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preprint/preprint__108a10cb9c1440165eb07da0c93914dd20b3bbecd3cc16aaa49a8c13587e4294/images_list.json

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+[]

preprint/preprint__10d0d51a8bce57917eb5eb8e73969ca23dbe6411a8411b870aaf2abb658b6420/images_list.json

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+[
+  {
+    "type": "image",
+    "img_path": "images/Figure_1.jpg",
+    "caption": "Fig. 1 Comparative genomics analysis of two fungal isolates reveals PAVs. a The overview of chromosomal features of V991 and 1cd3-2 with genetics and epigenetic data. 1, Chromosomes of V991. ii, Gene density (bin=10Kb). Outer, V991; Inner, 1cd3-2. iii, TE content (bin=10Kb). iv, Methylation loci including 6mA and 4mc (bin=100Kb). v, The distribution of SNP (bin=100Kb). vi, GC content (bin=100Kb). Red line, V991; Blue line, 1cd3-2. b Specific genes encode smaller protein with significant P-value < 2.2e-16 (Wilcox.test). c Specific genes are less likely to encode proteins with function domains (own at least one Gene Ontology annotation). Statistical analyses were performed using a Student's t test: \\(^{**}P<0.01\\). d Specific genes are more likely to encode candidate effectors (EffectorP classification). Statistical analyses were performed using a Student's t test: \\(^{**}P<0.05\\). e The specific genes distribution in each chromosome. f PAV regions have higher frequency of specific genes than random genomic regions. g-i On chromosome 5, a large insertion incident (106 Kb) affects 21 genes including 20 specific genes, intriguingly 3 of them are secretion proteins (SP3, SP5, SP6). g PAV region is verified by PacBio sequence reads separately mapping to two genomes. h Gene and TE distribution in the PAV region. i Maximum gene regulation level for each gene from upper panel. PCR validation of PAV regions (j) and specific genes (SP3, SP5, SP6) (k).",
+    "footnote": [],
+    "bbox": [
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+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_2.jpg",
+    "caption": "Fig. 2 In planta transcriptomes analysis of cotton-V. dahliae interaction. a The phenotype of cotton infected with V. dahliae (V991) at 0, 3, 6, 9, 12,15,18 and 21 dpi. The scale bar stands for 2 cm. b Ratio of V991 and 1cd3-2 reads identified at different time points in the transcriptomes. c, e Correlation analysis of patterns clearly cluster differentially expressed genes into two categories, with 3-9 days as stage I (the early infection or defense stage) and 12-21 days as stage II (the late infection or defense stage), respectively. Pearson correlation of gene expression profiles is used to define the similarity in gene expression profiles among different time points. Pearson's correlation coefficient is coded in the form of the size and color. d, f Gene ontology enrichment of upregulated genes responding to different stages, respectively. GeneRatio is the number of differentially expressed genes divided by the total number of genes associated with a specific pathway.",
+    "footnote": [],
+    "bbox": [
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+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_3.jpg",
+    "caption": "Fig. 3 V991 infection cause the burst of ROS in cotton leaves in the late stage. A expression profiling of differentially expressed genes between two stages in cotton (V991) and cotton (1cd3-2) transcriptomes, respectively. b ROS burst and cell death in cotton leaves after inoculation of V991 are visualized through DAB staining and trypan blue staining at 12dpi, respectively. The scale bar stands for 1 cm. c-H The low levels of ROS are found at stage I (3-6 dpi), and the beginning of the ROS burst occurred at 9 dpi and increase significantly to high levels at stage II (9-15 dpi). c-e The DCF (H2O2), HPF (OH+) and DHE (O2-) fluorescent dye intensity of the second true leaves of cotton plants among different time points after V991 inoculation, respectively. The scale bar stands for 50 μm. f-h The intensity calculation of DCF, HPF and DHE fluorescence. The values are means ± SD, n = 6. Statistical analyses were performed using a Student's t test: ns, Not significant; *P<0.05; **P<0.01; ***P<0.001.",
+    "footnote": [],
+    "bbox": [
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+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_4.jpg",
+    "caption": "Fig. 4 SP3 was a key virulence factor by interfering with the ROS accumulation of the host. a The signal peptide (SP) of SP3 is functional. Both YPRA and TTC indicate the successful secretion of the invertase. The empty pSUC2 and pSUC2-Avr1b-SP vectors were used as negative and positive controls, respectively. The scale bar stands for 5 mm. b Expression profile of SP3 during V. dahliae infection cotton. Transcript levels of SP3 were determined by qRT-PCR. The v991_EVM0005718 was used as the internal control gene. c-e Three independent SP3 deletion strains show compromised virulence on cotton, evidenced by (c) reduced stunting, and (d) decrease of disease indices compared with inoculation with wild-type V. dahliae (WT, V991). Verticillium wilt symptoms were photographed at 18 dpi. The values are means ± SD, n = 3. Statistical analyses were performed using a Student's t test. ns, Not significant; *P<0.05. The scale bar stands for 2 cm. e For the fungal recovery assay, the stem sections taken from cotton seedlings 18 days after V. dahliae inoculation were incubated at 25 °C on potato dextrose agar, photographed 4 days after inoculation. f-i H202 and OH- production in the second true leaves of cotton infected with WT and SP3 mutant (ΔSP3), respectively. f, h The DCF (H2O2) and HPF (OH+) fluorescent dye intensity of the second true leaves of cotton plants. The scale bar stands for 50 μm. g, i The intensity calculation of DCF and HPF fluorescence. The values are means ± SD, n = 3. Statistical analyses were performed using a Student's t test. *P<0.05. All of the experiments were repeated at least three times with similar results.",
+    "footnote": [],
+    "bbox": [
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+      ]
+    ],
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+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_5.jpg",
+    "caption": "Fig. 5 The characterization and distribution of PEI-MQDs. a The TEM image of PEI-MQDs. b The TEM size of PEI-MQDs. c The emission wavelength of PEI-MQDs in different bands. d The zeta potential of PEI-MQDs. e ROS scavenging capacities of PEI-MQDs. f Confocal imaging results showed colocalization of membrane autofluorescence with FITC-PEI-MQDs in the root of the cotton. PEI-MQDs were labelled with FITC. The scale bar stands for \\(20\\mu \\mathrm{m}\\) . g The calculated colocalization rate between FITC-PEI-MQDs and membrane. The values are means \\(\\pm \\mathrm{SD}\\) , \\(n = 3\\) . Statistical analyses were performed using a Student's t test: \\(^{**}P< 0.01\\) . All of the experiments were repeated at least three times with similar results.",
+    "footnote": [],
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+      ]
+    ],
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+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_6.jpg",
+    "caption": "Fig. 6 PE-MQDs improve cotton tolerance to V. dahliae by maintaining ROS homeostasis. a Disease symptoms of cotton plants at 18 days after V991 inoculation with and without PE-MQDs treatment. The scale bar stands for 2 cm. b Disease index of infected cotton plants. The values are means \\(\\pm\\) SD, n = 3. Statistical analyses were performed using a Student's t test. \\\\*P<0.01. c Fungal recovery assay. The scale bar stands for 2 cm. d Sections from cotyledonary node of cotton 16 d after V.dahliae infection. The scale bar stands for 100 \\(\\mu \\mathrm{m}\\) . e The qPCR analysis of the amount of fungal DNA. GHUB7 was used as an internal reference, and the ITS of the fungal ribosomal DNA was targeted. The values are means \\(\\pm\\) SD, n = 3. Statistical analyses were performed using a Student's t test. \\\\*P<0.01. All of the experiments were repeated at least three times with similar results. f PE-MQDs reduced MDA, H,O, content and increased catalase (CAT), glutathione peroxidase (GSH-Px) and peroxidase (POD) activities in the PE-MQDs treated plants at 12 dpi. f The MDA content. g The H,O, content. h The CAT activity. i The GPx activity. j The POD activity. The values are means \\(\\pm\\) SD, n = 4. Statistical analyses were performed using a Student's t test. \\\\*P<0.05. \\\\*P<0.01. k PE-MQDs helped to scavenge more ROS in the second true leaves of infected cotton plants. k The DCF (H,O), DHE (O,) and HPF (OH) fluorescent dye intensity of the second true leaves of cotton plants without and with PE-MQDs treatment at 18dpi, respectively. The scale bar stands for 50 \\(\\mu \\mathrm{m}\\) . n The intensity calculation of DCF, DHE and HPF fluorescence. The values are means \\(\\pm\\) SD, n = 6. Statistical analyses were performed using a Student's t test. \\\\*P<0.05. \\\\*P<0.01. \\\\*P<0.001.",
+    "footnote": [],
+    "bbox": [
+      [
+        72,
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+      ]
+    ],
+    "page_idx": 38
+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_7.jpg",
+    "caption": "Fig. 7 The interaction model between V.dahliae and cotton. a More secreted proteins secreted by V991 cause the burst of ROS in cottons leading to susceptibility. b The 1cd3-2 secreted less virulence proteins during the invasion and cotton showed a relatively resistant phenotype. c PEI-MQDs increase resistance to V991 by maintaining the homeostasis of ROS in cottons.",
+    "footnote": [],
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+        137,
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+        610,
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+      ]
+    ],
+    "page_idx": 39
+  }
+]

preprint/preprint__1101d4449753c0d3811408947f10da6a3b68d79fe46f4594e4cd268d8af9d117/images_list.json

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+    "type": "image",
+    "img_path": "images/Figure_2.jpg",
+    "caption": "Figure 2",
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+    "img_path": "images/Figure_3.jpg",
+    "caption": "Figure 3",
+    "footnote": [],
+    "bbox": [
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+    ],
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+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_unknown_0.jpg",
+    "caption": "C. Baseline",
+    "footnote": [],
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preprint/preprint__1101d4449753c0d3811408947f10da6a3b68d79fe46f4594e4cd268d8af9d117/preprint__1101d4449753c0d3811408947f10da6a3b68d79fe46f4594e4cd268d8af9d117_det.mmd

@@ -0,0 +1,674 @@
+<|ref|>title<|/ref|><|det|>[[44, 108, 900, 207]]<|/det|>
+# Interleukin-12 anchored drug conjugate (tolododekin alfa) in patients with advanced solid tumors: Results of a first-in-human Phase 1 trial  
+
+<|ref|>text<|/ref|><|det|>[[44, 230, 310, 276]]<|/det|>
+Jong Chul PARK jpark73@mgh.harvard.edu  
+
+<|ref|>text<|/ref|><|det|>[[44, 303, 704, 345]]<|/det|>
+Massachusetts General Hospital https://orcid.org/0000- 0002- 1052- 0734  
+
+<|ref|>text<|/ref|><|det|>[[44, 349, 645, 368]]<|/det|>
+Providence Cancer Center https://orcid.org/0000- 0003- 3948- 2708  
+
+<|ref|>text<|/ref|><|det|>[[44, 374, 705, 415]]<|/det|>
+Marcus Butler Princess Margaret Cancer Centre https://orcid.org/0000- 0002- 9840- 7057  
+
+<|ref|>text<|/ref|><|det|>[[44, 420, 234, 460]]<|/det|>
+Joseph Elsasl Ankya therapeutics  
+
+<|ref|>text<|/ref|><|det|>[[44, 465, 237, 506]]<|/det|>
+Robert Tighe Ankya Therapeutics  
+
+<|ref|>text<|/ref|><|det|>[[44, 511, 237, 551]]<|/det|>
+Sailaja Battula Ankya Therapeutics  
+
+<|ref|>text<|/ref|><|det|>[[44, 556, 237, 596]]<|/det|>
+Gail Iodice Ankya Therapeutics  
+
+<|ref|>text<|/ref|><|det|>[[44, 601, 245, 642]]<|/det|>
+Howard Kaufman Ankryra Therapeutics  
+
+<|ref|>text<|/ref|><|det|>[[44, 647, 179, 666]]<|/det|>
+John Kirkwood  
+
+<|ref|>text<|/ref|><|det|>[[50, 670, 673, 690]]<|/det|>
+UPMC Hillman Cancer Center https://orcid.org/0000- 0002- 3570- 4476  
+
+<|ref|>sub_title<|/ref|><|det|>[[44, 733, 103, 751]]<|/det|>
+## Article  
+
+<|ref|>text<|/ref|><|det|>[[44, 771, 738, 791]]<|/det|>
+Keywords: Anchored immunotherapy, Cancer, Interleukin- 12, Phase 1, Treatment  
+
+<|ref|>text<|/ref|><|det|>[[44, 809, 315, 828]]<|/det|>
+Posted Date: March 10th, 2025  
+
+<|ref|>text<|/ref|><|det|>[[44, 847, 475, 866]]<|/det|>
+DOI: https://doi.org/10.21203/rs.3.rs- 5984691/v1  
+
+<|ref|>text<|/ref|><|det|>[[44, 884, 914, 927]]<|/det|>
+License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[42, 46, 535, 65]]<|/det|>
+Additional Declarations: There is NO Competing Interest.  
+
+<|ref|>text<|/ref|><|det|>[[42, 100, 921, 144]]<|/det|>
+Version of Record: A version of this preprint was published at Nature Communications on September 29th, 2025. See the published version at https://doi.org/10.1038/s41467-025-63579-9.
+
+<--- Page Split --->
+<|ref|>title<|/ref|><|det|>[[114, 90, 881, 134]]<|/det|>
+# Interleukin-12 anchored drug conjugate (tolododekin alfa) in patients with advanced solid tumors: Results of a first-in-human Phase 1 trial  
+
+<|ref|>text<|/ref|><|det|>[[114, 177, 881, 280]]<|/det|>
+Jong Chul Park, \(\mathrm{MD}^{1}\) ; Brendan Curti, \(\mathrm{MD}^{2}\) ; Marcus Butler, \(\mathrm{MD}^{3}\) ; Joseph Elassal, \(\mathrm{MD}^{4}\) , MBA; Robert Tighe \(^{4}\) ; Sailaja Battula, \(\mathrm{PhD}^{4}\) ; Gail Iodice, \(\mathrm{RN}^{4}\) ; Howard L. Kaufman, \(\mathrm{MD}^{4}\) ; John M. Kirkwood, \(\mathrm{MD}^{5}\)  
+
+<|ref|>text<|/ref|><|det|>[[114, 290, 860, 308]]<|/det|>
+\(^{1}\) Massachusetts General Hospital, Boston, MA; Providence Portland Medical Center, Portland,  
+
+<|ref|>text<|/ref|><|det|>[[114, 313, 808, 331]]<|/det|>
+OR; Princess Margaret Cancer Center, Toronto, ON, Canada; Ankura Therapeutics, Inc.,  
+
+<|ref|>text<|/ref|><|det|>[[114, 336, 567, 354]]<|/det|>
+Cambridge, MA; University of Pittsburgh, Pittsburgh, PA  
+
+<|ref|>text<|/ref|><|det|>[[114, 350, 295, 368]]<|/det|>
+All correspondence to:  
+
+<|ref|>text<|/ref|><|det|>[[350, 351, 545, 420]]<|/det|>
+Jong Chul Park, MD  Harvard Medical School  25 Shattuck Street  Boston, MA 02115  
+
+<|ref|>text<|/ref|><|det|>[[350, 437, 604, 508]]<|/det|>
+Massachusetts General Hospital  55 Fruit Street  Boston, MA 02114  Phone: 617- 724- 4000  
+
+<|ref|>text<|/ref|><|det|>[[350, 525, 620, 543]]<|/det|>
+Email: jpark73@mgh.harvard.edu  
+
+<|ref|>text<|/ref|><|det|>[[116, 578, 766, 597]]<|/det|>
+Keywords: Anchored immunotherapy; Cancer; Interleukin- 12; Phase 1; Treatment
+
+<--- Page Split --->
+<|ref|>sub_title<|/ref|><|det|>[[115, 90, 223, 107]]<|/det|>
+## ABSTRACT  
+
+<|ref|>text<|/ref|><|det|>[[113, 125, 885, 460]]<|/det|>
+Anchored immunotherapy is a novel approach for retaining high doses of drugs within the tumor microenvironment improving the therapeutic window . Tolododekin alfa is a first- in- class anchored drug conjugate in which IL- 12 is linked to aluminum hydroxide. Safety and biologic activity was evaluated in a Phase 1 clinical trial in patients with accessible advanced solid tumors. The primary objectives were safety and tolerability. Secondary objectives included pharmacokinetics, pharmacodynamics, preliminary antitumor activity, and immunogenicity. Fifteen patients were enrolled and dosed at escalating doses of tolododekin alfa given by direct injection once every 3 weeks. There were no dose- limiting toxicities or treatment- related serious adverse events. PK/PD measurements demonstrated retention of drug in the tumor at all dose levels. Biologic activity was demonstrated by increased \(\mathrm{CD8^{+}}\) T cells, PD- L1 expression, and prolonged pro- inflammatory gene expression. Nine patients \((60\%)\) achieved disease control. These findings support continued clinical development of tolododekin alfa. ClinicalTrials.gov registration: NCT06171750.
+
+<--- Page Split --->
+<|ref|>sub_title<|/ref|><|det|>[[115, 90, 274, 108]]<|/det|>
+## INTRODUCTION  
+
+<|ref|>text<|/ref|><|det|>[[113, 118, 885, 427]]<|/det|>
+Immunotherapy has transformed the therapeutic landscape in oncology, most notably through immune checkpoint blockade for solid tumors and chimeric antigen receptor (CAR) T cell therapy for hematologic malignancies. \(^{1 - 2}\) In contrast to checkpoint blockade, which prevents T cell inhibition, immune agonists act directly on effector T and natural killer (NK) cells to stimulate proliferation and anti- tumor effector cell functions. While numerous immune agonists have shown robust therapeutic activity in preclinical tumor models, clinical development has been limited by the need for supraphysiologic systemic doses for efficacy, resulting in narrow therapeutic windows and poor pharmacokinetics (PK), requiring frequent administration. For example, high- dose interleukin- 2 (IL- 2) is approved for treating renal cell carcinoma and metastatic melanoma, but requires high- dose bolus infusions, leading to significant toxicity and requiring intensive care management. \(^{3 - 4}\) Similarly, other promising immune agonists, including those targeting CD28, 4- 1BB, CD40, and IL- 12, have been hindered by significant systemic toxicity. \(^{5 - 8}\)  
+
+<|ref|>text<|/ref|><|det|>[[113, 440, 885, 855]]<|/det|>
+To overcome these challenges, local retention of the agonist at the tumor site is a promising strategy. Localized delivery or tumor- targeted approaches are thought to enhance therapeutic efficacy by maximizing immune activation at the tumor site, which can promote systemic antitumor immunity, while minimizing systemic exposure and associated toxicities. \(^{9 - 10}\) Such strategies represent a pivotal opportunity to unlock the full potential of immune agonists in cancer therapy. However, the direct injection of agonists, such as recombinant IL- 12, into tumors has not proven successful in reducing toxicity, likely due to rapid diffusion out of the tumor resulting in systemic absorption. \(^{11}\) We have developed a drug platform that results in durable local retention within tumors by leveraging stable, non- covalent conjugation to an aluminum hydroxide scaffold. \(^{12}\) Tolododekin alfa (formerly ANK- 101) is constitutes by stable conjugation of full- length human IL- 12 to aluminum hydroxide, which serves as an inert scaffold for drug presentation. The drug is composed of the p35 and p40 IL- 12 subunits with an alum- binding peptide (ABP) extending from the C- terminus of the IL- 12 chain. The ABP contains serine residues that can be variably phosphorylated to create a negative charge allowing for interaction with hydroxyl residues on aluminum hydroxide. Brief admixture of the IL- 12- ABP with aluminum hydroxide prior to administration generates tolododekin alfa. \(^{12}\)
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[113, 87, 884, 291]]<|/det|>
+IL- 12 is produced by myeloid cells, \(\mathrm{CD4^{+}}\) T helper cells, and B- lymphoblastoid cells and regulates innate and adaptive immune responses. \(^{13 - 14}\) IL- 12 comprises two subunits: p35, which regulates signaling, and p40, essential for receptor binding. The p40 subunit is shared with other IL- 12 family members, such as IL- 23. \(^{13 - 14}\) IL- 12 induces interferon- \(\gamma\) (IFN- \(\gamma\) ) production, promotes Th1 differentiation, and enhances \(\mathrm{CD8^{+}}\) T cell and NK cell effector functions, playing a vital role in transitioning from innate to adaptive immunity and mediating tumor- antigen- specific responses. \(^{13 - 14}\) Systemic IL- 12 has previously demonstrated therapeutic activity in humans but was associated with unacceptable toxicity and mortality.  
+
+<|ref|>text<|/ref|><|det|>[[113, 307, 884, 510]]<|/det|>
+In a Phase 1 trial, recombinant IL- 12 (3- 1000 ng/kg/day) was administered via intravenous (IV) bolus for five days every three weeks. \(^{15}\) While two patients (melanoma, renal cell carcinoma) showed objective responses and four had stable disease, severe toxicities, including elevated hepatic transaminases and stomatitis, limited its use. A subsequent study with twice- weekly IV boluses (30- 700 ng/kg) established a maximum tolerated dose (MTD) of \(500\mathrm{ng / kg}\) . \(^{16}\) At doses above \(500\mathrm{ng / kg}\) significant dose- limiting toxicities (DLTs) included elevated transaminases, Grade 4 neutropenia, and hemolytic anemia. \(^{17}\) Despite some evidence of activity, systemic toxicity has hindered clinical development of IL- 12.  
+
+<|ref|>text<|/ref|><|det|>[[113, 526, 884, 729]]<|/det|>
+Preclinical studies of a murine surrogate of tolododekin alfa demonstrated prolonged retention within tumors for several weeks and showed single- agent therapeutic activity across various murine tumor models without evidence of toxicity. \(^{12}\) Murine tolododekin alfa promoted effector \(\mathrm{CD8^{+}}\) T cell recruitment, enhanced IFN- \(\gamma\) production, increased local PD- L1 expression, and induced durable pro- inflammatory gene expression. \(^{12}\) Additionally, a canine version of tolododekin alfa was evaluated in a Phase 1 clinical trial for dogs with advanced melanoma, showing a favorable safety profile with a \(29\%\) objective response rate and a \(47\%\) durable disease control rate. \(^{18}\)  
+
+<|ref|>text<|/ref|><|det|>[[113, 745, 884, 896]]<|/det|>
+Based on these findings, we hypothesized that tolododekin alfa would have an acceptable safety profile in human cancer patients, and PK modeling predicted drug retention locally and biologic activity that would result in local IFN- \(\gamma\) and PD- L1 upregulation. To test this hypothesis and gather preliminary data on efficacy and quality of life, a Phase 1 trial of tolododekin alfa was conducted in patients with advanced, superficially accessible solid tumors. The data herein represent the first- in- human report of an anchored drug conjugate in patients with advanced solid tumors.
+
+<--- Page Split --->
+<|ref|>sub_title<|/ref|><|det|>[[115, 120, 387, 138]]<|/det|>
+## MATERIALS AND METHODS  
+
+<|ref|>sub_title<|/ref|><|det|>[[115, 150, 382, 169]]<|/det|>
+## Patients and Eligibility Criteria  
+
+<|ref|>text<|/ref|><|det|>[[114, 179, 884, 330]]<|/det|>
+Patient eligibility included age 18 or greater and presence of a histologically confirmed solid tumor malignancy located in a superficial anatomic site (e.g., cutaneous, soft tissue, or lymph nodes). Patients had metastatic lesions and needed to exhibit progression of disease upon standard of care treatments. All patients were required to have measurable disease per RECISTv1.1 criteria. In addition, patients were required to meet established cardiac, hematologic, hepatic, and renal criteria.  
+
+<|ref|>text<|/ref|><|det|>[[114, 346, 884, 472]]<|/det|>
+The clinical trial was conducted in accordance with the clinical protocol, the Declaration of Helsinki ICH, and all applicable federal and local regulatory requirements. The clinical protocol was approved by the Institutional Review Boards (IRBs) at all study sites prior to patient screening. All patients were required to provide written, informed consent prior to any study procedures. The clinical protocol was registered at clinicaltrials.gov (NCT06171750).  
+
+<|ref|>sub_title<|/ref|><|det|>[[114, 489, 228, 508]]<|/det|>
+## Study Design  
+
+<|ref|>text<|/ref|><|det|>[[113, 517, 884, 799]]<|/det|>
+The study is an open- label Phase 1 multi- center trial with the primary objective of defining the safety and tolerability of tolododekin alfa. This report describes results from Part 1 of the study which enrolled patients with superficially accessible solid tumors (Part 2 is on- going and focuses on patients with visceral tumors). A standard \(3 + 3\) dose escalation design was utilized using six independent cohorts with tolododekin alfa concentration escalation across the following doses: 2 \(\mu \mathrm{g / mL}\) , \(6\mu \mathrm{g / mL}\) , \(20\mu \mathrm{g / mL}\) , \(60\mu \mathrm{g / mL}\) , \(120\mu \mathrm{g / mL}\) , and \(250\mu \mathrm{g / mL}\) (Suppl. Fig. 1). The first three cohorts used single patients to accelerate dose escalation based on preclinical modeling suggesting activity would be more likely at doses \(>60\mu \mathrm{g / mL}\) . To optimize dose selection, some cohorts were backfilled with additional patients. Tolododekin alfa was administered by direct injection every 3 weeks, and the volume was adjusted up to \(5\mathrm{mL}\) total at any visit based on the tumor volume as shown in Suppl. Table 1.  
+
+<|ref|>sub_title<|/ref|><|det|>[[115, 816, 444, 835]]<|/det|>
+## Dose Escalation and Safety Assessment  
+
+<|ref|>text<|/ref|><|det|>[[115, 845, 883, 890]]<|/det|>
+The incidence and description of DLTs- ) and treatment- emergent adverse events (TEAEs) were characterized according to the National Cancer Institute Common Terminology Criteria for
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[113, 87, 885, 345]]<|/det|>
+Adverse Events (CTCAE) v5.0. The study required that the first patient at any given dose be monitored for 21 days prior to enrollment of additional patients to confirm no DLTs. An adverse event was considered dose- limiting if it was possibly, probably, or definitely related to tolododekin alfa, occurred in the first 21 days after drug administration, and met the criteria outlined in Suppl. Table 2. In the event of a DLT, the cohort would be increased to six total patients, and if 2 DLTs occurred, the next lower dose would be considered the MTD. In the event that a DLT occurred in the first three cohorts, the cohort would be expanded to a full six patients. The safety was monitored by a Safety Review Committee (SRC), which was provided information on patient safety and efficacy data, laboratory assessments, and PK and PD data, when available. Dose escalation required approval by the SRC.  
+
+<|ref|>sub_title<|/ref|><|det|>[[115, 360, 667, 380]]<|/det|>
+## Manufacture and Analytical Characterization of Tolododekin alfa  
+
+<|ref|>text<|/ref|><|det|>[[113, 395, 885, 836]]<|/det|>
+Tolododekin alfa (IL- 12- ABP bulk drug substance and drug product) is expressed in a clonal Chinese Hamster Ovary (CHO) cell line utilizing the leap- in transposase \(\dot{\mathrm{O}}\) technology (ATUM). The molecule is co- expressed with the human kinase, FAM20C, an enzyme responsible for the phosphorylation of serine amino acids embedded in the C- terminal peptide (ABP) of the molecule. The phosphorylated amino acids are responsible for the binding of the IL- 12- ABP to aluminum hydroxide (Alhydrogel®) through ligand exchange. Tolododekin alfa is manufactured at the 1000 L process scale, defined by the nominal volume of the production bioreactor. The cells used to inoculate the production bioreactor originate from a single cell bank vial. Cell culture continues in the production bioreactor until harvest criteria are met. A single bioreactor harvest is purified as a single downstream process consisting of conventional chromatography and filtration steps resulting in the bulk drug substance. The drug product is manufactured sterile in a liquid formulation utilizing 2R glass vials. Both bulk drug substance and drug product undergo a battery of biochemical release assays, including an assessment of the phosphorylation levels of the drug and set on long- term stability studies. In addition, Alhydrogel®, formulated in \(\mathrm{H}_2\mathrm{O}\) , is purchased from CRODA (Denmark) and dialed in 2R glass vials. Finally, formulation buffer (diluent) is sterile manufactured in 6R vials and provided to the pharmacies. Tolododekin alfa is combined with appropriate amounts of Alhydrogel® and diluent to generate the clinical doses.
+
+<--- Page Split --->
+<|ref|>sub_title<|/ref|><|det|>[[115, 90, 293, 109]]<|/det|>
+## Drug Administration  
+
+<|ref|>text<|/ref|><|det|>[[113, 118, 885, 507]]<|/det|>
+Drug AdministrationTolododekin alfa (ANK- 101) is composed of two components: an IL- 12 with an alum binding peptide (designated IL- 12- ABP) and aluminum hydroxide (Alhydrogel®). The IL- 12- ABP is mixed with Alhydrogel® at room temperature for 30 minutes prior to administration and was delivered by intratumoral injection every 3 weeks for four cycles. At any given visit, patients could have multiple tumors injected provided these were accessible for injection and the total volume of tolododekin alfa did not exceed 5 mL. Drug volume was adjusted for lesion volume based on the nomogram shown in Suppl. Table 1. Ultrasound imaging was allowed to help guide injections when lesions could no longer be palpated clinically or when deemed safer by the investigator. Patients without significant adverse events or confirmed disease progression could be treated with another four cycles of tolododekin alfa every 3 weeks. While the initial protocol provided for patient follow- up for safety monitoring after 8 cycles, exceptions were made for patients to continue tolododekin alfa injections every 3 weeks beyond the 8 cycles based on investigator impression of clinical benefit to the patient. Continued treatment with tolododekin alfa required approval by the U.S. FDA and local IRB, and all patients provided new written, informed consent as well as being treated under a separate, single- patient protocol in each case.  
+
+<|ref|>sub_title<|/ref|><|det|>[[115, 522, 304, 540]]<|/det|>
+## Response Assessments  
+
+<|ref|>text<|/ref|><|det|>[[114, 550, 885, 755]]<|/det|>
+Response AssessmentsResponses were monitored by modified RECISTv1.1 criteria. The criteria were modified in this study to allow patients with progression to remain on study provided that there were no significant adverse events or evidence of clinical progression (to accommodate possible pseudo- progression). Patients were evaluable for best objective response assessment provided they had baseline measurements, received at least one dose of tolododekin alfa, and had at least one post- treatment measurement. All patients were required to have baseline computed tomography (CT) scans of the chest, abdomen, pelvis, and any specific sites of known disease as well as a magnetic resonance imaging (MRI) of the brain. Follow- up imaging with CT scans every 12 weeks was also performed.  
+
+<|ref|>sub_title<|/ref|><|det|>[[115, 802, 360, 820]]<|/det|>
+## Pharmacokinetic Assessment  
+
+<|ref|>text<|/ref|><|det|>[[115, 831, 884, 904]]<|/det|>
+Pharmacokinetic AssessmentPeripheral blood and tumor biopsy samples were collected pre- dose and at specified timepoints after tolododekin alfa injection (Suppl. Table 3). For PK measurements, IL- 12- ABP was measured using a validated Meso Scale Discovery (MSD) based assay with a lower limit of quantification
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[113, 87, 884, 293]]<|/det|>
+of \(10\mathrm{pg / mL}\) . IL- 12- ABP was captured on a pre- coated ECL capable plate. After addition of detection reagent, conjugate and read buffer, the reaction product is detected using an MSD ECL plate reader. A 5- parameter logistic (5- PL) curve fitting with \(1 / \mathrm{y}^2\) weighting was used to fit the standard curve. The electrochemiluminescence signal produced is directly proportional to the amount of IL- 12- ABP in human serum. PK parameters for IL- 12- ABP will be calculated from observed serum concentration data by standard non- compartmental analysis (NCA) for extravascular administration methods for all subjects in the PK analysis set using Phoenix WinNonlin® version 8.3.4 (Certara, NJ, USA).  
+
+<|ref|>sub_title<|/ref|><|det|>[[115, 309, 383, 328]]<|/det|>
+## Pharmacodynamic Assessments  
+
+<|ref|>text<|/ref|><|det|>[[113, 338, 884, 672]]<|/det|>
+Immunogenicity assessments included characterizing the development of anti- drug antibodies (ADA) toward IL- 12- ABP in serum using classical acid- dissociation combined with a bridging ECL based validated immunoassay. Briefly, first the acid- dissociated serum samples and PC (anti- IL- 12p70, positive control) are incubated with neutralization buffer and master mix of biotinylated IL- 12- ABP and ruthenylated IL- 12- ABP. An immune complex is formed between the two IL- 12- ABP conjugates and the anti- IL- 12- ABP antibody present in human serum samples and PCs during the incubation step. This immune- complex is then captured onto a streptavidin coated MSD plate via Biotin- Streptavidin bond. Excess unbound conjugate complex is removed by further washing of the wells, followed by addition of MSD 2X read buffer. The assay plate is then read using an MSD ECL plate reader. The electrochemiluminescence signal (RLU) generated during the plate reading is relative to the amount of anti- IL- 12- ABP antibodies present in the PCs and samples tested. All the samples were planned for analysis in a three- tier assay, which includes screening, confirmatory, and titer assays.  
+
+<|ref|>text<|/ref|><|det|>[[113, 687, 884, 865]]<|/det|>
+The serum cytokine analysis was performed using a qualified electrochemiluminescence based 10- Plex cytokine panel assay for the determination of IFN- y, IL- 1β, IL- 2, IL- 4, IL- 6, IL- 8, IL- 10, IL- 12p70, IL- 13, and TNF- \(\alpha\) in human serum. All proinflammatory biomarkers are captured on a MSD U- PLEX electrochemiluminescence (ECL) plate. After addition of conjugate and read buffer, the reaction product is detected using an MSD ECL plate reader. The ECL signal produced is relative to the amount of analyte in human serum. The concentrations of all analytes were back calculated from the non- linear regression of their respective standard curves.
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[113, 88, 884, 241]]<|/det|>
+For tumor biopsies, while freshly obtained biopsies were preferred, archival tissue was used as the baseline sample if the tissue was collected within 12 months of C1D1. When possible, tumor biopsies were done on both injected tumors and non- injected tumors. Paired pre- study and on- study biopsies were subsequently formalin- fixed and paraffin- embedded (FFPE), followed by H&E staining, and analyzed for expression of CD8 (clone C8/144B) and PD- L1 (clone 22C3 pharmDX<sup>FDA</sup> IVD Kit) by immunohistochemistry (IHC).  
+
+<|ref|>text<|/ref|><|det|>[[113, 255, 884, 407]]<|/det|>
+For NanoString profiling, RNA was extracted from FFPE slides and approximately \(100\mathrm{ng}\) of total RNA were run on the PanCancer IO \(360^{\mathrm{TM}}\) Panel (Bruker Spatial Biology) per the manufacturer's recommendation. Data analysis was performed via the IO 360 Data Analysis Service. P- values are adjusted within each analysis, gene or signature, and on the grouping variable level difference t- test using the Benjamini and Yekutieli False Discovery Rate (FDR) adjustment to account for correlations amongst the tests. All models are fit using the limma package in R.  
+
+<|ref|>sub_title<|/ref|><|det|>[[115, 423, 332, 442]]<|/det|>
+## Statistical Considerations  
+
+<|ref|>text<|/ref|><|det|>[[114, 459, 884, 559]]<|/det|>
+Statistical methods were primarily descriptive without formal hypothesis testing. The data cutoff for this report was December 11, 2024. Categorical variables were summarized using numbers and percentages. Continuous variables were summarized by total number (n), mean, standard deviation, median, and range (minimum and maximum). Data are summarized by dose cohort.  
+
+<|ref|>text<|/ref|><|det|>[[113, 573, 884, 802]]<|/det|>
+Total enrollment in the Part 1 dose escalation was expected to be 12- 36 patients based on 3 single participant dose cohorts at the lowest dose levels and up to 3 additional cohorts of 3- 6 DLT- evaluable participants, depending on DLT profile at each dose level. The overall objective response was defined by standard RECISTv1.1 criteria. The best overall response (BOR) was defined as the best objective response achieved at any time and was determined for each participant. Disease control was defined as subjects who achieved best response of complete response (CR), partial response (PR), or stable disease (SD) for at least 12 weeks. Duration of stable disease was calculated from the start of treatment until the date when PD was documented, or the last study visit or end of treatment if no PD has occurred.
+
+<--- Page Split --->
+<|ref|>sub_title<|/ref|><|det|>[[115, 90, 206, 107]]<|/det|>
+## RESULTS  
+
+<|ref|>sub_title<|/ref|><|det|>[[115, 120, 310, 138]]<|/det|>
+## Patient Characteristics  
+
+<|ref|>text<|/ref|><|det|>[[113, 149, 885, 352]]<|/det|>
+A total of fifteen patients were treated in this dose escalation monotherapy phase. (Part 1) The patient characteristics are summarized in Table 1. The median age was 68 years old (range, 36- 79) with 6 (40%) men and 9 (60%) women enrolled. The baseline ECOG performance status was 0 in 9 (60%) and 1 in 6 (40%) of the patients. All patients had received prior therapy with more than three regimens in 11 (73.3%) of the patients and less than or equal to three prior regimens in 4 (26.7%) of the patients (Suppl. Table 4). All but two patients had progressed on prior immunotherapy. The tumors treated were melanoma (n=7; 46.7%), head and neck cancer (n=4; 26.7%), breast cancer (n=2; 13.3%), and apocrine adenocarcinoma (n=1; 6.7%).  
+
+<|ref|>sub_title<|/ref|><|det|>[[115, 369, 206, 387]]<|/det|>
+## Treatment  
+
+<|ref|>text<|/ref|><|det|>[[113, 398, 885, 680]]<|/det|>
+Overall, 31 patients were screened for the trial and 15 patients were enrolled (Suppl. Fig 2). All 15 patients are included in the safety analysis set. Thirteen patients are included in the efficacy analysis as 2 subjects terminated the study prior to initial evaluation. One patient (in cohort 1; 2 \(\mu \mathrm{g / mL}\) ) withdrew consent after one injection due to social reasons after completing the DLT period. Three patients were removed from treatment prior to completing all four cycles, all for disease progression. Three patients received all four planned cycles and eight patients received more than four cycles. No patients discontinued study treatment due to an adverse event. The median number of doses administered was 4 (range 1- 8) per patient and the median number of tumors injected per patient was 1 (range 1- 6). The median volume injected per patient was 8 mL (range 0.5- 32mL). Further details describing dosing and drug volume injected per patient are described in Table 1.  
+
+<|ref|>sub_title<|/ref|><|det|>[[115, 697, 170, 715]]<|/det|>
+## Safety  
+
+<|ref|>text<|/ref|><|det|>[[114, 726, 885, 903]]<|/det|>
+There were no DLTs reported in any patients. There were no Grade 3 or greater treatment- related TEAEs or serious adverse events (SAEs) in any patient. There was one Grade 3 SAE of soft tissue infection in a patient treated with tolododekin alfa at the \(20 \mu \mathrm{g / mL}\) cohort, which resolved with systemic antibiotics and was considered unrelated to tolododekin alfa. The most common treatment- related adverse events are summarized in Table 2. The most common of these were fatigue (n=2), influenza like illness (n=2), and myalgia (n=2). Of note, there were three reports of Grade 1 fever in two patients (at both the \(20 \mu \mathrm{g / mL}\) and \(120 \mu \mathrm{g / mL}\) cohorts) that may have been
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[114, 88, 883, 188]]<|/det|>
+due to mild cytokine release syndrome. Pre- medication with acetaminophen was allowed after the fevers, and this event did not recur with subsequent dosing. There were no significant laboratory abnormalities noted, and there were no changes in neutrophil- to- lymphocyte (NLR) ratio in any patients **Suppl. Fig. 3**.  
+
+<|ref|>sub_title<|/ref|><|det|>[[114, 204, 490, 223]]<|/det|>
+## Pharmacokinetics and Anti-Drug Antibodies  
+
+<|ref|>text<|/ref|><|det|>[[113, 233, 884, 438]]<|/det|>
+The PK analyses from all patients after the first cycle of treatment are summarized in **Fig. 1A (left)** and **Suppl. Table 5**. A general trend of dose- dependent increases in systemic drug exposure was observed; however, substantial variability was noted in individual PK profiles within and across dose cohorts. Serum concentrations in the first 8 patients (Cohorts 1- 4) ranged from unquantifiable in 4 patients to very low levels, with a \(\mathrm{C_{max}}\) range of 19.2–139 pg/mL. In contrast, systemic IL- 12- ABP levels were more readily detectable in Cohorts 5 and 6, with a median \(\mathrm{C_{max}}\) of 103 pg/mL (range, 18.2–693 pg/mL) and a median \(\mathrm{T_{max}}\) of 24 hours (range, 0.5–168 hours). In most patients with measurable PK profiles, IL- 12- ABP remained detectable up to the last timepoint (168 hours).  
+
+<|ref|>text<|/ref|><|det|>[[114, 453, 883, 552]]<|/det|>
+To evaluate tumor retention efficiency, the \(\mathrm{C_{max}}\) was expressed as a percentage of the total administered dose (Suppl. Table 6). Results indicated that only a minute fraction of the total dose \((0.02–0.67\%)\) was present systemically at \(\mathrm{C_{max}}\) , indicating that tolodeokin alfa efficiently anchors the bioactive IL- 12- ABP component within tumors.  
+
+<|ref|>text<|/ref|><|det|>[[113, 567, 884, 718]]<|/det|>
+Following the second treatment cycle, systemic levels of IL- 12- ABP were consistently detected in all patients. Similar to observations from Cycle 1, serum concentrations after Cycle 2 remained generally low with a median across all Cohorts of 59.5pg/mL (range, 13.8- 1010 pg/mL). Significant inter- and intra- cohort variability persisted, with a trend of higher IL- 12- ABP levels at doses \(>60\mu \mathrm{g / mL}\) (Fig. 1A and Suppl. Table 7). Total systemic exposures following Cycle 2 were notably lower compared to Cycle 1, with all patients exhibiting a \(\mathrm{T_{last}}\) of 48 hours (Fig. 1A [right]).  
+
+<|ref|>text<|/ref|><|det|>[[114, 733, 884, 833]]<|/det|>
+The presence of circulating ADA against the IL- 12- ABP protein was assessed at baseline and after Cycles 1 and 3. Among the first 12 patients (Cohorts 1–5), only two patients tested positive for ADA, with the positivity apparent after Cycle 3 (Suppl. Table 8). These preliminary findings suggest low immunogenicity of the IL- 12- ABP protein.  
+
+<|ref|>sub_title<|/ref|><|det|>[[115, 850, 370, 868]]<|/det|>
+## Induction of Serum Cytokines
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[113, 87, 886, 423]]<|/det|>
+Peripheral blood samples were collected according to the schedule in Suppl. Table 3, and serum was isolated for cytokine analysis. IFN- \(\gamma\) is the primary cytokine produced in response to IL- 12 signaling. Post- treatment increases in systemic IFN- \(\gamma\) levels were detected in all patients (Fig. 1B). In Cohorts 1–3, levels of circulating IFN- \(\gamma\) were low, with a median peak of 77.9 pg/mL (range, 17.7–496.4 pg/mL). A dose- dependent upward trend was observed, with Cohorts 4–6 exhibiting a median peak of 559.9 pg/mL; however, levels varied widely (range, 19.0–6,083 pg/mL). For most patients, peak IFN- \(\gamma\) levels occurred 48 hours after dosing. In patients that received multiple treatment cycles, pre- dose IFN- \(\gamma\) returned to undetectable levels and was reliably re- induced with subsequent doses. Changes in the other cytokines measured were minimal, except for IL- 12p70, where the elevated levels were attributed to circulating IL- 12- ABP (Fig.1C). Overall, these findings indicate that tolododekin alfa is biologically active following local administration and induces IL- 12- mediated immune activation, supported by evidence of a dose- dependent pharmacodynamic response, and this activity is persistent with repeat dosing.  
+
+<|ref|>sub_title<|/ref|><|det|>[[115, 439, 494, 458]]<|/det|>
+## Remodeling of the Tumor Microenvironment  
+
+<|ref|>text<|/ref|><|det|>[[113, 468, 886, 777]]<|/det|>
+Tumor biopsies were collected as outlined in Suppl. Table 2 to assess treatment- related changes in the TME. In biopsies collected 21 days post- treatment, differential gene expression analysis demonstrated consistent upregulation of multiple markers indicative of immune activation and counter- regulatory mechanisms, compared to their paired pre- treatment samples (Fig. 2A and 2B). Significant increases were observed in gene expression signatures associated with T cells, Th1 cells, \(\mathrm{CD8^{+}}\) T cells, exhausted \(\mathrm{CD8^{+}}\) T cells, cytotoxic cells, dendritic cells, macrophages, and regulatory T cells. The most highly upregulated genes included those encoding the IL2 receptor alpha chain (IL2RA); IL12RB2, STAT4 (IL- 12 signaling), immune checkpoint molecules (CTLA4, PD- 1, TIGIT, PD- L1, PD- L2); chemokines (CXCL9, CXCR3, CCL5); cytotoxic effector molecules (PRF1, GZMA, GZMK), M1 macrophages (CD38 and CD86) (Fig. 2B). These gene expression changes were consistently observed across all evaluable dosing cohorts, in all tumor types assessed, and in both injected and non- injected tumors (Fig. 2C).  
+
+<|ref|>text<|/ref|><|det|>[[113, 791, 885, 891]]<|/det|>
+Upregulation of the Tumor Inflammatory Score (TIS) and \(\mathrm{CD8^{+}}\) T cell gene signatures were associated with treatment outcomes, with patients achieving partial response (PR) or stable disease (SD) exhibiting significantly higher scores compared to those with progressive disease (PD) (Fig. 2D). The TME was further characterized using IHC to detect and quantify tumor infiltration by
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[113, 88, 884, 213]]<|/det|>
+\(\mathrm{CD8^{+}}\) T cells, as well as to assess PD- L1 expression by immune cells and tumor cells. Compared to paired baseline biopsies, most post- treatment samples exhibited a marked increase in the infiltration of \(\mathrm{CD8^{+}}\) T cells (Fig. 2E) and PD- L1 expression (Fig. 2F), consistent with the NanoString gene profiling results. Treatment induced a clear upregulation of PD- L1 on tumor cells (TPS), immune cells (IC) as well as the combined positive score (CPS) (Suppl. Table 9).  
+
+<|ref|>text<|/ref|><|det|>[[113, 228, 884, 432]]<|/det|>
+In a patient with metastatic bladder cancer treated with \(60~\mu \mathrm{g / mL}\) tolododekin alfa, changes in the TME were observed after the first injection of tolododekin alfa (Fig. 2D). These changes included a \(\sim 6\) - fold increase in the levels of \(\mathrm{CD8^{+}}\) T cells (Fig. 2E), and a 10- fold increase in the Combined Positive Score (CPS) for PD- L1 expression (Fig. 2F). Biopsies collected from this tumor after Cycle 3 showed a return to pre- treatment levels of \(\mathrm{CD8^{+}}\) T cells (Suppl Fig. 4A) and PD- L1 expression (Suppl Fig. 4B). However, the lesion exhibited near complete regression with decreased cellularity and increased necrosis, potentially reflecting an effective immune response followed by contraction as the tumor was destroyed (Fig. 3D).  
+
+<|ref|>text<|/ref|><|det|>[[113, 448, 884, 730]]<|/det|>
+Similar post- treatment changes in \(\mathrm{CD8^{+}}\) T cell infiltration and PD- L1 expression were observed in patients with other tumor types, including Stage IV metastatic breast cancer (treated at \(20~\mu \mathrm{g / mL}\) ), acral lentiginous melanoma (treated at \(20~\mu \mathrm{g / mL}\) tolododekin alfa), cutaneous melanoma (treated at \(60~\mu \mathrm{g / mL}\) ), and metastatic HNSCC (treated at \(60~\mu \mathrm{g / mL}\) ). In the HNSCC patient, a 4.3- fold increase in \(\mathrm{CD8^{+}}\) T cells and a 23.3- fold increase in PD- L1 CPS were observed after the first tolododekin alfa injection (Fig. 3E & 3F). Consistent with prior observations in the metastatic bladder cancer patient, the lesion appeared highly necrotic with no viable tumor cells detected, further supporting the hypothesis of local immune contracture following tumor cell elimination (Fig. 3D). Taken together, these data suggest that treatment with tolododekin alfa induces a profound reprogramming of the TME, consistent with both the known biology of IL- 12 and previous preclinical observations<sup>12</sup>.  
+
+<|ref|>sub_title<|/ref|><|det|>[[115, 747, 186, 765]]<|/det|>
+## Efficacy  
+
+<|ref|>text<|/ref|><|det|>[[114, 776, 884, 900]]<|/det|>
+The overall disease control rate was \(60.0\%\) by standard RECISTv1.1 criteria (Table 3). Spider plots of responses across all treatment cohorts demonstrated that 9 patients had stable disease across all doses (Fig. 3A). Further, one patient achieved a best objective partial response after four cycles but then progressed with a new lesion. Six patients remain on active treatment and are being followed for response. At a median follow- up of 3.1 months, the median duration of stable disease
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[113, 87, 884, 266]]<|/det|>
+was 4.9 months (range 2.5- 5.4 months) which is summarized in the swimmer plot (Fig. 3B). Although serial pathology for response was not included in the study, serial photography documented complete regression in several injected lesions in a patient with metastatic bladder cancer treated with tolododekin alfa at \(60\mathrm{mg / mL}\) (Fig. 3C). Biopsy of injected lesions were available in three patients (melanoma, bladder carcinoma, and head and neck squamous cell carcinoma) after 1 and 3 cycles of treatment that demonstrated absence of viable tumor with residual inflammation (Fig. 3D).
+
+<--- Page Split --->
+<|ref|>sub_title<|/ref|><|det|>[[115, 90, 238, 108]]<|/det|>
+## DISCUSSION  
+
+<|ref|>text<|/ref|><|det|>[[113, 118, 884, 375]]<|/det|>
+Tolododekin alfa is a novel IL- 12- anchored drug conjugate designed for prolonged retention within the tumor microenvironment. The goal of this modification was to mediate anti- tumor immunity without on- target, off- tumor toxicity. In this first- in- human study, tolododekin alfa was well tolerated and there were no DLTs or Grade 3 or greater treatment- related TEAEs or SAEs. The most common treatment- related adverse events were low grade constitutional symptoms, including fatigue, influenza like illness, and myalgias (Table 2). Two patients exhibited Grade 1 fever, which may have been related to mild cytokine release syndrome, but both cases resolved without incident and did not recur after pre- medication of the patient with acetaminophen. Of note, there were no significant elevations of hepatic transaminases, neutropenia, the neutrophil to lymphocyte ratio (NLR), or other laboratory abnormalities (Suppl. Fig. 3).  
+
+<|ref|>text<|/ref|><|det|>[[113, 388, 884, 881]]<|/det|>
+Evidence that the IL- 12 anchored drug conjugate was retained at the tumor site was provided in the PK analyses. Following direct injection tolododekin alfa, systemic levels of IL- 12- ABP were generally very low, particularly in the first four dosing cohorts. Systemic levels were higher in cohorts after dose escalation, with the highest \(\mathrm{C_{max}}\) of \(1010\mathrm{pg / mL}\) observed after the second treatment cycle in a Cohort 5 patient who received a total dose of \(300\mu \mathrm{g}\) . In this patient, the \(\mathrm{C_{max}}\) corresponded to just \(0.58\%\) of the total administered dose. By way of comparison, \(\mathrm{C_{max}}\) values at the MTD ( \(500\mathrm{ng / kg}\) ) of IV administered IL- 12 were reported to be \(\sim 3,000\mathrm{pg / mL}^{15}\) , which represents \(\sim 24\%\) of the total dose assuming a body weight of \(70\mathrm{kg}\) . In all patients, following both the first and second treatment cycles, \(\mathrm{C_{max}}\) levels of tolododekin alfa accounted for only a small fraction (well under \(1.0\%\) ) of the total administered dose, indicating highly efficient tumor retention. The observed low systemic exposure, driven by effective tumor retention, is an expected characteristic of anchored immunotherapy, because the drug is stably linked to aluminum hydroxide. Systemic exposure decreased substantially following the second treatment cycle. This reduction is unlikely attributable to antidrug antibodies (ADA), as no ADA were detected in pre- dose samples. Other plausible explanations include changes in the TME that may have influenced systemic drug absorption. These could involve increased target- mediated drug disposition due to upregulated IL- 12R expression, alterations in stromal architecture that impact drug diffusion, enhanced local drug sequestration within the tumor, increased absorption of tolododekin alfa by infiltrating lymphocytes, or increased intratumoral clearance mechanisms.
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[113, 87, 885, 345]]<|/det|>
+Durable drug retention was also seen in preclinical studies of murine tolododekin alfa utilizing \(^{125}\mathrm{I}\) - SPECT imaging. These indicated that \(\sim 40\%\) of the drug conjugate remained in the tumor 28 days after injection. \(^{12}\) In a Phase 1 study conducted in dogs with advanced mucosal melanoma, a canine version of tolododekin alfa induced pro- inflammatory gene expression that persisted in some dogs for up to 84 days and was associated with recruitment of T cells to the tumor site, increased local PD- L1 expression, and induced serum IFN- \(\gamma\) . \(^{18}\) In this canine study, therapeutic activity was observed without dose- limiting toxicities suggesting that local drug retention is similar across multiple species. A mixed- effect PD model based on preclinical data suggested that systemic IFN- \(\gamma\) production and PD- L1 expression in the tumor microenvironment (doi: 10.1158/1535-7163.MCT- 24- 0317) would serve as key indicators of bioactivity in humans. \(^{19}\)  
+
+<|ref|>text<|/ref|><|det|>[[113, 360, 885, 566]]<|/det|>
+The favorable safety profile supports that patients can tolerate up to \(250~\mu \mathrm{g / mL}\) of tolododekin alfa. Previous PK modeling and drug release studies have suggested that further dose escalation is unlikely to yield further drug biodistribution or improve IL- 12 signaling. \(^{19}\) These data further suggest that continued dose escalation may be unlikely to improve therapeutic activity. Indeed, the prolonged retention of IL- 12 locally may be sufficient to mediate anti- tumor activity even at lower administered doses. Consistent with this, we found disease control at all dosing cohorts except for the lowest dose of \(2\mu \mathrm{g / mL}\) Thus, \(250~\mu \mathrm{g / mL}\) was selected as the recommended dose for further expansion.  
+
+<|ref|>text<|/ref|><|det|>[[113, 582, 885, 890]]<|/det|>
+In line with the PK modeling prediction, evaluation of systemic and local pharmacodynamic biomarkers confirmed the biologic activity of tolododekin alfa. Indeed, we observed a dose- dependent increase in systemic IFN- \(\gamma\) levels (Fig. 1B) and consistent re- induction of transient IFN- \(\gamma\) with each treatment cycle (Fig. 1C). We also observed remodeling of the tumor microenvironment (TME) across multiple tumor types and dose levels. Upregulation of immune- related gene signatures, particularly those associated with \(\mathrm{CD8}^+\) T cells and Th1- driven responses, was accompanied by increased infiltration of \(\mathrm{CD8}^+\) T cells and macrophages, consistent with data from preclinical studies. \(^{12,18 - 20}\) Gene profiling showed increased expression of CXCL9, CXCR3, and CCL5, which likely explains how \(\mathrm{CD8}^+\) T cells and myeloid cells were recruited to the tumor site. Preclinical studies suggested that infiltrating macrophages were skewed to an M1 phenotype, \(^{20}\) and Nanostring data suggest trends towards M1 macrophage phenotype (CD38, CD86) (Fig.3B).
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[113, 87, 884, 267]]<|/det|>
+We also demonstrated elevated PD- L1 expression in the tumor microenvironment with CPS scores increasing more than 2- to 3- fold in some patients. Cell population analysis suggested that PD- L1 expression was often most profound on infiltrating myeloid cells rather than on tumor cells (Suppl. Table 9), which may have been in the process of dying and/or immune- mediated elimination. This suggests that tolododekin alfa may be useful in combination with PD- 1/PD- L1 checkpoint inhibitors where increased CPS scores are often associated with increased efficacy. These findings align with preclinical observations and are hallmarks of IL- 12- mediated immune stimulation.  
+
+<|ref|>text<|/ref|><|det|>[[113, 280, 884, 721]]<|/det|>
+Initial results suggest that patients who were heavily pre- treated experienced clinical benefit with durable disease control in \(60\%\) of the patients with one PR by BOR. The reason for limited objective responses may relate to traditional CT imaging underestimating true pathologic responses as has been reported for other local immunotherapy agents. \(^{21,22}\) Consistent with this we observed pathologic responses in three patients despite having measurable disease by CT imaging (see Fog. 3 FD). Another reason may relate to the presence of highly advanced disease as all patients had progressed following multiple lines of prior therapy and three patients developed disease progression prior to being able to complete all four planned cycles. Disease control was \(81.8\%\) in the patients who completed at least four cycles of treatment. These data are also consistent with the canine IL- 12 anchored drug conjugate in dogs with advanced melanoma disease control was associated with a durable overall survival benefit. \(^{18}\) Importantly, six subjects remain on treatment and may achieve further benefit. Further studies may need to incorporate scheduled tumor biopsies or inclusion of circulating tumor DNA to better assess clinical benefit beyond imaging alone. Notably, there was a long duration of responses seen in most of the patients who achieved disease control (Table 3 and Fig. 3B). Patients who achieved disease control also demonstrated significant increases in \(\mathrm{CD8 + }\) T cell and pro- inflammatory gene signatures suggesting that biologic changes were associated with clinical benefit.  
+
+<|ref|>text<|/ref|><|det|>[[114, 735, 884, 886]]<|/det|>
+In summary, this Phase 1 first- in- human study demonstrated that tolododekin alfa, an IL- 12 anchored drug conjugate is well tolerated at doses up to \(250~\mu \mathrm{g / mL}\) , exhibits durable local tumor retention, induces recruitment of \(\mathrm{CD8^{+}}\) T cells to the tumor microenvironment, increases PD- L1 expression, and may have therapeutic activity. Anchoring IL- 12 further resulted in delivering nearly ten- fold higher doses of IL- 12 compared to systemic IL- 12 without systemic toxicity. Tolododekin alfa merits further investigation for advanced cancer alone and in combination with
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[113, 88, 883, 135]]<|/det|>
+PD- 1/PD- L1 checkpoint inhibitors. Anchored drug conjugates provide a new strategy for improving the therapeutic window of active anti- cancer drugs.
+
+<--- Page Split --->
+<|ref|>sub_title<|/ref|><|det|>[[115, 90, 250, 108]]<|/det|>
+## REFERENCES  
+
+<|ref|>text<|/ref|><|det|>[[115, 119, 848, 191]]<|/det|>
+1. Sun, Q., Hong, Z., Zhang, C. et al. Immune checkpoint therapy for solid tumours: clinical dilemmas and future trends. Sig Transduct Target Ther. 2023; 8, 320 https://doi.org/10.1038/s41392-023-01522-4  
+
+<|ref|>text<|/ref|><|det|>[[115, 208, 850, 254]]<|/det|>
+2. Mitra A, Barua A, Huang L, Ganguly S, Feng Q, He B. From bench to bedside: the history and progress of CAR T cell therapy. Front Immunol. 2023;14:1188049.  
+
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+3. Schwartz RN, Stover L, Dutcher JP. Managing toxicities of high-dose interleukin-2. Oncology (Williston Park). 2002; Nov;16(11 Suppl 13):11-20. PMID: 12469935.  
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+4. Pachella LA, Madsen LT, Dains JE. The Toxicity and Benefit of Various Dosing Strategies for Interleukin-2 in Metastatic Melanoma and Renal Cell Carcinoma. J Adv Pract Oncol. 2015 May-Jun;6(3):212-21.  
+
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+5. Suntharalingam G, Perry MR, Ward S, Brett SJ, Castello-Cortes A, Brunner MD, Panoskaltsis N. Cytokine storm in a phase 1 trial of the anti-CD28 monoclonal antibody TGN1412. N Engl J Med. 2006 Sep 7;355(10):1018-28. doi: 10.1056/NEJMoa063842.  
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+6. Segal NH, Logan TF, Hodi FS, McDermott D, Melero I, Hamid O, Schmidt H, Robert C, Chiarion-Sileni V, Ascierto PA, Maio M, Urba WJ, Gangadhar TC, Suryawanshi S, Neely J, Jure-Kunkel M, Krishnan S, Kohrt H, Sznol M, Levy R. Results from an Integrated Safety Analysis of Urelumab, an Agonist Anti-CD137 Monoclonal Antibody. Clin Cancer Res. 2017 Apr 15;23(8):1929-1936. doi: 10.1158/1078-0432.CCR-16-1272.  
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+7. Salomon R, Dahan R. Next Generation CD40 Agonistic Antibodies for Cancer Immunotherapy. Front Immunol. 2022 Jul 13;13:940674. doi: 10.3389/fimmu.2022.940674. PMID: 35911742; PMCID: PMC9326085.  
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+8. Car BD, Eng VM, Lipman JM, Anderson TD. The toxicology of interleukin-12: a review. Toxicol Pathol. 1999 Jan-Feb;27(1):58-63. doi: 10.1177/019262339902700112. PMID: 10367675.  
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+9. Wittrup KD, Kaufman HL, Schmidt MM, Irvine DJ. Intratumorally anchored cytokine therapy. Expert Opin Drug Deliv. 2022 Jun;19(6):725-732. doi: 10.1080/17425247.2022.2084070.
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[115, 88, 876, 161]]<|/det|>
+10. Sheth RA, Wehrenberg-Klee E, Patel SP, Brock KK, Fotiadis N, de Baere T. Intratumoral Injection of Immunotherapeutics: State of the Art and Future Directions. Radiology. 2024 Jul;312(1):e232654. doi: 10.1148/radiol.232654. PMID: 39078294; PMCID: PMC11294769.  
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+11. van Herpen CM, Looman M, Zonneveld M, Scharenborg N, de Wilde PC, van de Locht L, Merkx MA, Adema GJ, de Mulder PH. Intratumoral administration of recombinant human interleukin 12 in head and neck squamous cell carcinoma patients elicits a T-helper 1 profile in the locoregional lymph nodes. Clin Cancer Res. 2004 Apr 15;10(8):2626-35. doi: 10.1158/1078-0432.ccr-03-0304. PMID: 15102664.  
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+12. Battula S, Papastotisis G, Kaufman HL, Wittrup KD, Schmidt MM: Intratumoral aluminum hydroxide-anchored IL-12 drives potent antitumor activity by remodeling the tumor microenvironment. JCI Insight 8:e168224, 2023.  
+
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+13. Watford WT, Moriguchi M, Morinobu A, O'Shea JJ. The biology of IL-12: coordinating innate and adaptive immune responses. Cytokine Growth Factor Rev. 2003 Oct;14(5):361-8. doi: 10.1016/s1359-6101(03)00043-1. PMID: 12948519.  
+
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+14. Del Vecchio M, Bajetta E, Canova S, Lotze MT, Wesa A, Parmiani G, Anichini A. Interleukin-12: biological properties and clinical application. Clin Cancer Res. 2007 Aug 15;13(16):4677-85. doi: 10.1158/1078-0432.CCR-07-0776. PMID: 17699845.  
+
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+15. Atkins MB, Robertson MJ, Gordon M, et al: Phase I evaluation of intravenous recombinant human interleukin 12 in patients with advanced malignancies. Clin Cancer Res 3:409-417, 1997.  
+
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+16. Gollob JA, Mier JW, Veenstra K, et al: Phase I trial of twice-weekly intravenous interleukin12 in patients with metastatic renal cell cancer or malignant melanoma: ability to maintain IFN-gamma induction is associated with clinical response. Clin Cancer Res 6:1678-1692, 2000.  
+
+<|ref|>text<|/ref|><|det|>[[115, 787, 883, 832]]<|/det|>
+17. Gollob JA, Veenstra KG, Mier JW, Atkins MB: Agranulocytosis and hemolytic anemia in patients with renal cell cancer treated with interleukin-12. J Immunother 24:91-98, 2001.  
+
+<|ref|>text<|/ref|><|det|>[[115, 849, 830, 894]]<|/det|>
+18. Barbosa MMP, Kamerer R, Schmit J, et al: Preclinical evaluation of an anchored immunotherapy strategy with aluminum hydroxide-tethered interleukin-12 in dogs with
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[135, 88, 850, 134]]<|/det|>
+advanced malignant melanoma. Mol Cancer Ther, doi: 10.1158/1535-7163.MCT- 24- 0317, 2024.  
+
+<|ref|>text<|/ref|><|det|>[[115, 150, 870, 223]]<|/det|>
+19. Mistry HB, Hodson D, Battula S, Schmidt MM, Tighe R, Kaufman HL, Chassagnole C. A pharmacokinetic and pharmacodynamic model of an interleukin-12 (IL-12) anchored drug conjugate for the treatment of solid tumors. Mol Cancer Ther, in press.  
+
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+20. Fabian KP, Santiago-Sanchez G, Padget MR, Lassoued W, Allen CT, Battula S, Kaufman H, Hodge JW. Alum-anchored IL-12 combined with cytotoxic chemotherapy and immune checkpoint blockade enhanced antitumor immune responses in head and neck cancer models. J Immunother Cancer. 2024 Oct 23;12(10):e009712. doi: 10.1136/jitc-2024-009F712.  
+
+<|ref|>text<|/ref|><|det|>[[115, 353, 881, 504]]<|/det|>
+21. Anditbacka RH, Kaufman HL, Collichio F, Amatruda T, Senzer N, Chesney J, Delman KA, Spitler LE, Puzanov I, Agarwala SS, Milhem M, Cranmer L, Curti B, Lewis K, Ross M, Guthrie T, Linette GP, Daniels GA, Harrington K, Middleton MR, Miller WH Jr, Zager JS, Ye Y, Yao B, Li A, Doleman S, VanderWalde A, Gansert J, Coffin RS. Talimogene Laherparepvec Improves Durable Response Rate in Patients With Advanced Melanoma. J Clin Oncol. 2015 Sep 1;33(25):2780-8. doi: 10.1200/JCO.2014.58.3377. Epub 2015 May 26. PMID: 26014293.  
+
+<|ref|>text<|/ref|><|det|>[[115, 519, 844, 644]]<|/det|>
+22. Goldmacher GV, Khilnani AD, Anditbacka RHI, Luke JJ, Hodi FS, Marabelle A, Harrington K, Perrone A, Tse A, Madoff DC, Schwartz LH. Response Criteria for Intratumoral Immunotherapy in Solid Tumors: itRECIST. J Clin Oncol. 2020 Aug 10;38(23):2667-2676. doi: 10.1200/JCO.19.02985. Epub 2020 Jun 18. PMID: 32552274; PMCID: PMC7402995.
+
+<--- Page Split --->
+<|ref|>sub_title<|/ref|><|det|>[[115, 90, 293, 108]]<|/det|>
+## FIGURE LEGENDS  
+
+<|ref|>text<|/ref|><|det|>[[113, 124, 884, 380]]<|/det|>
+Figure 1. Systemic pharmacokinetics and pharmacodynamics of tolododekin alfa. (A) Serial serum samples were obtained as per the study protocol and mean plasma concentration of IL- 12- ABP versus time after Cycle 1 (left) and after Cycle 2 (right) are shown. Systemic levels of IL- 12- ABP are in general low, and there was a trend in dose dependent exposure observed at higher dosing cohorts. (B) Levels of circulating IFN- \(\gamma\) are shown by treatment cohort following Cycle 1 dosing. A transient and low systemic exposure of IFN- \(\gamma\) with a peak at 48 hours after dosing was observed. (C) Composite dose cohort maps summarizing the systemic levels of a panel of cytokines following treatment with repeated doses of tolododekin alfa. Tolododekin alfa treatment led to minimal changes in systemic cytokine levels supporting the local immune activation at the injected site by tolododekin alfa.  
+
+<|ref|>text<|/ref|><|det|>[[112, 395, 884, 783]]<|/det|>
+Figure 2. Tolododekin- alfa treatment results in remodeling of the tumor microenvironment. (A- D) Transcriptional analysis (Nanostring IO360TM Panel) of tumor tissue biopsies collected at baseline (C1D1) and 21 days post treatment after one dose of tolododekin alfa (C2D1). (A) The 'All Signatures' forest plot shows the differential expression between time points (C2D1 vs C1D1) for each signature on an unadjusted scale. The vertical axis is shown at fold change equal to zero, the 'black triangle' indicates a significant difference in the signature as assessed by univariate analysis (significance is not adjusted for multiple comparisons) (B) Volcano plot shows the most highly upregulated IL- 12 pathway related genes as indicated (C) The heatmap displays differential signature score per patient across all evaluable dosing cohorts, in all tumor types assessed, and in both injected and non- injected tumors, after one cycle (C2D1) and after three cycles of doing (C4D1) (D) Upregulation of the Tumor Inflammatory Score (TIS) and CD8<sup>+</sup> T cell gene signatures were associated with treatment outcomes (E- F) Representative IHC images of (E) CD8<sup>+</sup> and (F) PD- L1 staining of paired tumor biopsies pre and post- treatment (21 days) from individual patients across different dosing cohorts and tumor types as indicated. The bar graphs indicate corresponding %positive cells as scored by the pathologist.  
+
+<|ref|>text<|/ref|><|det|>[[114, 834, 883, 907]]<|/det|>
+Figure 3. Clinical activity of tolododekin alfa. (A) Spider plot depicting % change in sum diameters per RECIST in target lesions across all evaluable dosing cohorts. (B) Swimmer plot for individual patients across dosing cohorts. (C) Photograph of metastatic bladder cancer patient
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[113, 87, 884, 187]]<|/det|>
+before (left) and after four cycles (right); arrow indicates injected lesion and regression seen in injected and satellite lesions after injection. (D) Representative H&E images from individual patients pre- and post- treatment with tolododekin alfa at indicated times show decreased tumor cellularity and increased necrosis.
+
+<--- Page Split --->
+<|ref|>table_caption<|/ref|><|det|>[[115, 92, 685, 106]]<|/det|>
+Table 1. Patient Demographics and Baseline Disease Characteristics
+
+<|ref|>table<|/ref|><|det|>[[115, 120, 698, 880]]<|/det|>
+
+<table><tr><td></td><td>N=15</td></tr><tr><td>Age (years)</td><td></td></tr><tr><td>Median (min,max)</td><td>68.0 (36,79)</td></tr><tr><td>Sex</td><td></td></tr><tr><td>Female</td><td>9 (60.0)</td></tr><tr><td>Male</td><td>6 (40.0)</td></tr><tr><td>Race [n (%)]</td><td></td></tr><tr><td>White</td><td>12 (80.0)</td></tr><tr><td>Not reported</td><td>3 (20.0)</td></tr><tr><td>Ethnicity</td><td></td></tr><tr><td>Not Hispanic or Latino</td><td>14 (93.3)</td></tr><tr><td>Not Reported</td><td>1 (6.7)</td></tr><tr><td>Baseline ECOG [n (%)]</td><td></td></tr><tr><td>0</td><td>9 (60.0)</td></tr><tr><td>1</td><td>6 (40.0)</td></tr><tr><td>Solid Tumor Type [n, (%)]</td><td></td></tr><tr><td>Melanoma</td><td>7 (46.7)</td></tr><tr><td>Head and Neck</td><td>4 (26.7)</td></tr><tr><td>Breast Cancer</td><td>2 (13.3)</td></tr><tr><td>Bladder Cancer</td><td>1 (6.7)</td></tr><tr><td>Cutaneous Squamous Cell Carcinoma</td><td>0</td></tr><tr><td>Other Cancer Type</td><td>1 (6.7)</td></tr><tr><td>Prior Systemic Therapies [n, (%)]</td><td></td></tr><tr><td>≤3</td><td>4 (26.7)</td></tr><tr><td>&gt;3</td><td>11 (73.3)</td></tr><tr><td>Prior IO Therapies [n, (%)]</td><td></td></tr><tr><td>Yes</td><td>13 (86.7)</td></tr><tr><td>No</td><td>2 (13.3)</td></tr><tr><td>Number of doses administered</td><td></td></tr><tr><td>Median (min,max)</td><td>4 (1,8)</td></tr><tr><td>Number of tumors injected per patient</td><td></td></tr><tr><td>Median (min,max)</td><td>1 (1,6)</td></tr><tr><td>Number of volume (mL) used per patient</td><td></td></tr><tr><td>Median (min,max)</td><td>8 (0.5,32)</td></tr></table>
+
+<--- Page Split --->
+<|ref|>table_caption<|/ref|><|det|>[[88, 120, 461, 139]]<|/det|>
+Table 2. All Related Treatment Emergent Adverse Events
+
+<|ref|>table<|/ref|><|det|>[[88, 152, 835, 789]]<|/det|>
+
+<table><tr><td></td><td>Cohort 1<br>2 μg/mL<br>(N=1)</td><td>Cohort 2<br>6 μg/mL<br>(N=1)</td><td>Cohort 3<br>20 μg/mL<br>(N=4)</td><td>Cohort 4<br>60 μg/mL<br>(N=3)</td><td>Cohort 5<br>120 μg/mL<br>(N=3)</td><td>Cohort 6<br>250 μg/mL<br>(N=3)</td><td>Highest<br>Grade</td><td>Total<br>(N=15)</td></tr><tr><td>Preferred Term</td><td>n(%)</td><td>n(%)</td><td>n(%)</td><td>n(%)</td><td>n(%)</td><td>n (%)</td><td></td><td>n(%)</td></tr><tr><td>Any study treatment related AE</td><td>0</td><td>0</td><td>3(75.0)</td><td>1(33.3)</td><td>2(66.7)</td><td>2(66.7)</td><td>2</td><td>8(53.3)</td></tr><tr><td>Fatigue</td><td>0</td><td>0</td><td>0</td><td>1(33.3)</td><td>1(33.3)</td><td>0</td><td>2</td><td>2(13.3)</td></tr><tr><td>Influenza like illness</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1(33.3)</td><td>1(33.3)</td><td>2</td><td>2(13.3)</td></tr><tr><td>Myalgia</td><td>0</td><td>0</td><td>1(25.0)</td><td>0</td><td>1(33.3)</td><td>0</td><td>2</td><td>2(13.3)</td></tr><tr><td>Abdominal distension</td><td>0</td><td>0</td><td>1(25.0)</td><td>0</td><td>0</td><td>0</td><td>1</td><td>1(6.7)</td></tr><tr><td>Abdominal pain</td><td>0</td><td>0</td><td>1(25.0)</td><td>0</td><td>0</td><td>0</td><td>1</td><td>1(6.7)</td></tr><tr><td>Arthralgia</td><td>0</td><td>0</td><td>1(25.0)</td><td>0</td><td>0</td><td>0</td><td>2</td><td>1(6.7)</td></tr><tr><td>Chills</td><td>0</td><td>0</td><td>1(25.0)</td><td>0</td><td>0</td><td>0</td><td>1</td><td>1(6.7)</td></tr><tr><td>Conjunctivitis</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1(33.3)</td><td>0</td><td>2</td><td>1(6.7)</td></tr><tr><td>Cough</td><td>0</td><td>0</td><td>1(25.0)</td><td>0</td><td>0</td><td>0</td><td>1</td><td>1(6.7)</td></tr><tr><td>Cytokine release syndrome</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1(33.3)</td><td>0</td><td>1</td><td>1(6.7)</td></tr><tr><td>Diarrhea</td><td>0</td><td>0</td><td>1(25.0)</td><td>0</td><td>0</td><td>0</td><td>1</td><td>1(6.7)</td></tr><tr><td>Dry mouth</td><td>0</td><td>0</td><td>1(25.0)</td><td>0</td><td>0</td><td>0</td><td>1</td><td>1(6.7)</td></tr><tr><td>Face oedema</td><td>0</td><td>0</td><td>1(25.0)</td><td>0</td><td>0</td><td>0</td><td>2</td><td>1(6.7)</td></tr><tr><td>Injection site pain</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1(33.3)</td><td>2</td><td>1(6.7)</td></tr><tr><td>Malaise</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1(33.3)</td><td>0</td><td>1</td><td>1(6.7)</td></tr><tr><td>Mucosal inflammation</td><td>0</td><td>0</td><td>1(25.0)</td><td>0</td><td>0</td><td>0</td><td>1</td><td>1(6.7)</td></tr><tr><td>Nasal congestion</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1(33.3)</td><td>1</td><td>1(6.7)</td></tr><tr><td>Pruritus</td><td>0</td><td>0</td><td>1(25.0)</td><td>0</td><td>0</td><td>0</td><td>1</td><td>1(6.7)</td></tr><tr><td>Pyrexia</td><td>0</td><td>0</td><td>1(25.0)</td><td>0</td><td>0</td><td>0</td><td>1</td><td>1(6.7)</td></tr><tr><td>Vomiting</td><td>0</td><td>0</td><td>1(25.0)</td><td>0</td><td>0</td><td>0</td><td>1</td><td>1(6.7)</td></tr></table>
+
+<--- Page Split --->
+<|ref|>table<|/ref|><|det|>[[87, 152, 880, 732]]<|/det|>
+<|ref|>table_caption<|/ref|><|det|>[[88, 115, 565, 140]]<|/det|>
+Table 3. Summary of RECISTv1.1 and best objective responses by cohort   
+
+<table><tr><td></td><td></td><td>Cohort 1<br>2 μg/mL<br>N=1</td><td>Cohort 2<br>10 μg/mL<br>N=1</td><td>Cohort 3<br>20 μg/mL<br>N=4</td><td>Cohort 4<br>60 μg/mL<br>N=3</td><td>Cohort 5<br>120 μg/mL<br>N=3</td><td>Cohort 6<br>250 μg/mL<br>N=3</td><td>Total<br>N=15</td></tr><tr><td rowspan="5">Overall<br>Objective<br>Response<br>RECIST 1.1</td><td>Complete</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Partial</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Stable</td><td>1</td><td>2</td><td>3</td><td>1</td><td>2</td><td>9</td><td></td></tr><tr><td>Progressive</td><td></td><td>2</td><td></td><td>2</td><td>1</td><td>5</td><td></td></tr><tr><td>Not<br>Evaluable</td><td>1</td><td></td><td></td><td></td><td></td><td></td><td>1</td></tr><tr><td rowspan="5">Best<br>Objective<br>Response<br>Target<br>Lesions</td><td>Disease<br>Control</td><td>1</td><td>2</td><td>3</td><td>1</td><td>2</td><td>9</td><td></td></tr><tr><td>Complete</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Partial</td><td></td><td>1</td><td></td><td></td><td></td><td>1</td><td></td></tr><tr><td>Stable</td><td>1</td><td>2</td><td>3</td><td>1</td><td>2</td><td>9</td><td></td></tr><tr><td>Progressive</td><td></td><td>1</td><td></td><td>2</td><td>1</td><td>4</td><td></td></tr><tr><td></td><td>Not<br>Evaluable</td><td>1</td><td></td><td></td><td></td><td></td><td>1</td><td></td></tr></table>
+
+<--- Page Split --->
+<|ref|>image<|/ref|><|det|>[[99, 155, 850, 899]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[88, 118, 145, 140]]<|/det|>
+<center>Figure 2 </center>
+
+<--- Page Split --->
+<|ref|>image<|/ref|><|det|>[[100, 170, 444, 508]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[90, 118, 145, 140]]<|/det|>
+<center>Figure 3 </center>  
+
+<|ref|>image<|/ref|><|det|>[[100, 600, 222, 808]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[100, 599, 221, 620]]<|/det|>
+<center>C. Baseline </center>  
+
+<|ref|>image<|/ref|><|det|>[[325, 600, 499, 808]]<|/det|>  
+
+<|ref|>image<|/ref|><|det|>[[520, 204, 860, 520]]<|/det|>  
+
+<|ref|>image<|/ref|><|det|>[[500, 550, 874, 880]]<|/det|>
+
+<--- Page Split --->
+<|ref|>title<|/ref|><|det|>[[114, 92, 306, 106]]<|/det|>
+# Supplementary Tables
+
+<|ref|>title<|/ref|><|det|>[[114, 122, 787, 137]]<|/det|>
+# Supplementary Table 1. Determination of dose volume based on tumor diameter
+
+<|ref|>table<|/ref|><|det|>[[115, 180, 750, 404]]<|/det|>
+
+<table><tr><td>Lesion Size<br>(Maximum Diameter)</td><td>Volume of tolododekin alfa Injection*</td></tr><tr><td>&lt;1.5 cm</td><td>Up to 0.5 mL</td></tr><tr><td>&gt;1.5 to 2.5 cm</td><td>Up to 1.0 mL</td></tr><tr><td>&gt;2.5 to 5.0 cm</td><td>Up to 2 mL</td></tr><tr><td>&gt;5.0 to 10.0 cm</td><td>Up to 4 mL</td></tr><tr><td>&gt;10.0 cm</td><td>Up to 5 mL</td></tr><tr><td colspan="2">*Maximum of 5 mL at each visit.</td></tr></table>
+
+<--- Page Split --->
+<|ref|>sub_title<|/ref|><|det|>[[88, 116, 524, 142]]<|/det|>
+## Supplementary Table 2. Protocol definition of dose limiting toxicity  
+
+<|ref|>sub_title<|/ref|><|det|>[[99, 168, 577, 192]]<|/det|>
+## All Toxicities Graded Using NCI CTCAE v5.0 Based on Investigator Assessment  
+
+<|ref|>text<|/ref|><|det|>[[98, 202, 901, 245]]<|/det|>
+The occurrence of any of the following toxicities during the DLT evaluation period will be considered a DLT, if assessed by the Investigator to be possibly, probably, or definitely related to study treatment administration:  
+
+<|ref|>text<|/ref|><|det|>[[99, 248, 388, 270]]<|/det|>
+1. Grade 4 nonhematologic toxicity (not laboratory)  
+
+<|ref|>text<|/ref|><|det|>[[99, 283, 520, 306]]<|/det|>
+2. Grade 4 hematologic toxicity lasting \(\geq 7\) days, except thrombocytopenia:  
+
+<|ref|>text<|/ref|><|det|>[[168, 318, 415, 339]]<|/det|>
+·Grade 4 thrombocytopenia of any duration  
+
+<|ref|>text<|/ref|><|det|>[[168, 345, 579, 366]]<|/det|>
+·Grade 3 thrombocytopenia associated with clinically significant bleeding  
+
+<|ref|>text<|/ref|><|det|>[[99, 373, 925, 435]]<|/det|>
+3. Any nonhematologic AE \(\geq\) Grade 3 in severity should be considered a DLT, with the following exceptions: Grade 3 fatigue lasting \(\leq 3\) days; Grade 3 diarrhea, nausea, or vomiting without use of anti-emetics or anti-diarrheals per SOC; Grade 3 rash without use of corticosteroids or anti-inflammatory agents per SOC  
+
+<|ref|>text<|/ref|><|det|>[[99, 448, 451, 471]]<|/det|>
+4. Any Grade 3 or Grade 4 nonhematologic laboratory value if:  
+
+<|ref|>text<|/ref|><|det|>[[168, 483, 645, 620]]<|/det|>
+·Clinically significant medical intervention is required to treat the participant, or ·The abnormality leads to hospitalization, or ·The abnormality persists for \(>1\) week ·The abnormality results in a Drug induced Liver Injury (DILI) ·Exceptions: Clinically nonsignificant, treatable, or reversible laboratory abnormalities  
+
+<|ref|>text<|/ref|><|det|>[[99, 630, 344, 652]]<|/det|>
+5. Febrile neutropenia Grade 3 or Grade 4:  
+
+<|ref|>text<|/ref|><|det|>[[168, 661, 923, 760]]<|/det|>
+·Grade 3 is defined as ANC \(< 1000 / \mathrm{mm}^3\) with a single temperature of \(>38.3^{\circ}\mathrm{C}\) ( \(101^{\circ}\mathrm{F}\) ) or a sustained temperature of \(\geq 38^{\circ}\mathrm{C}\) ( \(100.4^{\circ}\mathrm{F}\) ) for more than 1 hour ·Grade 4 is defined as ANC \(< 1000 / \mathrm{mm}^3\) with a single temperature of \(>38.3^{\circ}\mathrm{C}\) ( \(101^{\circ}\mathrm{F}\) ) or a sustained temperature of \(\geq 38^{\circ}\mathrm{C}\) ( \(100.4^{\circ}\mathrm{F}\) ) for more than 1 hour, with life-threatening consequences and urgent intervention indicated  
+
+<|ref|>text<|/ref|><|det|>[[99, 763, 205, 782]]<|/det|>
+6. Grade 5 toxicity  
+
+<|ref|>text<|/ref|><|det|>[[99, 789, 377, 810]]<|/det|>
+7. Grade \(\geq 3\) Cytokine Release Syndrome (CRS)
+
+<--- Page Split --->
+<|ref|>table_caption<|/ref|><|det|>[[113, 92, 867, 107]]<|/det|>
+Supplementary Table 3. Schedule of pharmacokinetic and pharmacodynamic assessments
+
+<|ref|>table<|/ref|><|det|>[[115, 120, 904, 648]]<|/det|>
+
+<table><tr><td rowspan="2">Cycle and Day</td><td rowspan="2">Collection Timepoints</td><td colspan="2">PK and Immunogenicity<br>Sampling</td><td colspan="2">PD Biomarkers<br>Sampling</td></tr><tr><td>PK Collections (Serum)</td><td>ADA<br>Collections (Serum)</td><td>Serum<br>Cytokine (Serum)</td><td>PD<br>Biomarkers (Tissue)</td></tr><tr><td rowspan="4">C1D1</td><td>Pre*-dose</td><td>X</td><td>X</td><td>X</td><td>Biopsy</td></tr><tr><td>30 min post-dose (± 5 min)</td><td>X</td><td></td><td></td><td></td></tr><tr><td>3 hrs post-dose (± 15 min)</td><td>X</td><td></td><td></td><td></td></tr><tr><td>6 hrs post-dose (± 15 min)</td><td>X</td><td></td><td>X</td><td></td></tr><tr><td>C1D2</td><td>24 hrs post-dose (± 2 hrs)</td><td>X</td><td></td><td>X</td><td></td></tr><tr><td>C1D3</td><td>48 hrs post-dose (± 2 hrs)</td><td>X</td><td></td><td>X</td><td></td><td></td></tr><tr><td rowspan="4">C2D1</td><td>Pre-dose</td><td>X</td><td>X</td><td>X</td><td>Biopsy</td></tr><tr><td>30 min post-dose (± 5 min)</td><td>X</td><td></td><td></td><td></td></tr><tr><td>3 hrs post-dose (± 15 min)</td><td>X</td><td></td><td></td><td></td></tr><tr><td>6 hrs post-dose (± 15 min)</td><td>X</td><td></td><td>X</td><td></td></tr><tr><td>C2D2</td><td>24 hrs post-dose (± 2 hrs)</td><td>X</td><td></td><td>X</td><td></td></tr><tr><td>C2D3</td><td>48 hrs post-dose (± 2 hrs)</td><td>X</td><td></td><td>X</td><td></td><td></td></tr><tr><td rowspan="2">C4D1</td><td>Pre-dose</td><td>X</td><td>X</td><td>X</td><td>Biopsy</td></tr><tr><td>6 hrs post-dose (± 15 min)</td><td>X</td><td></td><td>X</td><td></td></tr><tr><td>C5D1</td><td>Pre-dose</td><td>X</td><td></td><td>X</td><td></td></tr><tr><td>C8D1</td><td>Pre-dose</td><td>X</td><td></td><td>X</td><td></td></tr><tr><td>EOT</td><td></td><td></td><td></td><td></td><td>Biopsy</td></tr><tr><td>EOS</td><td></td><td></td><td>X</td><td></td><td></td></tr></table>
+
+<|ref|>text<|/ref|><|det|>[[113, 679, 881, 710]]<|/det|>
+Abbreviations: ADA = anti-drug antibody; C = cycle; D = day; hrs = hours; min = minute; PK = pharmacokinetic;PD = pharmacodynamic; EOS: End of Study; EOT: End of Treatment.
+
+<--- Page Split --->
+<|ref|>table<|/ref|><|det|>[[85, 150, 945, 787]]<|/det|>
+<|ref|>table_caption<|/ref|><|det|>[[88, 115, 492, 140]]<|/det|>
+Supplementary Table 4. Prior therapies for each study subject   
+
+<table><tr><td>Subject</td><td>Diagnosis</td><td>Prior Therapies (starting with most recent)</td></tr><tr><td>101-101</td><td>Melanoma</td><td>XTX202 (adjuvant), T-VEC (adjuvant), Pembrolizumab – best response PD to all</td></tr><tr><td>101-201</td><td>Apocrine Adenocarcinoma</td><td>Carboplatin/Paclitaxel, Carboplatin/Paclitaxel/Herceptin – best response SD to Herceptin</td></tr><tr><td>104-301</td><td>Bladder Cancer</td><td>Nivolumab, Enfortumab Vedotin, Atezolizumab, Herceptin/Pertuzumab, Cisplatin/Gemcitabine, BCG – best response CR to Atezo</td></tr><tr><td>101-302</td><td>SCCHN</td><td>Carboplatin (neoadjuvant), Paclitaxel (adjuvant), Pembrolizumab (adjuvant), Fluorouracil, Docetaxel, Cisplatin – best response PD to all</td></tr><tr><td>104-303</td><td>Breast Cancer (HER2/ER+)</td><td>Elacestrant, Palazestrant, Everolimus/Tamoxifen, Falodex/Ibrance, Exemestane/Zoladex, Tamoxifen, Doxorubicin/Cyclophosphamide/Paclitaxel (adjuvant) – best response CR to Exemestane/Zoladex</td></tr><tr><td>201-304</td><td>Melanoma (acral)</td><td>Pembrolizumab, Ipilimumab, Dacarbazine – best response PD to all</td></tr><tr><td>105-401</td><td>Melanoma (BRAF+)</td><td>Ipilimumab, Dabrafenib/Mekinist, Nivolumab (adjuvant) – best response SD to all</td></tr><tr><td>105-402</td><td>Melanoma</td><td>Duvelisib/Nivolumab, Pembrolizumab, T3011, Ipilimumab/Nivolumab, Pembrolizumab, CMP-001, Pembrolizumab – best response PD to all</td></tr><tr><td>101-403</td><td>SCCHN</td><td>Pembrolizumab, Cisplatin – best response CR to Cisplatin</td></tr><tr><td>201-501</td><td>Melanoma (ROS1+)</td><td>SGN-BB228, CA027-002/Ipilimumab/Nivolumab, Pembrolizumab, IL-2 – best response PD to all</td></tr><tr><td>104-502</td><td>Breast Cancer (PD-L1+)</td><td>Futibatinib, Trodelvy, Carboplatin/Gemcitabine/Pembrolizumab, Capecitabine, Doxorubicin/Cyclophosphamide/Paclitaxel – best response CR to Capecitabine</td></tr><tr><td>101-503</td><td>SCCHN</td><td>Pembrolizumab, Carboplatin/Paclitaxel/Pembrolizumab – best response PD to all</td></tr><tr><td>101-601</td><td>SCCHN</td><td>Paclitaxel/Carboplatin, Pembrolizumab/10E8, Pembrolizumab/ALX148 – best response PD to all</td></tr><tr><td>105-602</td><td>Melanoma</td><td>Nivolumab, Ipilimumab/Pembrolizumab – best response PD to all</td></tr></table>
+
+<--- Page Split --->
+<|ref|>table<|/ref|><|det|>[[85, 222, 936, 728]]<|/det|>
+<|ref|>table_caption<|/ref|><|det|>[[88, 186, 696, 211]]<|/det|>
+Supplementary Table 5. Summary of tolododekin alfa systemic pharmacokinetics after cycle 1   
+
+<table><tr><td>Patient ID</td><td>Dose Level (μg/mL)</td><td>Total Dose (μg)</td><td>AUC0-48 (h*pg/mL)</td><td>AUC0-0-inf (h*pg/mL)</td><td>AUC0-0-inf (h*pg/mL)(pg/mL)</td><td>Cmax (pg/mL)</td><td>Clast (pg/mL)</td><td>Tmax (h)</td><td>Tlast (h)</td><td>T1/2 (h)</td><td>Cl/F (L/h)</td><td>Vd/F (L)</td></tr><tr><td>101-101</td><td>2</td><td>8</td><td>/</td><td>/</td><td>/</td><td>BQ</td><td>BQ</td><td>/</td><td>/</td><td>/</td><td>/</td><td>/</td></tr><tr><td>101-201</td><td>6</td><td>18</td><td>2541.62</td><td>689.25</td><td>/</td><td>19.2</td><td>12.2</td><td>48.00</td><td>168.00</td><td>/</td><td>/</td><td>/</td></tr><tr><td>104-301</td><td>20</td><td>80</td><td>/</td><td>/</td><td>/</td><td>BQ</td><td>BQ</td><td>/</td><td>/</td><td>/</td><td>/</td><td>/</td></tr><tr><td>101-302</td><td>20</td><td>60</td><td>1540.71</td><td>1540.71</td><td>1877.07</td><td>64.7</td><td>12.2</td><td>0.50</td><td>48.00</td><td>19.11</td><td>53.27</td><td>1468.80</td></tr><tr><td>104-303</td><td>20</td><td>80</td><td>12489.86</td><td>5712.23</td><td>16589.60</td><td>173.0</td><td>34.1</td><td>6.00</td><td>168.00</td><td>83.34</td><td>6.03</td><td>724.71</td></tr><tr><td>105-401</td><td>60</td><td>30</td><td>/</td><td>/</td><td>/</td><td>BQ</td><td>BQ</td><td>/</td><td>/</td><td>/</td><td>/</td><td>/</td></tr><tr><td>105-402</td><td>60</td><td>240</td><td>/</td><td>/</td><td>/</td><td>BQ</td><td>BQ</td><td>/</td><td>/</td><td>/</td><td>/</td><td>/</td></tr><tr><td>105-403</td><td>60</td><td>240</td><td>12422.48</td><td>4902.67</td><td>15408.74</td><td>139.0</td><td>28.5</td><td>0.50</td><td>168.00</td><td>72.63</td><td>19.47</td><td>2040.03</td></tr><tr><td>201-501</td><td>120</td><td>300</td><td>12509.65</td><td>7263.10</td><td>15559.44</td><td>365.0</td><td>27.8</td><td>3.00</td><td>168.00</td><td>76.04</td><td>38.56</td><td>4230.41</td></tr><tr><td>104-502</td><td>120</td><td>480</td><td>34349.47</td><td>16469.47</td><td>/</td><td>693.0</td><td>151.0</td><td>0.50</td><td>168.00</td><td>/</td><td>/</td><td>/</td></tr><tr><td>101-503</td><td>120</td><td>120</td><td>8866.92</td><td>3557.47</td><td>/</td><td>103.0</td><td>24.6</td><td>24.00</td><td>168.00</td><td>/</td><td>/</td><td>/</td></tr><tr><td>101-601</td><td>250</td><td>250</td><td>2983.10</td><td>632.10</td><td>/</td><td>20.3</td><td>18.9</td><td>48.00</td><td>168.00</td><td>/</td><td>/</td><td>/</td></tr></table>  
+
+<|ref|>text<|/ref|><|det|>[[88, 768, 750, 790]]<|/det|>
+Note: PK parameters were not derived for patient 105- 603 in Cycle 1 since only one reportable concentration was reported  
+
+<|ref|>text<|/ref|><|det|>[[88, 803, 911, 872]]<|/det|>
+Abbreviations: AUC, area under the time- concentration curve; BQ, Below limit of quantitation; \(C_{\max}\) , maximum drug concentration; \(C_{\text{last}}\) , last measurable drug concentration; \(T_{\text{max}}\) , time to reach maximum serum concentration, \(T_{\text{last}}\) , time of last measurable drug concentration, \(T_{1 / 2}\) , half- life, CL/F, apparent clearance; Vd/F, apparent volume of distribution; /, not calculable.
+
+<--- Page Split --->
+<|ref|>table_caption<|/ref|><|det|>[[114, 90, 770, 107]]<|/det|>
+Supplementary Table 6. Cmax as fraction of total dose after treatment Cycle 1
+
+<|ref|>table<|/ref|><|det|>[[115, 118, 927, 470]]<|/det|>
+
+<table><tr><td>Patient ID</td><td>Dose<br>(μg/mL)</td><td>Total Dose (μg)</td><td>Patient<br>Bodyweight (kg)</td><td>Estimated<br>Serum Volume (mL)</td><td>Cmax<br>(pg/mL)</td><td>Cmax as % of Total Dose</td></tr><tr><td>101-201</td><td>6</td><td>18</td><td>101.20</td><td>3636</td><td>19.20</td><td>0.43</td></tr><tr><td>101-302</td><td>20</td><td>80</td><td>65.70</td><td>2628</td><td>64.70</td><td>0.21</td></tr><tr><td>104-303</td><td>20</td><td>60</td><td>58.50</td><td>2340</td><td>173.00</td><td>0.67</td></tr><tr><td>105-403</td><td>60</td><td>240</td><td>93.80</td><td>3752</td><td>139.00</td><td>0.22</td></tr><tr><td>201-501</td><td>120</td><td>300</td><td>86.20</td><td>3448</td><td>365.00</td><td>0.42</td></tr><tr><td>104-502</td><td>120</td><td>480</td><td>97.00</td><td>3880</td><td>693.00</td><td>0.56</td></tr><tr><td>101-503</td><td>120</td><td>120</td><td>75.60</td><td>3024</td><td>103.00</td><td>0.26</td></tr><tr><td>101-601</td><td>250</td><td>250</td><td>54.90</td><td>2196</td><td>20.3</td><td>0.02</td></tr><tr><td>101-603</td><td>250</td><td>250</td><td>94.2</td><td>3768</td><td>18.2</td><td>0.05</td></tr></table>
+
+<|ref|>text<|/ref|><|det|>[[114, 506, 407, 518]]<|/det|>
+Abbreviations: \(C_{max}\) , maximum drug concentration
+
+<|ref|>text<|/ref|><|det|>[[114, 520, 531, 533]]<|/det|>
+Estimated serum volume calculation \(=\text {patient}\)  bodyweight in \(kg*40mL\) 
+
+<|ref|>text<|/ref|><|det|>[[114, 533, 813, 548]]<|/det|>
+\(C_{max}\)  as fraction of total dose calculation \(=((C_{max}\)  in \(pg/mL/10^{5})*\)  estimated serum volume in mL) / total dose in \(\mu g*100\)
+
+<--- Page Split --->
+<|ref|>table<|/ref|><|det|>[[87, 150, 928, 693]]<|/det|>
+<|ref|>table_caption<|/ref|><|det|>[[88, 115, 559, 140]]<|/det|>
+Supplementary Table 7. Systemic PK parameters after treatment cycle 2   
+
+<table><tr><td>Patient ID</td><td>Dose Level (μg/mL)</td><td>Total Dose (μg/mL)</td><td>AUC0-t (h*pg/mL)</td><td>AUC0-48 (h*pg/mL)</td><td>AUC0-inf (h*pg/mL)</td><td>Cmax (pg/mL)</td><td>Clast (pg/mL)</td><td>Tmax (h)</td><td>Tlast (h)</td><td>T1/2 (h)</td><td>Cl/F (L/h)</td><td>Vd/F (L)</td></tr><tr><td>101-201</td><td>6</td><td>18</td><td>/</td><td>/</td><td>/</td><td>22.9</td><td>22.9</td><td>48.00</td><td>48.00</td><td>/</td><td>/</td><td>/</td></tr><tr><td>104-301</td><td>20</td><td>80</td><td>659.61</td><td>944.67</td><td>/</td><td>50.0</td><td>17.5</td><td>0.50</td><td>24.00</td><td>/</td><td>/</td><td>/</td></tr><tr><td>101-302</td><td>20</td><td>80</td><td>83.30</td><td>/</td><td>/</td><td>15.4</td><td>12.7</td><td>0.50</td><td>6.00</td><td>/</td><td>/</td><td>/</td></tr><tr><td>104-303</td><td>20</td><td>60</td><td>8948.29</td><td>8948.29</td><td>/</td><td>232</td><td>123</td><td>6.00</td><td>48.00</td><td>/</td><td>/</td><td>/</td></tr><tr><td>105-401</td><td>60</td><td>36</td><td>602.25</td><td>602.25</td><td>/</td><td>13.8</td><td>11.3</td><td>3.00</td><td>48.00</td><td>/</td><td>/</td><td>/</td></tr><tr><td>105-402</td><td>60</td><td>240</td><td>907.74</td><td>907.74</td><td>/</td><td>20.7</td><td>18.8</td><td>24.00</td><td>48.00</td><td>/</td><td>/</td><td>/</td></tr><tr><td>105-403</td><td>60</td><td>240</td><td>1831.84</td><td>1831.84</td><td>/</td><td>63.9</td><td>35.9</td><td>24.00</td><td>48.00</td><td>/</td><td>/</td><td>/</td></tr><tr><td>201-501</td><td>120</td><td>600</td><td>15281.83</td><td>15281.83</td><td>/</td><td>1010</td><td>88.2</td><td>0.50</td><td>48.00</td><td>/</td><td>/</td><td>/</td></tr><tr><td>104-502</td><td>120</td><td>480</td><td>19337.33</td><td>19337.33</td><td>/</td><td>541</td><td>541</td><td>48.00</td><td>48.00</td><td>/</td><td>/</td><td>/</td></tr><tr><td>101-503</td><td>120</td><td>240</td><td>2566.40</td><td>2566.40</td><td>/</td><td>75.3</td><td>53.8</td><td>24.00</td><td>48.00</td><td>/</td><td>/</td><td>/</td></tr><tr><td>101-601</td><td>250</td><td>250</td><td>1376.65</td><td>1376.65</td><td>/</td><td>59.5</td><td>19.5</td><td>3.00</td><td>48.00</td><td>/</td><td>/</td><td>/</td></tr></table>  
+
+<|ref|>text<|/ref|><|det|>[[88, 707, 901, 780]]<|/det|>
+Abbreviations: AUC, area under the time- concentration curve; \(C_{\mathrm{max}}\) , maximum drug concentration; \(C_{\mathrm{last}}\) , last measurable drug concentration; \(T_{\mathrm{max}}\) , time to reach maximum serum concentration, \(T_{\mathrm{last}}\) , time of last measurable drug concentration, \(T_{1 / 2}\) , half- life, CL/F, apparent clearance; Vd/F, apparent volume of distribution. \(l =\) not calculable.
+
+<--- Page Split --->
+<|ref|>table<|/ref|><|det|>[[113, 115, 922, 666]]<|/det|>
+<|ref|>table_caption<|/ref|><|det|>[[113, 90, 687, 108]]<|/det|>
+Supplementary Table 8. IL-12-ABP anti-drug antibody (ADA) levels   
+
+<table><tr><td>Cohort #</td><td>Subject</td><td>Timepoints</td><td>Mean Response</td><td>%CV</td><td>Assay Cutpoint</td><td>Screening Assay Result</td><td>% Inhibition</td><td>Overall Result</td></tr><tr><td rowspan="2">Cohort 1 (2 μg/mL)</td><td>101-101</td><td>C1D1 Pre-dose</td><td>61</td><td>2.318</td><td>65.265</td><td>Negative Not Collected</td><td>NA</td><td>Negative</td></tr><tr><td>101-101</td><td>C2D1 Pre-dose</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td rowspan="3">Cohort 2 (6 μg/mL)</td><td>101-201</td><td>C1D1 Pre-dose</td><td>63.5</td><td>1.114</td><td>65.265</td><td>Negative</td><td>NA</td><td>Negative</td></tr><tr><td>101-201</td><td>C2D1 Pre-dose</td><td>63</td><td>2.245</td><td>65.265</td><td>Negative</td><td>NA</td><td>Negative</td></tr><tr><td>101-201</td><td>C4D1 Pre-dose</td><td>66.5</td><td>1.063</td><td>76.95</td><td>Negative</td><td>NA</td><td>Negative</td></tr><tr><td rowspan="5">Cohort 3 (20 μg/mL)</td><td>104-301</td><td>C1D1 Pre-dose</td><td>65</td><td>0.00</td><td>65.265</td><td>Negative</td><td>NA</td><td>Negative</td></tr><tr><td>104-301</td><td>C2D1 Pre-dose</td><td>66</td><td>4.285</td><td>76.95</td><td>Negative</td><td>NA</td><td>Negative</td></tr><tr><td>104-301</td><td>C4D1 Predose</td><td>58</td><td>4.877</td><td>69.682</td><td>Negative</td><td>NA</td><td>Negative</td></tr><tr><td>104-301</td><td>EOS</td><td>50.5</td><td>9.801</td><td>69.682</td><td>Negative</td><td>NA</td><td>Negative</td></tr><tr><td>Cohort 3 (20 μg/mL)</td><td>101-302</td><td>C1D1 Pre-dose</td><td>66.5</td><td>5.317</td><td>76.95</td><td>Negative</td><td>NA</td><td>Negative</td></tr><tr><td rowspan="5">Cohort 4 (60 μg/mL)</td><td>101-302</td><td>C2D1 Pre-dose</td><td>62.5</td><td>3.394</td><td>76.95</td><td>Negative</td><td>NA</td><td>Negative</td></tr><tr><td>101-302</td><td>EOS</td><td>62.5</td><td>1.131</td><td>69.682</td><td>Negative</td><td>NA</td><td>Negative</td></tr><tr><td>104-303</td><td>C1D1 Predose</td><td>55</td><td>7.714</td><td>69.682</td><td>Negative</td><td>NA</td><td>Negative</td></tr><tr><td>104-303</td><td>C2D1 Predose</td><td>55.5</td><td>1.274</td><td>69.682</td><td>Negative</td><td>NA</td><td>Negative</td></tr><tr><td>104-303</td><td>C4D1 Predose</td><td>60</td><td>0.00</td><td>69.682</td><td>Negative</td><td>NA</td><td>Negative</td></tr><tr><td rowspan="5">Cohort 4 (60 μg/mL)</td><td>105-401</td><td>C1D1 Pre-dose</td><td>65</td><td>4.351</td><td>76.95</td><td>Negative</td><td>NA</td><td>Negative</td></tr><tr><td>105-401</td><td>C2D1 Pre-dose</td><td>69.5</td><td>1.017</td><td>76.95</td><td>Negative</td><td>NA</td><td>Negative</td></tr><tr><td>105-401</td><td>C4D1 Predose</td><td>14869.5</td><td>2.639</td><td>69.682</td><td>Confirmatory</td><td>99.3</td><td>Positive</td></tr><tr><td>105-401</td><td>EOS</td><td>15021</td><td>1.45</td><td>64.41</td><td>Confirmatory</td><td>99.3</td><td>Positive</td></tr><tr><td>Cohort 4 (60 μg/mL)</td><td>105-402</td><td>C1D1 Pre-dose</td><td>63</td><td>2.245</td><td>76.95</td><td>Negative</td><td>NA</td><td>Negative</td></tr><tr><td rowspan="5">Cohort 5 (120 μg/mL)</td><td>105-402</td><td>C2D1 Predose</td><td>57</td><td>0</td><td>69.682</td><td>Negative</td><td>NA</td><td>Negative</td></tr><tr><td>105-402</td><td>C4D1 Predose</td><td>61.5</td><td>1.15</td><td>69.682</td><td>Negative</td><td>NA</td><td>Negative</td></tr><tr><td>101-403</td><td>C1D1 Predose</td><td>58.5</td><td>6.044</td><td>69.682</td><td>Negative</td><td>NA</td><td>Negative</td></tr><tr><td>101-403</td><td>C2D1 Predose</td><td>62.5</td><td>3.394</td><td>69.682</td><td>Negative</td><td>NA</td><td>Negative</td></tr><tr><td>101-403</td><td>C4D1 Predose</td><td>57</td><td>2.481</td><td>69.682</td><td>Negative</td><td>NA</td><td>Negative</td></tr><tr><td rowspan="5">Cohort 5 (120 μg/mL)</td><td>201-501</td><td>C1 D1 Predose</td><td>58</td><td>4.877</td><td>69.682</td><td>Negative</td><td>NA</td><td>Negative</td></tr><tr><td>201-501</td><td>C2 D1 Predose</td><td>57</td><td>7.443</td><td>69.682</td><td>Negative</td><td>NA</td><td>Negative</td></tr><tr><td>104-502</td><td>C1 D1 Predose</td><td>57.5</td><td>1.23</td><td>69.682</td><td>Negative</td><td>NA</td><td>Negative</td></tr><tr><td>104-502</td><td>C2 D1 Predose</td><td>62</td><td>6.843</td><td>69.682</td><td>Negative</td><td>NA</td><td>Negative</td></tr><tr><td>101-503</td><td>C1 D1 Predose</td><td>57</td><td>2.481</td><td>69.682</td><td>Negative</td><td>NA</td><td>Negative</td></tr><tr><td rowspan="3">Cohort 6 (250 μg/mL)</td><td>101-503</td><td>C2 D1 Predose</td><td>53</td><td>8.005</td><td>69.682</td><td>Negative</td><td>NA</td><td>Negative</td></tr><tr><td>101-503</td><td>C4 D1 Predose</td><td>33638</td><td>0.95</td><td>64.41</td><td>Confirmatory</td><td>99.5</td><td>Positive</td></tr><tr><td>101-601</td><td>C1 D1 Predose</td><td>53</td><td>5.337</td><td>64.41</td><td>Negative</td><td>NA</td><td>Negative</td></tr><tr><td>Cohort 6 (250 μg/mL)</td><td>101-601</td><td>C2 D1 Predose</td><td>53.5</td><td>6.608</td><td>64.41</td><td>Negative</td><td>NA</td><td>Negative</td></tr></table>
+
+<--- Page Split --->
+<|ref|>table<|/ref|><|det|>[[85, 150, 945, 780]]<|/det|>
+<|ref|>table_caption<|/ref|><|det|>[[88, 116, 463, 140]]<|/det|>
+Supplementary Table 9.PD-L1 expression per cell type   
+
+<table><tr><td rowspan="2">Parameter</td><td colspan="3">101-201</td><td colspan="3">104-301</td><td colspan="3">101-302</td><td colspan="3">104-303</td><td colspan="3">105-401</td><td colspan="3">105-402</td><td colspan="3">101-403</td><td colspan="3">104-502</td></tr><tr><td>C1D1</td><td>C2D1</td><td>C4D1</td><td>C1D1</td><td>C2D1</td><td>C4D1</td><td></td><td>C1D1</td><td>C2D1</td><td>C1D1</td><td>C2D1</td><td>C1D1</td><td></td><td>C2D1</td><td>C1D1</td><td>C2D1</td><td></td><td>C1D1</td><td>C2D1</td><td>C4D1</td><td></td><td>C1D1</td><td>C2D1</td></tr><tr><td>% PD-L1 Tumor cells positive (TPS)</td><td>0</td><td>0</td><td>1</td><td>0</td><td>1</td><td>0</td><td>3</td><td>0</td><td>2</td><td>7</td><td>0</td><td>0</td><td>3</td><td>10</td><td>5</td><td>3</td><td>40</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>% PD-L1 Immune cells positive out of total tumor cells (IC)</td><td>0</td><td>2</td><td>1</td><td>10</td><td>50</td><td>5</td><td>5</td><td>0</td><td>3</td><td>15</td><td>0</td><td>0</td><td>2</td><td>1</td><td>1</td><td>0</td><td>30</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>% PD-L1 positive lymphocytes out of total tumor cells</td><td>0</td><td>1</td><td>1</td><td>5</td><td>30</td><td>3</td><td>4</td><td>0</td><td>2</td><td>20</td><td>100</td><td>0</td><td>2</td><td>1</td><td>0</td><td>0</td><td>25</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>% PD-L1 positive lymphoid cells out of total lymphoid cells</td><td>0</td><td>10</td><td>50</td><td>50</td><td>50</td><td>20</td><td>80</td><td>0</td><td>3</td><td>80</td><td>0</td><td>0</td><td>50</td><td>1</td><td>0</td><td>0</td><td>5</td><td>*QNS. Necrotic tissue, no viable tumor</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>% PD-L1 positive macrophages out of total tumor cells</td><td>0</td><td>1</td><td>0</td><td>5</td><td>20</td><td>1</td><td>0</td><td>0</td><td>1</td><td>20</td><td>0</td><td>0</td><td>2</td><td>0</td><td>0</td><td>0</td><td>1</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>% PD-L1 positive macrophages out of total macrophages</td><td>0</td><td>10</td><td>0</td><td>50</td><td>50</td><td>10</td><td>50</td><td>0</td><td>3</td><td>80</td><td>0</td><td>0</td><td>40</td><td>0</td><td>0</td><td>0</td><td>1</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Combined Positive Score (CPS)</td><td>0</td><td>2</td><td>2</td><td>10</td><td>51</td><td>5</td><td>8</td><td>0</td><td>5</td><td>22</td><td>0</td><td>0</td><td>5</td><td>11</td><td>6</td><td>3</td><td>70</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr></table>
+
+<--- Page Split --->
+<|ref|>sub_title<|/ref|><|det|>[[115, 90, 358, 107]]<|/det|>
+## SUPPLEMENTARY FIGURES  
+
+<|ref|>text<|/ref|><|det|>[[113, 121, 864, 161]]<|/det|>
+Supplementary Figure 1. Diagram of dose escalation and enrollment in tolododekin alfa Phase 1 study  
+
+<|ref|>image<|/ref|><|det|>[[115, 174, 911, 512]]<|/det|>
+
+<--- Page Split --->
+<|ref|>image<|/ref|><|det|>[[113, 180, 881, 644]]<|/det|>
+
+<--- Page Split --->
+<|ref|>image<|/ref|><|det|>[[135, 130, 857, 860]]<|/det|>
+
+<--- Page Split --->
+<|ref|>sub_title<|/ref|><|det|>[[114, 90, 875, 130]]<|/det|>
+## Supplementary Figure 4. Remodeling of tumor microenvironment after three cycles of tolododekin alfa  
+
+<|ref|>text<|/ref|><|det|>[[114, 142, 870, 216]]<|/det|>
+Representative IHC images of (A) CD8 and (B) PD- L1 staining of paired tumor biopsies pre (C1D1) and post- treatment from individual patients at indicated times (C2D1 and C4D1), different dosing cohorts and tumor types as indicated. The bar graphs indicate corresponding %positive cells as scored by the pathologist.  
+
+<|ref|>image<|/ref|><|det|>[[146, 235, 741, 761]]<|/det|>
+
+<--- Page Split --->
+<|ref|>sub_title<|/ref|><|det|>[[116, 90, 363, 108]]<|/det|>
+## CORRESPONDING AUTHOR  
+
+<|ref|>text<|/ref|><|det|>[[114, 121, 490, 140]]<|/det|>
+Jong C. Park, MD; jpark@mgh.harvard.edu  
+
+<|ref|>sub_title<|/ref|><|det|>[[115, 152, 204, 170]]<|/det|>
+## SUPPORT  
+
+<|ref|>text<|/ref|><|det|>[[115, 184, 565, 202]]<|/det|>
+This work was supported by Ankura Therapeutics, Inc.  
+
+<|ref|>sub_title<|/ref|><|det|>[[115, 214, 690, 233]]<|/det|>
+## AUTHORS' DISCLOSURE OF POTENTIAL CONFLICTS OF INTEREST  
+
+<|ref|>text<|/ref|><|det|>[[113, 245, 880, 848]]<|/det|>
+Dr Park reports Institutional research funding provided by A2 biotherapeutics, ALX Oncology, Ankura Therapeutics and Inhibrx. Dr Curti has served in an advisory role for Merck. He has received honorarium from Clinigen Group and Sanofi, and institutional research funding from Bristol Meyers Squibb and the Clinigen Group. Dr Curti reports a patent for Biomarkers OX40 response. Dr Butler has served in an advisory role for Adaptimmune, EMD Serono, Genzyme, Glaxosmithkline, IDEAYA Biosciences, Immunocore, Immunovaccine, InstilBio, Iovance Biotherapeutics, LaRoche Posay, Medison, Novartis, Pfizer, Regeneron, Sun Pharma and Merck. He received honorarium from Bristol Myers Squibb, Merck, Novartis, Roche and Sanofi. Dr Butler also received research funding and provided expert testimony for Merck. Joseph Elassal, Sailaja Battula, and Gail Iodice report employment at Ankura Therapeutics. Robert Tighe holds stock in Ankura Therapeutics and is a former employee. Howard Kaufman has served in an advisory role at Castle Biosciences, Marengo Therapeutics, Midatech Pharma, Tatum Bioscience, and Virogin Biotech. He reports employment at Ankura therapeutics, and holds stock in Immunearing and Replimune. Dr Kaufman reports honorarium at Society of Immunotherapy for Cancer. Dr Kirkwood served in an advisory role for Amgen, Ankura Therapeutics, Applied Clinical Intelligence, AXIO Research, Becker Pharmaceutic, Bristol Myers Squibb, Cancer Network, Cancer Study group, Checkmate Pharmaceuticals, Cytomx Therapeutics, DermTech, Fenix Group International, Harbour BioMed, Immunocore, iOnctura, Iovance Biotherapeutics, IQVIA, Scopus Biopharma, SR One Capital Management, Takeda, Valar Labs, Istari Ocology, Jazz Pharmaceuticals, Lytix Biopharma, Magnolia Innovation, Merck, Natara, Novartis, Oncocyte, Oncosec, PATHAI, Pfizer, Piper Sandler, PyroJas Corporation, Regeneron, and Replimune. Institutional research funding provided by Amgen, Bristol- Myers Squibb, Checkmate Pharmaceuticals, Harbour BioMed, Immunocore, Immvira, Iovance Biotherapeutics, Lion Biotechnologies, Novartis, Takeda, and Verastem. Dr Kirkwood has received travel accommodation and expenses from Ankura therapeutics, Bristol- Myers Squibb, checkmate Pharmaceuticals, Iovance Biotherapeutics, and Regeneron.  
+
+<|ref|>sub_title<|/ref|><|det|>[[116, 857, 350, 875]]<|/det|>
+## AUTHOR CONTRIBUTIONS  
+
+<|ref|>text<|/ref|><|det|>[[115, 889, 830, 908]]<|/det|>
+Conception and design: Joe Elassal, Gail Iodice, Sailaja Battula, Howard L. Kaufman
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[115, 90, 422, 108]]<|/det|>
+Administrative support: Gail Iodice  
+
+<|ref|>text<|/ref|><|det|>[[115, 121, 565, 140]]<|/det|>
+Provision of study materials or patients: All authors  
+
+<|ref|>text<|/ref|><|det|>[[115, 153, 504, 170]]<|/det|>
+Collection and assembly of data: All authors  
+
+<|ref|>text<|/ref|><|det|>[[115, 185, 504, 202]]<|/det|>
+Data analysis and interpretation: All authors  
+
+<|ref|>text<|/ref|><|det|>[[115, 216, 385, 234]]<|/det|>
+Manuscript writing: All authors  
+
+<|ref|>text<|/ref|><|det|>[[115, 247, 473, 265]]<|/det|>
+Final approval of manuscript: All authors  
+
+<|ref|>text<|/ref|><|det|>[[115, 279, 565, 297]]<|/det|>
+Accountable for all aspects of the work: All authors  
+
+<|ref|>sub_title<|/ref|><|det|>[[115, 341, 315, 359]]<|/det|>
+## ACKNOWLEDGMENTS  
+
+<|ref|>text<|/ref|><|det|>[[115, 373, 883, 435]]<|/det|>
+The authors wish to thank all the patients and their families who participated in the clinical trial. We also thank Saran Vardhanabhuti, PhD for statistical support and Lynn Nicole for regulatory guidance.
+
+<--- Page Split --->
+<|ref|>sub_title<|/ref|><|det|>[[115, 90, 203, 107]]<|/det|>
+## CONTEXT  
+
+<|ref|>sub_title<|/ref|><|det|>[[115, 122, 240, 140]]<|/det|>
+## Key Objective  
+
+<|ref|>text<|/ref|><|det|>[[115, 153, 854, 193]]<|/det|>
+The objective was to determine if tolododekin alfa, an IL- 12 anchored drug conjugate, is safe and tolerated by patients with advanced superficial solid tumors.  
+
+<|ref|>sub_title<|/ref|><|det|>[[115, 207, 314, 224]]<|/det|>
+## Knowledge Generated  
+
+<|ref|>text<|/ref|><|det|>[[115, 238, 884, 365]]<|/det|>
+The study supports that tolododekin alfa is safe up to doses of \(250\mu \mathrm{g / mL}\) and exhibits a PK/PD profile consistent with prolonged retention in the tumor microenvironment and activation of host immune responses across a range of solid tumor types. Clinical efficacy was also suggested by a high rates of disease control across multiple doses, and a long duration of response. Standard RECIST endpoints may underestimate the pathologic responses and further studies are needed to confirm efficacy in specific indications.  
+
+<|ref|>sub_title<|/ref|><|det|>[[115, 377, 211, 394]]<|/det|>
+## Relevance  
+
+<|ref|>text<|/ref|><|det|>[[115, 408, 884, 534]]<|/det|>
+This study demonstrates that an IL- 12 anchored drug conjugate is well tolerated, induces IL12 biologic activity, and may have clinical activity in patients with advanced cancer. The data further supports that anchored IL- 12 is retained within the tumor improving the therapeutic window considerably. This has implications for further development of the IL- 12 anchored drug conjugate but could also provide evidence for durable drug retention for other antitumor neoplastic agents with a limited therapeutic window.
+
+<--- Page Split --->

preprint/preprint__110f144be0aab50a1863472bf27521f640645a260b7405624f274aaea5f1b4e0/images_list.json

@@ -0,0 +1,167 @@
+[
+  {
+    "type": "image",
+    "img_path": "images/Figure_1.jpg",
+    "caption": "Figure 1: Flow Chart of Ghana Financial Incentives Trial",
+    "footnote": [],
+    "bbox": [
+      [
+        180,
+        202,
+        814,
+        737
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+    ],
+    "page_idx": 6
+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_unknown_0.jpg",
+    "caption": "Model 1: Vaccine Intention",
+    "footnote": [],
+    "bbox": [
+      [
+        130,
+        190,
+        525,
+        360
+      ]
+    ],
+    "page_idx": 16
+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_unknown_1.jpg",
+    "caption": "Model 2: Vaccine Intention Low-High",
+    "footnote": [],
+    "bbox": [
+      [
+        545,
+        191,
+        933,
+        370
+      ]
+    ],
+    "page_idx": 16
+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_unknown_2.jpg",
+    "caption": "Model 3: Reported Vaccine",
+    "footnote": [],
+    "bbox": [
+      [
+        130,
+        393,
+        525,
+        570
+      ]
+    ],
+    "page_idx": 16
+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_unknown_3.jpg",
+    "caption": "Model 4: Reported Vaccine Low-High",
+    "footnote": [],
+    "bbox": [
+      [
+        545,
+        394,
+        933,
+        570
+      ]
+    ],
+    "page_idx": 16
+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_unknown_4.jpg",
+    "caption": "Model 5: Actual Vaccine",
+    "footnote": [],
+    "bbox": [
+      [
+        130,
+        593,
+        525,
+        775
+      ]
+    ],
+    "page_idx": 16
+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_unknown_5.jpg",
+    "caption": "Model 6: Actual Vaccine Low-High",
+    "footnote": [],
+    "bbox": [
+      [
+        545,
+        595,
+        933,
+        775
+      ]
+    ],
+    "page_idx": 16
+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_3.jpg",
+    "caption": "Figure 3: Balance on Standardized Mean Differences for Full Phase I Sample: Unadjusted compared to propensity score weighted sample.",
+    "footnote": [],
+    "bbox": [
+      [
+        115,
+        300,
+        923,
+        633
+      ]
+    ],
+    "page_idx": 45
+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_4.jpg",
+    "caption": "Figure 4: Balance on Standardized Mean Differences for Phase III Spill-over Sample: Unadjusted compared to propensity score weighted sample.",
+    "footnote": [],
+    "bbox": [
+      [
+        115,
+        92,
+        949,
+        425
+      ]
+    ],
+    "page_idx": 46
+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_5.jpg",
+    "caption": "Figure 5: BART: Heterogeneous Treatment Effects",
+    "footnote": [],
+    "bbox": [
+      [
+        115,
+        238,
+        941,
+        710
+      ]
+    ],
+    "page_idx": 55
+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_6.jpg",
+    "caption": "Figure 6: BART: Heterogeneous Treatment Effects",
+    "footnote": [],
+    "bbox": [
+      [
+        180,
+        120,
+        814,
+        830
+      ]
+    ],
+    "page_idx": 56
+  }
+]

preprint/preprint__114b2c7af2a7edcc27507d19510f53d2b72a74abd673eef2f33846e234ceb8b7/images_list.json

@@ -0,0 +1,77 @@
+[
+  {
+    "type": "image",
+    "img_path": "images/Figure_1.jpg",
+    "caption": "FIG. 1: Phase diagram for \\(\\alpha = E_{B} / E_{F}\\) vs \\(T\\) . The instability temperature for the \\(d\\) -wave superconductor, \\(T_{i}\\) , defines the transition from a Fermi liquid (light blue) to the SC state. At weak coupling the pairing state is a homogeneous \\(d\\) -wave superconductor (gold). Increasing \\(\\alpha\\) the system develops critical fluctuations at finite momentum and the \\(d\\) -wave SC state becomes unstable toward a non-homogeneous SC state (pink and purple regions). \\(T^{*}\\) is the temperature at which the momentum rigidity parameter \\(c_{2}\\) vanishes. The fluctuating PDW (pink) condenses below a coherence temperature \\(T_{c}\\) into a long-range ordered state that can be an homogeneous \\(d\\) -wave SC state (gold) or a PDW (purple) depending on the coupling strength, schematically represented by a solid line. \\(T_{c}\\) coincides with \\(T_{i}\\) at weak coupling while at strong coupling it is expected that \\(T_{c} < T_{i}\\) [6]. Note that the actual instability temperature of the FPDW, \\(\\bar{T}_{i}\\) , is somewhat higher than \\(T_{i}\\) (see Supplementary Material). Temperatures are renormalized by the energy range of the pairing, \\(\\Lambda\\) that is the largest energy scale of our model.",
+    "footnote": [],
+    "bbox": [
+      [
+        84,
+        65,
+        485,
+        283
+      ]
+    ],
+    "page_idx": 2
+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_2.jpg",
+    "caption": "FIG. 2: Mean-field results for the spatially homogeneous \\(d\\) -wave superconductor. (a,b) Self-consistent solutions of the pairing order parameter \\(\\Delta (T)\\) and the chemical potential \\(\\mu (T)\\) for three representative values of \\(\\alpha\\) . Temperatures are normalized to the instability temperature \\(T_{i}\\) defined as the temperature at which the static pairing susceptibility \\(L_{q = 0}\\) diverges, while \\(\\Delta\\) and \\(\\mu\\) are scaled with \\(\\Lambda\\) . (c) Instability temperature \\(T_{i}\\) and chemical potential \\(\\mu_{i} \\equiv \\mu (T = T_{i})\\) as a function of \\(\\alpha\\) . (d) \\(T = 0\\) solutions: \\(\\Delta_{0} \\equiv \\Delta (T = 0)\\) and chemical potential \\(\\mu_{0} \\equiv \\mu (T = 0)\\) as a function of \\(\\alpha\\) . For comparison we show also the results of the isotropic s-wave case in dashed lines. Computations are performed using \\(\\Lambda = 11\\) , \\(E_{F} = 2.2\\) in units of \\(2m = 1\\) .",
+    "footnote": [],
+    "bbox": [
+      [
+        88,
+        61,
+        480,
+        365
+      ]
+    ],
+    "page_idx": 4
+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_3.jpg",
+    "caption": "FIG. 3: Coefficients of the momentum-expansion of the static susceptibility as a function of the coupling strength \\(\\alpha = E_{B} / E_{F}\\) at \\(T = T_{i}\\) . (a) The momentum rigidity \\(c_{2}(\\alpha)\\) for \\(d\\) -wave (solid line) and \\(s\\) -wave pairing interaction (dashed line). In the anisotropic \\(d\\) -wave case \\(c_{2}\\) becomes negative at intermediate coupling, \\(\\alpha \\sim 0.7\\) indicating that the homogeneous \\(d\\) -wave SC is unstable. Inset: \\(c_{2}(\\mu_{i})\\) , the sign change of the momentum rigidity occurs around the same range in which \\(\\mu_{i}\\) turns from positive to negative values. The momentum rigidity for the isotropic \\(s\\) -wave case remains positive regardless the coupling strength. (b) \\(c_{n}(\\alpha)\\) coefficients, \\(n = 2,4,6\\) , for the \\(d\\) -wave pairing. The positive value of \\(c_{6}\\) allows to recover the stability of the action. The computation of the higher order coefficients allows to define the finite momentum of the critical mode (see Fig. 4) and the relative instability temperature. We use here the same set of parameters of Fig. 2 and plot the results in dimensionless units i.e. \\(c_{n} \\equiv c_{n} \\Lambda^{n / 2}\\) .",
+    "footnote": [],
+    "bbox": [
+      [
+        515,
+        60,
+        917,
+        208
+      ]
+    ],
+    "page_idx": 4
+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_4.jpg",
+    "caption": "FIG. 4: Momentum dependence of the sixth order expansion of \\(L^{-1}\\) at \\(T_{i}\\) (dimensionless units). At weak coupling, \\(\\alpha = 0.22\\) , we find the homogeneous \\(d\\) -wave SC. The momentum rigidity \\(c_{2}\\) is large and positive, and the minimum of the inverse of the susceptibility is at \\(q = 0\\) . At intermediate coupling, \\(\\alpha \\sim 0.7\\) , \\(c_{2}\\) vanishes and the minimum of \\(L^{-1}\\) appears at a finite \\(\\bar{Q}\\) of order 1. Same set of parameters of Fig. 2",
+    "footnote": [],
+    "bbox": [
+      [
+        108,
+        63,
+        460,
+        268
+      ]
+    ],
+    "page_idx": 5
+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_5.jpg",
+    "caption": "FIG. 5: \\(I_{2}(k_{x},k_{y})\\) color maps at \\(T = 0\\) and \\(T = T_{i}\\) and \\(\\alpha = 2.0\\) for the \\(s\\) -wave and \\(d\\) -wave case. The anisotropy of the interactions affects the momentum dependence of the propagator both in the SC and normal phase. This reflects in a strong momentum dependence of the contribution to the momentum rigidity parameter. For the \\(d\\) -wave case negative contributions to the rigidity are found both at \\(T = 0\\) and \\(T = T_{i}\\) from \\(k\\) -points close to the nodal region. We use the same set of parameters of Fig. 2 and plot the result in dimensionless units for momenta \\(|k_{i}| / \\sqrt{\\Lambda} < \\pi\\) , with \\(i = x, y\\) .",
+    "footnote": [],
+    "bbox": [
+      [
+        86,
+        61,
+        468,
+        355
+      ]
+    ],
+    "page_idx": 6
+  }
+]

preprint/preprint__114b2c7af2a7edcc27507d19510f53d2b72a74abd673eef2f33846e234ceb8b7/preprint__114b2c7af2a7edcc27507d19510f53d2b72a74abd673eef2f33846e234ceb8b7_det.mmd

@@ -0,0 +1,245 @@
+<|ref|>title<|/ref|><|det|>[[44, 108, 949, 175]]<|/det|>
+# Microscopic mechanism for fluctuating pair density wave  
+
+<|ref|>text<|/ref|><|det|>[[44, 196, 460, 220]]<|/det|>
+Chandan Setty ( settychandan@gmail.com )  
+
+<|ref|>text<|/ref|><|det|>[[53, 221, 185, 238]]<|/det|>
+Rice University  
+
+<|ref|>text<|/ref|><|det|>[[44, 244, 185, 262]]<|/det|>
+Laura Fanfarillo  
+
+<|ref|>text<|/ref|><|det|>[[53, 265, 839, 285]]<|/det|>
+SISSA International School for Advanced Studies https://orcid.org/0000- 0002- 6452- 8520  
+
+<|ref|>text<|/ref|><|det|>[[44, 290, 185, 308]]<|/det|>
+Peter Hirschfeld  
+
+<|ref|>text<|/ref|><|det|>[[53, 312, 231, 330]]<|/det|>
+University of Florida  
+
+<|ref|>sub_title<|/ref|><|det|>[[44, 372, 102, 389]]<|/det|>
+## Article  
+
+<|ref|>text<|/ref|><|det|>[[44, 408, 679, 429]]<|/det|>
+Keywords: superconductors, fluctuating pair density wave, pairing phases  
+
+<|ref|>text<|/ref|><|det|>[[44, 446, 345, 466]]<|/det|>
+Posted Date: November 18th, 2021  
+
+<|ref|>text<|/ref|><|det|>[[44, 485, 474, 504]]<|/det|>
+DOI: https://doi.org/10.21203/rs.3.rs- 1021881/v1  
+
+<|ref|>text<|/ref|><|det|>[[44, 521, 910, 565]]<|/det|>
+License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License
+
+<--- Page Split --->
+<|ref|>title<|/ref|><|det|>[[226, 63, 776, 81]]<|/det|>
+# Microscopic mechanism for fluctuating pair density wave  
+
+<|ref|>text<|/ref|><|det|>[[99, 93, 912, 157]]<|/det|>
+Chandan Setty \(^{\oplus ,\dagger ,1,2}\) Laura Fanfarillo \(^{\oplus ,\diamond ,1,3}\) and P. J. Hirschfeld \(^{\otimes 1}\) \(^{1}\) Department of Physics, University of Florida, Gainesville, Florida, USA \(^{2}\) Department of Physics and Astronomy, Rice Center for Quantum Materials, Rice University, Houston, Texas 77005, USA \(^{3}\) Scuola Internazionale Superiore di Studi Avanzati (SISSA), Via Bonomea 265, 34136 Trieste, Italy  
+
+<|ref|>text<|/ref|><|det|>[[175, 163, 830, 336]]<|/det|>
+In weakly coupled BCS superconductors, only electrons within a tiny energy window around the Fermi energy, \(E_{F}\) , form Cooper pairs. This may not be the case in strong coupling superconductors such as cuprates, FeSe, SrTiO \(_3\) or cold atom condensates where the pairing scale, \(E_{B}\) , becomes comparable or even larger than \(E_{F}\) . In cuprates, for example, a plausible candidate for the pseudogap state at low doping is a fluctuating pair density wave, but no microscopic model has yet been found which supports such a state. In this work, we write an analytically solvable model to examine pairing phases in the strongly coupled regime and in the presence of anisotropic interactions. Already for moderate coupling we find an unusual finite temperature phase, below an instability temperature \(T_{i}\) , where local pair correlations have non- zero center- of- mass momentum but lack long- range order. At low temperature, this fluctuating pair density wave can condense either to a uniform \(d\) - wave superconductor or the widely postulated pair- density wave phase depending on the interaction strength. Our minimal model offers a unified microscopic framework to understand the emergence of both fluctuating and long range pair density waves in realistic systems.  
+
+<|ref|>text<|/ref|><|det|>[[86, 360, 487, 678]]<|/det|>
+Spatially uniform superconducting (SC) order formed from Cooper pairs with zero center- of- mass momentum is the energetically favored ground state in the conventional theory of Bardeen, Cooper and Schrieffer (BCS) [1]. Equivalently, the SC instability is signaled by a divergence in the static pair- fluctuation propagator, \(L(\mathbf{q}, \Omega = 0)\) , at \(\mathbf{q} = 0\) once the pair instability temperature, \(T_{i}\) , is achieved [2]. On the other hand, a non- uniform order with non- zero center- of- mass momentum Cooper pair can occur when the divergence of the pair fluctuation propagator is shifted to non- zero \(\mathbf{q}\) . First proposed by Fulde and Farrell (FF) [3] and independently by Larkin and Ovchinnikov (LO) [4], these solutions are stabilized in the presence of explicit time- reversal symmetry breaking from an external magnetic field. A modulated order parameter can also be realized in the presence of time- reversal symmetry where the spatial average of the gap vanishes. Termed pair- density waves (PDWs), these states are posited to exist in a variety of systems, including high- temperature cuprate superconductors (for a review, see Ref. [5] and references therein).  
+
+<|ref|>text<|/ref|><|det|>[[86, 689, 487, 914]]<|/det|>
+While PDWs have been subject to much theoretical [7- 20] and numerical [21- 27] interest, a clear- cut analytically solvable model describing their origin from microscopic ingredients is lacking. From the experimental point of view, the interest for modulated pairing phases has been triggered by increasing experimental evidence for short- ranged PDW order in the underdoped region of the phase diagram of cuprates [27- 38]. In particular, [33] reported the first clear observation via scanning tunneling spectroscopy of a vortex- induced PDW in \(\mathrm{Bi}_{2}\mathrm{Sr}_{2}\mathrm{CaCu}_{2}\mathrm{O}_{8}\) at low temperature. More recent STM experiments provide further evidence in favor of a short- range PDW coexisting with the \(d\) - wave superconductivity in the SC phase and evolving into a PDW state in the pseudogap region [27, 38]. This phase is characterized by a gap at finite temperatures but lacks long- range order, and can be characterized as a "fluctuating pair density wave", locally pinned by disorder. Such a state also provides an explanation for many other experimental signatures of the cuprates, including the existence of vestigial charge density wave order arising from partial melting of a PDW [5, 16, 39]. However, there is currently no microscopic model supporting this picture. Hence it is urgent to seek a unified framework that subsumes both fluctuating and long- range ordered PDW phases under a single paradigm by providing a concrete description of their origin.  
+
+<|ref|>text<|/ref|><|det|>[[516, 360, 916, 541]]<|/det|>
+ized by a gap at finite temperatures but lacks long- range order, and can be characterized as a "fluctuating pair density wave", locally pinned by disorder. Such a state also provides an explanation for many other experimental signatures of the cuprates, including the existence of vestigial charge density wave order arising from partial melting of a PDW [5, 16, 39]. However, there is currently no microscopic model supporting this picture. Hence it is urgent to seek a unified framework that subsumes both fluctuating and long- range ordered PDW phases under a single paradigm by providing a concrete description of their origin.  
+
+<|ref|>text<|/ref|><|det|>[[516, 545, 916, 666]]<|/det|>
+In this Article, we show that a Fermi liquid subjected to a finite anisotropic interaction is unstable toward a modulated SC phase in the strong coupling limit. Whether this phase is a "fluctuating" PDW (FPDW) or long- range order PDW is determined by temperature as well as the coupling strength defined by the ratio \(\alpha = E_{B} / E_{F}\) , with \(E_{F}\) the Fermi energy and \(E_{B}\) the bound state energy for pair formation.  
+
+<|ref|>text<|/ref|><|det|>[[516, 670, 916, 896]]<|/det|>
+Our strategy is to solve the self- consistent gap equation for the homogeneous \(d\) - wave superconductor and analyze the momentum dependence of the SC fluctuations. The expansion of the static pair propagator \(L_{\mathbf{q}}\) in powers of momentum transfer \(\mathbf{q}\) , can reveal, in fact, critical fluctuations of Cooper pairs with finite center- of- mass momentum, that makes the homogeneous solution unstable towards a modulated SC phase. This is indeed what we find already at intermediate coupling \(\alpha \sim 0.7\) . The emergence of such a state is linked to the existence of fluctuating terms that lower the momentum rigidity of the Cooper pairs. These terms directly follow from the anisotropy of the pairing interaction that affects the momentum dependence of the pairing susceptibility already in the normal phase.  
+
+<|ref|>text<|/ref|><|det|>[[530, 900, 916, 915]]<|/det|>
+Our results are summarized in the phase diagram,
+
+<--- Page Split --->
+<|ref|>image<|/ref|><|det|>[[84, 65, 485, 283]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[85, 297, 488, 549]]<|/det|>
+<center>FIG. 1: Phase diagram for \(\alpha = E_{B} / E_{F}\) vs \(T\) . The instability temperature for the \(d\) -wave superconductor, \(T_{i}\) , defines the transition from a Fermi liquid (light blue) to the SC state. At weak coupling the pairing state is a homogeneous \(d\) -wave superconductor (gold). Increasing \(\alpha\) the system develops critical fluctuations at finite momentum and the \(d\) -wave SC state becomes unstable toward a non-homogeneous SC state (pink and purple regions). \(T^{*}\) is the temperature at which the momentum rigidity parameter \(c_{2}\) vanishes. The fluctuating PDW (pink) condenses below a coherence temperature \(T_{c}\) into a long-range ordered state that can be an homogeneous \(d\) -wave SC state (gold) or a PDW (purple) depending on the coupling strength, schematically represented by a solid line. \(T_{c}\) coincides with \(T_{i}\) at weak coupling while at strong coupling it is expected that \(T_{c} < T_{i}\) [6]. Note that the actual instability temperature of the FPDW, \(\bar{T}_{i}\) , is somewhat higher than \(T_{i}\) (see Supplementary Material). Temperatures are renormalized by the energy range of the pairing, \(\Lambda\) that is the largest energy scale of our model. </center>  
+
+<|ref|>text<|/ref|><|det|>[[86, 581, 488, 912]]<|/det|>
+Fig. 1. \(T_{i}\) is the instability temperature of the homogeneous \(d\) - wave state. The analysis of fluctuations allows us to define two different regimes. At weak coupling, \(\alpha \ll 1\) , the uniform \(d\) - wave paired state is the ground state; at larger \(\alpha\) (strong coupling), SC fluctuations at finite momentum lead to two modulated pairing phases – the \(T = 0\) PDW ground state and a higher temperature FPDW phase that condenses into a PDW ordered phase below a coherence temperature \((T_{c})\) . As expected in the BCS limit, the instability temperature \(T_{i}\) and the coherence temperature \(T_{c}\) coincide at weak coupling, while they decouple in the strong coupling regime where we find well formed pairs with no coherence. We do not perform here any calculation of the coherence temperature inside the modulated phase, however in analogy with results obtained for homogeneous \(s\) - wave superconductors in the strong coupling limit [6] we anticipate \(T_{c} < T_{i}\) for \(\alpha > 1\) . The FPDW is found for temperature \(T_{c} < T < T_{i}\) and it is characterized by pairs with finite momentum with no coherence. At \(T = 0\) , the ground state can be either the uniform \(d\) - wave solution or the long- range PDW depending on the value of \(\alpha\) . Hence our model captures two  
+
+<|ref|>text<|/ref|><|det|>[[515, 65, 916, 110]]<|/det|>
+key experimentally postulated modulated Cooper phases – a FPDW and a long- range PDW – in a single unified scheme.  
+
+<|ref|>text<|/ref|><|det|>[[515, 113, 917, 491]]<|/det|>
+The mechanism we present in this paper predicts spatially modulated pairing phases for \(\alpha = E_{B} / E_{F} > 1\) , i.e. in strongly coupled electronic systems, with anisotropic interactions. Examples of low- density electronic materials include the Fe- based superconductor FeSe where quantum oscillations [40] as well as transport and scanning tunneling spectroscopy [41] show that both the electron and hole pockets are tiny with Fermi energies comparable or even smaller than the SC gap and for which we find several proposals of BCS- BEC cross- over physics in the literature [42- 44]. Other "mixed- band" superconductors such as O vacancy- or Nb- doped SrTiO₃ have one partially filled band with a large Fermi surface while the Fermi level intersects the other at or close to the band bottom [45]. Even if these materials typically have more than one band close to or crossing the Fermi level, the results from our minimal model may eventually provide a suitable starting- point for the analysis of possible instabilities towards modulated pairing states in dilute multiorbital superconductors. Our results may also be relevant to the recent observation of superconductivity in twisted- bilayer graphene [46] where interactions can be large compared to the bandwidth leading to large inter- particle distances [47] and hence possible strongly- coupled Cooper pairing.  
+
+<|ref|>text<|/ref|><|det|>[[515, 494, 917, 780]]<|/det|>
+The modulated phases we propose in this work, that include both the long- range ordered PDW as well as the FPDW at finite temperature, are distinct from earlier proposals in literature. Loder and coworkers [11], considered similar models characterized by nearest neighbor attractive interaction with \(d\) - wave symmetry and found Cooper pairing with finite center- of- mass momentum above a critical interaction strength. In Refs. [19, 20], a modulated superconducting state is found in models which have correlated pair- hopping interactions. Other models that admit modulated SC ground states were proposed in the context of cold atoms [10] where local interactions were considered in systems with multiple bands. Those references focused on the analysis of the long- range ordered state (mainly at zero temperature) without exploring the FPDW phase. The key contribution of our work is it provides an analytically tractable model where both fluctuating and long- range ordered PDWs can be explained under a single unified framework.  
+
+<|ref|>sub_title<|/ref|><|det|>[[683, 820, 748, 833]]<|/det|>
+## MODEL  
+
+<|ref|>text<|/ref|><|det|>[[515, 853, 917, 914]]<|/det|>
+Let's consider a single band SC system. The kinetic part of the Hamiltonian reads \(H_{0} = \sum_{\mathbf{k}\sigma} \xi_{\mathbf{k}} c_{\mathbf{k}\sigma} c_{\mathbf{k}\sigma}\) , where \(\xi_{\mathbf{k}} = \epsilon_{\mathbf{k}} - \mu\) , \(\mu\) is the chemical potential, \(\epsilon_{\mathbf{k}} = \mathbf{k}^{2} / 2m\) the parabolic dispersion and we further assume
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[85, 65, 398, 80]]<|/det|>
+\(2m = 1\) . The pairing interaction is given by  
+
+<|ref|>equation<|/ref|><|det|>[[210, 90, 484, 123]]<|/det|>
+\[H_{I} = -g\sum_{\mathbf{q}}\theta_{\mathbf{q}}^{\dagger}\theta_{\mathbf{q}}, \quad (1)\]  
+
+<|ref|>text<|/ref|><|det|>[[85, 134, 444, 150]]<|/det|>
+\(g\) is the constant SC coupling and \(\theta_{\mathbf{q}}\) is defined as  
+
+<|ref|>equation<|/ref|><|det|>[[176, 160, 484, 195]]<|/det|>
+\[\theta_{\mathbf{q}} = \sum_{\mathbf{k}}f_{\mathbf{k},\mathbf{q}}c_{-\mathbf{k} + \frac{\mathbf{q}}{2},\downarrow}c_{\mathbf{k} + \frac{\mathbf{q}}{2},\uparrow}. \quad (2)\]  
+
+<|ref|>text<|/ref|><|det|>[[85, 205, 486, 310]]<|/det|>
+where \(f_{\mathbf{k},\mathbf{q}} = (h_{\mathbf{k} - \mathbf{q} / 2} + h_{\mathbf{k} + \mathbf{q} / 2}) / 2\) is a form factor. In this work \(h_{\mathbf{k}}\) can be any anisotropic form factor; we consider, e.g. \(h_{\mathbf{k}} = \left(k_{x}^{2} - k_{y}^{2}\right) / \Lambda\) with \(d\) - wave form. Our results do not depend qualitatively on the exact form of the anisotropy, provided it is strong enough, but they are distinct from the conventional \(s\) - wave case \(f_{\mathbf{k},\mathbf{q}} = 1\) . Here \(\Lambda\) is the pairing energy scale.  
+
+<|ref|>text<|/ref|><|det|>[[85, 310, 486, 384]]<|/det|>
+We use the standard Hubbard- Stratonovich transformation to decouple the interaction term, Eq.1 and to derive the effective action in term of the bosonic pairing field \(\Delta\) (for a detailed derivation see Supplemental Material).  
+
+<|ref|>text<|/ref|><|det|>[[85, 384, 486, 520]]<|/det|>
+In standard BCS superconductors, the mean- field value of the pairing field is defined by minimizing the action with respect to the homogeneous \(\mathbf{q} = 0\) value of \(\Delta\) and then solving this equation together with the one for the chemical potential. To study fluctuations of the pairing field around the mean- field value, we instead analyze the gaussian action obtained by retaining up to the second order in the fluctuating field with arbitrary momentum \(\mathbf{q}\) given by  
+
+<|ref|>equation<|/ref|><|det|>[[197, 530, 484, 562]]<|/det|>
+\[S_{G}[\Delta_{\mathbf{q}}] = \sum_{\mathbf{q}}L_{\mathbf{q}}^{-1}\Delta_{\mathbf{q}}^{2}. \quad (3)\]  
+
+<|ref|>text<|/ref|><|det|>[[85, 575, 486, 620]]<|/det|>
+The static pairing susceptibility is explicitly given by \(L_{\mathbf{q}}^{- 1} = g^{- 1} + \Pi_{\mathbf{q}}\) , where the particle- particle propagator reads  
+
+<|ref|>equation<|/ref|><|det|>[[85, 627, 504, 667]]<|/det|>
+\[\Pi_{\mathbf{q}} = \frac{T}{V}\sum_{\mathbf{k}n}\frac{(i\omega_{n} + \xi_{\mathbf{k} + \mathbf{q}})(i\omega_{n} - \xi_{\mathbf{k}}) - f_{\mathbf{k},0}f_{\mathbf{k} + \mathbf{q},0}\Delta^{2}}{(\omega_{n}^{2} + E_{\mathbf{k}}^{2})(\omega_{n}^{2} + E_{\mathbf{k} + \mathbf{q}}^{2})} f_{\mathbf{k},\mathbf{q}}^{2}, \quad (4)\]  
+
+<|ref|>text<|/ref|><|det|>[[85, 677, 486, 737]]<|/det|>
+with \(E_{\mathbf{k}}^{2} = \xi_{\mathbf{k}}^{2} + f_{\mathbf{k},0}^{2}\Delta^{2}\) . Here \(T\) is the temperature and \(V\) the volume. Note that since \(2m = 1\) , energies have dimensions of 2- D \(V^{- 1}\) , and \(L_{\mathbf{q}}^{- 1}\) is therefore dimensionless.  
+
+<|ref|>text<|/ref|><|det|>[[85, 738, 486, 767]]<|/det|>
+The static susceptibility can be expanded in the hydrodynamic limit as  
+
+<|ref|>equation<|/ref|><|det|>[[225, 777, 485, 796]]<|/det|>
+\[L_{\mathbf{q}}^{-1} = c_{0} + c_{2}q^{2}. \quad (5)\]  
+
+<|ref|>text<|/ref|><|det|>[[85, 808, 486, 912]]<|/det|>
+The instability temperature is defined as the highest temperature at which the susceptibility diverges, i.e. \(c_{0} = g^{- 1} + \Pi_{0}|_{T = T_{i}} = 0\) , as we assume that the minimum of the action, Eq. 3, is associated with the homogeneous order parameter. The coefficient \(c_{2} = (\partial^{2}L_{\mathbf{q}}^{- 1} / \partial q^{2}|_{q = 0}) / 2\) provides instead information about the momentum rigidity of the fluctuating Cooper pairs i.e. the energy needed to  
+
+<|ref|>text<|/ref|><|det|>[[515, 66, 916, 186]]<|/det|>
+move the center- of- mass momentum of the Cooper pairs from zero to a finite value. A negative momentum rigidity, \(c_{2}< 0\) , implies that finite momentum fluctuations can lower the energy of the system making the homogeneous SC solution unstable. This means that the highest temperature at which the pairing susceptibility, Eq. 5, diverges is actually associated to a critical mode with finite momentum.  
+
+<|ref|>text<|/ref|><|det|>[[515, 189, 916, 441]]<|/det|>
+In what follows we analyze the momentum- dependence of the static susceptibility, Eq. 5, looking for a sign change of the momentum rigidity parameter \(c_{2}\) and using it as a proxy to identify possible spatially modulated SC regions in the phase diagram. It is worth noticing that \(c_{2}\) is directly affected by the momentum properties of the pairing susceptibility i.e. the pairing symmetry. From Eq. 4, it is easy to verify that the anisotropy of the interactions affects the momentum dependence of the propagator not only in the SC phase via the symmetry of the SC order parameter, but also above the instability temperature \(T_{i}\) where \(\Delta = 0\) due to the overall form factor \(f_{\mathbf{k},\mathbf{q}}^{2}\) at the numerator. This reflects in a strong momentum dependence of the contributions to the rigidity parameter depending on the symmetry of the pairing interaction. We discuss below how this affects the development of critical finite- momentum fluctuations.  
+
+<|ref|>sub_title<|/ref|><|det|>[[597, 472, 835, 486]]<|/det|>
+## RESULTS AND DISCUSSION  
+
+<|ref|>text<|/ref|><|det|>[[515, 504, 916, 805]]<|/det|>
+The mean- field analysis for the homogeneous \(d\) - wave superconductor is shown in Fig.2. In panels (a)- (b) we report the self- consistent numerical mean- field solutions for the pairing function \(\Delta\) and the chemical potential \(\mu\) as a function of temperature \(T\) for three representative cases of the pairing strength \(\alpha = E_{B} / E_{F} = 0.5,1.0,2.0\) , where for simplicity the weak- coupling expression \(E_{B} = \Lambda e^{- 2 / g}\) is used at all \(\alpha\) . In panels (c)- (d) we show the same mean- field results at \(T = T_{i}\) and \(T = 0\) as a function of \(\alpha\) . The change of sign of the chemical potential with increasing coupling strength is well- known from the BCS- BEC crossover problem [48- 52]. In the weak- coupling regime, the pairs are loosely bound and we recover the BCS expression \(\mu \sim E_{F}\) . As the interaction increases, all fermions strongly bind in pairs and \(\mu\) becomes negative and proportional to \(- E_{B}\) . In both the weak and strong coupling limits, the curves are similar to those derived for \(s\) - wave superconductors in [52], showing that the \(d\) - wave symmetry of the pairing interaction does not affect the mean- field results qualitatively.  
+
+<|ref|>text<|/ref|><|det|>[[515, 807, 916, 896]]<|/det|>
+We first study the SC fluctuations above the instability temperature by analyze the static pairing susceptibility in the hydrodynamic limit, Eq. 5. The mass term \(c_{0}\) is positive and vanishes as the temperature approaches the instability temperature as expected from a Ginzburg- Landau description of the transition.  
+
+<|ref|>text<|/ref|><|det|>[[530, 898, 916, 912]]<|/det|>
+The analysis of the momentum rigidity of the fluctuat
+
+<--- Page Split --->
+<|ref|>image<|/ref|><|det|>[[88, 61, 480, 365]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[85, 380, 488, 553]]<|/det|>
+<center>FIG. 2: Mean-field results for the spatially homogeneous \(d\) -wave superconductor. (a,b) Self-consistent solutions of the pairing order parameter \(\Delta (T)\) and the chemical potential \(\mu (T)\) for three representative values of \(\alpha\) . Temperatures are normalized to the instability temperature \(T_{i}\) defined as the temperature at which the static pairing susceptibility \(L_{q = 0}\) diverges, while \(\Delta\) and \(\mu\) are scaled with \(\Lambda\) . (c) Instability temperature \(T_{i}\) and chemical potential \(\mu_{i} \equiv \mu (T = T_{i})\) as a function of \(\alpha\) . (d) \(T = 0\) solutions: \(\Delta_{0} \equiv \Delta (T = 0)\) and chemical potential \(\mu_{0} \equiv \mu (T = 0)\) as a function of \(\alpha\) . For comparison we show also the results of the isotropic s-wave case in dashed lines. Computations are performed using \(\Lambda = 11\) , \(E_{F} = 2.2\) in units of \(2m = 1\) . </center>  
+
+<|ref|>text<|/ref|><|det|>[[86, 589, 488, 878]]<|/det|>
+ing pairs above \(T_{i}\) is shown in Fig. 3. The weak coupling region is characterized by a standard regime of fluctuations with \(c_{2} > 0\) . Here Cooper pairs with zero center- of- mass momentum are stable. Increasing \(\alpha\) , the momentum rigidity for the \(d\) - wave pairing interaction (continuous line) monotonically decreases and becomes negative at intermediate coupling, \(\alpha > 0.7\) , as shown in Fig. 3(a). This means that finite momentum critical fluctuations grow, increasing the coupling strength up to a critical value of the interaction for which the homogeneous SC solution can become unstable toward a modulated phase. Notice that \(c_{2}\) becomes very small and eventually changes sign in the crossover between weak and strong coupling where also the chemical potential changes sign from positive to negative, see inset Fig. 3(a). The result changes qualitatively for the isotropic \(s\) - wave interaction (dashed line) where the rigidity parameter decreases but remains positive even at strong coupling for the set of model parameter of our study [53].  
+
+<|ref|>text<|/ref|><|det|>[[86, 883, 488, 912], [516, 472, 660, 486]]<|/det|>
+To characterize the modulated SC state and check its stability, we expand the static susceptibility to higher order in momentum   
+
+<|ref|>image<|/ref|><|det|>[[515, 60, 917, 208]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[515, 221, 918, 448]]<|/det|>
+<center>FIG. 3: Coefficients of the momentum-expansion of the static susceptibility as a function of the coupling strength \(\alpha = E_{B} / E_{F}\) at \(T = T_{i}\) . (a) The momentum rigidity \(c_{2}(\alpha)\) for \(d\) -wave (solid line) and \(s\) -wave pairing interaction (dashed line). In the anisotropic \(d\) -wave case \(c_{2}\) becomes negative at intermediate coupling, \(\alpha \sim 0.7\) indicating that the homogeneous \(d\) -wave SC is unstable. Inset: \(c_{2}(\mu_{i})\) , the sign change of the momentum rigidity occurs around the same range in which \(\mu_{i}\) turns from positive to negative values. The momentum rigidity for the isotropic \(s\) -wave case remains positive regardless the coupling strength. (b) \(c_{n}(\alpha)\) coefficients, \(n = 2,4,6\) , for the \(d\) -wave pairing. The positive value of \(c_{6}\) allows to recover the stability of the action. The computation of the higher order coefficients allows to define the finite momentum of the critical mode (see Fig. 4) and the relative instability temperature. We use here the same set of parameters of Fig. 2 and plot the results in dimensionless units i.e. \(c_{n} \equiv c_{n} \Lambda^{n / 2}\) . </center>   
+
+<|ref|>equation<|/ref|><|det|>[[555, 490, 916, 529]]<|/det|>
+\[L_{\mathbf{q}}^{-1} = \sum_{n}c_{n}\mathbf{q}^{n},\quad \mathrm{with}\quad c_{n} = \frac{1}{n}\frac{\partial^{n}L_{\mathbf{q}}^{-1}}{\partial\mathbf{q}^{n}}\bigg|_{{\mathbf{q}} = 0} \quad (6)\]  
+
+<|ref|>text<|/ref|><|det|>[[516, 535, 917, 625]]<|/det|>
+We report the coefficients of the momentum expansion at \(T_{i}\) in Fig. 3(b). Results are shown as a function of \(\alpha\) for the coupling regime in which \(c_{2} \lesssim 0\) . We need to expand the susceptibility up \(n = 6\) to find \(c_{6} > 0\) , since for our set of model parameters \(c_{4} < 0\) as in the conventional BCS case.  
+
+<|ref|>text<|/ref|><|det|>[[516, 626, 917, 912]]<|/det|>
+We analyze the momentum dependence of the static susceptibility at \(T_{i}\) in Fig. 4, where we show the expansion of Eq. 6 up to sixth order for different values of \(\alpha\) . At the instability temperature, \(c_{0} = 0\) by definition and the minimum of the function is determined by the higher order coefficients. At weak coupling, where \(c_{2}\) is large and positive, the minimum of \(L_{\mathbf{q}}^{- 1}\) is located at zero momentum. As the pairing interaction increases \(c_{2}\) becomes small and eventually changes sign at \(\alpha \sim 0.7\) . Here, since \(c_{4} < 0\) , the minimum shifts discontinuously to a finite momentum \(\bar{Q}\) , i.e. by increasing the interactions the modulated phase emerges at \(T_{i}\) via a first order transition from the homogeneous \(d\) - wave SC solution, in analogy with the results found at \(T = 0\) in [11, 19]. The non- zero value of \(\bar{Q}\) at \(\alpha \sim 0.7\) signals the formation of the FPDW state with finite momentum pairing but no long range coherent order. Note that the finite order parameter jump \(\bar{Q}\) is a non- universal quantity and depends on microscopic details of the chosen model, as is a
+
+<--- Page Split --->
+<|ref|>image<|/ref|><|det|>[[108, 63, 460, 268]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[85, 282, 488, 375]]<|/det|>
+<center>FIG. 4: Momentum dependence of the sixth order expansion of \(L^{-1}\) at \(T_{i}\) (dimensionless units). At weak coupling, \(\alpha = 0.22\) , we find the homogeneous \(d\) -wave SC. The momentum rigidity \(c_{2}\) is large and positive, and the minimum of the inverse of the susceptibility is at \(q = 0\) . At intermediate coupling, \(\alpha \sim 0.7\) , \(c_{2}\) vanishes and the minimum of \(L^{-1}\) appears at a finite \(\bar{Q}\) of order 1. Same set of parameters of Fig. 2 </center>  
+
+<|ref|>text<|/ref|><|det|>[[86, 407, 488, 528]]<|/det|>
+feature of any generic first order transition. The momentum characterizing the modulated phase shifts toward larger values increasing the coupling parameters. In the strong coupling regime, \(\alpha \gg 1\) , the minimum occurs at \(q / \sqrt{\Lambda} \gg 1\) , (not shown), for this range of the interaction the analysis of the momentum characterizing the modulated phase requires the implementation of a non perturbative approach.  
+
+<|ref|>text<|/ref|><|det|>[[86, 531, 488, 666]]<|/det|>
+The sign change of the momentum rigidity parameter discussed at \(T = T_{i}\) can be traced down in temperature (dashed line in Fig. 1). At \(T = 0\) the homogeneous \(d\) - wave state becomes unstable, now toward a PDW, for a slightly higher value of the coupling where the chemical potential \(\mu\) also changes sign (see Fig.2d). The stability of the PDW phase requires expanding up to the sixth- order, \(c_{4} < 0\) , \(c_{6} > 0\) as we show in the Supplementary Material.  
+
+<|ref|>text<|/ref|><|det|>[[86, 670, 488, 895]]<|/det|>
+The results of our numerical study are summarized in the phase diagram of Fig. 1. We characterized the SC region below \(T_{i}\) by the sign of the momentum rigidity parameter (dashed line). The sign change of the \(c_{2}\) coefficient at strong coupling signals the presence of critical SC fluctuations at finite momentum that make the \(d\) - wave homogeneous state unstable toward either an FPDW or PDW. The pink and purple regions indicate the FPDW and the long- range ordered PDW state at high and low temperatures respectively. We leave for future work the explicit calculation of the coherence temperature below which the FPDW condenses. The color gradient indicates approximately the expected \(T_{c}(\alpha)\) behaviour based on previous analysis of the coherence energy scale for the homogeneous \(s\) - wave SC state [6].  
+
+<|ref|>text<|/ref|><|det|>[[102, 898, 488, 912]]<|/det|>
+Analytical calculations of the momentum rigidity can  
+
+<|ref|>text<|/ref|><|det|>[[516, 65, 917, 216]]<|/det|>
+be easily performed within a simplified model in which the chemical potential is used as parameter. Both at \(T_{i}\) and \(T = 0\) , we find qualitatively the same results discussed within the numerical study. In particular, within the analytical calculations sketched in the Supplementary Material, the momentum rigidity parameter follows the chemical potential behaviour, i.e. \(c_{2}(\mu) < 0\) for \(\mu < 0\) . This relation is qualitatively in agreement with the numerical study performed computing self- consistently \(\mu (\alpha)\) , as one can see from the inset of Fig. 3(a).  
+
+<|ref|>text<|/ref|><|det|>[[516, 218, 917, 640]]<|/det|>
+The strategy implemented here to investigate how finite momentum fluctuations become critical at strong coupling is based on the analysis of the momentum rigidity parameter. This method presents two main advantages with respect to other theoretical approaches. On the hand, as already discussed, it allows us to explore the finite temperature regime and analyze the FPDW state. On the other hand, it provides a physical understanding of the importance of the anisotropy of the pairing interactions in the development of the modulated phase. As one can see in Eq. 4, the symmetry of the pairing interactions dramatically affects the momentum dependence of the propagator not only in the SC phase, but also in the normal one when \(\Delta = 0\) due to the overall form factor \(f_{\mathbf{k},\mathbf{q}}^{2}\) . This is reflected in a strong momentum dependence of the contribution to the momentum rigidity parameter. In fact, after performing analytically the Matsubara summation, the computation of the \(c_{2}\) coefficient reduces to an integral over the Brillouin zone \(c_{2} = \frac{1}{V} \sum_{\mathbf{k}} I_{2}(\mathbf{k})\) . The expression for \(I_{2}\) is given in the Supplemental Material, but here we show here in Fig. 5 2D maps of \(I_{2}(\mathbf{k})\) for both \(s\) - wave and \(d\) - wave at \(T = 0\) and \(T = T_{i}\) . In the isotropic \(s\) - wave case, the contributions to the momentum rigidity coming from different momenta, \(I_{2}(\mathbf{k})\) , are positive at any \((k_{x}, k_{y})\) . On the other hand, in the \(d\) - wave case the contributions to the momentum rigidity coming from the nodal regions are negative and dominate the overall sign of the \(c_{2}\) coefficient.  
+
+<|ref|>sub_title<|/ref|><|det|>[[652, 668, 780, 682]]<|/det|>
+## CONCLUSIONS  
+
+<|ref|>text<|/ref|><|det|>[[516, 701, 916, 912]]<|/det|>
+A consistent explanation for the occurrence of both static and fluctuating Cooper pairs with finite momentum in the phase diagram of materials such as cuprates has been a long- standing problem. This is primarily because an identification of the microscopic ingredients driving such exotic pairing has been elusive. The results in this paper point toward a simple and unified framework that naturally promotes both fluctuating and static pair- density wave (FPDW and PDW) phases over their zero momentum counterparts. Fig. 1 summarizes the main conclusions of our work, supported not only by numerical evaluations but also transparent analytical estimates (see Supplemental Material). The two key ingredients resulting in a high temperature FPDW and low tem
+
+<--- Page Split --->
+<|ref|>image<|/ref|><|det|>[[86, 61, 468, 355]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[85, 369, 488, 504]]<|/det|>
+<center>FIG. 5: \(I_{2}(k_{x},k_{y})\) color maps at \(T = 0\) and \(T = T_{i}\) and \(\alpha = 2.0\) for the \(s\) -wave and \(d\) -wave case. The anisotropy of the interactions affects the momentum dependence of the propagator both in the SC and normal phase. This reflects in a strong momentum dependence of the contribution to the momentum rigidity parameter. For the \(d\) -wave case negative contributions to the rigidity are found both at \(T = 0\) and \(T = T_{i}\) from \(k\) -points close to the nodal region. We use the same set of parameters of Fig. 2 and plot the result in dimensionless units for momenta \(|k_{i}| / \sqrt{\Lambda} < \pi\) , with \(i = x, y\) . </center>  
+
+<|ref|>text<|/ref|><|det|>[[86, 533, 488, 806]]<|/det|>
+perature PDW phases are a) anisotropic (e.g. \(d\) - wave) pair interactions and b) intermediate to strong coupling ratio of \(\alpha = \frac{E_{B}}{E_{F}}\) , where \(E_{B}\) is the pair binding energy for two electrons on the Fermi surface in the presence of an attractive interaction, and \(E_{F}\) is the Fermi energy. Well below a critical value of \(\alpha \sim 0.7\) , only uniform zero momentum \(d\) - wave pairing is favored. In the approximate range of \(0.7 \lesssim \alpha \lesssim 1.5\) , the FPDW phase, characterized by a negative momentum rigidity \(c_{2}\) and positive \(c_{6}\) (see Fig. 3), is stable over a range of temperatures below the instability temperature \(T_{i}\) . However, in this range of \(\alpha\) a uniform \(d\) - wave pair is still favored at zero temperature. For \(\alpha \gtrsim 1.5\) , the PDW phase is more stable than a uniform solution at \(T = 0\) and a finite momentum pair exists for all temperatures below \(T_{i}\) . The modulation wave vector \(\mathbf{Q}\) of the paired phases is determined by the ratio \(\alpha\) and acquires a jump with increasing \(\alpha\) as in a first order transition (see Fig. 4).  
+
+<|ref|>text<|/ref|><|det|>[[86, 807, 488, 913]]<|/det|>
+The FPDW and PDW phases are stabilized by contributions to the fluctuation free energy arising from momenta close to the nodal regions in the Brillouin zone. These contributions, which also should drive strong anisotropy in the phase stiffness near \(T_{i}\) , are suppressed (enhanced) at weak (strong) coupling thus leading to a modulated phase above a critical pairing strength. This  
+
+<|ref|>text<|/ref|><|det|>[[513, 65, 916, 308]]<|/det|>
+simplified picture is confirmed from our numerical calculations (Fig. 5). Finally, while our work primarily focuses on the instability temperature \(T_{i}\) in the strong coupling limit, the behavior of the condensation temperature \(T_{c}\) and the fluctuations around the PDW ground state in this setting are open problems that will require further investigations. Our work does not consider the competing effects of a nematic superconducting phase that has been phenomenologically found to suppress the PDW at \(T > 0\) in 2D [8, 12]. In addition, even if allowed by our model, we have not addressed the possible coexistence at low \(T\) of a PDW and a homogeneous \(d\) - wave superconductor, as suggested by cuprate experiments [5, 27]. Our results as such set the stage for future microscopic descriptions of modulated superconductivity in strongly coupled materials.  
+
+<|ref|>sub_title<|/ref|><|det|>[[614, 337, 819, 350]]<|/det|>
+## ACKNOWLEDGEMENTS  
+
+<|ref|>text<|/ref|><|det|>[[513, 369, 916, 490]]<|/det|>
+We thank P. Abbamonte, B.M. Andersen, S. Caprara, A. Chubukov, E. Fradkin, S. A. Kivelson, M. Granath and A. Toschi for useful discussions and suggestions. L. F. acknowledges support by the European Union's Horizon 2020 research and innovation programme through the Marie Sklodowska- Curie grant SuperCoop (Grant No 838526). This work is supported by the DOE grant number DE- FG02- 05ER46236.  
+
+<|ref|>text<|/ref|><|det|>[[513, 504, 875, 560]]<|/det|>
+\(\oplus\) These authors contributed equally to this work. \(\dagger\) csctty@rice.edu \(\diamond\) laura.fanfariello@ufl.edu \(\otimes\) pjh@phys.ufl.edu  
+
+<|ref|>text<|/ref|><|det|>[[520, 620, 919, 912]]<|/det|>
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+
+<--- Page Split --->
+<|ref|>sub_title<|/ref|><|det|>[[44, 42, 312, 70]]<|/det|>
+## Supplementary Files  
+
+<|ref|>text<|/ref|><|det|>[[44, 93, 765, 113]]<|/det|>
+This is a list of supplementary files associated with this preprint. Click to download.  
+
+<|ref|>text<|/ref|><|det|>[[61, 130, 191, 149]]<|/det|>
+SMPDW.pdf
+
+<--- Page Split --->

preprint/preprint__118e48f8906d9884fe96b830f39986096147254471f7b700649dc45674684d7f/preprint__118e48f8906d9884fe96b830f39986096147254471f7b700649dc45674684d7f.mmd

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+
+# Unfolding the multiscale structure of networks with dynamical Ollivier-Ricci curvature  
+
+Adam Gosztolai ( \(\boxed{ \begin{array}{r l} \end{array} }\) adam.gosztolai@epfl.ch) École Polytechnique Fédérale de Lausanne https://orcid.org/0000- 0002- 0699- 5825  
+
+Alexis Arnaudon Imperial College London  
+
+## Article  
+
+Keywords: network structure, learning algorithms, network processes, Ollivier- Ricci curvature  
+
+Posted Date: February 16th, 2021  
+
+DOI: https://doi.org/10.21203/rs.3.rs- 222407/v1  
+
+License: © \(\circledast\) This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License  
+
+Version of Record: A version of this preprint was published at Nature Communications on July 27th, 2021. See the published version at https://doi.org/10.1038/s41467- 021- 24884- 1.
+
+<--- Page Split --->
+
+# Unfolding the multiscale structure of networks with dynamical Ollivier-Ricci curvature  
+
+Adam Gosztolai\* \(^{1}\) and Alexis Arnaudon \(^{1,2}\)  
+
+\(^{1}\) Neuroengineering Laboratory, Brain Mind Institute & Interfaculty Institute of Bioengineering, EPFL, Lausanne, Switzerland \(^{2}\) Department of Mathematics, Imperial College London, London, United Kingdom  
+
+## Abstract  
+
+Defining the geometry of networks is typically associated with embedding in low- dimensional spaces such as manifolds. This approach has helped design efficient learning algorithms, unveil network symmetries and study dynamical network processes. However, the choice of embedding space is network- specific, and incompatible spaces can result in information loss. Here, we define a dynamic edge curvature for the study of arbitrary networks measuring the similarity between pairs of dynamical network processes seeded at nearby nodes. We show that the evolution of the curvature distribution exhibits gaps at characteristic timescales indicating bottleneck- edges that limit information spreading. Importantly, curvature gaps robustly encode communities until the phase transition of detectability, where spectral clustering methods fail. We use this insight to derive geometric modularity optimisation and demonstrate it on the European power grid and the C. elegans homeobox gene regulatory network finding previously unidentified communities on multiple scales. Our work suggests using network geometry for studying and controlling the structure of and information spreading on networks.  
+
+Real- world networks are rarely embedded in physical or Euclidean spaces, which complicates their analysis. However, to correctly represent node similarities, it is typical to assume that the network's nodes lie in a low- dimensional subspace, such as a manifold or linear subspace \(^{1}\) . Having this geometric backbone permits the efficient functioning of standard clustering methods, including ones based on Euclidean geometric features such as k- means or expectation maximisation \(^{2}\) . A related means of geometrising networks is possible by embedding nodes into a continuous space. For example, the hyperbolic space of constant negative curvature provides a natural parametrisation of complex networks to unveil their self- similar clusters across scales \(^{3,4}\) . Likewise, embedding networks into a geometric space based on a suitable distance metric between dynamical network processes has helped reveal their functional organisation \(^{5,6}\) . However, in general, there is no guarantee that a network is compatible with a given metric space without suffering significant distortion \(^{7}\) . At the same time, a network may have several, not necessarily self- similar, geometric representations arising, for example, from clusters at multiple resolutions \(^{8}\) . Thus, there is a need for a geometric notion that does not require embedding, yet allows studying the multiscale structure of a general class of networks.  
+
+A promising candidate is the Ollivier- Ricci (OR) curvature \(^{9}\) , which measures the change of local connectivity from one node to another, given by the cost of transporting a unit of mass between their respective neighbourhoods. Instead of imposing a geometry on the network through embedding, the OR curvature induces an effective geometry that has precise interpretation in limiting cases. In fact, it is the only one among a number of discrete curvature notions \(^{10,11}\) known to converge rigorously to the traditional Ricci curvature of a Riemannian manifold \(^{12}\) . The OR curvature is also related to graph theoretical objects, including the local clustering coefficient and bounds on the spectrum of the graph Laplacian \(^{13,14}\) , and has
+
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+
+lead to advances in applications such as studying the robustness of economic networks \(^{15}\) , characterising the human brain structural connectivity \(^{16}\) and designing clustering heuristics \(^{17,18}\) .  
+
+However, several properties of the OR curvature hinder its widespread applicability to study network clusters. Since it depends on structural neighbourhoods, it lacks a resolution parameter to define a geometry on different resolutions, that is necessary to study the multiscale structure in real- world networks. Further, the OR curvature of an edge is a local quantity, in a sense that it is controlled by the degree of its endpoints \(^{13}\) . Thus, it may provide a suboptimal geometric representation of sparse networks - including many real- world networks where each node connects only to a few others - in which node degrees vary widely. This lack of robustness of the OR curvature for sparse networks also precludes its use for studying from a geometric perspective the phase transition occurring as the community structure gets weaker and become abruptly undetectable \(^{19 - 21}\) . In other words, there is a need for a geometric notion that does not rely on embeddings, robustly captures multiscale clusters in real networks, and captures the phase transition at the limit of cluster detection.  
+
+## 54 Results  
+
+## 55 Dynamical Ollivier-Ricci curvature from graph diffusion  
+
+We address this need by combining two distinct frameworks - network- driven dynamical processes and geometry with OR curvature. The spreading of network- driven dynamical processes is shaped by the heterogeneity of the network. In turn, one may infer the network structure by observing properties of their evolution. We focus on Markov diffusion processes \(^{8,22 - 24}\) , a class of linear dynamical systems which is rich enough to capture several properties of nonlinear processes on networks \(^{25,26}\) . On a connected network \(G\) weighted by pairwise distances \(w_{ij}\) , the continuous time diffusion is constructed by the standard procedure \(^{27}\) of defining the normalised graph Laplacian matrix \(\mathbf{L} := \mathbf{K}^{- 1}(\mathbf{K} - \mathbf{A})\) , where \(\mathbf{K}\) is the diagonal matrix of node degrees with \(K_{ii} = \sum_{j} A_{ij}\) and \(\mathbf{A}\) is the weighted adjacency matrix encoding similarities between nodes. For example, one may simply take \(A_{ij} = \max_{ij} w_{ij} - w_{ij}\) , or \(A_{ij} = e^{- w_{ij}}\) . Then, the probability measure of the diffusion started from the unit mass \(\delta_{i}\) on node \(i\) (Fig. 1a, b) evolves according to  
+
+\[\mathbf{p}_{i}(\tau) = \delta_{i}e^{-\tau \mathbf{L}}. \quad (1)\]  
+
+In analogy to the Ricci curvature on a manifold, the classical OR curvature \(^{9,28}\) measures the change of one- step neighbourhoods between nodes (see Methods and Supplementary Fig. 1a for background). Here, instead of structural neighbourhoods we consider distributions generated by diffusion processes across scales \(\tau\) . Specifically, we start a diffusion process at each node \(i = 1, \ldots , n\) to obtain a set of measures \(\mathbf{p}_{i}(\tau)\) . We then define the dynamic Ollivier- Ricci curvature of an edge as the distance of adjacent pairs of measures relative to the distance of their starting points  
+
+\[\kappa_{ij}(\tau) := 1 - \frac{\mathcal{W}_{1}(\mathbf{p}_{i}(\tau), \mathbf{p}_{j}(\tau))}{w_{ij}}, \quad (2)\]  
+
+whenever \(ij\) is an edge and 0 otherwise. Intuitively, Eq. (2) measures how much 'closer' diffusions get over time when started \(w_{ij}\) distance apart, measured by \(\mathcal{W}_{1}\) , the optimal transport distance \(^{29}\) . It is obtained as a solution to a minimisation problem (Eq. (13) in Methods) and encodes the least cost of transporting the measure \(\mathbf{p}_{i}(\tau)\) to \(\mathbf{p}_{j}(\tau)\) via the edges on the graph. The minimiser of this problem is the optimal transport plan represented as a matrix \(\zeta (\tau)\) . The entries of this matrix shown on Fig. 1c, d quantify how much mass is moved between each pair of nodes \(u\) and \(v\) along their connecting geodesic of length \(d_{uv}\) (Fig. 1e).  
+
+As expected, our definition recovers the classical OR curvature \(^{9}\) as a first- order approximation for small times \(\tau \ll 1\) . Indeed, \(\mathbf{p}_{i}(\tau) \simeq \delta_{i}\mathbf{K}^{- 1}\mathbf{A}\) is the one- step measure encoding the local connectivity. Further, the dynamical OR curvature inherits the geometric intuition of the classical definition. Notably, on canonical trees- like and cliques- like networks the \(\kappa_{ij}(\tau)\) is negative and positive, respectively, for all finite scales \(\tau\) analogously to the Ricci curvature on hyperboloids and spheres (Supplementary Fig. 1b, c).
+
+<--- Page Split --->
+![](images/Figure_1.jpg)
+
+<center>Figure 1: Dynamical Ollivier-Ricci curvature capturing the spreading of diffusion processes. a Snapshot at time \(\log \tau = 0.15\) of a pair of diffusion measures \(\mathbf{p}_{i}(\tau)\) and \(\mathbf{p}_{j}(\tau)\) started at nodes \(i\) , \(j\) of a stochastic block model network ( \(n = 120\) with four equal clusters with edge probabilities 0.8 within clusters and 0.1 or 0.02 between clusters). When \(i\) and \(j\) are in the same cluster, the measures overlap significantly. The size of half-circles is proportional to the amount of mass on the respective nodes. b For \(i^{\prime}\) , \(j^{\prime}\) in different clusters the measures remain largely disjoint. c Optimal transport plan \(\zeta (\tau)\) superimposed with \(\mathbf{p}_{i}(\tau)\) , \(\mathbf{p}_{j}(\tau)\) . When \(i\) , \(j\) (colored dashed lines) lie in the same cluster only diagonal elements \(\zeta_{uu}\) are positive, meaning only geodesics within a cluster transport significant mass. The white dashed lines correspond to the four clusters. d Same as c, but with diffusions started at nodes \(i^{\prime}\) and \(j^{\prime}\) in different clusters. Only entries \(\zeta_{uv}\) with \(u\) and \(v\) in different clusters have significant nonzero weight. e Geodesic distance matrix showing the block structure of the network. f The evolution of the edge curvatures (Eq. (2)) against time, with the highlighted lines corresponding to edges in a, b. Here \(\kappa_{ij}(\tau) \simeq 0.75\) indicates scales when local mixing occurs between diffusion pairs. The dashed vertical lines show two such scales ( \(\log \tau = 0.15\) , 0.43). g, h Graph edges coloured by the curvature reveals the clusters at the two scales. </center>  
+
+In the following we are interested in studying the curvature distribution across edges when the network structure deviates from these canonical topologies.  
+
+## Edge curvature gap differences in rate of information spreading  
+
+Most real- world networks exhibit organisation on several scales. As an illustration, the unweighted stochastic block model (SBM) network \(^{30}\) of four equal- size clusters contains two nontrivial scales if the edges are drawn independently with probability 0.8 within clusters and 0.1 or 0.02 between clusters (Fig. 1a, b). We show that multiscale structure can be revealed by scanning through a finite range of scales \(\tau\) and studying snapshots of curvature distribution across edges.  
+
+The characteristic scales of a network are related to the overlap between pairs of diffusion measures \(\mathbf{p}_{i}(\tau)\) , \(\mathbf{p}_{j}(\tau)\) . This overlap depends on the starting points \(i\) , \(j\) and on network clusters which can confine diffusions on well- connected regions for long times before reaching the stationary state \(\pi^{8,22,23,31}\) , with \(\pi_{i} = \mathbf{K}_{ii} / \sum_{i} \mathbf{K}_{ii}\) . This transient phenomenon is reflected by the structure of the optimal transport matrix \(\zeta (\tau)\) . If \(i\) , \(j\) lie within the same subnetwork, the measures quickly overlap (Fig. 1a) and only diagonal entries of \(\zeta (\tau)\) are positive (Fig. 1c), weighing only short, within- cluster geodesics. By contrast, started at different subnetworks, the measures remain almost disjoint (Fig. 1b) and \(\zeta (\tau)\) is forced to select longer
+
+<--- Page Split --->
+
+geodesics (Fig. 1d, e), reflected by the large entries in the off- diagonal block.  
+
+The evolution of the edge curvature \(\kappa_{ij}(\tau)\) (Fig. 1f) aggregates the information in \(\zeta (\tau)\) into a single number that is related to the rate of mass exchange between subnetworks at a given scale. We see in Fig. 1f that, initially, when all nodes support disjoint point masses and the diffusions have not yet mixed, \(\begin{array}{r}{\lim_{\tau \to 0}\kappa_{ij}(\tau)\to 1 - \mathcal{W}_{1}(\delta_{i},\delta_{j}) / d_{ij} = 0} \end{array}\) . At the other extreme, as the diffusions reach stationary state, \(\begin{array}{r}{\lim_{\tau \to \infty}\kappa_{ij}(\tau)\to 1 - \mathcal{W}_{1}(\pi ,\pi) / d_{ij} = 1} \end{array}\) . At intermediate scales, the curvature can take values between 1 and some finite negative number depending on the graph. We find that, as the curvature of an edge evolves, the scale at which it approaches unity indicates how easy it is to propagate information between the subnetworks. More precisely, in the Methods, we prove that this scale gives an upper bound on the mixing time \(\tau_{ij}^{\mathrm{mix}}\) of the diffusion pair, namely,  
+
+\[\begin{array}{l}{\tau_{ij}^{\mathrm{mix}}:= \frac{1}{2}\sum_{uv}|\zeta_{uv}(\tau) - \zeta_{uv}(\infty)|}\\ {\leq \min \{\tau :\kappa_{ij}(\tau)\geq 0.75\} ,} \end{array} \quad (3)\]  
+
+where \(\zeta (\tau)\) is the optimal transport plan with marginals \(\mathbf{p}_{i}(\tau)\) and \(\mathbf{p}_{j}(\tau)\) . Note that \(\kappa_{ij}(\tau)\geq 0.75\) does not imply that the corresponding diffusion processes have approached stationary state independently, but only that they exchange negligible mass at that or larger scales.  
+
+Importantly, a gap in the distribution of curvatures appears when the curvature exceeds 0.75 for some edges while remaining less than 0.75 for others indicating a network bottleneck that limits mass flow. To illustrate this, Fig. 1f shows three groups of edges, those with most positive curvature are found within clusters, while the other two groups of edges are found between pairs of clusters. Figs. 1g, h correspond to two scales on Fig. 1f ( \(\log \tau = 0.15\) , 0.43) where the curvature has exceeded 0.75 for some groups of edges, indicating the diffusions are well mixed within these groups, but not across other edges for which the curvature is less than 0.75. The latter mark bottleneck edges which lie between the expected partitions with 4 and 2 clusters, respectively. This simple example shows the importance of the scale parameter \(\tau\) in our curvature definition to capture the network structure at multiple scales. Before applying this to real networks, we take a closer look at the curvature gap in the theoretical context of the stochastic block model.  
+
+## Curvature gap is a robust indicator of clusters in stochastic block models  
+
+Since in our example any pair of diffusions are supported by one (Fig. 1a) or two (Fig. 1b) clusters, we focus on studying the subgraph \(G\) induced by two clusters (Fig. 2a). The subgraph \(G\) is a realisation of \(\mathcal{G} = \mathrm{SSBM}(n / 2, p_{\mathrm{in}}, p_{\mathrm{out}})\) , the symmetric SBM composed of two planted partitions of equal size. Edges are generated independently with probability \(p_{\mathrm{in}}\) within- clusters and probability \(p_{\mathrm{out}}\) between- clusters. We will denote the ground truth with \(C_{i}\in \{1, - 1\}\) for each node \(i\) and define \(\bar{k} = n(p_{\mathrm{in}} + p_{\mathrm{out}}) / 2\) as the average degree.  
+
+Classical spectral clustering methods \(^{27}\) perform well for dense graphs (Fig. 2a), where \(\bar{k}\) is an increasing function of \(n\) . This suppresses fluctuations for large \(n\) causing a spectral gap to appear when the eigenvalue \(\lambda_{c}\) of the Laplacian matrix \(\mathbf{L}\) of \(G\) separates from bulk eigenvalues arising from randomness \(^{27}\) (Fig. 2c). In this dense regime, \(\lambda_{c}\) is well approximated by \(\langle \lambda_{c}\rangle_{\mathcal{G}} = 2p_{\mathrm{out}} / (p_{\mathrm{in}} + p_{\mathrm{out}})\) , the second eigenvalue of the ensemble averaged Laplacian \(\langle \mathbf{L}\rangle_{\mathcal{G}}\) (see Supplementary Note 1). Since \(\lambda_{c}\) can be identified due to the spectral gap, clustering involves simply labelling nodes by the sign of the entries of the corresponding eigenvector, which also approximates the ensemble average eigenvector \(\phi_{c}(u) = 1 / \sqrt{n}\) when \(C_{u} = 1\) and \(- 1 / \sqrt{n}\) when \(C_{u} = - 1\) . However, for sparse graphs (Fig. 2b), where \(\bar{k}\) is constant (independent of \(n\) ), the spectral gap ceases to exist \(^{32}\) (Fig. 2d). Thus, spectral algorithms relying on identifying \(\lambda_{c}\) perform no better than chance. To perform clustering in this regime, one needs to go beyond spectral clustering using, for example, the belief propagation method in statistical physics or the related non- backtracking operator whose spectrum is better behaved \(^{19,21}\) .
+
+<--- Page Split --->
+![](images/Figure_2.jpg)
+
+<center>Figure 2: Edge curvature gap indicates the presence of clusters where spectral clustering fails. Two-partition symmetric SBM graph in the a dense regime ( \(p_{\mathrm{in}} = 0.5\) , \(p_{\mathrm{out}} = 0.1\) ) and b sparse regime ( \(p_{\mathrm{in}} = 8 / n\) , \(p_{\mathrm{out}} = 0.5 / n\) ). Edges are coloured by the curvature ( \(n = 100\) , \(\log \tau = 0.83\) ). c, d The histogram of eigenvalues obtained from five SBM realisations in dense and sparse regime, respectively. In the dense regime, the eigenvalue \(\lambda_{c}\) corresponding to the community structure is well separated from the bulk eigenvalues, but overlaps in the sparse regime. e, f The evolution of edge curvatures driven against diffusion time. A gap between the curvatures of within-edges and between-edges is associated with the presence of clusters. When \(\kappa_{ij} > 0.75\) (horizontal dashed line) the diffusions are well mixed across the respective edges. The curvature gap is maximal at \(\tau_{\kappa} \approx \lambda_{c}^{-1}\) (orange and black vertical lines). g There is no curvature gap in the limiting ER graph (inset, \(p_{\mathrm{in}} = p_{\mathrm{out}} = (8 + 0.5) / (2n)\) ). h Maximal curvature gap averaged over 20 SBM realisations for each fixed \(\bar{k}\) with \(10^{4}\) nodes, against edge density ratio. The horizontal line marks the estimated background noise level. The intersection of this line with the mean curvature gap defines \(r_{k}^{*}\) , the largest possible edge density ratio to detect clusters. i Phase diagram of critical edge density ratio against average degree. The numerically obtained critical edge density ratios computed from the curvature gap are superimposed with the theoretical Kesten-Stigum detection limit (dashed line) and show excellent agreement. Gray shaded area denotes the regime where detection is possible. </center>  
+
+To see how robustly the dynamical OR curvature indicates the presence of clusters in the symmetric SBM, let us construct a measure on the curvature evolution. To this end, we define the curvature gap as the difference between the mean curvatures of within- and between- edges  
+
+\[\delta \kappa (\tau):= \frac{1}{\sigma}\left|\langle \kappa_{ij}(\tau)\rangle_{C_i = C_j} - \langle \kappa_{ij}(\tau)\rangle_{C_i\neq C_j}\right| \quad (4)\]  
+
+where the averages are on within and between- edges, normalised by \(\sigma = \sqrt{\frac{1}{2}\left(\sigma_{\mathrm{within}}^{2} + \sigma_{\mathrm{between}}^{2}\right)}\) in terms of the standard deviations of both sets of curvatures. This measure is adapted from the sensitivity index in signal detection theory, known to be, asymptotically, the most powerful statistical test for discriminating two distributions. Large curvature gap \(\delta \kappa (\tau)\) indicates that the within and between edges have curvatures different enough for the clusters to be recovered (Fig. 2e, f). Correspondingly, in the limits \(\tau \to 0, \infty\)
+
+<--- Page Split --->
+![](images/Figure_3.jpg)
+
+<center>Figure 3: Detecting communities using pairs of diffusions near the weak recovery limit. a Difference in eigenvectors \(\Delta \phi_{s}\) (Eq. (8)) between diffusion processes started at adjacent nodes for a single sparse SBM network \((p_{\mathrm{in}} = 3 / n\) , \(p_{\mathrm{out}} = 0.5 / n\) , \(n = 10^{4}\) ). Each dot marks \((\lambda_{s},\Delta \phi_{s})\) for the 50 smallest eigenvectors, colored by the correlation of the corresponding eigenvector with the ground truth, shown in the inset. b The eigenvector with the highest \(\Delta \phi_{s}\) encodes the cluster structure (solid line), whereas the second eigenvector \(\phi_{2}\) , used by spectral clustering methods, are driven by high random fluctuations. c Correlation of eigenvectors with ground truth against distance to KS limit ( \(n = 10^{5}\) , \(\bar{k} = 3\) ). The eigenvector identified by the highest \(\Delta \phi_{s}\) approaches the correlation with the ground truth of the best eigenvector in the spectrum. All eigenvectors become uncorrelated with the ground truth at the KS limit. </center>  
+
+where the curvatures are uniform across the graph \(\delta \kappa (\tau)\) vanishes and, likewise, in the absence of structure \((p_{\mathrm{in}}\approx p_{\mathrm{out}}\) in the Erdos- Renyi (ER) limit) we have \(\delta \kappa (\tau) = 0\) for all \(\tau\) (Fig. 2g). At intermediate scales, we find that the scale of maximal curvature gap occurs at \(\tau_{\kappa}\) at which point the curvatures of within- edges is \(\kappa_{ij}(\tau_{\kappa})\approx 0.75\) . In agreement with Eq. (3), this indicates well- mixed diffusions across these edges relative to low- curvature bottleneck edges between clusters, which indicate incomplete mixing. We also find that \(\tau_{\kappa}\approx \lambda_{c}^{- 1}\) (Fig. 2e, f). These results show that positive curvature gap is associated with the presence of clusters.  
+
+What is the minimum curvature gap needed to detect clusters? Previous works on the limits of cluster detection has shown that if the clusters are too weak (high \(r:= p_{\mathrm{out}} / p_{\mathrm{in}}\) ) or the graph too sparse (low \(\bar{k}\) ), no algorithm can assign the vertices to communities better than chance, or distinguish \(G\) from an Erdos- Renyi graph ( \(r = 1\) ). This is known as the limit of weak- recovery or detection and is characterised by the Kesten- Stigum (KS) threshold \(r = r_{\mathrm{KS}} = (\bar{k} - \sqrt{\bar{k}}) / (\bar{k} + \sqrt{\bar{k}})^{19,20,34}\) .  
+
+To study this limit, we sampled 20 networks from \(\mathcal{G}\) for a range of \(\bar{k}\) and \(r\) . For each sample, we computed the maximal curvature gap \(\delta \kappa^{*}:= \max_{\tau}\delta \kappa (\tau)\) and formed the ensemble average quantity \(\langle \delta \kappa^{*}\rangle_{\mathcal{G}}\) . As \(r\) increases for a given \(\bar{k}\) we observe that \(\langle \delta \kappa^{*}\rangle_{\mathcal{G}}\) decreases exponentially until a certain noise level (Fig. 2h). The critical edge density ratio \(r_{\bar{k}}^{*}\) can be estimated as the smallest \(r\) where \(\langle \delta \kappa^{*}\rangle_{\mathcal{G}}\) dropped below a threshold background noise level, estimated here at 0.035 (black horizontal line). This choice of threshold is not absolute, as it is affected by the finite- size effect of the SBM graphs. An analytical derivation of this threshold is out of scope of this work, but our numerical experiment clearly shows that the curvature gap detects a signal from the planted partitions up to the KS limit (Fig. 2i).  
+
+## Geometric cluster detection in the sparse regime  
+
+Given that the curvature gap (Eq. (4)) indicates the presence of clusters until the fundamental KS limit we asked if this information could be used to recover the ground truth partition. The definition of curvature gap (Eq. (4)) suggests looking for equilibrium configurations of the unit- temperature Boltzmann distribution over the cluster assignments \(C\) ,  
+
+\[\mathbb{P}(C|\kappa)\propto e^{\sum_{ij}\kappa_{ij}(\tau)\delta (C_i,C_j)}, \quad (5)\]  
+
+where \(\kappa\) is a matrix with entries \(\kappa_{ij}\) and the sum is over all edges \(ij\) . The distribution involves only within- edges because finding those is equivalent to finding between- edges, up to a normalisation factor.
+
+<--- Page Split --->
+
+The distribution \(\mathbb{P}(C|\kappa)\) is important because all of its equilibrium states are equivalent and correlate with the ground truth partition of the symmetric SBM \(\mathcal{G}\) . To see this, we connect \(\mathbb{P}(C|\kappa)\) to the posterior distribution \(\mathbb{P}(C|G)\) of the cluster assignments obtained given the graph drawn from \(\mathcal{G}\) . In the sparse regime, the likelihood of observing \(G\) with a given cluster assignment \(C\) is  
+
+\[\mathbb{P}(G|C)\propto \prod_{ij}\left(\frac{p_{in}}{p_{\mathrm{out}}}\right)^{\delta (C_i,C_j)}\propto \mathbb{P}(C|G) \quad (6)\]  
+
+(see Eq. (17) in Methods). The second part of Eq. (6) results from Bayes' theorem using a uniform prior on \(C\) , since a priori all configurations are equally likely. It has been previously shown \(^{19}\) that \(P(C|G)\) is equivalent to the Boltzmann distribution of an Ising model with constant interaction strength  
+
+\[\mathbb{P}(C|G)\propto e^{\beta \sum_{ij}\delta (C_i,C_j)} \quad (7)\]  
+
+with inverse temperature \(\beta = \log (p_{\mathrm{in}} / p_{\mathrm{out}})\approx p_{\mathrm{in}} - p_{\mathrm{out}}\) . Note that one of the equilibrium states is trivial assigning all nodes to one cluster. However, asymptotically ( \(n\to \infty\) ) the probability of this state vanishes and the Boltzmann distribution is uniform over all other configurations with group sizes \(n / 2\) and \(p_{\mathrm{out}}n / 2\) between- edges \(^{19}\) . The fact that one of these states is the ground truth partition, and all equilibrium states of Eq. (7) are equivalent up to a permutation of nodes within clusters means they are indistinguishable from the ground truth partition.  
+
+Due to the equivalence between Eq. (6) and (7), to prove the equivalence between Eq. (5) and (6) we show that Eq. (5) can also be reduced to Eq. (7). The main insight is that the dynamical OR curvature (Eq. (2)) is constructed using pairs of diffusions, as opposed to single diffusions. Thus, eigenmodes arising from random fluctuations are reflected equally in the spectrum of both diffusions and cancel out upon taking differences over all adjacent node pairs. This allows recovering the community eigenvector \(\phi_{c}\) even in the sparse regime where when there is no spectral gap and \(\lambda_{c}\) is no longer identifiable from the spectrum (Fig. 2d). Specifically, using pairs of diffusions, we use the spectral expansion to write \(\sum_{ij}\left(p_{i}^{u}(\tau) - p_{j}^{u}(\tau)\right) = \sum_{s}e^{\lambda_{s}\tau}\phi_{s}\Delta \phi_{s}\) where  
+
+\[\Delta \phi_{s}:= \sum_{ij}\left(\phi_{s}(i) - \phi_{s}(j)\right). \quad (8)\]  
+
+We find that, on a single SBM realisation, \(\Delta \phi_{s}\) is large for only a few eigenvectors \(\phi_{s}\) and diminishing for others Fig. 3a). Importantly only those eigenvectors with large \(\Delta \phi_{s}\) correlate strongly with the ground truth (Fig. 3a inset). As seen in Fig. 3b, the best eigenvector is not \(\phi_{2}\) , i.e., the one whose eigenvalue is second in the spectrum and is used by spectral clustering methods, but the one whose eigenvalue is inside the bulk in Fig. 2d and thus cannot be identified by looking at the spectrum alone. The correlation with the ground truth for \(\phi_{c}\) with the highest \(\Delta \phi_{s}\) averaged over 50 SBM realisations remains close to the highest achievable among all eigenvectors as the KS bound is approached. Meanwhile, \(\phi_{2}\) is suboptimal (Fig. 3c). We also found that, close to the KS bound, often a few other eigenvectors with similarly high \(\Delta \phi_{s}\) appear, suggesting an improved clustering method combining several top eigenvectors, but this is out of scope here.  
+
+To express the curvature in the exponent of Eq. (5) we use the dual formulation of the optimal transport distance (Eq. (14) in Methods). The fact that \(\Delta \phi_{c}\) dominates the contribution from other eigenvectors, allows us to approximate \(\sum_{ij}\left(p_{i}^{u}(\tau) - p_{j}^{u}(\tau)\right) = e^{\lambda_{c}\tau}\phi_{c}\Delta \phi_{c} + \epsilon_{\phi}\propto e^{\lambda_{c}\tau}\phi_{c} + \epsilon_{\phi}\) , where \(\epsilon_{\phi}\) is an asymptotically small term. We use this expression, together with the duality formula (Eq. (14)) to express Eq. (5). Finally, in the sparse regime, we may make a tree- like approximation of the neighbourhoods of \(i\) and \(j\) to find that Eq. (5) reduces to  
+
+\[\mathbb{P}(C|\kappa)\propto e^{|p_{\mathrm{in}} - p_{\mathrm{out}}|\sum_{ij}\delta (C_i,C_j)}. \quad (9)\]
+
+<--- Page Split --->
+
+We refer the reader to the Methods for details. Eq. (9) is the same as Eq. (7) when the communities are assortative \((p_{\mathrm{in}} > p_{\mathrm{out}})\) . We then conclude that the curvatures encode the communities of the symmetric SBM and allow it to be recovered until close to the Kesten- Stigum bound.  
+
+In the next section, we present a clustering algorithm based on this insight that can find multiscale clusters in real- world networks.  
+
+## Geometric modularity for the multiscale clustering of networks  
+
+To exploit the property of the dynamical OR curvature to give multiple geometric representations, we develop a multiscale graph clustering algorithm for real- world networks. Using Eq. (5), we introduce the geometric modularity function  
+
+\[Q_{\kappa}(C,\tau) = \frac{1}{2m_{\kappa}}\sum_{ij}\left(\kappa_{ij}(\tau) - \kappa_{0}\right)\delta (C_{i},C_{j}), \quad (10)\]  
+
+where \(2m_{\kappa} = \sum_{ij}|\kappa_{ij}|\) is a normalisation factor and \(\kappa_{0} = \max_{ij}\kappa_{ij}(\tau_{\mathrm{min}})\) is a constant ensuring that all edges have small non- positive curvature at the smallest computed scale \(\tau_{\mathrm{min}}\) . Hence optimising Eq. (10) at small times yields separate communities for each node whereas at large times, when \(\kappa_{ij}(\tau)\to 1\) for all \(ij\) , all nodes are merged to a single community. At intermediate scales, the curvatures will have negative and positive values on different edges, making the detection of non- trivial clusters possible without a statistical null- model. This is in contrast to classical modularity \(^{35}\) , which minimises the expected number of edges between clusters, and requires a statistical null- model (typically the configuration model), which can hinder identifying functional communities based on dynamics \(^{5}\) .  
+
+To detect robust partitions at several scales, we sample the cluster landscape \(Q_{\kappa}(C,\tau)\) at a sequence of scales \(\tau\) spanning the entire dynamical range of the curvature and, at each \(\tau\) , optimising Eq. (10) using the Louvain algorithm \(^{36,37}\) with 200 random initialisations. At a given \(\tau\) , we take the cluster with the highest geometric modularity and deem it robust if it has a low variation of information \(\mathrm{VI}_{\tau}\) against 50 other randomly chosen clusters at this scale, as well as low variation of information \(\mathrm{VI}_{\tau \tau^{\prime}}\) against the best cluster assignments at nearby scales \(\tau^{\prime}\) . As an example, we show in Fig. 4a the result of this computation on our four- partition SBM graph with two hard- coded scales. We clearly see two large plateaus with low \(\mathrm{VI}_{\tau}\) and \(\mathrm{VI}_{\tau \tau^{\prime}}\) , corresponding to robust clusters, shown in Fig. 4b,c. At the smallest scales we find no robust communities shown by the sharp increase in the number of communities and the large \(\mathrm{VI}_{\tau}\) .  
+
+Due to the link between high edge curvature and well- mixed state (Eq. (3)), we expected that at robust scales the clusters will correspond to those regions which have a high amount of redundant information, and thus can be disconnected without affecting the dynamics within them. To see this, we applied this clustering algorithm to the European power grid graph in Fig. 4d,e,f, an unweighted network of major electrical lines, which has been previously analysed for robustness \(^{38}\) , multiscale communities \(^{39}\) and centrality \(^{24}\) . The multiscale community structure can be clearly seen with the many minima of the \(\mathrm{VI}_{\tau}\) function in Fig. 4d. We displayed two scales in Fig. 4e,f which unfold parts of the power grid which have been historically independently developed. The smaller scale (at around \(\log \tau = - 0.95\) ) marks countries or economical and historical alliances (Skandinavia, Benelux, Czechoslovakia, Balkans, etc.). Likewise, the larger scale (at around \(\log \tau = - 0.5\) ) divides historical Eastern- Western Europe. Interestingly with the boundary in Germany runs along the iron curtain, which also demarcates the regions between major electricity companies.  
+
+Finally, we analysed a recent dataset of homeobox gene expression in single neurons of \(C\) . elegans in Fig. 4g, h, i and \(\mathrm{j}^{40}\) . This work found based on a multivariate linear regression that the homeobox gene expression profile in a given anatomical neuron class can explain on average 74% of the expression level of the remaining genes in that neuron class. We therefore asked whether the homeobox gene expression profile has sufficient information to cluster neurons into their known anatomical classes.  
+
+The data contains a binary feature vector for each of the 301 neurons, indicating the presence of a protein expressed by any of the 105 homeobox genes in the given neuron. To convert this data into a
+
+<--- Page Split --->
+![](images/Figure_4.jpg)
+
+<center>Figure 4: Clustering networks based on multiscale geometric modularity a Clustering statistics computed based on \(10^{2}\) Louvain realisations for the multiscale stochastic block model graph. Vertical dashed lines show scales at which stable clusters are detected based on low variation of information at a given scale and persistent low variation of information between Louvain realisations across scales. The communities obtained at these scales are shown on b for \(\log \tau = -2.3\) and c for \(\log \tau = -1\) . Edges are coloured by the curvatures at the respective scales. d Clustering statistics for the European power grid. Two representative stable scales are shown e for \(\log \tau = -0.95\) and f for \(\log \tau = -0.5\) . g Clustering statistics for and the network of C. elegans single-neuron homeobox gene expressions show a plateau of stable scales with very similar partitions. h Clustering statistics obtained with Markov stability shows stable scales only at small times with single-node communities, indicating overfitting, and many non-robust partitions at larger scales with high variation of information. i Distance from ground truth based on structural neuronal types or the predicted clusters. Geometric modularity obtained significantly better performance than Markov stability. j Clustering of the C. elegans homeobox gene expression data obtained from geometric modularity optimisation superimposed with the ground truth. </center>
+
+<--- Page Split --->
+
+graph with nodes being neurons, we first eliminated all homeobox genes co- expressed in none or more than \(90\%\) of the neurons to retain 67 homeobox genes. We then constructed an all- to- all graph adjacency matrix weighted by the Jaccard similarity index between expression profiles of neurons. To increase the number of edges with negative curvature, thus improve the detection at the smallest scales, we sparsified this network using a geometric sparsification method \(^{41}\) with parameter \(\gamma = 0.01\) . This method retains at most a fraction \(\gamma\) edges of the original graph as minimum spanning tree augmented by edges relevant for preserving local or global geometry of the graph.  
+
+The results of our clustering algorithm on this graph is shown on Fig. 4g and compared with the result of Markov stability \(^{8}\) (Fig. 4h), a multiscale method based on persistence of diffusions. Geometric modularity obtains a large range of robust scales with highly similar clusters - as shown by the low \(\mathrm{VI}_{\tau}\) and \(\mathrm{VI}_{\tau \tau^{\prime}}\) . These scales correlate closely with the known ground truth of 117 anatomical neuron classes (Fig. 4i). In contrast, for Markov stability \(^{8}\) , the scales with low \(VI_{\tau}\) overfit the graph finding too many clusters (Fig. 4h) which correlate less with the ground truth (Fig. 4i). Likewise, hierarchical clustering fails to identify the ground truth communities \(^{40}\) . On Fig. 4j we superimpose the best clustering from geometric modularity against the ground- truth. We observe little differences, apart from VA and AS nodes as well as VD and DD often clustered together. Careful look reveals close biological relationship between these classes; all four classes correspond to motor neurons, with pairs expressing the same neurotransmitters – VA, AS expressing acetylcholine and VD, DD expressing gamma- aminobutyric acid (GABA). These novel results give direct quantitative support to the claim that homeobox gene expression patterns encode structural neuron types. We also observe other stable partitions at larger scale, but they did not correlate the ground- truth.  
+
+Overall, these results give a strong demonstration that our method is able to find stable clusters in sparse graphs, and provide meaningful insights into distinct types of real- world networks.  
+
+## Discussion  
+
+We introduced the concept of dynamical Ollivier- Ricci (OR) curvature which defines an effective geometry from pairs of diffusion processes on the network. Instead of imposing the requirement of a manifold approximation or embedding, used by previous geometric approaches \(^{4 - 6,23}\) , our approach constructs a geometric object - the weighted and signed edge curvature matrix - capturing progressively coarser features as the diffusion processes evolve.  
+
+Real- world networks often exhibit community structure on multiple scales, based on difference between the rates of information propagation in regions the network on various timescales. We showed that the edge curvature matrix carries a precise meaning in this context and bounds the rate of information flow across edges. Consequentially, curvature gaps, differences between edge curvatures within and between regions, indicate network bottlenecks. This result does not rely on the dynamics being linear diffusions, making it suitable to study the interaction of arbitrary dynamical processes. We expect that, in the future, this approach can be used to tune the geometry of the graph to control the flux or interaction of network- driven dynamical processes, for example, leading to better insights to synchronisation problems or metapopulation models \(^{42}\) .  
+
+Although diffusion processes constructed from the graph Laplacian have been explored for network clustering \(^{8,23}\) , our work differs in the use of diffusion pairs to construct the curvature. Two diffusions pick up random variations in the graph independently, which can be exploited to average out non- informative fluctuations. On stochastic block models, this feature allows the curvature gap to robustly indicate clusters in the sparse regime down to the fundamental limit, where clustering methods relying on the spectral gap in the Laplacian fail \(^{32}\) . We also found a new measure of eigenvalue quality, able to select the best eigenvector to be used in spectral methods. Interestingly, the edge curvatures are defined on the set of shortest paths which cannot contain the same edge twice, a subset of the set of non- backtracking walks. Our results are therefore consistent with previous works on the limits of cluster detection using statistical physics objects including the spectrum of non- backtracking operator \(^{21}\) or related message passing approaches \(^{19}\) . We expect this insight to provide a new avenue to study the fundamental limits of efficient clustering from
+
+<--- Page Split --->
+
+a geometric perspective.  
+
+Finally, using geometric modularity we built an easy- to- use algorithm to study the multiscale community structure of real networks. We demonstrated on the European power grid network and a recent dataset of \(C\) . elegans single- neuron homeobox gene expressions that our method can find robust and interpretable communities on multiple scales on diverse datasets without the tendency of overfitting.  
+
+Overall, we expect our insights connecting dynamical processes, geometry and network clustering to open new avenues to studying and controlling the structural and dynamical properties of networks.  
+
+## Acknowledgements  
+
+AG acknowledges support from an HFSP Cross- disciplinary Postdoctoral Fellowship (LT000669/2020- C). We thank Mauricio Barahona for insightful discussions on this topic, Jonas Braun and István Tomon for their helpful comments on the manuscript and Daniel Morales for inspiring us to analyse the \(C\) . elegans dataset.  
+
+## Author contributions  
+
+A.G. and A.A. contributed equally to this work.  
+
+## Code availability  
+
+The code to reproduce the results in our paper and to perform geometric modularity optimisation is available at https://github.com/agosztolai/geometric_clustering.  
+
+## Data availability  
+
+The raw data supporting the results is available from the authors upon request.  
+
+## Methods  
+
+## Classical Ollivier-Ricci edge curvature  
+
+To contrast the dynamical Ollivier Ricci curvature in Eq. (2), we recap the definition of the classical formulation \(^{9}\) , which is a generalisation of the Ricci curvature of manifolds in differential geometry. Briefly, consider two close points \(x\) and \(y\) on a manifold as well as a vector \(\mathbf{v}\) on the tangent plane at \(x\) and another tangent vector \(\mathbf{v}^{\prime}\) in the tangent plane at \(y\) that is parallel to \(\mathbf{v}\) , i.e., obtained by parallel transport along the geodesic connecting \(x, y\) (Supplementary Figure 1a). These vectors shift \(x\) and \(y\) to nearby points \(x^{\prime}\) and \(y^{\prime}\) , which will be at a distance \(d_{x^{\prime}y^{\prime}} \approx d_{xy}(1 - ||\mathbf{v}||^{2}K_{w} / 2)\) , where \(K_{w}\) is the sectional curvature. The Ricci curvature \(Ric_{xy}\) between points \(x, y\) is then defined as the average sectional curvature and is proportional to  
+
+\[R i c_{x y}\propto 1 - \frac{\langle d_{x^{\prime}y^{\prime}}\rangle}{d_{x y}}, \quad (11)\]  
+
+where \(\langle \cdot \rangle\) denotes the average over all vectors \(\mathbf{w}, \mathbf{w}^{\prime}\) running over the unit sphere in the tangent planes at \(x\) and \(y\) . In other words, it measures how much geodesics expand or contract on average around points \(x, y\) . On flat planes the geodesics stay equally separated hence \(Ric_{xy} = 0\) , on spheres the geodesics contract hence \(Ric_{xy} > 0\) , whereas in hyperbolic spaces they expand, hence \(Ric_{xy} < 0\) (Supplementary Figure 1a).  
+
+The classical Ollivier- Ricci curvature \(^{9}\) is defined by direct analogy to this. Consider two adjacent nodes \(i\) and \(j\) and place weights on their immediate neighbours in proportion to the edge weights, namely, \(\mathbf{p}_{i} = \delta_{i}\mathbf{K}^{- 1}\mathbf{A}\) . Then the Ollivier Ricci curvature becomes  
+
+\[\kappa_{i j} = 1 - \frac{\mathcal{W}_{1}(\mathbf{p}_{i},\mathbf{p}_{j})}{d_{i j}}. \quad (12)\]  
+
+Contrast this expression to the dynamical Ollivier Ricci curvature in Eq. (2), which considers diffusion measures which weight progressively larger neighbourhoods as \(\tau\) increases.  
+
+We remark that Eq. (12) and the dynamical OR curvature in (2) are valid for any two nodes \(i, j\) if the denominator is replaced by the weighted geodesic distance between \(i\) and \(j\) , but for this work, it suffices to consider adjacent nodes. Indeed, for any non- adjacent nodes \(uv\) , \(\kappa_{uv} \geq \kappa_{u^{\prime}v^{\prime}}\) , where \(u^{\prime}v^{\prime}\) is an adjacent pair lying on the geodesic connecting \(u, v\) (Proposition 19 in Ref. \(^{9}\) ), meaning that local curvatures control global curvatures.
+
+<--- Page Split --->
+
+347 Wasserstein distance  
+
+348 To measure the distance between a pair of measures \(\mathbf{p}_{i}(\tau)\) and \(\mathbf{p}_{j}(\tau)\) we use the optimal transport distance \(^{29}\) (also known as 349 1- Wasserstein or earth- mover distance), defined as  
+
+\[\begin{array}{l}{\mathcal{W}_{1}(\mathbf{p}_{i}(\tau),\mathbf{p}_{j}(\tau)) = \min_{\zeta}\sum_{u v}d_{u v}\zeta_{u v},}\\ {\mathrm{subject~to}\sum_{v}\zeta_{u v} = p_{i}^{u}(\tau),\quad \sum_{u}\zeta_{u v} = p_{j}^{v}(\tau).} \end{array} \quad (13)\]  
+
+350 The constraints in Eq. (13) ensure that the optimal transport plan \(\zeta (\tau) \in \mathbb{R}^{n \times n}\) is a coupling of the measures \(\mathbf{p}_{i}(\tau)\) , \(\mathbf{p}_{j}(\tau)\) , i.e., \(\zeta (\tau)\) is a joint distribution that admits \(\mathbf{p}_{i}(\tau)\) and \(\mathbf{p}_{j}(\tau)\) as marginals.  
+
+352 An equivalent formulation of this distance can be constructed from the Kantorovich- Rubinstein duality \(^{29}\) , given by  
+
+\[\mathcal{W}_{1}(\mathbf{p}_{i}(\tau),\mathbf{p}_{j}(\tau)) = \sup_{f}\sum_{u}f(u)[p_{i}^{u}(\tau) - p_{j}^{u}(\tau)] \quad (14)\]  
+
+353 where the supremum is taken over all 1- Lipschitz functions \(f\) on the graph, that is,  
+
+\[|f(u) - f(v)|\leq d_{u v} \quad (15)\]  
+
+354 for any node pair \(u\) , \(v\) .  
+
+355 Computational complexity  
+
+356 The computational complexity of our clustering method is determined by three components: the computation of the diffusion measures (Eq. (1)), the computation of the optimal transport distance (Eq. (13)) and the computation of the clustering.  
+
+357 We compute each diffusion measure in Eq. (1) by the scaling and squaring algorithm of Ref. \(^{43}\) . To our knowledge, the complexity of this algorithm is not known, but we found it to be better than computing the matrix exponential which runs in time \(\mathcal{O}(n^{3})\) and then multiplying by the initial condition. The exact computation of Eq. (13) is performed by interior point methods which have a complexity \(\mathcal{O}(n^{3} \log n)\) . However, note that in our work the explicit computation of \(\zeta (\tau)\) is not required and, moreover, there is typically a significant overlap between the measures \(\mathbf{p}_{i}\) , \(\mathbf{p}_{j}\) whenever \(i\) , \(j\) lie in the same highly connected region. Based on these observations we use recent approximate algorithms to compute the transport cost permitting near \(\mathcal{O}(n)\) - time computation of the optimal transport distance \(^{44,45}\) . This yields a computational complexity of \(\mathcal{O}(nm)\) for sparse graphs with worst case \(\mathcal{O}(n^{3})\) . These methods also allow GPU parallelisation, which we recommend using for large \((n \gg 10^{3})\) and dense graphs.  
+
+The third component is the complexity of the Louvain algorithm which is linear \(\mathcal{O}(n)\) for sparse networks \(^{36}\) .  
+
+368 Upper bound on the mixing time in terms of curvature  
+
+369 Here we prove inequality (3), which gives an upper bound on the mixing time of the coupled diffusions with measures \(\mathbf{p}_{i}(\tau)\) , \(\mathbf{p}_{j}(\tau)\) in terms the dynamical OR curvature. The \(\epsilon\) - mixing time is defined as the smallest \(\tau\) where the law of the coupled process, the optimal transport plan \(\zeta (\tau)\) , is within an \(\epsilon\) radius of the stationary distribution  
+
+\[\tau_{ij}(\epsilon):= \min \{\tau :||\zeta (\tau) - \zeta (\infty)||_{\mathrm{TV}}\leq \epsilon \} , \quad (16)\]  
+
+372 where the notion of "close to stationarity" is quantified by the total variation distance \(\begin{array}{r}{||\zeta (\tau) - \zeta (\infty)||_{\mathrm{TV}}:= \frac{1}{2}\sum_{u v}|\zeta_{u v}(\tau) - } \end{array}\) \(\zeta_{u v}(\infty)]\) . Since \(\mathbf{p}_{i}(\tau)\) and \(\mathbf{p}_{j}(\tau)\) are marginals of \(\zeta (\tau)\) we have that  
+
+\[\tau_{ij}(\epsilon) = \min \{\tau :||\mathbf{p}_{i}(\tau) - \pi ||_{\mathrm{TV}} + ||\mathbf{p}_{j}(\tau) - \pi ||_{\mathrm{TV}}\leq \epsilon \}\] \[\qquad = \min \{\tau :||\mathbf{p}_{i}(\tau) - \mathbf{p}_{j}(\tau)||_{\mathrm{TV}}\leq \epsilon \} ,\]  
+
+374 where we used the independence of the diffusion processes. From here, we may follow Ref. \(^{46}\) and use the Csiszar- KullbackPinsker inequality for the optimal transport distance  
+
+\[||\mathbf{p}_{j}(\tau) - \mathbf{p}_{j}(\infty)||_{\mathrm{TV}}\leq (1 / d_{0})\mathcal{W}_{1}(\mathbf{p}_{j}(\tau),\mathbf{p}_{j}(\tau)),\]  
+
+376 where \(d_{0} = \min_{ij}d_{ij}\) is a global graph constant, which can therefore be absorbed into \(\epsilon\) . This gives an upper bound  
+
+\[\tau_{ij}(\epsilon^{\prime})\leq \min \{\tau :\mathcal{W}_{1}(\mathbf{p}_{i}(\tau),\mathbf{p}_{j}(\tau))\leq \epsilon^{\prime}\}\] \[\qquad = \min \{\tau :\kappa_{ij}(\tau)\geq 1 - \epsilon^{\prime}\} ,\]  
+
+377 with \(\epsilon^{\prime} = d_{0}\epsilon\) which is what we set out to show. Note that choosing any \(\epsilon^{\prime}\in (0,1 / 2)\) ensures exponential convergence rate to the stationary measure \(^{47}\) and by convention, we take the middle of this range and define \(\tau_{ij}^{\mathrm{mix}}:= \tau_{ij}^{\mathrm{mix}}(1 / 4)\) to obtain Eq. (3).
+
+<--- Page Split --->
+
+## Connection between geometric modularity and the symmetric stochastic block model  
+
+In this section, we prove that the Boltzmann distribution of cluster assignments given the edge curvatures \(\mathbb{P}(C|\kappa)\) (Eq. (5)) has equilibrium states which are indistinguishable from the ground truth partition of the SBM. We show this by reducing \(\mathbb{P}(C|\kappa)\) as well as the posterior distribution \(\mathbb{P}(C|G)\) , to the same constant interaction Ising model (Eq. (7)). In the remainder of this section we work in the sparse regime, where \(p_{\mathrm{in}}\) , \(p_{\mathrm{out}} = O(1 / n)\) .  
+
+First, we recap the well- known equivalence of the SBM and the Ising model \(^{19}\) . Let \(E\) denote the set of edges. The probability distribution of the symmetric SBM for two clusters can be written as \(^{30}\)  
+
+\[\begin{array}{r l} & {\mathbb{P}(G|C) = p_{\mathrm{out}}^{\mathrm{e}}(1 - p_{\mathrm{out}})^{\binom{n}{2} -e}\times}\\ & {\qquad \times \prod_{i j\in E}\left(\frac{p_{\mathrm{in}}}{p_{\mathrm{out}}}\right)^{\delta (C_{i},C_{j})}\prod_{i j\notin E}\left(\frac{1 - p_{\mathrm{in}}}{1 - p_{\mathrm{out}}}\right)^{\delta (C_{i},C_{j})}}\\ & {\qquad \propto \prod_{i j\in E}\left(\frac{p_{\mathrm{in}}}{p_{\mathrm{out}}}\right)^{\delta (C_{i},C_{j})}} \end{array} \quad (17)\]  
+
+where \(e\) is the total number of edges and in the last line we used that the effect of non- edges is weak in the sparse regime. Therefore, by Bayes' theorem with uniform prior one obtains the posterior distribution \(\mathbb{P}(C|G) \propto \mathbb{P}(G|C)\) . As a result, the probability of clusters generated by the SBM is equivalent to the Ising model with uniform interaction with Boltzmann distribution given by Eq. (7) \(^{19}\) .  
+
+Second, we reduce the Boltzmann distribution of clusters given the edge curvature to same Ising model in Eq. (7). From Eq. (5) we have  
+
+\[\begin{array}{r l} & {\mathbb{P}(C|\kappa)\propto e^{\sum_{i j}\kappa_{i j}(\tau)\delta (C_{i},C_{j})}}\\ & {\qquad \propto e^{\sum_{i j}[1 - \mathcal{W}_{1}(\mathbf{p}_{i}(\tau),\mathbf{p}_{j}(\tau))]\delta (C_{i},C_{j})},} \end{array} \quad (18)\]  
+
+where in the last line we used the definition of the curvature in Eq. (2). Comparing Eq. (18) with Eq. (7) note that \(1 - \mathcal{W}_{1}(\mathbf{p}_{i}(\tau),\mathbf{p}_{j}(\tau))\) is non- constant and has a non- linear dependence on the scale \(\tau\) . However, it is possible to express it in terms of \(p_{\mathrm{in}}\) , \(p_{\mathrm{in}}\) to make the connection to the Ising model. Let us write the diffusion measures in Eq. (1) in terms of the spectral decomposition of \(\mathbf{L}\) as  
+
+\[p_{i}^{k}(\tau) = \delta_{i}\sum_{s = 1}^{n}e^{-\lambda_{s}\tau}\phi_{s}(k)\phi_{s}(l) = \sum_{s = 1}^{n}e^{-\lambda_{s}\tau}\phi_{s}(\boldsymbol {k})\phi_{s}(\boldsymbol {i}). \quad (19)\]  
+
+At this point let us remark that in the dense regime where \(p_{\mathrm{in}}\) , \(p_{\mathrm{out}} = O(1)\) , the first two eigenmodes \((\lambda_{1},\phi_{1})\) and \((\lambda_{c},\phi_{c})\) dominate and the second eigenmode contains the anti- symmetric eigenvector \(\phi_{c}(u) = 1 / \sqrt{n}\) when \(C_{u} = 1\) and \(- 1 / \sqrt{n}\) when \(C_{u} = 2\) that is associated with the community structure (Fig. 2c). Thus, one can follow spectral clustering methods \(^{27}\) to find the sparsest cut between clusters using \(\phi_{c}\) . In contrast, in the sparse regime, the dominant eigenmodes will be driven by random fluctuations in the node degrees across the graph \(^{48}\) , thus spectral clustering algorithms based on \(\mathbf{L}\) are suboptimal (Fig. 2d).  
+
+However, the coupled diffusion pair allows for cancelling out random fluctuations in their spectrum. To see this, consider for a between- edge \(ij\) the difference  
+
+\[\begin{array}{r l r} & {} & {\sum_{i j\in E}p_{i}^{k}(\tau) - p_{j}^{k}(\tau) = \sum_{i j\in E}\sum_{s = 1}^{n}e^{-\lambda_{s}\tau}\phi_{s}(k)[\phi_{s}(i) - \phi_{s}(j)]}\\ & {} & {= \sum_{s = 1}^{n}e^{-\lambda_{s}\tau}\phi_{s}(k)\Delta \phi_{s},} \end{array} \quad (20)\]  
+
+where \(\Delta \phi_{s}\) is defined in Eq. (8). The first term involves the constant eigenvector \(\phi_{1}\) corresponding to the stationary state. Therefore, \(\phi_{1}(i) = \phi_{1}(j)\) for all \(ij\) and thus its contributions cancels out when taking differences. Further, for eigenvectors \(\phi_{s}\) with \(s \neq 1\) , \(c\) we have asymptotically \((n \to \infty)\) that  
+
+\[\Delta \phi_{s}\to 0\]  
+
+(Fig. 3). As a result, the only contribution we are left with is coming from the anti- symmetric eigenmode \((\lambda_{c},\phi_{c})\) . Thus we have that  
+
+\[\sum_{i j\in E}(p_{i}^{u}(\tau) - p_{j}^{u}(\tau)) = \left\{ \begin{array}{l l}{\epsilon_{\phi},} & {\mathrm{if~}C_{i} = C_{j},}\\ {e^{-\lambda_{s}\tau}\phi_{c}\Delta \phi_{c} + \epsilon_{\phi},} & {\mathrm{if~}C_{i}\neq C_{j},} \end{array} \right. \quad (21)\]  
+
+where \(\epsilon_{\phi}\) represents the contribution from the random eigenvectors which is negligible in the limit \(n \to \infty\) .  
+
+To compute \(\mathcal{W}_{1}\) in the exponent of Eq. (18), we use Kantorovich- Rubinstein duality (Eq. (14)). Using Eq. (21) in Eq. (14) and ignoring asymptotically small terms, we consider the quantity  
+
+\[\sum_{i j\in E}\sum_{u}f(u)\left[p_{i}^{u}(\tau) - p_{j}^{u}(\tau)\right]\]
+
+<--- Page Split --->
+
+\[\begin{array}{l}{= e^{-\lambda_{c}\tau}\sum_{u}f(u)\phi_{c}(u)}\\ {= \frac{e^{-\lambda_{c}\tau}}{n}\bigg[\sum_{u:C_{u} = 1}f(u) - \sum_{u:C_{u} = 2}f(u)\bigg]}\\ {= \frac{e^{-\lambda_{c}\tau}}{n}\bigg[\sum_{u:C_{u} = 1}(f(u) - f(i)) - \sum_{u:C_{u} = 2}(f(u) - f(j))}\\ {\qquad +\sum_{u:C_{u} = 1}f(i) - \sum_{u:C_{u} = 2}f(j)\bigg].} \end{array} \quad (22)\]  
+
+412 In the sparse regime, we may make a tree- like approximation in the neighbourhood of \(i\) . This means that the number of \(i\) neighbours of \(i\) at distance \(q\) inside the cluster is \(p_{n}^{q}(n / 2)^{q}\) , ignoring terms of order \(O(1 / n)\) and beyond. Considering only \(i\) nodes at unit distance ( \(q = 1\) ), we approximate Eq. (22) as  
+
+\[\begin{array}{r l} & {\frac{e^{-\lambda_{c}\tau}}{n}\bigg[\sum_{\substack{u:C_{u}=1}}\left(f(u)-f(i)\right)-\sum_{\substack{u:C_{u}=2}}\left(f(u)-f(i)\right)}\\ & {\qquad+\sum_{\substack{u:C_{u}=1}}\left(f(u)-f(j)\right)-\sum_{\substack{u:C_{u}=2}}\left(f(u)-f(j)\right)}\\ & {\qquad+\sum_{\substack{u:C_{u}=1}}\left(f(i)-\sum_{\substack{u:C_{u}=2}}\left(f(i)+\sum_{\substack{u:C_{u}=1}}\left(f(j)-\sum_{\substack{u:C_{u}=2}}\left(f(j)\right)\right)\right)\right)}\\ & {\qquad=\frac{e^{-\lambda_{c}\tau}}{n}\bigg[\sum_{\substack{u:C_{u}=1}}\left(f(u)-f(i)\right)-\sum_{\substack{u:C_{u}=2}}\left(f(u)-f(i)\right)}\\ & {\qquad+\sum_{\substack{u:C_{u}=1}}\left(f(u)-f(j)\right)-\sum_{\substack{u:C_{u}=2}}\left(f(u)-f(j)\right)}\\ & {\qquad+\sum_{\substack{u:C_{u}=1}}\left(f(u)-f(j)\right)-\sum_{\substack{u:C_{u}=2}}\left(f(u)-f(j)\right)}\\ & {\qquad+\frac{n}{2}p_{n}(f(i)-f(j))-\frac{n}{2}p_{n}(f(i)-f(j))\bigg].} \end{array} \quad (22)\]  
+
+415 Then, taking the supremum over all 1- Lipschitz functions \(f\) , we obtain  
+
+\[\begin{array}{l}{\sum_{i j\in E}\mathcal{W}_{1}(\mathbf{p}_{i}(\tau),\mathbf{p}_{j}(\tau))(1 - \delta (C_{i},C_{j}))}\\ {\approx e^{-\lambda_{c}\tau}(p_{\mathrm{in}} + p_{\mathrm{out}})\left(1 + \frac{|p_{\mathrm{in}} - p_{\mathrm{out}}|}{2(p_{\mathrm{in}} + p_{\mathrm{out}})}\right)} \end{array} \quad (23)\]  
+
+416 Substituting this into Eq. (18) and noting that \(p_{\mathrm{in}} + p_{\mathrm{out}}\) is constant we obtain at a fixed \(\tau\)  
+
+\[\mathbb{P}(C|\kappa)\propto \exp \left[\left(\frac{|p_{\mathrm{in}} - p_{\mathrm{out}}|}{2(p_{\mathrm{in}} + p_{\mathrm{out}})}\right)\sum_{i\in E}\delta (C_{i},C_{j})\right],\]  
+
+417 which up to a constant of proportionality equals the expression in Eq. (9).  
+
+## 418 References  
+
+419 [1] Tenenbaum, J. B., de Silva, V. & Langford, J. C. A global geometric framework for nonlinear dimensionality reduction. Science 290, 2319- 2323 (2000).  
+
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+<--- Page Split --->
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+
+<--- Page Split --->
+![](images/Supplementary_Figure_1.jpg)
+
+<center>Supplementary Figure 1: Ricci and dynamical Ollivier Ricci curvature on canonical surfaces and graph structures. a Ricci curvature on a manifold. The geodesic distance of close points \(x\) and \(y\) on average changes when translated by parallel vectors \(\mathbf{v}\) and \(\mathbf{v}^{\prime}\) on the unit circle in the tangent planes at \(x\) and \(y\) . On planes the points remain equidistant, on spheres the points contract and on hyperbolic surface they expand. b Canonical graphs with edges coloured by the dynamical OR curvature (Eq. (2)) for \(\tau = 1\) show that positively and negatively curved graphs have qualitatively different topologies. Away from the boundaries, tree-like topologies are negatively curved, grid-like topologies are flat (zero curvature), whereas clique-like topologies attain positive curvature. Nodes are coloured by the average edge curvature across the neighbours. c Analogously, the differential geometric notion of Ricci curvature is negative on hyperbolic surfaces, zero on planes and positive on spherical surfaces. </center>
+
+<--- Page Split --->
+
+## Supplementary Note 1  
+
+In this section, we compute the spectrum of the expected normalised Laplacian matrix of the symmetric SBM \(\mathcal{G}(n,k_{\mathrm{in}} / n,k_{\mathrm{out}} / n)\) . Here \(k_{\mathrm{in}}\) and \(k_{\mathrm{out}}\) are constants representing the expected number of edges within and across clusters, respectively. The expected adjacency matrix of the symmetric stochastic block model is:  
+
+\[\langle \mathbf{A}\rangle_{\mathcal{G}} = \left(\begin{array}{c c}{\frac{k_{\mathrm{in}}}{n}\mathbf{1}_{n / 2\times n / 2}} & {\frac{k_{\mathrm{out}}}{n}\mathbf{1}_{n / 2\times n / 2}}\\ {\frac{k_{\mathrm{out}}}{n}\mathbf{1}_{n / 2\times \mathrm{n / 2}}} & {\frac{k_{\mathrm{in}}}{n}\mathbf{1}_{n / 2\times n / 2}}\end{array}\right). \quad (1)\]  
+
+Then, the expected normalised Laplacian matrix is given by  
+
+\[\langle \mathbf{L}\rangle_{\mathcal{G}} = \mathbf{I} - \left(\frac{\frac{2k_{\mathrm{in}}}{n(k_{\mathrm{in}} + k_{\mathrm{out}})}\mathbf{1}_{n / 2\times n / 2}}{\frac{2k_{\mathrm{out}}}{n(k_{\mathrm{in}} + k_{\mathrm{out}})}\mathbf{1}_{n / 2\times n / 2}}\frac{\frac{2k_{\mathrm{out}}}{n(k_{\mathrm{in}} + k_{\mathrm{out}})}\mathbf{1}_{n / 2\times n / 2}}{\frac{2k_{\mathrm{in}}}{n(k_{\mathrm{in}} + k_{\mathrm{out}})}\mathbf{1}_{n / 2\times n / 2}}\right) \quad (2)\]  
+
+The first eigenvector is \(\phi_{1} = \mathbf{1}_{n} / \sqrt{n}\in \mathbb{R}^{n}\) , with the corresponding eigenvalue being \(\lambda_{1} = 0\) . The second eigenvector has two values, one on each cluster of the SBM. Taking \(\phi_{c}(u) = 1 / \sqrt{n}\) for \(1\leq u\leq n / 2\) and \(- 1 / \sqrt{n}\) for \(n / 2< u\leq n\) one has asymptotically  
+
+\[\langle \mathbf{L}\rangle_{\mathcal{G}}\phi_{c} = \left[1 - \frac{k_{\mathrm{in}}}{k_{\mathrm{in}} + k_{\mathrm{out}}}\frac{2}{n} -\frac{k_{\mathrm{in}}}{k_{\mathrm{in}} + k_{\mathrm{out}}} \frac{2}{n} \left(\frac{n}{2} -1\right) + \frac{k_{\mathrm{out}}}{k_{\mathrm{in}} + k_{\mathrm{out}}} \frac{2}{n} \frac{n}{2}\right]\phi_{c} \xrightarrow{n\to\infty} \frac{2k_{\mathrm{out}}}{k_{\mathrm{in}} + k_{\mathrm{out}}}\phi_{c}. \quad (3)\]
+
+<--- Page Split --->
+
+## Figures  
+
+![](images/Figure_1.jpg)
+
+<center>Figure 1 </center>  
+
+Please see the manuscript file to view figure caption.
+
+<--- Page Split --->
+![](images/Figure_2.jpg)
+
+<center>Figure 2 </center>  
+
+Please see the manuscript file to view figure caption.  
+
+![](images/Figure_3.jpg)
+
+<center>Figure 3 </center>  
+
+Please see the manuscript file to view figure caption.
+
+<--- Page Split --->
+![](images/Figure_4.jpg)
+
+<center>Figure 4 </center>  
+
+Please see the manuscript file to view figure caption.
+
+<--- Page Split --->

preprint/preprint__1da3cc07656e2b2ad084cb5e938a2f126f69f88db62135e01903242c34fe8e44/preprint__1da3cc07656e2b2ad084cb5e938a2f126f69f88db62135e01903242c34fe8e44.mmd

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+
+# African bush pigs exhibit porous species boundaries and appeared in Madagascar concurrently with human arrival  
+
+Rasmus Heller ( \(\boxed{\bullet}\) rheller@bio.ku.dk) University of Copenhagen https://orcid.org/0000- 0001- 6583- 6923  
+
+Renzo Balboa University of Copenhagen https://orcid.org/0000- 0003- 2821- 8020  
+
+Laura Bertola University of Copenhagen  
+
+Anna Bruniche- Olse University of Copenhagen  
+
+Malthe Sebro Rasmussen Department of Biology, University of Copenhagen, Denmark https://orcid.org/0000- 0002- 2982- 6258  
+
+Xiaodong Liu University of Copenhagen  
+
+Guillaume Besnard Universit&#x00E9  
+
+Jordi Salmona Universite Paul Sabatier  
+
+Cindy Santander Section for Computational and RNA Biology, Department of Biology, University of Copenhagen, Copenhagen, Denmark https://orcid.org/0000- 0003- 3021- 6809  
+
+Shixu He University of Copenhagen  
+
+Dietmar Zinner Deutsches Primatenzentrum GmbH https://orcid.org/0000- 0003- 3967- 8014  
+
+Miguel Pedrono CIRAD  
+
+Vincent Muvanika Makerere University  
+
+Charles Masembe Makerere University  
+
+Mikkel Schubert University of Copenhagen
+
+<--- Page Split --->
+
+Josiah Kuja University of Copenhagen  
+
+Liam Quinn University of Copenhagen https://orcid.org/0000- 0002- 3597- 2948  
+
+Genis Garcia- Erill University of Copenhagen  
+
+Rianja Rakotoarivony CIRAD  
+
+Margarida Henrique Instituto Gulbenkian de Ciencia  
+
+Long Lin University of Copenhagen  
+
+Xi Wang University of Copenhagen  
+
+Michael Heaton  
+
+US Meat Animal Research Center, ARS USDA, Clay Center, Nebraska https://orcid.org/0000- 0003- 1386- 1208  
+
+Timothy Smith  
+
+US Meat Animal Research Center, ARS USDA, Clay Center, Nebraska https://orcid.org/0000- 0003- 1611- 6828  
+
+Kristian Hanghoj  
+
+University of Copenhagen  
+
+Mikkel- Holger Sinding University of Copenhagen  
+
+Anagaw Atickem  
+
+University of Oslo, Centre for Ecological and Evolutionary Synthesis  
+
+Lounes chikhi  
+
+Christian Roos  
+
+German Primate Center, Leibniz Institute for Primate Research https://orcid.org/0000- 0003- 0190- 4266  
+
+Philippe Gaubert  
+
+Université, Toulouse Paul Sabatier  
+
+Hans Siegismund  
+
+University of Copenhagen  
+
+Ida Moltke  
+
+University of Copenhagen https://orcid.org/0000- 0001- 7052- 8554  
+
+Anders Albrechtsen  
+
+University of Copenhagen
+
+<--- Page Split --->
+
+Keywords: whole genomes, genomics, population genetics, metapopulations, introgression, hybridisation, speciation, Suidae  
+
+Posted Date: September 6th, 2023  
+
+DOI: https://doi.org/10.21203/rs.3.rs- 3289506/v1  
+
+License: © © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License  
+
+Additional Declarations: There is NO Competing Interest.  
+
+Version of Record: A version of this preprint was published at Nature Communications on January 3rd, 2024. See the published version at https://doi.org/10.1038/s41467- 023- 44105- 1.
+
+<--- Page Split --->
+
+# African bush pigs exhibit porous species boundaries and appeared in Madagascar concurrently with human arrival  
+
+## Authors  
+
+Renzo F. Balboa1#, Laura D. Bertola1#, Anna Bruniche- Olsen1#, Malthe Sebro Rasmussen1, Xiaodong Liu1, Guillaume Besnard2, Jordi Salmona2, Cindy G. Santander1, Shixu He1, Dietmar Zinner3,4,5, Miguel Pedrono6, Vincent Muvanika7, Charles Masembe8, Mikkel Schubert1, Josiah Kuja1, Liam Quinn1, Genis Garcia- Erill1, Rianja Rakotoarivony6, Margarida Henrique9, Long Lin1, Xi Wang1, Michael P. Heaton10, Timothy P. L. Smith10, Kristian Hanghoj1, Mikkel- Holger S. Sinding1, Anagaw Atickem11, Lounès Chikhi2,9, Christian Roos12, Philippe Gaubert2, Hans R. Siegismund1, Ida Moltke1\*, Anders Albrechtsen1\* and Rasmus Heller1\*  
+
+1 Department of Biology, University of Copenhagen, Copenhagen, Denmark  2 Laboratoire Evolution et Diversité Biologique (EDB), UMR 5174, CNRS, IRD, Université Toulouse Paul Sabatier, 31062 Toulouse, France  3 Cognitive Ecology Laboratory, German Primate Center, Leibniz Institute for Primate Research; 37077 Göttingen, Germany  4 Department of Primate Cognition, Georg-August-Universität Göttingen; 37077 Göttingen, Germany  5 Leibniz Science Campus Primate Cognition; 37077 Göttingen, Germany  6 UMR ASTRE, CIRAD, Campus International de Baillarguet, Montpellier, France  7 College of Agricultural and Environmental Sciences, Makerere University, Kampala, Uganda  8 College of Natural Sciences, Makerere University, Kampala, Uganda  9 Instituto Gulbenkian de Ciência, Oeiras, Portugal  10 USDA, ARS, US Meat Animal Research Center, Clay Center, Nebraska, USA  11 Department of Zoological Sciences, Addis Ababa University, PO Box 1176, Addis Ababa, Ethiopia  12 Gene Bank of Primates and Primate Genetics Laboratory, German Primate Center, Leibniz Institute for Primate Research, Kellnerweg 4, 37077 Göttingen, Germany  13 These authors contributed equally  14 Corresponding/senior author
+
+<--- Page Split --->
+
+## Abstract  
+
+Several African mammals exhibit a phylogeographic pattern where closely related taxa are split between West/Central and East/Southern Africa, but their evolutionary relationships and histories remain controversial. Bushpigs (Potamochoerus larvatus) and red river hogs (P. porcus) are recognised as separate species due to morphological distinctions, a perceived lack of interbreeding at contact, and putatively old divergence times, but historically, they were considered conspecific. Moreover, the presence of Malagasy bushpigs as the sole large terrestrial mammal shared with the African mainland raises intriguing questions about its origin and arrival in Madagascar. Analyses of 67 whole genomes revealed a genetic continuum between the two species, with putative signatures of historical gene flow, variable \(F_{ST}\) values, and a recent divergence time (<500,000 years). Thus, our study challenges key arguments for splitting Potamochoerus into two species and suggests their speciation might be incomplete. Our findings also indicate that Malagasy bushpigs diverged from southern African populations and underwent a limited bottleneck 1,000- 5,000 years ago, concurrent with human arrival in Madagascar. These results shed new light on the evolutionary history of an iconic and widespread African genus and provide insight into the longstanding biogeographic puzzle surrounding the bushpig's presence in Madagascar.  
+
+Keywords: whole genomes, genomics, population genetics, metapopulations, introgression, hybridisation, speciation, Suidae.
+
+<--- Page Split --->
+
+## 54 Introduction  
+
+The African Suidae lineage contains six recognised extant species: common warthog (Phacochoerus africanus), desert warthog (Ph. aethiopicus), giant forest hog (Hylochoerus meinertzhageni), wild boar (Sus scrofa), red river hog (Potamochoerus porcus) and bushpig (P. larvatus) \(^{1,2}\) . There are several unresolved aspects of the evolutionary history of African pigs, including a controversial timeline for their divergence which stems from molecular estimates that predate fossil records by millions of years, and the unresolved role of gene flow between lineages \(^{3,4}\) . The two members of the genus Potamochoerus — red river hog and bushpig — were historically considered conspecific, despite considerable morphological differences \(^{5,6}\) . They occur parapatrically in Western/Central (W/C) Africa and Eastern/Southern (E/S) Africa with some populations possibly having abutting or slightly overlapping ranges \(^{2}\) (Fig. 1a). Based primarily on morphological differences and a lack of evidence that these taxa hybridise at contact, Grubb proposed the currently accepted nomenclature, regarding them as two distinct species \(^{7,8}\) .  
+
+The distribution of the two Potamochoerus species is similar to that found in several other African mammals that have ecologically comparable sister (sub)species pairs. The W/C and E/S divide has been highlighted as one of the most important biogeographic patterns in Africa, and is potentially connected to the initial divergence between hominins and apes \(^{9}\) , even if at a different time scale. This evolutionary divergence into W/C and E/S lineages occurred relatively recently for some mammalian taxa such as the African buffalo (Syncerus caffer) \(^{10}\) and the lion (Panthera leo) \(^{11}\) leading to sub speciation, whereas in other taxa, an older split led to full speciation, e.g. in African elephants (Loxodonta sp.) \(^{12}\) and baboons (Papio sp.) \(^{13}\) . For all species mentioned above, a hybrid zone has been identified where the ranges of diverged lineages overlap \(^{8}\) . Although possible hybridisation between the two Potamochoerus species has been suggested \(^{7}\) , the evolutionary connection and the geographic context of a likely suture zone are still poorly understood \(^{5,14}\) . Recent range contractions limit the overlap of the two species ranges to Uganda and Democratic Republic of Congo (DR Congo). However, South Sudan and possibly Ethiopia were part of a suture zone in the recent past, when the red river hog range extended further towards the East (Fig. 1a) \(^{5}\) . The evolutionary processes occurring in these suture zones, found recurrently across many taxa, e.g. in western Uganda \(^{10,15}\) , are of particular interest for understanding speciation and the phylogeography of African mammals in general.  
+
+Bushpig populations on Madagascar provide an interesting case of possible human- mediated range expansion. The bushpig represents a biogeographic anomaly in being the only large,
+
+<--- Page Split --->
+
+wild terrestrial mammal to be shared between the African continent and the island of Madagascar<sup>16</sup>. These land masses separated about 150 million years ago, leading to a largely divergent fauna and flora<sup>17,18</sup>. For some Malagasy taxa, such as lemurs, it has long been debated whether colonisation of Madagascar could have taken place through island hopping or temporal land bridges<sup>19</sup>. It is now commonly accepted that some of these taxa arrived on Madagascar by rafting on floats of vegetation, and that successful colonisation events and subsequent radiation led to the diversity seen today<sup>20,21</sup>. For bushpigs, it has been proposed that the most plausible explanation is that they were introduced to Madagascar by humans, possibly through the Comoros Islands<sup>22,23</sup>; however, this has not been conclusively verified. Humans are believed to have been present in Madagascar no earlier than 11,000 years ago<sup>24</sup>, with some authors claiming that there is no proof of human presence older than 2,000 years<sup>25</sup>. Nevertheless, most authors agree that there were no significant numbers of humans until 1,000- 1,500 years ago with the arrival of populations from South- Eastern Africa (Bantu speakers) and South- East Asia (Austronesian speakers)<sup>24,26,27</sup>. Radiocarbon dating of archaeological remains suggests that bushpigs, as well as zebu, sheep and goats, were established in southwest Madagascar between 700- 1,200 years ago; however, this estimate may be influenced by the scarce data available for Malagasy bushpigs<sup>28</sup>. To our knowledge, there is only one study which attempted to estimate the arrival of bushpigs on Madagascar based on genetic data; this study suggested a split time of 480 kya based on mitochondrial DNA (mtDNA) divergence times, which is not in line with a proposed human- mediated introduction to the island<sup>29</sup>. In addition to the time of arrival, the source population for Malagasy bushpigs is still unknown, where despite detailed morphological studies, these have been unable to conclusively resolve their mainland origin<sup>8,30</sup>. The existing genetic data tentatively suggest an origin from Central Southern Africa<sup>29</sup>. If bushpigs were indeed introduced to Madagascar by humans, it presents another suite of questions as there is no archaeological or other evidence of domestication of bushpigs ever occurring despite them being an important protein source for many rural communities<sup>31</sup>. For example, the transportation of such a large non- domesticated mammal over the wide (> 400 km) Mozambique channel remains an unsolved mystery, and may provide an indirect indication that populations located on the south- eastern African coast mastered oceanic travel beyond fishing<sup>29</sup>. Alternatively, a much older divergence time could provide indirect proof of a very early African presence in Madagascar.  
+
+In this study, we present new data and population genomic analyses of 67 whole genomes from \*Potamochoerus\*, including 32 bushpigs from Madagascar. We investigate their population structure and genetic diversity, and infer gene flow between the two taxa. We also estimate the degree of evolutionary divergence between the bushpig and red river hog relative
+
+<--- Page Split --->
+
+to co- occurring species that represent incomplete or full speciation. Finally, we address the question of when and from where in Africa the bushpig colonised Madagascar, clarifying several details regarding the origin of Malagasy bushpigs. Our analyses present new insights that will improve our understanding of African biogeography, and help settle a major question regarding prehistoric human activities shaping biodiversity patterns in Africa.  
+
+## Results  
+
+## Sampling and filtering  
+
+Whole genome sequencing data were generated for 71 Potamochoerus samples across the two species' ranges, including 23 red river hogs and 48 bushpigs (3x- 101x, mean \~12.8x; Fig. 1a; Supplementary Data 1). All samples were mapped to a chromosome- level common warthog reference genome, and rigorous site filtering applied to reduce downstream errors (see Methods; Supplementary Data 1). Two red river hog samples, from Cameroon and DR Congo, were excluded due to high sequencing error rates (Supplementary Fig. S1). Four samples, two from Equatorial Guinea (Eq Guinea) and two from Ethiopia, were deemed to originate from the same individual and were merged into one sample for their respective localities (Supplementary Fig. S2). A total of 13 samples were first degree relatives (parent- offspring or full siblings), of which 11 were from Madagascar and two were from Uganda. Depending on the specific requirements of the various downstream analyses, these samples were excluded. In summary, whole genome sequencing data from 67 pigs from 13 countries were analysed in this study, of which 54 were not closely related, including 18 that were sequenced at medium- high depth (≥14x; Fig. 1a; Supplementary Data 1). A summary of datasets, analyses and methods used is provided in Supplementary Data 1.
+
+<--- Page Split --->
+![](images/Figure_1.jpg)
+
+<center>Figure 1. Sampling and population structure of red river hogs and bushpis. a) Sampling map of all 67 pig individuals used within this study, coloured by country of origin. Ranges for red river hogs and bushpis are shaded in red and blue, respectively \(^{32,33}\) . b) Principal component analysis (PCA) for 67 pigs, showing the first two principal components, coloured by country. c) Unrooted neighbour-joining tree based on pairwise identity-by-state (IBS), coloured by country ( \(n = 67\) ). d) Inferred ancestry proportions for 54 unrelated samples using NGSadmix \(^{34}\) , assuming \(K = 2\) (upper barplot) and \(K = 8\) (bottom barplot). Coloured lines above and below population labels indicate species designations; red - red river hogs, blue - bushpis. Pairwise correlations of residuals as estimated by evalAdmix \(^{35}\) are shown above and below the respective NGSadmix barplots. Gh - Ghana, To - Togo, Ga - Gabon, DRC - Democratic Republic of Congo, Zi - Zimbabwe, SA - South Africa. </center>
+
+<--- Page Split --->
+
+## Localised population structure and no recent admixture between red river hogs and bushpigs  
+
+We first aimed to gain insights into the population structure of red river hogs and African bushpigs, specifically examining genetic differentiation between populations of both species \(^{34}\) . Principal component analysis (PCA) revealed that the first two principal components exhibited a spatial distribution pattern reflecting the taxonomic and geographic origins of the sampled pigs; the red river hog samples clustered together, with only the Congo individuals being closer to the bushpigs than the other red river hogs and the Malagasy samples formed a separate cluster from the other bushpigs (Fig. 1b). A neighbour- joining tree using identity- by- state delineated a clear division between red river hogs and bushpigs, displaying a basal split between the two groups (Fig. 1c). The tree also revealed more localised population substructure, including the Malagasy samples forming a clade separate from the other bushpigs.  
+
+We next inferred ancestry proportions within both putative species to further explore population substructure. Assuming the number of ancestral populations was 2 ( \(K = 2\) ), the result largely aligned with the pattern observed in PC1- PC2 (Fig. 1d). Notably, we did not observe a clear separation of red river hogs and bushpigs at \(K = 2\) , even when excluding Madagascar samples (Supplementary Fig. S3). It is worth noting that evalAdmix \(^{35}\) indicated unresolved substructure, suggesting that this pattern should not be interpreted as the result of admixture and these numbers as admixture proportions. We obtained a much better fit by assuming a higher number of ancestral populations ( \(K = 8\) , Fig. 1d; Supplementary Fig. S4) and were able to assign most geographic locations to their own ancestral population. However, this analysis did not reveal evidence of recent gene flow between bushpigs and red river hogs.  
+
+## Moderate differentiation and gene flow between red river hogs and bushpigs through Uganda  
+
+Genetic differentiation between all pairs of populations was assessed using Hudson's \(F_{ST}\) and \(D_{xy}\) between unrelated individuals (Fig. 2a) \(^{36,37}\) . \(F_{ST}\) values generally correlate with geographic distance (Fig. 2a; Supplementary Fig. S5). Notably, Ethiopia and Madagascar exhibited higher \(F_{ST}\) values (0.345- 0.581 and 0.168- 0.546, respectively). Excluding these populations, \(F_{ST}\) values within red river hog populations ranged from 0.023- 0.286, while \(F_{ST}\) values within bushpigs were between 0.044 and 0.175. When comparing across species, \(F_{ST}\) excluding Ethiopia was higher (0.232- 0.546), though not markedly so when compared to the most differentiated population pairs in within- species comparisons. \(D_{xy}\) values exhibited a similar trend, displaying increased nucleotide diversity between species relative to within- species
+
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+
+comparisons (Fig. 2a). As with \(F_{\mathrm{ST}}\) , \(D_{xy}\) also correlated with geographic distance with the exception of Tanzania; this population had increased \(D_{xy}\) relative to other populations. Notably, \(D_{xy}\) for Ethiopia was similar to those between other bushpig and red river hog populations, suggesting that the high \(F_{\mathrm{ST}}\) observed for Ethiopia was likely driven by lower within- population diversity. In contrast, the Ugandan bushpig population exhibited a reduced \(D_{xy}\) relative to other bushpig populations, suggesting potential gene flow with the red river hogs. In fact, the lowest \(D_{xy}\) between species was between the Ugandan and Congolese populations, which were also the two geographically closest.
+
+<--- Page Split --->
+![](images/Figure_2.jpg)
+
+<center>Figure 2. Genetic differentiation and gene flow between African bushigis and red river hogs. a) Genetic differentiation as described by pairwise Hudson's \(F_{ST}\) 36 and \(D_{XY}\) 37 for 54 unrelated individuals, rounded to three significant figures. Circles on the diagonal correspond to populations as in b). Coloured lines above and next to population labels indicate species designations; red - red river hogs, blue - bushigis. b) Estimated effective migration surfaces using EEMS 38. Circles are coloured by country of origin. \(\log_{10}(m)\) describes the effective migration rate relative to the overall migration rate across indicated regions. The East African Rift Valley is depicted by grey lines. c) D-statistics between populations using the common warthog as an outgroup, constructed as D(H1,H2,H3,Warthog). A significant non-zero positive value, as depicted by the red arrow in the graphic for each panel, provides evidence for gene flow between H3 and H2, relative to H1 (i.e. H2 is closer to H3 than H1) 39. Upper panel - D-statistics testing for gene flow signals between African bushigis (H3) and non-Ghana red river hogs (H2). Lower panel - D-statistics testing for gene flow signals between red river hogs (H3) and non-Malagasy bushigis (H2). Error bars represent \(\pm\) three standard errors from the estimated D-statistic. RRH - Red river hog; ABP - African bushig; SA - South Africa. </center>
+
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+
+Given the observed \(F_{\mathrm{ST}}\) and \(D_{xy}\) values, we explored spatial patterns of gene flow between species (Fig. 2b) \(^{38}\) . A general barrier through the Central African rainforest and following the East African Rift Valley was observed, separating W/C and E/S populations. Within each of the species ranges, connectivity was high, with the exception of Malagasy and non- Malagasy bushpigs where we observed a barrier across the Mozambique Channel, particularly with the northernmost non- Malagasy populations. We also observed a decrease in effective migration in Ethiopia. This is in contrast with Uganda where we observed weak gene flow barriers, suggesting a corridor of gene flow connectivity involving Uganda.  
+
+To detect potential gene flow patterns between the two species, we then tested for ancient admixture events. \(f\) - branch statistics revealed putative signals of gene flow between bushpigs and red river hogs, showing extensive gene flow involving Uganda (Supplementary Fig. S6). D- statistics were then used specifically to test whether there is increased allele sharing between red river hogs and bushpigs within the putative suture zone, compared to populations further from the suture zone (e.g. Ghana and Madagascar). Two tests were designed in order to test this hypothesis. We first set the westernmost population (Ghana) as H1, each of the red river hogs as H2 and each of the bushpigs as H3 (Fig. 2c; upper panel). This revealed that all red river hog populations showed signs of gene flow from African bushpigs, decreasing in signal from Central Africa to the westernmost red river hog populations, and with a particularly strong signal from Uganda. Similarly, to test for gene flow in the opposite direction, we performed similar tests with the easternmost bushpig population, Madagascar as H1, each of the remaining red river hog populations as H2, and each of the bushpig populations as H3 (Fig. 2c; lower panel). We observed a similar result, whereby we perceived a signal decrease towards more eastern and southern populations. Notably, this signal was particularly strong in Ethiopia and Uganda, suggesting substantial gene flow between red river hogs and these bushpig populations. These results suggest that there is or has been gene flow between the two taxa currently identified as species, and that the gradient of allele sharing between them is consistent with isolation by distance, where genetic similarity is strongest in populations from Central Africa. Additionally, these results could also be interpreted as a complex network of populations connected by genetic exchange, either recent or ancient.  
+
+## Demographic histories and genetic diversity  
+
+Demographic histories of the surveyed populations were next explored (Fig. 3a) \(^{40,41}\) . All PSMC curves overlapped from the most ancient past until \(\sim 500\) kya, where we observed a stark difference in PSMC trajectories between red river hog and bushpig individuals. All red river hog populations first experienced a moderate increase (population expansion assuming
+
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+panmixia) followed by a more recent contraction \(\sim 50\) kya. In contrast, bushpig individuals exhibited PSMC curves that followed three different trajectories: i) the populations in Tanzania, Zimbabwe, South Africa and Madagascar exhibited relatively constant (i.e. horizontal) curves until \(\sim 10\) kya; ii) the Ugandan population showed a demographic history more similar to red river hogs than to the remaining bushpig populations, particularly between 100- 500 kya and; iii) the Ethiopian population showed a history characterised by a declining and low PSMC curve \(\sim 200\) kya. Given the results reported above, the unique demographic histories in Uganda and Ethiopia could be influenced by their geographic location as a place of introgression between the two taxa.  
+
+![](images/Figure_3.jpg)
+
+<center>Figure 3. Genetic diversity of African pigs. a) Effective population sizes over time for 18 medium-high depth pig samples as estimated by PSMC, assuming a mutation rate of \(\mu = 1.49\mathrm{e}^{-8}\) per site per generation and a generation time of six years \(^{4,42}\) . b) Genome-wide heterozygosity measurements described as the proportion of heterozygous sites per bp across each individual genome. Medium-high depth (n = 18; red triangles) and low-depth samples (n = 49; blue circles) are shown. c) Estimated genome-wide runs of homozygosity (ROH) proportions for 18 medium-high depth individuals. Each bar represents a single individual, grouped by their population. Proportions of differing ROH length intervals are shown as subdivisions within bars. d) Linkage-disequilibrium decay for populations with five or more samples, described as mean \(r^2\) values for SNP pairs 0-5 Mb apart (n = 5 for each population). </center>
+
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+Per- sample heterozygosity was next explored as a measure of genetic diversity, differing at both a species and population level (Fig. 3b). Heterozygosity was generally lower in bushpigs when compared to red river hogs, with the exception of Uganda and Tanzania which had similar heterozygosity levels to populations of DR Congo and Equatorial Guinea. The bushpig population in Ethiopia exhibited extremely low genetic diversity, one third of that of the highest, Tanzania (Fig. 3b). This was consistent with elevated \(F_{ST}\) values, reduced connectivity in EEMS and the low effective population size estimated by PSMC (Fig. 2a). Heterozygosity in Madagascar was also relatively low, but similar to that of Zimbabwe and South Africa.  
+
+Runs of homozygosity (ROH) were then explored within medium- high depth genomes, where the fraction of ROH with length \(>1\) Mb (FRoH) affected 3- 27% of the genome across all individuals (Fig. 3c). There was no systematic difference in FRoH between red river hogs and bushpigs. Ugandan individuals generally had low levels of FRoH except for one individual, while Ethiopian individuals had relatively long FRoH, supporting the low genetic diversity described within this population. The Madagascar individual had the largest FRoH out of all individuals tested, and exhibited the largest proportion for each length class \(< 10\) Mb for all samples excluding Ghana, and \(< 5\) Mb for all samples. This is consistent with results comparing linkage disequilibrium (LD) decay between different populations with at least five unrelated individuals, where Madagascar exhibited increased LD (Fig. 3d).  
+
+Taken together, these results suggest that the evolutionary histories of red river hogs and bushpigs vary markedly. In light of previous results, we find further evidence that Uganda is likely a region of strong introgression, and that the Ethiopian population underwent strong drift after gene flow with red river hogs. Finally, we find that Malagasy individuals had similar population histories and a level of genetic diversity comparable with other southern bushpig populations, but had increased FRoH and LD.  
+
+## Bushpig arrival in Madagascar coincides with the expansion of Bantu speakers  
+
+The timing of arrival and geographical origin of bushpigs in Madagascar is still unresolved, as previous lines of evidence, e.g. estimated split times and fossil records, appear to be contradictory. We therefore explored the putative founding of this population. We first measured the amount of shared history between the Malagasy population and each of the other populations (Fig. 4a) \(^{43}\) . Our results suggested that amongst sampled populations, those from South Africa and Zimbabwe have the longest shared history with Madagascar. This was consistent with our results exploring gene flow and connectivity, which showed a weaker
+
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+barrier between Madagascar and these two southern populations when compared with other bushpig populations (Fig. 2b,c), the neighbour- joining tree (Fig. 1c) and \(D_{xy}\) values (Fig. 2a).  
+
+Split times between populations were next examined, including the species split between red river hogs and African bushpigs and the split between bushpig populations on mainland Africa and Madagascar (Fig. 4b) \(^{44}\) . The species split was estimated to have occurred \~300 kya, and consistent with the outgroup f3 results, Madagascar exhibited the lowest split times with populations in South Africa and Zimbabwe (\~850 and \~500 years ago, respectively). This further suggested that either one of these or an unsampled population within the same geographic region was the population of origin. Additionally, we investigated putative recent demographic events for the Madagascar population (Fig. 4c) \(^{45}\) . This analysis suggested that the Malagasy population experienced a severe bottleneck, likely a result of a founder event between 1- 5 kya. This result was also consistent with the high \(\mathrm{F_{ROH}}\) (Fig. 3c) and the high LD (Fig. 3d) characterising this population.  
+
+![](images/Figure_4.jpg)
+
+<center>Figure 4. Origin and timing of bushpigs in Madagascar. a) Outgroup f3 statistics in the form f3 (Madagascar, X; Warthog), where X describes different sampling localities. b) Population split times with Madagascar as estimated by the TT method using individual pairs of medium-high depth samples (n = 18). T1 and T2 values, describing population split times, are shown as red and blue points, respectively. A mutation rate of \(\mu = 1.49\mathrm{e}^{-8}\) per site per generation and a generation time of six years were assumed \(^{4,42}\) . kya - thousands of years ago. c) Recent effective population sizes inferred based on unrelated Madagascar individuals using popSizeABC \(^{45}\) (n = 21). Shaded region - estimated timing of human arrival in Madagascar \(^{24,26,27}\) ; red line - estimated timing of earliest known bushpig fossils in Madagascar (MBP) \(^{46}\) ; black line - estimated timing of Bantu speakers arrival in Madagascar, as estimated in Pierron et al. \(^{26}\) . </center>
+
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+
+## Discussion  
+
+Biodiversity patterns on the African continent show striking similarities across multiple species, including a division between lineages in W/C Africa and in E/S Africa, with known hybridisation zones spanning across Uganda, South Sudan, and Ethiopia \(^{10,12,13,47}\) . Although hybridisation between the red river hog and the bushpig has been suggested before, this has not yet been studied in detail and the level of evolutionary divergence between them remains contentious. Moreover, bushpigs also represent the anomaly of being the only large wild mammal which occurs both on mainland Africa and Madagascar, but due to limited data from Malagasy bushpigs, the scenario of colonisation is still largely unknown. Our study is the first to investigate the evolutionary histories of red river hogs and bushpigs at the genome scale, allowing for a better understanding of the processes leading to the formation of two distinct taxa and the colonisation of Madagascar.  
+
+## Divergence and introgression between red river hogs and bushpigs  
+
+Under relatively simple models, we found that the estimated split times between red river hogs and bushpigs could be estimated at \(\sim 300\) kya (Fig. 4b) and that this time was in the same range as W/C- E/S split time estimates for other African species which show a similar phylogeographic pattern, including African buffalo ( \(\sim 273\) kya \(^{10}\) ), baboon ( \(\sim 320\) kya) \(^{13}\) , giraffe ( \(\sim 280\) kya; submitted), warthog ( \(\sim 226\) kya) \(^{4}\) , lion ( \(\sim 245\) kya) \(^{11}\) , and spotted hyena ( \(\sim 360\) kya) \(^{48}\) . Although red river hogs and bushpigs are widely considered to be distinct species, in the examples mentioned above, the divergent populations are typically considered to belong to the same species, except for baboons, elephants and with an ongoing taxonomic debate about the species status of giraffes \(^{12,49 - 51}\) . In a previous study, Gongora et al. \(^{3}\) estimated the divergence time between red river hogs and bushpigs at 2,710 kya, thus lending further support for a species distinction between red river hogs and bushpigs. However, this estimate was obtained with a wide confidence interval of 200- 4,800 kya \(^{3}\) . Our results suggest (and corroborate recent findings) that divergence times between African suid taxa have thus far been overestimated \(^{4}\) . This includes red river hogs and bushpigs, where our analyses represent a much younger divergence time and more reticulated evolutionary history than previously known.  
+
+Furthermore, our results indicate a complex history of population structure (i.e. a metapopulation or two interconnected metapopulations), with possible periods of increased and decreased connectivity between populations. PSMC curves can be interpreted as representing changes in coalescent effective population size, as is usually done, but this interpretation relies on a very strong assumption of total panmixia \(^{52}\) . If this assumption is
+
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+violated, changes in PSMC curves may alternatively reflect changes in gene flow \(^{53,54}\) . Thus, an alternative explanation for the observed PSMC curves is that there was a major fragmentation period between 500 kya and 100- 200 kya, and a second period more recently, possibly between 100 and 30 kya. Such complex histories could lead to overestimates of divergence times.  
+
+Our findings, therefore, have implications for the ongoing taxonomic debate about Potamochoerus and for the interpretation of genetic data in this group. The current taxonomic definition of \(P\) . porcus and \(P\) . larvatus was primarily based on morphology and a lack of 'convincing evidence' that they interbreed when they come into contact \(^{8}\) , a case which has been previously disputed by previous authors who favour a single- species taxonomy \(^{32,55}\) . Our results suggest that the two taxa could be a case of incomplete speciation. However, we emphasise that in cases such as Potamochoerus, different species concepts might arrive at different conclusions about whether speciation has gone to completion, and we note that taxonomic revisions should draw on various types of data and evidence, e.g. morphology and behaviour \(^{56}\) , which were not considered in the present study.  
+
+The impact of changing climate and habitat availability on the complex evolutionary history between red river hog and bushpig is showcased by the Ethiopian population, which has received substantial red river hog gene flow and was fixed for a divergent red river hog mtDNA lineage (Supplementary Fig. S7). Ethiopia is further characterised by a low effective population size and is strongly affected by drift, as illustrated by relatively long ROHs and high \(F_{ST}\) values. Hence, Ethiopian bushpigs show contrasting evidence of connectivity and isolation, possibly caused by historical fluctuations in the equatorial forest belt across Africa. These fluctuations could have facilitated intermittent contact and hybridisation between red river hogs and bushpigs as the forest expanded, followed by isolation of resident populations as the forests receded. In line with this, the taxonomic status of Potamochoerus in Ethiopia remains unresolved \(^{57}\) and there is anecdotal evidence of African buffaloes in Ethiopia that strongly resemble the forest buffaloes of distant central and western Africa \(^{58}\) . Similarly, the observation of Potamochoerus admixture in Uganda coincides with the present- day boundary between two of Africa's major mammalian biogeographical regions, the Guinean- Congolian and the Sudanian core regions \(^{59}\) , an area which also constitutes well- known hybridisation zones for several large- mammal taxa, including elephants \(^{60}\) , and subspecies of buffalo \(^{10}\) and kob (Kobus kob) \(^{61}\) . However, without more samples from adjacent locations, such as east DRC, the epicentre of hybridisation is speculative.
+
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+
+## Origin of Malagasy bushpigs  
+
+Our results support the hypothesis that bushpigs were introduced by humans from southeastern Africa into Madagascar \(\sim 1,000 - 1,500\) years ago \(^{25,26}\) and possibly as early as 5,000 years ago. A previous estimate for the most recent common ancestor of mainland and Malagasy bushpigs at 480 kya \(^{29}\) contradicts this; however, this could be partially caused by problematic temporal calibration and by the limited information contained in mtDNA sequences. Although we cannot pinpoint the precise source population from which the Malagasy bushpigs were introduced with great certainty, our results suggest an origin in southern Africa, as corroborated by the Zimbabwe and South African populations being closer than all other populations when using NGSadmix, evolutionary distances, outgroup f3 statistics, divergence times and EEMS. Our estimate of an effective population size of 1,000 individuals during the bottleneck 1,500 years ago is surprisingly high, assuming that the founder event was a single occurrence involving a limited number of individuals carried to Madagascar by ship. However, the estimate is supported by their observed heterozygosity level, which is similar to levels observed in southern Africa, although we cannot know to what extent southern African populations have been subjected to drift since the founding of the Malagasy population. Multiple introductions, spanning over a longer period of time, or even with animals sourced from different mainland populations (including already admixed individuals), may have inflated this estimate, and we also caution that popSizeABC may not be able to accurately reflect changes in population size happening within a few generations, such as those occurring during a bottleneck with very rapid subsequent regrowth and may also be influenced by population structure.  
+
+Despite these limitations, our results provide, to our knowledge, the clearest evidence yet of a recent introduction of bushpigs to Madagascar mediated by humans, most likely populations which started to arrive on Madagascar from southern Africa at least 1,500 years ago and possibly much earlier \(^{26}\) . Bushpigs likely became established on the island as livestock together with zebu, goats and sheep 700- 1,200 years ago based on \(^{14}\mathrm{C}\) bone analyses \(^{28}\) , coinciding with the extinction of Madagascar's megafauna likely as the result of human activities such as hunting, pastoralism and farming \(^{62}\) . Lending strong support to this hypothesis, our dating results are in line with the oldest fossils of Malagasy bushpigs ( \(\sim 1,000\) years ago) \(^{46}\) . Blench \(^{22}\) hypothesised that human migrants reaching Madagascar must have captured bushpigs in southeastern Africa, introduced them to Madagascar, and made an attempt to domesticate them. Etymological problems over the naming of Malagasy bushpigs (i.e. with a term usually used for bovine in South- East Asia) highlight that there are still outstanding questions regarding the cultural perception and uses of bushpigs in early
+
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+Malagasy settlers, composed of both Bantu-speaking and Austronesian-speaking people. Furthermore, the alleged morphological variation between Malagasy subpopulations \(^{5}\) , including the suggestion that they are distinct subspecies \(^{8}\) , had led to suggestions of multiple, distinct introduction pulses through the Comoros Islands and the North Mozambique current \(^{63}\) . However, from the PCA, NGAadmix and IBS tree we did not identify substantial structure within the island, which is consistent with a relatively homogeneous founder population.  
+
+Although we have samples across most of the species' range, we acknowledge that there are some gaps in our inferences. A more even spread across the species ranges, and especially more sampling localities from within the putative suture zone, e.g. southern DR Congo, would increase our understanding of the evolutionary dynamics near the suture zone. In addition, more sampling localities along the East African coastline could help to more precisely identify the source populations for the colonisation of Madagascar \(^{5}\) .  
+
+Overall, our study sheds new light on the distribution of genomic diversity and the evolutionary histories of two closely related African pig taxa. It provides yet another example of diverged taxa with a suture zone around western Uganda, as has been shown for numerous other taxa and is characteristic for African mammal phylogeography. The recent split times, moderate and large values of \(F_{ST}\) as geographic distance increases and ancestral gene flow between the bushpigs and red river hogs suggest that their evolutionary divergence is young and incomplete, a perspective that should be taken into account in future taxonomic assessments and management plans. Furthermore, our data from Malagasy bushpigs suggest that bushpigs indeed colonised the island by hitchhiking along with the accelerating human colonisation of Madagascar occurring around the onset of the Medieval period. These insights provide answers for long- standing questions regarding the distribution of biodiversity in Africa and the mysterious presence of African bushpigs on Madagascar.  
+
+## Methods  
+
+## Sample collection and laboratory protocol  
+
+Tissue samples used in this study were sourced from several different collections, detailed in Supplementary Data 1. For non- USDA samples, the QIAGEN DNeasy Blood & Tissue Kit (QIAGEN, Valencia, CA, USA) was used for DNA extraction following the manufacturer's protocol. RNase was added to all samples to ensure RNA- free genomic DNA. DNA concentrations were then measured using a Qubit 2.0 Fluorometer and Nanodrop before using gel electrophoresis to check the quality of genomic DNA.
+
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+Bushpig hide samples contributed by the USDA were salted, acidified and dried after collection in the field. Samples were purchased by the USDA from willing sellers and stored at \(- 20^{\circ}C\) until DNA extraction by standard phenol/chloroform procedures. DNA was dissolved in a solution of \(10 \text{mM TrisCl}\) , \(1 \text{mM EDTA (TE, pH 8.0)}\) and stored at \(4^{\circ}C\) . Sample quality and concentrations were measured by ultraviolet spectrophotometry and double-stranded DNA fluorometry (DeNovix Inc., Wilmington, DE USA; QuantiFluoONE, Promega, Madison, WI, USA).  
+
+## Sequencing and mapping  
+
+All samples were sequenced using Illumina paired- end 150- bp reads. This included 53 samples which were sequenced to low depth ( \(\sim 3 - 6\times\) depth of coverage) on the Illumina NovaSeq platform and 16 samples sequenced to medium- high depth ( \(\sim 14 - 49\times\) ) on the Illumina HiSeq2500, NextSeq500 or NextSeq2000 platforms (Illumina Inc., San Diego, CA, USA). Sequencing data were then assessed using FastQC \(^{64}\) and MultiQC \(^{65}\) . Publicly available data from two red river hog samples from Nigeria were also used in this study (Supplementary Data 1) \(^{66}\) .  
+
+The sequencing reads were mapped to the chromosome level assemblies for the common warthog (Phacochoerus africanus, accession number: GCA_016906955.1) using a development version of the PALEOmIX BAM pipeline \(^{67}\) (https://github.com/MikkelSchubert/paleomix; branch 'pub/2022/africa').  
+
+Reads were processed using AdapterRemoval v2.3.2 \(^{67}\) to remove adapter contamination and to merge overlapping reads in order to improve read fidelity. Adapter sequences published by Illumina and BGI were used for trimming. Reads were merged using the --collapse- conservatively option, which assigns 'N' to any mismatching position in the alignment for which both bases have the same quality. No trimming of Ns or low- quality bases was performed, and only empty reads resulting from primer- dimers were excluded. Trimmed reads were subsequently mapped using BWA- mem v0.7.17- r1188 \(^{68}\) . Reads were post- processed using samtools v1.11 \(^{69}\) commands 'sort' and 'calmd', and putative PCR duplicates flagged using the 'markdup' command and PALEOmIX 'rmdup_collapsed', for paired and unmerged reads respectively.  
+
+The resulting BAM alignments were filtered to remove unmapped reads, secondary alignments, PCR duplicates, and supplementary alignments, and reads flagged as having
+
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+failed QC. We furthermore removed alignments with an inferred insert size \(< 50\) bp or \(>1,000\) bp, and reads where less than 50 bp or \(50\%\) were mapped to the reference genome. Finally, we removed pairs of reads mapping to different contigs or in an unexpected orientation and reads for which the mate had been removed by any of the above criteria.  
+
+## Reference genome and site quality filters  
+
+We estimated the mappability of the warthog reference genome using GENMAP \(^{70}\) . Here, we used 100- bp k- mers allowing for two mismatches (- K 100 - E 2) and the remaining parameters set to default settings. All sites with a mappability score \(< 1\) were excluded from downstream analyses. RepeatMasker v4.1.1 \(^{71}\) was used to identify repeat elements in the warthog genome assembly, utilising 'rmblast' as the search engine and 'mammal' as the query species with default settings. Repeat regions identified with RepeatMasker were masked to limit mismapping in these regions. Annotated sex chromosomes and scaffolds that were not assembled into chromosomes were also excluded.  
+
+We also removed genomic regions with unusually high heterozygosity to avoid mismapping artefacts driven by multimapping on paralogous and other repetitive regions. We first estimated genotype likelihoods for SNPs using Angsd \(^{72}\) with the GATK model (- GL 2), - minimum mapping quality of 30 (- minMapQ 30), a minimum base quality of 30 (- minQ 30), a \(p\) - value of \(1e^{- 6}\) to call SNPs (- snp_pval \(1e^{- 6}\) ) and kept only SNPs with minor allele frequency (MAF) \(>0.05\) (- minmaf 0.05). Genotype likelihoods were then used as input for PCAngsd's per site Hardy- Weinberg equilibrium (HWE) test \(^{73}\) , which estimates inbreeding coefficients (F), and a likelihood ratio test statistic (LRT) for evidence of deviation from HWE, while controlling for population structure. The PCAngsd MAP test \(^{73}\) was also used to select the optimal number of principal components in each case. Sites with \(F < - 0.9\) and LRT \(>24\) were subsequently removed as they may have been driven by mapping artefacts, and therefore all regions within 10 kb from such sites were also discarded. We ran this analysis separately for red river hogs and bushpigs samples.  
+
+Finally, we removed sites with extreme depth. We estimated the global depth (read count across all samples) for each site using Angsd \(^{72}\) (- minMapQ 30 - minQ 30 - doCounts 1 - doDepth 1 - dumpCounts 1 - maxdepth 4000). This was done separately for each species for all (n = 67), unrelated (n = 54) and medium- high depth samples (n = 18) (Supplementary Data 1). Only autosomal chromosomes were included. From the global depth we calculated the upper 1% and lower 3% percentiles and visually inspected the plots before deciding on a threshold for excluding sites with extreme sequencing depth. Only sites that were within the
+
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+thresholds for both low- and medium- high depth samples were used in the downstream analyses.  
+
+## Sample filters  
+
+We identified and excluded samples with high sequencing error rates based on the "perfect individual" approach \(^{74}\) . The rationale behind this approach is that any sample in the dataset should have equal genetic distance to the outgroup and therefore samples with excess/deficit of derived alleles would be interpreted as errors. As the "perfect individual" we used a high- depth individual from Ghana (BPigGha0038; Supplementary Data 1). This sample was processed with Angsd \(^{72}\) to create a consensus sequence (- doFasta 2) taking the most commonly observed base as the consensus (- doAncError 2) while setting the base quality to at least 30 (- minQ 30). We chose the common warthog as an outgroup and mapped all samples to the consensus using BWA excluding sex chromosomes, the mitogenome, repeats and sites with mappability \(< 1\) . Individuals with high error rates (>0.001) were removed from downstream analyses (Supplementary Fig. S1).  
+
+We then considered relatedness between samples, where we identified and removed potential relatives and duplicated samples using the methodology described in IBSRELATE \(^{75}\) . First, we calculated the Site Allele Frequency (SAF) likelihood in Angsd \(^{72}\) for each individual. We used the genotype likelihood- based approach assuming HWE (- doSaf 1). The warthog genome was used as ancestral reference (- anc), a minimum mapping quality of 30 (- minMapQ 30), a minimum base quality of 30 (- minQ 30), and the GATK method (- GL 2). Then, we inferred the two- dimensional site frequency spectra (2D- SFS) pairwise among all possible combinations of individuals. To limit the computational time we limited the number of sites surveyed to the first 50,000 sites. Based on the 2D- SFS we calculated: R0, R1 and KING- robust kinship \(^{75,76}\) , which can be used to identify close familiar relatives. For the analysis we combined all the data from bushpig and red river hog in this analysis in order to account for potentially interspecies duplicates or mislabeled samples. We identified and removed an individual from each pair of first and second degree relatives.  
+
+## Imputation  
+
+Imputation was performed using BEAGLE3 \(^{77}\) from genotype likelihoods (GLs) estimated in Angsd \(^{72}\) . GLs were estimated using the GATK genotype likelihood model (- GL 2) and only keeping sites that had a \(p\) - value less than \(1e^{- 6}\) (- SNP_pval 1e- 6) for being variable in addition to only keeping sites that passed initial QC (- sites) as well as using a minimum MAF of 0.025 (- minMAF 0.025). We assumed the major allele was fixed and the minor was unknown when
+
+<--- Page Split --->
+
+estimating GLs (- doMajorMinor 1 - doMAF 2). We further filtered imputation results by only keeping sites with an imputation score \(R^2 > 0.95\) and which had a maximum of \(5\%\) missingness after applying a \(>0.95\) posterior probability cutoff on genotype calls.  
+
+## PCA, IBS and population structure  
+
+Beagle GL input files were first generated using Angsd \(^{72}\) , keeping only the sites that passed QC, with additional filters of removing tri- allelic sites, and with a minor allele frequency filter of 0.05. We used PCAngsd v1.02 \(^{73}\) to estimate the covariance matrix and identify potentially population structure for all individuals. A pairwise identity- by- state (IBS) matrix was then generated using Angsd, using the sample filters and including the - dolBS 1 flag. A neighbour- joining tree was then estimated using this matrix using the ape library in R \(^{78}\) .  
+
+## NGSadmix & evalAdmix  
+
+Admixture proportions for each population were inferred based on GL using NGSadmix \(^{34}\) . A Beagle file, using the same filters to investigate population structure with PCAngsd was taken and randomly thinned to contain one million sites for computational practicality. We ran NGSadmix with \(K = 2\) to \(K = 9\) until the model converged, where the top 3 maximum likelihood runs were within 10 log- likelihood units of each other or until a limit of 4000 independent runs was reached without convergence. \(K = 9\) did not converge after 4000 independent runs, likely constrained by the number of samples per population. Model- based analyses of population structure make a set of assumptions about the data (e.g., individuals are unrelated, are in HWE, exhibit no LD, and that each ancestral population is represented by multiple unadmixed individuals with no subsequent drift). Therefore, we calculated the correlations of residuals using evalAdmix \(^{35}\) for each pair of individuals to evaluate model fit, and to test whether the data violated some of these assumptions for \(K\) ancestral clusters.  
+
+## Population differentiation \((F_{ST} / D_{xy})\)  
+
+To quantify the extent of genetic differentiation between red river hog and bushpig populations, we used Hudson's estimator for genome- wide \(F_{ST}\) \(^{36}\) . This analysis encompassed two approaches: one utilising called genotypes for the 18 medium- high depth genomes (Supplementary Fig. S5), and another utilising all 54 unrelated genomes and estimating values from population- level 2D- SFS inferred from genotype likelihoods using winsfs (https://github.com/malthesr/winsfs). We also calculated the absolute genome- wide nucleotide divergence \((D_{xy})\) for all population pairs using the same approach.
+
+<--- Page Split --->
+
+## 613 Estimation of effective migration surfaces (EEMS)  
+
+613 Estimation of effective migration surfaces (EEMS)To investigate effective migration and gene flow connectivity between populations, we used the Estimated Effective Migration Surfaces (EEMS) program \(^{38}\) . A distance matrix was created from individual- level 2D- SFS estimated from GLs, and was used as input for the program. EEMS was run using 300 demes for three independent runs of 30 million iterations, discarding the first 15 million as burn- in. Convergence was assessed visually and by using the Gelman–Rubin diagnostic in the coda R package \(^{79}\) .  
+
+## 620 D-and f-branch statistics  
+
+620 D- and f- branch statisticsTo explore signatures of introgression between populations of red river hogs and African bushpigs, the Dsuite package \(^{80}\) was utilised on variable sites of medium- high depth individuals as input, with the topology of a neighbour- joining tree based on pairwise Hudson's \(F_{ST}\) between individual pairs using the ape library in R \(^{36,78}\) and the common warthog as an outgroup (Supplementary Data 1). The Dtrios function in Dsuite calculates the D- statistics for all possible trio combinations, which are then used for calculating f- branch statistics, using the f- branch command. A summary of these results within the provided phylogenetic framework is presented as a heatmap (Supplementary Fig. S6).  
+
+## 629 PSMc  
+
+629 PSMCThe Pairwise Sequentially Markovian Coalescent (PSMC) algorithm \(^{40,41}\) was used to infer changes in historical population sizes by including all individuals sequenced at medium- high depth. PSMc was run with default parameters. In addition to the size quality filter, we also excluded sites based on the average depth per individual divided by three as a minimum, and twice the average depth per individual as a maximum. We used a mutation rate of \(1.49e^{- 8}\) per site per generation and a generation time of six years, as described for warthogs \(^{4,42}\) .  
+
+## 636 Heterozygosity  
+
+636 HeterozygosityGenetic diversity of pig populations were approximated through the estimation of genome- wide heterozygosity. Individual- level heterozygosity was estimated in Angsd \(^{72}\) using individual- level site frequency spectra, measured as the proportion of heterozygous loci per sample. The GATK genotype likelihood model was utilised in Angsd (- GL 2), with minimum quality filters on mapping (- minMapQ 30) and base quality (- minQ 30), while reducing the amount of reads with excessive mismatches (- C 50).
+
+<--- Page Split --->
+
+## Runs of homozygosity  
+
+Runs of homozygosity (ROH) analyses were performed using PLINK v1.9 \(^{81}\) . PLINK files included only filtered variable sites within medium- high depth samples ( \(n = 18\) ), with an additional depth filter (10 reads minimum) and at least two heterozygous reads to make a heterozygous call. In order to generate more accurate ROH regions, we further excluded SNPs with \(\mathrm{MAF}< 0.05\) ( \(\sim \text{maf}\) ) and missing genotype calls ( \(\sim \text{geno}\) ) \(< 0.05\) . For each individual, we then used PLINK with \(\sim \text{homozyg}\) to scan the ROH regions, with scanning window modifiers ( \(\sim \text{homozyg}\) - window- het 3 \(\sim \text{homozyg}\) - window- missing 20). SNP sites with \(>50\%\) heterozygous genotypes across individuals were also excluded.  
+
+## LDdecay  
+
+Linkage disequilibrium (LD) decay curves were generated for four populations which included at least five samples (Cameroon, Eq Guinea, Madagascar, and Tanzania) to reduce the potential bias among the variable sample sizes among populations \(^{82}\) . We calculated LD using the relate R package \(^{83}\) for each population using imputed polymorphic sites from chr16. These sites were thinned to \(10\%\) of the original data using PLINK 1.9 ( \(\sim \text{thin 0.1}\) ) function \(^{81}\) to minimise computational time. Pairwise LD was calculated using 36,417 SNPs and 5 Mb physical distance, at which the curves plateaued.  
+
+## Outgroup f3 statistics  
+
+To further test gene flow between the Malagasy population and other red river hog and bushpig populations, outgroup f3 statistics were calculated based on genotype calls from medium- high depth individuals using ADMIXTOOLS2 \(^{43}\) . f2 statistics were first calculated for each population using five million bp blocks. Using the common warthog as an outgroup, outgroup f3 was estimated in the form of f3 (Madagascar, X; Warthog), where X represents different populations of red river hogs and bushpigs.  
+
+## TT and split time estimations  
+
+Population split times were estimated from unfolded individual 2D- SFS from genotype calls between medium- high depth samples using the Two- Two (TT) method \(^{44,84}\) , polarised against the common warthog, desert warthog (Supplementary Data 1) and the domestic pig (SRA: SAMN28197093). T1 and T2 values were calculated using formulae described in Sjödin et al. \(^{44}\) using a custom R script with a mutation rate of \(1.49e^{-8}\) per site per generation and a generation time of six years as in PSMC analyses \(^{4,42}\) .
+
+<--- Page Split --->
+
+## PopSizeABC  
+
+In order to estimate recent population size changes that cannot be captured by PSMC, we used popSizeABC \(^{45}\) on imputed data, with a focus on a sufficiently large sample of unrelated individuals (n = 21) in the Malagasy bushpig population. PopSizeABC takes VCF files per chromosome as input, and estimates linkage disequilibrium curves and site frequency spectra for tested populations. PopSizeABC population size estimates require multiple simulations of demographic scenarios to compare a posterior distribution of simulation derived parameters to those observed in the real data. For this analysis, 210,000 simulations were performed for 100 2- Mb regions per simulation as per the suggested settings in the popSizeABC publication for the software. A minimum MAF threshold of 0.1 was applied to calculation of the site frequency spectra and 0.2 for calculation of the linkage disequilibrium curves, again in accordance with the suggested parameters in Boitard et al. \(^{45}\) . The same recombination rate, mutation rate and generation time used in PSMC and TT were used in popSizeABC.  
+
+## Data availability  
+
+Data used in this study are described within the Article, Supplementary Data 1 and Supplementary Information. All data are available upon request.  
+
+## Acknowledgements  
+
+We thank Amal Al- Chaer for her invaluable help with DNA extractions. We are also grateful to Peter Arctander, who organised sample collections between the 1980s and 1990s and to David Moyer for contributing Tanzanian bushpig samples, collected between 1995- 1997 and subsequently stored in the collection at the University of Copenhagen. We would also like to acknowledge our collaborators from African wildlife management authorities for granting express permission to use samples within this study. We thank Komlan Afiademanyo (Université de Lomé, Togo), Flobert Njikou (Université de Yaoundé I, Cameroon) and Gabriel Ngua (ANDEGE, Equatorial Guinea) for collecting samples of red river hogs in Western and Central Africa between 2007 and 2010. RFB, XL and IM are supported by a Villum Young Investigator grant (19114) awarded to IM. AA, RFB, LL and ZL are funded by the Novo Nordisk Foundation (NNF20OC0061343). ABO is supported by a Carlsberg Foundation Reintegration Fellowship (CF19- 0427). MSR and AA are supported by the Independent Research Fund Denmark (grant number: 8021- 00360B). GGE and RH are supported by a Danmarks Frie Forskningsfond Sapere Aude research grant (DFF8049- 00098B), and RH is further supported by a Carlsberg Young Researcher grant (CF21- 0497). GB, JS, LC, and PG are members of the EDB laboratory, which is supported by the Laboratoire d'Excellence (LABEX) CEBA (grant
+
+<--- Page Split --->
+
+708 ANR- 10- LABX- 25- 01) and LABEX TULIP (grant ANR- 10- LABX- 0041), both managed by the 709 Agence Nationale de la Recherche in France.  
+
+## 710 Author information  
+
+## 711 Contributions  
+
+712 RFB: methodology, analysis, writing - original draft; LDB: methodology, analysis, writing - original draft; ABO: methodology, analysis, writing - review & editing; MSR: methodology & analysis; XL: methodology & analysis; GB: sampling, writing - review & editing; JS: writing - review & editing; CGS: analysis, writing - review & editing; SH: analysis; DZ: writing - review & editing; MP: sampling, writing - review & editing; VM: sampling; CM: sampling; MS: methodology, writing - review & editing; JK: analysis; LQ: analysis; GGE: analysis, writing - review & editing; RR: analysis, MH: analysis; LL: analysis; XW: analysis; MPH: sampling, writing - review & editing; TPLS: sampling, writing - review & editing; KH: analysis; MSS: sampling, writing - review & editing; AA: sampling; LC: writing - review & editing; CR: analysis, writing - review & editing; PG: sampling, writing - review & editing; HRS: sampling, writing - review & editing; IM: supervision, writing - original draft; AA: supervision, writing - original draft, RH: supervision, writing - original draft. All authors proofread and approved the final version of the manuscript.  
+
+## 725 Ethics declarations  
+
+## 726 Competing interests  
+
+The authors declare no competing interests.  
+
+## 728 Materials and correspondence  
+
+Materials requests and correspondence: Renzo F. Balboa (renzo.balboa@bio.ku.dk), Laura D. Bertola (laura.bertola@bio.ku.dk), Ida Moltke (ida@bio.ku.dk), Anders Albrechtsen (aalbrechtsen@bio.ku.dk), and Rasmus Heller (rheller@bio.ku.dk).
+
+<--- Page Split --->
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+## Supplementary Files  
+
+This is a list of supplementary files associated with this preprint. Click to download.  
+
+SupplementaryData1. xlsxSupplementaryInformationforsubmission.pdf
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+# Inhibition of novel human-HPV hybrid ecDNA enhancers reduces oncogene expression and tumor growth in oropharyngeal cancer  
+
+Joseph Califano jcalifano@health.ucsd.edu  
+
+UCSD Takuya Nakagawa Chiba University https://orcid.org/0000- 0003- 1216- 4822 Jens Luebeck University of California, San Diego https://orcid.org/0000- 0003- 4391- 979X Kaiyuan Zhu University of California at San Diego Joshua Lange University of California at San Diego https://orcid.org/0000- 0002- 4246- 6058 Roman Sasik University of California, San Diego https://orcid.org/0000- 0002- 9266- 8993 Chad Phillips UCSD Sayed Sadat UCSD Sara Javadzadeh UCSD Qian Yang UCSD Allen Wang UCSD https://orcid.org/0000- 0001- 9870- 7888 Kersi Pestonjamasp UCSD Sara Rosenthal University of California San Diego Kathleen Fisch University of California San Diego https://orcid.org/0000- 0002- 0117- 7444 Paul Mischel
+
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+Stanford University https://orcid.org/0000- 0002- 4560- 2211  
+
+Vineet Bafna  University of California, San Diego  
+
+## Article  
+
+Keywords:  
+
+Posted Date: September 5th, 2024  
+
+DOI: https://doi.org/10.21203/rs.3.rs- 4636308/v1  
+
+License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License  
+
+## Additional Declarations:  
+
+Yes there is potential Competing Interest. P.S.M. is a co- founder, chairs the scientific advisory board (SAB) of and has equity interest in Boundless Bio Inc. (BBI). P.S.M. is also an advisor with equity for Asteroid Therapeutics and is an advisor to Sage Therapeutics. V.B. is a co- founder, consultant, SAB member and has equity interest in Boundless Bio and Abterra. J.T.L is an employee of BBI. J.L. previously consulted for BBI. The other authors have no conflicts of interest to disclose.  
+
+All animal experiments were obtained under approval and under oversight from the UC San Diego Institutional Animal Care and Use Committee (IACUC) Office.  
+
+Version of Record: A version of this preprint was published at Nature Communications on March 26th, 2025. See the published version at https://doi.org/10.1038/s41467- 025- 57447- 9.
+
+<--- Page Split --->
+
+1 Inhibition of novel human- HPV hybrid ecDNA enhancers 2 reduces oncogene expression and tumor growth in 3 oropharyngeal cancer  
+
+4 5 Takuya Nakagawa \(^{1,2,3*}\) , Jens Luebeck \(^{4}\) , Kaiyuan Zhu \(^{4}\) , Joshua T. Lange \(^{5}\) , Roman 6 Sasik \(^{6}\) , Chad Phillips \(^{1}\) , Sayed Sadat \(^{1}\) , Sara Javadzadeh \(^{4}\) , Qian Yang \(^{7}\) , Allen Wang \(^{7}\) , 7 Kersi Pestonjamasp \(^{8}\) , Brin Rosenthal \(^{6}\) , Kathleen M. Fisch \(^{6}\) , Paul Mischel \(^{5}\) , Vineet 8 Bafna \(^{4}\) , & Joseph A. Califano \(^{1,9*}\) 9 10 Author affiliation: \(^{1}\) Moores Cancer Center, University of California, San Diego, La 11 Jolla, CA, USA; 12 \(^{2}\) Department of Otorhinolaryngology, Head and Neck Surgery, Chiba University, 13 Graduate School of Medicine, Chiba, Japan; 14 \(^{3}\) Health and Disease Omics Center, Chiba University, Chiba, Japan; 15 \(^{4}\) Department of Computer Science and Engineering, UC San Diego, La Jolla, CA, 16 USA; 17 \(^{5}\) Department of Pathology, Stanford University School of Medicine, Stanford, CA, 18 USA; 19 \(^{6}\) Center for Computational Biology and Bioinformatics, University of California, San 20 Diego, La Jolla, CA, USA; 21 \(^{7}\) Center for Epigenomics, Department of Cellular and Molecular Medicine, University 22 of California, San Diego, La Jolla, CA, USA; 23 \(^{8}\) Cancer Center Microscopy Core, University of California, San Diego, La Jolla, CA, 24 USA; 25
+
+<--- Page Split --->
+
+26 9 Department of Otolaryngology - Head and Neck Surgery, University of California, San Diego, La Jolla, CA, USA.  
+
+# \\*Corresponding to:  
+
+30 Joseph Califano, 31 Department of Otolaryngology- Head and Neck Surgery, University of California, San Diego, La Jolla, CA, USA 92093. Phone: 858- 822- 6100; email: jcalifano@health.ucsd.edu; and 34 Takuya Nakagawa, 35 Moores Cancer Center, University of California, San Diego, La Jolla, CA, USA; 36 Phone: 858- 822- 6100; email: tanakagawa@health.ucsd.edu and tnakagawa@chiba- u.jp 37 Running title: Targeted therapy for hybrid ecDNA enhancers in HPV- associated oropharyngeal cancer  
+
+40 Word count: 2854 words 41 Conflict of interest: P.S.M. is a co- founder, chairs the scientific advisory board (SAB) of 42 and has equity interest in Boundless Bio Inc. (BBI). P.S.M. is also an advisor with equity 43 for Asteroid Therapeutics and is an advisor to Sage Therapeutics. V.B. is a co- founder, 44 consultant, SAB member and has equity interest in Boundless Bio and Abterra. J.T.L is 45 an employee of BBI. J.L. previously consulted for BBI. The other authors have no 46 conflicts of interest to disclose.
+
+<--- Page Split --->
+
+## Abstract  
+
+Extrachromosomal circular DNA (ecDNA) have been found in most types of human cancers, and ecDNA incorporating viral genomes has recently been described, specifically in human papillomavirus (HPV)- mediated oropharyngeal cancer (OPC). However, the molecular mechanisms of human- viral hybrid ecDNA (hybrid ecDNA) for carcinogenesis remains elusive.  
+
+We characterized the epigenetic status of hybrid ecDNA using HPVOPC cell lines and patient- derived tumor xenografts, identifying HPV oncogenes E6/E7 in hybrid ecDNA were flanked by novel somatic DNA enhancers and HPV L1 enhancers, with strong cis- interaction. Targeting of these enhancers by clustered regularly interspaced short palindromic repeats interference or hybrid ecDNA by bromodomain and extra- terminal inhibitor reduced E6/E7 expression, and significantly inhibited in vitro and/or in vivo growth only in ecDNA(+) models. HPV DNA in hybrid ecDNA structures are associated with novel somatic and HPV enhancers in hybrid ecDNA that drive HPV oncogene expression and carcinogenesis, and can be targeted with ecDNA disrupting therapeutics.  
+
+(149 words)
+
+<--- Page Split --->
+
+## Introduction  
+
+Extrachromosomal circular DNA (ecDNA) represents a frequent driver of focal oncogene amplification during carcinogenesis, and the subsequent high expression of the genes carried on ecDNA \(^{1 - 3}\) . This phenomenon is facilitated by the circular structure and open- chromatin configuration of ecDNA \(^{4}\) , allowing transcriptional factors to easily access ecDNA. The unique spatial reorganization of ecDNA is thought to create new topological domains, providing opportunities for novel interactions between oncogenes and distant regulatory elements, and ecDNA mediated cis- interactions between enhancers and oncogenes (“enhancer hijacking,”). Oncogenes residing on chromosomal DNA in the human genome may also be activated using so- called “mobile enhancers” from free- floating ecDNA \(^{5}\) . Furthermore, ecDNA segments in close proximity may also interact through “ecDNA hubs” \(^{6,7}\) .  
+
+Recent studies have shown that in Human papillomavirus (HPV) mediated cancers, human DNA and viral genomes can form circular human- viral hybrid ecDNA (hybrid ecDNA). HPV is an approximately 8kbp circular double- stranded DNA virus and includes oncogenes called E6 and E7 \(^{8,9}\) . HPV- mediated oropharyngeal cancer (HPVOPC) incidence has dramatically increased over the last 2 decades and has become one of the fastest growing cause of solid organ cancer death in the US \(^{10,11}\) . In our previous analysis, hybrid ecDNA was identified in 16 out of 56 cases (around 30%) of HPVOPC \(^{12}\) . While somatic integration of HPV oncogenes into the human genome is known as a carcinogenic driver of HPVOPC \(^{13,14}\) , the molecular mechanisms by which hybrid ecDNA drives carcinogenesis have not been well described. Understanding these mechanisms and potential therapeutic vulnerabilities in hybrid ecDNA(+) HPVOPC are an important, unmet challenge in understanding and treating HPVOPC.  
+
+We hypothesized that in addition to oncogene amplification, HPV oncogene transcriptional upregulation via cis- interactions in hybrid ecDNA between enhancers and
+
+<--- Page Split --->
+
+HPV oncogenes, and proximity of hybrid ecDNA and HPV episomal segments themselves are deeply involved in mediating the carcinogenic molecular functions of HPVOPC.  
+
+Here, we investigated the chromatin status of hybrid ecDNA using HPVOPC cell lines and PDX tumors and identified active enhancers that were newly created in hybrid ecDNA. The HPV oncogenes E6/E7 inside hybrid ecDNA were flanked by novel somatic enhancers in addition to novel HPV enhancers within the L1 region. HiC-seq identified a strong interaction between these enhancers and E6/E7, supporting a cis-interaction inside hybrid ecDNA. Inactivation of this enhancer by clustered regularly interspaced short palindromic repeats (CRISPR) interference reduced the expression of E6/E7 and proliferation. Furthermore, bromodomain and extra-terminal (BET) inhibitor treatment targeting hybrid ecDNA structures in ecDNA(+) tumors resulted in the significant inhibition of tumor growth in both cell lines and PDX tumors not seen in hybrid ecDNA(- ) tumors, suggesting new avenues for therapeutic intervention in HPVOPC.
+
+<--- Page Split --->
+
+## Results  
+
+## Identification of hybrid ecDNA in HPVOPC cell lines and PDX tumors  
+
+To determine hybrid ecDNA status, we performed WGS and RNA- seq on 2 HPVOPC cell lines, HMS001 and SCC154, and 2 patient- derived xenograft (PDX) tumors from clinical tumor samples of HPVOPC patients, described here as PDX_A and PDX_C. As negative control, we used the cell- line NOKSI, (spontaneously immortalized human cell line derived from normal oral mucosa that is HPV negative and known to not carry ecDNA). Analysis of WGS and RNA- seq data showed the presence of hybrid DNA molecules and hybrid transcripts in HMS001 and PDX_A but not in PDX_C, SCC154 or NOKSI. Using Amplicon Architect (AA), hybrid ecDNA was detected in HMS001 and PDX_A, but not in SCC154, PDX_B or NOKSI (Fig. 1A-D). Across samples, each sequence of hybrid ecDNA was unique, consistent with our previous analyses in clinical samples of HPVOPC<sup>12</sup>. Long read DNA- seq also detected structural variants for both viral and human segments on the same sequenced molecule, consistent with the AA results (Supplementary Fig. 1). For the cytogenetic validation of each hybrid ecDNA, multi- DNA FISH targeting each human genome and HPV genome on hybrid ecDNA was performed using a metaphase- spread in cell line and short- time cultured PDX tumors. Overlapping of somatic and HPV probes was located outside of condensed chromosomes in HMS001 (hybrid ecDNA(+)) and PDX_A, confirming the existence of hybrid ecDNA (Fig. 1E and F), but no such overlapping probes were found in SCC154 (hybrid ecDNA-; Fig. 1G and H). We also observed HPV- only signals only in HMS001, PDX_A, and SCC154 showing the existence of episomal HPV DNA in each sample (Fig. 1E- G). The mean copy numbers of HPV/cell in HMS001, PDX_A, SCC154, and NOKSI were 4.14, 4.11, 2.3, and 0 respectively (Supplementary Table 1).  
+
+## Novel enhancers are created in hybrid ecDNA structures
+
+<--- Page Split --->
+
+To elucidate chromatin status of hybrid ecDNA, we performed ChIP- seq for H3K27ac (activation mark), H3K4me1 (enhancer mark), and H3K4me3 (promoter mark), and ATAC- seq on HPVOPC cell lines and PDX tumors. Contrary to our expectations, previously described somatic enhancer regions were not hijacked, but clusters of H3K4me1 and H3K27ac peaks, indicative of active enhancers, were found in the regions with hybrid ecDNA (Fig. 2A). Intriguingly, a novel cluster of H3K4me1 and H3K27ac peaks was observed in HMS001, but not in SCC154 or NOKSI, suggesting the creation of a novel enhancer element in the hybrid ecDNA (Fig. 2A). Strikingly, the HPV integration site within hybrid ecDNA exists in the center of these novel enhancers in hybrid ecDNA. Specifically, H3K4me1 and H3K27ac enrichment was noted in the HPV L1 region within the hybrid ecDNA enhancer as part of a larger enhancer region and ATAC- seq confirmed these enhancers exist in open chromatin regions (Fig. 2B). In addition, the E6/E7 promoter was surrounded by these enhancers and making enhancement- promoter complex. The same pattern was observed within the hybrid ecDNA region in PDX_A (Fig. 2C and D). Although the somatic genomic regions associated with each hybrid ecDNA were quite different and unique between HMS001 and PDX_A, we found that in each case, the E6/E7 promoter was surrounded by newly created enhancers in somatic regions and an enhancer in the HPV L1 region. Based on these data, we hypothesized that HPV gene expression, specifically E6/E7, was upregulated by newly created somatic human and HPV hybrid enhancer complexes on hybrid ecDNA. These phenomena only occurred in hybrid ecDNA samples, suggesting a hybrid ecDNA- specific mechanism.  
+
+## Human and viral genomes on hybrid ecDNA interact directly with each other  
+
+To elucidate enhancer and HPV DNA interactions in hybrid ecDNA, HiC- seq was performed using the same cell lines and PDX tumors. Human somatic genomic sequences
+
+<--- Page Split --->
+
+on hybrid ecDNA were divided into 2 segments (S1 and S2). The S1 enhancer region closely interacted with the HPV L1 region and the S2 enhancer region closely interacted with HPV E6/E7 regions in HMS001 (Fig. 3A and B). On the other hand, there was no such interaction in SCC154 that lacked hybrid ecDNA (Fig. 3C). This phenomenon was confirmed in PDX tumors as well (Fig. 3D-F). In PDX_A, the enhancer existed only in the S1 segment, and the S1 enhancer region closely interacted with HPV L1 and E6/E7 regions (Fig. 3D and E), and there was no such interaction in hybrid ecDNA- PDX_C (Fig. 3F). Although each hybrid ecDNA structure was unique, each of the novel somatic enhancer regions closely interacted with HPV genome, confirming the direct interaction of the human and viral genomes in hybrid ecDNA.  
+
+## CRISPR interference targeting of enhancers on hybrid ecDNA blocks HPV oncogene expression  
+
+To elucidate the functional role of newly created enhancers on hybrid ecDNA, we performed CRISPR interference, targeting specific hybrid ecDNA enhancers in HMS001. We generated dCas9- KRAB stable cell lines; HMS001, SCC154, and NOKSI. gRNAs targeting S1: the long part (gRNA#1) and the S2: the short part (gRNA#2) of the enhancer on hybrid ecDNA of HMS001 and nontarget controls were used (Fig. 4A). We confirmed the expression of dCas9 after doxycycline induction (Fig. 4B and C, Supplementary Fig. 2A and B). Consistent with our hypothesis, E6 and E7 expression were specifically reduced by the repression of the S2 enhancer region on hybrid ecDNA by CRISPR interference (CRISPRi) (Fig. 4D). This phenomenon was not seen in SCC154 or NOKSI without hybrid ecDNA (Supplementary Fig. 2C and D), supporting the notion that the enhancer on hybrid ecDNA was newly and uniquely created. Furthermore, compared to nontarget controls, S2 repression significantly inhibited the proliferation only in HMS001 ( \(P = 0.006\) ) (Fig. 4E-G). This indicates that novel hybrid enhancer regions in hybrid
+
+<--- Page Split --->
+
+ecDNA specifically drive expression of HPV oncogenes.  
+
+## Hybrid ecDNA and HPV episomes are physically associated and can be targeted therapeutically in vitro and in vivo  
+
+Next, we investigated the potential for novel HPVOPC therapeutic strategies targeting hybrid ecDNA. We focused using the Bromo- and Extra- Terminal domain (BET) inhibitors as potential therapeutic agents that specifically target BRD4 as a key linker of ecDNA<sup>6</sup>. We hypothesized that disruption of the direct interaction of the enhancer in the somatic genome that interacts with HPV E6/E7 on hybrid ecDNA would inhibit HPV oncogene expression and reduce downstream gene targeting and proliferation.  
+
+To investigate the spatial properties of hybrid ecDNA in the cells, we performed multi- probe FISH using an EYA2 probe and an HPV probe in super resolution with and without JQ1 treatment for HMS001 cells. FISH signals for hybrid ecDNA and episomal HPV only tended to accumulate in the nucleus, and these signals were significantly reduced after JQ1 treatment. This suggests that the copy number of hybrid ecDNA and episomal HPV decreased after JQ1 treatment (Fig. 5A and B).  
+
+To investigate gene expression changes after JQ1 treatment, we at first checked the E6/E7 expression after JQ1 treatment (Fig. 6A). E6/7 expression was only downregulated in the hybrid ecDNA(+) HMS001 cell line—not in hybrid ecDNA(−) SCC154. MYC is one of the key oncogenes regulated by BRD4, and as expected, MYC was downregulated by JQ1 even in hybrid ecDNA(−) cell lines. (Fig. 6B, C, and Supplementary Fig. 3). In addition, upon JQ1 treatment, E6/E7 expression was reduced in a concentration- dependent manner (100nM and 1μM) at 6h after JQ1 treatment and more rapidly at 24 h (Fig. 6B and C). A similar result was seen in proliferation assays in HMS001 and SCC154, in which JQ1 treatment significantly inhibited tumor growth only in hybrid ecDNA(+) HPVOPC, but not in ecDNA(−) HPVOPC (*P = 0.03, P = 0.12, respectively)
+
+<--- Page Split --->
+
+(Fig. 6D). Comparison of ChIP-seq data between JQ1 treatment and DMSO in HMS001 indicated that while we found many BRD4 peaks significantly reduced after JQ1 treatment, we found many H3K27ac peaks significantly enriched after JQ1 treatment (Supplementary Fig. 4A and 4B). To clarify specific pathways affected by chromatin alterations, 810 differentially upregulated H3K27ac peaks along with H3K4me1 peaks (FC>2 and Idfr<0.3) were identified. Gene Ontology analysis showed that GO terms reported to be suppressed by HPV infection were restored, such as, “epithelial cell differentiation” and “Apoptosis” (Supplementary Fig. 4C). On the other hand, HiC-seq showed no significant changes after JQ1 treatment, suggesting that the structure of the hybrid ecDNA itself is not affected by JQ1 treatment (Supplementary Fig. 4D and 4E).  
+
+We investigated JQ1 treatment in an in vivo model using a patient- derived xenograft model of HPVOPC (Fig. 7A) with hybrid ecDNA(+) (PDX_A) and hybrid ecDNA(- ) (PDX_C and PDX004). In the PDX_A JQ1 treatment group, tumor growth was significantly inhibited compared to the vehicle control group (tumor volume: \(P = 2 \times 10^{- 5}\) , tumor weight: \(P = 1 \times 10^{- 4}\) respectively) (Fig. 7B- F). Consistent with our hypothesis, E6/E7 expression was also reduced after JQ1 treatment in this model (\(P < 1 \times 10^{- 4}\) ) (Fig. 7G). On the other hand, in PDX_C and PDX004: hybrid ecDNA- models, JQ1 did not significantly inhibit tumor growth (Fig. 7H- M), suggesting specific targeting of hybrid ecDNA(+) tumors. Furthermore, multi- FISH targeting hybrid ecDNA using a short- time culture cell line from PDX_A tumors showed reduction of hybrid ecDNA FISH signals after JQ1 treatment (Supplementary Fig. 5A- C) and ChIP- seq results also identified changes in 496 H3K27ac peaks along with H3K4me1 peaks (FC>2 and Idfr<0.3). GO analysis of genes associated with these changes include “epithelial cell differentiation”, and “regulation of p38MAPK cascade” (Supplementary Fig. 6A- C). HiC- seq showed no significant changes after JQ1 treatment in PDX_A as well as in HMS001 (Supplementary Fig. 6D and 6E). A total of 248 K27ac peaks along with K4me1 peaks were common to
+
+<--- Page Split --->
+
+hybrid ecDNA(+) cell line and PDX treated with JQ1, again including GO terms such as "epithelial cell differentiation" and "positive regulation of apoptotic process", suggesting changes associated with HPV inhibition (Supplementary Fig. 7). Taken together, these data suggest the potential for specific therapy targeting hybrid ecDNA through interruption of HPV mediated gene expression changes.
+
+<--- Page Split --->
+
+## Discussion  
+
+While the formation of ecDNA is commonly associated with DNA damage, such as chromosome shattering (chromothripsis), and breakage- fusion- bridge cycles (BFBs) \(^{2,3,15,16}\) , viral integration into the human genome also induces genomic instability \(^{17,18,19,20}\) and causes formation of hybrid ecDNA and carcinogenesis. Although HPV integration often occurs in non- coding regions and some hot spots as previously reported, the location of integration is thought to be random \(^{21,22}\) . Furthermore, HPV integration is reported to change chromatin accessibility status and activate surrounding genes \(^{23,24}\) , but the mechanisms behind this phenomenon are yet to be elucidated. Although HPV integration around originally existing enhancers, and hijacking of known enhancers has been reported previously \(^{25}\) , we have observed that HPV integration creates novel enhancers in ecDNA that did not exist in OPC samples without HPV integration at that locus. We found HPV genome integration sites were surrounded by newly created enhancers and these regions formed hybrid ecDNA in both HPVOPC cell lines and PDX tumors from clinical HPVOPC patients. In addition, we also demonstrated the HPV genome itself can serve as an enhancer region within ecDNA. This is perhaps one reason why HPV integration may be seen as "random" or not biased to integration with known enhancer/promoter regions: HPV genome sequences themselves can serve as enhancers and can induce enhancer activity in genome regions that do not serve as enhancers in the normal physiologic state.  
+
+In our study, cis- interaction between HPV and newly created enhancers on hybrid ecDNA was confirmed by HiC- seq, and CRISPRi targeting this enhancer reduced the expression of E6/E7 and proliferation consistently. Interestingly, in HMS001, HPV is surrounded on both sides by newly created enhancers, but only one enhancer (S2) showed inhibition of HPV E6/E7 expression and cell growth. This is consistent with the interaction in HiC- seq data, which also showed strong interaction between S2 and E6/E7
+
+<--- Page Split --->
+
+in HMS001. In this case, the S1 enhancer was working the enhancer complex with L1 enhancer of HPV. We also made the novel observation that HPV genome regions, including the L1 region, can serve as enhancers when integrated into hybrid ecDNA structures. Detailed examination of the hybrid ecDNA structure along with the ChIP- seq data shows that an enhancer of HPV in cell line (HMS001) or PDX models, forms a complex with an enhancer in the L1 region of HPV. This is consistent with the fact that the suppression of HPV E6/E7 expression did not occur by repression of the enhancer on the human side alone. Although the cell lines and PDX from different patients had completely different hybrid ecDNAs, they shared the composition of newly created enhancers as well as HPV L1 enhancers flanking E6/E7, suggesting that L1 regions might be key enhancer regions driving HPV expression in hybrid structures.  
+
+Furthermore, we explored the role of hybrid ecDNA association with episomal HPV in HPVOPC. Although it was difficult to specifically define 'hybrid ecDNA hubs' in our analysis, hybrid ecDNAs and episomal HPV were found nearby each other in the nucleus, and HPV E6/E7 expression was strikingly reduced after JQ1 treatment. It is reasonable that the copy number of HPV in hybrid ecDNA is not as high as copy number of the oncogene in other cancer types<sup>6</sup>, because hybrid ecDNA can drive high levels of HPV transcription by coexisting with episomal HPV.  
+
+In the past few years, development of therapies that target ecDNA has been accelerating<sup>26</sup>. In this study, we identified that targeting the enhancer on hybrid ecDNA by CRISPRi reduced the expression of E6/E7 and proliferation in vitro model in HPVOPC. Furthermore, we showed that specific targeting of hybrid ecDNA with a BET inhibitor in HPVOPC was able to effectively reduce tumor growth and oncogene expression in both in vivo and in vitro models. We have now seen in this study using the first preclinical model targeted at hybrid ecDNA, that BET inhibitors are a potential new targeted therapy for patients with hybrid ecDNA(+) tumors. This provides a rationale for
+
+<--- Page Split --->
+
+biomarker driven targeting of hybrid ecDNA(+) HPVOPC, and investigation of other types of treatment for ecDNA(+) HPVOPC that specifically targets ecDNA.  
+
+## Acknowledgements  
+
+We thank Cynthia Fox for English language editing. Funding includes support from the Gleiberman Head and Neck Cancer Center, National Institutes of Health, Grants 2UL1TR001442- 08 of CTSA.  
+
+## Statement of author contributions  
+
+T.N.: Conceptualization, data curation, formal analysis, investigation, visualization, methodology, writing - original draft, writing - review and editing. JL, Software, bioinformatic analysis, investigation, visualization, methodology, writing - review and editing. K.Z.: Software, bioinformatic analysis, investigation, visualization, methodology, writing - review and editing. J.T.L.: Methodology. R.S.: Data curation, visualization, methodology. C.P.: Data curation, investigation. S.S.: Investigation. S.J.: Data curation, investigation. Q.Y.: Data curation, investigation. A.W.: writing - review and editing. K.P.: visualization, methodology. B.R.: Writing - review and editing. K.F.: Writing - review and editing. P.M.: Writing - review and editing. V.B.: Conceptualization, resources, supervision, funding acquisition, investigation, writing - original draft, writing - review and editing. J.A.C: Conceptualization, formal analysis, resources, supervision, funding acquisition, investigation, writing - original draft, project administration, writing - review and editing.
+
+<--- Page Split --->
+
+## Materials and Methods  
+
+## Clinical samples  
+
+Clinical specimens for whole- genome sequencing (WGS) and RNA- seq were collected from HPVOPC patients who underwent surgery at Jacobs Medical Center at the University of California, San Diego (UCSD) Health. Written informed consent was obtained from all patients. These samples were shared with the UCSD Human Research Protection Program (institutional review board (IRB)- approved protocol HRPP# 181755) by way of the Moores Cancer Center Biorepository and Tissue Technology resource. Two independent pathologists confirmed that the purity of the primary tumor was at least \(80\%\) . HPV status was determined by p16 immunohistochemistry or in- situ hybridization.  
+
+## Patient-derived xenografts (PDX)  
+
+Female 4- week- old nonobese diabetic/severe combined immunodeficiency (NOD/SCID) mice were purchased from The Jackson Laboratory. Fresh clinical HPVOPC tumor samples were extracted at surgery and cut into approximately 3mm pieces and transplanted into 6- 8- week- old female NOD/SCID mice. Xenografting procedures were described previously \(^{27,28}\) .  
+
+## Cell culture  
+
+SCC154 was purchased from the American Type Culture Collection (ATCC). HMS001 was a gift from the James Rocco lab (Ohio State). NOKSI was a gift from the lab of Silvio Gutkind. SCC154 and NOKSI were grown in DMEM (Sigma- Aldrich, St. Louis, MO). HMS001 was grown in 2/3 of DMEM and 1/3 of F12 Medium (Sigma- Aldrich). All media were supplemented with 10% FBS (Sigma- Aldrich), 1% penicillin/streptomycin (Sigma- Aldrich), and plasocin (InvivoGen, Toulouse, France). Cells were cultured at \(37^{\circ}\mathrm{C}\) with 5% \(\mathrm{CO_2}\) .
+
+<--- Page Split --->
+
+For short- time cell culture of PDX tumors, PDX mice were euthanized for tissue retrieval, and tumors were dissected. Then, tumors were digested and isolated for short- time cell culture. Procedures were described elsewhere<sup>29</sup>. Cells were grown in defined keratinocyte serum free media (Invitrogen, Carlsbad, CA) supplemented with 1% antibiotics, 5 ng/ml mouse epidermal growth factor (Invitrogen) and \(2 \times 10^{- 11}\) M cholera (Sigma- Aldrich) at \(37^{\circ}\) C with 5% CO<sub>2</sub>.  
+
+## Whole genome sequencing  
+
+DNA was extracted using the QIAquick DNA mini kit (Qiagen, Hilden, Germany) for high- quality extraction per the manufacturer instructions. Library preparation and sequencing was performed with the Illumina NovaSeq 6000 at the UCSD IGM Genomics Center. Hg38 and HPV genome sequences (accession number: AY686584.1) were used for reference genome.  
+
+## Amplicon Architect  
+
+WGS data were analyzed using AmpliconSuite- pipeline, which encompasses AmpliconArchitect<sup>30</sup> v1.3. r3 and AmpliconClassifier<sup>2</sup> v0.5.1 to detect hybrid ecDNA, using the GRCh38_viral reference genome option (https://github.com/AmpliconSuite). In brief, CNVkit v0.9.9 was employed for copy number segmentation and estimation. Segments with copy number \(\geq 2.5\) copies above chromosome arm ploidy, as well as viral genome regions with CN>=1 were extracted using the AmpliconSuite- pipeline and used as seed regions for analysis of focal amplifications. For each seed region, AmpliconArchitect searched the region and nearby loci for discordant read pairs, which are indicative of genomic structural rearrangements. Genomic segments are defined based on the positions of gene breakpoints and changes in copy number. AmpliconArchitect utilizes structural variant signatures, such as discordant paired- end reads and CNV
+
+<--- Page Split --->
+
+boundaries, to partition all intervals into segments. It then constructs an amplicon graph based on these segments and decomposes the graph into genome paths and cycles that explain the observed changes in copy number and structural variation. The AmpliconClassifier script was used to classify amplicons into categories such as ecDNA, breakage- fusion- bridge, complex non- cyclic, linear, and no focal amplification based on rules related to patterns of structural variation, copy number and decomposed genome paths from AmpliconArchitect. Circular visualizations of ecDNA genome structure and annotation tracks were generated using CycleViz (https://github.com/AmpliconSuite/CycleViz)31.  
+
+## RNA extraction and RNA sequencing  
+
+RNA was extracted following the protocol of the Qiagen RNeasy Plus Mini Kit (Qiagen, Hilden, Germany). RNA concentration, purity, and integrity were verified using a NanoDrop spectrophotometer (Thermo Fisher Scientific, Waltham, MA). In addition, an RNA Integrity Number (RIN) of 7.0 or greater by TapeStation was required for quality assessment. Library preparation and RNA sequencing were performed by the UCSD IGM Genomics Center utilizing an Illumina NovaSeq 6000.  
+
+## Long read DNA-sequencing  
+
+High molecular weight DNA(>40kb) was extracted using the Nanobind CBB kit or Nanobing tissue kit (PacBio, Menlo Park, CA) following manufacturer's recommendations. Long read DNA sequencing was performed using 1 SMRT Cell per sample. Library preparation and sequencing were performed by the UCSD IGM Genomics Center with the Illumina NovaSeq 6000.  
+
+## ChIP-seq
+
+<--- Page Split --->
+
+For ChIP experiments, \(1 \times 10^{7}\) cells for cell lines, and \(25 \mathrm{mg}\) tumors for PDX were used for each condition. Antibody for each immunoprecipitation using H3K4me3 (#9751, \(2 \mu \mathrm{g}\) , Cell Signaling Technology, Danvers, MA), H3K4me1 (#C5326S, \(2 \mu \mathrm{g}\) , Cell Signaling Technology), H3K27ac (#91194, \(4 \mu \mathrm{g}\) , Active Motif, Carlsbad, CA), BRD4 (#A301- 985A50, \(7.5 \mu \mathrm{g}\) , Bethyl Laboratories, Montgomery, TX), and IgG for negative control was used. We used the SimpleChIP Kit (Cell Signaling Technology, Danvers, MA) and followed the manufacturer's protocol. Library preparation and sequencing was performed with the Illumina NovaSeq 6000 at the UC San Diego IGM Genomics Center.  
+
+## ATAC-seq  
+
+The precise protocol was previously shown<sup>32</sup>. Briefly, around \(50 \mathrm{mg}\) of pulverized PDX tumor tissue with liquid nitrogen and thawed cell lines ( \(3 \times 10^{6}\) ) were used for nuclei preparation. PDX tumor tissue was dissociated by GentleMACS machine in MACS buffer [ Tris- HCl, \(\mathrm{pH} = 8\) (Thermo Fisher Scientific), 5 \(\mathrm{CaCl}_2\) (G- Biosciences, St. Louis, MO), \(3 \mathrm{mM}\) Mg- acetate (Sigma- Aldrich), \(2 \mathrm{mM}\) EDTA (Thermo Fisher Scientific), \(0.6 \mathrm{mM}\) DTT (Sigma- Aldrich) and Protease inhibitor (Roche, Basel, Switzerland) in Molecular biology water (Corning, Corning, NY)]. Nuclei were pelleted by centrifugation for \(5 \mathrm{min}\) at \(500 \mathrm{x g}\) at \(4^{\circ}\mathrm{C}\) . Permeabilized nuclei were obtained by resuspending nuclei pellet in \(1 \mathrm{mL}\) Nuclear Permeabilization Buffer [ \(0.2\%\) IGEPAL- CA630 (Sigma- Aldrich), \(1 \mathrm{mM}\) DTT (Sigma- Aldrich), Protease inhibitor (Roche, Basel, Switzerland), \(5\%\) BSA (Sigma- Aldrich) in PBS (Thermo Fisher Scientific)], and incubating for \(10 \mathrm{min}\) on a rotator at \(4^{\circ}\mathrm{C}\) . For cell lines, permeabilized nuclei were obtained by resuspending cells in \(250 \mu \mathrm{L}\) Nuclear Permeabilization Buffer and incubating for \(5 \mathrm{min}\) on a rotator at \(4^{\circ}\mathrm{C}\) . Nuclei were then pelleted by centrifugation for \(5 \mathrm{min}\) at \(500 \mathrm{x g}\) at \(4^{\circ}\mathrm{C}\) . The pellet was resuspended in \(25 \mu \mathrm{L}\) ice- cold Tagmentation Buffer [ \(33 \mathrm{mM}\) Tris- acetate \((\mathrm{pH} = 7.8)\) (Thermo Fisher Scientific), \(66 \mathrm{mM}\) K- acetate (Sigma- Aldrich), \(11 \mathrm{mM}\) Mg- acetate
+
+<--- Page Split --->
+
+(Sigma-Aldrich), 16 % DMF (Merk Millipore, Darmstadt, Germany) in Molecular biology water (Corning, Corning, NY)]. An aliquot was then taken and counted by hemocytometer to determine nuclei concentration. Approximately 50,000 nuclei were resuspended in \(20\mu \mathrm{L}\) ice- cold Tagmentation Buffer, and incubated with \(1\mu \mathrm{L}\) Tagmentation enzyme (Illumina) at \(37^{\circ}\mathrm{C}\) for \(60\mathrm{min}\) with shaking \(500\mathrm{rpm}\) . The. tagmented DNA was purified using MinElute PCR purification kit (Qiagen). The libraries were amplified using NEBNext High- Fidelity 2X PCR Master Mix (NEB, Ipswich, MA) with primer extension at \(72^{\circ}\mathrm{C}\) for \(5\mathrm{min}\) , denaturation at \(98^{\circ}\mathrm{C}\) for 30s, followed by 8 cycles of denaturation at \(98^{\circ}\mathrm{C}\) for \(10\mathrm{s}\) , annealing at \(63^{\circ}\mathrm{C}\) for \(30\mathrm{s}\) and extension at \(72^{\circ}\mathrm{C}\) for \(60\mathrm{s}\) . Amplified libraries were then purified using MinElute PCR purification kit (Qiagen), and two size selection steps were performed using SPRIselect bead (Beckman Coulter, Brea, CA) at \(0.55\mathrm{X}\) and \(1.5\mathrm{X}\) bead- to- sample volume rations, respectively.  
+
+Final libraries were quantified using Qubit (Thermo Fisher Scientific) and checked for library size distribution using 4200 TapeStation (Agilent Technologies, Santa Clara, CA). Library preparation and sequencing was performed with the Illumina NovaSeq 6000 at the UCSD IGM Genomics Center.  
+
+## ChIP-seq and ATAC-seq analysis  
+
+Raw sequence data were first trimmed for sequencing adapters using fastp \(^{33}\) , then aligned to human genome (hg38) augmented by the HPV type 16 genome sequence as an extra chromosome (accession number AY686584.1), using STAR aligner \(^{34}\) with chimeric alignments allowed in the output. Peak calling was done in an unbiased way on an artificial sample created as the union of reads sampled randomly (with equal weight per sample) from all samples of the same type (ATAC- seq, H3K27ac, etc.), using HOMER (Hypergeometric Optimization of Motif EnRichtment) \(^{35}\) . Peak quantitation was done for
+
+<--- Page Split --->
+
+each sample individually by counting reads aligning to the peak regions determined in the previous step.  
+
+## Peak normalization and differential analysis  
+
+Raw read numbers in peak regions of all samples of the same type were normalized separately. For the H3K27ac mark, we have two replicates each of HMS001 and PDX_A treated with either vehicle or JQ1 (8 samples total). Normalization proceeds under the assumption that the tallest peaks should be comparable across samples. To this end, we found peak regions in which the raw peak values are among the top 30- percentile for each sample. To this subset of all peak regions, we apply the Relative Log Expression (RLE) normalization method \(^{36}\) . The normalization factors per sample are then applied to all peak regions. This normalization method is insensitive to potentially different levels of low- level “background” signal in different samples. Differentially acetylated regions (H3K27ac mark) were identified as follows: Since we have only two biological replicates per condition, and \(\sim 10^{4}\) tests to carry out, the standard \(t\) - test has very low power. Instead, for each peak region and each comparison we calculate a \(z\) - score, in which the standard deviation in the denominator is modeled as a function of mean signal across samples being compared. This function is obtained as the loess regression fit through the calculated standard deviation data versus mean signal, using all eight samples together as if they were replicates. The approximate \(z\) - scores in every comparison are then assessed for significance by the empirical Bayes method \(^{37}\) . The result is a posterior error probability \(fdr\) assigned to each peak region in every comparison.  
+
+## Finding significantly differentially activated peaks (H3K27ac) within active promoters (H3K4me3) and enhancers (H3K4me1)  
+
+We look for intercept of significantly differentially activated genomic regions (H3K27ac peaks with significance operationally defined as \(fdr < 0.3\) ) with a) peak
+
+<--- Page Split --->
+
+regions defined by the active promoter mark H3K4me3 or b) by the enhancer mark H3K4me1.  
+
+## HiC library preparation and sequencing  
+
+HiC experiments were performed based on the protocol of Arima- HiC Kit (Arima Genomics, Carlsbad, CA). Briefly, chromatin obtained from each cell line or PDX tumor was first cross- linked and digested using a restriction enzyme cocktail. The digested ends were labeled with biotinylated nucleotides and ligated to capture the sequence and structure of the genome. The ligated DNA was purified and fragmented, and the enriched biotinylated fragments were subjected to a custom library preparation protocol using an Arima Library preparation module. Sequencing was performed with the Illumina NovaSeq 6000 by Arima Genomics.  
+
+## Hi-C data processing.  
+
+Hi- C data were processed by the runHiC python package (https://pypi.org/project/runHiC/) with a custom reference genome composed of human genome assembly hg38 augmented by the HPV type 16 genome as an extra chromosome. The runHiC package can remove duplicate reads, assign reads to restriction fragments, filter out invalid interaction pairs, and generate binned interaction matrices in \*.mcool format. We specified the enzyme name as Arima in the filtering step to remove the read pair that maps to the same restriction fragment. We used a bin size as small as 1kbp in the binning step to generate a high- resolution interaction matrix for each chromosome. Given the primary ecDNA structure predicted by AmpliconArchitect (and validated by long reads), we extracted the submatrices from each genomic interval composing the ecDNA and assembled these submatrices into one matrix corresponding to ecDNA according to the order and orientation of the corresponding intervals on the ecDNA.
+
+<--- Page Split --->
+
+Finally, we applied ICE normalization on the resulting matrix and visualized them in Fig. 3.  
+
+## Metaphase chromosome spreads  
+
+Cultured cells were enriched in metaphase by treatment with KaryoMAX Colcemid (Gibco) at \(100\mathrm{ngml^{- 1}}\) overnight. After washing once with PBS, single- cell suspensions were incubated in \(75\mathrm{mM}\) KCl for \(15\mathrm{min}\) at \(37^{\circ}\mathrm{C}\) . Carnoy's fixative (3:1 methanol:glacial acetic acid) was used for cell fixation and cells were spun down. Cells were washed 3 more times with fixative solution and dropped onto humidified glass slides.  
+
+## Fluorescence in situ hybridization (FISH)  
+
+Fixed cells on slides from cell lines or primary cultured cells from PDX were equilibrated in 2xSCC buffer and dehydrated in \(70\%\) , \(85\%\) and \(100\%\) ethyl alcohol for approximately \(2\mathrm{min}\) each. FISH probes for human genome parts of hybrid ecDNA (EYA2 and NDUFC1 FISH probe) were purchased from EMPIRE genomics (Depew, NY), and HPV16 probes were purchased from Arbor Biosciences (Ann Arbor, MI). Diluted FISH probes in hybridization buffer were added to the sample and covered with a coverslip. Slides were denatured at \(72^{\circ}\mathrm{C}\) for \(1\mathrm{min}\) and hybridized overnight at \(37^{\circ}\mathrm{C}\) . The slides were then washed with \(0.4\times \mathrm{SSC}\) , and \(2\times \mathrm{SSC} - 0.1\%\) Tween 20. DAPI was applied to the samples for \(1\mathrm{min}\) . before washing again and mounting with Prolong Gold.  
+
+## Microscopy  
+
+Confocal images were collected on a Nikon confocal plus STORM system using a 100 x 1.49 NA TIRF objective (Moores Cancer Center Shared Resources). For STORM, samples were mounted in STORM buffer (50 mM Tris, pH 8.0, 10 mM NaCl, \(10\%\) glucose, 0.1 M mercaptoethanolamine 56 units/ml glucose oxidase, and 340 units/ml
+
+<--- Page Split --->
+
+catalase) and duplicate images were collected at the same pixel size (0.16 micrometers and 256x256 field of view) in the confocal mode and in the super- resolution mode using an ANDOR IXON3 Ultra DU897 EMCCD camera to enable overlay of the confocal (DAPI) and STORM images. For super- resolution images, TIRF illumination settings were used as appropriate to enhance the signal- to- noise ratio. The images were collected at frame rate (about 15 millisecond exposure time using a 256x256 pixel area of the camera chip) using the sequential illumination setting in the STORM acquisition module in Nikon Elements software (version 4.6). Laser power was adjusted so that 50- 350 localization events were recorded per channel in each 256x256 pixel area frame. Acquisition was stopped once 1 – 3 million localization events were recorded. Analysis of the image stacks was carried out using the STORM analysis module of the Elements software. The STORM images were superimposed on the confocal DAPI images for context.  
+
+## CRISPRi  
+
+We used doxycycline- inducible dCas9- KRAB plasmid (Addgene, 50917) for CRISPRi experiments<sup>38</sup>. Lentiviral transduction of this plasmid to HMS001 was performed using HEK293T cell (ATCC, CRL- 3216) with CRISPR&MISSION Lentiviral Packaging Mix (Sigma- Aldrich). Stable dCas9- expressing cells were confirmed by Western blot for Cas9 (Cell Signaling Technology). Lentiviral transduction was also performed using each target sgRNA. SgRNAs were created to target the enhancers on hybrid ecDNA S1 (#1) and S2 (#2) with non- targeting control (nonT) (GenScript, Piscataway, NJ) (Supplemental Table 2). To confirm each gene expression from transduced cell lines, Western blotting for Cas9 (#14697S, 1:1000, Cell Signaling Technology), MYC (#13987S, 1:1000, Cell Signaling Technology), E6 (#GTX132686, 1:100, GeneTex, Irvine, CA), E7 (#GTX133411, 1:100, GeneTex), and GAPDH (#2118S, 1:6000, Cell
+
+<--- Page Split --->
+
+Signaling Technology) or quantitative PCR for Cas9 and ACTB were used (Supplementary Table 3). Difference in proliferation was measured as the ratio of the relative absorbance 2 days after the start of measurement between doxycycline(+) vs doxycycline(- ).  
+
+## BET inhibitor treatment for cell lines  
+
+The effect of BET inhibitor was investigated on cell line HMS001 and SCC154. Proliferation was investigated in the presence of DMSO (negative control), as well as JQ1 (10nM, 100nM, and \(1\mu \mathrm{M}\) ) (SML1524, Sigma- Aldrich). Cells were seeded in 96- well plates at a density of 8,000 cells/well for HMS001 and 4,000 cells/well for SCC154. Relative absorbance was measured at day 0 and 2 days after the JQ1 or DMSO treatment. To confirm each gene expression after treatment, Western blotting for MYC (#13987S, 1:1000, Cell Signaling Technology), E6 (#GTX132686, 1:100, GeneTex), E7 (#GTX133411, GeneTex), and GAPDH (#2118S, 1:6000, Cell Signaling Technology) or quantitative PCR for MYC, E6/E7, and ACTB were used (Supplemental Table 3). For proliferation experiments, each datapoint is the average of 5 replicates with standard error represented by error bars, and all experiments were repeated at least 3 times demonstrating consistent results.  
+
+## BET inhibitor treatment for PDX  
+
+When the tumor volume of PDX reached about \(150 - 250 \mathrm{mm}^3\) , these mice were randomly divided into 2 groups (each group, \(\mathrm{n} = 6\) ). Mice were treated daily with either JQ1 at \(50\mathrm{mg / kg}\) IP or vehicle control. Tumor samples were harvested after 2 weeks of JQ1 treatment. To confirm each gene expression after treatment, quantitative PCR for E6/E7 and ACTB was performed (Supplemental Table 2)
+
+<--- Page Split --->
+
+## Gene Ontology (GO) analysis  
+
+Gene annotation enrichment analysis was conducted using the Functional Annotation tool of Metascape (http://metascape.org/gp/ index.html#/main/step1)39  
+
+## Statistical analysis  
+
+Comparison of the results of qPCR, proliferation assay, PDX tumor volume, and PDX tumor weight in each group was analyzed by Student's \(t\) - test using GraphPad Prism version 10.1.  
+
+## Data access  
+
+WGS, long read DNA- seq, RNA- seq, ChIP- seq, ATAC- seq, HiC- seq of each cell line and PDX tumor data were submitted to the GEO database under the accession number OO (We are in the process of registration, and will update once we got the numbers).
+
+<--- Page Split --->
+
+## 591 Reference list  
+
+592 1. Turner, K.M. et al. Extrachromosomal oncogene amplification drives tumour evolution and genetic heterogeneity. Nature 543, 122- 125 (2017). 595 2. Kim, H. et al. Extrachromosomal DNA is associated with oncogene amplification and poor outcome across multiple cancers. Nat Genet 57, 891- 897 (2020). 598 3. Yi, E., Chamorro Gonzalez, R., Henssen, A.G. & Verhaak, R.G.W. Extrachromosomal DNA amplifications in cancer. Nat Rev Genet 23, 760- 771 (2022). 601 4. Wu, S. et al. Circular ecDNA promotes accessible chromatin and high oncogene expression. Nature 575, 699- 703 (2019). 603 5. Zhu, Y. et al. Oncogenic extrachromosomal DNA functions as mobile enhancers to globally amplify chromosomal transcription. Cancer Cell 39, 694- 707 e7 (2021). 606 6. Hung, K.L. et al. ecDNA hubs drive cooperative intermolecular oncogene expression. Nature 600, 731- 736 (2021). 607 7. Weiser, N.E., Hung, K.L. & Chang, H.Y. Oncogene Convergence in Extrachromosomal DNA Hubs. Cancer Discov 12, 1195- 1198 (2022). 610 8. Stransky, N. et al. The mutational landscape of head and neck squamous cell carcinoma. Science 333, 1157- 60 (2011). 612 9. Chung, C.H. & Gillison, M.L. Human papillomavirus in head and neck cancer: its role in pathogenesis and clinical implications. Clin Cancer Res 15, 6758- 62 (2009). 615 10. Lechner, M., Jones, O.S., Breeze, C.E. & Gilson, R. Gender- neutral HPV vaccination in the UK, rising male oropharyngeal cancer rates, and lack of HPV awareness. Lancet Infect Dis 19, 131- 132 (2019). 618 11. Lechner, M., Liu, J., Masterson, L. & Fenton, T.R. HPV- associated oropharyngeal cancer: epidemiology, molecular biology and clinical management. Nat Rev Clin Oncol 19, 306- 327 (2022). 621 12. Pang, J. et al. Extrachromosomal DNA in HPV- Mediated Oropharyngeal Cancer Drives Diverse Oncogene Transcription. Clin Cancer Res 27, 6772- 6786 (2021). 624 13. Pett, M. & Coleman, N. Integration of high- risk human papillomavirus: a key event in cervical carcinogenesis? J Pathol 212, 356- 67 (2007). 627 14. Reuschenbach, M. et al. Methylation status of HPV16 E2- binding sites classifies subtypes of HPV- associated oropharyngeal cancers. Cancer 121, 1966- 76 (2015).
+
+<--- Page Split --->
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+630 15. Shoshani, O. et al. Chromothripsis drives the evolution of gene 631 amplification in cancer. Nature 591, 137- 141 (2021). 632 16. Kohl, N.E. et al. Transposition and amplification of oncogene- related 633 sequences in human neuroblastomas. Cell 35, 359- 67 (1983). 634 17. Duensing, S. & Munger, K. The human papillomavirus type 16 E6 635 and E7 oncoproteins independently induce numerical and structural 636 chromosome instability. Cancer Res 62, 7075- 82 (2002). 637 18. Kadaja, M. et al. Genomic instability of the host cell induced by the 638 human papillomavirus replication machinery. EMBO J 26, 2180- 91 639 (2007). 640 19. Akagi, K. et al. Genome- wide analysis of HPV integration in human 641 cancers reveals recurrent, focal genomic instability. Genome Res 24, 185- 99 (2014). 642 20. Akagi, K. et al. Intratumoral Heterogeneity and Clonal Evolution 644 Induced by HPV Integration. Cancer Discov 13, 910- 927 (2023). 645 21. Hu, Z. et al. Genome- wide profiling of HPV integration in cervical 646 cancer identifies clustered genomic hot spots and a potential 647 microhomology- mediated integration mechanism. Nat Genet 47, 158- 648 63 (2015). 649 22. Kamal, M. et al. Human papilloma virus (HPV) integration signature 650 in Cervical Cancer: identification of MACROD2 gene as HPV hot spot 651 integration site. Br J Cancer 124, 777- 785 (2021). 652 23. Karimzadeh, M. et al. Human papillomavirus integration transforms 653 chromatin to drive oncogenesis. Genome Biol 24, 142 (2023). 654 24. Mima, M. et al. Tumorigenic activation around HPV integrated sites 655 in head and neck squamous cell carcinoma. Int J Cancer 152, 1847- 656 1862 (2023). 657 25. Cao, C. et al. HPV- CCDC106 integration alters local chromosome 658 architecture and hijacks an enhancer by three- dimensional genome 659 structure remodeling in cervical cancer. J Genet Genomics 47, 437- 660 450 (2020). 661 26. Yan, X., Mischel, P. & Chang, H. Extrachromosomal DNA in cancer. 662 Nat Rev Cancer 24, 261- 273 (2024). 663 27. Kelley, D.Z. et al. Integrated Analysis of Whole- Genome ChIP- Seq 664 and RNA- Seq Data of Primary Head and Neck Tumor Samples 665 Associates HPV Integration Sites with Open Chromatin Marks. 666 Cancer Res 77, 6538- 6550 (2017). 667 28. Ando, M. et al. Chromatin dysregulation and DNA methylation at 668 transcription start sites associated with transcriptional repression in 669 cancers. Nat Commun 10, 2188 (2019).
+
+<--- Page Split --->
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+670 29. Iglesias-Bartolome, R. et al. mTOR inhibition prevents epithelial stem cell senescence and protects from radiation-induced mucositis. Cell Stem Cell 11, 401- 14 (2012). 673 30. Deshpande, V. et al. Exploring the landscape of focal amplifications in cancer using AmpliconArchitect. Nat Commun 10, 392 (2019). 675 31. Luebeck, J. et al. Extrachromosomal DNA in the cancerous transformation of Barrett's oesophagus. Nature 616, 798- 805 (2023). 677 32. Buenrostro, J.D., Wu, B., Chang, H.Y. & Greenleaf, W.J. ATAC- seq: A Method for Assaying Chromatin Accessibility Genome- Wide. Curr Protoc Mol Biol 109, 21 29 1- 21 29 9 (2015). 680 33. Chen, S., Zhou, Y., Chen, Y. & Gu, J. fastp: an ultra- fast all- in- one FASTQ preprocessor. Bioinformatics 34, 1884- i890 (2018). 681 34. Dobin, A. et al. STAR: ultrafast universal RNA- seq aligner. Bioinformatics 29, 15- 21 (2013). 683 35. Heinz, S. et al. Simple combinations of lineage- determining transcription factors prime cis- regulatory elements required for macrophage and B cell identities. Mol Cell 38, 576- 89 (2010). 686 36. Anders, S. & Huber, W. Differential expression analysis for sequence count data. Genome Biol 11, R106 (2010). 689 37. Efron, B. Microarrays, empirical Bayes and the two- groups model. Statistical Science 23, 1- 22 (2008). 691 38. Thakore, P.I. et al. Highly specific epigenome editing by CRISPR- Cas9 repressors for silencing of distal regulatory elements. Nat Methods 12, 1143- 9 (2015). 693 39. Tripathi, S. et al. Meta- and Orthogonal Integration of Influenza "OMICs" Data Defines a Role for UBR4 in Virus Budding. Cell Host Microbe 18, 723- 35 (2015).
+
+<--- Page Split --->
+
+710 **Figure legends** 
+
+711 
+
+712 **Fig. 1: Identification of hybrid ecDNA in HPVOPC using AA and FastViFI and validation of hybrid ecDNA by multi-FISH.** 
+
+713 Examples of circular hybrid ecDNA suggested by AA results in HMS001 (A and C) and PDX tumor PDX_A (B and D). Multi-FISH using each ecDNA specific probe (green) and HPV specific probe (red) for metaphase spread cells showed overlapping of each probe signal in the same place only in hybrid ecDNA(+) samples (yellow allow in E and F). Hybrid ecDNA(+) samples also showed the red HPV signals alone (white arrowhead in E and F), suggesting HPV-only epitomes. Hybrid ecDNA(-) cell line SCC154 only showed the red HPV signal (white arrowhead in G). Control cell line NOKSI did not show any signal (H). Scale bar shows 10μm. 
+
+724 **Fig. 2: Detecting active enhancer using ChIP-seq, and new identification of HPV integration mechanism in hybrid ecDNA.** 725 ChIP-seq results of Input, K4me3 (promoter), K4me1 (enhancer), and K27ac (activation mark) for NOKSI (normal control), SCC154 (hybrid ecDNA- HPVOPC) and HMS001 (hybrid ecDNA+ HPVOPC) were shown. Promoter is indicated by the light green box, enhancers by the yellow box, and the sequences included in hybrid ecDNA of HMS001 by the light blue box. The active enhancer is marked by the red oval (top) (A). Expanded hybrid ecDNA sequence tracks are shown (bottom), and include an expanded enhancer map showing HPV integration occurred in the exact center of the active enhancer mark (bottom) (A). The HPV integration site and its ChIP-seq results are also shown. On the other hand, active enhancers already existing originally were not included in hybrid ecDNA (bottom) (A). (B)Hybrid ecDNA in HMS001 with ATAC-seq (top) and ChIP-seq (bottom) are shown in the CycleViz plot. A related figure using NOKSI (normal control), PDX_C (hybrid ecDNA- HPVOPC), and PDX_A (hybrid ecDNA+ HPVOPC) is shown (C and D). The newly created active enhancer made a complex with 2 promoters (bottom) (C). 
+
+741 **Fig. 3: Human and viral genomes on hybrid ecDNA interacted directly with each other.** 742 Cis-interactions between enhancer and HPV in hybrid ecDNA were analyzed by HiC-seq. Human genome regions on hybrid ecDNA were divided into 2 segments (S1 and S2). The S1 enhancer region closely interacted with the HPV L1 region, and the S2 enhancer region closely interacted with the HPV E6/E7 regions in HMS001 (black arrow in A and B). On the other hand, there was no such interaction in SCC154 that lacked hybrid ecDNA (C). This phenomenon was confirmed in PDX tumors (D-F). In PDX_A, the enhancer existed only in the S1 segment, and the S1 enhancer region closely interacted with the HPV L1 and E6/E7 regions in PDX_A (black and yellow arrows in D and E). On the
+
+<--- Page Split --->
+
+other hand, there was no such interaction in PDX_C that lacked hybrid ecDNA (F). Although each hybrid ecDNA structure was unique, each of the novel human enhancer regions closely interacted with HPV, confirming the direct interaction of the human and viral genomes on the hybrid ecDNAs. 
+
+## Fig. 4: CRISPR interference targeting enhancers on hybrid ecDNA blocks HPV oncogene expression. 
+
+CRISPR interference, using dCas9-KRAB to target enhancers on the hybrid ecDNA of HMS001, was performed. gRNAs targeting S1: the long part (gRNA#1) and S2: the short part (gRNA#2) of the enhancer on hybrid ecDNA of HMS001 and nontarget controls were used (A). The expression of dCas9 after doxycycline induction was confirmed by qPCR and Western blotting (B and C). MYC and GAPDH were used as controls (C). Western blotting results of E6 and E7 of each CRISPRi condition indicate E6 and E7 expression were reduced by CRISPRi targeting the S2 enhancer (D). Proliferation assay results targeting the S2 enhancer and nonT in HMS001, SCC154, and NOKSI indicate Dox induction inhibited proliferation only when targeting S2 in HMS001 (**P=0.006) (E), but not when targeting SCC154 or NOKSI (F and G). 
+
+## Fig. 5: Hybrid ecDNA and HPV epitomes are associated and reduced after JQ1 treatment. 
+
+Multi-probe FISH, using an EYA2 probe and an HPV probe on hybrid ecDNA on super resolution DMSO and JQ1 treatment of HMS001 cells, is shown (A and B). Hybrid ecDNA was observed nearby in the nucleus along with episomal HPV in the “no treatment” condition (A). Each signal (green: EYA2, red: HPV, and blue: DAPI) were also shown separately (bottom). (A). FISH signals of hybrid ecDNA and episomal HPV were reduced after JQ1 treatment (B). Scale bar shows 5μm (0.2μm in expanded picture). Cartoon illustrating how hybrid ecDNA hub disruption may decrease transcription is illustrated (right in A and B). 
+
+## Fig. 6: JQ1 treatment on hybrid ecDNA significantly blocks HPV oncogene expression and proliferation. 
+
+JQ1 treatment on HMS001 and SCC154 were performed. The schema of the experiments were shown (A). qPCR results of MYC and E6/E7 were shown. ACTB was used internal control. MYC and E6/E7 expressions were reduced in a concentration-dependent manner in qPCR (**P = 4 x10⁻³, ***P = 4 x10⁻⁴, respectively) at 24h (B). MYC and E6/E7 expressions were reduced in a concentration-dependent manner in western blotting at 6h and 24 h after JQ1 treatment (C). Proliferation assay using JQ1 for HMS001 and SCC154 were shown. JQ1 treatment significantly inhibited tumor growth only in HMS001 in 1uM, but not in SCC154 (**P = 0.03, P = 0.12, respectively) (D).
+
+<--- Page Split --->
+
+789
+790 Fig. 7: JQ1 inhibited proliferation only in hybrid ecDNA(+) HPVOPC PDX tumors.
+791 JQ1 treatment was performed in HPVOPC PDX models. Six PDX_A (hybrid ecDNA+)
+792 mice were divided into a vehicle control group and a JQ1 treatment group. Each mouse
+793 possessed tumors in both flanks (A). Tumors were harvested after 2 weeks of vehicle
+794 control or JQ1 treatment (B). Tumors were harvested after 2 weeks of JQ1 treatment or
+795 vehicle control. Tumor volumes of each condition are shown (C-E). In the PDX_A JQ1
+796 treatment group, tumor growth was significantly inhibited compared to the vehicle
+797 control group (tumor volume: \(P = 2 \times 10^{-5}\), tumor weight: \(P = 1 \times 10^{-4}\) respectively) (C and
+798 F).qPCR of E6/E7 is shown between JQ1 treatment and vehicle control. E6/E7 expression
+799 was also reduced after JQ1 treatment ( \(P < 1 \times 10^{-4}\) ) (G). JQ1 treatment for hybrid ecDNA-
+800 HPVOPC PDX (PDX_C and PDX004) was also performed (H-M). Neither tumor volume
+801 nor tumor weight were inhibited significantly compared to the vehicle control group in
+802 PDX_C (H-J) and PDX004 (K-M).
+803
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+## Supplementary Files  
+
+This is a list of supplementary files associated with this preprint. Click to download.  
+
+- HybridecDNASupplementaryFigTN062524finalsubmit.pdf- SupplementaryTable1.pdf- SupplementaryTable2.pdf- SupplementaryTable3.pdf
+
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+  {
+    "type": "image",
+    "img_path": "images/Figure_1.jpg",
+    "caption": "Figure 1. The time-resolved phosphoproteome from a single-cell to a 16-cell embryo and its cell cycle assignment. (a) Schematic representation of the workflow. Single Xenopus eggs and embryos were collected followed by cell lysis, protein digestion, phosphopeptide enrichment and high-resolution proteomics analysis. (b) STRING network of functionally associated proteins undergoing dynamic phosphorylation (each node represents a protein). Vicinity clustering reveals three main groups (yellow, blue and orange) with a high degree of association. Radar plots show the corresponding GO terms (adjusted p value \\(< 0.05\\) ) for each group (axes show -Log₁₀(adj p value) for each GO term). (c) Hierarchical clustering of",
+    "footnote": [],
+    "bbox": [
+      [
+        117,
+        131,
+        880,
+        699
+      ]
+    ],
+    "page_idx": 16
+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_2.jpg",
+    "caption": "Figure 2. Switch-like mitotic phosphorylation in vivo. (a) Schematic representation of the workflow. Samples were collected over two cell divisions and enriched phosphopeptides were subjected to targeted proteomics analysis. (b) Heat map shows a highly synchronous wave of phosphorylation preceding each of the two cell divisions. Dashed lines depict times when cell divisions were recorded. (c) Single phosphosite plots from selected proteins. Each dot represents a biological replicate \\((n = 3)\\) . Dashed lines depict times when cell divisions were recorded. (d) Single phosphosite plot of CDK1 inhibitory phosphorylation (Y15).",
+    "footnote": [],
+    "bbox": [
+      [
+        113,
+        81,
+        880,
+        570
+      ]
+    ],
+    "page_idx": 18
+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_3.jpg",
+    "caption": "Fig. 3. The cell cycle phosphoproteome is characterised by intrinsic disorder and MLO components. (a) Scheme illustrating hypothetical enrichment of phosphorylation in disordered regions when taking into account amino acid compositional bias. (b) Scatter plot of expected vs observed phosphorylated Ser/Thr for each protein of human and Xenopus phosphoprotein datasets. FDR thresholds of \\(5\\%\\) and \\(1\\%\\) are marked in yellow and red respectively. Circles: proteins with at least one dynamic phosphorylation in Xenopus, or human CDK1 subfamily substrates, respectively. (c) Boxplots showing expected vs observed phosphorylated Ser/Thr among all phosphoproteins detected (left), phosphoproteins with at least one dynamic phosphosite (middle), and dynamic phosphoproteins also detected as CDK1 subfamily targets in humans (right). Distributions were compared with the Wilcoxon signed-rank test. \\(*p<0.05\\) ,",
+    "footnote": [],
+    "bbox": [
+      [
+        115,
+        80,
+        881,
+        666
+      ]
+    ],
+    "page_idx": 19
+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_4.jpg",
+    "caption": "Fig. 4. CDK-mediated phosphorylation regulates phase separation of a model IDP. (a) Top, scheme of the human Ki-67 protein (FHA, forkhead-associated domain; PP1, PP1 phosphatase-binding domain; CD, conserved domain; LR, leucine arginine-rich domain).",
+    "footnote": [],
+    "bbox": [
+      [
+        118,
+        82,
+        825,
+        831
+      ]
+    ],
+    "page_idx": 21
+  }
+]

preprint/preprint__1e09d1f0c744080040ce271de3a8b26e159b4dd20eaf5d470baf8cba1c52d41e/preprint__1e09d1f0c744080040ce271de3a8b26e159b4dd20eaf5d470baf8cba1c52d41e_det.mmd

@@ -0,0 +1,341 @@
+<|ref|>title<|/ref|><|det|>[[44, 107, 796, 175]]<|/det|>
+# A CDK-mediated phosphorylation switch of disordered protein condensation  
+
+<|ref|>text<|/ref|><|det|>[[44, 195, 567, 235]]<|/det|>
+Maarten Alteaar (m.alteaar@uu.nl) Utrecht University https://orcid.org/0000- 0001- 5093- 5945  
+
+<|ref|>text<|/ref|><|det|>[[44, 241, 210, 280]]<|/det|>
+Juan Valverde Utrecht University  
+
+<|ref|>text<|/ref|><|det|>[[44, 288, 189, 326]]<|/det|>
+Geronimo Dubra CNRS  
+
+<|ref|>text<|/ref|><|det|>[[44, 334, 567, 374]]<|/det|>
+Henk W.P. Van den Toom Utrecht University https://orcid.org/0000- 0002- 0270- 5763  
+
+<|ref|>text<|/ref|><|det|>[[44, 380, 461, 420]]<|/det|>
+Guido van Mierlo EPFL https://orcid.org/0000- 0001- 5883- 0339  
+
+<|ref|>text<|/ref|><|det|>[[44, 427, 672, 467]]<|/det|>
+Michiel Vermeulen Radboud University Nijmegen https://orcid.org/0000- 0003- 0836- 6894  
+
+<|ref|>text<|/ref|><|det|>[[44, 473, 567, 513]]<|/det|>
+Albert Heck Utrecht University https://orcid.org/0000- 0002- 2405- 4404  
+
+<|ref|>text<|/ref|><|det|>[[44, 519, 315, 559]]<|/det|>
+Carlos Elena- Real CBS, University of Montpellier  
+
+<|ref|>text<|/ref|><|det|>[[44, 566, 315, 605]]<|/det|>
+Aurélie Fournet CBS, University of Montpellier  
+
+<|ref|>text<|/ref|><|det|>[[44, 611, 311, 651]]<|/det|>
+Emile Al Ghoul IGH, University of Montpellier  
+
+<|ref|>text<|/ref|><|det|>[[44, 658, 330, 697]]<|/det|>
+Dhanvantri Chahar IGMM, University of Montpellier  
+
+<|ref|>text<|/ref|><|det|>[[44, 704, 234, 744]]<|/det|>
+Austin Haider University of Denver  
+
+<|ref|>text<|/ref|><|det|>[[44, 751, 670, 791]]<|/det|>
+Matteo Paloni CBS, University of Montpellier https://orcid.org/0000- 0003- 4841- 9321  
+
+<|ref|>text<|/ref|><|det|>[[44, 797, 933, 838]]<|/det|>
+Angelos Constantinou Institute of Human Genetics, UMR9002 CNRS- UM, 141 rue de la Cardonille, 34396 Montpellier, France.  
+
+<|ref|>text<|/ref|><|det|>[[44, 844, 330, 883]]<|/det|>
+Alessandro Barducci Centre de Biochimie Structurale  
+
+<|ref|>text<|/ref|><|det|>[[44, 890, 234, 929]]<|/det|>
+Kingshuk Ghosh University of Denver  
+
+<|ref|>text<|/ref|><|det|>[[44, 936, 177, 954]]<|/det|>
+Nathalie Sibille
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[56, 46, 315, 65]]<|/det|>
+CBS, University of Montpellier  
+
+<|ref|>text<|/ref|><|det|>[[44, 70, 160, 108]]<|/det|>
+Pau Bernadó  CBS  
+
+<|ref|>text<|/ref|><|det|>[[44, 116, 400, 157]]<|/det|>
+Puck Knipscheer  https://orcid.org/0000- 0003- 4198- 0132  
+
+<|ref|>text<|/ref|><|det|>[[44, 163, 466, 204]]<|/det|>
+Liliana Krasinska  CNRS  https://orcid.org/0000- 0002- 6858- 0852  
+
+<|ref|>text<|/ref|><|det|>[[44, 209, 814, 250]]<|/det|>
+Daniel Fisher  French National Centre for Scientific Research  https://orcid.org/0000- 0002- 0822- 3482  
+
+<|ref|>text<|/ref|><|det|>[[44, 290, 285, 311]]<|/det|>
+Biological Sciences - Article  
+
+<|ref|>text<|/ref|><|det|>[[44, 328, 137, 348]]<|/det|>
+Keywords:  
+
+<|ref|>text<|/ref|><|det|>[[44, 366, 336, 386]]<|/det|>
+Posted Date: February 24th, 2022  
+
+<|ref|>text<|/ref|><|det|>[[44, 404, 474, 424]]<|/det|>
+DOI: https://doi.org/10.21203/rs.3.rs- 1370895/v1  
+
+<|ref|>text<|/ref|><|det|>[[44, 441, 910, 485]]<|/det|>
+License: © This work is licensed under a Creative Commons Attribution 4.0 International License.  Read Full License
+
+<--- Page Split --->
+<|ref|>title<|/ref|><|det|>[[137, 85, 860, 147]]<|/det|>
+# A CDK-mediated phosphorylation switch of disordered protein condensation  
+
+<|ref|>text<|/ref|><|det|>[[115, 163, 883, 280]]<|/det|>
+Authors: Juan Manuel Valverde \(^{1,2\dagger}\) , Geronimo Dubra \(^{3,4\dagger}\) , Henk van den Toorn \(^{1,2}\) , Guido van Mierlo \(^{5}\) , Michiel Vermeulen \(^{5}\) , Albert J.R. Heck \(^{1,2}\) , Carlos Elena- Real \(^{6}\) , Aurélie Fournet \(^{6}\) , Emile Al Ghoul \(^{7}\) , Dhanvantri Chahar \(^{3,4}\) , Austin Haider \(^{8}\) , Matteo Paloni \(^{6}\) , Angelos Constantinou \(^{7}\) , Alessandro Barducci \(^{6}\) , Kingshuk Ghosh \(^{8}\) , Nathalie Sibille \(^{6}\) , Pau Bernado \(^{6}\) , Puck Knipscheer \(^{9}\) , Liliana Krasinska \(^{3,4\ddagger}\) , Daniel Fisher \(^{3,4\ddagger \ast}\) , Maarten Alteaar \(^{1,2\ddagger \ast}\)  
+
+<|ref|>title<|/ref|><|det|>[[75, 310, 223, 326]]<|/det|>
+# 8 Affiliations:  
+
+<|ref|>text<|/ref|><|det|>[[111, 339, 884, 785]]<|/det|>
+\(^{1}\) Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, Utrecht, 3584 CH Utrecht, Netherlands. \(^{2}\) Netherlands Proteomics Center, Padualaan 8, 3584 CH Utrecht, Netherlands. \(^{3}\) IGMM, University of Montpellier, CNRS, Inserm, Montpellier, France. \(^{4}\) Equipe Labellisée LIGUE 2018, Ligue Nationale Contre le Cancer, Paris, France. \(^{5}\) Department of Molecular Biology, Faculty of Science, Radboud Institute for Molecular Life Sciences, Oncode Institute, Radboud University Nijmegen, 6525 GA Nijmegen, the Netherlands. \(^{6}\) CBS, University of Montpellier, INSERM, CNRS, Montpellier, France. \(^{7}\) IGH, University of Montpellier, CNRS, Montpellier, France. \(^{8}\) Department of Physics and Astronomy, and Department of Molecular and Cellular Biophysics, University of Denver, Denver, Colorado 80208, USA. \(^{9}\) Oncode Institute, Hubrecht Institute- KNAW and University Medical Center, Utrecht, 3584 CT, Netherlands. \(^{*}\) Correspondence to: m.alteaar@uu.nl and daniel.fisher@igmm.cnrs.fr \(^{\dagger \ddagger}\) Equal contributions
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[113, 78, 883, 720]]<|/det|>
+Cell cycle transitions arise from collective changes in protein phosphorylation states triggered by cyclin- dependent kinases (CDKs), but conceptual and mechanistic explanations for the abrupt cellular reorganisation that occurs upon mitotic entry are lacking. Specific interactions between distinct CDK- cyclin complexes and sequence motifs encoded in substrates might result in highly ordered phosphorylation<sup>1</sup>, while bistability in the mitotic CDK1 control network can trigger switch- like phosphorylation<sup>2</sup>. Yet the dynamics of mitotic phosphorylation has not been demonstrated in vivo, and the roles of most cell cycle- regulated phosphorylations are unclear. Here, we show evidence that switch- like phosphorylation of intrinsically disordered proteins (IDPs) by CDKs contributes to mitotic cellular reorganisation by controlling protein- protein interactions and phase separation. We studied protein phosphorylation in single Xenopus embryos throughout synchronous cell cycles, performed parallel assignment of cell cycle phases using egg extracts, and analysed dynamics of mitotic phosphorylation using quantitative targeted phosphoproteomics. This provided a high- resolution map of dynamic phosphosites from the egg to the 16- cell embryo and showed that mitotic phosphorylation occurs on entire protein complexes involved in diverse subcellular processes and is switch- like in vivo. Most cell cycle- regulated phosphosites occurred in CDK consensus motifs and located to intrinsically disordered regions. We found that substrates of CDKs and other cell cycle kinases are significantly more disordered than phosphoproteins in general, a principle conserved from yeast to humans, while around half are components of membraneless organelles (MLOs), whose assembly is thought to involve phase separation. Analytical modelling predicts modulation of homotypic IDP interactions by CDK- mediated phosphorylation, which was confirmed by biophysical and biochemical analysis of a model IDP, Ki- 67. These results highlight the dynamic control of intrinsic disorder as a conserved hallmark of the cell cycle and suggest a mechanism for CDK- mediated mitotic cellular reorganisation.  
+
+<|ref|>sub_title<|/ref|><|det|>[[118, 731, 168, 747]]<|/det|>
+## Main  
+
+<|ref|>text<|/ref|><|det|>[[115, 760, 883, 903]]<|/det|>
+To explain behaviour of complex systems, such as the cell cycle, two general strategies have been used<sup>3</sup>: top- down identification of system components, such as screens which have identified hundreds of CDK substrates<sup>4- 9</sup> and cell cycle- regulated proteins<sup>10</sup>, and bottom- up molecular analysis of the structural effects of individual phosphorylations on single proteins<sup>11</sup>. Yet it has proven challenging to use studies performed at such different scales to reconcile different models of CDK- mediated phosphorylation. We reasoned that understanding how
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[115, 82, 882, 275]]<|/det|>
+thousands of mitotic phosphorylations<sup>12</sup> bring about an ordered cell cycle transition would require a multidisciplinary quantitative approach involving cell biology, biochemistry, bioinformatics and biophysics. A sine qua non is a time- resolved map of in vivo cell cycle phosphorylation in a system devoid of artifacts arising from cell synchronisation<sup>13,14</sup>, and with temporal resolution that alternative approaches, like centrifugal elutriation<sup>15</sup> or FACS<sup>16</sup> cannot provide. Dynamic phosphorylation states cannot be determined from cell populations<sup>17</sup>, while single- cell proteomics studies<sup>18,19</sup> currently have insufficient sensitivity and reproducibility for low stoichiometry and dynamic targets.  
+
+<|ref|>sub_title<|/ref|><|det|>[[118, 288, 625, 307]]<|/det|>
+## A high-resolution map of in vivo cell cycle phosphorylation  
+
+<|ref|>text<|/ref|><|det|>[[115, 319, 882, 536]]<|/det|>
+We took advantage of the naturally synchronous early cell cycles of Xenopus laevis embryos<sup>20,21</sup> to perform quantitative phosphoproteomics in vivo, using a sensitive phosphopeptide enrichment strategy<sup>22</sup>. We collected single embryos at 15- minute intervals while recording visual cues of cell divisions. Phosphopeptides from each embryo were purified, separated by nano- LC and analysed by mass spectrometry (Fig. 1a). We identified 4583 high- confidence phosphosites mapping to 1843 proteins (Extended data Fig. 1a; Data S1), most being phosphoseries (Extended data Fig. 1b). Individual embryo phosphorylation states strongly correlated (Extended data Fig. 1c). We thus generated a dynamic map of protein phosphorylation from an unfertilised egg to a 16- cell embryo.  
+
+<|ref|>text<|/ref|><|det|>[[115, 548, 882, 910]]<|/det|>
+We focused on 1032 sites whose variation in phosphorylation over time was statistically significant (hereafter denoted “dynamic phosphosites”) which occurred on 646 proteins. Gene ontology (GO) and network analysis revealed high functional association and interconnectivity between groups of proteins involved in RNA binding and the nuclear pore complex (NPC), DNA replication and chromatin remodeling, and microtubule regulation (Fig. 1b). Hierarchical clustering uncovered four distinct groups that reflect cell cycle- regulated behaviour (Fig. 1c; Data S1). The levels of clusters A and B phosphosites were highest in eggs and post- fertilisation, and decreased during the first round of DNA replication, suggesting that dephosphorylation of these sites may prepare the zygote for upcoming cell divisions<sup>23</sup>. GO analysis for group A highlighted proteins involved in RNA regulation and nuclear organisation, including the NPC and nuclear transport, chromosomal structure and segregation (Extended data Fig. 1d), as also observed in a recent study on meiosis exit<sup>24</sup>. Cluster B phosphosites were enriched in regulators of RNA biosynthesis and stability, translation, actin, DNA replication and repair (Extended data Fig. 1d). Cluster C phosphosites progressively increased after meiotic exit, while cluster D phosphosites had a clear oscillating signature with upregulation
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[115, 83, 882, 374]]<|/det|>
+preceding each cell division. GO analysis of cluster C shows dominance of interphase cell cycle processes including DNA replication, RNA- related processes and chromosome organisation (Extended data Fig. 1d), and included phosphosites displaying a reciprocal oscillating trend and a lower amplitude compared to cluster D sites. Several such sites, e.g. S31 of the replication licensing protein MCM4, were from monophosphorylated peptides, while the multiphosphorylated forms were found in cluster D (Extended data Fig. 1e). Thus, cluster C contains the earliest phosphorylations of proteins that are highly phosphorylated at mitosis. Cluster D shows coordinated phosphorylation of multiple members of protein complexes involved in diverse processes, suggesting a common mechanism of regulation (Extended data Fig. 1f). Importantly, phosphoproteome changes were not simply a reflection of changes in abundance of the corresponding proteins (Extended data Fig. 2), which are generally negligible during Xenopus early development<sup>25</sup>.  
+
+<|ref|>text<|/ref|><|det|>[[115, 385, 882, 700]]<|/det|>
+We assigned in vivo embryo phosphosites to different cell cycle stages by comparing with phosphorylation patterns of replicating or mitotic egg extracts (Fig. 1d). Replication was initiated by adding purified sperm chromatin to interphase egg extracts and quantified over time (Fig. 1e, top), while mitosis was triggered by adding recombinant cyclin B and verified microscopically. We also used egg extracts arrested at meiotic metaphase II (Cytostatic Factor, CSF- arrested). Overall, we identified 6937 phosphosites, which included 71% of the sites identified in vivo (Fig. 1f, Data S1). 1728 sites varied between S and M- phase, including 693 sites upregulated in S- phase and 1035 in mitosis (Fig. 1e, Data S1). GO analysis of interphase and mitotic sites revealed processes enriched in in vivo cluster C and cluster D, respectively (Extended data Fig. 3a). Several DNA- replication factors, including MCM4 and RIF1, showed multi- site phosphorylation specifically in S- phase (Extended data Fig. 3b). This phosphoproteomics dataset greatly increases the known repertoire of phosphorylation sites upregulated during S- phase<sup>12</sup>.  
+
+<|ref|>text<|/ref|><|det|>[[115, 712, 882, 903]]<|/det|>
+We next analysed the cell cycle behaviour of dynamic phosphosites that we found in vivo (Extended data Fig. 3c). Most embryo cluster A sites were upregulated in both CSF- arrested meiotic extracts and mitotic extracts, highlighting the global similarities of regulation of meiotic and mitotic M- phase, despite the additional activity of the Mos/MEK/MAP kinase pathway in meiosis. Around half of embryo cluster B sites were present only in interphase, while the rest showed a minimum phosphorylation in late S- phase, confirming their dephosphorylation during the first round of DNA replication. As expected, most sites from embryo clusters C and D were part of the in vitro S- phase and mitotic groups, respectively.
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[115, 83, 881, 151]]<|/det|>
+Therefore, single embryo data can successfully identify cell cycle-dependent phosphorylation. In mitosis, as expected, monophosphorylated species are reduced because multisite phosphorylation emerges (Extended data Fig. 3d; Extended data Fig. 1e).  
+
+<|ref|>sub_title<|/ref|><|det|>[[118, 165, 382, 183]]<|/det|>
+## Predominance of CDK targets  
+
+<|ref|>text<|/ref|><|det|>[[115, 195, 883, 486]]<|/det|>
+Analysis of kinase consensus motifs showed that proline- directed (S/T- P) sites, which conform to the minimal consensus for CDKs, comprise \(51\%\) of all detected phosphosites in vivo and \(60\%\) of dynamic sites (Extended data Fig. 4a). Around \(10\%\) of all phosphosites matched the full CDK1- family consensus site: S/TPxK/R. Replicating and mitotic extracts displayed a similar trend (Extended data Fig. 4a). Putative CDK targets dominated all clusters, with \(80\%\) of sites in cluster D in vivo and mitotic clusters in vitro conforming to the minimal CDK motif (Fig.1g, Extended data Fig. 4b, c). Consensus sites of other kinases such as Aurora, Polo- like kinase (PLK), DBF4- dependent kinase (DDK) and Casein kinase I and II were present to a lesser extent (Extended data Fig. 4b, d). In meiotic M- phase, MAP kinases, which have the same consensus motif as CDKs, are likely responsible for sites specific to embryo cluster A or CSF extracts, but these kinases are inactivated during early embryonic cell cycles<sup>26</sup>, suggesting that most of the other dynamic proline- directed phosphorylations are due to CDKs.  
+
+<|ref|>text<|/ref|><|det|>[[115, 498, 883, 764]]<|/det|>
+Although few direct CDK substrates have been characterised in Xenopus, they are likely conserved between vertebrates. We therefore manually curated a set of 654 human CDK1- subfamily targets (Data S2; see Supplementary Methods for sources). 303 of these have Xenopus homologues among the 1843 phosphoproteins we detected, and 149 were present among the 646 proteins with dynamic phosphosites in Xenopus embryos (Fig. 1h). Thus, the predominance of CDK motifs among dynamic phosphosites reflects a high proportion of bona fide CDK substrates. This is a conservative estimate, since we only considered proline- directed sites as CDK motifs, although we found that \(10 - 20\%\) of human and yeast CDK substrates (Data S2; see Supplementary Methods for sources) were non- proline- directed (Extended data Fig. 4e), confirming a recent finding<sup>33</sup>. These data reinforce the dominant role of CDKs in cell cycle- regulated phosphorylation.  
+
+<|ref|>sub_title<|/ref|><|det|>[[118, 777, 508, 795]]<|/det|>
+## Mitotic phosphorylation is switch-like in vivo  
+
+<|ref|>text<|/ref|><|det|>[[115, 809, 881, 900]]<|/det|>
+We next determined whether mitotic phosphorylation of individual phosphosites is progressive or switch- like in vivo. We analysed dynamics of 64 cluster D sites from diverse protein complexes in single embryos every 180- seconds using quantitative targeted phosphoproteomics<sup>27- 29</sup> by parallel reaction monitoring<sup>30</sup>, thereby obtaining a quantitative
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[115, 83, 883, 423]]<|/det|>
+description of mitotic phosphorylation in vivo at extremely high- time resolution (Fig. 2a). This revealed parallel and abrupt upregulation of all phosphosites preceding each cell division (Fig. 2b, c), indicating switch- like phosphorylation of diverse protein complexes at mitotic onset. This was not due to oscillation of CDK1- Y15 inhibitory phosphorylation, which was downregulated over time (Fig. 2d), as previously reported<sup>31</sup>, consistent with lack of corresponding phosphorylation of the CDK1- Y15- regulatory enzymes, CDC25 and WEE1. In contrast, oscillating phosphorylations on NIPA and the APC/C, which regulate mitotic cyclin accumulation, as well as Greatwall kinase, which activates the PP2A inhibitors Arpp19/ENSA, were apparent (Extended data Fig. 5a). These data suggest that control of mitotic cyclin levels and PP2A activity, and therefore the overall CDK/phosphatase activity ratio<sup>2</sup>, suffices for switch- like mitotic phosphorylation whereas regulated CDK1- Y15 phosphorylation is not essential (Extended data Fig. 5b). This is consistent with the self- sufficiency of futile cycles of opposing enzymes in generating switch- like network output in the absence of allosteric regulation<sup>32</sup>.  
+
+<|ref|>sub_title<|/ref|><|det|>[[118, 435, 620, 454]]<|/det|>
+## The cell cycle phosphoproteome is intrinsically disordered  
+
+<|ref|>text<|/ref|><|det|>[[115, 466, 883, 905]]<|/det|>
+We wondered whether the diverse dynamic phosphoproteins share common structural features facilitating switch- like CDK- mediated phosphorylation. Phosphosites in general are often located in intrinsically disordered regions (IDRs) of proteins<sup>34</sup>, which is also true for yeast and mouse CDK sites<sup>35,8,9</sup>. Yet previous analyses did not exclude the possibility that this is an artefact due to the enrichment of serine, threonine and proline in disordered regions, which is consistently predicted across the entire proteome of Xenopus, human and yeast (Extended data Fig. 6a). We corrected for this compositional bias, and found that phosphorylatable residues in IDR are indeed more highly phosphorylated than those in ordered regions (Fig. 3a- c). This enrichment was increased for proteins with at least one site displaying dynamic phosphorylation; the same was true for human CDK substrates (Fig. 3b, c). To estimate the differential phosphorylation of disordered sites globally, we calculated the ratio of dynamically phosphorylated (Xenopus) or CDK- phosphorylated (yeast, human) to non- phosphorylated serine and threonine in both disordered and structured regions (Extended data Fig. 6b; see Methods). This confirmed that cell cycle- regulated phosphorylation is largely skewed towards disordered regions and that CDKs preferentially phosphorylate disordered sites (Fig. 3d, Extended data Fig. 6c). We then asked whether this is also true for substrates of other protein kinases. We analysed the mitotic PLK and Aurora kinases, DYRK kinases, which promote mitotic phosphorylation of several IDPs<sup>36</sup>, NEK kinases, which have roles in centrosome
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[115, 84, 882, 177]]<|/det|>
+duplication and various stages of mitosis, and MAP kinases, which share the proline- directed S/T consensus site. For each kinase, documented phosphosites were strongly enriched in IDRs (Extended data Fig. 6c, d), supporting the idea that phosphorylation of residues in IDRs is kinetically favoured<sup>34</sup>.  
+
+<|ref|>text<|/ref|><|det|>[[115, 189, 882, 479]]<|/det|>
+To explain the dominance of CDK- mediated phosphorylation in the cell cycle, we surmised that their substrates might be more disordered than phosphoproteins in general. We therefore determined the percentage of disordered residues of proteins in our datasets, compared to the rest of their respective phosphoproteins (Data S3). This revealed that, on average, both Xenopus dynamic phosphoproteins and human and yeast CDK substrates contain approximately twice the proportion of disordered amino acids as other phosphoproteins (Fig. 3e, Extended data Fig. 6e), putting them among the top quartile of proteins with the most disorder in the proteome. If this reflects the importance of disordered proteins for the cell cycle generally, then substrates of other cell cycle kinases might also be more disordered than other phosphoproteins. Indeed, targets of most cell cycle kinases are significantly more disordered than targets of MAP kinase (Fig. 3f), whose phosphosites are also proline- directed and preferentially located in IDRs (Extended data Fig. 6d).  
+
+<|ref|>sub_title<|/ref|><|det|>[[117, 492, 613, 510]]<|/det|>
+## Enrichment of MLO components among CDK substrates  
+
+<|ref|>text<|/ref|><|det|>[[115, 523, 882, 862]]<|/det|>
+We thus reasoned that phosphorylation may have been selected to regulate the functions of IDPs during the cell cycle. IDPs are key components of membrane- less organelles (MLO), many of which (e.g. Cajal bodies, nucleoli, nuclear pore complexes, splicing speckles) are thought to arise by phase separation (PS)<sup>37</sup>, are disassembled in mitosis, and can be regulated by phosphorylation<sup>36,38,39</sup>. To corroborate our hypothesis, we analysed available data on cellular localisation for each of our curated human CDK substrates. We found that 257 (39.2%) are present in MLOs, including key IDPs such as coilin (Cajal bodies), nucleophosmin, nucleolin and Ki- 67 (nucleoli), 53BP1 (53BP1 bodies), nucleoporins (NPC) and PML (PML bodies) (Fig. 3g). We then manually curated an MLO proteome from human proteomics studies (Data S4; See Supplementary Methods for sources). Homologues of 204 dynamic Xenopus phosphoproteins (31.6%) localise to MLOs, as do 73 of the 149 proteins (50%) that show dynamic phosphorylation in Xenopus and are CDK substrates in human (Fig. 3g). The vast majority of proline- directed phosphosites and confirmed CDK sites in these proteins were located in predicted IDRs (Extended data Fig. 7).  
+
+<|ref|>sub_title<|/ref|><|det|>[[117, 875, 441, 892]]<|/det|>
+## CDKs regulate IDR phase separation
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[115, 83, 882, 300]]<|/det|>
+Both stochastic and specific interactions between IDPs contribute to PS and MLO assembly<sup>37,40,41</sup>. We hypothesised that cell cycle kinase-mediated phosphorylation might modulate such interactions. We first applied a machine learning classifier<sup>42</sup> to predict whether cell cycle-regulated phosphoproteins have an increase in average propensity for PS (PSAP score). Indeed, we observed a sharp increase in the PSAP score, from the proteome to the phosphoproteome, and a further increase for dynamic phosphoproteins, with the highest score for mitotic cluster D (Extended data Fig. 8a). Similarly, the propensity for PS is far higher amongst targets of most cell cycle kinases (CDK, Aurora, PLK, but not NEK) and DYRK kinases than the overall phosphoproteome, but less so for MAP kinase substrates.  
+
+<|ref|>text<|/ref|><|det|>[[115, 312, 882, 724]]<|/det|>
+Next, to better understand the biochemical effects of their cell cycle- regulated phosphorylation, we analysed a selection of IDRs from CDK substrates. We applied a general heteropolymer theory that uses sequence charge decoration matrices (SCDM), based on electrostatic interactions only, to identify intra- chain interaction topology<sup>43,44</sup>. Since this should correlate with inter- chain interactions that promote PS, SCDMs provide indirect insights to propensity to phase separate. Of the 12 IDPs tested, 7 (nucleolin, nucleophosmin, NUP53, ELYS, MCM4, 53BP1 and the splicing factor SF3B1) had SCDM maps showing visibly decreased self- association propensity (increased red regions in Extended data Fig. 8b), implying reduced propensity to phase separate, upon CDK- site phosphorylation. Conversely, for SRRM2, CDK- mediated phosphorylation is predicted to increase intra- chain attraction (Extended data Fig. 8b) and hence PS tendency. For 4 proteins (MDC1, TICRR, COILIN, and CDT1), SCDM maps were inconclusive. To further analyse these trends, we calculated radius of gyration of several IDRs using all- atom simulation. Effects of phosphorylation on CDT1 (28.4Å to 30.3Å), TICRR (56.2Å to 57.3Å) and coilin (39 Å to 37.9 Å) were minor, while MCM4 IDR expands upon phosphorylation (21.9Å to 26.3Å), consistent with SCDM analysis. Overall, these data suggest that phosphorylation is a key regulator of homotypic interactions, an important element of PS propensity, of most IDRs.  
+
+<|ref|>text<|/ref|><|det|>[[115, 736, 882, 904]]<|/det|>
+To test this hypothesis, we focused on a model CDK substrate, Ki- 67, an IDP that organises heterochromatin structure<sup>45</sup> and perichromosomal layer formation from nucleolar components in mitosis<sup>46,47</sup>. Ki- 67 contains a multivalent Ki- 67 repeat domain that is highly phosphorylated in mitosis by CDKs (Fig. 4a), which regulates its perichromosomal localisation<sup>48</sup>. SCDM analysis predicted that phosphorylation of full- length Ki- 67 should promote self- interaction and thus PS, but this cannot be attributed to interactions of its repeat motif alone, since phosphorylation of the latter is predicted to reduce homotypic interactions (Fig. 4b). In
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[113, 78, 883, 821]]<|/det|>
+agreement, coarse- grained (CG) molecular dynamics (MD) simulations (Extended data Movies 1 and 2) showed that the radius of gyration of full- length Ki- 67 decreased upon phosphorylation (Fig. 4c, left) while that of a single consensus repeat motif increased (Fig. 4c, right). MD simulations also showed that PS is enhanced by increasing repeat valency and counteracted by phosphorylation (Fig. 4d), consistent with SCDM analysis. To test these predictions experimentally, we first used the optogenetic Cry2 “optodroplet” system<sup>49</sup> with full length Ki- 67 or a series of deletion mutants. Full- length Ki- 67 localised to the nucleolus, as expected, but exposure to blue light caused rapid appearance of small round foci in the nucleoplasm, which was dependent on the level of induced Ki- 67 expression, consistent with PS (Extended data Fig. 9a). Importantly, promoting CDK- mediated phosphorylation by inhibiting PP2A with okadac acid<sup>2</sup> led to foci formation in the absence of blue light, while pan- CDK inhibition with purvalanol A prevented induction of foci upon light (Fig. 4e, f). These results indicate that, as predicted by SCDM and MD, phosphorylation of full- length Ki- 67 promotes PS. Results were similar for constructs lacking the C- terminal LR domain, that binds chromatin, or the N- terminal domain, which is required for the nucleolar localisation of Ki- 67 (Extended data Fig. 9b). Finally, we purified a consensus repeat polypeptide (Extended data Fig. 10a) and phosphorylated it in vitro with recombinant CDK complexes. Nuclear Magnetic Resonance spectroscopy showed a reduced amide proton spectral dispersion typical for an IDP, and confirmed appearance of 7 phosphorylated residues upon incubation with purified CDKs and ATP (Fig. 4g). We mapped phosphorylation sites and intensity by phosphoproteomics and Phos- Tag- SDS- PAGE, indicating stoichiometric phosphorylation (Extended data Fig. 10b, c). Purified GFP- tagged Ki- 67 repeat motif could phase- separate in vitro, and, as predicted, this was abolished upon full phosphorylation by CDK (Fig. 4h). Taken together, these results confirm that CDK- mediated phosphorylation is able to both promote or inhibit homotypic interactions that contribute to PS, and suggest that Ki- 67 may have several competing modes of PS that are differentially regulated by phosphorylation. Our data suggest a mechanism for Ki- 67- mediated mitotic targeting of nucleolar components to the perichromosomal layer<sup>45,46</sup> via CDK- mediated phosphorylation, which reduces PS of several major nucleolar IDPs, thus triggering nucleolar disassembly, while simultaneously promoting PS of Ki- 67 bound to chromatin to recruit nucleolar components.  
+
+<|ref|>text<|/ref|><|det|>[[115, 828, 881, 896]]<|/det|>
+In conclusion, this work reveals in vivo that CDK- dependent mitotic phosphorylation occurs in a switch- like manner on diverse proteins whose common denominators are a high level of disorder and localisation to MLOs. Furthermore, our data show that CDK- mediated
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[115, 84, 882, 177]]<|/det|>
+phosphorylation regulates homotypic interactions between IDPs, which may coordinate diverse cellular processes during the cell cycle. While this is not incompatible with models in which high- affinity interactions contribute to MLO formation by \(\mathrm{PS}^{50,51}\) , it suggests that cell cycle control may be less specific than previously thought.  
+
+<|ref|>text<|/ref|><|det|>[[115, 206, 883, 595]]<|/det|>
+Acknowledgments: We thank Merlijn Witte for technical assistance with the Xenopus laevis egg fertilization experiments, Ariane Abrieu for a gift of CSF egg extracts, and Markus Raschle from the Technical University of Kaiserslautern for providing the Xenopus laevis protein database. Funding: AJRH and MA acknowledge support from the Horizon 2020 program INFRAIA project Epic- XS (Project 823839) and the NWO funded Netherlands Proteomics Centre through the National Road Map for Large- scale Infrastructures program X- Omics (Project 184.034.019) of the Netherlands Proteomics Centre. JMV is supported by scholarships from the Ministry of Science and Technology of Costa Rica (MICITT) and the University of Costa Rica (UCR). PK and MV are funded by the Oncode Institute which is financed by the Dutch Cancer Society and by the gravitation program CancerGenomiCs.nl from the Netherlands Organisation for Scientific Research (NWO). DF and LK are Inserm employees. GD is funded by the Institut National de Cancer, France (INCa) PRT- K programme (PRT- K17 \(n^{\circ}\) 2018- 023). The Fisher lab is funded by the Ligue Nationale Contre le Cancer, France (EL2018.LNCC/DF) and INCa (PLBIO18- 094). The CBS is a member of France- BioImaging (FBI) and the French Infrastructure for Integrated Structural Biology (FRISBI), supported by the French National Research Agency (ANR- 10- INBS- 04- 01 and ANR- 10- INBS- 05).  
+
+<|ref|>text<|/ref|><|det|>[[117, 607, 882, 700]]<|/det|>
+Author contributions: MA and DF conceived and supervised the project. JMV, PK and LK designed and interpreted experiments. JMV, HT, AH, GvM, LK and GD performed experiments and interpreted the data. MV supervised GvM. JMV, LK, GD, DF and MA wrote the paper.  
+
+<|ref|>text<|/ref|><|det|>[[118, 713, 626, 731]]<|/det|>
+Competing interests: Authors declare no competing interests.  
+
+<|ref|>text<|/ref|><|det|>[[118, 744, 880, 787]]<|/det|>
+Data and materials availability: All data is available in the main text or the supplementary materials. All data, code, and materials are available on request.  
+
+<|ref|>sub_title<|/ref|><|det|>[[118, 809, 348, 826]]<|/det|>
+## Supplementary Materials:  
+
+<|ref|>text<|/ref|><|det|>[[118, 832, 340, 870]]<|/det|>
+Materials and Methods  Extended data figures 1- 10  
+
+<|ref|>text<|/ref|><|det|>[[118, 877, 214, 892]]<|/det|>
+Data S1- S4
+
+<--- Page Split --->
+<|ref|>sub_title<|/ref|><|det|>[[117, 133, 216, 149]]<|/det|>
+## References
+
+<--- Page Split --->
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+17. Purvis, J. E. & Lahav, G. Encoding and decoding cellular information through signaling dynamics. Cell 152, 945–956 (2013).18. Budnik, B., Levy, E., Harmange, G. & Slavov, N. SCoPE-MS: mass spectrometry of single mammalian cells quantifies proteome heterogeneity during cell differentiation. Genome Biol. 19, 161 (2018).19. Lombard-Banek, C., Moody, S. A., Manzini, M. C. & Nemes, P. Microsampling Capillary Electrophoresis Mass Spectrometry Enables Single-Cell Proteomics in Complex Tissues: Developing Cell Clones in Live Xenopus laevis and Zebrafish Embryos. Anal Chem 91, 4797–4805 (2019).20. Newport, J. & Kirschner, M. A major developmental transition in early Xenopus embryos: I. characterization and timing of cellular changes at the midblastula stage. Cell 30, 675–686 (1982).21. Newport, J. W. & Kirschner, M. W. Regulation of the cell cycle during early Xenopus development. Cell 37, 731–42 (1984).22. Post, H. et al. Robust, Sensitive, and Automated Phosphopeptide Enrichment Optimized for Low Sample Amounts Applied to Primary Hippocampal Neurons. J. Proteome Res. 16, 728–737 (2017).23. Clift, D. & Schuh, M. Restarting life: fertilization and the transition from meiosis to mitosis. Nat Rev Mol Cell Biol 14, 549–562 (2013).24. Presler, M. et al. Proteomics of phosphorylation and protein dynamics during fertilization and meiotic exit in the Xenopus egg. Proc. Natl. Acad. Sci. U.S.A. 114, E10838–E10847 (2017).25. Peuchen, E. H. et al. Phosphorylation Dynamics Dominate the Regulated Proteome during Early Xenopus Development. Sci Rep 7, 15647 (2017).26. Ferrell, J. E., Jr., Wu, M., Gerhart, J. C. & Martin, G. S. Cell cycle tyrosine phosphorylation of p34cdc2 and a microtubule-associated protein kinase homolog in Xenopus oocytes and eggs. Mol Cell Biol 11, 1965–71 (1991).27. Lawrence, R. T., Searle, B. C., Llovet, A. & Villén, J. Plug-and-play analysis of the human phosphoproteome by targeted high-resolution mass spectrometry. Nat Methods 13, 431–434 (2016).28. Schmidlin, T. et al. Assessment of SRM, MRM3, and DIA for the targeted analysis of phosphorylation dynamics in non-small cell lung cancer. PROTEOMICS 16, 2193–2205 (2016).29. Schmidlin, T. et al. High-Throughput Assessment of Kinome-wide Activation States. Cell Systems 9, 366–374.e5 (2019).
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+32. Goldbeter, A. & Koshland, D. E., Jr. An amplified sensitivity arising from covalent modification in biological systems. Proc Natl Acad Sci U S A 78, 6840–4 (1981). 
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+33. Suzuki, K. et al. Identification of non-Ser/Thr-Pro consensus motifs for Cdk1 and their roles in mitotic regulation of C2H2 zinc finger proteins and Ect2. Scientific Reports 5, 7929 (2015). 
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+34. Iakoucheva, L. M. et al. The importance of intrinsic disorder for protein phosphorylation. Nucleic Acids Res. 32, 1037–1049 (2004). 
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+35. Moses, A. M., Hériche, J.-K. & Durbin, R. Clustering of phosphorylation site recognition motifs can be exploited to predict the targets of cyclin-dependent kinase. Genome Biol. 8, R23 (2007). 
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+36. Rai, A. K., Chen, J.-X., Selbach, M. & Pelkmans, L. Kinase-controlled phase transition of membraneless organelles in mitosis. Nature 559, 211–216 (2018). 
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+37. Shimobayashi, S. F., Ronceray, P., Sanders, D. W., Haataja, M. P. & Brangwynne, C. P. Nucleation landscape of biomolecular condensates. Nature 599, 503–506 (2021). 
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+38. Berchtold, D., Battich, N. & Pelkmans, L. A Systems-Level Study Reveals Regulators of Membrane-less Organelles in Human Cells. Mol. Cell 72, 1035-1049.e5 (2018). 
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+39. Hur, W. et al. CDK-Regulated Phase Separation Seeded by Histone Genes Ensures Precise Growth and Function of Histone Locus Bodies. Dev Cell 54, 379-394.e6 (2020). 
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+40. Lin, Y.-H., Wu, H., Jia, B., Zhang, M. & Chan, H. S. Assembly of model postsynaptic densities involves interactions auxiliary to stoichiometric binding. Biophys J 121, 157–171 (2022). 
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+41. Ghosh, K. Stoichiometric versus stochastic interaction in models of liquid-liquid phase separation. Biophys J 121, 4–6 (2022). 
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+42. van Mierlo, G. et al. Predicting protein condensate formation using machine learning. Cell Rep 34, 108705 (2021). 
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+43. Huihui, J. & Ghosh, K. An analytical theory to describe sequence-specific inter-residue distance profiles for polyampholytes and intrinsically disordered proteins. J Chem Phys 152, 161102 (2020).
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+44. Huihui, J. & Ghosh, K. Intrachain interaction topology can identify functionally similar intrinsically disordered proteins. Biophys J 120, 1860-1868 (2021).  
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+45. Sobecki, M. et al. The cell proliferation antigen Ki-67 organises heterochromatin. Elife 5, e13722 (2016).  
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+46. Booth, D. G. et al. Ki-67 is a PP1-interacting protein that organises the mitotic chromosome periphery. Elife 3, e01641 (2014).  
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+47. Hayashi, Y., Kato, K. & Kimura, K. The hierarchical structure of the perichromosomal layer comprises Ki67, ribosomal RNAs, and nucleolar proteins. Biochemical and Biophysical Research Communications 493, 1043-1049 (2017).  
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+48. Hégarat, N. et al. Cyclin A triggers Mitosis either via the Greatwall kinase pathway or Cyclin B. The EMBO Journal 39, e104419 (2020).  
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+49. Shin, Y. et al. Spatiotemporal Control of Intracellular Phase Transitions Using Light-Activated optoDroplets. Cell 168, 159-171.e14 (2017).  
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+50. Feng, Z., Jia, B. & Zhang, M. Liquid-Liquid Phase Separation in Biology: Specific Stoichiometric Molecular Interactions vs Promiscuous Interactions Mediated by Disordered Sequences. Biochemistry 60, 2397-2406 (2021).  
+
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+51. Musacchio, A. On the role of phase separation in the biogenesis of membraneless compartments. EMBO J e109952 (2022) doi:10.15252/embj.2021109952.
+
+<--- Page Split --->
+<|ref|>image<|/ref|><|det|>[[117, 131, 880, 699]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[115, 703, 881, 894]]<|/det|>
+<center>Figure 1. The time-resolved phosphoproteome from a single-cell to a 16-cell embryo and its cell cycle assignment. (a) Schematic representation of the workflow. Single Xenopus eggs and embryos were collected followed by cell lysis, protein digestion, phosphopeptide enrichment and high-resolution proteomics analysis. (b) STRING network of functionally associated proteins undergoing dynamic phosphorylation (each node represents a protein). Vicinity clustering reveals three main groups (yellow, blue and orange) with a high degree of association. Radar plots show the corresponding GO terms (adjusted p value \(< 0.05\) ) for each group (axes show -Log₁₀(adj p value) for each GO term). (c) Hierarchical clustering of </center>
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[115, 83, 883, 352]]<|/det|>
+439 significantly changing phosphosites (ANOVA, Benjamini- Hochberg correction, FDR 0.05), 440 reveals 4 clusters with distinct regulation (A- D). Dashed boxes in clusters A and D are zoomed- 441 in to highlight dynamic phosphorylation patterns (dashed lines depict the time points of cell 442 division). (d) Scheme of the experiment in the Xenopus egg extract. (e) Top: quantification of 443 DNA replication in each biological replicate. Below: Hierarchical clustering of dynamic 444 phosphosites (ANOVA, Benjamini- Hochberg correction, FDR 0.05) reveals differential 445 regulation of phosphosites during S- phase and mitosis. (f) Overlap between in vivo (embryo) 446 and in vitro (egg extract) phosphoproteomics. (g) Proportion of phosphosites according to their 447 potential upstream kinase for each cluster in the in vivo (top) and in vitro (bottom) experiments. 448 (h) Circle plots presenting enrichment of homologues of human CDK substrates among 449 Xenopus phosphoproteins detected in vivo and those with dynamic phosphosites.
+
+<--- Page Split --->
+<|ref|>image<|/ref|><|det|>[[113, 81, 880, 570]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[115, 580, 883, 747]]<|/det|>
+<center>Figure 2. Switch-like mitotic phosphorylation in vivo. (a) Schematic representation of the workflow. Samples were collected over two cell divisions and enriched phosphopeptides were subjected to targeted proteomics analysis. (b) Heat map shows a highly synchronous wave of phosphorylation preceding each of the two cell divisions. Dashed lines depict times when cell divisions were recorded. (c) Single phosphosite plots from selected proteins. Each dot represents a biological replicate \((n = 3)\) . Dashed lines depict times when cell divisions were recorded. (d) Single phosphosite plot of CDK1 inhibitory phosphorylation (Y15). </center>
+
+<--- Page Split --->
+<|ref|>image<|/ref|><|det|>[[115, 80, 881, 666]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[115, 672, 881, 914]]<|/det|>
+<center>Fig. 3. The cell cycle phosphoproteome is characterised by intrinsic disorder and MLO components. (a) Scheme illustrating hypothetical enrichment of phosphorylation in disordered regions when taking into account amino acid compositional bias. (b) Scatter plot of expected vs observed phosphorylated Ser/Thr for each protein of human and Xenopus phosphoprotein datasets. FDR thresholds of \(5\%\) and \(1\%\) are marked in yellow and red respectively. Circles: proteins with at least one dynamic phosphorylation in Xenopus, or human CDK1 subfamily substrates, respectively. (c) Boxplots showing expected vs observed phosphorylated Ser/Thr among all phosphoproteins detected (left), phosphoproteins with at least one dynamic phosphosite (middle), and dynamic phosphoproteins also detected as CDK1 subfamily targets in humans (right). Distributions were compared with the Wilcoxon signed-rank test. \(*p<0.05\) , </center>
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[113, 83, 883, 448]]<|/det|>
+\(^{**}\mathrm {p}<0.01\) , \(^{***}\mathrm {p}<0.001\) . (d) Plots showing the common Odds Ratio of Ser/Thr phosphorylation in structured and ordered regions calculated with the Fisher's test (see Extended data Fig. 6b, c). For all organisms, the disordered regions were calculated with three different disorder predictors. The disordered fraction is presented in a colour scale. (e) Violin plots of the distribution of disordered residues per protein for CDK targets vs the rest of the phosphoproteome for human and yeast, and dynamic phosphoproteins vs the rest of the phosphoproteome for Xenopus. Intrinsic disorder was calculated with three different predictors (IUPred, SPOT, and VSL2b). Statistical significance was evaluated with the Wilcoxon–Mann–Whitney test; \(^{***}\mathrm {p}<0.001\) . (f) Violin plot (left) showing the distribution of disordered residues per protein for CDK, MAPK, Aurora, PLK, NEK and DYRK kinase targets vs the rest of the phosphoproteome for human targets. Statistical significance was assessed by Kruskal-Wallis ANOVA, and pairwise comparisons were performed with Dunn’s post-hoc tests. The adjusted p-values (Benjamini-Hochberg) are shown in a tile plot (right). (g) Human CDK1 subfamily targets, Xenopus dynamic phosphoproteins, and the intersection of both sets, that are present in our manually curated proteome of membraneless organelles.
+
+<--- Page Split --->
+<|ref|>image<|/ref|><|det|>[[118, 82, 825, 831]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[115, 831, 880, 896]]<|/det|>
+<center>Fig. 4. CDK-mediated phosphorylation regulates phase separation of a model IDP. (a) Top, scheme of the human Ki-67 protein (FHA, forkhead-associated domain; PP1, PP1 phosphatase-binding domain; CD, conserved domain; LR, leucine arginine-rich domain). </center>
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[113, 80, 883, 896]]<|/det|>
+Highlighted, Ki- 67 repeat consensus motif. Bottom, diagram of IUPred score over the length of human Ki- 67. Regions with scores \(>0.5\) (orange) are considered to be disordered, and \(< 0.5\) (grey) structured. Blue vertical lines indicate Ser and Thr residues; yellow circles, known Ser/Thr- Pro phosphosites; green circles, confirmed CDK1 subfamily phosphorylations. (b) Sequence Charge Decoration Matrix (SCDM) maps for full length Ki- 67 (FL, left) and Ki- 67 consensus repeat (CR, right), depicting the contribution of electrostatic interaction dictating the distance between two amino acid residues i and j (shown in x and y axes). The values of SCDM for different residue pairs (i,j) are shown using colour schemes with red and blue denoting positive (repulsive) and negative (attractive) values, respectively. The lower and upper triangles indicate SCDM map for the unphosphorylated (non- P) and phosphorylated (P) sequences, respectively. Confirmed and putative (Ser/Thr- Pro) CDK phosphorylation sites are indicated with red circles. (c) Dependency of the radius of gyration (Rg) on the simulation temperature in single- chain MD simulations for full chain Ki- 67 (left) and consensus repeat (right). The reference temperature is the \(\theta\) temperature of the non- phosphorylated molecule for full chain and consensus repeat, respectively. Reported error bars are obtained by block analysis over 10 blocks. (d) Binodal curves from phase coexistence simulations of the Ki- 67 consensus repeat sequence. For each temperature, filled circles indicate the dilute phase density and squares indicate the coexisting dense phase density. Empty circles indicate the fitted critical temperature (Tc) of each system. The Tc of the non- phosphorylated monomer (light blue empty circle) was the reference for the normalisation of the temperature values. The light gray dashed line indicates the total concentration used in the simulations. The reference temperature is the \(\theta\) temperature of the non- phosphorylated molecule for full chain and consensus repeat, respectively. Reported error bars are obtained by block analysis over 10 blocks. (e) Representative fluorescent images of HEK- 293 cells expressing opto- Ki- 67 (FL) construct before (Light Off) and after (Light On) exposure to blue light. Cells were pretreated for 1h with either vehicle (DMSO), 0.5 μM okadaic acid (OA), to inhibit protein phosphatase 2, or 5 μM purvalanol A (PA), to inhibit CDKs. DNA was stained with Hoechst 33258; scale bars, 10μm. (f) Violin plot presenting quantification of results from (e); the number of foci per nucleus was counted. Statistical significance was assessed by one- way ANOVA on ranks (Kruskal–Wallis test) and pairwise post- hoc comparisons using the Mann–Whitney test. P- values were adjusted by the Benjamini- Hochberg method. (g) Overlaid NMR \(^1\mathrm{H}\) - \(^{15}\mathrm{N}\) HSQC of unphosphorylated (blue) and CDK- phosphorylated (red) GFP- tagged Ki- 67 consensus repeat. Each cross- peak corresponds to one residue. The seven new deshielded cross peaks (highlighted by a black flag)
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[58, 83, 883, 252]]<|/det|>
+appearing above 8.5 ppm in \(^1\mathrm{H}\) correspond to phosphorylated serines or threonines ( \(^1\mathrm{H}\) downfield chemical shift perturbation on phosphorylated Ser/Thr residues due to phosphate electronegativity). Non phosphorylated Ser/Thr residues are surrounded by a black oval. (h) Representative fluorescence images of in vitro phase separation assay with purified GFP- tagged Ki- 67 consensus repeat (CR), non- phosphorylated (non- P) or in vitro phosphorylated with recombinant CDK1- cyclin B- CKS1 (P), at indicated dextran concentrations and time points; scale bars, \(10\mu \mathrm{m}\) .
+
+<--- Page Split --->
+<|ref|>sub_title<|/ref|><|det|>[[43, 42, 311, 70]]<|/det|>
+## Supplementary Files  
+
+<|ref|>text<|/ref|><|det|>[[43, 92, 765, 113]]<|/det|>
+This is a list of supplementary files associated with this preprint. Click to download.  
+
+<|ref|>text<|/ref|><|det|>[[59, 130, 415, 338]]<|/det|>
+SupplementaryInformation.pdf DATAS1Phosphoproteomicsdata.xlsx DATAS2CDKphosphorylationdata.xlsx DATAS3Proteindisorderprediction.xlsx DATAS4HumanproteinsinMLOs.xlsx SupplementarymovieS1.mpg SupplementarymovieS2A.mpg SupplementarymovieS2B.mpg
+
+<--- Page Split --->

preprint/preprint__1e1bedff740ea0146a02a6d351b3babf234584dc6acf3acb3873b690d8852174/images_list.json

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preprint/preprint__1e369c655e2cff1cb25b6641a4035c982b0a10c2ec5eb94ed8a1982a90990f7c/images_list.json

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+    "caption": "Fig. 1 | Design and simulation of the \\(\\chi^{(2)}\\) integrated waveguide with birefringence PM based on a ZGP crystal. (a) Calculated normalized gain spectrum at a pump wavelength of \\(2.4 \\mu \\mathrm{m}\\) for a bulk ZGP crystal, at various PM angles. A PM angle of \\(\\theta = 48.3^{\\circ}\\) is designed as marked by the orange dashed line to generate the broadest gain bandwidth. (b) Simulated electric field distributions in the \\(\\chi^{(2)}\\) waveguide of pump wave at \\(2.4 \\mu \\mathrm{m}\\) in TE\\(_{00}\\) mode (top) and a typical idler wave at \\(8.0 \\mu \\mathrm{m}\\) in TM\\(_{00}\\) mode (bottom). (c) Calculated propagation losses of fundamental modes in the designed \\(\\chi^{(2)}\\) waveguide in TE and TM polarizations, in a broad spectral range of 2 - 11 \\(\\mu \\mathrm{m}\\) . The results indicate that the propagation loss of fundamental TE and TM mode is less than 0.15 dB/cm and 0.25 dB/cm, respectively. The raise of waveguide losses in the wavelength range of 9 - 11 \\(\\mu \\mathrm{m}\\) is attributed to the absorption of fused silica substrate peaked at 9.5 \\(\\mu \\mathrm{m}\\) , as shown in the top inset which depicts the absorption coefficient (imaginary part of complex refractive index) of silica, as a function of wavelength\\(^{19}\\) . Meanwhile, the material loss of ZGP crystal is minimal in the spectral range of 2-11 \\(\\mu \\mathrm{m}\\) as shown by the measured transmission spectrum of a 10-mm-thick uncoated ZGP crystal in the bottom inset of Fig. 1(c). (d) The simulated GVD of TE\\(_{00}\\) modes in a wavelength range of 2 to 11 \\(\\mu \\mathrm{m}\\) , in the designed \\(\\chi^{(2)}\\) ZGP waveguide (green) and bulk ZGP (black), respectively. Owing to multi-wavelength-scale dimensions, the integrated waveguide exhibits nearly an identical GVD profile to that of the bulk material, which",
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+  {
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+    "img_path": "images/Figure_2.jpg",
+    "caption": "Fig. 2| Fabrication steps of the \\(\\chi^{(2)}\\) integrated waveguide with birefringence PM based on a ZGP crystal. (a, b) Step1: a bulk ZGP crystal with a PM angle of \\(48.3^{\\circ}\\) for type I PM (the designed optical axis (OA), with respect to the incident wave vector \\(k\\) , is marked by a white dashed line) and an anti-reflection coating in the spectral range of \\(1.7 - 3 \\mu \\mathrm{m}\\) and \\(5 - 13 \\mu \\mathrm{m}\\) is bonded to a fused silica substrate, through using the ultraviolet curing optical adhesives. (c, d) Step 2: the bonded bulk ZGP crystal with the designed PM angle is lapped and polished to a thickness of \\(\\sim 40 \\mu \\mathrm{m}\\) by employing an optical grinding machine. (e, f) Step 3: the ultrafast laser direct writing technique is adopted to form a ridge waveguide structure on the lapped ZGP wafer. (h) The photograph shows the grinded ZGP-on-SiO₂ wafer. (i) Scanning electron microscope images of the fabricated \\(\\chi^{(2)}\\) waveguide. Closely packed waveguide arrays are fabricated to fully utilize the \\(\\chi^{(2)}\\) wafer, while no field could be coupled between the adjacent waveguides.",
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+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_3.jpg",
+    "caption": "Fig. 3| Experimental setup and characterizations of the spectra, output power, pump threshold and parametric gain of the on-",
+    "footnote": [],
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+  {
+    "type": "image",
+    "img_path": "images/Figure_4.jpg",
+    "caption": "Fig. 4| The characterization of broadband spectral tuning and shaping from the on-chip integrated \\(\\chi^{(2)}\\) device with birefringence",
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+  {
+    "type": "image",
+    "img_path": "images/Figure_5.jpg",
+    "caption": "Fig. 5| Comparison of quantum efficiency and pump threshold of the demonstrated \\(\\chi^{(2)}\\) waveguide based on birefringence PM",
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preprint/preprint__1e369c655e2cff1cb25b6641a4035c982b0a10c2ec5eb94ed8a1982a90990f7c/preprint__1e369c655e2cff1cb25b6641a4035c982b0a10c2ec5eb94ed8a1982a90990f7c.mmd

@@ -0,0 +1,256 @@
+
+# On-chip highly efficient octave-spanning long-wavelength mid-infrared optical parametric generation with a 74% quantum efficiency  
+
+Bo Ho Sichuan University Xuemei Yang Sichuan University Jiangen Wu Shenzhen Technology University Siyi Lu Sichuan University Hang Yang Sichuan University Zhe Long Sichuan University Linzhen He Sichuan University Kan Tian Sichuan University Weizhe Wang Sichuan University Yang Li Sichuan University Han Wu Sichuan University https://orcid.org/0000- 0003- 0674- 0862 Wenlong Li Dien PHOTOELECTRIC Technology Co., Ltd. Huan Yang Sichuan University Qi Jie Wang Nanyang Technological University https://orcid.org/0000- 0002- 9910- 1455 Houkun Liang ( hkliang@scu.edu.cn ) Sichuan University https://orcid.org/0000- 0002- 6894- 0491
+
+<--- Page Split --->
+
+## Article  
+
+## Keywords:  
+
+Posted Date: December 1st, 2022  
+
+DOI: https://doi.org/10.21203/rs.3.rs- 2300201/v1  
+
+License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License  
+
+Additional Declarations: There is NO Competing Interest.  
+
+Version of Record: A version of this preprint was published at Nature Communications on November 6th, 2023. See the published version at https://doi.org/10.1038/s41467- 023- 42912- 0.
+
+<--- Page Split --->
+
+1 On-chip highly efficient octave-spanning long-wavelength mid-infrared optical parametric generation with a \(74\%\) quantum efficiency  
+
+4 Bo Hu<sup>1,5</sup>, Xuemei Yang<sup>1,5</sup>, Jiangen Wu<sup>2,5</sup>, Siyi Lu<sup>1</sup>, Hang Yang<sup>1</sup>, Zhe Long<sup>1</sup>, Linzhen He<sup>1</sup>, 5 Kan Tian<sup>1</sup>, Weizhe Wang<sup>1</sup>, Yang Li<sup>1</sup>, Han Wu<sup>1</sup>, Wenlong Li<sup>3</sup>, Huan Yang<sup>2\*</sup>, Qi Jie Wang<sup>4</sup> 6 and Houkun Liang<sup>1\*</sup> 7 <sup>1</sup> School of Electronics and Information Engineering, Sichuan University, Chengdu, Sichuan 610064, China 8 <sup>2</sup> Sino-German College of Intelligent Manufacturing, Shenzhen Technology University, Shenzhen, 9 Guangdong 518118, China 10 <sup>3</sup> Chengdu Dien PHOTOELECTRIC Technology Co., Ltd. Chengdu, Sichuan 610100, China 11 <sup>4</sup> School of Electrical & Electronic Engineering & The Photonics Institute, Nanyang Technological 12 University 639798, Singapore, Singapore 13 <sup>5</sup> These authors contributed equally: Bo Hu, Xuemei Yang, Jiangen Wu 14 The realization of integrated broadband mid-infrared (MIR) lasers has enormous impacts in 15 promoting MIR spectroscopy for various important applications. On-chip MIR supercontinuum and 16 frequency combs have been demonstrated based on cubic nonlinearities, but unfortunately third-order 17 nonlinear conversions inherently have low efficiencies. Here, we propose and demonstrate for the first 18 time a \(\chi^{(2)}\) parametric integrated device based on birefringence phase matching with a high quantum
+
+<--- Page Split --->
+
+efficiency and low pump threshold. In a ZnGeP2- based integrated waveguide, an octave- spanning spectrum covering 5 - 11 μm is generated through optical parametric generation. A quantum conversion efficiency of 74% as a new record in MIR parametric processes is achieved. The threshold energy is found to be as low as \(\sim 616\) pJ, reduced by more than 1- order of magnitude as compared to the state- of- the- art MIR parametric conversions. Moreover, a universal and cost- effective fabrication technique for integrated nonlinear photonics is demonstrated extendable to various other \(\chi^{(2)}\) crystals. In the recent decade, tremendous efforts have been spent towards the generation of ultra- broadband mid- infrared (MIR, 3- 20 μm) laser sources for various applications including frequency metrology and trace- gas sensing<sup>1</sup>. Among various approaches, despite techniques such as multi- stacks of gain regions have been explored<sup>2</sup>, quantum cascaded lasers still face limitations in producing broadband emissions. Up to now, nonlinear frequency conversions have become the main route for generating coherent ultra- broadband MIR radiations, which however generally consists of large and complicated laser apparatuses<sup>3,4</sup>. An unremitting pursuit marching toward compact and efficient MIR conversions has led to substantial progress of MIR laser sources towards on- chip integrated photonic devices. Integrated MIR emitters based on cubic polarizations (\(\chi^{(3)}\)) namely Kerr nonlinearities have been intensively investigated in material systems such as silicon (Si), silicon nitride (SiN), germanium (Ge) and chalcogenide leveraging on the advancement of semiconductor and CMOS technologies<sup>5-10</sup>. Broadband MIR supercontinuum with the wavelength extending up to 13 μm has been demonstrated in a SiGe waveguide<sup>9</sup>. However, \(\chi^{(3)}\) nonlinear response is inherently weak, which results in low conversion efficiency (< 1%) and requires high pump threshold. On the other hand, more efficient quadratic nonlinearity (\(\chi^{(2)}\))- based integrated nonlinear devices for parametric conversions such as optical parametric generation/amplification (OPG/OPA), and difference- frequency generation (DFG) are
+
+<--- Page Split --->
+
+expected to be a promising approach for generation of highly- efficient, low- threshold and ultra- broadband MIR lasers.  
+
+\(\chi^{(2)}\) - based integrated devices have been realized empowered by quasi- phase matching technique \(^{11 - 14}\) . OPG emitting in the wavelength range of \(1700 - 2700 \mathrm{nm}\) has been demonstrated with a pump threshold of \(60 \mathrm{fJ}\) and a parametric gain of \(118 \mathrm{dB / cm}\) in an integrated waveguide based on periodically- poled lithium niobate (PPLN) \(^{11}\) . In order to extend the wavelength into the MIR molecular fingerprint regime, particularly beyond \(5 \mu \mathrm{m}\) , the orientation- patterned gallium arsenide (OP- GaAs) platform has been employed as an on- chip OPG, which is the only integrated \(\chi^{(2)}\) device so far demonstrated in the long- wavelength MIR region \(^{14}\) . Nevertheless, high efficiency of parametric conversion or broadband emission have not been demonstrated in integrated OP- GaAs platforms, and sophisticated fabrication procedures are required for the necessity of orientational patterning, which poses stringent constraints on the selection of nonlinear media. In addition, dry- etching techniques such as inductively coupled plasma etching are made use for the fabrication of OP- GaAs waveguides, which restricts the cross- sectional area of the integrated device. Therefore, there is vital urgency and importance to unlock new \(\chi^{(2)}\) on- chip platforms with other phase- matching (PM) techniques.  
+
+In this work, we, for the first time to the best of our knowledge, propose and demonstrate the \(\chi^{(2)}\) parametric waveguide platform with birefringence PM taking the advantages of high birefringence \(\chi^{(2)}\) in non- oxide nonlinear crystals, such as \(\mathrm{ZnGeP_2}\) (ZGP), \(\mathrm{AgGsS_2}\) , GaSe and \(\mathrm{CdSiP_2}\) which are attractive for broadband long- wavelength MIR generation \(^{15 - 18}\) . OPG in the long- wavelength MIR region from the \(\chi^{(2)}\) integrated device is experimentally exploited, driven at a central wavelength of \(2.4 \mu \mathrm{m}\) . The generated idler pulse has an octave spectrum spanning from 5 to \(11 \mu \mathrm{m}\) . Owing to the efficient quadratic nonlinear response, tight spatial/temporal confinement and elongated interaction length in the \(\chi^{(2)}\) waveguide with birefringence
+
+<--- Page Split --->
+
+PM, an ultra-low threshold pulse energy and peak power are demonstrated as low as \(\sim 616\) pJ and \(1.9 \mathrm{kW}\) , respectively. More strikingly, a quantum conversion efficiency of \(74\%\) is achieved as a new record of MIR parametric processes, which is enhanced by more than 2 folds compared to the state-of-art parametric conversions in the long-wavelength MIR region. The saturated parametric gain of \(>60\) dB/cm is observed at a pump energy of \(7.9 \mathrm{nJ}\) . Besides the remarkable laser specifications, the on-chip device is fabricated by bonding and grinding the ZGP nonlinear crystal with a designed PM angle, followed by patterning using ultrafast laser direct writing (ULDW) technique, which provides a universal and cost-effective fabrication method for nonlinear photonic devices out of various birefringent crystals. This work opens an exciting routine for achieving integrated broadband MIR lasers based on birefringent nonlinear crystals with efficient quadratic nonlinear response. The demonstrated platform and methodology are promising to trigger the blossom of on-chip integrated MIR nonlinear photonics and practical applications of MIR spectroscopy and metrology.  
+
+## Integrated \(\chi^{(2)}\) device with birefringence PM: design and fabrication  
+
+A ridge nonlinear waveguide is designed for the proof- of- concept demonstration of the integrated \(\chi^{(2)}\) device with birefringence PM. The ZGP crystal is chosen as the material platform for its high \(\chi^{(2)}\) nonlinearities ( \(d_{36} \sim 75 \mathrm{pm / V}\) ), mature growth technique, and broad MIR transparency window ( \(\sim 0.73 - 12 \mu \mathrm{m}\) ) \(^{15}\) . Traditionally, crystal angle is twisted to fulfil the birefringence PM condition. Thus, in integrated devices with birefringence PM, with a fixed crystal angle, it is crucial to have an ultrabroad PM bandwidth such that different pump and signal wavelengths could be adapted, and the spectral tunable parametric conversion could be realized. Fortunately, in a bulk ZGP crystal, Type- I PM pumped at \(\sim 2.4 \mu \mathrm{m}\) could provide an ultra- broadband PM bandwidth for an idler wavelength spanning from 5 to \(11 \mu \mathrm{m}\) , as calculated in Fig. 1(a), with a PM angle
+
+<--- Page Split --->
+
+83 designed as \(48.3^{\circ}\) . Besides the ultrabroad PM bandwidth, a multi- wavelength- scale ZGP ridge waveguide 84 structure on fused silica \(\mathrm{(SiO_2)}\) substrate is designed for the balance of tight field confinement, small 85 propagation loss, and ease of pump coupling. In the simulation of electric field distribution in the \(\chi^{(2)}\) 86 waveguide, the waveguide dimensions are designed as 35 and \(40\mu \mathrm{m}\) in width and height, respectively. In 87 addition, a sidewall angle of \(85^{\circ}\) is also considered in the simulation to mimic the actual fabricated device. 88 The moderate waveguide dimension and high refractive index contrast between the ZGP waveguide core and 89 cladding layers (air or \(\mathrm{SiO_2}\) ) contribute to an excellent mode confinement. As illustrated in simulated mode 90 cross sections in Fig. 1(b), fundamental mode profiles of the \(2.4\mu \mathrm{m}\) pump and \(8\mu \mathrm{m}\) idler in transverse 91 electric \(\mathrm{(TE_{00})}\) and magnetic \(\mathrm{(TM_{00})}\) polarizations, corresponding to ordinary and extraordinary waves, 92 respectively, of ZGP crystal are well confined and overlapped in the \(\chi^{(2)}\) waveguide. Notably, confinement 93 factors of both \(\mathrm{TE_{00}}\) and \(\mathrm{TM_{00}}\) modes are calculated to be greater than \(99\%\) in the entire interested MIR 94 spectral range. Moreover, the propagation loss of fundamental modes including the material absorption in the 95 designed \(\chi^{(2)}\) waveguide in a wavelength range of 2 to \(11\mu \mathrm{m}\) are calculated, as presented in Fig. 1(c). It is 96 revealed that the pump wave at \(2.4\mu \mathrm{m}\) in \(\mathrm{TE_{00}}\) mode and the long- wavelength MIR idler spanning from 5 to 97 \(11\mu \mathrm{m}\) with \(\mathrm{TM_{00}}\) polarization have a propagation loss \(< 0.01\) dB/cm and \(0.25\mathrm{dB / cm}\) , respectively, which 98 guarantees a good transmission of parametric waves in the \(\chi^{(2)}\) waveguide. The raise of waveguide losses in 99 the wavelength range of 9 - 11 \(\mu \mathrm{m}\) is attributed to the absorption of fused silica substrate peaked at \(9.5\mu \mathrm{m}\) , 100 as shown in the top inset which depicts the absorption coefficient of silica, as a function of wavelength19. It 101 is worth mentioning that the material loss of ZGP crystal is minimal in the spectral range of 2-11 \(\mu \mathrm{m}\) as shown 102 by the measured transmission spectrum of a 10- mm- thick uncoated ZGP crystal in the bottom inset of Fig. 103 1(c) (The Fresnell reflection is not subtracted).
+
+<--- Page Split --->
+
+Dispersion engineering is another critical process for the design of integrated \(\chi^{(2)}\) devices. The group velocity dispersion (GVD) and group velocity mismatch (GVM) between pump and idler pulses in the \(\chi^{(2)}\) waveguide are calculated and presented in Fig. 1(d). Owing to the multi- wavelength- scale geometry, the effect of waveguide dispersion is weak, and thus the ZGP waveguide features a nearly identical GVD profile with that of the bulk material, which simplifies the design of integrated \(\chi^{(2)}\) devices with birefringence PM. In addition, the GVD of \(2.4 \mu \mathrm{m}\) pump pulse is calculated as a small value of \(470 \mathrm{fs}^2 /\mathrm{mm}\) , which implies that the temporal profile of the pump pulse is not perturbed with respect to the \(320 \mathrm{fs}\) pulse width, while propagating in the \(10\mathrm{- mm - long}\chi^{(2)}\) waveguide. On the other hand, the calculated maximum GVM between the pump at a wavelength of \(2.4 \mu \mathrm{m}\) and the idler spanning from 6 to \(12 \mu \mathrm{m}\) is less than \(30 \mathrm{fs / mm}\) , indicating a small temporal walk off for parametric waves propagating in the \(10\mathrm{- mm - long}\mathrm{ZGP}\) waveguide.  
+
+Based on the designed parameters, a unique fabrication technique which combines procedures of nonlinear crystal bonding with the designed PM angle, \(\chi^{(2)}\) wafer lapping and ULDW is proposed and demonstrated based on a ZGP crystal, as depicted in Fig. 2. As the first step, a bulk ZGP crystal (DPT, YSZGP) with the dimension of \(4\) (width) \(\times 3\) (thickness) \(\times 10\) (length) \(\mathrm{mm}^3\) is cut into a crystal angle of \(48.3^{\circ}\) for type I PM. The bulk crystal with an anti- reflection coating in the spectral range of \(1.7 - 3 \mu \mathrm{m}\) and \(5 - 13 \mu \mathrm{m}\) is then bonded to a fused silica substrate using ultraviolet curing optical adhesives. The bonded bulk ZGP crystal is subsequently lapped and polished to a thickness of \(\sim 40 \mu \mathrm{m}\) . Fig. 2(h) shows the photograph of lapped ZGP- on- \(\mathrm{SiO}_2\) wafer. Finally, the ULDW technique is adopted to form a ridge waveguide structure on the lapped ZGP wafer (see Fig. 2(e, f)). More detailed descriptions of the ULDW process are presented in Methods. Scanning electron microscope images of the fabricated ZGP ridge waveguides based on the invented fabrication technique for \(\chi^{(2)}\) devices are also displayed in Fig. 2(i). It is observed that the structured
+
+<--- Page Split --->
+
+ZGP waveguide exhibits a side- wall roughness less than \(10 \mu \mathrm{m}\) , and the waveguide facets are nearly identical to those of unprocessed ZGP films, indicating a small scattering loss and potentially good coupling efficiency of the fabricated \(\chi^{(2)}\) waveguide. Notably, ULDW with optimized processing parameters is efficient for etching high- quality structures with tens of micrometer depth in \(\chi^{(2)}\) nonlinear crystals, with minor constraint on materials to be processed, which provides a unique merit compared to traditional CMOS dry- etching techniques such as inductively coupled plasma etching. It is therefore suggested that the demonstrated new technique could provide a universal method for large- scale three- dimensional fabrication of integrated \(\chi^{(2)}\) device.  
+
+## Highly efficient and low threshold parametric generation  
+
+To characterize the parametric conversion in the fabricated on- chip \(\chi^{(2)}\) device with birefringence PM, OPG is performed to measure the parametric gain bandwidth and saturated gain value \(^{11,12}\) with the experimental setup illustrated in Fig. 3(a). The pump source is a home- built OPA centered at \(2.4 \mu \mathrm{m}\) with \(320 \mathrm{fs}\) pulse width at a repetition rate of \(500 \mathrm{kHz}\) . The spectrum of the pump laser is presented in Fig. 3(b). (More details of the MIR OPA as the pump source are included in Methods). The \(2.4 \mu \mathrm{m}\) pump beam is coupled into the ZGP waveguide focused by an uncoated \(\mathrm{CaF}_2\) lens with a \(40 \mathrm{- mm}\) focal length, while the generated long- wavelength MIR idler is collected by a ZnSe lens with a \(25 \mathrm{- mm}\) focal length and an anti- reflection coating in the spectral range of \(2 - 13 \mu \mathrm{m}\) . An input coupling efficiency of \(15.3\%\) and an output collecting efficiency of \(34\%\) are measured and estimated, respectively (More details of the measurement and calculation of coupling efficiencies are presented in Methods). A long- pass filter with a cutoff wavelength of \(4.5 \mu \mathrm{m}\) is used to remove the residual co- propagating pump and signal beams, and a MIR hollow core fiber with a \(500 \mu \mathrm{m}\) core diameter is employed to transmit the generated long- wavelength MIR radiation. The spectrum of the
+
+<--- Page Split --->
+
+generated idler is characterized by using a grating- scanning monochromator equipped with a lock- in amplifier and a liquid nitrogen cooled Mercury Cadmium Telluride (MCT) detector. The average power of the generated idler wave is measured using the MCT detector and a power meter.  
+
+Measured OPG spectra at different pump energy (peak power) are shown in Fig. 3(c). Driven at a pulse energy of \(3.2 \mathrm{nJ}\) , corresponding to a peak power of \(9.4 \mathrm{kW}\) , the OPG spectrum spans from \(6.6\) to \(9.6 \mu \mathrm{m}\) . As the pump energy is increased to \(8.2 \mathrm{nJ}\) , a macroscopic broadening of OPG spectra is observed because of the parametric gain bandwidth broadening \(^{11}\) which could tolerate certain deviation of the PM angle from the designed value caused by fabrication imperfections. Notably, the generated MIR idler wave has an octave- spanning spectrum covering \(5.4\) to \(10.3 \mu \mathrm{m}\) at \(-20 \mathrm{dB}\) , which is in good agreement with the simulated result in Fig. 1(a). The output power of the generated MIR idler from the ZGP waveguide is plotted in Fig. 3(d), as a function of pump pulse energy and average power. An OPG threshold of \(616 \mathrm{pJ}\) is measured from the ZGP waveguide, which is reduced by more than 1- order of magnitude, compared to the state- of- the- art MIR parametric conversions based on bulk crystals, thanks to the strong quadratic nonlinear response, tight mode confinement, good spatial/temporal overlap and long interaction range in the \(\chi^{(2)}\) device. It is worth mentioning that the threshold measurement is limited by the transmission loss of ZnSe lenses and LPF, the noise floor of the used MCT detector and \(300 \mathrm{ms}\) integration time of the lock- in detection. Even lower threshold energy value is expected with a pump source at a higher repetition rate. Increasing the pump pulse energy, the OPG output power grows exponentially. The long- wavelength MIR OPG power is measured as \(-0.72 \mathrm{mW}\) , at a pump pulse energy of \(6.5 \mathrm{nJ}\) and an average power of \(3.25 \mathrm{mW}\) , as presented in Fig. 3(d), corresponding to a power efficiency of \(22\%\) and a quantum efficiency of \(74\%\) . Moreover, as shown in Fig. 3(e), the measured saturated gain of the nonlinear waveguide is \(\sim 60 \mathrm{dB / cm}\) , which indicates that a decent
+
+<--- Page Split --->
+
+parametric gain could be provided by the integrated \(\chi^{(2)}\) device \(^{11,12}\) . The inset of Fig. 3(e) is the measured beam profile of the long- wavelength MIR idler from the ZGP waveguide. The revealed spatial intensity distribution is in excellent agreement with the simulated mode profile shown in Fig. 1(b).  
+
+The OPG spectral tunability from the \(\chi^{(2)}\) waveguide with birefringence PM is investigated by scanning the pump wavelength in the range of 2.3- 2.7 \(\mu \mathrm{m}\) . As shown in Fig. 4(a), pumped at 2.4 \(\mu \mathrm{m}\) , a MIR idler spectrum centered at 8 \(\mu \mathrm{m}\) is obtained. When the pump wavelength is increased to 2.5 \(\mu \mathrm{m}\) , owing to better PM condition at two wings of the spectrum, an octave- spanning idler covering 4.5 to 12 \(\mu \mathrm{m}\) is generated. Further increasing the pump wavelength to 2.6 \(\mu \mathrm{m}\) abruptly reduces the parametric bandwidth, and a relatively narrow- band idler spectrum centered at 9.3 \(\mu \mathrm{m}\) is measured, as presented in Fig. 4(c). A semiclassical simulation of optical parametric generation is also conducted to study the spectral evolution in the \(\chi^{(2)}\) waveguide with birefringence PM at different pump wavelengths, by solving the \(\chi^{(2)}\) - based coupled- wave equations. Parametric seed with noise field represented by a complex Gaussian distribution with zero mean and a half- a- photon energy variance is adopted to mimic vacuum fluctuations \(^{20}\) . The simulation results shown in Figs. 4(d- f) qualitatively agree well with the experimental results. We therefore suggest that spectral shaping of the integrated parametric device could be realized by tuning the pump wavelength, which would broaden its applications in MIR spectroscopy and metrology.  
+
+## Discussion  
+
+Over the last decade, long- wavelength MIR parametric sources have been demonstrated in bulk nonlinear crystals facilitated either by birefringence or quasi- PM techniques. However, the quantum efficiency is usually low, limited by factors such as spatial/temporal walk- off, non- perfect beam overlap, large interaction area and short parametric coupling length. Fig. 5(a) compares reported quantum efficiencies of the state- of
+
+<--- Page Split --->
+
+art parametric conversions including OPA \(^{17,38 - 42}\) (blue squares), OPG \(^{35 - 37}\) (pink circles), DFG \(^{21 - 27}\) (orange diamonds), and intra-pulse difference-frequency generation \(^{4,28 - 34}\) (IPDFG) (green triangles) based on bulk nonlinear crystals in the long-wavelength MIR range ( \(\lambda > 5 \mu \mathrm{m}\) ), with that of the demonstrated on-chip integrated \(\chi^{(2)}\) waveguide (red star). Typically, OPAs/OPGs in bulk nonlinear crystals operate with microjoule or even higher pump energy. DFGs and IPDFGs pumped by mode-locked lasers at high-repetition ( \(\sim \mathrm{MHz}\) magnitude) allow lower pump pulse energy of nanojoules level, but the quantum conversion efficiency is limited to \(\sim 20\%\) or lower. Owing to the small interaction area, good spatial/temporal overlap, and inherently elongated interaction length originated from the waveguide structure, a quantum efficiency of \(74\%\) at a small pump energy of \(\sim 6.5 \mathrm{nJ}\) is obtained in the integrated \(\chi^{(2)}\) waveguide, which is enhanced by more than 2 folds compared to the traditional parametric conversions. In addition, anther merit of the demonstrated on-chip \(\chi^{(2)}\) device is reflected as the ultra-low threshold. As displayed in Fig. 5(b), the measured threshold pulse energy and peak power from the demonstrated \(\chi^{(2)}\) waveguide is compared with those of OPGs in \(\chi^{(2)}\) bulk media including PPLN \(^{43 - 46}\) and OP- GaAs \(^{35}\) crystals. Ultra-low threshold pulse energy and peak power measured as \(616 \mathrm{pJ}\) and \(1.9 \mathrm{kW}\) are obtained, respectively, which is more than 1-order of magnitude lower than those of reported OPGs in literature.  
+
+## Conclusions and outlook  
+
+In summary, we, for the first time to the best of our knowledge, propose and demonstrate the integrated photonic platform based on the \(\chi^{(2)}\) nonlinear crystal with birefringence PM. A unique fabrication technique which combines procedures of nonlinear crystal bonding, \(\chi^{(2)}\) wafer lapping and ULDW is employed to fabricate the ZGP \(\chi^{(2)}\) waveguide, which could be extended as a universal technique for integrated nonlinear photonics out of other birefringent crystals. High- gain ( \(>60 \mathrm{dB}\) ) and low- threshold ( \(616 \mathrm{pJ}\) ) long- wavelength
+
+<--- Page Split --->
+
+MIR OPG with an octave- spanning bandwidth is demonstrated in the \(\chi^{(2)}\) waveguide. Tunable MIR spectra in the wavelength range of 5 to \(12\mu \mathrm{m}\) are achieved by scanning the pump wavelength. With the strong quadratic nonlinear response, tight mode confinement, extraordinary spatial/temporal overlap of the parametric waves, and elongated parametric interaction length in the demonstrated \(\chi^{(2)}\) waveguide, a record quantum efficiency of \(74\%\) are demonstrated.  
+
+Based on the proof- of- concept demonstration, more advanced explorations of MIR integrated nonlinear photonics such as on- chip IPDFG \(^{47}\) , MIR femtosecond lasers with ultra- high repetition rate and \(\chi^{(2)}\) frequency comb generation \(^{48}\) could be pursued. Moreover, it is worth mentioning that more compact femtosecond lasers operating around \(2.4\mu \mathrm{m}\) with decent output power have experienced substantial development in the last decade. Kerr- lens mode- locked \(\mathrm{Cr^{2 + }}\) : \(\mathrm{ZnS / ZnSe}\) oscillators \(^{49}\) and MIR fiber lasers based on soliton self- frequency shift \(^{50}\) could output up to hundreds of kW peak power which is well above the pump threshold of the demonstrated \(\chi^{(2)}\) integrated parametric generator. This would further improve the compactness of the entire system. We therefore believe that the on- chip integrated \(\chi^{(2)}\) device with birefringence PM fabricated by the new technique invented in this work, equipped with advanced pump sources would flourish the MIR integrated photonics researches and promote practical applications of MIR spectroscopy and metrology with compact systems.  
+
+## Methods  
+
+The pump source: KTiOPO4 (KTP) based OPA. The pump source starts with a customized Yb- fiber laser (Yacto, YF- FL- 50- 100- IR) emitting 1030 nm pulses with a duration of 260 fs at 500 kHz repetition rate. A small fraction of pulse energy \(\sim 8\mu \mathrm{J}\) is focused into a 15- mm- long YAG crystal generating a stable white light
+
+<--- Page Split --->
+
+spanning from 1.1 to \(1.9 \mu \mathrm{m}\) in the near- infrared region. An 8- mm- long anti- reflection coated KTP crystal cut at \(\theta = 44.5^{\circ}\) , \(\phi = 0^{\circ}\) for type II PM is used in the OPA. The idler wavelength is tunable from 2.4 to \(2.8 \mu \mathrm{m}\) by twisting the PM angle. The output power of the idler wave is measured up to \(500 \mathrm{mW}\) by using a \(15 \mathrm{W}\) pump. The pulse duration of idler wave is characterized as 320 fs with some uncompensated dispersion inherited from the OPA system, through a home- built interferometer autocorrelator.  
+
+\(\chi^{(2)}\) waveguide fabrication. A bulk ZGP crystal cut into a PM angle of \(48.3^{\circ}\) for type I PM is bonded to a fused silica substrate using ultraviolet curing optical adhesives (NOA61). The bonded ZGP crystal is subsequently lapped and polished by a plane precision ring polishing machine (Jian Su XB, LP10C/08C) to a thickness of \(\sim 40 \mu \mathrm{m}\) . In the process of ultrafast laser direct writing, the \(520 \mathrm{nm}\) femtosecond laser beam (Spectra- Physics Spirit HE 1040- 30- SHG) with a pulse width of \(300 \mathrm{fs}\) at a repetition rate of \(250 \mathrm{kHz}\) is focused to the ZGP surface by an F- Theta lens with a focal spot size of \(10 \mu \mathrm{m}\) in diameter. A coaxial charge- coupled device imaging system is equipped to achieve an accurate focusing. The laser processing parameters are optimized with the laser fluence, scanning speed, scanning spacing, and processing cycles as \(1.53 \mathrm{J / cm^2}\) , \(100 \mathrm{mm / s}\) , \(5 \mu \mathrm{m}\) , and \(2\) , respectively. A vacuum cleaner is turned on during the laser processing to remove the debris produced in the femtosecond laser fabrication. The \(\chi^{(2)}\) wafer is cleaned using an ultrasonic cleaner after laser processing in deionized water for 5 seconds. With above procedures and parameters, the waveguide side- wall roughness is estimated to be less than \(10 \mu \mathrm{m}\) , and the waveguide facets are nearly identical to those of unprocessed ZGP films.  
+
+Calibrations of the coupling and collecting efficiency. The coupling efficiency is measured by replacing the \(\chi^{(2)}\) waveguide with a pinhole of \(100 \mu \mathrm{m}\) in diameter, and record the transmitted power ratio of the 2.4 \(\mu \mathrm{m}\) laser beam. The coupling efficiency is measured as \(15.3\%\) which is several times larger than those of
+
+<--- Page Split --->
+
+PPLN waveguides with sub- wavelength apertures, benefited from the multi- wavelength- scale dimensions in the fabricated \(\chi^{(2)}\) device. On the collecting side, the generated long- wavelength MIR idler radiation is collected by a 1- inch ZnSe lens with a focal length of \(25\mathrm{mm}\) , corresponding to a numerical aperture (NA) of 0.45, and an anti- reflection coating in the spectral range of 2- 13 \(\mu \mathrm{m}\) . Using the method demonstrated in Ref. 11, 12, the OPG average photon number could be expressed as \(\langle n\rangle = a\sinh^2 (2gl)\) , where \(a\) is the overall detection efficiency including the output- coupling loss and imperfect detection induced loss, \(g\) indicates the parameter gain coefficient. Here, \(g\) is in directly proportion to \(\sqrt{\eta P}\) , where \(\eta\) represents the nonlinear interaction coefficient and \(P\) is the pump power. When the parametric gain is larger than \(10\mathrm{dB}\) , \(\langle n\rangle\) could be approximated as \(\langle n\rangle = a\exp (2gl)\) . In addition, the measured average OPG power could be expressed as \(P_{OPG} = \langle n\rangle hv_{f_{rep}}\) , from which \(\langle n\rangle\) could be fitted and obtained, where \(h\) is the Planck constant, \(\nu\) represents the idler frequency and \(f_{rep}\) indicates the repetition frequency. Hence, the parameter \(a\) could be estimated, and the collection efficiency is obtained as \(34\%\) .  
+
+Data availability. The data that support the findings of this study are available from the corresponding author upon request.  
+
+## Reference  
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+
+<--- Page Split --->
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+359 49. Mirov. S. B. et al. Frontiers of Mid-IR Lasers Based on Transition Metal Doped Chalcogenides. IEEE 360 J. Sel. Topics Quantum Electron. 24, 1601829 (2018). 361 50. Tiliouine, I. et al. Fiber-based source of 500 kW mid-infrared solitons. Opt. Lett. 46, 5890-5893 (2021). 362
+
+<--- Page Split --->
+
+## Acknowledgement:  
+
+This work was supported by National Natural Science Foundation of China (62075144, 12175157), Sichuan Outstanding  
+
+Youth Science and Technology Talents (2022JDJQ0031), and Engineering Featured Team Fund of Sichuan University  
+
+(2020SCUNG105).  
+
+## Materials & Correspondence:  
+
+Correspondence should be addressed to Huan Yang yanghuan@sztu.edu.cn and HouKun Liang  
+
+hkliang@scu.edu.cn.  
+
+## Author contributions:  
+
+H. K. Liang conceived and designed the experiment. B. Hu and X. Yang carried out the experiment of OPG  
+
+measurement. H. K. Liang, Y. Li, H. Wu, Q. J. Wang designed ZGP waveguide. B. Hu, J. Wu, W. Li and H.  
+
+Yang fabricated ZGP waveguide. B. Hu, X. Yang, S. Lu, H. Yang, and Z. Long conducted the theoretical  
+
+simulations. X. Yang, K. Tian, L. He and W. Wang built the MIR OPA. H. K. Liang, B. Hu, X. Yang, J. Wu,  
+
+H. Yang and H. Wu wrote the manuscript. B. Hu, X. Yang and J. Wu contributed equally. All authors discussed  
+
+the results and contributed to the manuscript.
+
+<--- Page Split --->
+![](images/Figure_1.jpg)
+
+<center>Fig. 1 | Design and simulation of the \(\chi^{(2)}\) integrated waveguide with birefringence PM based on a ZGP crystal. (a) Calculated normalized gain spectrum at a pump wavelength of \(2.4 \mu \mathrm{m}\) for a bulk ZGP crystal, at various PM angles. A PM angle of \(\theta = 48.3^{\circ}\) is designed as marked by the orange dashed line to generate the broadest gain bandwidth. (b) Simulated electric field distributions in the \(\chi^{(2)}\) waveguide of pump wave at \(2.4 \mu \mathrm{m}\) in TE\(_{00}\) mode (top) and a typical idler wave at \(8.0 \mu \mathrm{m}\) in TM\(_{00}\) mode (bottom). (c) Calculated propagation losses of fundamental modes in the designed \(\chi^{(2)}\) waveguide in TE and TM polarizations, in a broad spectral range of 2 - 11 \(\mu \mathrm{m}\) . The results indicate that the propagation loss of fundamental TE and TM mode is less than 0.15 dB/cm and 0.25 dB/cm, respectively. The raise of waveguide losses in the wavelength range of 9 - 11 \(\mu \mathrm{m}\) is attributed to the absorption of fused silica substrate peaked at 9.5 \(\mu \mathrm{m}\) , as shown in the top inset which depicts the absorption coefficient (imaginary part of complex refractive index) of silica, as a function of wavelength\(^{19}\) . Meanwhile, the material loss of ZGP crystal is minimal in the spectral range of 2-11 \(\mu \mathrm{m}\) as shown by the measured transmission spectrum of a 10-mm-thick uncoated ZGP crystal in the bottom inset of Fig. 1(c). (d) The simulated GVD of TE\(_{00}\) modes in a wavelength range of 2 to 11 \(\mu \mathrm{m}\) , in the designed \(\chi^{(2)}\) ZGP waveguide (green) and bulk ZGP (black), respectively. Owing to multi-wavelength-scale dimensions, the integrated waveguide exhibits nearly an identical GVD profile to that of the bulk material, which </center>
+
+<--- Page Split --->
+
+simplifies the design of integrated \(\chi^{(2)}\) devices with birefringence PM. In addition, the GVD of the \(2.4 \mu \mathrm{m}\) pump pulse is calculated as a small value of \(470 \mathrm{fs}^2 /\mathrm{mm}\) , which implies that the temporal profile of the pump pulse is not perturbed with respect to the \(320 \mathrm{fs}\) pulse width, while propagating in the \(10\mathrm{- mm - long}\chi^{(2)}\) waveguide. In addition, the simulated GVM between the pump wave at \(2.4 \mu \mathrm{m}\) for the \(\mathrm{TE}_{00}\) modes and the signal wave (blue)/idler wave (red) in the spectral range of \(3 - 5 \mu \mathrm{m} / 6 - 12 \mu \mathrm{m}\) for the \(\mathrm{TM}_{00}\) modes are also plotted. The maximum GVM between the pump and idler pulses in the spectral range of \(6 - 12 \mu \mathrm{m}\) is less than \(30 \mathrm{fs} / \mathrm{mm}\) , implying a small temporal walk off, for a \(\sim 320 \mathrm{fs}\) pump pulse propagating in the \(10\mathrm{- mm - long}\mathrm{ZGP}\) waveguide.
+
+<--- Page Split --->
+![](images/Figure_2.jpg)
+
+<center>Fig. 2| Fabrication steps of the \(\chi^{(2)}\) integrated waveguide with birefringence PM based on a ZGP crystal. (a, b) Step1: a bulk ZGP crystal with a PM angle of \(48.3^{\circ}\) for type I PM (the designed optical axis (OA), with respect to the incident wave vector \(k\) , is marked by a white dashed line) and an anti-reflection coating in the spectral range of \(1.7 - 3 \mu \mathrm{m}\) and \(5 - 13 \mu \mathrm{m}\) is bonded to a fused silica substrate, through using the ultraviolet curing optical adhesives. (c, d) Step 2: the bonded bulk ZGP crystal with the designed PM angle is lapped and polished to a thickness of \(\sim 40 \mu \mathrm{m}\) by employing an optical grinding machine. (e, f) Step 3: the ultrafast laser direct writing technique is adopted to form a ridge waveguide structure on the lapped ZGP wafer. (h) The photograph shows the grinded ZGP-on-SiO₂ wafer. (i) Scanning electron microscope images of the fabricated \(\chi^{(2)}\) waveguide. Closely packed waveguide arrays are fabricated to fully utilize the \(\chi^{(2)}\) wafer, while no field could be coupled between the adjacent waveguides. </center>
+
+<--- Page Split --->
+![](images/Figure_3.jpg)
+
+<center>Fig. 3| Experimental setup and characterizations of the spectra, output power, pump threshold and parametric gain of the on- </center>  
+
+chip integrated \(\chi^{(2)}\) device with birefringence PM based on a ZGP crystal. (a) The schematic of the experimental setup for OPG from the \(\chi^{(2)}\) waveguide. The \(2.4 \mu \mathrm{m}\) pump beam is coupled into the ZGP waveguide focused by an uncoated \(\mathrm{CaF}_2\) lens with a \(40 - \mathrm{mm}\) focal length, while the generated long-wavelength MIR idler is collected by a ZnSe lens with a \(25 - \mathrm{mm}\) focal length and an anti-reflection coating in the spectral range of \(2 - 13 \mu \mathrm{m}\) . A long-pass filter (LPF, Edmund #68- 655) with a cutoff wavelength of \(4.5 \mu \mathrm{m}\) is used to remove the residual co- propagating pump and signal beams. The spectrum of the generated idler is characterized by using a grating- scanning monochromator (Zolix Omni- \(\lambda 500i\) ) equipped with a lock- in amplifier (SRS, SR830) and a liquid nitrogen cooled Mercury Cadmium Telluride (MCT) detector (Judson, DMCT16- De01). The average power of the generated idler wave is measured using the MCT detector and a power meter (Ophir, 3A). (b) The pump spectrum centered at \(2.4 \mu \mathrm{m}\) . (c) The measured OPG spectra from the \(\chi^{(2)}\) waveguide with
+
+<--- Page Split --->
+
+different pump peak powers. The generated MIR idler wave has an octave-spanning spectrum covering 5.4 to \(10.3 \mu \mathrm{m}\) at - 20 dB, which  
+
+is in good agreement with the simulated result in Fig. 1(a). The noise floor is marked by a grey dotted line. (d) The measured OPG  
+
+output power shown in a linear scale as a function of pump pulse energy and average power. The measured pump threshold energy is  
+
+\(\sim 616 \mathrm{pJ}\) , corresponding to a peak power of \(1.9 \mathrm{kW}\) . In addition, When the pump energy is \(6.5 \mathrm{nJ}\) corresponding to an average power of  
+
+\(3.25 \mathrm{mW}\) , a long- wavelength MIR power of \(0.72 \mathrm{mW}\) is detected from the \(\chi^{(2)}\) waveguide, corresponding to a power efficiency of \(22\%\)  
+
+and a quantum efficiency of \(74\%\) . Meanwhile, further increasing the pump energy, the output power tends to saturate. Particularly, when  
+
+the pump pulse energy is larger than \(9 \mathrm{nJ}\) , the MIR output power decreases, due to parametric back conversion. (e) The normalized OPG  
+
+intensity in a logarithm scale at different pump energy. A saturated parametric gain of \(>60 \mathrm{dB / cm}\) is observed at a pump energy of \(7.9\)  
+
+\(\mathrm{nJ}\) , which indicates that a decent parametric gain could be provided by the integrated \(\chi^{(2)}\) device. The inset shows the measured beam  
+
+profile of MIR OPG from the \(\chi^{(2)}\) integrated waveguide, which agrees well with the simulated field profile shown in Fig. 1(b).
+
+<--- Page Split --->
+![](images/Figure_4.jpg)
+
+<center>Fig. 4| The characterization of broadband spectral tuning and shaping from the on-chip integrated \(\chi^{(2)}\) device with birefringence </center>  
+
+PM, when scanning the pump wavelength. (a-c) and (d-f) are the measured and simulated OPG spectra pumped at \(2.4 \mu \mathrm{m}\) , \(2.5 \mu \mathrm{m}\) and \(2.6 \mu \mathrm{m}\) , respectively. (a) A MIR idler spectrum centered at \(8 \mu \mathrm{m}\) is measured pumped at \(2.4 \mu \mathrm{m}\) wavelength. (b) As the pump wavelength is increased to \(2.5 \mu \mathrm{m}\) , an octave-spanning idler covering \(4.5\) to \(12 \mu \mathrm{m}\) is generated, owing to a better PM condition at two wings of the spectrum. (c) Further increasing the pump wavelength to \(2.6 \mu \mathrm{m}\) , narrow-band idler spectrum centered at \(9.3 \mu \mathrm{m}\) is measured due to the reduced parametric bandwidth. (d-f) A semi-classical simulation of optical parametric generation is also conducted to study the spectral evolution in the \(\chi^{(2)}\) waveguide with birefringence PM at different pump wavelengths, by solving the \(\chi^{(2)}\) - based coupled-wave equations. The measurement and simulation results qualitatively agree well with each other.
+
+<--- Page Split --->
+![](images/Figure_5.jpg)
+
+<center>Fig. 5| Comparison of quantum efficiency and pump threshold of the demonstrated \(\chi^{(2)}\) waveguide based on birefringence PM </center>  
+
+with the state- of- art parametric conversions. (a) Summary of quantum efficiencies and saturated pump pulse energy of parametric  
+
+sources including representative OPA \(^{17,38,42}\) , OPG \(^{35,37}\) , DFG \(^{21,27}\) and IPDFG \(^{24,28,34}\) in the long- wavelength MIR region (>5 μm) based on  
+
+bulk nonlinear crystals, and the demonstrated on- chip integrated OPG based on ZGP \(\chi^{(2)}\) waveguide. A quantum efficiency of 74% at a  
+
+pump pulse energy of 6.5 nJ is demonstrated, which is enhanced by more than 2 folds compared to the state- of- art parametric conversions.  
+
+(b) Comparison of the threshold pulse energy and peak power of the demonstrated OPG in the \(\chi^{(2)}\) waveguide based on birefringence  
+
+PM and other MIR OPGs with quasi- PM, based on PPLN \(^{43,46}\) and OP- GaAs crystals \(^{35}\) . The measured threshold pump energy of ZGP \(\chi^{(2)}\)  
+
+waveguide is 616 pJ, corresponding to a peak power of 1.9 kW, which is reduced by more than 1- order of magnitude compared to the  
+
+reported OPGs in the literature.
+
+<--- Page Split --->

preprint/preprint__1e369c655e2cff1cb25b6641a4035c982b0a10c2ec5eb94ed8a1982a90990f7c/preprint__1e369c655e2cff1cb25b6641a4035c982b0a10c2ec5eb94ed8a1982a90990f7c_det.mmd

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+<|ref|>title<|/ref|><|det|>[[44, 106, 843, 210]]<|/det|>
+# On-chip highly efficient octave-spanning long-wavelength mid-infrared optical parametric generation with a 74% quantum efficiency  
+
+<|ref|>text<|/ref|><|det|>[[42, 228, 340, 930]]<|/det|>
+Bo Ho Sichuan University Xuemei Yang Sichuan University Jiangen Wu Shenzhen Technology University Siyi Lu Sichuan University Hang Yang Sichuan University Zhe Long Sichuan University Linzhen He Sichuan University Kan Tian Sichuan University Weizhe Wang Sichuan University Yang Li Sichuan University Han Wu Sichuan University https://orcid.org/0000- 0003- 0674- 0862 Wenlong Li Dien PHOTOELECTRIC Technology Co., Ltd. Huan Yang Sichuan University Qi Jie Wang Nanyang Technological University https://orcid.org/0000- 0002- 9910- 1455 Houkun Liang ( hkliang@scu.edu.cn ) Sichuan University https://orcid.org/0000- 0002- 6894- 0491
+
+<--- Page Split --->
+<|ref|>sub_title<|/ref|><|det|>[[44, 46, 102, 63]]<|/det|>
+## Article  
+
+<|ref|>sub_title<|/ref|><|det|>[[44, 83, 135, 101]]<|/det|>
+## Keywords:  
+
+<|ref|>text<|/ref|><|det|>[[44, 120, 335, 140]]<|/det|>
+Posted Date: December 1st, 2022  
+
+<|ref|>text<|/ref|><|det|>[[44, 159, 474, 179]]<|/det|>
+DOI: https://doi.org/10.21203/rs.3.rs- 2300201/v1  
+
+<|ref|>text<|/ref|><|det|>[[42, 196, 910, 240]]<|/det|>
+License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License  
+
+<|ref|>text<|/ref|><|det|>[[44, 257, 530, 277]]<|/det|>
+Additional Declarations: There is NO Competing Interest.  
+
+<|ref|>text<|/ref|><|det|>[[42, 312, 944, 355]]<|/det|>
+Version of Record: A version of this preprint was published at Nature Communications on November 6th, 2023. See the published version at https://doi.org/10.1038/s41467- 023- 42912- 0.
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[103, 153, 856, 268]]<|/det|>
+1 On-chip highly efficient octave-spanning long-wavelength mid-infrared optical parametric generation with a \(74\%\) quantum efficiency  
+
+<|ref|>text<|/ref|><|det|>[[100, 300, 850, 850]]<|/det|>
+4 Bo Hu<sup>1,5</sup>, Xuemei Yang<sup>1,5</sup>, Jiangen Wu<sup>2,5</sup>, Siyi Lu<sup>1</sup>, Hang Yang<sup>1</sup>, Zhe Long<sup>1</sup>, Linzhen He<sup>1</sup>, 5 Kan Tian<sup>1</sup>, Weizhe Wang<sup>1</sup>, Yang Li<sup>1</sup>, Han Wu<sup>1</sup>, Wenlong Li<sup>3</sup>, Huan Yang<sup>2\*</sup>, Qi Jie Wang<sup>4</sup> 6 and Houkun Liang<sup>1\*</sup> 7 <sup>1</sup> School of Electronics and Information Engineering, Sichuan University, Chengdu, Sichuan 610064, China 8 <sup>2</sup> Sino-German College of Intelligent Manufacturing, Shenzhen Technology University, Shenzhen, 9 Guangdong 518118, China 10 <sup>3</sup> Chengdu Dien PHOTOELECTRIC Technology Co., Ltd. Chengdu, Sichuan 610100, China 11 <sup>4</sup> School of Electrical & Electronic Engineering & The Photonics Institute, Nanyang Technological 12 University 639798, Singapore, Singapore 13 <sup>5</sup> These authors contributed equally: Bo Hu, Xuemei Yang, Jiangen Wu 14 The realization of integrated broadband mid-infrared (MIR) lasers has enormous impacts in 15 promoting MIR spectroscopy for various important applications. On-chip MIR supercontinuum and 16 frequency combs have been demonstrated based on cubic nonlinearities, but unfortunately third-order 17 nonlinear conversions inherently have low efficiencies. Here, we propose and demonstrate for the first 18 time a \(\chi^{(2)}\) parametric integrated device based on birefringence phase matching with a high quantum
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[140, 130, 857, 877]]<|/det|>
+efficiency and low pump threshold. In a ZnGeP2- based integrated waveguide, an octave- spanning spectrum covering 5 - 11 μm is generated through optical parametric generation. A quantum conversion efficiency of 74% as a new record in MIR parametric processes is achieved. The threshold energy is found to be as low as \(\sim 616\) pJ, reduced by more than 1- order of magnitude as compared to the state- of- the- art MIR parametric conversions. Moreover, a universal and cost- effective fabrication technique for integrated nonlinear photonics is demonstrated extendable to various other \(\chi^{(2)}\) crystals. In the recent decade, tremendous efforts have been spent towards the generation of ultra- broadband mid- infrared (MIR, 3- 20 μm) laser sources for various applications including frequency metrology and trace- gas sensing<sup>1</sup>. Among various approaches, despite techniques such as multi- stacks of gain regions have been explored<sup>2</sup>, quantum cascaded lasers still face limitations in producing broadband emissions. Up to now, nonlinear frequency conversions have become the main route for generating coherent ultra- broadband MIR radiations, which however generally consists of large and complicated laser apparatuses<sup>3,4</sup>. An unremitting pursuit marching toward compact and efficient MIR conversions has led to substantial progress of MIR laser sources towards on- chip integrated photonic devices. Integrated MIR emitters based on cubic polarizations (\(\chi^{(3)}\)) namely Kerr nonlinearities have been intensively investigated in material systems such as silicon (Si), silicon nitride (SiN), germanium (Ge) and chalcogenide leveraging on the advancement of semiconductor and CMOS technologies<sup>5-10</sup>. Broadband MIR supercontinuum with the wavelength extending up to 13 μm has been demonstrated in a SiGe waveguide<sup>9</sup>. However, \(\chi^{(3)}\) nonlinear response is inherently weak, which results in low conversion efficiency (< 1%) and requires high pump threshold. On the other hand, more efficient quadratic nonlinearity (\(\chi^{(2)}\))- based integrated nonlinear devices for parametric conversions such as optical parametric generation/amplification (OPG/OPA), and difference- frequency generation (DFG) are
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[140, 134, 855, 188]]<|/det|>
+expected to be a promising approach for generation of highly- efficient, low- threshold and ultra- broadband MIR lasers.  
+
+<|ref|>text<|/ref|><|det|>[[140, 205, 857, 625]]<|/det|>
+\(\chi^{(2)}\) - based integrated devices have been realized empowered by quasi- phase matching technique \(^{11 - 14}\) . OPG emitting in the wavelength range of \(1700 - 2700 \mathrm{nm}\) has been demonstrated with a pump threshold of \(60 \mathrm{fJ}\) and a parametric gain of \(118 \mathrm{dB / cm}\) in an integrated waveguide based on periodically- poled lithium niobate (PPLN) \(^{11}\) . In order to extend the wavelength into the MIR molecular fingerprint regime, particularly beyond \(5 \mu \mathrm{m}\) , the orientation- patterned gallium arsenide (OP- GaAs) platform has been employed as an on- chip OPG, which is the only integrated \(\chi^{(2)}\) device so far demonstrated in the long- wavelength MIR region \(^{14}\) . Nevertheless, high efficiency of parametric conversion or broadband emission have not been demonstrated in integrated OP- GaAs platforms, and sophisticated fabrication procedures are required for the necessity of orientational patterning, which poses stringent constraints on the selection of nonlinear media. In addition, dry- etching techniques such as inductively coupled plasma etching are made use for the fabrication of OP- GaAs waveguides, which restricts the cross- sectional area of the integrated device. Therefore, there is vital urgency and importance to unlock new \(\chi^{(2)}\) on- chip platforms with other phase- matching (PM) techniques.  
+
+<|ref|>text<|/ref|><|det|>[[140, 639, 857, 874]]<|/det|>
+In this work, we, for the first time to the best of our knowledge, propose and demonstrate the \(\chi^{(2)}\) parametric waveguide platform with birefringence PM taking the advantages of high birefringence \(\chi^{(2)}\) in non- oxide nonlinear crystals, such as \(\mathrm{ZnGeP_2}\) (ZGP), \(\mathrm{AgGsS_2}\) , GaSe and \(\mathrm{CdSiP_2}\) which are attractive for broadband long- wavelength MIR generation \(^{15 - 18}\) . OPG in the long- wavelength MIR region from the \(\chi^{(2)}\) integrated device is experimentally exploited, driven at a central wavelength of \(2.4 \mu \mathrm{m}\) . The generated idler pulse has an octave spectrum spanning from 5 to \(11 \mu \mathrm{m}\) . Owing to the efficient quadratic nonlinear response, tight spatial/temporal confinement and elongated interaction length in the \(\chi^{(2)}\) waveguide with birefringence
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[140, 133, 857, 550]]<|/det|>
+PM, an ultra-low threshold pulse energy and peak power are demonstrated as low as \(\sim 616\) pJ and \(1.9 \mathrm{kW}\) , respectively. More strikingly, a quantum conversion efficiency of \(74\%\) is achieved as a new record of MIR parametric processes, which is enhanced by more than 2 folds compared to the state-of-art parametric conversions in the long-wavelength MIR region. The saturated parametric gain of \(>60\) dB/cm is observed at a pump energy of \(7.9 \mathrm{nJ}\) . Besides the remarkable laser specifications, the on-chip device is fabricated by bonding and grinding the ZGP nonlinear crystal with a designed PM angle, followed by patterning using ultrafast laser direct writing (ULDW) technique, which provides a universal and cost-effective fabrication method for nonlinear photonic devices out of various birefringent crystals. This work opens an exciting routine for achieving integrated broadband MIR lasers based on birefringent nonlinear crystals with efficient quadratic nonlinear response. The demonstrated platform and methodology are promising to trigger the blossom of on-chip integrated MIR nonlinear photonics and practical applications of MIR spectroscopy and metrology.  
+
+<|ref|>sub_title<|/ref|><|det|>[[144, 567, 620, 583]]<|/det|>
+## Integrated \(\chi^{(2)}\) device with birefringence PM: design and fabrication  
+
+<|ref|>text<|/ref|><|det|>[[140, 601, 857, 875]]<|/det|>
+A ridge nonlinear waveguide is designed for the proof- of- concept demonstration of the integrated \(\chi^{(2)}\) device with birefringence PM. The ZGP crystal is chosen as the material platform for its high \(\chi^{(2)}\) nonlinearities ( \(d_{36} \sim 75 \mathrm{pm / V}\) ), mature growth technique, and broad MIR transparency window ( \(\sim 0.73 - 12 \mu \mathrm{m}\) ) \(^{15}\) . Traditionally, crystal angle is twisted to fulfil the birefringence PM condition. Thus, in integrated devices with birefringence PM, with a fixed crystal angle, it is crucial to have an ultrabroad PM bandwidth such that different pump and signal wavelengths could be adapted, and the spectral tunable parametric conversion could be realized. Fortunately, in a bulk ZGP crystal, Type- I PM pumped at \(\sim 2.4 \mu \mathrm{m}\) could provide an ultra- broadband PM bandwidth for an idler wavelength spanning from 5 to \(11 \mu \mathrm{m}\) , as calculated in Fig. 1(a), with a PM angle
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[138, 131, 857, 875]]<|/det|>
+83 designed as \(48.3^{\circ}\) . Besides the ultrabroad PM bandwidth, a multi- wavelength- scale ZGP ridge waveguide 84 structure on fused silica \(\mathrm{(SiO_2)}\) substrate is designed for the balance of tight field confinement, small 85 propagation loss, and ease of pump coupling. In the simulation of electric field distribution in the \(\chi^{(2)}\) 86 waveguide, the waveguide dimensions are designed as 35 and \(40\mu \mathrm{m}\) in width and height, respectively. In 87 addition, a sidewall angle of \(85^{\circ}\) is also considered in the simulation to mimic the actual fabricated device. 88 The moderate waveguide dimension and high refractive index contrast between the ZGP waveguide core and 89 cladding layers (air or \(\mathrm{SiO_2}\) ) contribute to an excellent mode confinement. As illustrated in simulated mode 90 cross sections in Fig. 1(b), fundamental mode profiles of the \(2.4\mu \mathrm{m}\) pump and \(8\mu \mathrm{m}\) idler in transverse 91 electric \(\mathrm{(TE_{00})}\) and magnetic \(\mathrm{(TM_{00})}\) polarizations, corresponding to ordinary and extraordinary waves, 92 respectively, of ZGP crystal are well confined and overlapped in the \(\chi^{(2)}\) waveguide. Notably, confinement 93 factors of both \(\mathrm{TE_{00}}\) and \(\mathrm{TM_{00}}\) modes are calculated to be greater than \(99\%\) in the entire interested MIR 94 spectral range. Moreover, the propagation loss of fundamental modes including the material absorption in the 95 designed \(\chi^{(2)}\) waveguide in a wavelength range of 2 to \(11\mu \mathrm{m}\) are calculated, as presented in Fig. 1(c). It is 96 revealed that the pump wave at \(2.4\mu \mathrm{m}\) in \(\mathrm{TE_{00}}\) mode and the long- wavelength MIR idler spanning from 5 to 97 \(11\mu \mathrm{m}\) with \(\mathrm{TM_{00}}\) polarization have a propagation loss \(< 0.01\) dB/cm and \(0.25\mathrm{dB / cm}\) , respectively, which 98 guarantees a good transmission of parametric waves in the \(\chi^{(2)}\) waveguide. The raise of waveguide losses in 99 the wavelength range of 9 - 11 \(\mu \mathrm{m}\) is attributed to the absorption of fused silica substrate peaked at \(9.5\mu \mathrm{m}\) , 100 as shown in the top inset which depicts the absorption coefficient of silica, as a function of wavelength19. It 101 is worth mentioning that the material loss of ZGP crystal is minimal in the spectral range of 2-11 \(\mu \mathrm{m}\) as shown 102 by the measured transmission spectrum of a 10- mm- thick uncoated ZGP crystal in the bottom inset of Fig. 103 1(c) (The Fresnell reflection is not subtracted).
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[140, 133, 857, 480]]<|/det|>
+Dispersion engineering is another critical process for the design of integrated \(\chi^{(2)}\) devices. The group velocity dispersion (GVD) and group velocity mismatch (GVM) between pump and idler pulses in the \(\chi^{(2)}\) waveguide are calculated and presented in Fig. 1(d). Owing to the multi- wavelength- scale geometry, the effect of waveguide dispersion is weak, and thus the ZGP waveguide features a nearly identical GVD profile with that of the bulk material, which simplifies the design of integrated \(\chi^{(2)}\) devices with birefringence PM. In addition, the GVD of \(2.4 \mu \mathrm{m}\) pump pulse is calculated as a small value of \(470 \mathrm{fs}^2 /\mathrm{mm}\) , which implies that the temporal profile of the pump pulse is not perturbed with respect to the \(320 \mathrm{fs}\) pulse width, while propagating in the \(10\mathrm{- mm - long}\chi^{(2)}\) waveguide. On the other hand, the calculated maximum GVM between the pump at a wavelength of \(2.4 \mu \mathrm{m}\) and the idler spanning from 6 to \(12 \mu \mathrm{m}\) is less than \(30 \mathrm{fs / mm}\) , indicating a small temporal walk off for parametric waves propagating in the \(10\mathrm{- mm - long}\mathrm{ZGP}\) waveguide.  
+
+<|ref|>text<|/ref|><|det|>[[140, 495, 857, 875]]<|/det|>
+Based on the designed parameters, a unique fabrication technique which combines procedures of nonlinear crystal bonding with the designed PM angle, \(\chi^{(2)}\) wafer lapping and ULDW is proposed and demonstrated based on a ZGP crystal, as depicted in Fig. 2. As the first step, a bulk ZGP crystal (DPT, YSZGP) with the dimension of \(4\) (width) \(\times 3\) (thickness) \(\times 10\) (length) \(\mathrm{mm}^3\) is cut into a crystal angle of \(48.3^{\circ}\) for type I PM. The bulk crystal with an anti- reflection coating in the spectral range of \(1.7 - 3 \mu \mathrm{m}\) and \(5 - 13 \mu \mathrm{m}\) is then bonded to a fused silica substrate using ultraviolet curing optical adhesives. The bonded bulk ZGP crystal is subsequently lapped and polished to a thickness of \(\sim 40 \mu \mathrm{m}\) . Fig. 2(h) shows the photograph of lapped ZGP- on- \(\mathrm{SiO}_2\) wafer. Finally, the ULDW technique is adopted to form a ridge waveguide structure on the lapped ZGP wafer (see Fig. 2(e, f)). More detailed descriptions of the ULDW process are presented in Methods. Scanning electron microscope images of the fabricated ZGP ridge waveguides based on the invented fabrication technique for \(\chi^{(2)}\) devices are also displayed in Fig. 2(i). It is observed that the structured
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[140, 133, 857, 404]]<|/det|>
+ZGP waveguide exhibits a side- wall roughness less than \(10 \mu \mathrm{m}\) , and the waveguide facets are nearly identical to those of unprocessed ZGP films, indicating a small scattering loss and potentially good coupling efficiency of the fabricated \(\chi^{(2)}\) waveguide. Notably, ULDW with optimized processing parameters is efficient for etching high- quality structures with tens of micrometer depth in \(\chi^{(2)}\) nonlinear crystals, with minor constraint on materials to be processed, which provides a unique merit compared to traditional CMOS dry- etching techniques such as inductively coupled plasma etching. It is therefore suggested that the demonstrated new technique could provide a universal method for large- scale three- dimensional fabrication of integrated \(\chi^{(2)}\) device.  
+
+<|ref|>sub_title<|/ref|><|det|>[[144, 424, 545, 440]]<|/det|>
+## Highly efficient and low threshold parametric generation  
+
+<|ref|>text<|/ref|><|det|>[[140, 456, 857, 875]]<|/det|>
+To characterize the parametric conversion in the fabricated on- chip \(\chi^{(2)}\) device with birefringence PM, OPG is performed to measure the parametric gain bandwidth and saturated gain value \(^{11,12}\) with the experimental setup illustrated in Fig. 3(a). The pump source is a home- built OPA centered at \(2.4 \mu \mathrm{m}\) with \(320 \mathrm{fs}\) pulse width at a repetition rate of \(500 \mathrm{kHz}\) . The spectrum of the pump laser is presented in Fig. 3(b). (More details of the MIR OPA as the pump source are included in Methods). The \(2.4 \mu \mathrm{m}\) pump beam is coupled into the ZGP waveguide focused by an uncoated \(\mathrm{CaF}_2\) lens with a \(40 \mathrm{- mm}\) focal length, while the generated long- wavelength MIR idler is collected by a ZnSe lens with a \(25 \mathrm{- mm}\) focal length and an anti- reflection coating in the spectral range of \(2 - 13 \mu \mathrm{m}\) . An input coupling efficiency of \(15.3\%\) and an output collecting efficiency of \(34\%\) are measured and estimated, respectively (More details of the measurement and calculation of coupling efficiencies are presented in Methods). A long- pass filter with a cutoff wavelength of \(4.5 \mu \mathrm{m}\) is used to remove the residual co- propagating pump and signal beams, and a MIR hollow core fiber with a \(500 \mu \mathrm{m}\) core diameter is employed to transmit the generated long- wavelength MIR radiation. The spectrum of the
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[141, 133, 857, 224]]<|/det|>
+generated idler is characterized by using a grating- scanning monochromator equipped with a lock- in amplifier and a liquid nitrogen cooled Mercury Cadmium Telluride (MCT) detector. The average power of the generated idler wave is measured using the MCT detector and a power meter.  
+
+<|ref|>text<|/ref|><|det|>[[141, 240, 857, 878]]<|/det|>
+Measured OPG spectra at different pump energy (peak power) are shown in Fig. 3(c). Driven at a pulse energy of \(3.2 \mathrm{nJ}\) , corresponding to a peak power of \(9.4 \mathrm{kW}\) , the OPG spectrum spans from \(6.6\) to \(9.6 \mu \mathrm{m}\) . As the pump energy is increased to \(8.2 \mathrm{nJ}\) , a macroscopic broadening of OPG spectra is observed because of the parametric gain bandwidth broadening \(^{11}\) which could tolerate certain deviation of the PM angle from the designed value caused by fabrication imperfections. Notably, the generated MIR idler wave has an octave- spanning spectrum covering \(5.4\) to \(10.3 \mu \mathrm{m}\) at \(-20 \mathrm{dB}\) , which is in good agreement with the simulated result in Fig. 1(a). The output power of the generated MIR idler from the ZGP waveguide is plotted in Fig. 3(d), as a function of pump pulse energy and average power. An OPG threshold of \(616 \mathrm{pJ}\) is measured from the ZGP waveguide, which is reduced by more than 1- order of magnitude, compared to the state- of- the- art MIR parametric conversions based on bulk crystals, thanks to the strong quadratic nonlinear response, tight mode confinement, good spatial/temporal overlap and long interaction range in the \(\chi^{(2)}\) device. It is worth mentioning that the threshold measurement is limited by the transmission loss of ZnSe lenses and LPF, the noise floor of the used MCT detector and \(300 \mathrm{ms}\) integration time of the lock- in detection. Even lower threshold energy value is expected with a pump source at a higher repetition rate. Increasing the pump pulse energy, the OPG output power grows exponentially. The long- wavelength MIR OPG power is measured as \(-0.72 \mathrm{mW}\) , at a pump pulse energy of \(6.5 \mathrm{nJ}\) and an average power of \(3.25 \mathrm{mW}\) , as presented in Fig. 3(d), corresponding to a power efficiency of \(22\%\) and a quantum efficiency of \(74\%\) . Moreover, as shown in Fig. 3(e), the measured saturated gain of the nonlinear waveguide is \(\sim 60 \mathrm{dB / cm}\) , which indicates that a decent
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[141, 133, 856, 225]]<|/det|>
+parametric gain could be provided by the integrated \(\chi^{(2)}\) device \(^{11,12}\) . The inset of Fig. 3(e) is the measured beam profile of the long- wavelength MIR idler from the ZGP waveguide. The revealed spatial intensity distribution is in excellent agreement with the simulated mode profile shown in Fig. 1(b).  
+
+<|ref|>text<|/ref|><|det|>[[141, 243, 857, 691]]<|/det|>
+The OPG spectral tunability from the \(\chi^{(2)}\) waveguide with birefringence PM is investigated by scanning the pump wavelength in the range of 2.3- 2.7 \(\mu \mathrm{m}\) . As shown in Fig. 4(a), pumped at 2.4 \(\mu \mathrm{m}\) , a MIR idler spectrum centered at 8 \(\mu \mathrm{m}\) is obtained. When the pump wavelength is increased to 2.5 \(\mu \mathrm{m}\) , owing to better PM condition at two wings of the spectrum, an octave- spanning idler covering 4.5 to 12 \(\mu \mathrm{m}\) is generated. Further increasing the pump wavelength to 2.6 \(\mu \mathrm{m}\) abruptly reduces the parametric bandwidth, and a relatively narrow- band idler spectrum centered at 9.3 \(\mu \mathrm{m}\) is measured, as presented in Fig. 4(c). A semiclassical simulation of optical parametric generation is also conducted to study the spectral evolution in the \(\chi^{(2)}\) waveguide with birefringence PM at different pump wavelengths, by solving the \(\chi^{(2)}\) - based coupled- wave equations. Parametric seed with noise field represented by a complex Gaussian distribution with zero mean and a half- a- photon energy variance is adopted to mimic vacuum fluctuations \(^{20}\) . The simulation results shown in Figs. 4(d- f) qualitatively agree well with the experimental results. We therefore suggest that spectral shaping of the integrated parametric device could be realized by tuning the pump wavelength, which would broaden its applications in MIR spectroscopy and metrology.  
+
+<|ref|>sub_title<|/ref|><|det|>[[144, 712, 220, 725]]<|/det|>
+## Discussion  
+
+<|ref|>text<|/ref|><|det|>[[141, 748, 857, 873]]<|/det|>
+Over the last decade, long- wavelength MIR parametric sources have been demonstrated in bulk nonlinear crystals facilitated either by birefringence or quasi- PM techniques. However, the quantum efficiency is usually low, limited by factors such as spatial/temporal walk- off, non- perfect beam overlap, large interaction area and short parametric coupling length. Fig. 5(a) compares reported quantum efficiencies of the state- of
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[140, 130, 857, 660]]<|/det|>
+art parametric conversions including OPA \(^{17,38 - 42}\) (blue squares), OPG \(^{35 - 37}\) (pink circles), DFG \(^{21 - 27}\) (orange diamonds), and intra-pulse difference-frequency generation \(^{4,28 - 34}\) (IPDFG) (green triangles) based on bulk nonlinear crystals in the long-wavelength MIR range ( \(\lambda > 5 \mu \mathrm{m}\) ), with that of the demonstrated on-chip integrated \(\chi^{(2)}\) waveguide (red star). Typically, OPAs/OPGs in bulk nonlinear crystals operate with microjoule or even higher pump energy. DFGs and IPDFGs pumped by mode-locked lasers at high-repetition ( \(\sim \mathrm{MHz}\) magnitude) allow lower pump pulse energy of nanojoules level, but the quantum conversion efficiency is limited to \(\sim 20\%\) or lower. Owing to the small interaction area, good spatial/temporal overlap, and inherently elongated interaction length originated from the waveguide structure, a quantum efficiency of \(74\%\) at a small pump energy of \(\sim 6.5 \mathrm{nJ}\) is obtained in the integrated \(\chi^{(2)}\) waveguide, which is enhanced by more than 2 folds compared to the traditional parametric conversions. In addition, anther merit of the demonstrated on-chip \(\chi^{(2)}\) device is reflected as the ultra-low threshold. As displayed in Fig. 5(b), the measured threshold pulse energy and peak power from the demonstrated \(\chi^{(2)}\) waveguide is compared with those of OPGs in \(\chi^{(2)}\) bulk media including PPLN \(^{43 - 46}\) and OP- GaAs \(^{35}\) crystals. Ultra-low threshold pulse energy and peak power measured as \(616 \mathrm{pJ}\) and \(1.9 \mathrm{kW}\) are obtained, respectively, which is more than 1-order of magnitude lower than those of reported OPGs in literature.  
+
+<|ref|>sub_title<|/ref|><|det|>[[144, 676, 319, 691]]<|/det|>
+## Conclusions and outlook  
+
+<|ref|>text<|/ref|><|det|>[[140, 710, 857, 874]]<|/det|>
+In summary, we, for the first time to the best of our knowledge, propose and demonstrate the integrated photonic platform based on the \(\chi^{(2)}\) nonlinear crystal with birefringence PM. A unique fabrication technique which combines procedures of nonlinear crystal bonding, \(\chi^{(2)}\) wafer lapping and ULDW is employed to fabricate the ZGP \(\chi^{(2)}\) waveguide, which could be extended as a universal technique for integrated nonlinear photonics out of other birefringent crystals. High- gain ( \(>60 \mathrm{dB}\) ) and low- threshold ( \(616 \mathrm{pJ}\) ) long- wavelength
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[140, 133, 857, 295]]<|/det|>
+MIR OPG with an octave- spanning bandwidth is demonstrated in the \(\chi^{(2)}\) waveguide. Tunable MIR spectra in the wavelength range of 5 to \(12\mu \mathrm{m}\) are achieved by scanning the pump wavelength. With the strong quadratic nonlinear response, tight mode confinement, extraordinary spatial/temporal overlap of the parametric waves, and elongated parametric interaction length in the demonstrated \(\chi^{(2)}\) waveguide, a record quantum efficiency of \(74\%\) are demonstrated.  
+
+<|ref|>text<|/ref|><|det|>[[140, 315, 857, 690]]<|/det|>
+Based on the proof- of- concept demonstration, more advanced explorations of MIR integrated nonlinear photonics such as on- chip IPDFG \(^{47}\) , MIR femtosecond lasers with ultra- high repetition rate and \(\chi^{(2)}\) frequency comb generation \(^{48}\) could be pursued. Moreover, it is worth mentioning that more compact femtosecond lasers operating around \(2.4\mu \mathrm{m}\) with decent output power have experienced substantial development in the last decade. Kerr- lens mode- locked \(\mathrm{Cr^{2 + }}\) : \(\mathrm{ZnS / ZnSe}\) oscillators \(^{49}\) and MIR fiber lasers based on soliton self- frequency shift \(^{50}\) could output up to hundreds of kW peak power which is well above the pump threshold of the demonstrated \(\chi^{(2)}\) integrated parametric generator. This would further improve the compactness of the entire system. We therefore believe that the on- chip integrated \(\chi^{(2)}\) device with birefringence PM fabricated by the new technique invented in this work, equipped with advanced pump sources would flourish the MIR integrated photonics researches and promote practical applications of MIR spectroscopy and metrology with compact systems.  
+
+<|ref|>sub_title<|/ref|><|det|>[[144, 731, 214, 745]]<|/det|>
+## Methods  
+
+<|ref|>text<|/ref|><|det|>[[140, 766, 857, 852]]<|/det|>
+The pump source: KTiOPO4 (KTP) based OPA. The pump source starts with a customized Yb- fiber laser (Yacto, YF- FL- 50- 100- IR) emitting 1030 nm pulses with a duration of 260 fs at 500 kHz repetition rate. A small fraction of pulse energy \(\sim 8\mu \mathrm{J}\) is focused into a 15- mm- long YAG crystal generating a stable white light
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[140, 133, 856, 295]]<|/det|>
+spanning from 1.1 to \(1.9 \mu \mathrm{m}\) in the near- infrared region. An 8- mm- long anti- reflection coated KTP crystal cut at \(\theta = 44.5^{\circ}\) , \(\phi = 0^{\circ}\) for type II PM is used in the OPA. The idler wavelength is tunable from 2.4 to \(2.8 \mu \mathrm{m}\) by twisting the PM angle. The output power of the idler wave is measured up to \(500 \mathrm{mW}\) by using a \(15 \mathrm{W}\) pump. The pulse duration of idler wave is characterized as 320 fs with some uncompensated dispersion inherited from the OPA system, through a home- built interferometer autocorrelator.  
+
+<|ref|>text<|/ref|><|det|>[[140, 314, 857, 765]]<|/det|>
+\(\chi^{(2)}\) waveguide fabrication. A bulk ZGP crystal cut into a PM angle of \(48.3^{\circ}\) for type I PM is bonded to a fused silica substrate using ultraviolet curing optical adhesives (NOA61). The bonded ZGP crystal is subsequently lapped and polished by a plane precision ring polishing machine (Jian Su XB, LP10C/08C) to a thickness of \(\sim 40 \mu \mathrm{m}\) . In the process of ultrafast laser direct writing, the \(520 \mathrm{nm}\) femtosecond laser beam (Spectra- Physics Spirit HE 1040- 30- SHG) with a pulse width of \(300 \mathrm{fs}\) at a repetition rate of \(250 \mathrm{kHz}\) is focused to the ZGP surface by an F- Theta lens with a focal spot size of \(10 \mu \mathrm{m}\) in diameter. A coaxial charge- coupled device imaging system is equipped to achieve an accurate focusing. The laser processing parameters are optimized with the laser fluence, scanning speed, scanning spacing, and processing cycles as \(1.53 \mathrm{J / cm^2}\) , \(100 \mathrm{mm / s}\) , \(5 \mu \mathrm{m}\) , and \(2\) , respectively. A vacuum cleaner is turned on during the laser processing to remove the debris produced in the femtosecond laser fabrication. The \(\chi^{(2)}\) wafer is cleaned using an ultrasonic cleaner after laser processing in deionized water for 5 seconds. With above procedures and parameters, the waveguide side- wall roughness is estimated to be less than \(10 \mu \mathrm{m}\) , and the waveguide facets are nearly identical to those of unprocessed ZGP films.  
+
+<|ref|>text<|/ref|><|det|>[[141, 785, 857, 872]]<|/det|>
+Calibrations of the coupling and collecting efficiency. The coupling efficiency is measured by replacing the \(\chi^{(2)}\) waveguide with a pinhole of \(100 \mu \mathrm{m}\) in diameter, and record the transmitted power ratio of the 2.4 \(\mu \mathrm{m}\) laser beam. The coupling efficiency is measured as \(15.3\%\) which is several times larger than those of
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[141, 134, 857, 550]]<|/det|>
+PPLN waveguides with sub- wavelength apertures, benefited from the multi- wavelength- scale dimensions in the fabricated \(\chi^{(2)}\) device. On the collecting side, the generated long- wavelength MIR idler radiation is collected by a 1- inch ZnSe lens with a focal length of \(25\mathrm{mm}\) , corresponding to a numerical aperture (NA) of 0.45, and an anti- reflection coating in the spectral range of 2- 13 \(\mu \mathrm{m}\) . Using the method demonstrated in Ref. 11, 12, the OPG average photon number could be expressed as \(\langle n\rangle = a\sinh^2 (2gl)\) , where \(a\) is the overall detection efficiency including the output- coupling loss and imperfect detection induced loss, \(g\) indicates the parameter gain coefficient. Here, \(g\) is in directly proportion to \(\sqrt{\eta P}\) , where \(\eta\) represents the nonlinear interaction coefficient and \(P\) is the pump power. When the parametric gain is larger than \(10\mathrm{dB}\) , \(\langle n\rangle\) could be approximated as \(\langle n\rangle = a\exp (2gl)\) . In addition, the measured average OPG power could be expressed as \(P_{OPG} = \langle n\rangle hv_{f_{rep}}\) , from which \(\langle n\rangle\) could be fitted and obtained, where \(h\) is the Planck constant, \(\nu\) represents the idler frequency and \(f_{rep}\) indicates the repetition frequency. Hence, the parameter \(a\) could be estimated, and the collection efficiency is obtained as \(34\%\) .  
+
+<|ref|>text<|/ref|><|det|>[[142, 567, 854, 617]]<|/det|>
+Data availability. The data that support the findings of this study are available from the corresponding author upon request.  
+
+<|ref|>sub_title<|/ref|><|det|>[[143, 650, 217, 663]]<|/det|>
+## Reference  
+
+<|ref|>text<|/ref|><|det|>[[140, 667, 857, 845]]<|/det|>
+1. Schliesser, A. et al. Mid-infrared frequency combs. Nat. Photonics 6, 440-449 (2012).  
+2. Yao, Y. et al. Mid-infrared quantum cascade lasers. Nat. Photonics 6, 432-439 (2012).  
+3. Liang, H. K. et al. High-energy mid-infrared sub-cycle pulse synthesis from a parametric amplifier. Nat. Commun. 8, 141 (2017).  
+4. Pupeza, I. et al. High-power sub-two-cycle mid-infrared pulses at 100 MHz repetition rate. Nat. Photonics 9, 721-725 (2015).  
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+6. Soref, R. Mid-infrared photonics in silicon and germanium. Nat. Photonics 4, 495-497 (2010).  
+7. Hu, T. et al. Silicon photonic platforms for mid-infrared applications. Photonics Res. 5, 417-430 (2017).
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+8. Sinobad, M. et al. Mid-infrared octave spanning supercontinuum generation to 8.5 μm in silicon-germanium waveguides. Optica 5, 360-366 (2018).
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+12. Ledezma, L. et al. Intense optical parametric amplification in dispersion-engineered nanophotonic lithium niobate waveguides. Optica 9, 303-308 (2022).
+13. Jankowski, M. et al. Ultrabroadband nonlinear optics in nanophotonic periodically poled lithium niobate waveguides. Optica 7, 40-46 (2020).
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+17. Liu, K. et al. Multimicrojoule GaSe-based midinfrared optical parametric amplifier with an ultrabroad idler spectrum covering 4.2-16 μm. Opt. Lett. 44, 1003-1006 (2019).
+18. Lesko, D. M. B. et al. A six-octave optical frequency comb from a scalable few-cycle erbium fibre laser. Nat. Photonics 15, 281-286 (2021).
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+21. Churin, D. Kieu, K. & Peyghambarian, N. Difference frequency generation at 9 μm wavelength using a compact all-fiber laser source based on Thulium and Erbium fiber amplifiers. in Frontiers in Optics 2014, Optica Technical Digest (online).
+22. Sánchez, D. et al. Broadband mid-IR frequency comb with CdSiP₂ and AgGaS₂ from an Er, Tm:Ho fiber laser. Opt. Lett. 39, 6883-6886 (2014).
+23. Stibenz, G. Beutler. M. & Rimke, I. Femtosecond mid-IR difference-frequency generation in BaGa₂GeSe₆ from a 40 MHz optical parametric oscillator pumped at 1035 nm. in Conference on Lasers and Electro-Optics, OSA Technical Digest (online) (Optical Society of America, 2018), paper STh4F.5.
+24. Beutler, M. et al. Difference-frequency generation of fs and ps mid-IR pulses in LiInSe₂ based on Yb-fiber laser pump sources. Opt. Lett. 39, 4353-4355 (2014).
+25. Steinle. T. et al. Ultra-stable high average power femtosecond laser system tunable from 1.33 to 20 μm. Opt. Lett. 41, 4863-4866 (2016).
+26. Zhou, G. et al. Energy scalable, offset-free ultrafast mid-infrared source harnessing self-phase-modulation-enabled spectral selection. Opt. Lett. 43, 2953-2956 (2018).
+27. Phillips, C. R. et al. Widely tunable midinfrared difference frequency generation in orientation-patterned GaAs pumped with a femtosecond Tm-fiber system. Opt. Lett. 37, 2928-2930 (2012).
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+<--- Page Split --->
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+318 28. Timmers, H. et al. Molecular fingerprinting with bright, broadband infrared frequency combs. Optica 5, 719 727- 732 (2018). 320 29. Hoghooghi, N. et al. Broadband 1- GHz mid- infrared frequency comb. Light. Sci. Appl. 11, 264 (2022). 321 30. Zhang, J. et al. Intra- pulse difference- frequency generation of mid- infrared (2.7- 20 μm) by random quasi- phase- matching. Opt. Lett. 44, 2986- 2989 (2019). 323 31. Vasilyev, S. et al. Super- octave longwave mid- infrared coherent transients produced by optical rectification of few- cycle 2.5- μm pulses. Optica 6, 111- 114 (2019). 325 32. Elu, U. et al. Seven- octave high- brightness and carrier- envelope- phase- stable light source. Nat. Photonics 15, 277- 280 (2021). 327 33. Gaida, C. et al. Watt- scale super- octave mid- infrared intrapulse difference frequency generation. Light. Sci. Appl. 7, 94 (2018). 329 34. Liu, K. et al. High- energy mid- infrared intrapulse difference- frequency generation with 5.3% conversion efficiency driven at 3 μm. Opt. Express 27, 37706- 37713 (2019). 331 35. Kuo, P. S., Vodopyanov, K. L. & Fejer. M.M. Optical parametric generation of a mid- infrared continuum in orientation- patterned GaAs. Opt. Lett. 31, 71- 73 (2006). 333 36. Xu, L. et al. Thulium- fiber- laser- pumped, high- peak- power, picosecond, mid- infrared orientation- patterned GaAs optical parametric generator and amplifier. Opt. Lett. 42, 4036- 4039 (2017). 335 37. Chalus, O. et al. Optical parametric generation in CdSiP2. Opt. Lett. 35, 4142- 4144 (2010). 336 38. Seidel, M. et al. Multi- watt, multi- octave, mid- infrared femtosecond source. Sci. Adv. 4, eaag1526 (2018). 339 39. Hinkelmann, M. et al. High- repetition rate, mid- infrared, picosecond pulse generation with μJ- energies based on OPG/OPA schemes in 2- μm- pumped ZnGeP2. Opt. Express 28, 21499- 21508 (2020). 340 40. Penwell, S. B. et al. Single- stage MHz mid- IR OPA using LiGaS2 and a fiber laser pump source. Opt. Lett. 43, 1363- 1366 (2018). 341 41. Kanai, T. et al. Parametric amplification of 100 fs mid- infrared pulses in ZnGeP2 driven by a Ho:YAG chirped pulse amplifier. Opt. Lett. 42, 683- 686 (2017). 342 42. Malevich, P. et al. Broadband mid- infrared pulses from potassium titanyl arsenate/zinc germanium phosphate optical parametric amplifier pumped by Tm, Ho- fiber- seeded Ho:YAG chirped- pulse amplifier. Opt. Lett. 41, 930- 933 (2016). 343 43. Aadhi, A & Samanta, G. K. High power, high repetition rate, tunable broadband mid- IR source based on single- pass optical parametric generation of a femtosecond laser. Opt. Lett. 42, 2886- 2889 (2017). 344 44. Galvanauskas, A. et al. Fiber- laser- based femtosecond parametric generator in bulk periodically poled LiNbO3. Opt. Lett. 22, 105- 107 (1997). 345 45. Linnenbank, H. et al. Narrowband cw injection seeded high power femtosecond double- pass optical parametric generator at 43 MHz: Gain and noise dynamics. Opt. Express 24, 19558- 19566 (2016). 346 46. Fan, J. et al. Noise characteristics of high power fiber- laser pumped femtosecond optical parametric generation. Opt. Express 25, 24594- 24603 (2017). 347 47. Lind, A. J. et al. Mid- Infrared Frequency Comb Generation and Spectroscopy with Few- Cycle Pulses and \(\chi^{(2)}\) Nonlinear Optics. Phys. Rev. Lett. 124, 133904 (2020). 348 48. Lkuta, R. et al. Frequency comb generation in a quadratic nonlinear waveguide resonator. Opt. Express 26, 15551- 15558 (2018).
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[88, 125, 856, 196]]<|/det|>
+359 49. Mirov. S. B. et al. Frontiers of Mid-IR Lasers Based on Transition Metal Doped Chalcogenides. IEEE 360 J. Sel. Topics Quantum Electron. 24, 1601829 (2018). 361 50. Tiliouine, I. et al. Fiber-based source of 500 kW mid-infrared solitons. Opt. Lett. 46, 5890-5893 (2021). 362
+
+<--- Page Split --->
+<|ref|>sub_title<|/ref|><|det|>[[144, 135, 308, 152]]<|/det|>
+## Acknowledgement:  
+
+<|ref|>text<|/ref|><|det|>[[144, 177, 855, 195]]<|/det|>
+This work was supported by National Natural Science Foundation of China (62075144, 12175157), Sichuan Outstanding  
+
+<|ref|>text<|/ref|><|det|>[[144, 215, 855, 232]]<|/det|>
+Youth Science and Technology Talents (2022JDJQ0031), and Engineering Featured Team Fund of Sichuan University  
+
+<|ref|>text<|/ref|><|det|>[[144, 252, 265, 266]]<|/det|>
+(2020SCUNG105).  
+
+<|ref|>sub_title<|/ref|><|det|>[[144, 328, 397, 346]]<|/det|>
+## Materials & Correspondence:  
+
+<|ref|>text<|/ref|><|det|>[[144, 370, 855, 387]]<|/det|>
+Correspondence should be addressed to Huan Yang yanghuan@sztu.edu.cn and HouKun Liang  
+
+<|ref|>text<|/ref|><|det|>[[144, 408, 284, 423]]<|/det|>
+hkliang@scu.edu.cn.  
+
+<|ref|>sub_title<|/ref|><|det|>[[144, 444, 331, 460]]<|/det|>
+## Author contributions:  
+
+<|ref|>text<|/ref|><|det|>[[144, 479, 855, 497]]<|/det|>
+H. K. Liang conceived and designed the experiment. B. Hu and X. Yang carried out the experiment of OPG  
+
+<|ref|>text<|/ref|><|det|>[[144, 516, 855, 533]]<|/det|>
+measurement. H. K. Liang, Y. Li, H. Wu, Q. J. Wang designed ZGP waveguide. B. Hu, J. Wu, W. Li and H.  
+
+<|ref|>text<|/ref|><|det|>[[144, 553, 855, 570]]<|/det|>
+Yang fabricated ZGP waveguide. B. Hu, X. Yang, S. Lu, H. Yang, and Z. Long conducted the theoretical  
+
+<|ref|>text<|/ref|><|det|>[[144, 590, 855, 606]]<|/det|>
+simulations. X. Yang, K. Tian, L. He and W. Wang built the MIR OPA. H. K. Liang, B. Hu, X. Yang, J. Wu,  
+
+<|ref|>text<|/ref|><|det|>[[144, 626, 855, 643]]<|/det|>
+H. Yang and H. Wu wrote the manuscript. B. Hu, X. Yang and J. Wu contributed equally. All authors discussed  
+
+<|ref|>text<|/ref|><|det|>[[144, 663, 440, 678]]<|/det|>
+the results and contributed to the manuscript.
+
+<--- Page Split --->
+<|ref|>image<|/ref|><|det|>[[152, 168, 850, 450]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[140, 460, 855, 875]]<|/det|>
+<center>Fig. 1 | Design and simulation of the \(\chi^{(2)}\) integrated waveguide with birefringence PM based on a ZGP crystal. (a) Calculated normalized gain spectrum at a pump wavelength of \(2.4 \mu \mathrm{m}\) for a bulk ZGP crystal, at various PM angles. A PM angle of \(\theta = 48.3^{\circ}\) is designed as marked by the orange dashed line to generate the broadest gain bandwidth. (b) Simulated electric field distributions in the \(\chi^{(2)}\) waveguide of pump wave at \(2.4 \mu \mathrm{m}\) in TE\(_{00}\) mode (top) and a typical idler wave at \(8.0 \mu \mathrm{m}\) in TM\(_{00}\) mode (bottom). (c) Calculated propagation losses of fundamental modes in the designed \(\chi^{(2)}\) waveguide in TE and TM polarizations, in a broad spectral range of 2 - 11 \(\mu \mathrm{m}\) . The results indicate that the propagation loss of fundamental TE and TM mode is less than 0.15 dB/cm and 0.25 dB/cm, respectively. The raise of waveguide losses in the wavelength range of 9 - 11 \(\mu \mathrm{m}\) is attributed to the absorption of fused silica substrate peaked at 9.5 \(\mu \mathrm{m}\) , as shown in the top inset which depicts the absorption coefficient (imaginary part of complex refractive index) of silica, as a function of wavelength\(^{19}\) . Meanwhile, the material loss of ZGP crystal is minimal in the spectral range of 2-11 \(\mu \mathrm{m}\) as shown by the measured transmission spectrum of a 10-mm-thick uncoated ZGP crystal in the bottom inset of Fig. 1(c). (d) The simulated GVD of TE\(_{00}\) modes in a wavelength range of 2 to 11 \(\mu \mathrm{m}\) , in the designed \(\chi^{(2)}\) ZGP waveguide (green) and bulk ZGP (black), respectively. Owing to multi-wavelength-scale dimensions, the integrated waveguide exhibits nearly an identical GVD profile to that of the bulk material, which </center>
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[140, 134, 856, 330]]<|/det|>
+simplifies the design of integrated \(\chi^{(2)}\) devices with birefringence PM. In addition, the GVD of the \(2.4 \mu \mathrm{m}\) pump pulse is calculated as a small value of \(470 \mathrm{fs}^2 /\mathrm{mm}\) , which implies that the temporal profile of the pump pulse is not perturbed with respect to the \(320 \mathrm{fs}\) pulse width, while propagating in the \(10\mathrm{- mm - long}\chi^{(2)}\) waveguide. In addition, the simulated GVM between the pump wave at \(2.4 \mu \mathrm{m}\) for the \(\mathrm{TE}_{00}\) modes and the signal wave (blue)/idler wave (red) in the spectral range of \(3 - 5 \mu \mathrm{m} / 6 - 12 \mu \mathrm{m}\) for the \(\mathrm{TM}_{00}\) modes are also plotted. The maximum GVM between the pump and idler pulses in the spectral range of \(6 - 12 \mu \mathrm{m}\) is less than \(30 \mathrm{fs} / \mathrm{mm}\) , implying a small temporal walk off, for a \(\sim 320 \mathrm{fs}\) pump pulse propagating in the \(10\mathrm{- mm - long}\mathrm{ZGP}\) waveguide.
+
+<--- Page Split --->
+<|ref|>image<|/ref|><|det|>[[150, 157, 830, 415]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[142, 460, 855, 725]]<|/det|>
+<center>Fig. 2| Fabrication steps of the \(\chi^{(2)}\) integrated waveguide with birefringence PM based on a ZGP crystal. (a, b) Step1: a bulk ZGP crystal with a PM angle of \(48.3^{\circ}\) for type I PM (the designed optical axis (OA), with respect to the incident wave vector \(k\) , is marked by a white dashed line) and an anti-reflection coating in the spectral range of \(1.7 - 3 \mu \mathrm{m}\) and \(5 - 13 \mu \mathrm{m}\) is bonded to a fused silica substrate, through using the ultraviolet curing optical adhesives. (c, d) Step 2: the bonded bulk ZGP crystal with the designed PM angle is lapped and polished to a thickness of \(\sim 40 \mu \mathrm{m}\) by employing an optical grinding machine. (e, f) Step 3: the ultrafast laser direct writing technique is adopted to form a ridge waveguide structure on the lapped ZGP wafer. (h) The photograph shows the grinded ZGP-on-SiO₂ wafer. (i) Scanning electron microscope images of the fabricated \(\chi^{(2)}\) waveguide. Closely packed waveguide arrays are fabricated to fully utilize the \(\chi^{(2)}\) wafer, while no field could be coupled between the adjacent waveguides. </center>
+
+<--- Page Split --->
+<|ref|>image<|/ref|><|det|>[[186, 128, 797, 540]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[140, 551, 854, 565]]<|/det|>
+<center>Fig. 3| Experimental setup and characterizations of the spectra, output power, pump threshold and parametric gain of the on- </center>  
+
+<|ref|>text<|/ref|><|det|>[[140, 585, 856, 852]]<|/det|>
+chip integrated \(\chi^{(2)}\) device with birefringence PM based on a ZGP crystal. (a) The schematic of the experimental setup for OPG from the \(\chi^{(2)}\) waveguide. The \(2.4 \mu \mathrm{m}\) pump beam is coupled into the ZGP waveguide focused by an uncoated \(\mathrm{CaF}_2\) lens with a \(40 - \mathrm{mm}\) focal length, while the generated long-wavelength MIR idler is collected by a ZnSe lens with a \(25 - \mathrm{mm}\) focal length and an anti-reflection coating in the spectral range of \(2 - 13 \mu \mathrm{m}\) . A long-pass filter (LPF, Edmund #68- 655) with a cutoff wavelength of \(4.5 \mu \mathrm{m}\) is used to remove the residual co- propagating pump and signal beams. The spectrum of the generated idler is characterized by using a grating- scanning monochromator (Zolix Omni- \(\lambda 500i\) ) equipped with a lock- in amplifier (SRS, SR830) and a liquid nitrogen cooled Mercury Cadmium Telluride (MCT) detector (Judson, DMCT16- De01). The average power of the generated idler wave is measured using the MCT detector and a power meter (Ophir, 3A). (b) The pump spectrum centered at \(2.4 \mu \mathrm{m}\) . (c) The measured OPG spectra from the \(\chi^{(2)}\) waveguide with
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[140, 134, 857, 148]]<|/det|>
+different pump peak powers. The generated MIR idler wave has an octave-spanning spectrum covering 5.4 to \(10.3 \mu \mathrm{m}\) at - 20 dB, which  
+
+<|ref|>text<|/ref|><|det|>[[140, 170, 857, 185]]<|/det|>
+is in good agreement with the simulated result in Fig. 1(a). The noise floor is marked by a grey dotted line. (d) The measured OPG  
+
+<|ref|>text<|/ref|><|det|>[[140, 207, 857, 221]]<|/det|>
+output power shown in a linear scale as a function of pump pulse energy and average power. The measured pump threshold energy is  
+
+<|ref|>text<|/ref|><|det|>[[140, 243, 857, 257]]<|/det|>
+\(\sim 616 \mathrm{pJ}\) , corresponding to a peak power of \(1.9 \mathrm{kW}\) . In addition, When the pump energy is \(6.5 \mathrm{nJ}\) corresponding to an average power of  
+
+<|ref|>text<|/ref|><|det|>[[140, 279, 857, 293]]<|/det|>
+\(3.25 \mathrm{mW}\) , a long- wavelength MIR power of \(0.72 \mathrm{mW}\) is detected from the \(\chi^{(2)}\) waveguide, corresponding to a power efficiency of \(22\%\)  
+
+<|ref|>text<|/ref|><|det|>[[140, 315, 857, 329]]<|/det|>
+and a quantum efficiency of \(74\%\) . Meanwhile, further increasing the pump energy, the output power tends to saturate. Particularly, when  
+
+<|ref|>text<|/ref|><|det|>[[140, 351, 857, 365]]<|/det|>
+the pump pulse energy is larger than \(9 \mathrm{nJ}\) , the MIR output power decreases, due to parametric back conversion. (e) The normalized OPG  
+
+<|ref|>text<|/ref|><|det|>[[140, 387, 857, 401]]<|/det|>
+intensity in a logarithm scale at different pump energy. A saturated parametric gain of \(>60 \mathrm{dB / cm}\) is observed at a pump energy of \(7.9\)  
+
+<|ref|>text<|/ref|><|det|>[[140, 423, 857, 437]]<|/det|>
+\(\mathrm{nJ}\) , which indicates that a decent parametric gain could be provided by the integrated \(\chi^{(2)}\) device. The inset shows the measured beam  
+
+<|ref|>text<|/ref|><|det|>[[140, 459, 805, 473]]<|/det|>
+profile of MIR OPG from the \(\chi^{(2)}\) integrated waveguide, which agrees well with the simulated field profile shown in Fig. 1(b).
+
+<--- Page Split --->
+<|ref|>image<|/ref|><|det|>[[147, 131, 839, 383]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[140, 408, 854, 423]]<|/det|>
+<center>Fig. 4| The characterization of broadband spectral tuning and shaping from the on-chip integrated \(\chi^{(2)}\) device with birefringence </center>  
+
+<|ref|>text<|/ref|><|det|>[[140, 444, 857, 673]]<|/det|>
+PM, when scanning the pump wavelength. (a-c) and (d-f) are the measured and simulated OPG spectra pumped at \(2.4 \mu \mathrm{m}\) , \(2.5 \mu \mathrm{m}\) and \(2.6 \mu \mathrm{m}\) , respectively. (a) A MIR idler spectrum centered at \(8 \mu \mathrm{m}\) is measured pumped at \(2.4 \mu \mathrm{m}\) wavelength. (b) As the pump wavelength is increased to \(2.5 \mu \mathrm{m}\) , an octave-spanning idler covering \(4.5\) to \(12 \mu \mathrm{m}\) is generated, owing to a better PM condition at two wings of the spectrum. (c) Further increasing the pump wavelength to \(2.6 \mu \mathrm{m}\) , narrow-band idler spectrum centered at \(9.3 \mu \mathrm{m}\) is measured due to the reduced parametric bandwidth. (d-f) A semi-classical simulation of optical parametric generation is also conducted to study the spectral evolution in the \(\chi^{(2)}\) waveguide with birefringence PM at different pump wavelengths, by solving the \(\chi^{(2)}\) - based coupled-wave equations. The measurement and simulation results qualitatively agree well with each other.
+
+<--- Page Split --->
+<|ref|>image<|/ref|><|det|>[[150, 135, 850, 350]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[140, 369, 855, 384]]<|/det|>
+<center>Fig. 5| Comparison of quantum efficiency and pump threshold of the demonstrated \(\chi^{(2)}\) waveguide based on birefringence PM </center>  
+
+<|ref|>text<|/ref|><|det|>[[140, 405, 856, 420]]<|/det|>
+with the state- of- art parametric conversions. (a) Summary of quantum efficiencies and saturated pump pulse energy of parametric  
+
+<|ref|>text<|/ref|><|det|>[[140, 440, 856, 456]]<|/det|>
+sources including representative OPA \(^{17,38,42}\) , OPG \(^{35,37}\) , DFG \(^{21,27}\) and IPDFG \(^{24,28,34}\) in the long- wavelength MIR region (>5 μm) based on  
+
+<|ref|>text<|/ref|><|det|>[[140, 477, 856, 492]]<|/det|>
+bulk nonlinear crystals, and the demonstrated on- chip integrated OPG based on ZGP \(\chi^{(2)}\) waveguide. A quantum efficiency of 74% at a  
+
+<|ref|>text<|/ref|><|det|>[[140, 513, 856, 528]]<|/det|>
+pump pulse energy of 6.5 nJ is demonstrated, which is enhanced by more than 2 folds compared to the state- of- art parametric conversions.  
+
+<|ref|>text<|/ref|><|det|>[[140, 549, 856, 564]]<|/det|>
+(b) Comparison of the threshold pulse energy and peak power of the demonstrated OPG in the \(\chi^{(2)}\) waveguide based on birefringence  
+
+<|ref|>text<|/ref|><|det|>[[140, 584, 856, 600]]<|/det|>
+PM and other MIR OPGs with quasi- PM, based on PPLN \(^{43,46}\) and OP- GaAs crystals \(^{35}\) . The measured threshold pump energy of ZGP \(\chi^{(2)}\)  
+
+<|ref|>text<|/ref|><|det|>[[140, 620, 856, 636]]<|/det|>
+waveguide is 616 pJ, corresponding to a peak power of 1.9 kW, which is reduced by more than 1- order of magnitude compared to the  
+
+<|ref|>text<|/ref|><|det|>[[140, 657, 312, 670]]<|/det|>
+reported OPGs in the literature.
+
+<--- Page Split --->

preprint/preprint__1e5cdd750bc4e21002edbf5deec2c0603c5e61a8896d319fa5ba075a64d68f89/images_list.json

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+[
+  {
+    "type": "image",
+    "img_path": "images/Figure_1.jpg",
+    "caption": "Figure 1. Interaction and colocalization of SARS-CoV-2 M and ARF1. (A)",
+    "footnote": [],
+    "bbox": [
+      [
+        150,
+        140,
+        850,
+        490
+      ]
+    ],
+    "page_idx": 39
+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_2.jpg",
+    "caption": "Figure 2. ARF1 acts as a pro-viral host factor for SARS-CoV-2 and its variants.",
+    "footnote": [],
+    "bbox": [
+      [
+        147,
+        108,
+        852,
+        583
+      ]
+    ],
+    "page_idx": 41
+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_3.jpg",
+    "caption": "Figure 3. The role of ARF1 in SARS-CoV-2 infection and pathogenicity in vivo.",
+    "footnote": [],
+    "bbox": [
+      [
+        150,
+        103,
+        838,
+        600
+      ]
+    ],
+    "page_idx": 43
+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_4.jpg",
+    "caption": "Figure 4. ARF1 enables the accumulation of M to the EGRIC, boosting M-driven SARS-CoV-2 VLP assembly. (A, B) In contrast with the accumulation of M to the ERGIC in WT cells, the localization of M is dispersed and the concentration at the ERGIC is deprived in ARF1-KO cells. WT or ARF1-KO HeLa cells transfected with the M expression plasmid were fixed at 24 hpt for IFA with antibodies against the tag",
+    "footnote": [],
+    "bbox": [
+      [
+        161,
+        110,
+        850,
+        767
+      ]
+    ],
+    "page_idx": 45
+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_5.jpg",
+    "caption": "Figure 5. ARF1 small-molecule inhibitors disrupt the accumulation of ARF1 and hence M to the ERGIC and interfere with M-driven viral assembly, suppressing SARS-CoV-2 propagation. (A-D) ARF1 small-molecule inhibitors BFA and GCA inhibit SARS-CoV-2 propagation. Huh7-ACE2 cells were infected with SARS-CoV-2 at an MOI of 0.01 in the presence of the indicated drugs or DMSO control for 24 h.",
+    "footnote": [],
+    "bbox": [
+      [
+        160,
+        103,
+        820,
+        775
+      ]
+    ],
+    "page_idx": 47
+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_6.jpg",
+    "caption": "Figure 6. The N-terminal helix of ARF1 is the major binding domain of M and disrupts M localization to the ERGIC, when expressed alone. (A) Schematic diagram of GFP-tagged ARF1 truncated mutants used in this study. Several structural domains are indicated. Hel, helix; SW, switch; In SW, inter switch. (B) Mapping of the ARF1 domain targeted by M. HEK293T cells were co-transfected with the M-Stag expression plasmid and the plasmids encoding full-length or truncated ARF1 proteins fused with GFP or the pEGFP-N3 control plasmid. At 24 h post transfection, protein interactions were analyzed by S-pulldown and WB analyses. *, the major bands of ARF1 and its truncated mutants tagged with GFP in lysate inputs. (C-E) Colocalization analyses of M with representative ARF1 mutants (ARF1<sub>1-17</sub> and ARF1",
+    "footnote": [],
+    "bbox": [
+      [
+        156,
+        110,
+        850,
+        616
+      ]
+    ],
+    "page_idx": 49
+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_7.jpg",
+    "caption": "Figure 7. PEP17 interferes with M hijacking of ARF1 and thus M-driven viral assembly, inhibiting the propagation of SARS-CoV-2 and its variants. (A) PEP17 inhibits the M-ARF1 colocalization and the ERGIC accumulation of M. HeLa cells were transfected with the M expression plasmid. Six hours later, specifically at the",
+    "footnote": [],
+    "bbox": [
+      [
+        147,
+        100,
+        850,
+        797
+      ]
+    ],
+    "page_idx": 51
+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_8.jpg",
+    "caption": "Figure 8. PEP17 significantly attenuates SARS-CoV-2 infection and pathogenicity in hamsters. Six-week-old Syrian hamsters (n = 5/group) were infected with SARS-CoV-2 (10<sup>4</sup> TCID<sub>50</sub>) and then treated with PEP17 or CPS14",
+    "footnote": [],
+    "bbox": [
+      [
+        150,
+        100,
+        840,
+        808
+      ]
+    ],
+    "page_idx": 53
+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_unknown_0.jpg",
+    "caption": "Figure S1. Identification of ARF1 as an M-interacting protein by using S-pulldown coupled with mass spectrometry. HEK293T cells transfected with the control vector or the plasmid expressing S-tagged M (M-Stag) were lysed for S-pulldown assays at 24 hpt, followed by liquid chromatography coupled with tandem",
+    "footnote": [],
+    "bbox": [
+      [
+        160,
+        110,
+        833,
+        787
+      ]
+    ],
+    "page_idx": 55
+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_unknown_1.jpg",
+    "caption": "Figure S2. Validation of ARF1-KO HEK293 and HeLa cells generated by CRISPR-Cas9 editing. DNA sequencing of ARF1-KO HEK293 or HeLa cells (A-D) and WB analyses of ARF1-KO HeLa cells (E) are respectively shown. Regions encompassing the first two exons of ARF1 were amplified by PCR. The PCR products",
+    "footnote": [],
+    "bbox": [
+      [
+        156,
+        106,
+        840,
+        789
+      ]
+    ],
+    "page_idx": 57
+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_unknown_2.jpg",
+    "caption": "Figure S3. ARF1 that itself is localized to the ERGIC co-accumulates with M at the ERGIC in cells co-expressing M. HeLa cells were transfected with the M-Stag expression plasmid or control vector, together with the plasmid expressing GFP-tagged ARF1 (ARF1-GFP). At 24 hpt, cells were fixed for IFA analyses with the antibodies against Stag and ERGIC53. Nuclei were stained with Hoechst.",
+    "footnote": [],
+    "bbox": [
+      [
+        155,
+        108,
+        833,
+        303
+      ]
+    ],
+    "page_idx": 59
+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_unknown_3.jpg",
+    "caption": "Figure S4. ARF1(T31N) retains the ability to interact with M and inhibits M-driven assembly and viral propagation. (A) Interaction of ARF1(T31N) with M.",
+    "footnote": [],
+    "bbox": [
+      [
+        160,
+        108,
+        838,
+        320
+      ]
+    ],
+    "page_idx": 60
+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_unknown_4.jpg",
+    "caption": "Figure S5. Evaluation of the potential cytotoxicity of BFA, GCA, PEP17, and CPS14 to cells by CCK-8 assays. The indicated types of cells used in the relevant experiments of this study were treated with different doses of the inhibitors or peptides for \\(24\\mathrm{h}\\) , followed by the assessment of cytotoxicity by using CCK8 kit. No noticeable cytotoxicity of BFA, GCA, PEP17, or CPS14 to cells were observed under the concentrations used in this study. Graphs are shown as means \\(\\pm\\) SD, \\(\\mathrm{n}\\geq 3\\) biological replicates. ns, not significant.",
+    "footnote": [],
+    "bbox": [
+      [
+        156,
+        105,
+        844,
+        585
+      ]
+    ],
+    "page_idx": 61
+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_unknown_5.jpg",
+    "caption": "Figure S6. GBF1 but not PI4KB is required for SARS-CoV-2 propagation.",
+    "footnote": [],
+    "bbox": [
+      [
+        164,
+        108,
+        830,
+        288
+      ]
+    ],
+    "page_idx": 62
+  }
+]

preprint/preprint__1e802f90b1081cc23be601c3b6fc4853d0a86129c48fe79a724de81476b1676a/images_list.json

@@ -0,0 +1,77 @@
+[
+  {
+    "type": "image",
+    "img_path": "images/Figure_1.jpg",
+    "caption": "Fig. 1 | Electronic structure and antiferromagnetic (AF) band reconstruction of electron-doped cuprates. a Fermi surface (FS) and nodal band ARPES spectra of PLCCO as a function of electron doping \\((n)\\) . Non-SC, UD17, and OD11 indicate non-superconducting, underdoping, and overdoping, respectively, where the number indicates \\(T_{\\mathrm{c}}\\) in Kelvins. b Copper oxide plane with antiferromagnetically ordered spins. The black lines show the pristine unit-cell and the pink shaded square is the AF unit-cell. c Schematics of pristine (left) and AF pocket (right) FSs of electron-doped cuprates. The middle panel shows the band-folding process, attributed to the AF unit cell doubling shown in b. The pink shaded area is the AF Brillouin zone.",
+    "footnote": [],
+    "bbox": [
+      [
+        170,
+        125,
+        816,
+        451
+      ]
+    ],
+    "page_idx": 20
+  },
+  {
+    "type": "image",
+    "img_path": "images/Supplementary_Figure_3.jpg",
+    "caption": "Fig. 2 | Doping evolution of the nodal band in PLCCO. a ARPES spectra of the nodal band after Fermi–Dirac correction for various doping samples. OP indicates optimal doping. b Nodal band spectra with the maximum energy distribution curve (EDC) that brightens the “folded shadow band” forbidden in a (see Supplementary Fig. 3). c Schematic spectra of the FS at the nodal point and hot spot. The black peak at the nodal point indicates the nodal band spectrum. The red and green peaks show the \\(h\\) -pocket and \\(L\\) -circle component, respectively. d Superposed FS consisting of electron \\((e^{-})\\) and hole \\((h^{-})\\) pockets, and a large \\((L^{-})\\) circle. The pink dashed lines mark the AF zone boundary.",
+    "footnote": [],
+    "bbox": [
+      [
+        183,
+        118,
+        822,
+        512
+      ]
+    ],
+    "page_idx": 21
+  },
+  {
+    "type": "image",
+    "img_path": "images/Supplementary_Figure_4.jpg",
+    "caption": "Fig. 3 | Correlation between the Fermi hole pocket and superconductivity. a EDCs at the nodal point and hot spot for various dopings. Dashed and dotted lines guide the eye to the zero-energy intensity of each EDC. The red vertical arrows denote \\(Z_{\\text{hole}}\\) . B CPT calculation result as a function of \\(n\\) . The white dashed box highlights the nodal hole band. c \\(Z_{\\text{hole}}\\) versus \\(T_{\\text{c}}\\) . The red shaded area guides the eye. d \\(Z_{\\text{hole}}\\) , along with \\(T_{\\text{c}}\\) and \\(Z_{\\text{theory}}\\) , as a function of \\(n\\) . The dashed line marks the doping where the shallow hole band gap completely closes or underlying Lifshitz transition takes place (see Supplementary Fig. 4).",
+    "footnote": [],
+    "bbox": [
+      [
+        155,
+        125,
+        852,
+        580
+      ]
+    ],
+    "page_idx": 22
+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_4.jpg",
+    "caption": "Fig. 4 | Quantum critical-like transition with dissolution of glassy freezing near OP.",
+    "footnote": [],
+    "bbox": [
+      [
+        145,
+        123,
+        845,
+        530
+      ]
+    ],
+    "page_idx": 23
+  },
+  {
+    "type": "image",
+    "img_path": "images/Supplementary_Figure_5.jpg",
+    "caption": "Fig. 5 | Electronic, magnetic, and SC phase diagram of PLCCO. \\(T_{\\mathrm{N}}\\) , \\(T_{\\mathrm{f}}\\) , \\(T^{*}\\) , and LCP indicate Neel, spin-freezing, and fluctuating short-range antiferromagnetic (SR-AF) phase transition temperatures, and the Lifshitz-critical point, respectively. LR-AF and AF-glass represent the long-range antiferromagnetic order phase and clustered spin-glass phase, respectively. The schematic plot of SC and \\(Z_{\\mathrm{hole}}\\) dome is based on the data shown in Fig. 3d. The sign change in the Hall coefficient ( \\(R_{\\mathrm{H}}\\) ) at low- \\(T\\) with overdoping is shown in Supplementary Fig. 5.",
+    "footnote": [],
+    "bbox": [
+      [
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+      ]
+    ],
+    "page_idx": 24
+  }
+]

preprint/preprint__1e802f90b1081cc23be601c3b6fc4853d0a86129c48fe79a724de81476b1676a/preprint__1e802f90b1081cc23be601c3b6fc4853d0a86129c48fe79a724de81476b1676a.mmd

@@ -0,0 +1,315 @@
+
+# Interplay between Hole Superconductivity and Quantum Critical Antiferromagnetic Fluctuations in Electron-Doped Cuprates  
+
+Changyoung Kim changyoung@snu.ac.kr  
+
+Institute for Basic Science Dongjoon Song The University of British Columbia Suheon Lee Institute for Basic Science https://orcid.org/0000- 0002- 0705- 0452 Zecheng Shen Clemson University Woobin Jung Seoul National University Wonjun Lee Institute for Basic Science Sungkyun Choi Institute for Basic Science Wonshik Kyung Seoul National University https://orcid.org/0000- 0001- 8750- 5471 Saegyeol Jung Seoul National University Shigeyuki Ishida National Institute of Advanced Industrial Science and Technology https://orcid.org/0000- 0001- 9445- 8079 Yoshiyuki Yoshida National Institute of Advanced Industrial Science and Technology (AIST) Seung Ryong Park Incheon National University Hiroshi Eisaki AIST https://orcid.org/0000- 0002- 8299- 6416 Yao Wang Emory University https://orcid.org/0000- 0003- 1736- 0187
+
+<--- Page Split --->
+
+# Kwang-Yong Choi  
+
+Sungkyunkwan University  
+
+## Article  
+
+# Keywords:  
+
+Posted Date: September 19th, 2023  
+
+DOI: https://doi.org/10.21203/rs.3.rs- 3333329/v1  
+
+License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License  
+
+Additional Declarations: There is NO Competing Interest.  
+
+Version of Record: A version of this preprint was published at Nature Communications on March 20th, 2025. See the published version at https://doi.org/10.1038/s41467- 025- 57942- z.
+
+<--- Page Split --->
+
+# Interplay between Hole Superconductivity and Quantum Critical Antiferromagnetic Fluctuations in Electron-Doped Cuprates  
+
+## Authors  
+
+Dongjoon Song \(^{1,2*}\) , Suheon Lee \(^{3,4*}\) , Zecheng Shen \(^{5}\) , Woobin Jung \(^{2,6}\) , Wonjun Lee \(^{7}\) , Sungkyun Choi \(^{3,4}\) , Wonshik Kyung \(^{2,6}\) , Saegyeol Jung \(^{2,6}\) , S. Ishida \(^{8}\) , Y. Yoshida \(^{8}\) , Seung- Ryong Park \(^{9}\) , H. Eisaki \(^{8}\) , Yao Wang \(^{10}\) †, Kwang- Yong Choi \(^{11}\) †, and C. Kim \(^{2,6}\) †  
+
+## Affiliations  
+
+\(^{1}\) Stewart Blusson Quantum Matter Institute, University of British Columbia, Vancouver, BC V6T 1Z4, Canada \(^{2}\) Center for Correlated Electron Systems, Institute for Basic Science (IBS), Seoul 08826, Republic of Korea \(^{3}\) Center for Integrated Nanostructure Physics, Institute for Basic Science (IBS), Suwon 16419, Republic of Korea \(^{4}\) Sungkyunkwan University, Suwon 16419, Republic of Korea \(^{5}\) Department of Physics and Astronomy, Clemson University, Clemson, SC 29634, USA \(^{6}\) Department of Physics and Astronomy, Seoul National University, Seoul 08826, Republic of Korea \(^{7}\) Rare Isotope Science Project, Institute for Basic Science (IBS), Daejeon 34000, Republic of Korea \(^{8}\) National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Ibaraki 305- 8568, Japan \(^{9}\) Department of Physics, Incheon National University, Incheon 22012, Republic of Korea \(^{10}\) Department of Chemistry, Emory University, Atlanta, GA 30322, USA \(^{11}\) Department of Physics, Sungkyunkwan University, Suwon 16419, Republic of Korea  
+
+\*These authors contributed equally to this work.  
+
+†Corresponding author. Email: yao.wang@emory.edu (W.Y.); choisky99@skku.edu (K.- Y.C.); changyoung@snu.ac.kr (C.K.)
+
+<--- Page Split --->
+
+Antiferromagnetic spin fluctuations are the most promising candidate as the pairing glue of high critical temperature \((T_{\mathrm{c}})\) superconductivity. However, many- body states and intertwined orders in cuprates have made it difficult to determine how electrons interact with fluctuating spins to form Cooper pairs. Recent experimental and theoretical studies have suggested spin fluctuation- driven quasiparticle band folding, but the relationship between the resultant Fermi pockets and superconductivity remains unclear. Here, we investigated this relationship in electron- doped \(\mathrm{Pr}_{1 - x}\mathrm{LaCe}_x\mathrm{CuO}_{4\pm \delta}\) using angle- resolved photoemission and muon spin spectroscopy. By extracting the folded band component in the single- particle nodal band spectrum and analysing the muon relaxation rate, we discovered that \(T_{\mathrm{c}}\) is proportional to the quasiparticle weight of the nodal hole pocket in the regime of the fluctuating antiferromagnetic ground state around a presumed quantum critical point. Our experimental and numerical observations highlight the significance of the marked interplay between the electron correlation and antiferromagnetic fluctuations in enhancing the hole pocket and consequently driving superconductivity.  
+
+## Introduction  
+
+The microscopic mechanism of high critical temperature \((T_{\mathrm{c}})\) superconductivity in cuprates has remained a mystery for nearly four decades. Although various unconventional phases, such as pseudogap, charge order, and strange metal, have been found in over 200 superconducting (SC) compounds, a common feature in the doping temperature \((T)\) phase diagrams of cuprates is that \(T_{\mathrm{c}}\) forms a dome- like region near the antiferromagnetic (AF) order phase boundary \(^{1 - 3}\) . This proximity of the superconductivity and AF order makes AF spin fluctuation a compelling candidate as the pairing glue that mediates superconductivity \(^{4}\) . In this proposed electron- pairing mechanism, the electrons in a thin energy shell on opposite sides of the Fermi surface attract each other via their coupling with the spin fluctuations \(^{5}\) , highlighting that the nature of the paired electron state should be subject to fermionic quasiparticle state renormalized by electron- spin fluctuation coupling. Despite intensive angle- resolved photoelectron spectroscopy
+
+<--- Page Split --->
+
+(ARPES) studies that have observed quasiparticles of cuprates \(^{6,7}\) , how spin fluctuation coupling “dresses” the electrons and mediates superconductivity is still debated because the unconventional phases and strong electron correlation complicate interpretation of the quasiparticle spectrum.  
+
+Looking back at early ARPES experiments on electron- doped cuprates and concomitant mean- field studies, one of the most convincing demonstrations of the electron- spin interaction is that the emergence of a long- range (LR)- AF super- lattice potential with underdoping results in folding of the quasiparticle band and reconstructs the Fermi surface from a large circle ( \(L\) - circle) to a small hole ( \(h\) - ) and electron ( \(e\) - ) pockets (Fig. 1 and Supplementary Fig. 1) \(^{7,9}\) . However, incompatible with the mean- field approach assuming the LR- AF order, experimental studies have observed traces of the reconstructed pockets, even in the doping range of the SC phase beyond the LR- AF order phase boundary \(^{10,12}\) , suggesting possible contributions from the short- range (SR)- AF fluctuations or topological orders to the folding effect \(^{10}\) . In addition, previous ARPES studies have argued that the folded AF and unfolded pristine spectra are detected simultaneously in the single- particle spectrum \(^{7,13}\) , which is beyond the mean- field picture but subject to the strong electron correlation, pointing to the need for a more elaborate spectral analysis to interpret the AF reconstruction.  
+
+More importantly, magnetotransport studies have provided clues regarding the correlation between superconductivity and the AF Fermi pockets. Magnetoresistance and Shubnikov- de Haas quantum oscillation measurements of electron- doped \(\mathrm{Nd}_{2 - x}\mathrm{Ce}_x\mathrm{CuO}_{4\pm \delta}\) point to a contribution of the \(h\) - pocket at the nodal section to the conductivity only in the SC region, which disappears with termination of the SC dome \(^{11,12}\) . Although the external magnetic field applied for those magnetotransport measurements to suppress superconductivity might reinforce the spin order and pin the pockets in the SC region, the \(h\) - pocket with the superconductivity ultimately points to a possible driving role of spin- coupled quasiparticles in the superconductivity. In this regard, there are currently two important, related questions to answer: is the \(h\) - pocket tied to the superconductivity even in the ambient condition without an external magnetic field, and if this is the case, which magnetic ground state “gets along” with the pocket state and
+
+<--- Page Split --->
+
+promotes superconductivity?  
+
+To resolve this puzzle, we measured ARPES and zero- field muon spin rotation/relaxation \((\mu \mathrm{SR})\) over a wide doping range in electron- doped \(\mathrm{Pr}_{1 - x}\mathrm{LaCe}_x\mathrm{CuO}_{4\pm \delta}\) (PLCCO) and performed spectral simulations with the Hubbard model. By scrutinising the low- energy photoemission spectra near the Fermi energy \((E_{\mathrm{F}})\) , we found that the single- particle spectrum of the nodal quasiparticle band of PLCCO consists of both folded and unfolded components, as also shown by our numerical calculations based on cluster perturbation theory (CPT). Tracking the doping evolution of the folded AF branch in the nodal band spectrum, we find that the quasiparticle weight associated with the \(h\) - pocket forms a dome shape identical to the SC dome, which spans the SR- AF ground state phase region where the static- to- dynamic AF quantum phase transition occurs. This provides compelling evidence that superconductivity in electron- doped cuprates is driven by spin fluctuation- coupled quasiparticles that occupy the nodal \(h\) - pockets.  
+
+## Results  
+
+## Correlation between superconductivity and hole pocket  
+
+Figure 2a shows the doping- dependent ARPES spectrum of the low- energy quasiparticle state dispersing along the nodal direction, the so- called "nodal band", after dividing the Fermi- Dirac distribution function (also see Supplementary Fig. 2a). The most underdoped (UD) sample with \(n = 0.06\) , referred to as Non- SC, exhibits a clear energy gap that closes gradually as the electron doping \(n\) increases. According to the mean- filed picture, the gap originates from the splitting of quasiparticle band into upper and lower band due to effective AF \((\pi ,\pi)\) scattering and the gap closing signals a dissipation of AF super- lattice potential (Fig. 1 and Supplementary Fig. 1) \(^{7 - 9}\) . Although hole band dispersion is expected from the nodal band spectrum, the folded branch is invisible in Fig. 2a because its intensity is weak due to the matrix- element effect \(^{6}\) . After enhancing the visibility of the folded shadow band by normalizing the intensity of the spectrum with the maximum value of the energy distribution curves (EDCs) (for details of the analysis, see Supplementary Fig 3), the total hole band spectrum with the folding center at the AF zone boundary (pink dashed line) is obvious in the analysed data (see the spectra of UD
+
+<--- Page Split --->
+
+samples in Fig. 2b). Fig. 2b also shows that as the doping increases, the hole band edge lifts monotonically, leading to gap closure. Considering the rigid- band model in which electron doping enlarges the hole band gap, this observation contradicts the conventional model, but supports the AF reconstruction scenario. Note that since the gap feature disappears completely at around \(n = 0.17\) (Supplementary Fig. 4), we define this doping as a Lifshitz critical point where the underlying Fermi surface topology changes, i.e. a Lifshitz transition<sup>14</sup>, as signalled by the sign inversion in the Hall coefficient \(R_{\mathrm{H}}\) (Supplementary Fig. 5).  
+
+In addition to the gap closing, the intensity between the hole band top and \(E_{\mathrm{F}}\) increases gradually, which is known as in- gap state filling, while the folded AF branch (to the right of the red dashed line) becomes less well- defined (see Fig. 2a,b). From a phenomenological standpoint, the in- gap spectrum reflects the unfolded component of the quasiparticles that constitute the pristine \(L\) - circle Fermi surface. This simultaneous observation of the folded AF and unfolded pristine branch in a single- particle spectrum reflects the coexistence of spin interacting and spin- non- interacting states, which is beyond the conventional mean- field picture but is associated with the many- body effect in the fluctuating correlated electron system<sup>11,13,15- 17</sup>. The spectra measured at the position of hot spot also shows the similar in- gap state filling (Supplementary Fig. 2b), consistent with earlier studies<sup>18- 20</sup>. This indicates that in- gap state filling occurs universally along the entire \(L\) - circle Fermi surface contour. Consequently, due to the low intensity of the folded spectral features and intermixing with the pristine spectrum (see schematic nodal spectrum in Fig. 2c), isolating the \(h\) - pocket component from the nodal band spectrum and tracking its doping evolution is challenging.  
+
+To address this challenge, we further analysed the variation in the spectral distributions along the Fermi surface. Figure 3a displays the doping- dependent EDCs at the nodal point and hot spot. For the Non- SC sample with \(n = 0.06\) , the energy of the folded AF band peak at the nodal point and hot spot are \(\sim 30\) and \(\sim 100 \mathrm{meV}\) , respectively, with almost negligible in- gap spectral intensity at \(E_{\mathrm{F}}\) . On the other hand, increasing doping towards SC phase enhances the zero- energy intensity dramatically at both points (see doping evolution of dashed and dotted lines in Fig. 3a), accompanied by shifting and broadening
+
+<--- Page Split --->
+
+of the peaks. As mentioned with the spectral image above, the subtle gap at the nodal point for the SC samples results in significant intermixing of the AF and pristine components at \(E_{\mathrm{F}}\) (see the nodal spectrum in Fig. 2c). On the contrary, due to a relatively large gap size, the zero- energy in- gap state at the hot spot is considered to be dominated by the pristine component, while including a tiny fraction of the AF component (see the hot spot spectrum in Fig. 2c) \(^{19}\) . In this framework, assuming that the pristine Fermi surface has a nearly momentum- invariant spectral weight distribution along the \(L\) - circle contour \(^{7,9,21}\) , the difference in the zero- energy spectral intensity between the nodal point and hot spot provides an estimate of the quasiparticle weight beyond the pristine \(L\) - circle component from the nodal band spectrum. As a consequence, the quasiparticle weight obtained by integrating the difference around \(E_{\mathrm{F}}\) represents a relative occupation of the AF branch to the pristine branch, which serves as an underlying quasiparticle weight associated with the \(h\) - pocket, referred to as \(Z_{\mathrm{hole}}\) (the length of the red arrows in Fig. 3a) \(^{6}\) . This phenomenological analysis of the photoemission spectra for the SC samples presents a virtual fermiology consisting of the AF pockets and pristine \(L\) - circle (Fig. 2d), with the ratio between the two states determined by the doping concentration. A similar superposed Fermi surface is also proposed by a very recent ARPES study on the SC \(\mathrm{Nd}_{2 - x}\mathrm{Ce}_x\mathrm{CuO}_{4\pm \delta}^{13}\) .  
+
+We extract \(Z_{\mathrm{hole}}\) for all the samples and plot \(T_{\mathrm{c}}\) as a function of \(Z_{\mathrm{hole}}\) in Fig. 3c. Strikingly, \(T_{\mathrm{c}}\) increases in proportion to \(Z_{\mathrm{hole}}\) (see red shaded region in Fig. 3c), which provides a couple of valuable insights into the SC nature associated with the \(h\) - pocket. Firstly, considering that \(Z_{\mathrm{hole}}\) measures the occupancy of the hole band state at \(E_{\mathrm{F}}\) , the correlation between \(Z_{\mathrm{hole}}\) and \(T_{\mathrm{c}}\) suggests that the density of state (DOS) of the \(h\) - pocket is a key determinant of \(T_{\mathrm{c}}\) . Simultaneously, it also highlights that the pair interaction occurring between the holes at the pockets plays a crucial role in mediating the superconductivity. We further emphasize that the holes are quasiparticles dressed by AF fluctuations, which have been theoretically known to induce pairing correlations in both perturbative \(^{22}\) and strongly correlated regime \(^{23}\) . These interpretations from the ARPES results are supported by our numerical calculations as well as \(\mu \mathrm{SR}\) analysis, shown in the following sections.
+
+<--- Page Split --->
+
+## Numerical simulations of the hole pocket  
+
+We argue that the unconventional reconstruction of the Fermi surface, i.e. the coexisting \(L\) - circle and pockets, originates from strong correlation instead of static disorder. To demonstrate this, we simulated the single- band Hubbard model, a many- body model that describes the physics of cuprates, using \(\mathrm{CPT}^{24,25}\) . The CPT spectral simulation invoking electron correlation and SR- AF fluctuations allows us to trace the evolution of the AF branch. Figure 3b shows the resulting electronic structure as a function of \(n\) (see Methods and Supplementary Fig. 6 for details of the simulation). Unlike single band folding predicted by mean- field theory, the simulated spectral functions of this strongly correlated system show the coexistence of two peaks near the nodal Fermi surface. Although the two peaks are sharper and more separated in the simulation obtained from the clean model, the overall trend is consistent with the experimental findings that show an increase in the in- gap spectral weight at the hot spot, resulting in a peak- dip- hump- like feature in the EDC of OD samples (see Fig. 3a) \(^{19,20}\) .  
+
+By integrating the simulated single- particle spectrum around the folding centre \((\pi /2,\pi /2)\) of the hole band (inside the white dashed box in Fig. 3b), we further estimate the theoretical quasiparticle weight \(Z_{\mathrm{theory}}\) . Figure 3d presents \(Z_{\mathrm{theory}}\) , along with \(Z_{\mathrm{hole}}\) and \(T_{\mathrm{c}}\) , as a function of \(n\) . The overlap of \(Z_{\mathrm{hole}}\) and the SC domes again shows the scaling relation between \(Z_{\mathrm{hole}}\) and \(T_{\mathrm{c}}\) . The concurrent dome- shaped doping dependence of \(Z_{\mathrm{theory}}\) further indicates the role of the marked interplay of the electron correlation and AF fluctuations in shaping the observed \(Z_{\mathrm{hole}}\) dome. If the AF fluctuations act as the pairing glue \(^{21}\) , the dome structure of \(Z_{\mathrm{hole}}\) , and consequently \(T_{\mathrm{c}}\) , can be interpreted as the result of gradually developed quasiparticles with AF fluctuations. The quantitative difference between \(Z_{\mathrm{hole}}\) and \(Z_{\mathrm{theory}}\) is mainly due to two factors. First, the simplicity of the single- band Hubbard model and finite- size simulation neglects the impact of orbital fluctuations and LR interactions, which affect the self- energy. Second, while \(Z_{\mathrm{hole}}\) is obtained by integrating the experimental spectral weight in the immediate vicinity of \(E_{\mathrm{F}}\) , a comprehensive description of quasiparticles should involve a wider energy window.
+
+<--- Page Split --->
+
+## Hole pocket with antiferromagnetic quantum phase fluctuations  
+
+We now turn to the magnetic correlations of PLCCO, measured by zero- field \(\mu \mathrm{SR}\) spectroscopy. Figure 4a presents the time- differential \(\mu \mathrm{SR}\) spectra of the SC compounds at selected dopings and temperatures. For all samples, the muon spin polarization \(P_{z}(t)\) shows faster relaxation (drop) as the temperature \(T\) decreases, indicating an increase in the local magnetic fields. The lack of an oscillatory signal down to the base \(T = 2 \mathrm{K}\) implies the absence of LR order in the SC phase \(^{26,27}\) . For UD sample UD15, \(P_{z}(t)\) relaxes to 1/3 of its initial value below \(20 \mathrm{K}\) , suggesting the formation of SR clustered static order, i.e. a "spin- glass phase" \(^{26}\) . Notably, with the higher dopings, the low- \(T P_{z}(t)\) relaxes to values smaller than 1/3 of the initial asymmetry, while retaining its relatively fast relaxation. This implies that with increasing doping, the SR static order evolves to more dynamically fluctuating Cu spins \(^{26}\) .  
+
+In addition to the qualitative understanding of the muon spin relaxation, quantitative analysis of the relaxation rate \(\lambda (T)\) shines more light on the quantum phase transition across the optimal doping. Figure 4b, c shows the \(T\) - dependent \(\lambda (T)\) obtained by fitting the \(P_{z}(t)\) data (see Methods). For UD samples UD15 and UD20, two characteristic temperatures, \(T^{*}\) and \(T_{\mathrm{f}}\) , are identified in \(\lambda (T)\) (Fig. 4b). Between \(T^{*}\) and \(T_{\mathrm{f}}\) , \(\lambda (T)\) follows power law dependence, \(\lambda_{\mathrm{p}}(T) \sim T^{\alpha}\) (solid lines in Fig. 4b with the exponent \(\alpha\) in Supplementary Fig. 8e), reflecting a critical slowing of the Cu spin fluctuations with decreasing \(T\) . Below \(T_{\mathrm{f}}\) , the deviation of \(\lambda (T)\) from the extrapolated \(\lambda_{\mathrm{p}}(T)\) , as shown in Fig. 4b, demonstrates the build- up of the spin- glass (freezing) phase \(^{28 - 32}\) . As evident from the inset of Fig. 4b, the frozen spin contribution, \(\Delta \lambda (T) = \lambda_{\mathrm{p}}(T) - \lambda (T)\) , exhibits an order parameter- like increase with decreasing \(T\) . In comparison, for OD18, the absence of \(T_{\mathrm{f}}\) leads to levelling- off of \(\lambda (T)\) below \(T^{*}\) , indicating the dominance of truly dynamic AF fluctuations with overdoping (Fig. 4c) \(^{29}\) . More significantly, \(\lambda (T)\) of OP24 shows single power law behaviour below \(T^{*}\) (see the solid line in Fig. 4c), which means that quantum critical- like AF fluctuations dominate down to the low- \(T\) limit with optimal doping. Given that the single power law is a signature of critical behaviour near a ferromagnetic or AF quantum critical point (QCP) \(^{33 - 35}\) , the doping dependence of \(\lambda (T)\) indicates that the
+
+<--- Page Split --->
+
+optimal doping of PLCCO occurs near a putative QCP between the AF glass and dynamically fluctuating AF phase<sup>36</sup>, named the AF- glass QCP.  
+
+Summarising the ARPES and \(\mu \mathrm{SR}\) results in an \(n - T\) phase diagram (Fig. 5), we have firmly established the relationship between \(Z_{\mathrm{hole}}\) and antiferromagnetism. Both the mutual exclusion of the \(Z_{\mathrm{hole}}\) - dome and LR- AF phase<sup>27</sup> and the inverse proportion between the \(T_{\mathrm{f}}\) and \(Z_{\mathrm{hole}}\) signal competition between the \(h\) - pocket and static AF order. Recall that a similar anti- correlation between the nodal quasiparticle weight and AF order was suggested in \(t - t' - J\) model calculations<sup>37</sup>. The LR- AF phase boundary around \(n \sim 0.07\) also explains why \(Z_{\mathrm{hole}}\) emerges at larger doping than \(Z_{\mathrm{theory}}\) because the simulation excluded the LR interactions (Fig. 3d). Conversely, the dynamic SR- AF phase seems conducive to the formation of the \(h\) - pocket, which is supported by the proportionality of \(T^{*}\) and \(Z_{\mathrm{hole}}\) in the over- doped regime where static order is lacking. Consequently, the \(Z_{\mathrm{hole}}\) dome, i.e. the SC dome, is centred around the AF- glass QCP where the underlying static- to- dynamic quantum phase transition occurs. Thus, this phase diagram demonstrates that the AF band folding resulting in the formation of the \(h\) - pocket stems from the inhomogeneous magnetic ground state with persistent SR- AF fluctuations near the putative QCP. Note that the sign change in \(R_{\mathrm{H}}\) (dashed vertical line) also points to the presence of QCP, as suggested by previous studies of various electron- doped cuprates<sup>38- 40</sup>.  
+
+## Discussion  
+
+Our results give further insight into the link between AF instability and superconductivity. Since \(Z_{\mathrm{hole}}\) can be regarded as the DOS of the hole band at \((\pm \pi /2, \pm \pi /2)\) where \(|\nabla k (\omega)|\) vanishes, the marked enhancement of \(Z_{\mathrm{hole}}\) resembles the impact of a van Hove singularity (vHS)<sup>41- 44</sup>, namely a band- edge vHS. From this perspective, the AF- glass QCP can be interpreted as the vHS near \(E_{\mathrm{F}}\) enhancing the electronic susceptibility around \(2k_{\mathrm{F}} = Q\) \((\pi , \pi)\) (Fig. 1c) and triggering the instability in spin susceptibility ascribed to the quantum critical fluctuations<sup>41- 43</sup>. Indeed, \(Z_{\mathrm{hole}}\) is maximised as the leading peak of the hole band approaches 10 meV, which is in good agreement with the spin resonance energy of PLCCO<sup>45</sup> (Supplementary Fig. 3). Moreover, vHS has generally been considered key to strengthening superconductivity, not only in hole- doped cuprates<sup>41- 43</sup> but also in other
+
+<--- Page Split --->
+
+novel superconductors, including recently discovered twisted bilayer graphene<sup>46</sup>. Therefore, our observations suggest that in electron- doped cuprates, AF instability and superconductivity are intertwined through the hole band- edge vHS.  
+
+A remaining important question is why is superconductivity predominantly dictated by the formation of small \(h\) - pockets instead of relatively large \(e\) - pockets? It has been thought that the doped holes in cuprates enter the \(\mathrm{Cu3d - O2p}\) - hybridised state and cause spin frustrations, while doped electrons reside in the \(\mathrm{Cu3d}\) state and cancel the spins<sup>6</sup>. If the holes created by formation of the \(h\) - pocket play the same role as the nominally doped holes, our results signal the importance of the oxygen state or the spin frustrations for the superconductivity. On the other hand, referring to a previous study combining ARPES with \(t - t' - t'' - J\) model calculations for \(\mathrm{Nd}_{2 - x}\mathrm{Ce}_x\mathrm{CuO}_{4\pm \delta}\) , electrons in the \(e\) - pocket may be favourably coupled to the static AF order, which competes with superconductivity. Note that the \(h\) - pocket in five- layered hole- doped \(\mathrm{Ba}_2\mathrm{Ca}_4\mathrm{Cu}_5\mathrm{O}_{10}(\mathrm{F}_3\mathrm{O})_2\) recently observed by ARPES similarly implies that superconductivity can be induced by the \(h\) - pocket alone, without a contribution from the anti- nodal region<sup>48</sup>. Further systematic investigations of the orbital character of the \(h\) - pocket may provide clues regarding the origin of hole superconductivity in electron- doped cuprates.  
+
+## Methods  
+
+## Materials  
+
+Single crystals of PLCCO with \(x = 0.10\) , 0.15, and 0.18 were grown by the traveling- solvent floating- zone method. All of the crystal rods were cut into small pieces along the \(\mathrm{CuO}_2\) plane, and annealed in a high- purity \(\mathrm{N}_2\) gas atmosphere at \(920 - 930^{\circ}\mathrm{C}\) and vacuum with \(\sim 10^{- 5}\) Torr at \(790^{\circ}\mathrm{C}\) for \(10 - 24\mathrm{h}\) . Subsequent air annealing was performed on some of the samples, at a temperature between \(500\mathrm{and}800^{\circ}\mathrm{C}\) for \(5 - 10\mathrm{h}\) . We characterized the \(T_{\mathrm{c}}\) of each sample within \(10\%\) of the SC shielding volume fraction by measuring the magnetic susceptibility with a magnetic property measurement system (MPMS; Quantum
+
+<--- Page Split --->
+
+Design, San Diego, CA, USA). (see Supplementary Fig. 9) Hall resistivity was measured with a physical property measurement system (PPMS; Quantum Design).  
+
+## ARPES measurements  
+
+ARPES measurementsARPES experiments were performed at beam lines 5- 2 and 5- 4 of the Stanford Synchrotron Radiation Lightsource (SSRL, Menlo Park, CA, USA). Samples were cleaved in situ and experiments were performed at temperatures below \(30\mathrm{K}\) , at a pressure around \(4 \times 10^{- 11}\) Torr. We used linearly polarized \(16.5\mathrm{eV}\) photons with an overall energy resolution of \(\sim 12 - 15\mathrm{meV}\) for the doping dependence study described in the main text. The \(16.5\mathrm{eV}\) irradiation and linear polarization aligned with the \(c\) - axis component of the sample are suitable for the present study, as this combination tends to brighten the nodal band spectrum and enhance hot- spot features<sup>18</sup>.  
+
+## \(\mu \mathrm{SR}\) measurements  
+
+\(\mu \mathrm{SR}\) measurementsThe \(\mu \mathrm{SR}\) measurements were performed using the M20 beamline at the TRIUMF facility (Vancouver, British Columbia, Canada). A dozen pieces of sliced PLCCO crystal (typical area: \(1\mathrm{cm}^2\) ) were wrapped with silver foil and attached to the sample holder. Zero- field \(\mu \mathrm{SR}\) (ZF- \(\mu \mathrm{SR}\) ) measurements were carried out over the temperature range of \(2 - 200\mathrm{K}\) . The physical quantity measured was the evolution of the muon depolarization \(P_z(t) = [N_{\mathrm{B}}(t) - \alpha N_{\mathrm{F}}(t)] = [N_{\mathrm{B}}(t) + \alpha N_{\mathrm{F}}(t)]\) , where \(N_{\mathrm{F}}(t)\) and \(N_{\mathrm{B}}(t)\) are the number of positrons counted at detectors antiparallel and parallel to the incident muon spin direction, respectively. \(\alpha\) is the efficiency ratio between the forward and backward detectors. \(P_z(t)\) conveys information about the local magnetic field distribution at the muon stopping sites. All of the data were analyzed using the free musrfit software package<sup>49</sup>. All the \(\mu \mathrm{SR}\) spectra in Fig. 4a were obtained by subtracting the temperature- independent constant background from the raw data and then normalizing it with the theoretical initial asymmetry \(P_z(t = 0)\) estimated by the fittings. Details of analysis process are described in Supplementary Information.  
+
+## CPT Spectral Simulations
+
+<--- Page Split --->
+
+Cluster perturbation theory (CPT) is designed to be an efficient method to estimate the \(A(\mathbf{k},\omega)\) of strongly correlated systems \(^{24,25}\) . When dividing the infinite plane into clusters, the Hamiltonian could be split into \(H = H_{c} + H_{int}\) , where \(H_{c}\) contains the (open- boundary) intra- cluster operators and \(H_{int}\) contains the operators with inter- cluster indices (hopping terms for the Hubbard model). Restricting ourselves to zero temperature, we use exact diagonalization (ED) to exactly solve the cluster Green's function \(G_{c}(\omega)\) associated with the intra- cluster Hamiltonian \(H_{c}\) . Then the CPT method estimates Green's function by treating \(H_{int}\) perturbatively, giving  
+
+\[G(\mathbf{k},\omega) = \frac{G_{c}(\omega)}{1 - V(\mathbf{k})G_{c}(\omega)}\]  
+
+Here \(V(\mathbf{k}) = \sum_{\mathbf{R}}H_{int}e^{i\mathbf{k}\cdot \mathbf{R}}\) is the inter- cluster interactions projected to the intra- cluster coordinates. Taking the long- wavelength limit, we obtain the spectral function  
+
+\[A(\mathbf{k},\omega)_{CPT} = -\frac{1}{\pi N} Im\sum_{\sigma}G_{a,b}(\mathbf{k},\omega)e^{i\mathbf{k}\cdot (\mathbf{r}_a - \mathbf{r}_b)}\]  
+
+with a,b are intra- cluster site indices. In this paper we employ a \(4\times 4\) cluster as the exact spectral solver. We further use an \(8\times 8\) superclusters to obtain finer doping interval.  
+
+## Data availability  
+
+Data are available from the corresponding authors upon reasonable request.  
+
+## References  
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+
+## Acknowledgments  
+
+We acknowledge insightful discussions with Shin- ichi Uchida, George Sawatzky, Yu He, Dirk Wulferding, Mikyung Kim, Yangyang Li, Seyoung Park, Junwon Rhim, Ke- Jun Xu, and Alannah Hallas; ARPES experiments were performed at Beamline 5- 4 and 5- 2, Stanford Synchrotron Radiation Lightsource, SLAC National Accelerator Laboratory operated by the DOE Office of BES. (Proposal No. 5161); \(\mu \mathrm{SR}\) measurements were carried out on the M20 beamline at the TRIUMF facility (Vancouver, Canada). This project was undertaken thanks in part to funding from the Canada First Research Excellence Fund, Quantum Materials and Future Technologies Program and supported by the Institute for Basic Science in Korea (Grant No. IBS- R009- G2, IBS- R011- Y3); and JSPS KAKENHI (Grant No. 18H01860); The work at SKKU was supported by the National Research Foundation (NRF) of Korea (Grant Nos. 2020R1A2C3012367 and 2020R1A5A1016518). S.- R.P. acknowledges the National Research Foundation of Korea (NRF) (Grant No. NRF- 2020R1A2C1011439). Z.S. and Y.W. acknowledge support from the Air Force Office of Scientific Research Young Investigator Program under grant FA9550- 23- 1- 0153. The spectral simulations used resources of the National Energy Research Scientific Computing Center (NERSC), a U.S. Department of Energy Office of Science User Facility located at Lawrence Berkeley National Laboratory, operated under Contract No. DE- AC02- 05CH11231 using NERSC award BES- ERCAP0023181.  
+
+## Author contributions  
+
+D.S., S.L., S.- R.P., H.E., K.- Y.C., and C.K. conceived and designed the experiments with suggestions from S.C., S.I., and Y.Y.; Z.S. and Y.W. performed theoretical calculations; D.S., W.J., and S.I. grew and characterized the PLCCO single crystals; D.S., W.J., W.K.,
+
+<--- Page Split --->
+
+and S.J. performed the ARPES measurements; S.L., W.L., and K.- Y.C. performed the \(\mu \mathrm{SR}\) measurements; D.S., W.J., and S.J., analyzed the ARPES experimental data; S.L., and W.L. analyzed the \(\mu \mathrm{SR}\) experimental data; D.S., S.L., Y.W., S.C., W.K., K.- Y.C. and C.K. wrote the manuscript with input from S.- R.P., H.E., and contributions from all authors.  
+
+## Competing interests  
+
+The authors declare no competing interests.
+
+<--- Page Split --->
+![](images/Figure_1.jpg)
+
+<center>Fig. 1 | Electronic structure and antiferromagnetic (AF) band reconstruction of electron-doped cuprates. a Fermi surface (FS) and nodal band ARPES spectra of PLCCO as a function of electron doping \((n)\) . Non-SC, UD17, and OD11 indicate non-superconducting, underdoping, and overdoping, respectively, where the number indicates \(T_{\mathrm{c}}\) in Kelvins. b Copper oxide plane with antiferromagnetically ordered spins. The black lines show the pristine unit-cell and the pink shaded square is the AF unit-cell. c Schematics of pristine (left) and AF pocket (right) FSs of electron-doped cuprates. The middle panel shows the band-folding process, attributed to the AF unit cell doubling shown in b. The pink shaded area is the AF Brillouin zone. </center>
+
+<--- Page Split --->
+![](images/Supplementary_Figure_3.jpg)
+
+<center>Fig. 2 | Doping evolution of the nodal band in PLCCO. a ARPES spectra of the nodal band after Fermi–Dirac correction for various doping samples. OP indicates optimal doping. b Nodal band spectra with the maximum energy distribution curve (EDC) that brightens the “folded shadow band” forbidden in a (see Supplementary Fig. 3). c Schematic spectra of the FS at the nodal point and hot spot. The black peak at the nodal point indicates the nodal band spectrum. The red and green peaks show the \(h\) -pocket and \(L\) -circle component, respectively. d Superposed FS consisting of electron \((e^{-})\) and hole \((h^{-})\) pockets, and a large \((L^{-})\) circle. The pink dashed lines mark the AF zone boundary. </center>
+
+<--- Page Split --->
+![](images/Supplementary_Figure_4.jpg)
+
+<center>Fig. 3 | Correlation between the Fermi hole pocket and superconductivity. a EDCs at the nodal point and hot spot for various dopings. Dashed and dotted lines guide the eye to the zero-energy intensity of each EDC. The red vertical arrows denote \(Z_{\text{hole}}\) . B CPT calculation result as a function of \(n\) . The white dashed box highlights the nodal hole band. c \(Z_{\text{hole}}\) versus \(T_{\text{c}}\) . The red shaded area guides the eye. d \(Z_{\text{hole}}\) , along with \(T_{\text{c}}\) and \(Z_{\text{theory}}\) , as a function of \(n\) . The dashed line marks the doping where the shallow hole band gap completely closes or underlying Lifshitz transition takes place (see Supplementary Fig. 4). </center>
+
+<--- Page Split --->
+![](images/Figure_4.jpg)
+
+<center>Fig. 4 | Quantum critical-like transition with dissolution of glassy freezing near OP. </center>  
+
+a \(T\) - dependent zero- field muon spin rotation/relaxation spectra for samples UD15, UD20, OP24, and OD18. Solid lines show the fit to the data described in the Methods. b, c \(T\) - dependent relaxation rates \(\lambda (T)\) for UD15, UD20, OP24, and OD18. Solid and dashed arrows indicate the transition temperatures for the spin- freezing phase \(T_{\mathrm{f}}\) and fluctuating short- range- order phase \(T^{*}\) . The solid lines are power law fits, \(\lambda_{\mathrm{p}}(T) \sim T^{-\alpha}\) (see \(\alpha\) in Supplementary Fig. 8). The difference between the power law fit and data, \(\Delta \lambda (T) = \lambda_{\mathrm{p}}(T) - \lambda (T)\) , in the low- \(T\) range denotes the fraction of frozen spins in the inset of b.
+
+<--- Page Split --->
+![](images/Supplementary_Figure_5.jpg)
+
+<center>Fig. 5 | Electronic, magnetic, and SC phase diagram of PLCCO. \(T_{\mathrm{N}}\) , \(T_{\mathrm{f}}\) , \(T^{*}\) , and LCP indicate Neel, spin-freezing, and fluctuating short-range antiferromagnetic (SR-AF) phase transition temperatures, and the Lifshitz-critical point, respectively. LR-AF and AF-glass represent the long-range antiferromagnetic order phase and clustered spin-glass phase, respectively. The schematic plot of SC and \(Z_{\mathrm{hole}}\) dome is based on the data shown in Fig. 3d. The sign change in the Hall coefficient ( \(R_{\mathrm{H}}\) ) at low- \(T\) with overdoping is shown in Supplementary Fig. 5. </center>
+
+<--- Page Split --->
+
+## Supplementary Files  
+
+This is a list of supplementary files associated with this preprint. Click to download.  
+
+- PLCConNatComSupplementaryfinal.pdf
+
+<--- Page Split --->

preprint/preprint__1e802f90b1081cc23be601c3b6fc4853d0a86129c48fe79a724de81476b1676a/preprint__1e802f90b1081cc23be601c3b6fc4853d0a86129c48fe79a724de81476b1676a_det.mmd

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+<|ref|>title<|/ref|><|det|>[[44, 108, 930, 208]]<|/det|>
+# Interplay between Hole Superconductivity and Quantum Critical Antiferromagnetic Fluctuations in Electron-Doped Cuprates  
+
+<|ref|>text<|/ref|><|det|>[[44, 230, 280, 276]]<|/det|>
+Changyoung Kim changyoung@snu.ac.kr  
+
+<|ref|>text<|/ref|><|det|>[[44, 303, 645, 944]]<|/det|>
+Institute for Basic Science Dongjoon Song The University of British Columbia Suheon Lee Institute for Basic Science https://orcid.org/0000- 0002- 0705- 0452 Zecheng Shen Clemson University Woobin Jung Seoul National University Wonjun Lee Institute for Basic Science Sungkyun Choi Institute for Basic Science Wonshik Kyung Seoul National University https://orcid.org/0000- 0001- 8750- 5471 Saegyeol Jung Seoul National University Shigeyuki Ishida National Institute of Advanced Industrial Science and Technology https://orcid.org/0000- 0001- 9445- 8079 Yoshiyuki Yoshida National Institute of Advanced Industrial Science and Technology (AIST) Seung Ryong Park Incheon National University Hiroshi Eisaki AIST https://orcid.org/0000- 0002- 8299- 6416 Yao Wang Emory University https://orcid.org/0000- 0003- 1736- 0187
+
+<--- Page Split --->
+<|ref|>title<|/ref|><|det|>[[44, 43, 196, 61]]<|/det|>
+# Kwang-Yong Choi  
+
+<|ref|>text<|/ref|><|det|>[[55, 66, 280, 84]]<|/det|>
+Sungkyunkwan University  
+
+<|ref|>sub_title<|/ref|><|det|>[[44, 125, 103, 142]]<|/det|>
+## Article  
+
+<|ref|>title<|/ref|><|det|>[[44, 162, 135, 181]]<|/det|>
+# Keywords:  
+
+<|ref|>text<|/ref|><|det|>[[44, 200, 354, 220]]<|/det|>
+Posted Date: September 19th, 2023  
+
+<|ref|>text<|/ref|><|det|>[[44, 238, 475, 258]]<|/det|>
+DOI: https://doi.org/10.21203/rs.3.rs- 3333329/v1  
+
+<|ref|>text<|/ref|><|det|>[[42, 276, 914, 319]]<|/det|>
+License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License  
+
+<|ref|>text<|/ref|><|det|>[[42, 337, 535, 357]]<|/det|>
+Additional Declarations: There is NO Competing Interest.  
+
+<|ref|>text<|/ref|><|det|>[[42, 392, 930, 435]]<|/det|>
+Version of Record: A version of this preprint was published at Nature Communications on March 20th, 2025. See the published version at https://doi.org/10.1038/s41467- 025- 57942- z.
+
+<--- Page Split --->
+<|ref|>title<|/ref|><|det|>[[168, 130, 828, 181]]<|/det|>
+# Interplay between Hole Superconductivity and Quantum Critical Antiferromagnetic Fluctuations in Electron-Doped Cuprates  
+
+<|ref|>sub_title<|/ref|><|det|>[[140, 204, 226, 222]]<|/det|>
+## Authors  
+
+<|ref|>text<|/ref|><|det|>[[140, 230, 858, 300]]<|/det|>
+Dongjoon Song \(^{1,2*}\) , Suheon Lee \(^{3,4*}\) , Zecheng Shen \(^{5}\) , Woobin Jung \(^{2,6}\) , Wonjun Lee \(^{7}\) , Sungkyun Choi \(^{3,4}\) , Wonshik Kyung \(^{2,6}\) , Saegyeol Jung \(^{2,6}\) , S. Ishida \(^{8}\) , Y. Yoshida \(^{8}\) , Seung- Ryong Park \(^{9}\) , H. Eisaki \(^{8}\) , Yao Wang \(^{10}\) †, Kwang- Yong Choi \(^{11}\) †, and C. Kim \(^{2,6}\) †  
+
+<|ref|>sub_title<|/ref|><|det|>[[140, 320, 256, 339]]<|/det|>
+## Affiliations  
+
+<|ref|>text<|/ref|><|det|>[[137, 346, 860, 789]]<|/det|>
+\(^{1}\) Stewart Blusson Quantum Matter Institute, University of British Columbia, Vancouver, BC V6T 1Z4, Canada \(^{2}\) Center for Correlated Electron Systems, Institute for Basic Science (IBS), Seoul 08826, Republic of Korea \(^{3}\) Center for Integrated Nanostructure Physics, Institute for Basic Science (IBS), Suwon 16419, Republic of Korea \(^{4}\) Sungkyunkwan University, Suwon 16419, Republic of Korea \(^{5}\) Department of Physics and Astronomy, Clemson University, Clemson, SC 29634, USA \(^{6}\) Department of Physics and Astronomy, Seoul National University, Seoul 08826, Republic of Korea \(^{7}\) Rare Isotope Science Project, Institute for Basic Science (IBS), Daejeon 34000, Republic of Korea \(^{8}\) National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Ibaraki 305- 8568, Japan \(^{9}\) Department of Physics, Incheon National University, Incheon 22012, Republic of Korea \(^{10}\) Department of Chemistry, Emory University, Atlanta, GA 30322, USA \(^{11}\) Department of Physics, Sungkyunkwan University, Suwon 16419, Republic of Korea  
+
+<|ref|>text<|/ref|><|det|>[[140, 790, 533, 808]]<|/det|>
+\*These authors contributed equally to this work.  
+
+<|ref|>text<|/ref|><|det|>[[139, 830, 828, 872]]<|/det|>
+†Corresponding author. Email: yao.wang@emory.edu (W.Y.); choisky99@skku.edu (K.- Y.C.); changyoung@snu.ac.kr (C.K.)
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[137, 115, 863, 483]]<|/det|>
+Antiferromagnetic spin fluctuations are the most promising candidate as the pairing glue of high critical temperature \((T_{\mathrm{c}})\) superconductivity. However, many- body states and intertwined orders in cuprates have made it difficult to determine how electrons interact with fluctuating spins to form Cooper pairs. Recent experimental and theoretical studies have suggested spin fluctuation- driven quasiparticle band folding, but the relationship between the resultant Fermi pockets and superconductivity remains unclear. Here, we investigated this relationship in electron- doped \(\mathrm{Pr}_{1 - x}\mathrm{LaCe}_x\mathrm{CuO}_{4\pm \delta}\) using angle- resolved photoemission and muon spin spectroscopy. By extracting the folded band component in the single- particle nodal band spectrum and analysing the muon relaxation rate, we discovered that \(T_{\mathrm{c}}\) is proportional to the quasiparticle weight of the nodal hole pocket in the regime of the fluctuating antiferromagnetic ground state around a presumed quantum critical point. Our experimental and numerical observations highlight the significance of the marked interplay between the electron correlation and antiferromagnetic fluctuations in enhancing the hole pocket and consequently driving superconductivity.  
+
+<|ref|>sub_title<|/ref|><|det|>[[140, 541, 270, 560]]<|/det|>
+## Introduction  
+
+<|ref|>text<|/ref|><|det|>[[137, 582, 860, 872]]<|/det|>
+The microscopic mechanism of high critical temperature \((T_{\mathrm{c}})\) superconductivity in cuprates has remained a mystery for nearly four decades. Although various unconventional phases, such as pseudogap, charge order, and strange metal, have been found in over 200 superconducting (SC) compounds, a common feature in the doping temperature \((T)\) phase diagrams of cuprates is that \(T_{\mathrm{c}}\) forms a dome- like region near the antiferromagnetic (AF) order phase boundary \(^{1 - 3}\) . This proximity of the superconductivity and AF order makes AF spin fluctuation a compelling candidate as the pairing glue that mediates superconductivity \(^{4}\) . In this proposed electron- pairing mechanism, the electrons in a thin energy shell on opposite sides of the Fermi surface attract each other via their coupling with the spin fluctuations \(^{5}\) , highlighting that the nature of the paired electron state should be subject to fermionic quasiparticle state renormalized by electron- spin fluctuation coupling. Despite intensive angle- resolved photoelectron spectroscopy
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[137, 115, 860, 209]]<|/det|>
+(ARPES) studies that have observed quasiparticles of cuprates \(^{6,7}\) , how spin fluctuation coupling “dresses” the electrons and mediates superconductivity is still debated because the unconventional phases and strong electron correlation complicate interpretation of the quasiparticle spectrum.  
+
+<|ref|>text<|/ref|><|det|>[[137, 228, 860, 567]]<|/det|>
+Looking back at early ARPES experiments on electron- doped cuprates and concomitant mean- field studies, one of the most convincing demonstrations of the electron- spin interaction is that the emergence of a long- range (LR)- AF super- lattice potential with underdoping results in folding of the quasiparticle band and reconstructs the Fermi surface from a large circle ( \(L\) - circle) to a small hole ( \(h\) - ) and electron ( \(e\) - ) pockets (Fig. 1 and Supplementary Fig. 1) \(^{7,9}\) . However, incompatible with the mean- field approach assuming the LR- AF order, experimental studies have observed traces of the reconstructed pockets, even in the doping range of the SC phase beyond the LR- AF order phase boundary \(^{10,12}\) , suggesting possible contributions from the short- range (SR)- AF fluctuations or topological orders to the folding effect \(^{10}\) . In addition, previous ARPES studies have argued that the folded AF and unfolded pristine spectra are detected simultaneously in the single- particle spectrum \(^{7,13}\) , which is beyond the mean- field picture but subject to the strong electron correlation, pointing to the need for a more elaborate spectral analysis to interpret the AF reconstruction.  
+
+<|ref|>text<|/ref|><|det|>[[137, 586, 861, 878]]<|/det|>
+More importantly, magnetotransport studies have provided clues regarding the correlation between superconductivity and the AF Fermi pockets. Magnetoresistance and Shubnikov- de Haas quantum oscillation measurements of electron- doped \(\mathrm{Nd}_{2 - x}\mathrm{Ce}_x\mathrm{CuO}_{4\pm \delta}\) point to a contribution of the \(h\) - pocket at the nodal section to the conductivity only in the SC region, which disappears with termination of the SC dome \(^{11,12}\) . Although the external magnetic field applied for those magnetotransport measurements to suppress superconductivity might reinforce the spin order and pin the pockets in the SC region, the \(h\) - pocket with the superconductivity ultimately points to a possible driving role of spin- coupled quasiparticles in the superconductivity. In this regard, there are currently two important, related questions to answer: is the \(h\) - pocket tied to the superconductivity even in the ambient condition without an external magnetic field, and if this is the case, which magnetic ground state “gets along” with the pocket state and
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[139, 116, 377, 134]]<|/det|>
+promotes superconductivity?  
+
+<|ref|>text<|/ref|><|det|>[[138, 154, 860, 446]]<|/det|>
+To resolve this puzzle, we measured ARPES and zero- field muon spin rotation/relaxation \((\mu \mathrm{SR})\) over a wide doping range in electron- doped \(\mathrm{Pr}_{1 - x}\mathrm{LaCe}_x\mathrm{CuO}_{4\pm \delta}\) (PLCCO) and performed spectral simulations with the Hubbard model. By scrutinising the low- energy photoemission spectra near the Fermi energy \((E_{\mathrm{F}})\) , we found that the single- particle spectrum of the nodal quasiparticle band of PLCCO consists of both folded and unfolded components, as also shown by our numerical calculations based on cluster perturbation theory (CPT). Tracking the doping evolution of the folded AF branch in the nodal band spectrum, we find that the quasiparticle weight associated with the \(h\) - pocket forms a dome shape identical to the SC dome, which spans the SR- AF ground state phase region where the static- to- dynamic AF quantum phase transition occurs. This provides compelling evidence that superconductivity in electron- doped cuprates is driven by spin fluctuation- coupled quasiparticles that occupy the nodal \(h\) - pockets.  
+
+<|ref|>sub_title<|/ref|><|det|>[[140, 467, 216, 486]]<|/det|>
+## Results  
+
+<|ref|>sub_title<|/ref|><|det|>[[140, 508, 619, 527]]<|/det|>
+## Correlation between superconductivity and hole pocket  
+
+<|ref|>text<|/ref|><|det|>[[138, 546, 860, 888]]<|/det|>
+Figure 2a shows the doping- dependent ARPES spectrum of the low- energy quasiparticle state dispersing along the nodal direction, the so- called "nodal band", after dividing the Fermi- Dirac distribution function (also see Supplementary Fig. 2a). The most underdoped (UD) sample with \(n = 0.06\) , referred to as Non- SC, exhibits a clear energy gap that closes gradually as the electron doping \(n\) increases. According to the mean- filed picture, the gap originates from the splitting of quasiparticle band into upper and lower band due to effective AF \((\pi ,\pi)\) scattering and the gap closing signals a dissipation of AF super- lattice potential (Fig. 1 and Supplementary Fig. 1) \(^{7 - 9}\) . Although hole band dispersion is expected from the nodal band spectrum, the folded branch is invisible in Fig. 2a because its intensity is weak due to the matrix- element effect \(^{6}\) . After enhancing the visibility of the folded shadow band by normalizing the intensity of the spectrum with the maximum value of the energy distribution curves (EDCs) (for details of the analysis, see Supplementary Fig 3), the total hole band spectrum with the folding center at the AF zone boundary (pink dashed line) is obvious in the analysed data (see the spectra of UD
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[137, 114, 860, 308]]<|/det|>
+samples in Fig. 2b). Fig. 2b also shows that as the doping increases, the hole band edge lifts monotonically, leading to gap closure. Considering the rigid- band model in which electron doping enlarges the hole band gap, this observation contradicts the conventional model, but supports the AF reconstruction scenario. Note that since the gap feature disappears completely at around \(n = 0.17\) (Supplementary Fig. 4), we define this doping as a Lifshitz critical point where the underlying Fermi surface topology changes, i.e. a Lifshitz transition<sup>14</sup>, as signalled by the sign inversion in the Hall coefficient \(R_{\mathrm{H}}\) (Supplementary Fig. 5).  
+
+<|ref|>text<|/ref|><|det|>[[137, 327, 860, 690]]<|/det|>
+In addition to the gap closing, the intensity between the hole band top and \(E_{\mathrm{F}}\) increases gradually, which is known as in- gap state filling, while the folded AF branch (to the right of the red dashed line) becomes less well- defined (see Fig. 2a,b). From a phenomenological standpoint, the in- gap spectrum reflects the unfolded component of the quasiparticles that constitute the pristine \(L\) - circle Fermi surface. This simultaneous observation of the folded AF and unfolded pristine branch in a single- particle spectrum reflects the coexistence of spin interacting and spin- non- interacting states, which is beyond the conventional mean- field picture but is associated with the many- body effect in the fluctuating correlated electron system<sup>11,13,15- 17</sup>. The spectra measured at the position of hot spot also shows the similar in- gap state filling (Supplementary Fig. 2b), consistent with earlier studies<sup>18- 20</sup>. This indicates that in- gap state filling occurs universally along the entire \(L\) - circle Fermi surface contour. Consequently, due to the low intensity of the folded spectral features and intermixing with the pristine spectrum (see schematic nodal spectrum in Fig. 2c), isolating the \(h\) - pocket component from the nodal band spectrum and tracking its doping evolution is challenging.  
+
+<|ref|>text<|/ref|><|det|>[[137, 709, 860, 876]]<|/det|>
+To address this challenge, we further analysed the variation in the spectral distributions along the Fermi surface. Figure 3a displays the doping- dependent EDCs at the nodal point and hot spot. For the Non- SC sample with \(n = 0.06\) , the energy of the folded AF band peak at the nodal point and hot spot are \(\sim 30\) and \(\sim 100 \mathrm{meV}\) , respectively, with almost negligible in- gap spectral intensity at \(E_{\mathrm{F}}\) . On the other hand, increasing doping towards SC phase enhances the zero- energy intensity dramatically at both points (see doping evolution of dashed and dotted lines in Fig. 3a), accompanied by shifting and broadening
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[137, 115, 860, 555]]<|/det|>
+of the peaks. As mentioned with the spectral image above, the subtle gap at the nodal point for the SC samples results in significant intermixing of the AF and pristine components at \(E_{\mathrm{F}}\) (see the nodal spectrum in Fig. 2c). On the contrary, due to a relatively large gap size, the zero- energy in- gap state at the hot spot is considered to be dominated by the pristine component, while including a tiny fraction of the AF component (see the hot spot spectrum in Fig. 2c) \(^{19}\) . In this framework, assuming that the pristine Fermi surface has a nearly momentum- invariant spectral weight distribution along the \(L\) - circle contour \(^{7,9,21}\) , the difference in the zero- energy spectral intensity between the nodal point and hot spot provides an estimate of the quasiparticle weight beyond the pristine \(L\) - circle component from the nodal band spectrum. As a consequence, the quasiparticle weight obtained by integrating the difference around \(E_{\mathrm{F}}\) represents a relative occupation of the AF branch to the pristine branch, which serves as an underlying quasiparticle weight associated with the \(h\) - pocket, referred to as \(Z_{\mathrm{hole}}\) (the length of the red arrows in Fig. 3a) \(^{6}\) . This phenomenological analysis of the photoemission spectra for the SC samples presents a virtual fermiology consisting of the AF pockets and pristine \(L\) - circle (Fig. 2d), with the ratio between the two states determined by the doping concentration. A similar superposed Fermi surface is also proposed by a very recent ARPES study on the SC \(\mathrm{Nd}_{2 - x}\mathrm{Ce}_x\mathrm{CuO}_{4\pm \delta}^{13}\) .  
+
+<|ref|>text<|/ref|><|det|>[[137, 574, 860, 840]]<|/det|>
+We extract \(Z_{\mathrm{hole}}\) for all the samples and plot \(T_{\mathrm{c}}\) as a function of \(Z_{\mathrm{hole}}\) in Fig. 3c. Strikingly, \(T_{\mathrm{c}}\) increases in proportion to \(Z_{\mathrm{hole}}\) (see red shaded region in Fig. 3c), which provides a couple of valuable insights into the SC nature associated with the \(h\) - pocket. Firstly, considering that \(Z_{\mathrm{hole}}\) measures the occupancy of the hole band state at \(E_{\mathrm{F}}\) , the correlation between \(Z_{\mathrm{hole}}\) and \(T_{\mathrm{c}}\) suggests that the density of state (DOS) of the \(h\) - pocket is a key determinant of \(T_{\mathrm{c}}\) . Simultaneously, it also highlights that the pair interaction occurring between the holes at the pockets plays a crucial role in mediating the superconductivity. We further emphasize that the holes are quasiparticles dressed by AF fluctuations, which have been theoretically known to induce pairing correlations in both perturbative \(^{22}\) and strongly correlated regime \(^{23}\) . These interpretations from the ARPES results are supported by our numerical calculations as well as \(\mu \mathrm{SR}\) analysis, shown in the following sections.
+
+<--- Page Split --->
+<|ref|>sub_title<|/ref|><|det|>[[139, 116, 495, 134]]<|/det|>
+## Numerical simulations of the hole pocket  
+
+<|ref|>text<|/ref|><|det|>[[137, 154, 862, 469]]<|/det|>
+We argue that the unconventional reconstruction of the Fermi surface, i.e. the coexisting \(L\) - circle and pockets, originates from strong correlation instead of static disorder. To demonstrate this, we simulated the single- band Hubbard model, a many- body model that describes the physics of cuprates, using \(\mathrm{CPT}^{24,25}\) . The CPT spectral simulation invoking electron correlation and SR- AF fluctuations allows us to trace the evolution of the AF branch. Figure 3b shows the resulting electronic structure as a function of \(n\) (see Methods and Supplementary Fig. 6 for details of the simulation). Unlike single band folding predicted by mean- field theory, the simulated spectral functions of this strongly correlated system show the coexistence of two peaks near the nodal Fermi surface. Although the two peaks are sharper and more separated in the simulation obtained from the clean model, the overall trend is consistent with the experimental findings that show an increase in the in- gap spectral weight at the hot spot, resulting in a peak- dip- hump- like feature in the EDC of OD samples (see Fig. 3a) \(^{19,20}\) .  
+
+<|ref|>text<|/ref|><|det|>[[137, 489, 861, 828]]<|/det|>
+By integrating the simulated single- particle spectrum around the folding centre \((\pi /2,\pi /2)\) of the hole band (inside the white dashed box in Fig. 3b), we further estimate the theoretical quasiparticle weight \(Z_{\mathrm{theory}}\) . Figure 3d presents \(Z_{\mathrm{theory}}\) , along with \(Z_{\mathrm{hole}}\) and \(T_{\mathrm{c}}\) , as a function of \(n\) . The overlap of \(Z_{\mathrm{hole}}\) and the SC domes again shows the scaling relation between \(Z_{\mathrm{hole}}\) and \(T_{\mathrm{c}}\) . The concurrent dome- shaped doping dependence of \(Z_{\mathrm{theory}}\) further indicates the role of the marked interplay of the electron correlation and AF fluctuations in shaping the observed \(Z_{\mathrm{hole}}\) dome. If the AF fluctuations act as the pairing glue \(^{21}\) , the dome structure of \(Z_{\mathrm{hole}}\) , and consequently \(T_{\mathrm{c}}\) , can be interpreted as the result of gradually developed quasiparticles with AF fluctuations. The quantitative difference between \(Z_{\mathrm{hole}}\) and \(Z_{\mathrm{theory}}\) is mainly due to two factors. First, the simplicity of the single- band Hubbard model and finite- size simulation neglects the impact of orbital fluctuations and LR interactions, which affect the self- energy. Second, while \(Z_{\mathrm{hole}}\) is obtained by integrating the experimental spectral weight in the immediate vicinity of \(E_{\mathrm{F}}\) , a comprehensive description of quasiparticles should involve a wider energy window.
+
+<--- Page Split --->
+<|ref|>sub_title<|/ref|><|det|>[[139, 117, 694, 135]]<|/det|>
+## Hole pocket with antiferromagnetic quantum phase fluctuations  
+
+<|ref|>text<|/ref|><|det|>[[139, 155, 860, 420]]<|/det|>
+We now turn to the magnetic correlations of PLCCO, measured by zero- field \(\mu \mathrm{SR}\) spectroscopy. Figure 4a presents the time- differential \(\mu \mathrm{SR}\) spectra of the SC compounds at selected dopings and temperatures. For all samples, the muon spin polarization \(P_{z}(t)\) shows faster relaxation (drop) as the temperature \(T\) decreases, indicating an increase in the local magnetic fields. The lack of an oscillatory signal down to the base \(T = 2 \mathrm{K}\) implies the absence of LR order in the SC phase \(^{26,27}\) . For UD sample UD15, \(P_{z}(t)\) relaxes to 1/3 of its initial value below \(20 \mathrm{K}\) , suggesting the formation of SR clustered static order, i.e. a "spin- glass phase" \(^{26}\) . Notably, with the higher dopings, the low- \(T P_{z}(t)\) relaxes to values smaller than 1/3 of the initial asymmetry, while retaining its relatively fast relaxation. This implies that with increasing doping, the SR static order evolves to more dynamically fluctuating Cu spins \(^{26}\) .  
+
+<|ref|>text<|/ref|><|det|>[[138, 439, 860, 868]]<|/det|>
+In addition to the qualitative understanding of the muon spin relaxation, quantitative analysis of the relaxation rate \(\lambda (T)\) shines more light on the quantum phase transition across the optimal doping. Figure 4b, c shows the \(T\) - dependent \(\lambda (T)\) obtained by fitting the \(P_{z}(t)\) data (see Methods). For UD samples UD15 and UD20, two characteristic temperatures, \(T^{*}\) and \(T_{\mathrm{f}}\) , are identified in \(\lambda (T)\) (Fig. 4b). Between \(T^{*}\) and \(T_{\mathrm{f}}\) , \(\lambda (T)\) follows power law dependence, \(\lambda_{\mathrm{p}}(T) \sim T^{\alpha}\) (solid lines in Fig. 4b with the exponent \(\alpha\) in Supplementary Fig. 8e), reflecting a critical slowing of the Cu spin fluctuations with decreasing \(T\) . Below \(T_{\mathrm{f}}\) , the deviation of \(\lambda (T)\) from the extrapolated \(\lambda_{\mathrm{p}}(T)\) , as shown in Fig. 4b, demonstrates the build- up of the spin- glass (freezing) phase \(^{28 - 32}\) . As evident from the inset of Fig. 4b, the frozen spin contribution, \(\Delta \lambda (T) = \lambda_{\mathrm{p}}(T) - \lambda (T)\) , exhibits an order parameter- like increase with decreasing \(T\) . In comparison, for OD18, the absence of \(T_{\mathrm{f}}\) leads to levelling- off of \(\lambda (T)\) below \(T^{*}\) , indicating the dominance of truly dynamic AF fluctuations with overdoping (Fig. 4c) \(^{29}\) . More significantly, \(\lambda (T)\) of OP24 shows single power law behaviour below \(T^{*}\) (see the solid line in Fig. 4c), which means that quantum critical- like AF fluctuations dominate down to the low- \(T\) limit with optimal doping. Given that the single power law is a signature of critical behaviour near a ferromagnetic or AF quantum critical point (QCP) \(^{33 - 35}\) , the doping dependence of \(\lambda (T)\) indicates that the
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[137, 115, 858, 159]]<|/det|>
+optimal doping of PLCCO occurs near a putative QCP between the AF glass and dynamically fluctuating AF phase<sup>36</sup>, named the AF- glass QCP.  
+
+<|ref|>text<|/ref|><|det|>[[137, 179, 860, 567]]<|/det|>
+Summarising the ARPES and \(\mu \mathrm{SR}\) results in an \(n - T\) phase diagram (Fig. 5), we have firmly established the relationship between \(Z_{\mathrm{hole}}\) and antiferromagnetism. Both the mutual exclusion of the \(Z_{\mathrm{hole}}\) - dome and LR- AF phase<sup>27</sup> and the inverse proportion between the \(T_{\mathrm{f}}\) and \(Z_{\mathrm{hole}}\) signal competition between the \(h\) - pocket and static AF order. Recall that a similar anti- correlation between the nodal quasiparticle weight and AF order was suggested in \(t - t' - J\) model calculations<sup>37</sup>. The LR- AF phase boundary around \(n \sim 0.07\) also explains why \(Z_{\mathrm{hole}}\) emerges at larger doping than \(Z_{\mathrm{theory}}\) because the simulation excluded the LR interactions (Fig. 3d). Conversely, the dynamic SR- AF phase seems conducive to the formation of the \(h\) - pocket, which is supported by the proportionality of \(T^{*}\) and \(Z_{\mathrm{hole}}\) in the over- doped regime where static order is lacking. Consequently, the \(Z_{\mathrm{hole}}\) dome, i.e. the SC dome, is centred around the AF- glass QCP where the underlying static- to- dynamic quantum phase transition occurs. Thus, this phase diagram demonstrates that the AF band folding resulting in the formation of the \(h\) - pocket stems from the inhomogeneous magnetic ground state with persistent SR- AF fluctuations near the putative QCP. Note that the sign change in \(R_{\mathrm{H}}\) (dashed vertical line) also points to the presence of QCP, as suggested by previous studies of various electron- doped cuprates<sup>38- 40</sup>.  
+
+<|ref|>sub_title<|/ref|><|det|>[[140, 590, 248, 609]]<|/det|>
+## Discussion  
+
+<|ref|>text<|/ref|><|det|>[[137, 631, 860, 875]]<|/det|>
+Our results give further insight into the link between AF instability and superconductivity. Since \(Z_{\mathrm{hole}}\) can be regarded as the DOS of the hole band at \((\pm \pi /2, \pm \pi /2)\) where \(|\nabla k (\omega)|\) vanishes, the marked enhancement of \(Z_{\mathrm{hole}}\) resembles the impact of a van Hove singularity (vHS)<sup>41- 44</sup>, namely a band- edge vHS. From this perspective, the AF- glass QCP can be interpreted as the vHS near \(E_{\mathrm{F}}\) enhancing the electronic susceptibility around \(2k_{\mathrm{F}} = Q\) \((\pi , \pi)\) (Fig. 1c) and triggering the instability in spin susceptibility ascribed to the quantum critical fluctuations<sup>41- 43</sup>. Indeed, \(Z_{\mathrm{hole}}\) is maximised as the leading peak of the hole band approaches 10 meV, which is in good agreement with the spin resonance energy of PLCCO<sup>45</sup> (Supplementary Fig. 3). Moreover, vHS has generally been considered key to strengthening superconductivity, not only in hole- doped cuprates<sup>41- 43</sup> but also in other
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[139, 115, 859, 184]]<|/det|>
+novel superconductors, including recently discovered twisted bilayer graphene<sup>46</sup>. Therefore, our observations suggest that in electron- doped cuprates, AF instability and superconductivity are intertwined through the hole band- edge vHS.  
+
+<|ref|>text<|/ref|><|det|>[[139, 203, 860, 545]]<|/det|>
+A remaining important question is why is superconductivity predominantly dictated by the formation of small \(h\) - pockets instead of relatively large \(e\) - pockets? It has been thought that the doped holes in cuprates enter the \(\mathrm{Cu3d - O2p}\) - hybridised state and cause spin frustrations, while doped electrons reside in the \(\mathrm{Cu3d}\) state and cancel the spins<sup>6</sup>. If the holes created by formation of the \(h\) - pocket play the same role as the nominally doped holes, our results signal the importance of the oxygen state or the spin frustrations for the superconductivity. On the other hand, referring to a previous study combining ARPES with \(t - t' - t'' - J\) model calculations for \(\mathrm{Nd}_{2 - x}\mathrm{Ce}_x\mathrm{CuO}_{4\pm \delta}\) , electrons in the \(e\) - pocket may be favourably coupled to the static AF order, which competes with superconductivity. Note that the \(h\) - pocket in five- layered hole- doped \(\mathrm{Ba}_2\mathrm{Ca}_4\mathrm{Cu}_5\mathrm{O}_{10}(\mathrm{F}_3\mathrm{O})_2\) recently observed by ARPES similarly implies that superconductivity can be induced by the \(h\) - pocket alone, without a contribution from the anti- nodal region<sup>48</sup>. Further systematic investigations of the orbital character of the \(h\) - pocket may provide clues regarding the origin of hole superconductivity in electron- doped cuprates.  
+
+<|ref|>sub_title<|/ref|><|det|>[[140, 599, 231, 618]]<|/det|>
+## Methods  
+
+<|ref|>sub_title<|/ref|><|det|>[[140, 642, 225, 659]]<|/det|>
+## Materials  
+
+<|ref|>text<|/ref|><|det|>[[139, 679, 860, 848]]<|/det|>
+Single crystals of PLCCO with \(x = 0.10\) , 0.15, and 0.18 were grown by the traveling- solvent floating- zone method. All of the crystal rods were cut into small pieces along the \(\mathrm{CuO}_2\) plane, and annealed in a high- purity \(\mathrm{N}_2\) gas atmosphere at \(920 - 930^{\circ}\mathrm{C}\) and vacuum with \(\sim 10^{- 5}\) Torr at \(790^{\circ}\mathrm{C}\) for \(10 - 24\mathrm{h}\) . Subsequent air annealing was performed on some of the samples, at a temperature between \(500\mathrm{and}800^{\circ}\mathrm{C}\) for \(5 - 10\mathrm{h}\) . We characterized the \(T_{\mathrm{c}}\) of each sample within \(10\%\) of the SC shielding volume fraction by measuring the magnetic susceptibility with a magnetic property measurement system (MPMS; Quantum
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[139, 115, 858, 159]]<|/det|>
+Design, San Diego, CA, USA). (see Supplementary Fig. 9) Hall resistivity was measured with a physical property measurement system (PPMS; Quantum Design).  
+
+<|ref|>sub_title<|/ref|><|det|>[[140, 180, 339, 197]]<|/det|>
+## ARPES measurements  
+
+<|ref|>text<|/ref|><|det|>[[139, 217, 860, 413]]<|/det|>
+ARPES measurementsARPES experiments were performed at beam lines 5- 2 and 5- 4 of the Stanford Synchrotron Radiation Lightsource (SSRL, Menlo Park, CA, USA). Samples were cleaved in situ and experiments were performed at temperatures below \(30\mathrm{K}\) , at a pressure around \(4 \times 10^{- 11}\) Torr. We used linearly polarized \(16.5\mathrm{eV}\) photons with an overall energy resolution of \(\sim 12 - 15\mathrm{meV}\) for the doping dependence study described in the main text. The \(16.5\mathrm{eV}\) irradiation and linear polarization aligned with the \(c\) - axis component of the sample are suitable for the present study, as this combination tends to brighten the nodal band spectrum and enhance hot- spot features<sup>18</sup>.  
+
+<|ref|>sub_title<|/ref|><|det|>[[140, 434, 309, 451]]<|/det|>
+## \(\mu \mathrm{SR}\) measurements  
+
+<|ref|>text<|/ref|><|det|>[[139, 472, 861, 812]]<|/det|>
+\(\mu \mathrm{SR}\) measurementsThe \(\mu \mathrm{SR}\) measurements were performed using the M20 beamline at the TRIUMF facility (Vancouver, British Columbia, Canada). A dozen pieces of sliced PLCCO crystal (typical area: \(1\mathrm{cm}^2\) ) were wrapped with silver foil and attached to the sample holder. Zero- field \(\mu \mathrm{SR}\) (ZF- \(\mu \mathrm{SR}\) ) measurements were carried out over the temperature range of \(2 - 200\mathrm{K}\) . The physical quantity measured was the evolution of the muon depolarization \(P_z(t) = [N_{\mathrm{B}}(t) - \alpha N_{\mathrm{F}}(t)] = [N_{\mathrm{B}}(t) + \alpha N_{\mathrm{F}}(t)]\) , where \(N_{\mathrm{F}}(t)\) and \(N_{\mathrm{B}}(t)\) are the number of positrons counted at detectors antiparallel and parallel to the incident muon spin direction, respectively. \(\alpha\) is the efficiency ratio between the forward and backward detectors. \(P_z(t)\) conveys information about the local magnetic field distribution at the muon stopping sites. All of the data were analyzed using the free musrfit software package<sup>49</sup>. All the \(\mu \mathrm{SR}\) spectra in Fig. 4a were obtained by subtracting the temperature- independent constant background from the raw data and then normalizing it with the theoretical initial asymmetry \(P_z(t = 0)\) estimated by the fittings. Details of analysis process are described in Supplementary Information.  
+
+<|ref|>sub_title<|/ref|><|det|>[[140, 832, 367, 850]]<|/det|>
+## CPT Spectral Simulations
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[137, 114, 860, 310]]<|/det|>
+Cluster perturbation theory (CPT) is designed to be an efficient method to estimate the \(A(\mathbf{k},\omega)\) of strongly correlated systems \(^{24,25}\) . When dividing the infinite plane into clusters, the Hamiltonian could be split into \(H = H_{c} + H_{int}\) , where \(H_{c}\) contains the (open- boundary) intra- cluster operators and \(H_{int}\) contains the operators with inter- cluster indices (hopping terms for the Hubbard model). Restricting ourselves to zero temperature, we use exact diagonalization (ED) to exactly solve the cluster Green's function \(G_{c}(\omega)\) associated with the intra- cluster Hamiltonian \(H_{c}\) . Then the CPT method estimates Green's function by treating \(H_{int}\) perturbatively, giving  
+
+<|ref|>equation<|/ref|><|det|>[[380, 330, 618, 368]]<|/det|>
+\[G(\mathbf{k},\omega) = \frac{G_{c}(\omega)}{1 - V(\mathbf{k})G_{c}(\omega)}\]  
+
+<|ref|>text<|/ref|><|det|>[[137, 387, 858, 434]]<|/det|>
+Here \(V(\mathbf{k}) = \sum_{\mathbf{R}}H_{int}e^{i\mathbf{k}\cdot \mathbf{R}}\) is the inter- cluster interactions projected to the intra- cluster coordinates. Taking the long- wavelength limit, we obtain the spectral function  
+
+<|ref|>equation<|/ref|><|det|>[[295, 453, 700, 497]]<|/det|>
+\[A(\mathbf{k},\omega)_{CPT} = -\frac{1}{\pi N} Im\sum_{\sigma}G_{a,b}(\mathbf{k},\omega)e^{i\mathbf{k}\cdot (\mathbf{r}_a - \mathbf{r}_b)}\]  
+
+<|ref|>text<|/ref|><|det|>[[138, 515, 860, 560]]<|/det|>
+with a,b are intra- cluster site indices. In this paper we employ a \(4\times 4\) cluster as the exact spectral solver. We further use an \(8\times 8\) superclusters to obtain finer doping interval.  
+
+<|ref|>sub_title<|/ref|><|det|>[[139, 620, 309, 640]]<|/det|>
+## Data availability  
+
+<|ref|>text<|/ref|><|det|>[[139, 662, 751, 682]]<|/det|>
+Data are available from the corresponding authors upon reasonable request.  
+
+<|ref|>sub_title<|/ref|><|det|>[[139, 741, 253, 760]]<|/det|>
+## References  
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+<|ref|>text<|/ref|><|det|>[[137, 487, 825, 530]]<|/det|>
+43. He, Y. et al., Superconducting fluctuations in overdoped \(\mathrm{Bi}_{2}\mathrm{Sr}_{2}\mathrm{CaCu}_{2}\mathrm{O}_{8 + \delta}\) . Phys. Rev. X 11, 031068 (2020)  
+
+<|ref|>text<|/ref|><|det|>[[137, 536, 826, 604]]<|/det|>
+44. Kordyuk, A. A. Electronic band structure of optimal superconductors: From cuprates to ferropnictides and back again. Low Temperature Physics, 44(6), 477-486. (2018)  
+
+<|ref|>text<|/ref|><|det|>[[137, 610, 824, 653]]<|/det|>
+45. Wilson, S. D. et al. Resonance in the electron-doped high-transition-temperature superconductor \(\mathrm{Pr}_{0.88}\mathrm{LaCe}_{0.12}\mathrm{CuO}_{4 - \delta}\) . Nature, 442(7098), 59-62. (2006)  
+
+<|ref|>text<|/ref|><|det|>[[137, 659, 770, 702]]<|/det|>
+46. Cao, Y. et al. Unconventional superconductivity in magic-angle graphene superlattices. Nature 556, 43-50 (2018)  
+
+<|ref|>text<|/ref|><|det|>[[137, 708, 842, 775]]<|/det|>
+47. Kim, C. et al. Systematics of the photoemission spectral function of cuprates: Insulators and hole- and electron-doped superconductors. Phys. Rev. Lett. 80, 4245 (1998).  
+
+<|ref|>text<|/ref|><|det|>[[137, 782, 818, 825]]<|/det|>
+48. Kunisada, S. et al. Observation of small Fermi pockets protected by clean \(\mathrm{CuO}_{2}\) sheets of a high-\(T_{\mathrm{c}}\) superconductor. Science 369, 833-838 (2020).
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[137, 115, 822, 184]]<|/det|>
+49. Suter, A., Wojek, B.M. & Musrfit. A Free Platform-Independent Framework for \(\mu \mathrm{SR}\) Data Analysis. Physics Procedia 30, 69 http://dx.doi.org/10.1016/j.phpro.2012.04.042 (2012).  
+
+<|ref|>sub_title<|/ref|><|det|>[[140, 230, 327, 250]]<|/det|>
+## Acknowledgments  
+
+<|ref|>text<|/ref|><|det|>[[137, 271, 860, 710]]<|/det|>
+We acknowledge insightful discussions with Shin- ichi Uchida, George Sawatzky, Yu He, Dirk Wulferding, Mikyung Kim, Yangyang Li, Seyoung Park, Junwon Rhim, Ke- Jun Xu, and Alannah Hallas; ARPES experiments were performed at Beamline 5- 4 and 5- 2, Stanford Synchrotron Radiation Lightsource, SLAC National Accelerator Laboratory operated by the DOE Office of BES. (Proposal No. 5161); \(\mu \mathrm{SR}\) measurements were carried out on the M20 beamline at the TRIUMF facility (Vancouver, Canada). This project was undertaken thanks in part to funding from the Canada First Research Excellence Fund, Quantum Materials and Future Technologies Program and supported by the Institute for Basic Science in Korea (Grant No. IBS- R009- G2, IBS- R011- Y3); and JSPS KAKENHI (Grant No. 18H01860); The work at SKKU was supported by the National Research Foundation (NRF) of Korea (Grant Nos. 2020R1A2C3012367 and 2020R1A5A1016518). S.- R.P. acknowledges the National Research Foundation of Korea (NRF) (Grant No. NRF- 2020R1A2C1011439). Z.S. and Y.W. acknowledge support from the Air Force Office of Scientific Research Young Investigator Program under grant FA9550- 23- 1- 0153. The spectral simulations used resources of the National Energy Research Scientific Computing Center (NERSC), a U.S. Department of Energy Office of Science User Facility located at Lawrence Berkeley National Laboratory, operated under Contract No. DE- AC02- 05CH11231 using NERSC award BES- ERCAP0023181.  
+
+<|ref|>sub_title<|/ref|><|det|>[[140, 770, 356, 789]]<|/det|>
+## Author contributions  
+
+<|ref|>text<|/ref|><|det|>[[139, 811, 860, 878]]<|/det|>
+D.S., S.L., S.- R.P., H.E., K.- Y.C., and C.K. conceived and designed the experiments with suggestions from S.C., S.I., and Y.Y.; Z.S. and Y.W. performed theoretical calculations; D.S., W.J., and S.I. grew and characterized the PLCCO single crystals; D.S., W.J., W.K.,
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[137, 115, 860, 235]]<|/det|>
+and S.J. performed the ARPES measurements; S.L., W.L., and K.- Y.C. performed the \(\mu \mathrm{SR}\) measurements; D.S., W.J., and S.J., analyzed the ARPES experimental data; S.L., and W.L. analyzed the \(\mu \mathrm{SR}\) experimental data; D.S., S.L., Y.W., S.C., W.K., K.- Y.C. and C.K. wrote the manuscript with input from S.- R.P., H.E., and contributions from all authors.  
+
+<|ref|>sub_title<|/ref|><|det|>[[140, 296, 345, 316]]<|/det|>
+## Competing interests  
+
+<|ref|>text<|/ref|><|det|>[[140, 339, 494, 356]]<|/det|>
+The authors declare no competing interests.
+
+<--- Page Split --->
+<|ref|>image<|/ref|><|det|>[[170, 125, 816, 451]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[137, 473, 860, 689]]<|/det|>
+<center>Fig. 1 | Electronic structure and antiferromagnetic (AF) band reconstruction of electron-doped cuprates. a Fermi surface (FS) and nodal band ARPES spectra of PLCCO as a function of electron doping \((n)\) . Non-SC, UD17, and OD11 indicate non-superconducting, underdoping, and overdoping, respectively, where the number indicates \(T_{\mathrm{c}}\) in Kelvins. b Copper oxide plane with antiferromagnetically ordered spins. The black lines show the pristine unit-cell and the pink shaded square is the AF unit-cell. c Schematics of pristine (left) and AF pocket (right) FSs of electron-doped cuprates. The middle panel shows the band-folding process, attributed to the AF unit cell doubling shown in b. The pink shaded area is the AF Brillouin zone. </center>
+
+<--- Page Split --->
+<|ref|>image<|/ref|><|det|>[[183, 118, 822, 512]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[139, 525, 860, 714]]<|/det|>
+<center>Fig. 2 | Doping evolution of the nodal band in PLCCO. a ARPES spectra of the nodal band after Fermi–Dirac correction for various doping samples. OP indicates optimal doping. b Nodal band spectra with the maximum energy distribution curve (EDC) that brightens the “folded shadow band” forbidden in a (see Supplementary Fig. 3). c Schematic spectra of the FS at the nodal point and hot spot. The black peak at the nodal point indicates the nodal band spectrum. The red and green peaks show the \(h\) -pocket and \(L\) -circle component, respectively. d Superposed FS consisting of electron \((e^{-})\) and hole \((h^{-})\) pockets, and a large \((L^{-})\) circle. The pink dashed lines mark the AF zone boundary. </center>
+
+<--- Page Split --->
+<|ref|>image<|/ref|><|det|>[[155, 125, 852, 580]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[137, 590, 860, 779]]<|/det|>
+<center>Fig. 3 | Correlation between the Fermi hole pocket and superconductivity. a EDCs at the nodal point and hot spot for various dopings. Dashed and dotted lines guide the eye to the zero-energy intensity of each EDC. The red vertical arrows denote \(Z_{\text{hole}}\) . B CPT calculation result as a function of \(n\) . The white dashed box highlights the nodal hole band. c \(Z_{\text{hole}}\) versus \(T_{\text{c}}\) . The red shaded area guides the eye. d \(Z_{\text{hole}}\) , along with \(T_{\text{c}}\) and \(Z_{\text{theory}}\) , as a function of \(n\) . The dashed line marks the doping where the shallow hole band gap completely closes or underlying Lifshitz transition takes place (see Supplementary Fig. 4). </center>
+
+<--- Page Split --->
+<|ref|>image<|/ref|><|det|>[[145, 123, 845, 530]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[137, 541, 858, 560]]<|/det|>
+<center>Fig. 4 | Quantum critical-like transition with dissolution of glassy freezing near OP. </center>  
+
+<|ref|>text<|/ref|><|det|>[[137, 565, 860, 739]]<|/det|>
+a \(T\) - dependent zero- field muon spin rotation/relaxation spectra for samples UD15, UD20, OP24, and OD18. Solid lines show the fit to the data described in the Methods. b, c \(T\) - dependent relaxation rates \(\lambda (T)\) for UD15, UD20, OP24, and OD18. Solid and dashed arrows indicate the transition temperatures for the spin- freezing phase \(T_{\mathrm{f}}\) and fluctuating short- range- order phase \(T^{*}\) . The solid lines are power law fits, \(\lambda_{\mathrm{p}}(T) \sim T^{-\alpha}\) (see \(\alpha\) in Supplementary Fig. 8). The difference between the power law fit and data, \(\Delta \lambda (T) = \lambda_{\mathrm{p}}(T) - \lambda (T)\) , in the low- \(T\) range denotes the fraction of frozen spins in the inset of b.
+
+<--- Page Split --->
+<|ref|>image<|/ref|><|det|>[[231, 112, 771, 465]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[137, 479, 860, 644]]<|/det|>
+<center>Fig. 5 | Electronic, magnetic, and SC phase diagram of PLCCO. \(T_{\mathrm{N}}\) , \(T_{\mathrm{f}}\) , \(T^{*}\) , and LCP indicate Neel, spin-freezing, and fluctuating short-range antiferromagnetic (SR-AF) phase transition temperatures, and the Lifshitz-critical point, respectively. LR-AF and AF-glass represent the long-range antiferromagnetic order phase and clustered spin-glass phase, respectively. The schematic plot of SC and \(Z_{\mathrm{hole}}\) dome is based on the data shown in Fig. 3d. The sign change in the Hall coefficient ( \(R_{\mathrm{H}}\) ) at low- \(T\) with overdoping is shown in Supplementary Fig. 5. </center>
+
+<--- Page Split --->
+<|ref|>sub_title<|/ref|><|det|>[[43, 42, 312, 70]]<|/det|>
+## Supplementary Files  
+
+<|ref|>text<|/ref|><|det|>[[43, 93, 768, 113]]<|/det|>
+This is a list of supplementary files associated with this preprint. Click to download.  
+
+<|ref|>text<|/ref|><|det|>[[60, 130, 422, 150]]<|/det|>
+- PLCConNatComSupplementaryfinal.pdf
+
+<--- Page Split --->

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preprint/preprint__1ea13beaba38237f69e17a0b0ac6e90820da151be6ddca4447cff5ff3f07bea5/preprint__1ea13beaba38237f69e17a0b0ac6e90820da151be6ddca4447cff5ff3f07bea5.mmd

@@ -0,0 +1,329 @@
+
+# FGL2-targeted T cells induced tumor-specific brain resident TRM cells preventing glioblastoma recurrence  
+
+Qingnan Zhao Shanghai Jiao Tong University School of Medicine Ling- Yuan Kong  
+
+University of Texas MD Anderson Cancer Center  
+
+Shan Jiang The University of Texas Health Science Center at Houston  
+
+Xiangjun Tian The University of Texas MD Anderson Cancer Center  
+
+Jing Wang UT M.D. Anderson Cancer Center https://orcid.org/0000- 0002- 5398- 0802  
+
+Rintaro Hashizume Northwestern University  
+
+Jiemiao Hu The University of Texas MD Anderson Cancer Center https://orcid.org/0000- 0002- 5142- 0641  
+
+Zhiliang Jia The University of Texas MD Anderson Cancer Center  
+
+Natalie Fowlkes The University of Texas MD Anderson Cancer Center https://orcid.org/0000- 0002- 7711- 6031  
+
+Jun Yan University of Texas MD Anderson Cancer Center  
+
+Xeuqing Xia MD Anderson Cancer Center  
+
+Sofia Yi The University of Texas MD Anderson Cancer Center  
+
+Long Dao The University of Texas MD Anderson Cancer Center  
+
+David Masopust University of Minnesota https://orcid.org/0000- 0002- 9440- 3884  
+
+Hideho Okada UCSF, USA https://orcid.org/0000- 0003- 0076- 9920  
+
+Amy Heimberger
+
+<--- Page Split --->
+
+Northwestern University https://orcid.org/0000- 0002- 9970- 8695Shulin Li ( \(\bowtie\) SLi4@mdanderson.org)The University of Texas MD Anderson Cancer Center https://orcid.org/0000- 0001- 5657- 4183  
+
+## Article  
+
+Keywords: T- aFGL2, glioblastoma, Tissue- resident memory T cells, CD69, CXCR3  
+
+Posted Date: May 11th, 2022  
+
+DOI: https://doi.org/10.21203/rs.3.rs- 1632572/v1  
+
+License: © \(\circledast\) This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License  
+
+Version of Record: A version of this preprint was published at Nature Communications on February 10th, 2023. See the published version at https://doi.org/10.1038/s41467- 023- 36430- 2.
+
+<--- Page Split --->
+
+## Abstract  
+
+Tissue- resident memory T cells \((T_{\mathrm{RM}})\) specific to previously encountered pathogens have been characterized, but tumor- specific \(\mathsf{T}_{\mathsf{RM}}\) cells in brain have not been reported in the literature. We discovered that T cells armed with FGL2- blocking single- chain variable fragments (T- aFGL2) are able to induce tumor- specific \(\mathsf{CD8^{+}T_{RM}}\) cells, which prevent glioblastoma recurrence, a major obstacle in achieving long term survivors. These tumor- specific \(\mathsf{CD8^{+}T_{RM}}\) displayed a unique highly expanded T cell receptor repertoire distinct from that found in peripheral tissues. Notably, these \(\mathsf{CD8^{+}T_{RM}}\) cells could be transplanted into the brains of either immunocompetent or T cell deficient naive mice, transforming them to become immune reactive to tumor cells. The mechanism study found T- aFGL2 therapy boosted \(\mathsf{CD69^{+}CD8^{+}}\) memory T cells population in tumor bearing brains, which depend on CXCL9/10- CXCR3 signaling. These findings are the first to show tumor- specific brain resident \(\mathsf{CD8^{+}T_{RM}}\) generation via adoptive cellular treatment and may have promising implications for cancer immunotherapy.  
+
+## Significance Statement  
+
+1. T cells armed with FGL2-blocking single-chain variable fragments (T-aFGL2) were able to induce tumor-specific \(\mathsf{CD8^{+}T_{RM}}\) cells. 
+2. These tumor-specific \(\mathsf{CD8^{+}T_{RM}}\) cells are tumor reactive and transplantable. 
+3. The induced tumor-specific \(\mathsf{CD69^{+}CD62L^{-}CD8^{+}T_{RM}}\) cells, displayed a highly expanded T cell receptor repertoire. 
+4. CXCL9/10-CXCR3 signaling was associated with \(\mathsf{CD69^{+}CD8^{+}T_{RM}}\) induction.  
+
+## Introduction  
+
+Memory T cells provide rapid and effective immune protection against a wide variety of antigens, including those from environmental substances and malignant tumors. Memory T cells consist of two major populations: non- recirculating resident memory T \((T_{\mathsf{RM}})\) cells \(^{1,2}\) and recirculating memory T cells \(^{3}\) recirculating memory T cells include effector memory T \((T_{\mathsf{EM}})\) cells, central memory T cells \((T_{\mathsf{CM}})\) , and migratory memory T cells. Research has shown that \(T_{\mathsf{RM}}\) cells are more potent effectors than recirculating memory T cells \(^{4 - 7}\) . \(T_{\mathsf{RM}}\) cells, often bearing CD69 and the aEB7 integrin (CD103), and null of CD62L, offer dominant immunity to localized infection \(^{8}\) . CD103 binds to epithelial E-cadherin, while CD69 blocks cellular egress via inhibition of the function of Sphingosine-1-phosphate receptor-1 (S1PR1) \(^{9}\) , both help \(T_{\mathsf{RM}}\) cells retain in peripheral tissues. CD62L is lymphoid homing molecule, which helps peripheral T cells homing to secondary lymphoid organs. To date, \(T_{\mathsf{RM}}\) cells have been found in both barrier and non- barrier tissues, including the skins \(^{10,11}\) , brains \(^{12}\) , lungs \(^{13 - 17}\) , livers \(^{18}\) , and breasts \(^{19}\) , where they mediate long- lived protection against reinfection. However, how to induce \(T_{\mathsf{RM}}\) cell formation in tumor- bearing tissues,
+
+<--- Page Split --->
+
+especially glioblastoma (GBM) with high fatality rate, thus leading to tumor contraction and recurrence prevention, has rarely been studied.  
+
+As a member of the fibrinogen- like protein family, fibrinogen- like protein 2 (FGL2) possesses prothrombinase activity and immune regulatory functions in both viral infection and cancer development. Accumulating evidence shows that FGL2 acts as an immunosuppressive regulator, suppressing B cell, T cell, and dendritic cell (DC) functions by binding to FcγRIIB and regulating adaptive immunity via Th1 and Th2 cytokines<sup>20</sup>. Our previously published data showed that overexpression of FGL2 correlates with upregulated expression of negative immune checkpoints, decreased granulocyte- macrophage colony- stimulating factor induced CD103<sup>+</sup> DC differentiation, faster glioma progression, and poor clinical outcome in brain malignancies<sup>20- 23</sup>.  
+
+An analysis of TCGA data found an inverse correlation between FGL2 expression and patient with GBM survival<sup>22</sup>; therefore, FGL2 may serve as an attractive target for brain tumor immunotherapy. Indeed, FGL2- specific polyclonal antibodies showed antitumor activity against GBM tumor cells in syngeneic mouse models<sup>23</sup>. However, owing to their poor blood- brain barrier penetration, the potential of FGL2- blocking antibodies to suppress brain tumor progression is limited.  
+
+Here, to improve the efficacy of an FGL2- blocking antibody in treating brain tumors, we performed adoptive cell infusion of T cells armed with an FGL2- blocking single- chain variable fragment (scFv). These T cells bearing FGL2- blocking scFv (T- aFGL2) showed superior potent anti- tumor effects compared with control T cells (T- Ctr) without causing obvious toxicity at the therapeutic dose. Importantly, the long- term mouse survivors in the T- aFGL2 treatment group formed tumor- specific brain- resident memory CD8<sup>+</sup> T (CD8<sup>+</sup> T<sub>RM</sub>) cells that rejected rechallenged tumor cells in the brain. These CD8<sup>+</sup> T<sub>RM</sub> cells are CD69<sup>+</sup> CD8<sup>+</sup> T cells that display an expanded T cell receptor (TCR) repertoire. Of note, the CD8<sup>+</sup> T<sub>RM</sub> cells can be transplanted into the brains of naïve mice to convert these naïve mice into CD8<sup>+</sup> T<sub>RM</sub>- bearing mice, enabling these mice to reject tumor cells upon rechallenge. We report that T- aFGL2 treatment boosted CD69<sup>+</sup> CD62L<sup>+</sup> CD8<sup>+</sup> T cells population, and this effect was abolished when depleting either CXCR3 ligands CXCL9/10 or knockout of CXCR3 in the host mice, revealing an unanticipated novel link between CXCL9/10- CXCR3 signaling and tumor specific CD8<sup>+</sup> T<sub>RM</sub> formation in brains.  
+
+## Results  
+
+## T-aFGL2 treatment has limited antitumor cytotoxic T lymphocyte activity in vitro  
+
+To obtain superior FGL2 specific monoclonal antibodies (mAbs), we selected 75 clones of FGL2 mAbs through 3 independent hybridoma fusion and via an ELISA- based mouse FGL2 (mFGL2) binding assay (Supplementary Fig. 1a). 13 out of 75 clones showed strong binding activity to mFGL2 but not to his- tag. Human FGL2 (huFGL2) was then used to select mAbs that show bi- species binding reactivity (Supplementary Fig. 1b), and mouse FGL2 binding clone #4 also showed high binding affinity to human
+
+<--- Page Split --->
+
+FGL2 (Supplementary Fig. 1a, 1b). Additionally, clone #4 showed the most linear association between binding capacity and dilution (Supplementary Fig. 1c). Western blotting, immunofluorescence staining, and ELISA assay further validated the binding activity of FGL2 mAb- clone #4 to both mouse and human FGL2 (Supplementary Fig. 1d- 1f). To further test the effect of FGL2 blocking scFv, lentiviral constructs derived from FGL2 mAb- clone #4 were generated to arm T cells (Fig 1a). This construct contained scFv domains that aimed at recognizing and binding FGL2 (Fig 1a). To ensure that FGL2 scFv was expressed on the surface of T cells with the ability of movement, an EGFR- transmembrane (TM) domain was linked to the FGL2 scFv by a P2A linker (Fig 1a, 1b). The expression of FGL2 scFv on the T cell membrane was validated by the staining of the His tag domain (Fig 1c). The transduction efficiency of the activated mouse T cells was consistent and in the range of \(15\%\) to \(25\%\) (Fig 1c). To verify that T- aFGL2 cells can directly bind FGL2, we have established a microfluidics chip binding assay. As the data shown in Supplementary Fig.1g, T- aFGL2 cells can directly bind the FGL2 that is anchored on the chip via biotin- streptavidin covalent bond. The antitumor cytotoxic T lymphocyte activity of FGL2- scFv- armed T cells (T- aFGL2) against FGL2- expressing DBT cells, a mouse GBM cell line, was evaluated by measuring proportion of tumor cells, and granzyme B, interferon \(\gamma\) (IFNγ), and tumor necrosis factor \(\alpha\) (TNFα) positive T cells (Fig. 1d). T- aFGL2 cells expressed higher granzyme B levels than did T cells transfected with a control construct (T- Ctr) when cocultured with DBT cells at an effector- to- target ratio of 1:1. However, no significant difference was found in the proportion of tumor cells when cocultured with T- aFGL2 and T- Ctr cells, and T- aFGL2 and T- Ctr cells had comparable levels of IFNγ and TNFα, suggesting that T- aFGL2 may have limited direct tumor cell killing activity effects in vitro.  
+
+## T-aFGL2 treatment does not cause toxicity in immunocompetent mice  
+
+To evaluate the suitability of FGL2 as a target for T cell therapy with low risk of off- tumor on- target toxicity, we assessed the expression of FGL2 in human GBM tissues and normal human tissue arrays using FGL2 mAb- clone #4 from which the aFGL2 construct was derived. As shown in Fig. 1e, FGL2 was highly expressed in human GBM tissues but not in healthy medulla oblongata tissues. In healthy tissue arrays (Fig. 1e), major organs such as the brain, lung, breast, spleen, and muscle were FGL2 negative, while moderate expression of FGL2 was observed in the stomach, colon, and pancreas. (Fig. 1e). To assess the potential toxicity of T- aFGL2, we intravenously injected 5 million T- Ctr or T- aFGL2 cells into non- tumor- bearing 7- week- old immunocompetent Balb/c mice. Five days after T cell injection, we evaluated blood chemistry, organ toxicities, and immune cell populations in the spleen and bone marrow. As shown in Supplementary Fig. 2a and b, mice treated with T- aFGL2 exhibited no significant changes in immune cell counts in either the spleen or bone marrow. T- aFGL2 treatment caused no abnormalities in blood chemistry (Supplementary Fig. 2c), but mice treated with T- Ctr had significantly higher blood levels of albumin ( \(P = 0.0264\) ) and globulin ( \(P = 0.0181\) ) than did untreated mice (Supplementary Fig. 2c). Furthermore, a board certified veterinary pathologist (N.W.F.) observed no evident abnormality or aberrant T lymphocyte infiltration in tissue sections following T- aFGL2 cell infusion (Supplementary Fig. 3 and Supplementary Table 1). Taken together, these results show that T- aFGL2 therapy does not cause detectable organ toxicity in immunocompetent mice.
+
+<--- Page Split --->
+
+## T-aFGL2 therapy induces superior antitumor activity in vivo  
+
+To test the efficacy of T- aFGL2 therapy in vivo, we first validated expression of FGL2 in mouse GBM tissue. Brain tissues from immunocompetent syngeneic mouse GBM model (DBT tumor- bearing mice) were cryosectioned and stained with FGL2 mAb- clone #4. As shown in Fig. 2a, both tumor cells and surrounding stroma were positively stained for FGL2. Next, DBT- bearing Balb/c mice were used to evaluate the antitumor effects of T- aFGL2. Mice were inoculated with tumor cells and then treated with standard chemotherapy temozolomide (TMZ) on days 3, 4, and 5 to assimilate standard care, before administering T- Ctr or T- aFGL2 cells via the tail vein on days 6 and 13 after tumor cell inoculation (Fig. 2b). DBT is a very aggressive GBM tumor, and most DBT- bearing mice died within 3 weeks in the no treatment or T- Ctr group. T- aFGL2 treatment effectively suppressed DBT tumor progression, and tumors were eliminated in about \(30\%\) of the T- aFGL2- treated mice. These mice remained tumor free for up to 70 days before being used for a rechallenge study. In contrast, tumors progressed rapidly in T- Ctr- treated mice (Fig. 2c- e).  
+
+To confirm the anti- tumor efficacy of T- aFGL2 treatment, we also took advantage of another syngeneic mouse GL261 model. As shown in Fig. 2f- g, T- aFGL2 treatment, compared with T- Ctr treatment, suppressed this GBM tumor growth and extended mouse survival. Overall, T- aFGL2 showed superior antitumor properties in syngeneic malignant brain tumor models.  
+
+## T-aFGL2 treatment induces formation of tumor-specific \(\mathbf{CD8^{+}T_{RM}}\) like cells in the brain  
+
+We next evaluated whether long- term survivors that had been treated with T- aFGL2 cells developed memory T cells that were reactive to tumor cells. We rechallenged T- aFGL2- treated survivors with an intracranial (i.c.) injection of DBT cells (Fig. 3a). The re- challenged DBT cells were cleared within 7 days in T- aFGL2- treated survivors (Fig. 3b), and local re- exposure to DBT cells induced a rapid, more than 18- fold increase in the number of \(\mathbf{CD8^{+}}\) T cells in the brains of T- aFGL2- treated survivors compared to the number in naive brains (Fig. 3d, 3f). To investigate the tumor specificity of the generated memory T cells, we rechallenged DBT tumor- rejecting mice induced by T- aFGL2 treatment with 4T1 tumor cells (i.c.), which also developed tumors in the naive balb/c mice rapidly. We found these DBT- rejecting memory T cells failed to protect mice from 4T1 tumor cell challenge (Supplementary Fig. 4a). This rapid and intense tumor reactivity to DBT cells, but not to 4T1 cells, confirmed that tumor- specific memory \(\mathbf{CD8^{+}}\) T cells had developed in the brains of T- aFGL2- treated survivors.  
+
+To determine whether these tumor- specific memory \(\mathbf{CD8^{+}}\) T cells stayed in the vicinity of the tumor (ie, in the brain) or migrated throughout the body, we inoculated DBT cells subcutaneously into the flanks of T- aFGL2 survivors and naive mice (Fig. 3a). Interestingly, both groups of mice developed tumors under the skin (Fig. 3c), suggesting that the tumor- specific memory \(\mathbf{CD8^{+}}\) T cells in T- aFGL2- treated survivors were restricted to the brain. To confirm that tumor- reactive \(\mathbf{CD8^{+}}\) T cells only existed in the brain, we assessed T cells from the brains and draining lymph nodes (dLNs) of naive mice and T- aFGL2- treated survivors 7 days after the rechallenge with DBT cells. As shown in Fig. 3d and e, the ratio of \(\mathbf{CD8^{+}}\) T cells to \(\mathbf{CD4^{+}}\) T
+
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+cells in the brain was up to 8- fold higher in T- aFGL2- treated survivors than in naive mice. Moreover, the ratio of \(\mathsf{CD8^{+}}\) to \(\mathsf{CD4^{+}}\) T cells was 9- fold higher in the brains than in the LNs of T- aFGL2- treated survivors, suggesting that \(\mathsf{CD8^{+}}\) T cells, but not \(\mathsf{CD4^{+}}\) T cells, were the primary memory T cells controlling tumor cell growth, and that these \(\mathsf{CD8^{+}}\) T cells were only resident in the brain. Taken together, these data strongly indicate that T- aFGL2 treatment induced development of brain- resident tumor- specific \(\mathsf{CD8^{+}T_{RM}}\) like cells.  
+
+## \(\mathsf{CD8^{+}T_{RM}}\) like cells undergo recall expansion and reject tumor cells when being transplanted into naïve brains  
+
+To validate that \(\mathsf{CD8^{+}}\) T cells in the brains of T- aFGL2- treated survivors were \(\mathsf{CD8^{+}T_{RM}}\) cells, we sorted \(\mathsf{CD8^{+}}\) T cells from the brains, draining lymph nodes (dLNs), and peripheral blood (PB) of T- aFGL2- treated survivors on day 7 after tumor cell inoculation and adoptively transplanted these T cells along with DBT cells directly into the brains (intracranially) of naïve recipient mice (Fig. 4a). In contrast to both dLN and PB \(\mathsf{CD8^{+}}\) T cells, which failed to mount a recall response, brain \(\mathsf{CD8^{+}}\) T cells underwent expansion even when reseeded in the brain tissue in low numbers (3000 cells) (Fig. 4b, c), confirming that the \(\mathsf{CD8^{+}}\) T cells in the brains of T- aFGL2- treated survivors are bona fide \(\mathsf{T_{RM}}\) cells. As most of the \(\mathsf{CD8^{+}}\) T cells in tumor experienced brains were \(\mathsf{CD44^{+}}\) memory T cells, while the \(\mathsf{CD8^{+}}\) T cells in peripheral were not, we then compared the anti- tumor effect of \(\mathsf{CD44^{+}CD8^{+}T}\) cells in brain, dLNs, pLNs and PB to validate the results. In consistence with \(\mathsf{CD8^{+}}\) T cells, the \(\mathsf{CD44^{+}CD8^{+}T}\) cells in peripheral tissues didn't show protection against tumor cells in vivo (Supplementary Fig. 4b and 4c). Similar results was found in GL261 model (Supplementary Fig. 4d). To determine whether \(\mathsf{CD4^{+}}\) T cells in the brain behaved in a similar manner as \(\mathsf{CD8^{+}}\) T cells, we sorted \(\mathsf{CD4^{+}}\) and \(\mathsf{CD8^{+}}\) T cells from the brains of T- aFGL2- treated survivors and then co- inoculated with DBT cells into the brains of naïve recipient mice. As shown in Fig. 4b and c, \(\mathsf{CD4^{+}}\) T cells did not have the same tumor- cell- eliminating capacity as \(\mathsf{CD8^{+}}\) T cells, confirming that the induced brain resident \(\mathsf{CD8^{+}}\) T cells, but not \(\mathsf{CD4^{+}}\) T cells, in the brain provide immune surveillance of the previously encountered antigen.  
+
+To further investigate whether the adoptively transplanted \(\mathsf{CD8^{+}T_{RM}}\) cells could survive and remain in the brains of naïve recipient mice, we subsequently challenged the recipient mice with tumor cells on day 40 after adoptive \(\mathsf{CD8^{+}}\) T cells transplantation. We observed that the recipient mice rejected the rechallenged tumor cells (Fig. 4d, e). These findings show that \(\mathsf{CD8^{+}T_{RM}}\) cells were successfully transplanted into naïve brains, transforming naïve brains to become tumor rejecting brains. To further validate the function of transplanted \(\mathsf{CD8^{+}T_{RM}}\) cells and exclude the effect of host T cells, we transplanted the lymphocytes from brain with \(\mathsf{T_{RM}(T_{RM}BILs)}\) into brain of naïve immunodeficiency SCID mice and re- challenged these mice 35 days after the transplantation (Fig. 4f). The same as the parental \(\mathsf{T_{RM}}\) bearing mice, these \(\mathsf{T_{RM}}\) transplanted SCID mice showed anti- tumor capacity in the brain (Fig 4f- g); To verify the transplanted \(\mathsf{CD8^{+}}\) T cells are responsible for the protection, we re- challenged these SCID survivors with tumor cells combined with \(\mathsf{aCD8}\) , \(\mathsf{aCD4}\) or asialo GM1 antibodies to deplete \(\mathsf{CD8^{+}T}\) cells, \(\mathsf{CD4^{+}T}\) cells and NK cells
+
+<--- Page Split --->
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+respectively. Only depleting \(\mathsf{CD8^{+}T}\) cells, but not depleting \(\mathsf{CD4^{+}T}\) cells or NK cells, impaired this protection (Fig 4h and 4i). Taken together, \(\mathsf{CD8^{+}T_{RM}}\) like cells, in brains of T- aFGL2- treated survivors, which fulfill both memory and reactive functions against tumor cells, are tumor specific brain \(\mathsf{CD8^{+}T_{RM}}\) cells. Notably, these \(\mathsf{CD8^{+}T_{RM}}\) cells can be adoptively transferred into the naive brains with or without host T cells.  
+
+## \(\mathsf{CD8^{+}T_{RM}}\) cells establish classical \(\mathsf{T_{RM}}\) phenotype  
+
+To determine whether these \(\mathsf{CD8^{+}T_{RM}}\) like cells bear classical TRM phenotype, we checked CD69, CD103 and CD62L expression, which were used to identify \(\mathsf{T_{RM}}^{1,10,24}\) . To verify that the isolated BILs- CD8 \(^+\) T cells were brain restricted, we performed i.v. injection of the \(\mathsf{CD8\beta}\) antibody and found that over \(90\%\) of BILs- CD8 \(^+\) T cells were non- circulating brain resident T cells (Supplementary Fig. 5a). Here, we found that compared with \(\mathsf{CD44^{+}CD8^{+}T}\) cells in PB, the \(\mathsf{CD44^{+}CD8^{+}T}\) cells in \(\mathsf{T_{RM}}\) bearing brains with \(\mathsf{T_{RM}}\) were \(\mathsf{CD69^{+}}\) (either \(\mathsf{CD103^{+}}\) or \(\mathsf{CD103^{- }}\) ), and \(\mathsf{CD62L^{- }}\) (Fig. 4j). Similar results were found for \(\mathsf{CD4^{+}T}\) cells (Supplementary Fig. 5b). These findings show that \(\mathsf{CD8^{+}T_{RM}}\) like cells in brains established a classical \(\mathsf{T_{RM}}\) phenotype of \(\mathsf{CD69^{+}CD62L^{- }}\) . Together, both function and phenotype of the \(\mathsf{CD8^{+}T_{RM}}\) like cells in brains of T- aFGL2- treated survivors further validated that they are tumor specific brain \(\mathsf{CD8^{+}T_{RM}}\) cells.  
+
+## The function of \(\mathsf{CD8^{+}T_{RM}}\) cells is TCR-MHC-I dependent  
+
+Since TCR is generated through random rearrangement of genomic V(D)J segments and is the mediator of antigen recognition and binding by T lymphocytes, we next questioned whether \(\mathsf{CD8^{+}T_{RM}}\) cells displayed a unique TCR repertoire that was distinct from that found in the dLNs. To this end, we sorted \(\mathsf{CD44 + CD8 + T}\) (memory \(\mathsf{CD8 + T}\) ) cells from the brains and dLNs of T- aFGL2- treated survivors on day 20 after the third challenge with DBT cells via flow cytometric sorting, followed by TCRa and TCR \(\beta\) deep sequencing (Fig. 5a). The most abundant T cell clones- those with a frequency of more than \(5\% - \text{in} \mathsf{T_{RM}}\) bearing brains constituted more than \(60\%\) of the total TCRa and TCR \(\beta\) repertoire, whereas no T cell clones with a frequency of more than \(5\%\) were found in the TCR \(\beta\) repertoire of \(\mathsf{CD44^{+}CD8^{+}T}\) cells in the dLNs (Supplementary Fig. 5c). To further characterize the TCR repertoires of \(\mathsf{T_{RM}}\) cells and dLNs- \(\mathsf{CD44^{+}CD8^{+}T}\) cells, we analyzed sequences of complementarity determining region 3 (CDR3), which encompasses the V(D)J recombination junctions and encodes the vast majority of TCR variation. Moreover, all of the top 10 dominant CDR3 sequences in \(\mathsf{T_{RM}}\) cells encompassed the V/J recombination, but each dominant CDR3 sequence in dLNs- \(\mathsf{CD44^{+}CD8^{+}T}\) cells encompassed a unique V/J recombination (Fig. 5b and Supplementary Fig. 5d). Analysis of the V and J domain usage showed that, in one of the T- aFGL2- treated survivors, the most dominant clone of TCR \(\beta\) in \(\mathsf{T_{RM}}\) cells was grouped by V17/J1- 4, which was absent in dLNs (Fig. 5b). These data showed the presence and expansion of unique T cell clones and that there was no overlap of the highly occupied TCR clone in \(\mathsf{T_{RM}}\) cells with the TCR clone in dLNs- \(\mathsf{CD44^{+}CD8^{+}T}\) cells. Interestingly, each mouse of T- aFGL2- treated survivors bears different \(\mathsf{T_{RM}}\) clones
+
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+against different antigens. These data suggested that these highly expanded TCR repertoires of \(\mathsf{CD8^{+}}\) \(\mathsf{T}_{\mathsf{RM}}\) cells were associated, thus, with the rapid and robust response of \(\mathsf{T}_{\mathsf{RM}}\) cells against tumor cells.  
+
+To verify the robust response of \(\mathsf{CD8^{+}T_{RM}}\) cells against tumor cells is associated with the interaction between expanded TCR and MHC- I, we blocked MHC- I in vivo using aMHC- I antibody when transplanted \(\mathsf{CD8^{+}T_{RM}}\) cells into naive mice. Blocking MHC- I abolished the anti- tumor efficacy of the transplanted \(\mathsf{CD8^{+}T_{RM}}\) cells (Fig 5c- f), demonstrating TCR- MHC- I interaction is required for the proper function of \(\mathsf{CD8^{+}T_{RM}}\) cells in vivo.  
+
+## T-aFGL2 treatment-induced CD69 expression on \(\mathsf{CD8^{+}}\) memory T cells is essential for \(\mathsf{CD8^{+}T_{RM}}\) formation  
+
+To understand the cellular mechanisms by which the FGL2- blocking scFv induces the generation of \(\mathsf{CD8^{+}T_{RM}}\) cells, on day 4 after the second T cell infusion, we performed high- dimensional profiling of brain- infiltrating lymphocytes (BILs) using time- of- flight mass cytometry (CyTOF) with a panel of 37 antibodies that illustrated different immune populations (Fig. 6a). CyTOF data analysis divided BILs into 15 immune cell populations (Fig. 6b). As \(\mathsf{CD8^{+}}\) T cells are the primary functional cells rejecting tumor cells as shown in our transplant study (Fig. 4b), we focused on these cell populations. We found that the \(\mathsf{CD8^{+}}\) T cell population was composed of 2 subpopulations: \(\mathsf{CD69^{+}CD8^{+}}\) memory T cells ( \(\mathsf{CD69^{+}CD8^{+}T_{M}}\) ) and \(\mathsf{CD69^{- }CD8^{+}}\) memory T cells ( \(\mathsf{CD69^{- }CD8^{+}T_{M}}\) ) (Fig. 6b). Notably, the subset of \(\mathsf{CD69^{+}CD8^{+}T_{M}}\) cells was significantly larger in mice that underwent T- aFGL2 treatment than in those that received T- Ctr treatment (Fig. 6c, d). Indeed, CD69 has been reported to help in the retention of memory T cells in resident tissues through inhibiting expression of the S1P receptor, which can promote T cell circulation into the blood; high level expression of CD69 on T cells is an indicator of \(\mathsf{T_{RM}}\) cells \(^{24}\) . To further determine the phenotype of these \(\mathsf{CD69^{+}CD8^{+}T_{M}}\) cells, we compared their expression of T cell exhaustion markers with that of \(\mathsf{CD69^{- }CD8^{+}T_{M}}\) cells. As shown in Fig. 6e, \(\mathsf{CD69^{+}CD8^{+}T_{M}}\) cells had higher levels of Ki67, CD223 (LAG3), and CD279 (PD- 1) than did \(\mathsf{CD69^{- }CD8^{+}T_{M}}\) cells. This \(\mathsf{CD69^{HIPD- 1^{HILAG3^{Hl}}}}\) phenotype of highly proliferating \(\mathsf{CD69^{+}CD8^{+}T_{M}}\) cells has been reported to be most prominent in cells with \(\mathsf{T_{RM}}\) characteristics in different kinds of tissues, including lung \(^{15,25,26}\) , breast \(^{19}\) , and skin \(^{6}\) . These data indicated that T- aFGL2 treatment increased proliferating \(\mathsf{CD69^{+}CD8^{+}T_{M}}\) subsets with \(\mathsf{T_{RM}}\) characteristics, which may promote the transformation of these \(\mathsf{CD69^{+}CD8^{+}T_{M}}\) cells into \(\mathsf{T_{RM}}\) cells in the brain. To further validate the biological function of \(\mathsf{CD69}\) on \(\mathsf{CD8^{+}T_{RM}}\) , we blocked CD69 in vivo by \(\mathsf{aCD69}\) antibody. When transplanted the \(\mathsf{CD69^{+}CD8^{+}T_{RM}}\) cells with tumor cells into naive mice i.e., we found \(\mathsf{aCD69}\) antibody treatment didn't disrupt the anti- tumor efficacy of \(\mathsf{CD8^{+}T_{RM}}\) cells. However, when we re- challenge these mice with tumor cells i.e. on day 60 post the transplantation, the mice treated with \(\mathsf{aCD69}\) antibody lost the tumor- rejecting capacity (Fig. 6f- g), indicating that \(\mathsf{CD69}\) doesn't affect the executive function of \(\mathsf{T_{RM}}\) but is required for the prolonged residence and function of \(\mathsf{CD8^{+}T_{RM}}\) in brains.
+
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+Besides \(\mathsf{CD8^{+}}\) T cells, we analyzed the other immune subpopulations, including helper T cells (Th cells) and regulatory T cells (Tregs), DCs, macrophages, monocytes, and neutrophils. However, no significant difference in these subpopulations was observed between the T- aFGL2 and T- Ctr groups (Supplementary Fig. 6), suggesting that the antitumor effect induced by T- aFGL2 treatment is different from antibody therapy and may work mainly through regulating \(\mathsf{CD69^{+}CD8^{+}T_{M}}\) cells.  
+
+## T-aFGL2 induced \(\mathsf{CD8^{+}T_{RM}}\) formation is associated with CXCL9/10-CXCR3 axis  
+
+To further understand the molecular mechanism by which FGL2- blocking scFv induces \(\mathsf{CD69^{+}CD8^{+}T_{M}}\) cells generation, we analyzed the CYTOF data for the chemokine receptors (i.e. CCR2, CXCR3, CXCR2, and CX3CR1) that may affect the T cells infiltration and recruitment to tumor sites. Intriguingly, we found that T- aFGL2 treatment increased CXCR3 expression on \(\mathsf{CD69^{+}CD8^{+}T_{M}}\) cells in tumor bearing brains, compared with T- Ctr treatment (Fig. 7a). Flow cytometry data also verified that T- aFGL2 treatment, compared with T- Ctr, increased proportion of \(\mathsf{CXCR3^{+}CD69^{+}CD8^{+}T}\) cells among total \(\mathsf{CD8^{+}T}\) cells in tumor bearing brains (Fig. 7b), indicating that increased CXCR3 expression on \(\mathsf{CD8T}\) cells may play a role in mediating T- aFGL2 induced \(\mathsf{CD69^{+}CD8^{+}T_{RM}}\) cells formation. To validate this notion, we compared the anti- tumor efficacy of T- aFGL2 in treating tumor bearing wild type (WT) mice and CXCR3 deficient (CXCR3- ) mice. T- aFGL2 treatment didn't show protective effect in \(\mathsf{CXCR3^{- / - }}\) mice as did in WT mice (Fig. 7c). Besides, the \(\mathsf{CD69^{+}CD8^{+}T_{M}}\) population was reduced in \(\mathsf{CXCR3^{- / - }}\) mice compared with WT mice (Fig. 7d), suggesting that CXCR3 play a critical role in mediating T- aFGL2 induced \(\mathsf{CD69^{+}CD8^{+}T_{M}}\) cells generation and thus \(\mathsf{CD8^{+}T_{RM}}\) formation for protective function.  
+
+To characterize how the CXCR3 chemokine system mediates anti- tumor responses to T- aFGL2 treatment, we examined the expression of the CXCR3 chemokine ligands CXCL9 and CXCL10. Protein levels of CXCL9 and CXCL10 were markedly increased in tumor bearing brains after T- aFGL2 treatment compared to T- Ctr treatment (Fig. 7e). To determine the roles of CXCL9 and CXCL10 in T- aFGL2 induced tumor rejection and \(\mathsf{CD8^{+}T_{RM}}\) formation, we used aCXCL9 and aCXCL10 antibodies to blocking CXCL9 and CXCL10 in vivo. Consistent with our earlier findings (Fig. 7c- d), the therapeutic benefits of T- aFGL2 was lost when blocking CXCL9 and CXCL10 (Fig. 7f), indicating a critical role for CXCL9 and CXCL10 in T- aFGL2 immunotherapy. Moreover, the percentage of \(\mathsf{CD69^{+}CD8^{+}T_{M}}\) was increased upon T- aFGL2 treatment in control IgG group but not in aCXCL9 and aCXCL10 antibodies treatment group (Fig .7g), indicating the functional importance of CXCL9/10- CXCR3 axis for response to T- aFGL2 therapy and \(\mathsf{CD8^{+}T_{RM}}\) formation. Altogether, T- aFGL2 therapy induced tumor- reactive T cells' proliferation, secretion of granzyme B to control tumor progression, and increased CXCR3, CD69 expression on memory CD8+ T cells to help them retain in the brains, which will foster the formation of tumor specific brain resident \(\mathsf{CD8^{+}TRM}\) (Fig. 7h).  
+
+## Discussion
+
+<--- Page Split --->
+
+Increasing evidence has shown that \(\mathsf{T}_{\mathsf{RM}}\) cells have a promising role in the control of solid tumors \(^{27}\) . Early studies showed that intravaginal vaccination induced \(\mathsf{T}_{\mathsf{RM}}\) formation in cervicovaginal tissues resulting in the control of tumors in the genital tract \(^{28}\) . Nonetheless, whether adoptive cellular therapies (ACT) can foster T cells' development into \(\mathsf{T}_{\mathsf{RM}}\) cells remains unknown. Our study has provided an ACT- based treatment, T- aFGL2 cell therapy, which can program endogenous T cells into tumor specific \(\mathsf{CD8^{+}T_{RM}}\) cells. These \(\mathsf{T}_{\mathsf{RM}}\) cells had a highly expanded and specific TCR repertoire. After being transplanted into the brains of either immunocompetent or T cell deficient naïve mice, these \(\mathsf{T}_{\mathsf{RM}}\) cells transformed naïve mice to become tumor rejecting in the brains but not in peripheral tissues due to the tissue specific resident nature.  
+
+Retention in the resident tissue is required for \(\mathsf{T}_{\mathsf{RM}}\) cells to expand and to be functional \(^{10}\) . One of the mechanisms of \(\mathsf{T}_{\mathsf{RM}}\) retention is adhesion to home tissue, which is associated with overexpression of integrin molecules such as LFA- 1 (a \(\mathsf{L}\beta 2\) ) \(^{18}\) , VLA- 1(a \(\beta 1\) ) \(^{29}\) , and CD103 (a \(\mathsf{E}\beta 7\) ) \(^{10,29}\) , binding to adhesion molecules on the endothelium, and the extracellular matrix components collagen and laminin. Another mechanism attributed to retention of \(\mathsf{T}_{\mathsf{RM}}\) in the tissue is unresponsiveness to signals that promote recirculation. Expression of S1PR1 (encoded by \(S1pr1\) ) \(^{30}\) , CD62L (encoded by \(Sell\) ) \(^{31}\) , and CCR7 (encoded by \(Ccr7\) ) \(^{32}\) permits recirculation of memory T cells. It has been reported that CD69 suppresses memory T cells' recirculation potential by inhibiting surface expression of the S1P receptor \(^{24}\) . Indeed, most \(\mathsf{T}_{\mathsf{RM}}\) cells constitutively express \(\mathsf{CD69}^{5,9,33}\) . Although CD103, which binds to E- cadherin on epithelial cells, is expressed on the most- studied \(\mathsf{T}_{\mathsf{RM}}\) cells reside in epithelial tissue \(^{29}\) , CD69 is more commonly used than CD103 to identify \(\mathsf{T}_{\mathsf{RM}}\) cells in non- lymph organs. In our study, T- aFGL2, compared with T- Ctr, increased the population of \(\mathsf{CD69^{+}CD8^{+}T_{RM}}\) like cells (Ki67 \(^{\mathrm{H}}\) PD- 1 \(^{\mathrm{H}}\) LAG3 \(^{\mathrm{H}}\) ) in the brain tumor environment (Fig. 6b- d). The T- aFGL2 treatment induced \(\mathsf{CD8^{+}T_{RM}}\) (phenotype: \(\mathsf{CD69^{+}CD62L^{- }}\) ). Blocking CD69 appears to disrupt the residence of the brain- resident \(\mathsf{T}_{\mathsf{RM}}\) cells (Fig. 6f- g). Altogether, the increased tissue retention molecule CD69 expression on \(\mathsf{CD8^{+}T_{M}}\) cells in the brain can help retain these cells in the brain and promote their differentiation into brain- resident \(\mathsf{T}_{\mathsf{RM}}\) cells.  
+
+T- aFGL2 treatment- induced \(\mathsf{CD69^{+}CD8^{+}T_{RM}}\) formation is associated with CXCL9/10- CXCR3 axis, as either absence of CXCR3 or blocking CXCL9/10 abrogated the increased \(\mathsf{CD69^{+}CD8^{+}T_{M}}\) after T- aFGL2 treatment, and thus interrupted the anti- tumor efficacy of T- aFGL2 treatment (Fig. 7). The notion that CXCR3- CXCL9/10 axis promotes the generation of \(\mathsf{T}_{\mathsf{RM}}\) is consistent with the previous findings that CXCR3- CXCL9 axis is required for reinvigoration of intratumoral \(\mathsf{CD8^{+}T}\) cell responses in response to PD- 1 blockade \(^{34}\) , and exogenous application of CXCR3 ligands promoted the \(\mathsf{T}_{\mathsf{RM}}\) cells formation in the epithelium of the lower female reproductive tract \(^{35}\) .  
+
+It's reported that the immune suppressive FGL2 limits cytotoxic \(\mathsf{CD8^{+}T}\) - cell responses via FcγRIIb \(^{36}\) . To evaluate if the T cells express FcγRIIb, which can bind and be regulated by FGL2 in tumor microenvironment in our study, we detected the FcγRIIB expression on T cells in DBT tumor bearing
+
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+brains. T cells, both \(\mathsf{CD4^{+}T}\) cells and \(\mathsf{CD8^{+}T}\) cells, express FcγRIIB, though not all of them (Supplementary Fig. 7a). Moreover, wt T cells, but not FcγRIIB \(- / -\) T cells, can directly bind the FGL2 that is anchored on the chip via biotin- streptavidin interaction (supplementary Fig. 7b), verifying that T cells can directly bind FGL2 through FcγRIIB. Besides, T- aFGL2 treatment lost the superior anti- tumor effect, compared with T- Ctr, in FcγRIIB \(- / -\) mice (supplementary Fig. 7c), suggesting that FcγRIIB was regulated by FGL2. These data suggested that T cells in this study can bind and be regulated by FGL2 in tumor microenvironment through FcγRIIB, and T- aFGL2 treatment can disrupt this FcγRIIB- FGL2 interaction and boost the cytotoxic T cells responses.  
+
+To date, most studies of \(\mathsf{T}_{\mathsf{RM}}\) cells have focused on autoimmune diseases and infections. For example Malik et al. reported that melanoma antigen- specific skin- resident memory T cells are maintained in vitiligo- afflicted skin \(^{37}\) , but no adoptive cellular therapy that induces tumor- specific \(\mathsf{T}_{\mathsf{RM}}\) cells has yet been evaluated. Here, we performed T- aFGL2 adoptive cell treatment to brain tumor- bearing mice, and found that T- aFGL2 induced tumor- specific \(\mathsf{CD8^{+}T}_{\mathsf{RM}}\) cells in the brain. The phenotype of these \(\mathsf{CD8^{+}T}_{\mathsf{RM}}\) cells were either \(\mathsf{CD69^{+}CD103^{+}}\) or \(\mathsf{CD69^{+}CD103^{- }}\) . Besides the phenotype of these tumor- specific \(\mathsf{T}_{\mathsf{RM}}\) cells, we also characterized their TCR repertoire. TCR recognizes antigens presented by the major histocompatibility complex (MHC) on antigen- presenting cells and subsequently activates T cells and mediates the eradication of the antigen \(^{38 - 41}\) . However, the TCR repertoires of tumor- specific \(\mathsf{T}_{\mathsf{RM}}\) cells have not previously been characterized. From next generation RNA sequencing, we noted that the \(\mathsf{CD8^{+}T}_{\mathsf{RM}}\) cells had a highly expanded TCR \(\alpha \beta\) repertoire (Fig. 5). Moreover, no overlap of the TCR clone was found between \(\mathsf{T}_{\mathsf{RM}}\) cells in the brain and memory T cells in the periphery. This is distinct from influenza- specific lung- resident memory T cells, which maintain a wide diversity of TCR profiles \(^{13}\) . Blocking MHC- I abolished the anti- tumor efficacy of the \(\mathsf{CD8 + T}_{\mathsf{RM}}\) cells (Fig. 5c- f), demonstrating TCR- MHC- I interaction is required for the proper function of \(\mathsf{CD8^{+}T}_{\mathsf{RM}}\) cells in vivo. Further functional evaluation of this highly expanded TCR repertoire on \(\mathsf{T}_{\mathsf{RM}}\) cells will be an important part of future studies.  
+
+\(\mathsf{T}_{\mathsf{RM}}\) cells occupy frontline sites of infection and are positioned to respond most immediately and potently. The abundance of cells with \(\mathsf{T}_{\mathsf{RM}}\) cell characteristics in tumors often correlates with a favorable outcome \(^{19,42 - 44}\) ; thus, promoting tumor- specific \(\mathsf{T}_{\mathsf{RM}}\) cell formation in tumor tissues or adaptively transferring tumor- specific \(\mathsf{T}_{\mathsf{RM}}\) cells into tumor sites are promising approaches for treating patients with cancer. Wakim et al. found that virus- specific \(\mathsf{T}_{\mathsf{RM}}\) cells in the brain die rapidly upon isolation from the resident tissue and fail to undergo recall expansion after adoptive transfer into the bloodstream of an antigen- challenged recipient, indicating that these cells depend on the local milieu for their function and survival \(^{12,45}\) . Here, we were able to transfer \(\mathsf{CD8^{+}T}_{\mathsf{RM}}\) cells into the brains of both immunocompetent and T cells deficient naive mice and induce a tumor- specific reaction in the recipient mice (Fig. 4). To our knowledge, this is the first study in which tumor- specific \(\mathsf{CD8^{+}T}_{\mathsf{RM}}\) cells were successfully transferred into the same tissue of naive mice in vivo. Our success may be explained by the following factors: (1) \(\mathsf{T}_{\mathsf{RM}}\) cells should be transferred into the same tissue in naive mice where the \(\mathsf{T}_{\mathsf{RM}}\) cells originally resided; (2)
+
+<--- Page Split --->
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+\(\mathrm{T}_{\mathrm{RM}}\) cells should be cotransferred with antigens that can activate them. Besides yielding novel insights into these tumor specific brain resident \(\mathrm{CD8^{+}T_{RM}}\) cells, our study provides a valuable resource for further investigations of tumor- specific \(\mathrm{T}_{\mathrm{RM}}\) cell formation in the brain. Such studies will ultimately aid the development of strategies for immunotherapy of brain cancers.  
+
+## Declarations  
+
+## Acknowledgments  
+
+We appreciate Scientific Publications, Research Medical Library at MD Anderson for helping us edit our manuscript. We thank Dr. Leonid Metelitsa (Baylor College of Medicine) for providing DBT mouse glioma cells. for This research used the Flow Cytometry, Cellular Imaging Facility and DNA analysis Core Facility, Monoclonal Antibody Core Facility at MD Anderson, which are supported in part by the National Institutes of Health through MD Anderson's Cancer Center Support Grant P30 CA016672. Core Grant Citation. This work was supported by National Institutes of Health Grants R01 CA120895 and R01 CA200574.  
+
+## Authors' contributions  
+
+QZ and SL conceived and designed the experiments. QZ, LK, NWF, JY, JH, ZJ and XX conducted the experiments. RH were responsible for the mouse model. QZ, SJ, XT, and JW analyzed the data. QZ, SL, ABH, SY, LD, DM and HO edited and/or drafted the manuscript. SL supervised the study. All authors have read and approved the final version of the manuscript.  
+
+## References  
+
+1. Amsen, D., van Gisbergen, K., Hombrink, P. & van Lier, R. A. W. Tissue-resident memory T cells at the center of immunity to solid tumors. Nat Immunol 19, 538-546, doi:10.1038/s41590-018-0114-2 (2018).  
+2. Masopust, D., Vezys, V., Marzo, A. L. & Lefrançois, L. Preferential localization of effector memory cells in nonlymphoid tissue. Science 291, 2413-2417 (2001).  
+3. Watanabe, R. et al. Human skin is protected by four functionally and phenotypically discrete populations of resident and recirculating memory T cells. Science translational medicine 7, 279ra239-279ra239 (2015).  
+4. Welten, S. P. M., Sandu, I., Baumann, N. S. & Oxenius, A. Memory CD8 T cell inflation vs tissue-resident memory T cells: Same patrollers, same controllers? Immunol Rev 283, 161-175, doi:10.1111/imr.12649 (2018).  
+5. Beura, L. K. et al. CD4(+) resident memory T cells dominate immunosuppressive and orchestrate local recall responses. J Exp Med 216, 1214-1229, doi:10.1084/jem.20181365 (2019).  
+6. Dijkgraaf, F. E. et al. Tissue patrol by resident memory CD8(+) T cells in human skin. Nat Immunol 20, 756-764, doi:10.1038/s41590-019-0404-3 (2019).
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+14. Zens, K. D., Chen, J. K. & Farber, D. L. Vaccine-generated lung tissue-resident memory T cells provide heterosubtypic protection to influenza infection. JCI insight 1 (2016).  
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+19. Savas, P. et al. Single-cell profiling of breast cancer T cells reveals a tissue-resident memory subset associated with improved prognosis. Nat Med 24, 986-993, doi:10.1038/s41591-018-0078-7 (2018).  
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+20. Hu, J. et al. The duality of Fgl2-secreted immune checkpoint regulator versus membrane-associated procoagulant: therapeutic potential and implications. International reviews of immunology 35, 325-339 (2016).  
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+24. Mackay, L. K. et al. Cutting edge: CD69 interference with sphingosine-1-phosphate receptor function regulates peripheral T cell retention. The Journal of Immunology 194, 2059–2063 (2015).25. Hombrink, P. et al. Programs for the persistence, vigilance and control of human CD8 + lung-resident memory T cells. Nature immunology 17, 1467 (2016).26. Wang, Z. et al. PD-1hi CD8 + resident memory T cells balance immunity and fibrotic sequelae. Science immunology 4, eaaw1217 (2019).27. Amsen, D., van Gisbergen, K. P., Hombrink, P. & van Lier, R. A. Tissue-resident memory T cells at the center of immunity to solid tumors. Nature immunology 19, 538–546 (2018).28. Sun, Y.-Y. et al. Local HPV recombinant vaccinia boost following priming with an HPV DNA vaccine enhances local HPV-specific CD8 + T-cell–mediated tumor control in the genital tract. Clinical Cancer Research 22, 657–669 (2016).29. Gebhardt, T. et al. Memory T cells in nonlymphoid tissue that provide enhanced local immunity during infection with herpes simplex virus. Nature immunology 10, 524 (2009).30. Skon, C. N. et al. Transcriptional downregulation of S1pr1 is required for the establishment of resident memory CD8 + T cells. Nat Immunol 14, 1285–1293, doi:10.1038/ni.2745 (2013).31. Gebhardt, T. et al. Different patterns of peripheral migration by memory CD4 + and CD8 + T cells. Nature 477, 216–219 (2011).32. Kumar, B. V. et al. Human tissue-resident memory T cells are defined by core transcriptional and functional signatures in lymphoid and mucosal sites. Cell reports 20, 2921–2934 (2017).33. Buggert, M. et al. Identification and characterization of HIV-specific resident memory CD8 + T cells in human lymphoid tissue. Science immunology 3, eaar4526 (2018).34. Chow, M. T. et al. Intratumoral activity of the CXCR3 chemokine system is required for the efficacy of anti-PD-1 therapy. Immunity 50, 1498–1512. e1495 (2019).35. Shin, H. & Iwasaki, A. A vaccine strategy that protects against genital herpes by establishing local memory T cells. Nature 491, 463–467 (2012).36. Morris, A. B. et al. Signaling through the inhibitory Fc receptor FcγRIIB induces CD8 + T cell apoptosis to limit T cell immunity. Immunity 52, 136–150. e136 (2020).37. Malik, B. T. et al. Resident memory T cells in the skin mediate durable immunity to melanoma. Sci Immunol 2, doi:10.1126/sciimmunol.aam6346 (2017).38. Zhang, L. et al. Lineage tracking reveals dynamic relationships of T cells in colorectal cancer. Nature 564, 268–272, doi:10.1038/s41586-018-0694-x (2018).39. Ahmadzadeh, M. et al. Tumor-infiltrating human CD4 + regulatory T cells display a distinct TCR repertoire and exhibit tumor and neoantigen reactivity. Science immunology 4 (2019).40. Scheper, W. et al. Low and variable tumor reactivity of the intratumoral TCR repertoire in human cancers. Nature medicine 25, 89–94 (2019).41. Reuben, A. et al. Comprehensive T cell repertoire characterization of non-small cell lung cancer. Nature Communications 11, 1–13 (2020).
+
+<--- Page Split --->
+
+42. Djenidi, F. et al. CD8 + CD103 + tumor-infiltrating lymphocytes are tumor-specific tissue-resident memory T cells and a prognostic factor for survival in lung cancer patients. The Journal of Immunology 194, 3475–3486 (2015).  
+
+43. Webb, J. R., Milne, K., Watson, P., DeLeeuw, R. J. & Nelson, B. H. Tumor-infiltrating lymphocytes expressing the tissue resident memory marker CD103 are associated with increased survival in high-grade serous ovarian cancer. Clinical cancer research 20, 434–444 (2014).  
+
+44. Nizard, M. et al. Induction of resident memory T cells enhances the efficacy of cancer vaccine. Nature communications 8, 1–11 (2017).  
+
+45. Wakim, L. M., Woodward-Davis, A. & Bevan, M. J. Memory T cells persisting within the brain after local infection show functional adaptations to their tissue of residence. Proc Natl Acad Sci U S A 107, 17872–17879, doi:10.1073/pnas.1010201107 (2010).  
+
+## Figures  
+
+![](images/Figure_1.jpg)
+
+<center>Figure 1 </center>  
+
+Specific functional antitumor activity of T- aFGL2 in vitro.
+
+<--- Page Split --->
+
+a, Schematic of the vector encoding the FGL2- blocking scFv, linked to the transmembrane (TM) domain. The His- tag domain was used for detection of the scFv. The Ctr vector is the empty CMV construct. G4S: pentapeptide GGGGS; P2A: porcine teschovirus- 1. b, Cartoon schematic of a T cell transfected with the FGL2- blocking scFv vector (T- aFgl2). c, Representative flow cytometry histograms demonstrating FGL2- blocking scFv expression on mouse T cells following transduction. The scFv was detected with His- tag specific antibodies. d, Flow cytometry plots depicting no difference in proportion of tumor cells (DBT- GFP+) cocultured with T- Ctr and T- aFGL2 cells at E:T ratio of 4:1 for 72hrs (top panel); flow cytometry plots depicting increased granzyme B expression in T- aFGL2 cells (compared with T- Ctr) cocultured with DBT tumor cells at a ratio of 1:1 for 24 h; no difference in TNFα or IFNγ was detected between T- aFGL2 and T- Ctr. Data shown are mean ± SEM from 3 independent experiments. \(**P = 0.0025\) , two- way t- test. NS, not significant. e, Representative micrographs of FGL2 expression in GBM and the indicated normal human tissues assessed by staining with FGL2 mAb- clone #4 at the final concentration of 1 μg/mL. Micrographs are representative of at least two sections per tissue. Scale bars, 100 μm.  
+
+![](images/Figure_2.jpg)
+
+<center>Figure 2 </center>  
+
+Antitumor activity of T- aFGL2 in vivo.
+
+<--- Page Split --->
+
+a, Representative micrographs of FGL2 expression in mouse brains with glioma assessed by staining with FGL2 mAb- clone #4 at the final concentration of \(1 \mu \mathrm{g} / \mathrm{mL}\) . Slides stained without a primary Ab were used as negative controls. Scale bars, \(50 \mu \mathrm{m}\) . b, Schematic of the orthotopic glioma DBT model treated with temozolomide (TMZ) on days 3, 4, and 5 after tumor cell inoculation, followed by infusion of T- Ctr or T- aFGL2 on days 6 and 13. c, Representative bioluminescence images of DBT- luc tumor growth in the orthotopic glioma model shown in b. d, Representative H&E staining of brains from the orthotopic glioma model shown in b collected on day 14 after tumor cell inoculation. e, Kaplan- Meier survival curves of mice shown in b ( \(n = 11 - 15\) mice/group). \(***P = 0.0002\) (T- aFGL2 vs T- Ctr), \(****P < 0.0001\) (T- aFGL2 vs no treatment [NT] group), log- rank test. f, Schematic of the orthotopic glioma GL261 model. g, Kaplan- Meier survival curves of mice in f ( \(n = 6 - 7\) mice/group). \(**P = 0.0099\) log- rank test.  
+
+![](images/Figure_4.jpg)
+
+
+<--- Page Split --->
+
+## Figure 3  
+
+## T-aFGL2 treatment induced brain-resident tumor-specific memory T cells.  
+
+a, Schematic of re- challenge with tumor cells, either subcutaneously (s.c.) or intracranially (i.c.), in the long- term survivors of the orthotopic glioma DBT model on day 70 after first tumor cell inoculation. b, Kaplan- Meier survival curves (left) and representative bioluminescence images (right) of mice shown in a on day 0 and day 7 after second tumor cell inoculation (i.c.) \((n = 6)\) . \(***P = 0.0005\) , log- rank test. c, Tumor volume (left), representative bioluminescence images (middle) of mice on day 0 and day 7 after second tumor cell inoculation, and representative tumors (right) collected on day 11 after second tumor cell inoculation (s.c.) from the flanks of Balb/c mice. d, Representative flow cytometry plots depicting increase of \(\mathrm{CD8^{+}}\) T cells in the brains (BIL) of long- term survivors treated with T- aFGL2 (T- aFGL2 survivor). LN, lymph node. e, Graph showing the ratio of \(\mathrm{CD8^{+}}\) T cells to \(\mathrm{CD4^{+}}\) T cells in the brains of naïve mice and T- aFGL2 survivors and the LNs of T- aFGL2 survivors \((n = 3 - 5\) mice/group), two- way \(t\) - test. f, Graph showing \(\mathrm{CD8^{+}}\) T cell numbers in the brains of naïve mice and T- aFGL2 survivors \((n = 3 - 5\) mice/group), two- way \(t\) - test.
+
+<--- Page Split --->
+![](images/Figure_5.jpg)
+
+<center>Figure 4 </center>  
+
+\(\mathbf{T}_{\mathrm{RM}}\) - like cells can be adoptively transferred.  
+
+a, Schematic of experimental design. T- aFGL2- treated survivors were rechallenged with DBT tumor cells intracranially (i.c.) on days 70 and 100 after the first tumor cell inoculation. On day 7 after the third challenge with tumor cells, the mice were euthanized, and their brains, draining lymph nodes (dLN), and peripheral blood (PB) were collected. b, Representative bioluminescence images of naive Balb/c mice coinoculated i.c. with \(3 \times 10^{3}\) DBT glioma cells and \(3 \times 10^{3}\) T cells. Images show gliomas in mice coinoculated with \(\mathrm{CD8^{+}}\) T cells in the brain (BIL- \(\mathrm{CD8^{+}}\) T), \(\mathrm{CD4^{+}}\) T cells in the brain (BIL- \(\mathrm{CD4^{+}}\) T), \(\mathrm{CD8^{+}}\) T cells in peripheral blood (PB- \(\mathrm{CD8^{+}}\) T), and \(\mathrm{CD8^{+}}\) T cells in draining lymph nodes (dLN- \(\mathrm{CD8^{+}}\) T). \(\mathrm{CD8^{+}}\) T cells and \(\mathrm{CD4^{+}}\) T cells were sorted by flow cytometry on day 7 after the third challenge in T- aFGL2 survivors. c, Kaplan- Meier survival curves for mice in b ( \(n = 8 - 9\) mice/group). \(\mathrm{***P < 0.0001}\) , log- rank test. d, Representative bioluminescence images of naive Balb/c mice and mice bearing transplanted BIL- \(\mathrm{CD8^{+}}\) T
+
+<--- Page Split --->
+
+cells on days 0 and 4 after i.c. re- challenge with DBT cells on day 30 after BIL- CD8 \(^+\) T cells transplantation. e, Kaplan- Meier survival curves of mice in d \((n = 9\) mice/group). \(\yen 123,456,7\) \(P< 0.0001\) , log- rank test. f, Schematic of experimental design. \(3\times 10^{4}\mathrm{T}_{\mathrm{RM}}\) containing brain infiltrated lymphocyte cells \((\mathrm{T}_{\mathrm{RM}}\) BIL) were sorted by flow cytometry on day 7 after the third challenge in T- aFGL2 survivors, and coinoculated i.c. with \(3\times 10^{3}\) DBT cells into the naive SCID mice; 35 days after the transplantation, the SCID mice with transplanted \(\mathrm{T}_{\mathrm{RM}}\) - BILs were re- challenged with \(3\times 10^{3}\) DBT cells i.c. combined with antibodies blocking CD8, CD4 or asGM1 i.p. g, Kaplan- Meier survival curves of mice in f \((n = 4\sim 6\) mice/group). \(\yen 123,456,7\) \(P = 0.0003\) , log- rank test. h, Kaplan- Meier survival curves of of SCID mice bearing transplanted \(\mathrm{T}_{\mathrm{RM}}\) - BILs cells i.c. re- challenged with DBT cells combined with antibodies blocking CD8, CD4 or asGM1 i.p. i, Representative H&E staining of brains from h collected on day 14 after tumor cells re- challenge. j, Representative flow cytometry plots and graph showing ratio of \(\mathrm{CD69^{+}CD103^{+}}\) T cells, \(\mathrm{CD69^{+}CD103^{- }}\) T cells and \(\mathrm{CD69^{+}CD62L^{- }}\) T cells in brain and PB of T- aFgl2- treated survivors \((n = 5)\) , \(\yen 123,456,7\) \(P< 0.01\) , two- way \(t\) - test. g, Representative flow cytometry plots and graphs showing CD69 and CD103 expression on \(\mathrm{CD8^{+}}\) T cells in the brain and PB of T- aFGL2- treated survivors \((n = 3\sim 4)\) . \(\yen 123,456,7\) \(P< 0.001\) , two- way \(t\) - test.
+
+<--- Page Split --->
+![](images/Figure_6.jpg)
+
+<center>Figure 5 </center>  
+
+\(\mathsf{T}_{\mathsf{RM}}\) cells showed an expanded TCR repertoire.  
+
+a, Schematic of TCRα/β deep sequencing of \(\mathrm{CD8^{+}}\) T cells from the brains and dLNs of T- aFGL2- treated survivors. Cells were sorted via flow cytometry on day 20 after the third challenge with intracranially (i.c.) injected DBT tumor cells. b, Representative tree maps (top row) of \(\mathrm{TCR\alpha - T_{RM} - CD8^{+}T}\) , \(\mathrm{TCR\beta - T_{RM} - CD8^{+}T}\) , \(\mathrm{TCR\beta - dLNs - CD8^{+}T}\) clones. Each spot represents a unique entry: V- J- CDR3, and the size of a spot denotes its relative frequency; 3D map of V and J usage of \(\mathrm{TCR\alpha - T_{RM} - CD8^{+}T}\) , \(\mathrm{TCR\beta - T_{RM} - CD8^{+}T}\) , \(\mathrm{TCR\beta - dLNs - CD8^{+}T}\) clones (bottom row). c, Schematic of experimental design. Day \(1, 3 \times 10^{4} \mathrm{CD8^{+}T_{RM}}\) cells and \(3 \times\)
+
+<--- Page Split --->
+
+\(10^{3}\) DBT cells were coinoculated i.c. into the naïve Balb/c mice; day 0, 5, 10, 15, 20, and day 25 the mice were treated with IgG or MHC-I blocking antibodies (100μg/mouse,i.p.). d, Representative bioluminescence images of Balb/c mice on days 0 and 14 and day 28 after i.c. transplantation with \(\mathrm{CD8^{+}T_{RM}}\) and DBT cells. e, Kaplan-Meier survival curves of mice in f ( \(n = 4\) mice/group). \(*p = 0.0101\) , logrank test. f, Representative H&E staining of brains from d collected on day 40 after transplantation.  
+
+![](images/Figure_7.jpg)
+
+<center>Figure 6 </center>  
+
+## TaFGL2 treatment increased the \(\mathrm{CD69^{+}CD8^{+}T_{M}}\) cell subset.  
+
+a, Schematic of the experimental design. Four days after infusion of T- Ctr or T- aFGL2, brains were collected to isolate brain- infiltrating lymphocytes (BIL), which were then stained with antibodies conjugated to metal isotopes. Mass cytometry (CyTOF) single- cell data was clustered to identify common populations across the treatment groups. b, T- distributed stochastic neighbor embedding (tSNE) analysis of \(\mathrm{CD45^{+}}\) cells from the brain, colored by relative expression of CyTOF markers. Cell populations are indicated on the right. c, Composition of the \(\mathrm{CD8^{+}T}\) cell compartment in T- Ctr and T- aFGL2- treated DBT- bearing mice showing increased frequency of \(\mathrm{CD69^{+}CD8^{+}T_{M}}\) cells in the brains of T- aFGL2- treated mice.
+
+<--- Page Split --->
+
+d, Frequencies of total \(\mathsf{CD8^{+}}\) T cell population and subsets of \(\mathsf{CD8^{+}}\) T cells and \(\mathsf{CD4^{+}}\) T cells \((n = 4 - 5\) mice per group). e, Fold expression of Ki67, CD69, CD223, and CD279 on the \(\mathsf{CD69^{+}CD8^{+}T_{M}}\) subset and the \(\mathsf{CD69^{- }CD8^{+}T_{EM}}\) subset. f, Schematic of experimental design. Day 1, \(3 \times 10^{4} \mathsf{CD8^{+}T_{RM}}\) cells and \(3 \times 10^{3}\) DBT cells were coinnoculated i.c. into the naive Balb/c mice; day 0, 5, 10, 15, and day 20, the mice were treated with either IgG or CD69 blocking antibodies (150ug/mouse i.p.); day 60, Balb/c mice bearing transplanted \(\mathsf{CD8^{+}T_{RM}}\) were re-challenged with \(1 \times 10^{4}\) DBT cells (i.c.). g, Representative bioluminescence images of Balb/c mice on days 0 and 7 after i.c. re-challenge with DBT cells in f. Data are representative of two independent experiments. h, Kaplan- Meier survival curves of mice in f \((n = 3 \sim 4\) mice/group), log- rank test.  
+
+![PLACEHOLDER_23_0]
+
+<center>Figure 7 </center>
+
+<--- Page Split --->
+
+## TaFGL2 induced CD69+CD8+T M was associated with CXCL9/10-CXCR3 axis  
+
+a, Quantitative expression level of CCR2, CSF1R, CXCR2, CXCR3,and CX3CR1 on CD69+CD8+T M populations of T- Ctr and TaFGL2 group from CYTOF data. b, Representative flow cytometry plots and graphs showing TaFGL2 treatment increased CXCR3+CD69+CD8+T cells among total CD8+T cells in glioma bearing brains. c, Kaplan- Meier survival curves of GL261- bearing wild- type mice (WT) and CXCR3 deficient mice (CXCR3- ) treated with T- Ctr or TaFGL2 ( \(n = 5\) mice/group). \(*P\) <0.05, log- rank test. d, Quantitative data showing CD69+CD8+T M cells number per GL261- bearing brain on day 5\~7 post \(2^{\text{nd}}\) T cells therapy. \(*P< 0.05\) , two- way t- test. e, Quantitative protein analysis of CXCL9 and CXCL10 in DBT tumors from mice 4\~6 days after \(2^{\text{nd}}\) therapy of T- Ctr or TaFGL2. Data represent the mean \(\pm\) SEM; \(*p< 0.05\) , two- way t test. f, Kaplan- Meier survival curves of DBT- bearing Balb/c mice treated with T- Ctr or TaFGL2, combined with isotype control or anti- CXCL9 and anti- CXCL10 antibodies ( \(n = 5\) per group). \(*p< 0.05\) , log- rank test. g, Percentages of CD69+ out of CD44+CD8+T cells. Data represent the mean \(\pm\) SEM; \(**p< 0.01\) , \(***p< 0.001\) , one- way ANOVA with Tukey's multiple comparison test. h, Schematic illustration of cellular and molecular events underlying TaFGL2 induced tumor specific brain resident CD8+T RM. TaFGL2 cells block the FGL2 in the tumor microenvironment, resulting in CD69+CD8+T cells population enrichment and CXCL9/10 induction. These CD69+CD8+T cells were boosted through CXCL9/10- CXCR3 engagement. The CXCR3+CD69+CD8+T cells are candidate of tumor specific brain resident CD8+T RM as these cells show both TRM phenotype (CD44+CD69+CD62L- ), and proliferative activity (Ki67+) inside the brain tumors.  
+
+## Supplementary Files  
+
+This is a list of supplementary files associated with this preprint. Click to download.  
+
+Supplementary dataMethodandMaterialsV6. docx
+
+<--- Page Split --->

preprint/preprint__1ea13beaba38237f69e17a0b0ac6e90820da151be6ddca4447cff5ff3f07bea5/preprint__1ea13beaba38237f69e17a0b0ac6e90820da151be6ddca4447cff5ff3f07bea5_det.mmd

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+<|ref|>title<|/ref|><|det|>[[44, 106, 927, 214]]<|/det|>
+# FGL2-targeted T cells induced tumor-specific brain resident TRM cells preventing glioblastoma recurrence  
+
+<|ref|>text<|/ref|><|det|>[[44, 235, 555, 300]]<|/det|>
+Qingnan Zhao Shanghai Jiao Tong University School of Medicine Ling- Yuan Kong  
+
+<|ref|>text<|/ref|><|det|>[[55, 303, 470, 322]]<|/det|>
+University of Texas MD Anderson Cancer Center  
+
+<|ref|>text<|/ref|><|det|>[[44, 328, 555, 370]]<|/det|>
+Shan Jiang The University of Texas Health Science Center at Houston  
+
+<|ref|>text<|/ref|><|det|>[[44, 375, 510, 416]]<|/det|>
+Xiangjun Tian The University of Texas MD Anderson Cancer Center  
+
+<|ref|>text<|/ref|><|det|>[[44, 421, 697, 462]]<|/det|>
+Jing Wang UT M.D. Anderson Cancer Center https://orcid.org/0000- 0002- 5398- 0802  
+
+<|ref|>text<|/ref|><|det|>[[44, 468, 264, 508]]<|/det|>
+Rintaro Hashizume Northwestern University  
+
+<|ref|>text<|/ref|><|det|>[[44, 514, 865, 555]]<|/det|>
+Jiemiao Hu The University of Texas MD Anderson Cancer Center https://orcid.org/0000- 0002- 5142- 0641  
+
+<|ref|>text<|/ref|><|det|>[[44, 560, 510, 601]]<|/det|>
+Zhiliang Jia The University of Texas MD Anderson Cancer Center  
+
+<|ref|>text<|/ref|><|det|>[[44, 606, 865, 648]]<|/det|>
+Natalie Fowlkes The University of Texas MD Anderson Cancer Center https://orcid.org/0000- 0002- 7711- 6031  
+
+<|ref|>text<|/ref|><|det|>[[44, 653, 472, 694]]<|/det|>
+Jun Yan University of Texas MD Anderson Cancer Center  
+
+<|ref|>text<|/ref|><|det|>[[44, 699, 303, 739]]<|/det|>
+Xeuqing Xia MD Anderson Cancer Center  
+
+<|ref|>text<|/ref|><|det|>[[44, 745, 510, 787]]<|/det|>
+Sofia Yi The University of Texas MD Anderson Cancer Center  
+
+<|ref|>text<|/ref|><|det|>[[44, 792, 510, 833]]<|/det|>
+Long Dao The University of Texas MD Anderson Cancer Center  
+
+<|ref|>text<|/ref|><|det|>[[44, 839, 621, 880]]<|/det|>
+David Masopust University of Minnesota https://orcid.org/0000- 0002- 9440- 3884  
+
+<|ref|>text<|/ref|><|det|>[[44, 885, 510, 926]]<|/det|>
+Hideho Okada UCSF, USA https://orcid.org/0000- 0003- 0076- 9920  
+
+<|ref|>text<|/ref|><|det|>[[44, 931, 191, 950]]<|/det|>
+Amy Heimberger
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[50, 45, 872, 113]]<|/det|>
+Northwestern University https://orcid.org/0000- 0002- 9970- 8695Shulin Li ( \(\bowtie\) SLi4@mdanderson.org)The University of Texas MD Anderson Cancer Center https://orcid.org/0000- 0001- 5657- 4183  
+
+<|ref|>sub_title<|/ref|><|det|>[[44, 152, 102, 170]]<|/det|>
+## Article  
+
+<|ref|>text<|/ref|><|det|>[[44, 189, 738, 210]]<|/det|>
+Keywords: T- aFGL2, glioblastoma, Tissue- resident memory T cells, CD69, CXCR3  
+
+<|ref|>text<|/ref|><|det|>[[44, 228, 297, 248]]<|/det|>
+Posted Date: May 11th, 2022  
+
+<|ref|>text<|/ref|><|det|>[[44, 266, 474, 286]]<|/det|>
+DOI: https://doi.org/10.21203/rs.3.rs- 1632572/v1  
+
+<|ref|>text<|/ref|><|det|>[[42, 303, 910, 346]]<|/det|>
+License: © \(\circledast\) This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License  
+
+<|ref|>text<|/ref|><|det|>[[42, 380, 945, 424]]<|/det|>
+Version of Record: A version of this preprint was published at Nature Communications on February 10th, 2023. See the published version at https://doi.org/10.1038/s41467- 023- 36430- 2.
+
+<--- Page Split --->
+<|ref|>sub_title<|/ref|><|det|>[[44, 42, 159, 67]]<|/det|>
+## Abstract  
+
+<|ref|>text<|/ref|><|det|>[[40, 82, 951, 352]]<|/det|>
+Tissue- resident memory T cells \((T_{\mathrm{RM}})\) specific to previously encountered pathogens have been characterized, but tumor- specific \(\mathsf{T}_{\mathsf{RM}}\) cells in brain have not been reported in the literature. We discovered that T cells armed with FGL2- blocking single- chain variable fragments (T- aFGL2) are able to induce tumor- specific \(\mathsf{CD8^{+}T_{RM}}\) cells, which prevent glioblastoma recurrence, a major obstacle in achieving long term survivors. These tumor- specific \(\mathsf{CD8^{+}T_{RM}}\) displayed a unique highly expanded T cell receptor repertoire distinct from that found in peripheral tissues. Notably, these \(\mathsf{CD8^{+}T_{RM}}\) cells could be transplanted into the brains of either immunocompetent or T cell deficient naive mice, transforming them to become immune reactive to tumor cells. The mechanism study found T- aFGL2 therapy boosted \(\mathsf{CD69^{+}CD8^{+}}\) memory T cells population in tumor bearing brains, which depend on CXCL9/10- CXCR3 signaling. These findings are the first to show tumor- specific brain resident \(\mathsf{CD8^{+}T_{RM}}\) generation via adoptive cellular treatment and may have promising implications for cancer immunotherapy.  
+
+<|ref|>sub_title<|/ref|><|det|>[[44, 374, 350, 402]]<|/det|>
+## Significance Statement  
+
+<|ref|>text<|/ref|><|det|>[[55, 416, 920, 577]]<|/det|>
+1. T cells armed with FGL2-blocking single-chain variable fragments (T-aFGL2) were able to induce tumor-specific \(\mathsf{CD8^{+}T_{RM}}\) cells. 
+2. These tumor-specific \(\mathsf{CD8^{+}T_{RM}}\) cells are tumor reactive and transplantable. 
+3. The induced tumor-specific \(\mathsf{CD69^{+}CD62L^{-}CD8^{+}T_{RM}}\) cells, displayed a highly expanded T cell receptor repertoire. 
+4. CXCL9/10-CXCR3 signaling was associated with \(\mathsf{CD69^{+}CD8^{+}T_{RM}}\) induction.  
+
+<|ref|>sub_title<|/ref|><|det|>[[44, 601, 208, 627]]<|/det|>
+## Introduction  
+
+<|ref|>text<|/ref|><|det|>[[39, 640, 956, 934]]<|/det|>
+Memory T cells provide rapid and effective immune protection against a wide variety of antigens, including those from environmental substances and malignant tumors. Memory T cells consist of two major populations: non- recirculating resident memory T \((T_{\mathsf{RM}})\) cells \(^{1,2}\) and recirculating memory T cells \(^{3}\) recirculating memory T cells include effector memory T \((T_{\mathsf{EM}})\) cells, central memory T cells \((T_{\mathsf{CM}})\) , and migratory memory T cells. Research has shown that \(T_{\mathsf{RM}}\) cells are more potent effectors than recirculating memory T cells \(^{4 - 7}\) . \(T_{\mathsf{RM}}\) cells, often bearing CD69 and the aEB7 integrin (CD103), and null of CD62L, offer dominant immunity to localized infection \(^{8}\) . CD103 binds to epithelial E-cadherin, while CD69 blocks cellular egress via inhibition of the function of Sphingosine-1-phosphate receptor-1 (S1PR1) \(^{9}\) , both help \(T_{\mathsf{RM}}\) cells retain in peripheral tissues. CD62L is lymphoid homing molecule, which helps peripheral T cells homing to secondary lymphoid organs. To date, \(T_{\mathsf{RM}}\) cells have been found in both barrier and non- barrier tissues, including the skins \(^{10,11}\) , brains \(^{12}\) , lungs \(^{13 - 17}\) , livers \(^{18}\) , and breasts \(^{19}\) , where they mediate long- lived protection against reinfection. However, how to induce \(T_{\mathsf{RM}}\) cell formation in tumor- bearing tissues,
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[42, 45, 933, 88]]<|/det|>
+especially glioblastoma (GBM) with high fatality rate, thus leading to tumor contraction and recurrence prevention, has rarely been studied.  
+
+<|ref|>text<|/ref|><|det|>[[41, 104, 955, 291]]<|/det|>
+As a member of the fibrinogen- like protein family, fibrinogen- like protein 2 (FGL2) possesses prothrombinase activity and immune regulatory functions in both viral infection and cancer development. Accumulating evidence shows that FGL2 acts as an immunosuppressive regulator, suppressing B cell, T cell, and dendritic cell (DC) functions by binding to FcγRIIB and regulating adaptive immunity via Th1 and Th2 cytokines<sup>20</sup>. Our previously published data showed that overexpression of FGL2 correlates with upregulated expression of negative immune checkpoints, decreased granulocyte- macrophage colony- stimulating factor induced CD103<sup>+</sup> DC differentiation, faster glioma progression, and poor clinical outcome in brain malignancies<sup>20- 23</sup>.  
+
+<|ref|>text<|/ref|><|det|>[[42, 307, 936, 423]]<|/det|>
+An analysis of TCGA data found an inverse correlation between FGL2 expression and patient with GBM survival<sup>22</sup>; therefore, FGL2 may serve as an attractive target for brain tumor immunotherapy. Indeed, FGL2- specific polyclonal antibodies showed antitumor activity against GBM tumor cells in syngeneic mouse models<sup>23</sup>. However, owing to their poor blood- brain barrier penetration, the potential of FGL2- blocking antibodies to suppress brain tumor progression is limited.  
+
+<|ref|>text<|/ref|><|det|>[[40, 439, 956, 727]]<|/det|>
+Here, to improve the efficacy of an FGL2- blocking antibody in treating brain tumors, we performed adoptive cell infusion of T cells armed with an FGL2- blocking single- chain variable fragment (scFv). These T cells bearing FGL2- blocking scFv (T- aFGL2) showed superior potent anti- tumor effects compared with control T cells (T- Ctr) without causing obvious toxicity at the therapeutic dose. Importantly, the long- term mouse survivors in the T- aFGL2 treatment group formed tumor- specific brain- resident memory CD8<sup>+</sup> T (CD8<sup>+</sup> T<sub>RM</sub>) cells that rejected rechallenged tumor cells in the brain. These CD8<sup>+</sup> T<sub>RM</sub> cells are CD69<sup>+</sup> CD8<sup>+</sup> T cells that display an expanded T cell receptor (TCR) repertoire. Of note, the CD8<sup>+</sup> T<sub>RM</sub> cells can be transplanted into the brains of naïve mice to convert these naïve mice into CD8<sup>+</sup> T<sub>RM</sub>- bearing mice, enabling these mice to reject tumor cells upon rechallenge. We report that T- aFGL2 treatment boosted CD69<sup>+</sup> CD62L<sup>+</sup> CD8<sup>+</sup> T cells population, and this effect was abolished when depleting either CXCR3 ligands CXCL9/10 or knockout of CXCR3 in the host mice, revealing an unanticipated novel link between CXCL9/10- CXCR3 signaling and tumor specific CD8<sup>+</sup> T<sub>RM</sub> formation in brains.  
+
+<|ref|>sub_title<|/ref|><|det|>[[44, 751, 144, 775]]<|/det|>
+## Results  
+
+<|ref|>sub_title<|/ref|><|det|>[[44, 791, 736, 812]]<|/det|>
+## T-aFGL2 treatment has limited antitumor cytotoxic T lymphocyte activity in vitro  
+
+<|ref|>text<|/ref|><|det|>[[42, 829, 947, 943]]<|/det|>
+To obtain superior FGL2 specific monoclonal antibodies (mAbs), we selected 75 clones of FGL2 mAbs through 3 independent hybridoma fusion and via an ELISA- based mouse FGL2 (mFGL2) binding assay (Supplementary Fig. 1a). 13 out of 75 clones showed strong binding activity to mFGL2 but not to his- tag. Human FGL2 (huFGL2) was then used to select mAbs that show bi- species binding reactivity (Supplementary Fig. 1b), and mouse FGL2 binding clone #4 also showed high binding affinity to human
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[39, 44, 949, 499]]<|/det|>
+FGL2 (Supplementary Fig. 1a, 1b). Additionally, clone #4 showed the most linear association between binding capacity and dilution (Supplementary Fig. 1c). Western blotting, immunofluorescence staining, and ELISA assay further validated the binding activity of FGL2 mAb- clone #4 to both mouse and human FGL2 (Supplementary Fig. 1d- 1f). To further test the effect of FGL2 blocking scFv, lentiviral constructs derived from FGL2 mAb- clone #4 were generated to arm T cells (Fig 1a). This construct contained scFv domains that aimed at recognizing and binding FGL2 (Fig 1a). To ensure that FGL2 scFv was expressed on the surface of T cells with the ability of movement, an EGFR- transmembrane (TM) domain was linked to the FGL2 scFv by a P2A linker (Fig 1a, 1b). The expression of FGL2 scFv on the T cell membrane was validated by the staining of the His tag domain (Fig 1c). The transduction efficiency of the activated mouse T cells was consistent and in the range of \(15\%\) to \(25\%\) (Fig 1c). To verify that T- aFGL2 cells can directly bind FGL2, we have established a microfluidics chip binding assay. As the data shown in Supplementary Fig.1g, T- aFGL2 cells can directly bind the FGL2 that is anchored on the chip via biotin- streptavidin covalent bond. The antitumor cytotoxic T lymphocyte activity of FGL2- scFv- armed T cells (T- aFGL2) against FGL2- expressing DBT cells, a mouse GBM cell line, was evaluated by measuring proportion of tumor cells, and granzyme B, interferon \(\gamma\) (IFNγ), and tumor necrosis factor \(\alpha\) (TNFα) positive T cells (Fig. 1d). T- aFGL2 cells expressed higher granzyme B levels than did T cells transfected with a control construct (T- Ctr) when cocultured with DBT cells at an effector- to- target ratio of 1:1. However, no significant difference was found in the proportion of tumor cells when cocultured with T- aFGL2 and T- Ctr cells, and T- aFGL2 and T- Ctr cells had comparable levels of IFNγ and TNFα, suggesting that T- aFGL2 may have limited direct tumor cell killing activity effects in vitro.  
+
+<|ref|>sub_title<|/ref|><|det|>[[44, 514, 648, 534]]<|/det|>
+## T-aFGL2 treatment does not cause toxicity in immunocompetent mice  
+
+<|ref|>text<|/ref|><|det|>[[39, 550, 950, 939]]<|/det|>
+To evaluate the suitability of FGL2 as a target for T cell therapy with low risk of off- tumor on- target toxicity, we assessed the expression of FGL2 in human GBM tissues and normal human tissue arrays using FGL2 mAb- clone #4 from which the aFGL2 construct was derived. As shown in Fig. 1e, FGL2 was highly expressed in human GBM tissues but not in healthy medulla oblongata tissues. In healthy tissue arrays (Fig. 1e), major organs such as the brain, lung, breast, spleen, and muscle were FGL2 negative, while moderate expression of FGL2 was observed in the stomach, colon, and pancreas. (Fig. 1e). To assess the potential toxicity of T- aFGL2, we intravenously injected 5 million T- Ctr or T- aFGL2 cells into non- tumor- bearing 7- week- old immunocompetent Balb/c mice. Five days after T cell injection, we evaluated blood chemistry, organ toxicities, and immune cell populations in the spleen and bone marrow. As shown in Supplementary Fig. 2a and b, mice treated with T- aFGL2 exhibited no significant changes in immune cell counts in either the spleen or bone marrow. T- aFGL2 treatment caused no abnormalities in blood chemistry (Supplementary Fig. 2c), but mice treated with T- Ctr had significantly higher blood levels of albumin ( \(P = 0.0264\) ) and globulin ( \(P = 0.0181\) ) than did untreated mice (Supplementary Fig. 2c). Furthermore, a board certified veterinary pathologist (N.W.F.) observed no evident abnormality or aberrant T lymphocyte infiltration in tissue sections following T- aFGL2 cell infusion (Supplementary Fig. 3 and Supplementary Table 1). Taken together, these results show that T- aFGL2 therapy does not cause detectable organ toxicity in immunocompetent mice.
+
+<--- Page Split --->
+<|ref|>sub_title<|/ref|><|det|>[[43, 44, 555, 65]]<|/det|>
+## T-aFGL2 therapy induces superior antitumor activity in vivo  
+
+<|ref|>text<|/ref|><|det|>[[41, 82, 941, 354]]<|/det|>
+To test the efficacy of T- aFGL2 therapy in vivo, we first validated expression of FGL2 in mouse GBM tissue. Brain tissues from immunocompetent syngeneic mouse GBM model (DBT tumor- bearing mice) were cryosectioned and stained with FGL2 mAb- clone #4. As shown in Fig. 2a, both tumor cells and surrounding stroma were positively stained for FGL2. Next, DBT- bearing Balb/c mice were used to evaluate the antitumor effects of T- aFGL2. Mice were inoculated with tumor cells and then treated with standard chemotherapy temozolomide (TMZ) on days 3, 4, and 5 to assimilate standard care, before administering T- Ctr or T- aFGL2 cells via the tail vein on days 6 and 13 after tumor cell inoculation (Fig. 2b). DBT is a very aggressive GBM tumor, and most DBT- bearing mice died within 3 weeks in the no treatment or T- Ctr group. T- aFGL2 treatment effectively suppressed DBT tumor progression, and tumors were eliminated in about \(30\%\) of the T- aFGL2- treated mice. These mice remained tumor free for up to 70 days before being used for a rechallenge study. In contrast, tumors progressed rapidly in T- Ctr- treated mice (Fig. 2c- e).  
+
+<|ref|>text<|/ref|><|det|>[[43, 370, 930, 460]]<|/det|>
+To confirm the anti- tumor efficacy of T- aFGL2 treatment, we also took advantage of another syngeneic mouse GL261 model. As shown in Fig. 2f- g, T- aFGL2 treatment, compared with T- Ctr treatment, suppressed this GBM tumor growth and extended mouse survival. Overall, T- aFGL2 showed superior antitumor properties in syngeneic malignant brain tumor models.  
+
+<|ref|>sub_title<|/ref|><|det|>[[44, 477, 784, 500]]<|/det|>
+## T-aFGL2 treatment induces formation of tumor-specific \(\mathbf{CD8^{+}T_{RM}}\) like cells in the brain  
+
+<|ref|>text<|/ref|><|det|>[[41, 517, 953, 771]]<|/det|>
+We next evaluated whether long- term survivors that had been treated with T- aFGL2 cells developed memory T cells that were reactive to tumor cells. We rechallenged T- aFGL2- treated survivors with an intracranial (i.c.) injection of DBT cells (Fig. 3a). The re- challenged DBT cells were cleared within 7 days in T- aFGL2- treated survivors (Fig. 3b), and local re- exposure to DBT cells induced a rapid, more than 18- fold increase in the number of \(\mathbf{CD8^{+}}\) T cells in the brains of T- aFGL2- treated survivors compared to the number in naive brains (Fig. 3d, 3f). To investigate the tumor specificity of the generated memory T cells, we rechallenged DBT tumor- rejecting mice induced by T- aFGL2 treatment with 4T1 tumor cells (i.c.), which also developed tumors in the naive balb/c mice rapidly. We found these DBT- rejecting memory T cells failed to protect mice from 4T1 tumor cell challenge (Supplementary Fig. 4a). This rapid and intense tumor reactivity to DBT cells, but not to 4T1 cells, confirmed that tumor- specific memory \(\mathbf{CD8^{+}}\) T cells had developed in the brains of T- aFGL2- treated survivors.  
+
+<|ref|>text<|/ref|><|det|>[[41, 788, 950, 952]]<|/det|>
+To determine whether these tumor- specific memory \(\mathbf{CD8^{+}}\) T cells stayed in the vicinity of the tumor (ie, in the brain) or migrated throughout the body, we inoculated DBT cells subcutaneously into the flanks of T- aFGL2 survivors and naive mice (Fig. 3a). Interestingly, both groups of mice developed tumors under the skin (Fig. 3c), suggesting that the tumor- specific memory \(\mathbf{CD8^{+}}\) T cells in T- aFGL2- treated survivors were restricted to the brain. To confirm that tumor- reactive \(\mathbf{CD8^{+}}\) T cells only existed in the brain, we assessed T cells from the brains and draining lymph nodes (dLNs) of naive mice and T- aFGL2- treated survivors 7 days after the rechallenge with DBT cells. As shown in Fig. 3d and e, the ratio of \(\mathbf{CD8^{+}}\) T cells to \(\mathbf{CD4^{+}}\) T
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[41, 44, 956, 164]]<|/det|>
+cells in the brain was up to 8- fold higher in T- aFGL2- treated survivors than in naive mice. Moreover, the ratio of \(\mathsf{CD8^{+}}\) to \(\mathsf{CD4^{+}}\) T cells was 9- fold higher in the brains than in the LNs of T- aFGL2- treated survivors, suggesting that \(\mathsf{CD8^{+}}\) T cells, but not \(\mathsf{CD4^{+}}\) T cells, were the primary memory T cells controlling tumor cell growth, and that these \(\mathsf{CD8^{+}}\) T cells were only resident in the brain. Taken together, these data strongly indicate that T- aFGL2 treatment induced development of brain- resident tumor- specific \(\mathsf{CD8^{+}T_{RM}}\) like cells.  
+
+<|ref|>sub_title<|/ref|><|det|>[[42, 184, 928, 229]]<|/det|>
+## \(\mathsf{CD8^{+}T_{RM}}\) like cells undergo recall expansion and reject tumor cells when being transplanted into naïve brains  
+
+<|ref|>text<|/ref|><|det|>[[39, 245, 955, 660]]<|/det|>
+To validate that \(\mathsf{CD8^{+}}\) T cells in the brains of T- aFGL2- treated survivors were \(\mathsf{CD8^{+}T_{RM}}\) cells, we sorted \(\mathsf{CD8^{+}}\) T cells from the brains, draining lymph nodes (dLNs), and peripheral blood (PB) of T- aFGL2- treated survivors on day 7 after tumor cell inoculation and adoptively transplanted these T cells along with DBT cells directly into the brains (intracranially) of naïve recipient mice (Fig. 4a). In contrast to both dLN and PB \(\mathsf{CD8^{+}}\) T cells, which failed to mount a recall response, brain \(\mathsf{CD8^{+}}\) T cells underwent expansion even when reseeded in the brain tissue in low numbers (3000 cells) (Fig. 4b, c), confirming that the \(\mathsf{CD8^{+}}\) T cells in the brains of T- aFGL2- treated survivors are bona fide \(\mathsf{T_{RM}}\) cells. As most of the \(\mathsf{CD8^{+}}\) T cells in tumor experienced brains were \(\mathsf{CD44^{+}}\) memory T cells, while the \(\mathsf{CD8^{+}}\) T cells in peripheral were not, we then compared the anti- tumor effect of \(\mathsf{CD44^{+}CD8^{+}T}\) cells in brain, dLNs, pLNs and PB to validate the results. In consistence with \(\mathsf{CD8^{+}}\) T cells, the \(\mathsf{CD44^{+}CD8^{+}T}\) cells in peripheral tissues didn't show protection against tumor cells in vivo (Supplementary Fig. 4b and 4c). Similar results was found in GL261 model (Supplementary Fig. 4d). To determine whether \(\mathsf{CD4^{+}}\) T cells in the brain behaved in a similar manner as \(\mathsf{CD8^{+}}\) T cells, we sorted \(\mathsf{CD4^{+}}\) and \(\mathsf{CD8^{+}}\) T cells from the brains of T- aFGL2- treated survivors and then co- inoculated with DBT cells into the brains of naïve recipient mice. As shown in Fig. 4b and c, \(\mathsf{CD4^{+}}\) T cells did not have the same tumor- cell- eliminating capacity as \(\mathsf{CD8^{+}}\) T cells, confirming that the induced brain resident \(\mathsf{CD8^{+}}\) T cells, but not \(\mathsf{CD4^{+}}\) T cells, in the brain provide immune surveillance of the previously encountered antigen.  
+
+<|ref|>text<|/ref|><|det|>[[39, 677, 956, 944]]<|/det|>
+To further investigate whether the adoptively transplanted \(\mathsf{CD8^{+}T_{RM}}\) cells could survive and remain in the brains of naïve recipient mice, we subsequently challenged the recipient mice with tumor cells on day 40 after adoptive \(\mathsf{CD8^{+}}\) T cells transplantation. We observed that the recipient mice rejected the rechallenged tumor cells (Fig. 4d, e). These findings show that \(\mathsf{CD8^{+}T_{RM}}\) cells were successfully transplanted into naïve brains, transforming naïve brains to become tumor rejecting brains. To further validate the function of transplanted \(\mathsf{CD8^{+}T_{RM}}\) cells and exclude the effect of host T cells, we transplanted the lymphocytes from brain with \(\mathsf{T_{RM}(T_{RM}BILs)}\) into brain of naïve immunodeficiency SCID mice and re- challenged these mice 35 days after the transplantation (Fig. 4f). The same as the parental \(\mathsf{T_{RM}}\) bearing mice, these \(\mathsf{T_{RM}}\) transplanted SCID mice showed anti- tumor capacity in the brain (Fig 4f- g); To verify the transplanted \(\mathsf{CD8^{+}}\) T cells are responsible for the protection, we re- challenged these SCID survivors with tumor cells combined with \(\mathsf{aCD8}\) , \(\mathsf{aCD4}\) or asialo GM1 antibodies to deplete \(\mathsf{CD8^{+}T}\) cells, \(\mathsf{CD4^{+}T}\) cells and NK cells
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[42, 45, 933, 170]]<|/det|>
+respectively. Only depleting \(\mathsf{CD8^{+}T}\) cells, but not depleting \(\mathsf{CD4^{+}T}\) cells or NK cells, impaired this protection (Fig 4h and 4i). Taken together, \(\mathsf{CD8^{+}T_{RM}}\) like cells, in brains of T- aFGL2- treated survivors, which fulfill both memory and reactive functions against tumor cells, are tumor specific brain \(\mathsf{CD8^{+}T_{RM}}\) cells. Notably, these \(\mathsf{CD8^{+}T_{RM}}\) cells can be adoptively transferred into the naive brains with or without host T cells.  
+
+<|ref|>sub_title<|/ref|><|det|>[[42, 188, 468, 210]]<|/det|>
+## \(\mathsf{CD8^{+}T_{RM}}\) cells establish classical \(\mathsf{T_{RM}}\) phenotype  
+
+<|ref|>text<|/ref|><|det|>[[41, 229, 950, 456]]<|/det|>
+To determine whether these \(\mathsf{CD8^{+}T_{RM}}\) like cells bear classical TRM phenotype, we checked CD69, CD103 and CD62L expression, which were used to identify \(\mathsf{T_{RM}}^{1,10,24}\) . To verify that the isolated BILs- CD8 \(^+\) T cells were brain restricted, we performed i.v. injection of the \(\mathsf{CD8\beta}\) antibody and found that over \(90\%\) of BILs- CD8 \(^+\) T cells were non- circulating brain resident T cells (Supplementary Fig. 5a). Here, we found that compared with \(\mathsf{CD44^{+}CD8^{+}T}\) cells in PB, the \(\mathsf{CD44^{+}CD8^{+}T}\) cells in \(\mathsf{T_{RM}}\) bearing brains with \(\mathsf{T_{RM}}\) were \(\mathsf{CD69^{+}}\) (either \(\mathsf{CD103^{+}}\) or \(\mathsf{CD103^{- }}\) ), and \(\mathsf{CD62L^{- }}\) (Fig. 4j). Similar results were found for \(\mathsf{CD4^{+}T}\) cells (Supplementary Fig. 5b). These findings show that \(\mathsf{CD8^{+}T_{RM}}\) like cells in brains established a classical \(\mathsf{T_{RM}}\) phenotype of \(\mathsf{CD69^{+}CD62L^{- }}\) . Together, both function and phenotype of the \(\mathsf{CD8^{+}T_{RM}}\) like cells in brains of T- aFGL2- treated survivors further validated that they are tumor specific brain \(\mathsf{CD8^{+}T_{RM}}\) cells.  
+
+<|ref|>sub_title<|/ref|><|det|>[[44, 475, 524, 497]]<|/det|>
+## The function of \(\mathsf{CD8^{+}T_{RM}}\) cells is TCR-MHC-I dependent  
+
+<|ref|>text<|/ref|><|det|>[[39, 515, 960, 944]]<|/det|>
+Since TCR is generated through random rearrangement of genomic V(D)J segments and is the mediator of antigen recognition and binding by T lymphocytes, we next questioned whether \(\mathsf{CD8^{+}T_{RM}}\) cells displayed a unique TCR repertoire that was distinct from that found in the dLNs. To this end, we sorted \(\mathsf{CD44 + CD8 + T}\) (memory \(\mathsf{CD8 + T}\) ) cells from the brains and dLNs of T- aFGL2- treated survivors on day 20 after the third challenge with DBT cells via flow cytometric sorting, followed by TCRa and TCR \(\beta\) deep sequencing (Fig. 5a). The most abundant T cell clones- those with a frequency of more than \(5\% - \text{in} \mathsf{T_{RM}}\) bearing brains constituted more than \(60\%\) of the total TCRa and TCR \(\beta\) repertoire, whereas no T cell clones with a frequency of more than \(5\%\) were found in the TCR \(\beta\) repertoire of \(\mathsf{CD44^{+}CD8^{+}T}\) cells in the dLNs (Supplementary Fig. 5c). To further characterize the TCR repertoires of \(\mathsf{T_{RM}}\) cells and dLNs- \(\mathsf{CD44^{+}CD8^{+}T}\) cells, we analyzed sequences of complementarity determining region 3 (CDR3), which encompasses the V(D)J recombination junctions and encodes the vast majority of TCR variation. Moreover, all of the top 10 dominant CDR3 sequences in \(\mathsf{T_{RM}}\) cells encompassed the V/J recombination, but each dominant CDR3 sequence in dLNs- \(\mathsf{CD44^{+}CD8^{+}T}\) cells encompassed a unique V/J recombination (Fig. 5b and Supplementary Fig. 5d). Analysis of the V and J domain usage showed that, in one of the T- aFGL2- treated survivors, the most dominant clone of TCR \(\beta\) in \(\mathsf{T_{RM}}\) cells was grouped by V17/J1- 4, which was absent in dLNs (Fig. 5b). These data showed the presence and expansion of unique T cell clones and that there was no overlap of the highly occupied TCR clone in \(\mathsf{T_{RM}}\) cells with the TCR clone in dLNs- \(\mathsf{CD44^{+}CD8^{+}T}\) cells. Interestingly, each mouse of T- aFGL2- treated survivors bears different \(\mathsf{T_{RM}}\) clones
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[42, 46, 920, 92]]<|/det|>
+against different antigens. These data suggested that these highly expanded TCR repertoires of \(\mathsf{CD8^{+}}\) \(\mathsf{T}_{\mathsf{RM}}\) cells were associated, thus, with the rapid and robust response of \(\mathsf{T}_{\mathsf{RM}}\) cells against tumor cells.  
+
+<|ref|>text<|/ref|><|det|>[[42, 109, 940, 234]]<|/det|>
+To verify the robust response of \(\mathsf{CD8^{+}T_{RM}}\) cells against tumor cells is associated with the interaction between expanded TCR and MHC- I, we blocked MHC- I in vivo using aMHC- I antibody when transplanted \(\mathsf{CD8^{+}T_{RM}}\) cells into naive mice. Blocking MHC- I abolished the anti- tumor efficacy of the transplanted \(\mathsf{CD8^{+}T_{RM}}\) cells (Fig 5c- f), demonstrating TCR- MHC- I interaction is required for the proper function of \(\mathsf{CD8^{+}T_{RM}}\) cells in vivo.  
+
+<|ref|>sub_title<|/ref|><|det|>[[45, 252, 945, 275]]<|/det|>
+## T-aFGL2 treatment-induced CD69 expression on \(\mathsf{CD8^{+}}\) memory T cells is essential for \(\mathsf{CD8^{+}T_{RM}}\) formation  
+
+<|ref|>text<|/ref|><|det|>[[39, 285, 959, 923]]<|/det|>
+To understand the cellular mechanisms by which the FGL2- blocking scFv induces the generation of \(\mathsf{CD8^{+}T_{RM}}\) cells, on day 4 after the second T cell infusion, we performed high- dimensional profiling of brain- infiltrating lymphocytes (BILs) using time- of- flight mass cytometry (CyTOF) with a panel of 37 antibodies that illustrated different immune populations (Fig. 6a). CyTOF data analysis divided BILs into 15 immune cell populations (Fig. 6b). As \(\mathsf{CD8^{+}}\) T cells are the primary functional cells rejecting tumor cells as shown in our transplant study (Fig. 4b), we focused on these cell populations. We found that the \(\mathsf{CD8^{+}}\) T cell population was composed of 2 subpopulations: \(\mathsf{CD69^{+}CD8^{+}}\) memory T cells ( \(\mathsf{CD69^{+}CD8^{+}T_{M}}\) ) and \(\mathsf{CD69^{- }CD8^{+}}\) memory T cells ( \(\mathsf{CD69^{- }CD8^{+}T_{M}}\) ) (Fig. 6b). Notably, the subset of \(\mathsf{CD69^{+}CD8^{+}T_{M}}\) cells was significantly larger in mice that underwent T- aFGL2 treatment than in those that received T- Ctr treatment (Fig. 6c, d). Indeed, CD69 has been reported to help in the retention of memory T cells in resident tissues through inhibiting expression of the S1P receptor, which can promote T cell circulation into the blood; high level expression of CD69 on T cells is an indicator of \(\mathsf{T_{RM}}\) cells \(^{24}\) . To further determine the phenotype of these \(\mathsf{CD69^{+}CD8^{+}T_{M}}\) cells, we compared their expression of T cell exhaustion markers with that of \(\mathsf{CD69^{- }CD8^{+}T_{M}}\) cells. As shown in Fig. 6e, \(\mathsf{CD69^{+}CD8^{+}T_{M}}\) cells had higher levels of Ki67, CD223 (LAG3), and CD279 (PD- 1) than did \(\mathsf{CD69^{- }CD8^{+}T_{M}}\) cells. This \(\mathsf{CD69^{HIPD- 1^{HILAG3^{Hl}}}}\) phenotype of highly proliferating \(\mathsf{CD69^{+}CD8^{+}T_{M}}\) cells has been reported to be most prominent in cells with \(\mathsf{T_{RM}}\) characteristics in different kinds of tissues, including lung \(^{15,25,26}\) , breast \(^{19}\) , and skin \(^{6}\) . These data indicated that T- aFGL2 treatment increased proliferating \(\mathsf{CD69^{+}CD8^{+}T_{M}}\) subsets with \(\mathsf{T_{RM}}\) characteristics, which may promote the transformation of these \(\mathsf{CD69^{+}CD8^{+}T_{M}}\) cells into \(\mathsf{T_{RM}}\) cells in the brain. To further validate the biological function of \(\mathsf{CD69}\) on \(\mathsf{CD8^{+}T_{RM}}\) , we blocked CD69 in vivo by \(\mathsf{aCD69}\) antibody. When transplanted the \(\mathsf{CD69^{+}CD8^{+}T_{RM}}\) cells with tumor cells into naive mice i.e., we found \(\mathsf{aCD69}\) antibody treatment didn't disrupt the anti- tumor efficacy of \(\mathsf{CD8^{+}T_{RM}}\) cells. However, when we re- challenge these mice with tumor cells i.e. on day 60 post the transplantation, the mice treated with \(\mathsf{aCD69}\) antibody lost the tumor- rejecting capacity (Fig. 6f- g), indicating that \(\mathsf{CD69}\) doesn't affect the executive function of \(\mathsf{T_{RM}}\) but is required for the prolonged residence and function of \(\mathsf{CD8^{+}T_{RM}}\) in brains.
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[42, 46, 949, 162]]<|/det|>
+Besides \(\mathsf{CD8^{+}}\) T cells, we analyzed the other immune subpopulations, including helper T cells (Th cells) and regulatory T cells (Tregs), DCs, macrophages, monocytes, and neutrophils. However, no significant difference in these subpopulations was observed between the T- aFGL2 and T- Ctr groups (Supplementary Fig. 6), suggesting that the antitumor effect induced by T- aFGL2 treatment is different from antibody therapy and may work mainly through regulating \(\mathsf{CD69^{+}CD8^{+}T_{M}}\) cells.  
+
+<|ref|>sub_title<|/ref|><|det|>[[44, 180, 727, 202]]<|/det|>
+## T-aFGL2 induced \(\mathsf{CD8^{+}T_{RM}}\) formation is associated with CXCL9/10-CXCR3 axis  
+
+<|ref|>text<|/ref|><|det|>[[40, 222, 958, 540]]<|/det|>
+To further understand the molecular mechanism by which FGL2- blocking scFv induces \(\mathsf{CD69^{+}CD8^{+}T_{M}}\) cells generation, we analyzed the CYTOF data for the chemokine receptors (i.e. CCR2, CXCR3, CXCR2, and CX3CR1) that may affect the T cells infiltration and recruitment to tumor sites. Intriguingly, we found that T- aFGL2 treatment increased CXCR3 expression on \(\mathsf{CD69^{+}CD8^{+}T_{M}}\) cells in tumor bearing brains, compared with T- Ctr treatment (Fig. 7a). Flow cytometry data also verified that T- aFGL2 treatment, compared with T- Ctr, increased proportion of \(\mathsf{CXCR3^{+}CD69^{+}CD8^{+}T}\) cells among total \(\mathsf{CD8^{+}T}\) cells in tumor bearing brains (Fig. 7b), indicating that increased CXCR3 expression on \(\mathsf{CD8T}\) cells may play a role in mediating T- aFGL2 induced \(\mathsf{CD69^{+}CD8^{+}T_{RM}}\) cells formation. To validate this notion, we compared the anti- tumor efficacy of T- aFGL2 in treating tumor bearing wild type (WT) mice and CXCR3 deficient (CXCR3- ) mice. T- aFGL2 treatment didn't show protective effect in \(\mathsf{CXCR3^{- / - }}\) mice as did in WT mice (Fig. 7c). Besides, the \(\mathsf{CD69^{+}CD8^{+}T_{M}}\) population was reduced in \(\mathsf{CXCR3^{- / - }}\) mice compared with WT mice (Fig. 7d), suggesting that CXCR3 play a critical role in mediating T- aFGL2 induced \(\mathsf{CD69^{+}CD8^{+}T_{M}}\) cells generation and thus \(\mathsf{CD8^{+}T_{RM}}\) formation for protective function.  
+
+<|ref|>text<|/ref|><|det|>[[40, 556, 953, 888]]<|/det|>
+To characterize how the CXCR3 chemokine system mediates anti- tumor responses to T- aFGL2 treatment, we examined the expression of the CXCR3 chemokine ligands CXCL9 and CXCL10. Protein levels of CXCL9 and CXCL10 were markedly increased in tumor bearing brains after T- aFGL2 treatment compared to T- Ctr treatment (Fig. 7e). To determine the roles of CXCL9 and CXCL10 in T- aFGL2 induced tumor rejection and \(\mathsf{CD8^{+}T_{RM}}\) formation, we used aCXCL9 and aCXCL10 antibodies to blocking CXCL9 and CXCL10 in vivo. Consistent with our earlier findings (Fig. 7c- d), the therapeutic benefits of T- aFGL2 was lost when blocking CXCL9 and CXCL10 (Fig. 7f), indicating a critical role for CXCL9 and CXCL10 in T- aFGL2 immunotherapy. Moreover, the percentage of \(\mathsf{CD69^{+}CD8^{+}T_{M}}\) was increased upon T- aFGL2 treatment in control IgG group but not in aCXCL9 and aCXCL10 antibodies treatment group (Fig .7g), indicating the functional importance of CXCL9/10- CXCR3 axis for response to T- aFGL2 therapy and \(\mathsf{CD8^{+}T_{RM}}\) formation. Altogether, T- aFGL2 therapy induced tumor- reactive T cells' proliferation, secretion of granzyme B to control tumor progression, and increased CXCR3, CD69 expression on memory CD8+ T cells to help them retain in the brains, which will foster the formation of tumor specific brain resident \(\mathsf{CD8^{+}TRM}\) (Fig. 7h).  
+
+<|ref|>sub_title<|/ref|><|det|>[[44, 910, 191, 935]]<|/det|>
+## Discussion
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[39, 45, 960, 265]]<|/det|>
+Increasing evidence has shown that \(\mathsf{T}_{\mathsf{RM}}\) cells have a promising role in the control of solid tumors \(^{27}\) . Early studies showed that intravaginal vaccination induced \(\mathsf{T}_{\mathsf{RM}}\) formation in cervicovaginal tissues resulting in the control of tumors in the genital tract \(^{28}\) . Nonetheless, whether adoptive cellular therapies (ACT) can foster T cells' development into \(\mathsf{T}_{\mathsf{RM}}\) cells remains unknown. Our study has provided an ACT- based treatment, T- aFGL2 cell therapy, which can program endogenous T cells into tumor specific \(\mathsf{CD8^{+}T_{RM}}\) cells. These \(\mathsf{T}_{\mathsf{RM}}\) cells had a highly expanded and specific TCR repertoire. After being transplanted into the brains of either immunocompetent or T cell deficient naïve mice, these \(\mathsf{T}_{\mathsf{RM}}\) cells transformed naïve mice to become tumor rejecting in the brains but not in peripheral tissues due to the tissue specific resident nature.  
+
+<|ref|>text<|/ref|><|det|>[[39, 281, 958, 682]]<|/det|>
+Retention in the resident tissue is required for \(\mathsf{T}_{\mathsf{RM}}\) cells to expand and to be functional \(^{10}\) . One of the mechanisms of \(\mathsf{T}_{\mathsf{RM}}\) retention is adhesion to home tissue, which is associated with overexpression of integrin molecules such as LFA- 1 (a \(\mathsf{L}\beta 2\) ) \(^{18}\) , VLA- 1(a \(\beta 1\) ) \(^{29}\) , and CD103 (a \(\mathsf{E}\beta 7\) ) \(^{10,29}\) , binding to adhesion molecules on the endothelium, and the extracellular matrix components collagen and laminin. Another mechanism attributed to retention of \(\mathsf{T}_{\mathsf{RM}}\) in the tissue is unresponsiveness to signals that promote recirculation. Expression of S1PR1 (encoded by \(S1pr1\) ) \(^{30}\) , CD62L (encoded by \(Sell\) ) \(^{31}\) , and CCR7 (encoded by \(Ccr7\) ) \(^{32}\) permits recirculation of memory T cells. It has been reported that CD69 suppresses memory T cells' recirculation potential by inhibiting surface expression of the S1P receptor \(^{24}\) . Indeed, most \(\mathsf{T}_{\mathsf{RM}}\) cells constitutively express \(\mathsf{CD69}^{5,9,33}\) . Although CD103, which binds to E- cadherin on epithelial cells, is expressed on the most- studied \(\mathsf{T}_{\mathsf{RM}}\) cells reside in epithelial tissue \(^{29}\) , CD69 is more commonly used than CD103 to identify \(\mathsf{T}_{\mathsf{RM}}\) cells in non- lymph organs. In our study, T- aFGL2, compared with T- Ctr, increased the population of \(\mathsf{CD69^{+}CD8^{+}T_{RM}}\) like cells (Ki67 \(^{\mathrm{H}}\) PD- 1 \(^{\mathrm{H}}\) LAG3 \(^{\mathrm{H}}\) ) in the brain tumor environment (Fig. 6b- d). The T- aFGL2 treatment induced \(\mathsf{CD8^{+}T_{RM}}\) (phenotype: \(\mathsf{CD69^{+}CD62L^{- }}\) ). Blocking CD69 appears to disrupt the residence of the brain- resident \(\mathsf{T}_{\mathsf{RM}}\) cells (Fig. 6f- g). Altogether, the increased tissue retention molecule CD69 expression on \(\mathsf{CD8^{+}T_{M}}\) cells in the brain can help retain these cells in the brain and promote their differentiation into brain- resident \(\mathsf{T}_{\mathsf{RM}}\) cells.  
+
+<|ref|>text<|/ref|><|det|>[[41, 700, 955, 875]]<|/det|>
+T- aFGL2 treatment- induced \(\mathsf{CD69^{+}CD8^{+}T_{RM}}\) formation is associated with CXCL9/10- CXCR3 axis, as either absence of CXCR3 or blocking CXCL9/10 abrogated the increased \(\mathsf{CD69^{+}CD8^{+}T_{M}}\) after T- aFGL2 treatment, and thus interrupted the anti- tumor efficacy of T- aFGL2 treatment (Fig. 7). The notion that CXCR3- CXCL9/10 axis promotes the generation of \(\mathsf{T}_{\mathsf{RM}}\) is consistent with the previous findings that CXCR3- CXCL9 axis is required for reinvigoration of intratumoral \(\mathsf{CD8^{+}T}\) cell responses in response to PD- 1 blockade \(^{34}\) , and exogenous application of CXCR3 ligands promoted the \(\mathsf{T}_{\mathsf{RM}}\) cells formation in the epithelium of the lower female reproductive tract \(^{35}\) .  
+
+<|ref|>text<|/ref|><|det|>[[42, 893, 930, 960]]<|/det|>
+It's reported that the immune suppressive FGL2 limits cytotoxic \(\mathsf{CD8^{+}T}\) - cell responses via FcγRIIb \(^{36}\) . To evaluate if the T cells express FcγRIIb, which can bind and be regulated by FGL2 in tumor microenvironment in our study, we detected the FcγRIIB expression on T cells in DBT tumor bearing
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[41, 45, 955, 230]]<|/det|>
+brains. T cells, both \(\mathsf{CD4^{+}T}\) cells and \(\mathsf{CD8^{+}T}\) cells, express FcγRIIB, though not all of them (Supplementary Fig. 7a). Moreover, wt T cells, but not FcγRIIB \(- / -\) T cells, can directly bind the FGL2 that is anchored on the chip via biotin- streptavidin interaction (supplementary Fig. 7b), verifying that T cells can directly bind FGL2 through FcγRIIB. Besides, T- aFGL2 treatment lost the superior anti- tumor effect, compared with T- Ctr, in FcγRIIB \(- / -\) mice (supplementary Fig. 7c), suggesting that FcγRIIB was regulated by FGL2. These data suggested that T cells in this study can bind and be regulated by FGL2 in tumor microenvironment through FcγRIIB, and T- aFGL2 treatment can disrupt this FcγRIIB- FGL2 interaction and boost the cytotoxic T cells responses.  
+
+<|ref|>text<|/ref|><|det|>[[39, 245, 950, 641]]<|/det|>
+To date, most studies of \(\mathsf{T}_{\mathsf{RM}}\) cells have focused on autoimmune diseases and infections. For example Malik et al. reported that melanoma antigen- specific skin- resident memory T cells are maintained in vitiligo- afflicted skin \(^{37}\) , but no adoptive cellular therapy that induces tumor- specific \(\mathsf{T}_{\mathsf{RM}}\) cells has yet been evaluated. Here, we performed T- aFGL2 adoptive cell treatment to brain tumor- bearing mice, and found that T- aFGL2 induced tumor- specific \(\mathsf{CD8^{+}T}_{\mathsf{RM}}\) cells in the brain. The phenotype of these \(\mathsf{CD8^{+}T}_{\mathsf{RM}}\) cells were either \(\mathsf{CD69^{+}CD103^{+}}\) or \(\mathsf{CD69^{+}CD103^{- }}\) . Besides the phenotype of these tumor- specific \(\mathsf{T}_{\mathsf{RM}}\) cells, we also characterized their TCR repertoire. TCR recognizes antigens presented by the major histocompatibility complex (MHC) on antigen- presenting cells and subsequently activates T cells and mediates the eradication of the antigen \(^{38 - 41}\) . However, the TCR repertoires of tumor- specific \(\mathsf{T}_{\mathsf{RM}}\) cells have not previously been characterized. From next generation RNA sequencing, we noted that the \(\mathsf{CD8^{+}T}_{\mathsf{RM}}\) cells had a highly expanded TCR \(\alpha \beta\) repertoire (Fig. 5). Moreover, no overlap of the TCR clone was found between \(\mathsf{T}_{\mathsf{RM}}\) cells in the brain and memory T cells in the periphery. This is distinct from influenza- specific lung- resident memory T cells, which maintain a wide diversity of TCR profiles \(^{13}\) . Blocking MHC- I abolished the anti- tumor efficacy of the \(\mathsf{CD8 + T}_{\mathsf{RM}}\) cells (Fig. 5c- f), demonstrating TCR- MHC- I interaction is required for the proper function of \(\mathsf{CD8^{+}T}_{\mathsf{RM}}\) cells in vivo. Further functional evaluation of this highly expanded TCR repertoire on \(\mathsf{T}_{\mathsf{RM}}\) cells will be an important part of future studies.  
+
+<|ref|>text<|/ref|><|det|>[[39, 657, 951, 952]]<|/det|>
+\(\mathsf{T}_{\mathsf{RM}}\) cells occupy frontline sites of infection and are positioned to respond most immediately and potently. The abundance of cells with \(\mathsf{T}_{\mathsf{RM}}\) cell characteristics in tumors often correlates with a favorable outcome \(^{19,42 - 44}\) ; thus, promoting tumor- specific \(\mathsf{T}_{\mathsf{RM}}\) cell formation in tumor tissues or adaptively transferring tumor- specific \(\mathsf{T}_{\mathsf{RM}}\) cells into tumor sites are promising approaches for treating patients with cancer. Wakim et al. found that virus- specific \(\mathsf{T}_{\mathsf{RM}}\) cells in the brain die rapidly upon isolation from the resident tissue and fail to undergo recall expansion after adoptive transfer into the bloodstream of an antigen- challenged recipient, indicating that these cells depend on the local milieu for their function and survival \(^{12,45}\) . Here, we were able to transfer \(\mathsf{CD8^{+}T}_{\mathsf{RM}}\) cells into the brains of both immunocompetent and T cells deficient naive mice and induce a tumor- specific reaction in the recipient mice (Fig. 4). To our knowledge, this is the first study in which tumor- specific \(\mathsf{CD8^{+}T}_{\mathsf{RM}}\) cells were successfully transferred into the same tissue of naive mice in vivo. Our success may be explained by the following factors: (1) \(\mathsf{T}_{\mathsf{RM}}\) cells should be transferred into the same tissue in naive mice where the \(\mathsf{T}_{\mathsf{RM}}\) cells originally resided; (2)
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[42, 44, 944, 141]]<|/det|>
+\(\mathrm{T}_{\mathrm{RM}}\) cells should be cotransferred with antigens that can activate them. Besides yielding novel insights into these tumor specific brain resident \(\mathrm{CD8^{+}T_{RM}}\) cells, our study provides a valuable resource for further investigations of tumor- specific \(\mathrm{T}_{\mathrm{RM}}\) cell formation in the brain. Such studies will ultimately aid the development of strategies for immunotherapy of brain cancers.  
+
+<|ref|>sub_title<|/ref|><|det|>[[44, 163, 213, 190]]<|/det|>
+## Declarations  
+
+<|ref|>sub_title<|/ref|><|det|>[[44, 205, 207, 224]]<|/det|>
+## Acknowledgments  
+
+<|ref|>text<|/ref|><|det|>[[42, 242, 951, 377]]<|/det|>
+We appreciate Scientific Publications, Research Medical Library at MD Anderson for helping us edit our manuscript. We thank Dr. Leonid Metelitsa (Baylor College of Medicine) for providing DBT mouse glioma cells. for This research used the Flow Cytometry, Cellular Imaging Facility and DNA analysis Core Facility, Monoclonal Antibody Core Facility at MD Anderson, which are supported in part by the National Institutes of Health through MD Anderson's Cancer Center Support Grant P30 CA016672. Core Grant Citation. This work was supported by National Institutes of Health Grants R01 CA120895 and R01 CA200574.  
+
+<|ref|>sub_title<|/ref|><|det|>[[44, 394, 237, 413]]<|/det|>
+## Authors' contributions  
+
+<|ref|>text<|/ref|><|det|>[[42, 431, 947, 522]]<|/det|>
+QZ and SL conceived and designed the experiments. QZ, LK, NWF, JY, JH, ZJ and XX conducted the experiments. RH were responsible for the mouse model. QZ, SJ, XT, and JW analyzed the data. QZ, SL, ABH, SY, LD, DM and HO edited and/or drafted the manuscript. SL supervised the study. All authors have read and approved the final version of the manuscript.  
+
+<|ref|>sub_title<|/ref|><|det|>[[44, 544, 196, 570]]<|/det|>
+## References  
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+
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+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[50, 44, 920, 95]]<|/det|>
+42. Djenidi, F. et al. CD8 + CD103 + tumor-infiltrating lymphocytes are tumor-specific tissue-resident memory T cells and a prognostic factor for survival in lung cancer patients. The Journal of Immunology 194, 3475–3486 (2015).  
+
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+43. Webb, J. R., Milne, K., Watson, P., DeLeeuw, R. J. & Nelson, B. H. Tumor-infiltrating lymphocytes expressing the tissue resident memory marker CD103 are associated with increased survival in high-grade serous ovarian cancer. Clinical cancer research 20, 434–444 (2014).  
+
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+44. Nizard, M. et al. Induction of resident memory T cells enhances the efficacy of cancer vaccine. Nature communications 8, 1–11 (2017).  
+
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+45. Wakim, L. M., Woodward-Davis, A. & Bevan, M. J. Memory T cells persisting within the brain after local infection show functional adaptations to their tissue of residence. Proc Natl Acad Sci U S A 107, 17872–17879, doi:10.1073/pnas.1010201107 (2010).  
+
+<|ref|>sub_title<|/ref|><|det|>[[45, 328, 142, 353]]<|/det|>
+## Figures  
+
+<|ref|>image<|/ref|><|det|>[[63, 390, 933, 870]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[45, 895, 115, 914]]<|/det|>
+<center>Figure 1 </center>  
+
+<|ref|>text<|/ref|><|det|>[[45, 936, 535, 956]]<|/det|>
+Specific functional antitumor activity of T- aFGL2 in vitro.
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[39, 44, 950, 338]]<|/det|>
+a, Schematic of the vector encoding the FGL2- blocking scFv, linked to the transmembrane (TM) domain. The His- tag domain was used for detection of the scFv. The Ctr vector is the empty CMV construct. G4S: pentapeptide GGGGS; P2A: porcine teschovirus- 1. b, Cartoon schematic of a T cell transfected with the FGL2- blocking scFv vector (T- aFgl2). c, Representative flow cytometry histograms demonstrating FGL2- blocking scFv expression on mouse T cells following transduction. The scFv was detected with His- tag specific antibodies. d, Flow cytometry plots depicting no difference in proportion of tumor cells (DBT- GFP+) cocultured with T- Ctr and T- aFGL2 cells at E:T ratio of 4:1 for 72hrs (top panel); flow cytometry plots depicting increased granzyme B expression in T- aFGL2 cells (compared with T- Ctr) cocultured with DBT tumor cells at a ratio of 1:1 for 24 h; no difference in TNFα or IFNγ was detected between T- aFGL2 and T- Ctr. Data shown are mean ± SEM from 3 independent experiments. \(**P = 0.0025\) , two- way t- test. NS, not significant. e, Representative micrographs of FGL2 expression in GBM and the indicated normal human tissues assessed by staining with FGL2 mAb- clone #4 at the final concentration of 1 μg/mL. Micrographs are representative of at least two sections per tissue. Scale bars, 100 μm.  
+
+<|ref|>image<|/ref|><|det|>[[66, 368, 936, 844]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[43, 870, 116, 890]]<|/det|>
+<center>Figure 2 </center>  
+
+<|ref|>text<|/ref|><|det|>[[43, 913, 366, 933]]<|/det|>
+Antitumor activity of T- aFGL2 in vivo.
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[39, 44, 956, 271]]<|/det|>
+a, Representative micrographs of FGL2 expression in mouse brains with glioma assessed by staining with FGL2 mAb- clone #4 at the final concentration of \(1 \mu \mathrm{g} / \mathrm{mL}\) . Slides stained without a primary Ab were used as negative controls. Scale bars, \(50 \mu \mathrm{m}\) . b, Schematic of the orthotopic glioma DBT model treated with temozolomide (TMZ) on days 3, 4, and 5 after tumor cell inoculation, followed by infusion of T- Ctr or T- aFGL2 on days 6 and 13. c, Representative bioluminescence images of DBT- luc tumor growth in the orthotopic glioma model shown in b. d, Representative H&E staining of brains from the orthotopic glioma model shown in b collected on day 14 after tumor cell inoculation. e, Kaplan- Meier survival curves of mice shown in b ( \(n = 11 - 15\) mice/group). \(***P = 0.0002\) (T- aFGL2 vs T- Ctr), \(****P < 0.0001\) (T- aFGL2 vs no treatment [NT] group), log- rank test. f, Schematic of the orthotopic glioma GL261 model. g, Kaplan- Meier survival curves of mice in f ( \(n = 6 - 7\) mice/group). \(**P = 0.0099\) log- rank test.  
+
+<|ref|>image<|/ref|><|det|>[[60, 285, 829, 944]]<|/det|>
+
+<--- Page Split --->
+<|ref|>sub_title<|/ref|><|det|>[[44, 43, 117, 62]]<|/det|>
+## Figure 3  
+
+<|ref|>sub_title<|/ref|><|det|>[[44, 84, 671, 105]]<|/det|>
+## T-aFGL2 treatment induced brain-resident tumor-specific memory T cells.  
+
+<|ref|>text<|/ref|><|det|>[[40, 121, 953, 400]]<|/det|>
+a, Schematic of re- challenge with tumor cells, either subcutaneously (s.c.) or intracranially (i.c.), in the long- term survivors of the orthotopic glioma DBT model on day 70 after first tumor cell inoculation. b, Kaplan- Meier survival curves (left) and representative bioluminescence images (right) of mice shown in a on day 0 and day 7 after second tumor cell inoculation (i.c.) \((n = 6)\) . \(***P = 0.0005\) , log- rank test. c, Tumor volume (left), representative bioluminescence images (middle) of mice on day 0 and day 7 after second tumor cell inoculation, and representative tumors (right) collected on day 11 after second tumor cell inoculation (s.c.) from the flanks of Balb/c mice. d, Representative flow cytometry plots depicting increase of \(\mathrm{CD8^{+}}\) T cells in the brains (BIL) of long- term survivors treated with T- aFGL2 (T- aFGL2 survivor). LN, lymph node. e, Graph showing the ratio of \(\mathrm{CD8^{+}}\) T cells to \(\mathrm{CD4^{+}}\) T cells in the brains of naïve mice and T- aFGL2 survivors and the LNs of T- aFGL2 survivors \((n = 3 - 5\) mice/group), two- way \(t\) - test. f, Graph showing \(\mathrm{CD8^{+}}\) T cell numbers in the brains of naïve mice and T- aFGL2 survivors \((n = 3 - 5\) mice/group), two- way \(t\) - test.
+
+<--- Page Split --->
+<|ref|>image<|/ref|><|det|>[[72, 45, 920, 607]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[44, 636, 117, 655]]<|/det|>
+<center>Figure 4 </center>  
+
+<|ref|>text<|/ref|><|det|>[[44, 678, 419, 699]]<|/det|>
+\(\mathbf{T}_{\mathrm{RM}}\) - like cells can be adoptively transferred.  
+
+<|ref|>text<|/ref|><|det|>[[41, 716, 955, 953]]<|/det|>
+a, Schematic of experimental design. T- aFGL2- treated survivors were rechallenged with DBT tumor cells intracranially (i.c.) on days 70 and 100 after the first tumor cell inoculation. On day 7 after the third challenge with tumor cells, the mice were euthanized, and their brains, draining lymph nodes (dLN), and peripheral blood (PB) were collected. b, Representative bioluminescence images of naive Balb/c mice coinoculated i.c. with \(3 \times 10^{3}\) DBT glioma cells and \(3 \times 10^{3}\) T cells. Images show gliomas in mice coinoculated with \(\mathrm{CD8^{+}}\) T cells in the brain (BIL- \(\mathrm{CD8^{+}}\) T), \(\mathrm{CD4^{+}}\) T cells in the brain (BIL- \(\mathrm{CD4^{+}}\) T), \(\mathrm{CD8^{+}}\) T cells in peripheral blood (PB- \(\mathrm{CD8^{+}}\) T), and \(\mathrm{CD8^{+}}\) T cells in draining lymph nodes (dLN- \(\mathrm{CD8^{+}}\) T). \(\mathrm{CD8^{+}}\) T cells and \(\mathrm{CD4^{+}}\) T cells were sorted by flow cytometry on day 7 after the third challenge in T- aFGL2 survivors. c, Kaplan- Meier survival curves for mice in b ( \(n = 8 - 9\) mice/group). \(\mathrm{***P < 0.0001}\) , log- rank test. d, Representative bioluminescence images of naive Balb/c mice and mice bearing transplanted BIL- \(\mathrm{CD8^{+}}\) T
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[39, 45, 952, 404]]<|/det|>
+cells on days 0 and 4 after i.c. re- challenge with DBT cells on day 30 after BIL- CD8 \(^+\) T cells transplantation. e, Kaplan- Meier survival curves of mice in d \((n = 9\) mice/group). \(\yen 123,456,7\) \(P< 0.0001\) , log- rank test. f, Schematic of experimental design. \(3\times 10^{4}\mathrm{T}_{\mathrm{RM}}\) containing brain infiltrated lymphocyte cells \((\mathrm{T}_{\mathrm{RM}}\) BIL) were sorted by flow cytometry on day 7 after the third challenge in T- aFGL2 survivors, and coinoculated i.c. with \(3\times 10^{3}\) DBT cells into the naive SCID mice; 35 days after the transplantation, the SCID mice with transplanted \(\mathrm{T}_{\mathrm{RM}}\) - BILs were re- challenged with \(3\times 10^{3}\) DBT cells i.c. combined with antibodies blocking CD8, CD4 or asGM1 i.p. g, Kaplan- Meier survival curves of mice in f \((n = 4\sim 6\) mice/group). \(\yen 123,456,7\) \(P = 0.0003\) , log- rank test. h, Kaplan- Meier survival curves of of SCID mice bearing transplanted \(\mathrm{T}_{\mathrm{RM}}\) - BILs cells i.c. re- challenged with DBT cells combined with antibodies blocking CD8, CD4 or asGM1 i.p. i, Representative H&E staining of brains from h collected on day 14 after tumor cells re- challenge. j, Representative flow cytometry plots and graph showing ratio of \(\mathrm{CD69^{+}CD103^{+}}\) T cells, \(\mathrm{CD69^{+}CD103^{- }}\) T cells and \(\mathrm{CD69^{+}CD62L^{- }}\) T cells in brain and PB of T- aFgl2- treated survivors \((n = 5)\) , \(\yen 123,456,7\) \(P< 0.01\) , two- way \(t\) - test. g, Representative flow cytometry plots and graphs showing CD69 and CD103 expression on \(\mathrm{CD8^{+}}\) T cells in the brain and PB of T- aFGL2- treated survivors \((n = 3\sim 4)\) . \(\yen 123,456,7\) \(P< 0.001\) , two- way \(t\) - test.
+
+<--- Page Split --->
+<|ref|>image<|/ref|><|det|>[[88, 45, 940, 700]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[44, 725, 118, 743]]<|/det|>
+<center>Figure 5 </center>  
+
+<|ref|>text<|/ref|><|det|>[[44, 765, 444, 786]]<|/det|>
+\(\mathsf{T}_{\mathsf{RM}}\) cells showed an expanded TCR repertoire.  
+
+<|ref|>text<|/ref|><|det|>[[42, 805, 949, 952]]<|/det|>
+a, Schematic of TCRα/β deep sequencing of \(\mathrm{CD8^{+}}\) T cells from the brains and dLNs of T- aFGL2- treated survivors. Cells were sorted via flow cytometry on day 20 after the third challenge with intracranially (i.c.) injected DBT tumor cells. b, Representative tree maps (top row) of \(\mathrm{TCR\alpha - T_{RM} - CD8^{+}T}\) , \(\mathrm{TCR\beta - T_{RM} - CD8^{+}T}\) , \(\mathrm{TCR\beta - dLNs - CD8^{+}T}\) clones. Each spot represents a unique entry: V- J- CDR3, and the size of a spot denotes its relative frequency; 3D map of V and J usage of \(\mathrm{TCR\alpha - T_{RM} - CD8^{+}T}\) , \(\mathrm{TCR\beta - T_{RM} - CD8^{+}T}\) , \(\mathrm{TCR\beta - dLNs - CD8^{+}T}\) clones (bottom row). c, Schematic of experimental design. Day \(1, 3 \times 10^{4} \mathrm{CD8^{+}T_{RM}}\) cells and \(3 \times\)
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[41, 45, 945, 163]]<|/det|>
+\(10^{3}\) DBT cells were coinoculated i.c. into the naïve Balb/c mice; day 0, 5, 10, 15, 20, and day 25 the mice were treated with IgG or MHC-I blocking antibodies (100μg/mouse,i.p.). d, Representative bioluminescence images of Balb/c mice on days 0 and 14 and day 28 after i.c. transplantation with \(\mathrm{CD8^{+}T_{RM}}\) and DBT cells. e, Kaplan-Meier survival curves of mice in f ( \(n = 4\) mice/group). \(*p = 0.0101\) , logrank test. f, Representative H&E staining of brains from d collected on day 40 after transplantation.  
+
+<|ref|>image<|/ref|><|det|>[[60, 199, 930, 680]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[44, 708, 117, 727]]<|/det|>
+<center>Figure 6 </center>  
+
+<|ref|>sub_title<|/ref|><|det|>[[44, 751, 559, 772]]<|/det|>
+## TaFGL2 treatment increased the \(\mathrm{CD69^{+}CD8^{+}T_{M}}\) cell subset.  
+
+<|ref|>text<|/ref|><|det|>[[41, 790, 956, 954]]<|/det|>
+a, Schematic of the experimental design. Four days after infusion of T- Ctr or T- aFGL2, brains were collected to isolate brain- infiltrating lymphocytes (BIL), which were then stained with antibodies conjugated to metal isotopes. Mass cytometry (CyTOF) single- cell data was clustered to identify common populations across the treatment groups. b, T- distributed stochastic neighbor embedding (tSNE) analysis of \(\mathrm{CD45^{+}}\) cells from the brain, colored by relative expression of CyTOF markers. Cell populations are indicated on the right. c, Composition of the \(\mathrm{CD8^{+}T}\) cell compartment in T- Ctr and T- aFGL2- treated DBT- bearing mice showing increased frequency of \(\mathrm{CD69^{+}CD8^{+}T_{M}}\) cells in the brains of T- aFGL2- treated mice.
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[39, 45, 949, 262]]<|/det|>
+d, Frequencies of total \(\mathsf{CD8^{+}}\) T cell population and subsets of \(\mathsf{CD8^{+}}\) T cells and \(\mathsf{CD4^{+}}\) T cells \((n = 4 - 5\) mice per group). e, Fold expression of Ki67, CD69, CD223, and CD279 on the \(\mathsf{CD69^{+}CD8^{+}T_{M}}\) subset and the \(\mathsf{CD69^{- }CD8^{+}T_{EM}}\) subset. f, Schematic of experimental design. Day 1, \(3 \times 10^{4} \mathsf{CD8^{+}T_{RM}}\) cells and \(3 \times 10^{3}\) DBT cells were coinnoculated i.c. into the naive Balb/c mice; day 0, 5, 10, 15, and day 20, the mice were treated with either IgG or CD69 blocking antibodies (150ug/mouse i.p.); day 60, Balb/c mice bearing transplanted \(\mathsf{CD8^{+}T_{RM}}\) were re-challenged with \(1 \times 10^{4}\) DBT cells (i.c.). g, Representative bioluminescence images of Balb/c mice on days 0 and 7 after i.c. re-challenge with DBT cells in f. Data are representative of two independent experiments. h, Kaplan- Meier survival curves of mice in f \((n = 3 \sim 4\) mice/group), log- rank test.  
+
+<|ref|>image<|/ref|><|det|>[[55, 285, 930, 857]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[43, 899, 116, 918]]<|/det|>
+<center>Figure 7 </center>
+
+<--- Page Split --->
+<|ref|>sub_title<|/ref|><|det|>[[45, 46, 706, 68]]<|/det|>
+## TaFGL2 induced CD69+CD8+T M was associated with CXCL9/10-CXCR3 axis  
+
+<|ref|>text<|/ref|><|det|>[[40, 85, 958, 522]]<|/det|>
+a, Quantitative expression level of CCR2, CSF1R, CXCR2, CXCR3,and CX3CR1 on CD69+CD8+T M populations of T- Ctr and TaFGL2 group from CYTOF data. b, Representative flow cytometry plots and graphs showing TaFGL2 treatment increased CXCR3+CD69+CD8+T cells among total CD8+T cells in glioma bearing brains. c, Kaplan- Meier survival curves of GL261- bearing wild- type mice (WT) and CXCR3 deficient mice (CXCR3- ) treated with T- Ctr or TaFGL2 ( \(n = 5\) mice/group). \(*P\) <0.05, log- rank test. d, Quantitative data showing CD69+CD8+T M cells number per GL261- bearing brain on day 5\~7 post \(2^{\text{nd}}\) T cells therapy. \(*P< 0.05\) , two- way t- test. e, Quantitative protein analysis of CXCL9 and CXCL10 in DBT tumors from mice 4\~6 days after \(2^{\text{nd}}\) therapy of T- Ctr or TaFGL2. Data represent the mean \(\pm\) SEM; \(*p< 0.05\) , two- way t test. f, Kaplan- Meier survival curves of DBT- bearing Balb/c mice treated with T- Ctr or TaFGL2, combined with isotype control or anti- CXCL9 and anti- CXCL10 antibodies ( \(n = 5\) per group). \(*p< 0.05\) , log- rank test. g, Percentages of CD69+ out of CD44+CD8+T cells. Data represent the mean \(\pm\) SEM; \(**p< 0.01\) , \(***p< 0.001\) , one- way ANOVA with Tukey's multiple comparison test. h, Schematic illustration of cellular and molecular events underlying TaFGL2 induced tumor specific brain resident CD8+T RM. TaFGL2 cells block the FGL2 in the tumor microenvironment, resulting in CD69+CD8+T cells population enrichment and CXCL9/10 induction. These CD69+CD8+T cells were boosted through CXCL9/10- CXCR3 engagement. The CXCR3+CD69+CD8+T cells are candidate of tumor specific brain resident CD8+T RM as these cells show both TRM phenotype (CD44+CD69+CD62L- ), and proliferative activity (Ki67+) inside the brain tumors.  
+
+<|ref|>sub_title<|/ref|><|det|>[[44, 544, 310, 571]]<|/det|>
+## Supplementary Files  
+
+<|ref|>text<|/ref|><|det|>[[44, 595, 765, 615]]<|/det|>
+This is a list of supplementary files associated with this preprint. Click to download.  
+
+<|ref|>text<|/ref|><|det|>[[60, 634, 507, 653]]<|/det|>
+Supplementary dataMethodandMaterialsV6. docx
+
+<--- Page Split --->

preprint/preprint__1eb5a9097a98c358a3d5ddb23a47389b98cafbd877613684718e11c19c97fe39/images_list.json

@@ -0,0 +1,62 @@
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+  {
+    "type": "image",
+    "img_path": "images/Figure_1.jpg",
+    "caption": "FIGURE 1 | Scanning tunneling microscopy of graphene covered TaS2 (a) Schematic of the device and STM measurement setup. (b, left) Cartoon showing a single star-of-David (red) in TaS2. The black arrows denote the displacement of Ta atoms towards a central Ta atom. (b, right) The transition temperatures between the incommensurate CDW (ICCDW), nearly",
+    "footnote": [],
+    "bbox": [
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+      ]
+    ],
+    "page_idx": 5
+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_1.jpg",
+    "caption": "FIGURE 1 | Effects of interaction between graphene and TaS2. (a) and (b) are the measured dI/dV curves on bulk TaS2 and graphene/TaS2 respectively. The positions of the Hubbard band peaks and the broad features originating from the CDW distortion are labelled. The dashed lines in (b) indicate the bias voltages at which topography scans (g-i) were measured. (c) The calculated",
+    "footnote": [],
+    "bbox": [
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+      ]
+    ],
+    "page_idx": 7
+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_2.jpg",
+    "caption": "FIGURE 2 | Modelling co-tunneling and electrostatic screening (a) A schematic cross section of the tunneling junction. The mean graphene-TaS \\(_2\\) separation is \\(\\sim 0.55 \\mathrm{nm}\\) . Co-tunneling into the graphene and TaS2 layers was estimated by measuring the tunneling current decay constant \\(z_0\\) . (b) The integrated DOS of the graphene (blue curve) and the TaS2 (red curve) in the energy window \\([\\mathrm{E}_{\\mathrm{F}}, \\mathrm{E}_{\\mathrm{F}} + \\mathrm{eV}_{\\mathrm{b}}]\\) calculated using the DOS from DFT calculations. The integrated DOS for TaS2 is about an order of magnitude larger than that of graphene for large \\(\\mathrm{V}_{\\mathrm{b}}\\) . For \\(\\mathrm{V}_{\\mathrm{b}}\\) inside the Mott gap, the graphene DOS dominates. (c) A schematic which considers the screening of the CDW in TaS2 by the electrons in graphene. The bottom subplot shows the electron density (p) in the TaS2 as a function of position. The resulting electric potential (U(r)) denoted by the orange line, tunes the local charge density of the graphene layer. This is denoted by the shift of the charge neutrality point of graphene’s Dirac cones (blue) at two representative positions. The Fermi level is denoted by the dashed black line. The red squiggly lines denote the Hubbard bands of the TaS2. The hatched region shows the states within the energy window \\([\\mathrm{E}_{\\mathrm{F}}, \\mathrm{E}_{\\mathrm{F}} + \\mathrm{eV}_{\\mathrm{b}}]\\) for the two positions. There are more states available for tunneling in the graphene above the center of the DSs compared to the sides. This should result in a triangular CDW like pattern in topography measured at these tunneling conditions. This is at odds with the observed hexagonal honeycomb pattern. (d) A dI/dV spectrum measured on a bilayer graphene/TaS2 area which shows that the CNP is clearly around 0.3 eV. The Hubbard bands are greatly suppressed compared to the monolayer graphene/TaS2 owing to the increased separation of the topmost graphene layer from the TaS2, additional screening, and the lack of direct hybridization between the two materials.",
+    "footnote": [],
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+    ],
+    "page_idx": 10
+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_3.jpg",
+    "caption": "FIGURE 3| Ab initio band structure calculations. (a) Top view of \\(\\mathrm{G / TaS_2}\\) heterostructure. Blue, brown, and yellow spheres indicate C, Ta, and S atoms, respectively. Black, blue, and brown rhombuses show the \\(5 \\times 5\\) graphene/ \\(\\sqrt{13} \\times \\sqrt{13}\\) \\(\\mathrm{TaS_2}\\) supercell, graphene \\(1 \\times 1\\) unit cell, and 1T- \\(\\mathrm{TaS_2}\\) \\(1 \\times 1\\) unit cell, respectively. The graphene and \\(\\mathrm{TaS_2}\\) layer are twisted by \\(\\sim 13.9^{\\circ}\\) in this CDW",
+    "footnote": [],
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preprint/preprint__1eb5a9097a98c358a3d5ddb23a47389b98cafbd877613684718e11c19c97fe39/preprint__1eb5a9097a98c358a3d5ddb23a47389b98cafbd877613684718e11c19c97fe39.mmd

@@ -0,0 +1,333 @@
+
+# Proximity induced charge density wave in graphene/1T-TaS2  
+
+Eva Andrei eandrei@physics.rutgers.edu  
+
+Rutgers, The State University of New Jersey https://orcid.org/0000- 0002- 2516- 2749  
+
+Nikhil Tilak  rutgers  
+
+Michael Altvater  rutgers  
+
+Sheng- Hsiung Hung  National Tsing Hua University  
+
+Horng- Tay Jeng  Department of Physics, National Tsing Hua University, Hsinchu 30013 https://orcid.org/0000- 0002- 2881- 3826  
+
+Guohong Li  Rutgers University  
+
+Sang Wook cheong  Rutgers, The State University of New Jersey  
+
+Chung- Hou Chung  Physics Division, National Center for Theoretical Sciences  
+
+Taha Kaleem  Rutgers  
+
+Choong- Jae Won  Pohang University  
+
+Article  
+
+Keywords:  
+
+Posted Date: January 10th, 2024  
+
+DOI: https://doi.org/10.21203/rs.3.rs- 3779323/v1
+
+<--- Page Split --->
+
+License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License  
+
+Additional Declarations: There is NO Competing Interest.  
+
+Version of Record: A version of this preprint was published at Nature Communications on September 14th, 2024. See the published version at https://doi.org/10.1038/s41467-024-51608-y.
+
+<--- Page Split --->
+
+# Proximity induced charge density wave in graphene/1T-TaS2.  
+
+Nikhil Tilak \(^{1 + }\) , Michael Altvater \(^{1 + }\) , Sheng- Hsiung Hung \(^{2 + }\) , Guohong Li \(^{1}\) , Choong- Jae Won \(^{3}\) , Taha Kaleem \(^{1}\) , Sang- Wook Cheong \(^{1}\) , Chung- Hou Chung \(^{4,5}\) , Horng- Tay Jeng \(^{2,5,6}\) , and Eva Y. Andrei \(^{1}\)  
+
+1 Department of Physics and Astronomy, Rutgers, the State University of New Jersey, 136 Frelinghuysen Rd, Piscataway, New Jersey 08854, USA  
+
+2 Department of Physics, National Tsing Hua University, 101 Kuang Fu Road, Hsinchu 30013, Taiwan  
+
+3 Laboratory for Pohang Emergent Materials and Max Plank POSTECH Center for Complex Phase Materials, Department of Physics, Pohang University of Science and Technology, Pohang 37673, Korea  
+
+4 Department of Electrophysics, National Yang Ming Chiao Tung University, 1001 University Rd., Hsinchu, Taiwan 300  
+
+5 Physics Division, National Center for Theoretical Sciences, Taipei 10617, Taiwan  
+
+6 Institute of Physics, Academia Sinica, Taipei 11529, Taiwan  
+
+\*Corresponding authors E- mail: chung0523@nycu.edu.tw, jeng@phys.nthu.edu.tw, eandrei@physics.rutgers.edu  
+
++Equal contributors  
+
+## Abstract  
+
+The proximity- effect, a phenomenon whereby materials in close contact appropriate each other’s electronic- properties, is widely used in nano- scale devices to induce electron- correlations at heterostructure interfaces. Commonly observed proximity- induced correlation- effects include superconductivity, magnetism, and spin- orbit interactions. Thus far, however, proximity induced charge density waves (CDW) have not been rigorously explored, primarily because of screening in 3D metals and defect scattering at interfaces. Here, we report the observation of a CDW
+
+<--- Page Split --->
+
+proximity effect between graphene and the commensurate CDW in 1T- TaS<sub>2</sub> (henceforth called TaS<sub>2</sub> for brevity). Using scanning tunneling microscopy (STM) and spectroscopy (STS) together with theoretical modeling to probe the interface between graphene and a TaS<sub>2</sub> crystal, we demonstrate the existence of a proximity induced CDW within graphene. Furthermore, we observe that graphene modifies the band structure at the surface of TaS<sub>2</sub>, by providing mid- gap carriers and reducing the strength of electron correlations there. We show that the mechanism underlying the proximity induced CDW is well- described by short- range exchange interactions that are distinctly different from previously observed proximity effects.  
+
+## Introduction  
+
+The isolation and manipulation of atomically thin materials provides a ready- made two dimensional electron system<sup>1</sup> whose properties can be tuned by external knobs, such as stress or substrate morphology<sup>2- 6</sup>, leading to the emergence of correlated electron phases. Distinct from these external knobs, a very powerful approach to manipulate electron correlations is by contact proximity effects. It is well known that proximitizing materials that host correlated electron phases with a normal metal, induces correlations in the metal<sup>7</sup>. This is a direct consequence of the quantum mechanical properties of electrons in solids; specifically, the nonlocal nature of electrons. As quantum particles do not have a well- defined position, electronic states cannot abruptly change from one type of ordering to another at the interface between two materials. Consequently, correlated states persist into the normal metal where scattering events begin to destroy the coherence (and vice versa). In the case of 2D materials where scattering is reduced due to their atomically sharp interfaces, proximity- effects are particularly robust allowing correlated states to persist over long distances. The discovery of graphene and other
+
+<--- Page Split --->
+
+2D materials, together with the technology enabling 2D heterostructures has led to the observation of strong proximity effects at the atomic limit including proximity induced superconductivity, magnetism<sup>8</sup> and spin-orbit effects<sup>9- 16</sup>.  
+
+The CDW state is yet another correlated electron state which is driven by electron phonon coupling<sup>17</sup> (EPC) or alternatively via electron correlations<sup>18</sup>. It has been observed in systems such as unconventional superconductors<sup>19</sup> and many 2D transition- metal dichalcogenide (TMDs)<sup>20</sup>. However, identifying a proximity effect between a CDW material and a normal metal has been elusive. This is primarily due to screening in 3D metals and interface defect scattering. The use of 2D materials that were stacked in an inert atmosphere to avoid interface damage and contamination, together with sensitive local probes providing direct access to the charge distribution in graphene, has made it possible to overcome these hurdles<sup>21,22</sup>.  
+
+In this work, we present evidence of the emergence of a CDW in graphene produced by its proximity to the commensurate CDW (CCDW) in a \(\mathrm{TaS_2}\) substrate. While there are several recent experiments which have studied vertical heterostructures of graphene and a TMD with STM<sup>22- 24</sup>, it has proven difficult to experimentally isolate the electronic states in the graphene layer from those belonging to the TMD substrates making it difficult to definitively prove a proximity induced CDW in graphene. In this work, by using the coexisting CDW and Mott gap in \(\mathrm{TaS_2}\) , we can isolate the electronic states in the graphene layer and establish the presence of the proximity induced CDW. Using scanning tunneling microscopy (STM) and spectroscopy (STS) we show that the charge density modulation of the CCDW in \(\mathrm{TaS_2}\) persists within the contacted graphene layer. Additionally, we find that the Mott gap in \(\mathrm{TaS_2}\) is reduced due to its close contact with the Dirac carriers in graphene. By comparing with first- principle
+
+<--- Page Split --->
+
+76 calculations based on density- functional theory (DFT), we find that in addition to a global 77 charge- transfer between the two surfaces caused by the work- function difference, the proximity 78 induced CDW in graphene which is characterized by local charge modulation and lattice 79 distortion is described by a novel mechanism of short- range exchange interactions mediated by 80 second- order local electron hopping, which is distinctly different from superconducting, 81 magnetic and spin- orbit proximity effects.  
+
+## 82 Results and discussion  
+
+84 Scanning tunneling microscopy of graphene covered TaS2.  
+
+![](images/Figure_1.jpg)
+
+<center>FIGURE 1 | Scanning tunneling microscopy of graphene covered TaS2 (a) Schematic of the device and STM measurement setup. (b, left) Cartoon showing a single star-of-David (red) in TaS2. The black arrows denote the displacement of Ta atoms towards a central Ta atom. (b, right) The transition temperatures between the incommensurate CDW (ICCDW), nearly </center>
+
+<--- Page Split --->
+
+commensurate CDW (NCCDW) and commensurate CDW (CCDW) phases of TaS2. (c) Cartoon showing a monolayer graphene layer on top of TaS2. Here, the angle between the graphene layer and TaS2 is \(13.9^{\circ}\) to form a commensurate unit cell (cyan) for DFT calculations. (d) An STM topography image of graphene covered TaS2 measured at \(77\mathrm{K}\) using a tunneling set point of \(\mathrm{I} = 150\mathrm{pA}\) , \(\mathrm{V}_{\mathrm{b}} = 250\mathrm{mV}\) . Each bright spot corresponds to a star of David with a measured CDW wavelength of \(1.2\mathrm{nm}\) . This can also be seen in the 2D-FFT of the topography data (inset). (e) An STM topography image and its 2D FFT (inset) of a bulk TaS2 sample measured at \(77\mathrm{K}\) using a tunneling set point of \(\mathrm{I} = 150\mathrm{pA}\) , \(\mathrm{V}_{\mathrm{b}} = 500\mathrm{mV}\) .  
+
+TaS2 is comprised of hexagonal layers of tantalum atoms coordinated octahedrally by sulfur atoms. Below 550 K a high temperature metallic phase transits into an incommensurate CDW phase, followed by a nearly commensurate CDW (NCCDW) below \(350\mathrm{K}\) , and a CCDW below \(180\mathrm{K}^{21}\) (Fig. 1b, right). The CDW unit cell consists of a 13- atom cluster where 12 atoms displace from their high- temperature equilibrium positions toward the central, \(13^{\mathrm{th}}\) Ta atom forming a \(\sqrt{13} \times \sqrt{13}\) reconstructed supercell (Fig. 1b, left) also known as a David star (DS) structure. The CDW in TaS2 is driven by strong electron- phonon coupling which critically suppresses an acoustic phonon mode along the \(\Gamma \rightarrow \mathrm{M}\) direction \(^{25,26}\) (Supplementary Fig. 4) leading to the static displacement of the lattice with the wavevector \(\mathrm{Q}_{\mathrm{CDW}}\) , corresponding to the \(\sqrt{13} \times \sqrt{13}\) CDW. This soft phonon mode at the Kohn anomaly wavevector, \(\mathrm{Q}_{\mathrm{CDW}}\) , consists primarily of longitudinal vibrations of Ta atoms with a minor contribution from transverse vibrations of S atoms relative to the phonon propagating direction \(\mathrm{Q}_{\mathrm{CDW}}\) . The DS atomic arrangement involves the local lattice contraction around the center of the star, in which the bond lengths between Ta ions are shorter than those between Ta ions outside the star.  
+
+We studied vertical heterostructures consisting of thin TaS2 flakes (7- 50 nm) covered by monolayer graphene (Fig. 1a). The samples were assembled inside a dry argon- filled glovebox (<0.1 ppm \(\mathrm{O}_{2}\) , \(\mathrm{H}_{2}\mathrm{O}\) ) \(^{21,27}\) to avoid oxidation of the air sensitive surface of TaS2 and subsequently cooled to \(77\mathrm{K}\) inside a home- built STM \(^{28,29}\) . At this temperature TaS2 is in the CCDW regime. The STM topography (Fig. 1d) measured on a graphene- covered sample using a tunneling set- point of \(\mathrm{V}_{\mathrm{b}} = 250\mathrm{mV}\) , \(\mathrm{I} = 150\mathrm{pA}\) shows a triangular array of tall spots. The
+
+<--- Page Split --->
+
+116 lattice spacing for this triangular array is about 1.2 nm as measured directly from the 117 topography image and confirmed with its 2D- FFT (Fig. 1d, inset). This spacing is equal to the 118 expected CDW wavelength (LCDW) in TaS2 indicating that each tall spot corresponds to one DS. 119 The topography of the graphene covered sample thus reflects the distinct arrangement of the 120 DS clusters which are the hallmark of the CDW in TaS2. The topography is identical to that 121 measured on a bulk TaS2 sample without a graphene cover under similar tunneling conditions 122 (Fig. 1e). This is also true for typical negative bias setpoints of \(\mathrm{V_b} < - 300 \mathrm{mV}\) . Therefore, for 123 sufficiently large bias voltages of either sign, there is no noticeable difference in the STM 124 topography of TaS2 with or without a graphene cover. This is not surprising given the strong 125 CDW in TaS2 and the relatively small density of states in graphene compared to TaS2.  
+
+126 Interfacial charge transfer and reduction of on- site Coulomb repulsion  
+
+![](images/Figure_1.jpg)
+
+<center>FIGURE 1 | Effects of interaction between graphene and TaS2. (a) and (b) are the measured dI/dV curves on bulk TaS2 and graphene/TaS2 respectively. The positions of the Hubbard band peaks and the broad features originating from the CDW distortion are labelled. The dashed lines in (b) indicate the bias voltages at which topography scans (g-i) were measured. (c) The calculated </center>
+
+<--- Page Split --->
+
+DOS for bulk TaS2. (d) The calculated DOS for the heterostructure decomposed into the graphene DOS (blue curve) and TaS2(red curve) DOS respectively. The graphene DOS is multiplied by a factor (39) before adding the TaS2 DOS (black curve) for comparison with experiment. This accounts for the smaller tip-graphene separation. See SI for how the factor was estimated. I A map of dI/dV (Vb) vs position across 8 DSs in the graphene/TaS2 sample. The intensity of both the Hubbard bands is highest at the center of the DSs. (f) Three representative spectra from the colormI(e) demonstrate this intensity variation. (g-i) STM topography scans measured at bias voltages of -300 mV, -150 mV and +300 mV respectively. These energies are indicated by dashed lines in (b). The tunneling resistance (R) was kept constant at 1 GΩ for all scans. The small lateral mismatch between scans is a result of thermal drift of the sample. All scale bars are 2 nm.  
+
+The similarity between the topography scans of TaS2 with or without a graphene cover might suggest that there is little or no interaction between the two materials. Our spectroscopy data shows that this is not the case.  
+
+The band structure of bulk TaS2 consists of an isolated, half- filled, spin- degenerate, flat band at the Fermi level (EF) (Supplementary Figure 5). In the CCDW phase, the flat band splits into an occupied spin up lower Hubbard band (LHB) and empty spin down upper Hubbard band (UHB) with a Mott gap of \(\sim 0.5 \mathrm{eV}\) in between (ab initio calculation Fig. 2c). The other bands corresponding to the CDW distortion appear at energies below the LHB (CDW- VB) and above the UHB (CDW- CB) respectively. These spectroscopic features were observed in our experimentally measured dI/dV spectrum (Fig. 2a) on a bulk TaS2 sample and agree with previous reports<sup>20,31- 33</sup>.  
+
+The dI/dV spectrum measured on the graphene covered sample (Fig. 2b) similarly shows peaks at \(\sim - 270 \mathrm{meV}\) and \(+70 \mathrm{meV}\) which are identified as the LHB and UHB. This identification is supported by the intensity variation of these features as a function of distance from SD centers (Fig. 2e,f). However, the Mott gap between the two peaks appears to be absent. Instead, we observe a small but finite density of states. These states presumably belong to the graphene layer which is a gapless semi- metal whose low- energy dispersion consists of Dirac cones which touch at the charge neutrality point (CNP). Our ab initio calculations for monolayer graphene on monolayer TaS2 (Fig. 2d), decomposed into the graphene DOS and TaS2 DOS, reveal that the CNP of graphene is about \(0.3 \mathrm{eV}\) above \(\mathrm{E}_{\mathrm{F}}\) of the system, indicating that it is
+
+<--- Page Split --->
+
+hole doped. This kind of interfacial charge transfer is likely due to the differences in work function between the graphene \((\Phi_{\mathrm{G}} \sim 4.6 \mathrm{eV}^{34})\) and \(\mathrm{TaS_2(\Phi_{TaS_2} \sim 5.2 \mathrm{eV}^{35})}\) .  
+
+Another notable difference is that the separation between the Hubbard band peaks  
+
+( \(\sim 338 \mathrm{mV}\) ) is smaller by about \(31\%\) compared to the bare \(\mathrm{TaS_2}\) sample ( \(\sim 491 \mathrm{mV}\) ). We  
+
+attribute this to the reduction of the on- site Coulomb repulsion (U) in the graphene covered  
+
+\(\mathrm{TaS_2}\) . Graphene is known to be a semi- metal with high- mobility itinerant carriers. These highly  
+
+mobile electrons from graphene somewhat suppress the localized picture of the narrow  
+
+Hubbard d- bands in \(\mathrm{TaS_2}\) and thus reduce the on- site U value of Ta. Comparing the ab initio  
+
+calculated band structures (supplementary Fig. 6) for different U values with the STS results,  
+
+suggests that the graphene layer screens the Coulomb interaction in \(\mathrm{TaS_2}\) and the Hubbard U of  
+
+Ta is lowered from \(2.27 \mathrm{eV}\) to \(1.70 \mathrm{eV}\) . Therefore, we use a phenomenological value \(\mathrm{U} = 1.70\)  
+
+eV for our ab initio calculations (Fig. 2d) which reasonably reproduces the trend observed in  
+
+the STS measurements.
+
+<--- Page Split --->
+![](images/Figure_2.jpg)
+
+<center>FIGURE 2 | Modelling co-tunneling and electrostatic screening (a) A schematic cross section of the tunneling junction. The mean graphene-TaS \(_2\) separation is \(\sim 0.55 \mathrm{nm}\) . Co-tunneling into the graphene and TaS2 layers was estimated by measuring the tunneling current decay constant \(z_0\) . (b) The integrated DOS of the graphene (blue curve) and the TaS2 (red curve) in the energy window \([\mathrm{E}_{\mathrm{F}}, \mathrm{E}_{\mathrm{F}} + \mathrm{eV}_{\mathrm{b}}]\) calculated using the DOS from DFT calculations. The integrated DOS for TaS2 is about an order of magnitude larger than that of graphene for large \(\mathrm{V}_{\mathrm{b}}\) . For \(\mathrm{V}_{\mathrm{b}}\) inside the Mott gap, the graphene DOS dominates. (c) A schematic which considers the screening of the CDW in TaS2 by the electrons in graphene. The bottom subplot shows the electron density (p) in the TaS2 as a function of position. The resulting electric potential (U(r)) denoted by the orange line, tunes the local charge density of the graphene layer. This is denoted by the shift of the charge neutrality point of graphene’s Dirac cones (blue) at two representative positions. The Fermi level is denoted by the dashed black line. The red squiggly lines denote the Hubbard bands of the TaS2. The hatched region shows the states within the energy window \([\mathrm{E}_{\mathrm{F}}, \mathrm{E}_{\mathrm{F}} + \mathrm{eV}_{\mathrm{b}}]\) for the two positions. There are more states available for tunneling in the graphene above the center of the DSs compared to the sides. This should result in a triangular CDW like pattern in topography measured at these tunneling conditions. This is at odds with the observed hexagonal honeycomb pattern. (d) A dI/dV spectrum measured on a bilayer graphene/TaS2 area which shows that the CNP is clearly around 0.3 eV. The Hubbard bands are greatly suppressed compared to the monolayer graphene/TaS2 owing to the increased separation of the topmost graphene layer from the TaS2, additional screening, and the lack of direct hybridization between the two materials. </center>  
+
+In addition to the local height variation in the sample, STM topography also depends on the  
+
+total number of states in the energy window \([\mathrm{E}_{\mathrm{F}}, \mathrm{E}_{\mathrm{F}} + \mathrm{eV}_{\mathrm{b}}]\) . For CDW materials, the local
+
+<--- Page Split --->
+
+density of states variation dominates the topography signal. Since the integrated density of states of \(\mathrm{TaS_2}\) is orders of magnitude larger than that of the graphene (Fig. 3b) for large \(\mathrm{V_b}\) , we estimate that there is significant tunneling current into both the graphene and \(\mathrm{TaS_2}\) , despite the larger z separation between the tip and \(\mathrm{TaS_2}\) layer (Fig. 3a). Consequently, the topography scans measured on the graphene covered sample resemble that of \(\mathrm{TaS_2}\) as shown for \(\mathrm{V_b} = - 300 \mathrm{mV}\) (Fig. 2g) and \(\mathrm{V_b} = +300 \mathrm{mV}\) (Fig. 2i). Note that these two energy windows include the LHB and UHB of the \(\mathrm{TaS_2}\) respectively.  
+
+Surprisingly, the topography pattern measured at \(\mathrm{V_b} = - 150 \mathrm{mV}\) (Fig. 2h), which is in the Mott gap of \(\mathrm{TaS_2}\) , is hexagonal instead of triangular, but with the same periodicity as the CDW. A hexagonal lattice is the anti- lattice of a triangular lattice. This energy window includes only the states belonging to graphene since the \(\mathrm{TaS_2}\) layer is gapped (Fig. 3b) and thus reflects the electronic density redistribution in graphene due to its proximity to \(\mathrm{TaS_2}\) . This observation suggests that the electrons in the graphene layer also form a CDW- like pattern which is out- of- phase with the underlying \(\mathrm{TaS_2}\) CDW. We call this the proximity induced CDW in graphene. The hexagonal pattern was observed for multiple energy windows inside the \(\mathrm{TaS_2}\) Mott gap but never outside it (see Supplementary Fig. 9).  
+
+Note that this out- of- phase CDW cannot be explained by a simple screening picture (Fig. 3c). To wit: if one models the CDW in \(\mathrm{TaS_2}\) as a periodic modulation of electron density \((\rho (r))\) which peaks at the center of the DSs (Fig. 3c, bottom), then the electrostatic potential \((U(r))\) near the surface of \(\mathrm{TaS_2}\) will also peak near the center of the DSs (orange line in Fig. 3c). A graphene layer placed on top of \(\mathrm{TaS_2}\) will experience this spatially varying potential leading to spatial doping variation, i.e., shifting of the local CNP. This CNP variation is in addition to the average shift of the CNP \((\sim 0.3 \mathrm{eV})\) due to work function difference between
+
+<--- Page Split --->
+
+the two materials as discussed previously. If we consider an energy window in the Mott gap of the \(\mathrm{TaS_2}\) , then there are more graphene states (hatched regions in Fig. 3c) near the center of the SD compared to the region between neighboring DSs. This should result in a triangular CDW pattern in STM topography, which is in- phase with the underlying \(\mathrm{TaS_2}\) CDW. This is contrary to our observation of a hexagonal pattern, thus ruling out this simple picture.  
+
+We also studied an area of the sample covered with bilayer graphene. In this area the topography showed almost no CDW pattern. \(\mathrm{dI / dV}\) spectra (Fig. 3d) showed a bilayer graphene- like V shape with a pronounced dip near 0.3 V, indicating the CNP. Very weak features corresponding to the position of the Hubbard bands were also seen. These observations indicate that the proximity induced CDW relies on very short- range coupling between the graphene and \(\mathrm{TaS_2}\) .  
+
+Modeling the proximity induced CDW.  
+
+![](images/Figure_3.jpg)
+
+<center>FIGURE 3| Ab initio band structure calculations. (a) Top view of \(\mathrm{G / TaS_2}\) heterostructure. Blue, brown, and yellow spheres indicate C, Ta, and S atoms, respectively. Black, blue, and brown rhombuses show the \(5 \times 5\) graphene/ \(\sqrt{13} \times \sqrt{13}\) \(\mathrm{TaS_2}\) supercell, graphene \(1 \times 1\) unit cell, and 1T- \(\mathrm{TaS_2}\) \(1 \times 1\) unit cell, respectively. The graphene and \(\mathrm{TaS_2}\) layer are twisted by \(\sim 13.9^{\circ}\) in this CDW </center>
+
+<--- Page Split --->
+
+phase. (b) Side view of graphene/TaS₂ heterostructure overlaid with the charge density map (bubble-shaped color contour indicating the electron density) corresponding to the states at the two crossing points of the Dirac cone and Lower Hubbard band. The slight overlap between graphene and TaS₂ electron clouds gives rise to the interlayer coupling and proximity effect. Here, blue, red, and green spheres represent C, Ta, and S atoms, respectively. (c) GGA+U band structure of \(\sqrt{13} \times \sqrt{13}\) CDW reconstructed TaS₂ with U=2.27eV (d) GGA+U band structure of graphene/TaS₂ using a phenomenological value U = 1.70 eV. Owing to the charge transfer from graphene to TaS₂, the Fermi level (zero energy) moves from the lower Hubbard band to the upper Hubbard band, and the graphene-associated Dirac point at the K-point of the superstructure Brillouin zone is shifted to \(\sim\) 0.3 eV above the Fermi level (E_F) indicating hole doping. (e-g) The integrated DOS in the energy range from E_F to -300 mV, -200 mV and +300 mV respectively. DSs are overlaid for clarity. At -300 mV and +300 mV the DSs form a triangular lattice while at -200 mV they form a hexagonal lattice.  
+
+In addition to the global charge transfer between the graphene and TaS₂, we obtain local charge modulations and lattice distortions (see supplementary Fig. 8) mediated by the interfacial orbital hybridizations (Fig. 4b) through the crossing states of the graphene Dirac cone with the TaS₂ Hubbard bands (Fig 4d). Since different carbon atoms in the heterostructure unit- cell have a different registration with respect to the underlying CDW SD, it is natural to expect that the hybridization between graphene p₂- bands and TaS₂- d bands varies as a function of position within the supercell (Fig 4a). This can be seen in the calculated charge density map (Fig. 4b). Moreover, the local density of states is significantly affected by the energy- dependent interlayer hybridizations as can be seen in the calculated band structure (Fig. 4d), particularly within or outside the Mott gap of TaS₂. Thus, we calculate the total density of states in the energy windows [E_F, E_F + eV_b] for V_b=- 0.3 V, - 0.2 V and +0.3 V (Fig. 4e- g). They show triangular, hexagonal and triangular CDW patterns below, within, and above the Mott gap region, respectively, demonstrating the inverted behavior of the in- gap states. These results are in good agreement with our topography scans (Fig. 2g- i) and further support the out- of- phase proximity induced CDW in the graphene layer.
+
+<--- Page Split --->
+
+We believe that the proximity induced CDW is a novel effect driven by periodically varying  
+
+band hybridization. This can be modeled using a simplified Hamiltonian of the graphene/TaS2  
+
+system given by:  
+
+\[H = H_{d} + H_{c} + H_{t}, \quad (2)\]  
+
+\[H_{d} = \sum_{\langle i,j\rangle ,\sigma} - t_{i j}^{d}d_{i,\sigma}^{\dagger}d_{j,\sigma} + h.c. - \sum_{\langle i^{\prime},j^{\prime}\rangle ,\sigma}(\Delta_{i^{\prime},j^{\prime}}^{CDW}(i^{\prime},j^{\prime}))^{*}d_{i^{\prime},\sigma}^{\dagger}d_{j^{\prime},\sigma} + h.c. + \sum_{\langle i^{\prime},j^{\prime}\rangle}|\Delta_{i^{\prime},j^{\prime}}^{CDW}(i^{\prime},j^{\prime})|^{2}, \quad (2)\]  
+
+\[H_{c} = \sum_{\langle i,j\rangle ,\sigma} - t_{i j}^{c}c_{i,\sigma}^{\dagger}c_{j,\sigma} + h.c. = \sum_{k,\sigma}(\epsilon_{k} - \mu)c_{k,\sigma}^{\dagger}c_{k,\sigma},\]  
+
+\[H_{t} = -t\sum_{i,\sigma}c_{i,\sigma}^{\dagger}d_{i,\sigma} + h.c.,\]  
+
+where \(\mathrm{H}_{\mathrm{d}}\) \(\mathrm{(H_{c})}\) stands for the simplified Hamiltonian of the 1T- TaS2 (graphene) layer,  
+
+respectively, and \(\mathrm{H}_{\mathrm{t}}\) describes a weak charge transfer (hopping) term between these two layers  
+
+(we neglect a small mismatch in spatial locations between the nearest- neighbor sites of the  
+
+corresponding layer). The insulating \(\mathrm{TaS}_{2}\) layer shows CDW order with the order parameter  
+
+\(\Delta_{i,j}^{CDW}(i^{\prime},j^{\prime})\equiv \sum_{\sigma}\langle d_{i^{\prime},\sigma}^{\dagger}d_{j^{\prime},\sigma}\rangle\) where \(i^{\prime},j^{\prime}\) are sites within the CDW unit cell. The graphene  
+
+layer has the linear Dirac dispersion: \((\epsilon_{k} - \mu)\approx \hbar \nu_{F}|k - k_{F}|\) where \(\mathrm{v_{F}}\) \(\mathrm{k_{F}}\) are the Fermi  
+
+velocity and Fermi wavevector respectively. Here, i, j refer to the nearest- neighbor sites of the  
+
+corresponding lattices, and \(\mathrm{t_{ij}^{d(c)}}\) refers to the nearest- neighbor tight- binding hoping terms on  
+
+the 1T- TaS2 (graphene) layer, respectively.  
+
+Via the second order perturbation in the \(\mathrm{H}_{\mathrm{t}}\) term of the CDW unit cell, the following  
+
+exchange term \(\mathrm{H}_{\mathrm{t}}^{(2)}\) is generated:  
+
+\[H_{t}^{(2)} = t^{2}\sum_{\langle i^{\prime},j^{\prime}\rangle ,\sigma ,\sigma^{\prime}}c_{i^{\prime},\sigma}^{\dagger}d_{i^{\prime},\sigma}d_{j^{\prime},\sigma^{\prime}}^{\dagger}c_{j^{\prime},\sigma^{\prime}} + h.c.\]
+
+<--- Page Split --->
+
+A simple mean- field decoupling of \(\mathrm{H}_{\mathrm{t}}^{(2)}\) in terms of \(\Delta_{\mathrm{d}}^{\mathrm{CDW}}(\mathrm{i}^{\prime},\mathrm{j}^{\prime})\) (considering only \(\sigma = \sigma^{\prime}\) and assuming spin- isotropic CDW order \(\langle d_{j^{\prime},\uparrow}^{\dagger}d_{j^{\prime},\uparrow}\rangle = \langle d_{j^{\prime},\downarrow}^{\dagger}d_{j^{\prime},\downarrow}\rangle\) gives \(H_{\mathrm{t}}^{(2)}\to H_{\mathrm{t}}^{M F}\) with:  
+
+\[H_{\mathrm{t}}^{M F}\approx -t^{2} / 2\sum_{\langle i^{\prime},j^{\prime}\rangle ,\sigma}(\Delta_{d}^{C D W}(i^{\prime},j^{\prime}))^{*}c_{i^{\prime},\sigma}^{\dagger}c_{j^{\prime},\sigma} - t^{2} / 2\sum_{\langle i^{\prime},j^{\prime}\rangle ,\sigma}(\Delta_{d}^{C D W}(i^{\prime},j^{\prime}))^{*}\langle c_{i^{\prime},\sigma}^{\dagger}c_{j^{\prime},\sigma}\rangle +h.c.,\]  
+
+where the mean- field decoupling term \(\langle (c_{i^{\prime},\sigma}^{\dagger}c_{j^{\prime},\sigma})d_{i^{\prime},\sigma}^{\dagger}d_{j^{\prime},\sigma}\rangle\) in \(\mathrm{H}_{\mathrm{t}}^{(2)}\) is neglected since we expect \(\left|\langle c_{i^{\prime},\sigma}^{\dagger}c_{j^{\prime},\sigma}\rangle \right|\ll \left|\langle d_{i^{\prime},\sigma}^{\dagger}d_{j^{\prime},\sigma}\rangle \right|\) . The CDW proximity effect is manifested in \(\mathrm{H}_{\mathrm{t}}^{M F}\) as a weak CDW order \(\Sigma_{\sigma}\langle c_{i^{\prime},\sigma}^{\dagger}c_{j^{\prime},\sigma}\rangle\) is induced on the graphene layer by the second order charge transfer between the two layers with the following identification:  
+
+\[\Delta_{c}^{C D W}(i^{\prime},j^{\prime})\equiv -1 / 2\sum_{\sigma}\langle c_{i^{\prime},\sigma}^{\dagger}c_{j^{\prime},\sigma}\rangle^{*} = -t^{2} / 2\left(\Delta_{d}^{C D W}(i^{\prime},j^{\prime})\right)^{*},\]  
+
+or equivalently, \(\Sigma_{\sigma}\langle c_{i^{\prime},\sigma}^{\dagger}c_{j^{\prime},\sigma}\rangle = \mathrm{t}^{2}\Delta_{\mathrm{d}}^{\mathrm{CDW}}(\mathrm{i}^{\prime},\mathrm{j}^{\prime})\) . Via the above identification, the Hamiltonian \(\mathrm{H}_{\mathrm{t}}^{M F}\) can be expressed as:  
+
+\[H_{\mathrm{t}}^{M F} = \sum_{\langle i^{\prime},j^{\prime}\rangle ,\sigma}\Delta_{c}^{C D W}(i^{\prime},j^{\prime})c_{i^{\prime},\sigma}^{\dagger}c_{j^{\prime},\sigma} + h.c. + 2|\Delta_{c}^{C D W}(i^{\prime},j^{\prime})|^{2},\]  
+
+which leads to \(\Delta_{c}^{C D W}(i^{\prime},j^{\prime}) = - 1 / 2\sum_{\sigma}\langle c_{i^{\prime},\sigma}^{\dagger}c_{j^{\prime},\sigma}\rangle^{*}\) identified above via minimizing the free energy associated with \(\mathrm{H}_{\mathrm{t}}^{M F}\) with respect to \(\Delta_{\mathrm{c}}^{C D W}(\mathrm{i}^{\prime},\mathrm{j}^{\prime})\) . Note that from above derivations, we indeed find that \(\left|\langle c_{i^{\prime},\sigma}^{\dagger}c_{j^{\prime},\sigma}\rangle \right|\sim t^{2}\left|\langle d_{i^{\prime},\sigma}^{\dagger}d_{j^{\prime},\sigma}\rangle \right|\ll \left|\langle d_{i^{\prime},\sigma}^{\dagger}d_{j^{\prime},\sigma}^{\dagger}\rangle \right|\) , as expected. Note also that the CDW order parameters induced on graphene layer shows the opposite sign with respect to that on 1T- TaS \(_2\) layer, consistent with the hole- like (particle- like) CDW intensity on graphene (1T- TaS \(_2\) ) layer obtained from DFT calculations, respectively. Meanwhile, the opposite sign of the proximity induced CDW on the graphene layer with
+
+<--- Page Split --->
+
+respect to the CDW on the TaS2 layer as predicted within our mean- field theory is in perfect agreement with the "out- of- phase" relation between CDW patterns observed inside and outside of Mott gap by our STM measurement (see Fig. 2 g- i and corresponding text above). This agreement provides a strong case for CDW proximity in graphene. We emphasize here that the above mechanism based on charge transfer is distinct from all the previously realized proximity effects, including superconducting, magnetic, and spin- orbit proximity effects.  
+
+## Conclusions  
+
+In summary, we demonstrate, by STM/STS measurements and DFT calculations, the existence of a proximity induced CDW in graphene. We propose a model based on the short- range exchange interaction between carriers in graphene and the CDW hosted by the TaS2 crystal which qualitatively captures the main features of the CDW proximity effect. Concomitant with the proximity induced CDW in graphene, which is out- of- phase with that in TaS2, we observe a substantial reduction in the Mott gap of the TaS2 crystal, indicating the presence of proximity- induced mid- gap carriers which screen the Mott- Hubbard interaction. These observations open intriguing possibilities for engineering correlations and manipulating charge carriers in heterostructures. They suggest the prospect of non- linear electronic devices based on sliding and pinning of the CDW induced within the graphene layer and low power switches based on controlling correlation gaps through contact. Additional measurements including spin- resolved STM/STS, Raman spectroscopy, and electronic transport will further help elucidate the effects proposed in this work.
+
+<--- Page Split --->
+
+## Methods  
+
+Sample fabrication and STM measurements  
+
+Samples were fabricated by mechanical exfoliation of graphene and separately \(\mathrm{TaS_2}\) flakes  
+
+inside an argon- filled glovebox. \(\mathrm{TaS_2}\) flakes were exfoliated from a bulk 1T- TaS2 crystal (2D  
+
+semiconductor or grown by iodine chemical vapor transport) and transferred onto a passivated  
+
+SiO2- capped degenerately doped Si wafer. The graphene and 1T- TaS2 flakes were aligned  
+
+vertically and brought into close contact with micromanipulators under an optical microscope  
+
+and then heated to promote adhesion. Standard electron beam lithography and electrode  
+
+deposition (4- 5nm Ti/40- 50nm Au) were used to make electrical contact to the sample. After  
+
+removing the PMMA mask the resulting heterostructure was annealed (180- 220°C) in  
+
+hydrogen/argon (10% : 90%) to remove polymer residues followed by AFM tip sweeping.  
+
+STM and STS were performed using a homebuilt STM at 77 K in high- vacuum \(< 10^{- 5}\) Torr. To  
+
+locate the micron size samples we employed a technique using the STM tip (mechanically cut  
+
+Pt/Ir ) as a capacitive antenna29. Two such samples were measured by STM using several  
+
+mechanically cut Pt/Ir wire tips. Additionally, we also studied a \(\mathrm{TaS_2}\) flake transferred to a pre  
+
+patterned gold electrode using PDMS polymer. This sample was transferred to the STM system  
+
+with \(< 15\) mins of exposure to the atmosphere.  
+
+Ab initio calculation of band structure  
+
+We performed first- principle electronic structure calculations using the projector augmented  
+
+wave (PAW) approach within the framework of density functional theory (DFT) as  
+
+implemented in the Vienna ab initio Simulation Package (VASP)36- 38. The exchange correlation  
+
+is described in the Perdew- Burke- Ernzerhof (PBE) form of generalized gradient approximation  
+
+(GGA)39,40. To take the strong correlation of Ta d- electrons into consideration, we perform  
+
+generalized- gradient approximation plus on- site U (GGA+U) calculations with U=2.27eV for
+
+<--- Page Split --->
+
+bare, monolayer \(\mathrm{TaS_2}\) in accordance with previous DFT calculations \(^{30,41}\) . The lattice structure is theoretically optimized with the atomic forces converged within \(0.01 \mathrm{eV / Ang}\) .  
+
+Next, we calculated the band structure of a monolayer \(\mathrm{TaS_2}\) covered with graphene again using a GGA+U scheme (Fig. 2d). To enable this calculation, we considered a twist angle of 13.9 degrees between the graphene and \(\mathrm{TaS_2}\) layers. This forms a commensurate structure (Fig. 1d) because \(5 \times a_G \approx L_{CDW}\) , where \(a_G = 0.246 \mathrm{nm}\) is the lattice constant of graphene. We note however, that the main features of our results are insensitive to the choice of commensurate structures (Supplementary Figure 8). Furthermore, we chose a reduced value of \(\mathrm{U} = 1.7 \mathrm{eV}\) to reproduce the observed spacing between the Hubbard bands.  
+
+The calculated DOS of the heterostructure is shown in Fig. 2d and is decomposed into the states from graphene(blue) and \(\mathrm{TaS_2}\) (red) layers. However, we cannot directly compare their sum to the measured \(\mathrm{dI / dV}\) because the tunneling current has a greater contribution from the graphene than the \(\mathrm{TaS_2}\) due to the exponential decay of tunneling with tip- sample distance. We therefore calculate a weighted sum (black) of the projected DOS of graphene and \(\mathrm{TaS_2}\) which takes this into account (see Supplementary Fig. 11 for details). This is in good qualitative agreement with the measured \(\mathrm{dI / dV}\) curve (Fig. 2b). The transfer of electrons from the graphene layer to the \(\mathrm{TaS_2}\) layer also explains the shift of \(\mathrm{E_F}\) from the edge of the LHB in the \(\mathrm{TaS_2}\) to the edge of the UHB in graphene/\(\mathrm{TaS_2}\) .  
+
+## References  
+
+Geim & Grigorieva. Van der Waals heterostructures. Nature 499, 419- 425 (2013). Li, G., Luican, A. & dos Santos, J. M. B. L. a. H. Castro Neto, A. Reina, J. Kong, and EY Andrei, Observation of Van Hove singularities in twisted graphene layers. Nature Physics 6, 109- 109 (2010). Levy et al. Strain- Induced Pseudo- Magnetic Fields Greater Than 300 Tesla in Graphene Nanobubbles. Science 329, 544- 547 (2010).
+
+<--- Page Split --->
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+367 4 Carrillo, B. et al. Strained fold-assisted transport in graphene systems. Physical Review B 368 94, 125422- 125422 (2016). 369 5 Mao, J. & Cao, Y. Evidence of Flat Bands and Correlated States in Buckled Graphene 370 Superlattices. Nature 584 (2020). 371 6 Jiang, Y. et al. Visualizing Strain- Induced Pseudomagnetic Fields in Graphene through an 372 hBN Magnifying Glass. Nano Letters 17, 2839- 2843 (2017). 373 https://doi.org:10.1021/acs.nanolett.6b05228 374 7 Meissner. Superconductivity of Contacts with Interposed Barriers. Physical Review 117, 375 672- 680 (1960). 376 8 Hauser. Magnetic Proximity Effect. Physical Review 187, 580- 583 (1969). 377 9 Zhao et al. Tuning Phase Transitions in 1T- TaS2 via the Substrate. Nano Letters 17, 378 3471- 3477 (2017). 379 10 Frano et al. Long- range charge- density- wave proximity effect at cuprate/manganate 380 interfaces. Nature Materials 15, 831- 834 (2016). 381 11 Li, Zhu, Stewart & Hebard. Bi- 2212/1T- TaS2 Van der Waals junctions: Interplay of 382 proximity induced high- Tcsuperconductivity and CDW order. Scientific Reports 7, 4639- 383 4639 (2017). 384 12 Dreher et al. Proximity Effects on the Charge Density Wave Order and Superconductivity 385 in Single- Layer NbSe2. ACS Nano (2021). 386 13 Du, Skachko & Andrei. Josephson current and multiple Andreev reflections in graphene 387 SNS junctions. Phys. Rev B 77 (2008). 388 14 Heersche, Jarillo, H., Oostinga, Vandersypen & Morpurgo. Bipolar supercurrent in 389 graphene. Nature 446, 56- 59 (2007). 390 15 Avsar et al. Spin- orbit proximity effect in graphene. Nat Commun 5, 4875- 4875 (2014). 391 16 Liang et al. The magnetic proximity effect and electrical field tunable valley degeneracy 392 in MoS2/EuS van der Waals heterojunctions. Nanoscale 9, 9502- 9509 (2017). 393 17 Johannes, M. D. & Mazin, I. I. Fermi surface nesting and the origin of charge density 394 waves in metals. Physical Review B 77, 165135- 165135 (2008). 395 18 Overhauser, A. W. Exchange and correlation instabilities of simple metals. Physical 396 Review 167, 691- 691 (1968). 397 19 Keimer, Kivelson, Norman, Uchida & Zaanen. From quantum matter to high- temperature 398 superconductivity in copper oxides. Nature 518, 179- 186 (2015). 399 20 Lin, H. et al. Scanning tunneling spectroscopic study of monolayer 1 T- TaS 2 and 1 T- 400 TaSe 2. Nano Research 13, 133- 137- 133- 137 (2020). 401 21 Altvater et al. Charge Density Wave Vortex Lattice Observed in Graphene- Passivated 1T- 402 TaS2 by Ambient Scanning Tunneling Microscopy. Nano Lett 21, 6132- 6138 (2021). 403 22 Zhang, Watanabe, Taniguchi & LeRoy. Local characterization and engineering of 404 proximitized correlated states in graphene/NbSe2 vertical heterostructures. Physical 405 Review B 102, 085429- 085429 (2020). 406 23 Rahnejat, K. C. et al. Charge density waves in the graphene sheets of the superconductor 407 CaC6. Nature communications 2, 558- 558 (2011). 408 24 Chen, Y. et al. Visualizing the anomalous charge density wave states in graphene/NbSe2 409 heterostructures. Advanced Materials 32, 2003746- 2003746 (2020). 410 25 Chen, Singh, Lin & Pereira. Reproduction of the Charge Density Wave Phase Diagram in 411 1T- TiSe2 Exposes its Excitonic Character. Physical Review Letters 121, 226602- 226602 412 (2018).
+
+<--- Page Split --->
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+413 26 Zhang et al. Evidence for a Quasi- One- Dimensional Charge Density Wave in CuTe by 414 Angle- Resolved Photoemission Spectroscopy. Physical Review Letters 121, 206402- 415 206402 (2018). 416 27 Altvater et al. Observation of a topological defect lattice in the charge density wave of 417 1T- TaS2. Applied Physics Letters 119, 121601- 121601 (2021). 418 28 Li, Luican & Andrei. Scanning Tunneling Spectroscopy of Graphene on Graphite. 419 Physical Review Letters 102, 176804- 176804 (2009). 420 29 Li, Luican & Andrei. Self- navigation of a scanning tunneling microscope tip toward a 421 micron- sized graphene sample. Review of Scientific Instruments 82, 073701- 073701 422 (2011). 423 30 Qiao et al. Mottness Collapse in 1T- \$mathrm{TaS}_{2- x}\$ Se_ x\$\$ Transition- Metal 424 Dichalcogenide: An Interplay between Localized and Itinerant Orbitals. Physical Review 425 X 7 (2017). 426 31 Ma, L. et al. A metallic mosaic phase and the origin of Mott- insulating state in 1T- TaS2. 427 Nature communications 7, 1- 8- 1- 8 (2016). 428 Wang et al. Surface- Limited Superconducting Phase Transition on 1 T- TaS2. ACS Nano 429 12, 12619- 12628 (2018). 430 33 Cho, D. et al. Nanoscale manipulation of the Mott insulating state coupled to charge 431 order in 1 T- TaS2. Nature communications 7, 10453- 10453 (2016). 432 34 Yu, Y.- J. et al. Tuning the graphene work function by electric field effect. Nano letters 9, 433 3430- 3434- 3430- 3434 (2009). 434 35 Shimada, T., Ohuchi, F. S. & Parkinson, B. A. Work function and photothreshold of 435 layered metal dichalcogenides. Japanese journal of applied physics 33, 2696- 2698- 436 2696- 2698 (1994). 437 36 Kresse. Ab initio molecular dynamics for liquid metals. Journal of Non- Crystalline Solids 438 192- 193, 222- 229 (1995). 439 37 Kresse & Furthmüller. Efficiency of ab- initio total energy calculations for metals and 440 semiconductors using a plane- wave basis set. Computational Materials Science 6, 15- 50 441 (1996). 442 38 Kresse & Furthmüller. Efficient iterative schemes for ab initio total- energy calculations 443 using a plane- wave basis set. Physical Review B 54, 11169- 11186 (1996). 444 39 Perdew et al. Atoms, molecules, solids, and surfaces: Applications of the generalized 445 gradient approximation for exchange and correlation. Physical Review B 46, 6671- 6687 446 (1992). 447 40 Perdew & Wang. Pair- distribution function and its coupling- constant average for the spin- 448 polarized electron gas. Physical Review B 46, 12947- 12954 (1992). 449 41 Darancet, Millis & Marianetti. Three- dimensional metallic and two- dimensional 450 insulating behavior in octahedral tantalum dichalcogenides. Physical Review B 90, 451 045134- 045134 (2014).
+
+<--- Page Split --->
+
+## Acknowledgements  
+
+NT and EYA acknowledge support from the Department of Energy grant DOE- FG02- 99ER45742 and the Gordon and Betty Moore Foundation EPiQS initiative grant GBMF9453; MAA was supported by the National Science Foundation grant EFRI 1433307; GL was supported by Rutgers University; CJW was supported by The National Research Foundation of Korea (NRF), grant No. 2016K1A4A4A01922028 and 2020M3H4A2084417; SWC was supported by the Betty Moore Foundation’s EPiQS grant GBMF6402 and Rutgers University, C.- H. C. was supported by MOST (Grant NO.: 107- 2112- M- 009- 010- MY3, 110- 2112- M- A49- 018- MY3) and the NCTS of Taiwan, R.O.C., J.H.T acknowledges support from the Ministry of Science and Technology, Taiwan under grant: MOST 109- 2112- M- 007 - 034 - MY3, and from NCHC, CINC- NTU, AS- iMATE- 109- 13, and CQT- NTHU- MOE, Taiwan.
+
+<--- Page Split --->
+
+## Supplementary Files  
+
+This is a list of supplementary files associated with this preprint. Click to download.  
+
+SIGTaS2nmt.pdf
+
+<--- Page Split --->

preprint/preprint__1eb5a9097a98c358a3d5ddb23a47389b98cafbd877613684718e11c19c97fe39/preprint__1eb5a9097a98c358a3d5ddb23a47389b98cafbd877613684718e11c19c97fe39_det.mmd

@@ -0,0 +1,447 @@
+<|ref|>title<|/ref|><|det|>[[44, 108, 763, 176]]<|/det|>
+# Proximity induced charge density wave in graphene/1T-TaS2  
+
+<|ref|>text<|/ref|><|det|>[[44, 196, 355, 240]]<|/det|>
+Eva Andrei eandrei@physics.rutgers.edu  
+
+<|ref|>text<|/ref|><|det|>[[44, 268, 797, 288]]<|/det|>
+Rutgers, The State University of New Jersey https://orcid.org/0000- 0002- 2516- 2749  
+
+<|ref|>text<|/ref|><|det|>[[44, 293, 140, 333]]<|/det|>
+Nikhil Tilak  rutgers  
+
+<|ref|>text<|/ref|><|det|>[[44, 340, 188, 380]]<|/det|>
+Michael Altvater  rutgers  
+
+<|ref|>text<|/ref|><|det|>[[44, 386, 315, 427]]<|/det|>
+Sheng- Hsiung Hung  National Tsing Hua University  
+
+<|ref|>text<|/ref|><|det|>[[44, 432, 925, 496]]<|/det|>
+Horng- Tay Jeng  Department of Physics, National Tsing Hua University, Hsinchu 30013 https://orcid.org/0000- 0002- 2881- 3826  
+
+<|ref|>text<|/ref|><|det|>[[44, 502, 216, 542]]<|/det|>
+Guohong Li  Rutgers University  
+
+<|ref|>text<|/ref|><|det|>[[44, 548, 437, 589]]<|/det|>
+Sang Wook cheong  Rutgers, The State University of New Jersey  
+
+<|ref|>text<|/ref|><|det|>[[44, 594, 559, 635]]<|/det|>
+Chung- Hou Chung  Physics Division, National Center for Theoretical Sciences  
+
+<|ref|>text<|/ref|><|det|>[[44, 641, 216, 681]]<|/det|>
+Taha Kaleem  Rutgers  
+
+<|ref|>text<|/ref|><|det|>[[44, 687, 216, 727]]<|/det|>
+Choong- Jae Won  Pohang University  
+
+<|ref|>text<|/ref|><|det|>[[44, 770, 104, 786]]<|/det|>
+Article  
+
+<|ref|>text<|/ref|><|det|>[[44, 807, 137, 825]]<|/det|>
+Keywords:  
+
+<|ref|>text<|/ref|><|det|>[[44, 845, 330, 863]]<|/det|>
+Posted Date: January 10th, 2024  
+
+<|ref|>text<|/ref|><|det|>[[44, 883, 475, 901]]<|/det|>
+DOI: https://doi.org/10.21203/rs.3.rs- 3779323/v1
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[42, 44, 914, 88]]<|/det|>
+License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License  
+
+<|ref|>text<|/ref|><|det|>[[42, 105, 535, 125]]<|/det|>
+Additional Declarations: There is NO Competing Interest.  
+
+<|ref|>text<|/ref|><|det|>[[42, 161, 920, 205]]<|/det|>
+Version of Record: A version of this preprint was published at Nature Communications on September 14th, 2024. See the published version at https://doi.org/10.1038/s41467-024-51608-y.
+
+<--- Page Split --->
+<|ref|>title<|/ref|><|det|>[[118, 92, 881, 170]]<|/det|>
+# Proximity induced charge density wave in graphene/1T-TaS2.  
+
+<|ref|>text<|/ref|><|det|>[[135, 169, 861, 234]]<|/det|>
+Nikhil Tilak \(^{1 + }\) , Michael Altvater \(^{1 + }\) , Sheng- Hsiung Hung \(^{2 + }\) , Guohong Li \(^{1}\) , Choong- Jae Won \(^{3}\) , Taha Kaleem \(^{1}\) , Sang- Wook Cheong \(^{1}\) , Chung- Hou Chung \(^{4,5}\) , Horng- Tay Jeng \(^{2,5,6}\) , and Eva Y. Andrei \(^{1}\)  
+
+<|ref|>text<|/ref|><|det|>[[111, 270, 829, 310]]<|/det|>
+1 Department of Physics and Astronomy, Rutgers, the State University of New Jersey, 136 Frelinghuysen Rd, Piscataway, New Jersey 08854, USA  
+
+<|ref|>text<|/ref|><|det|>[[111, 315, 864, 354]]<|/det|>
+2 Department of Physics, National Tsing Hua University, 101 Kuang Fu Road, Hsinchu 30013, Taiwan  
+
+<|ref|>text<|/ref|><|det|>[[111, 360, 870, 419]]<|/det|>
+3 Laboratory for Pohang Emergent Materials and Max Plank POSTECH Center for Complex Phase Materials, Department of Physics, Pohang University of Science and Technology, Pohang 37673, Korea  
+
+<|ref|>text<|/ref|><|det|>[[111, 425, 863, 465]]<|/det|>
+4 Department of Electrophysics, National Yang Ming Chiao Tung University, 1001 University Rd., Hsinchu, Taiwan 300  
+
+<|ref|>text<|/ref|><|det|>[[111, 470, 774, 491]]<|/det|>
+5 Physics Division, National Center for Theoretical Sciences, Taipei 10617, Taiwan  
+
+<|ref|>text<|/ref|><|det|>[[111, 498, 603, 518]]<|/det|>
+6 Institute of Physics, Academia Sinica, Taipei 11529, Taiwan  
+
+<|ref|>text<|/ref|><|det|>[[111, 550, 775, 590]]<|/det|>
+\*Corresponding authors E- mail: chung0523@nycu.edu.tw, jeng@phys.nthu.edu.tw, eandrei@physics.rutgers.edu  
+
+<|ref|>text<|/ref|><|det|>[[111, 597, 275, 616]]<|/det|>
++Equal contributors  
+
+<|ref|>sub_title<|/ref|><|det|>[[111, 638, 214, 660]]<|/det|>
+## Abstract  
+
+<|ref|>text<|/ref|><|det|>[[110, 688, 877, 808]]<|/det|>
+The proximity- effect, a phenomenon whereby materials in close contact appropriate each other’s electronic- properties, is widely used in nano- scale devices to induce electron- correlations at heterostructure interfaces. Commonly observed proximity- induced correlation- effects include superconductivity, magnetism, and spin- orbit interactions. Thus far, however, proximity induced charge density waves (CDW) have not been rigorously explored, primarily because of screening in 3D metals and defect scattering at interfaces. Here, we report the observation of a CDW
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[111, 87, 875, 354]]<|/det|>
+proximity effect between graphene and the commensurate CDW in 1T- TaS<sub>2</sub> (henceforth called TaS<sub>2</sub> for brevity). Using scanning tunneling microscopy (STM) and spectroscopy (STS) together with theoretical modeling to probe the interface between graphene and a TaS<sub>2</sub> crystal, we demonstrate the existence of a proximity induced CDW within graphene. Furthermore, we observe that graphene modifies the band structure at the surface of TaS<sub>2</sub>, by providing mid- gap carriers and reducing the strength of electron correlations there. We show that the mechanism underlying the proximity induced CDW is well- described by short- range exchange interactions that are distinctly different from previously observed proximity effects.  
+
+<|ref|>sub_title<|/ref|><|det|>[[115, 389, 255, 411]]<|/det|>
+## Introduction  
+
+<|ref|>text<|/ref|><|det|>[[110, 432, 860, 878]]<|/det|>
+The isolation and manipulation of atomically thin materials provides a ready- made two dimensional electron system<sup>1</sup> whose properties can be tuned by external knobs, such as stress or substrate morphology<sup>2- 6</sup>, leading to the emergence of correlated electron phases. Distinct from these external knobs, a very powerful approach to manipulate electron correlations is by contact proximity effects. It is well known that proximitizing materials that host correlated electron phases with a normal metal, induces correlations in the metal<sup>7</sup>. This is a direct consequence of the quantum mechanical properties of electrons in solids; specifically, the nonlocal nature of electrons. As quantum particles do not have a well- defined position, electronic states cannot abruptly change from one type of ordering to another at the interface between two materials. Consequently, correlated states persist into the normal metal where scattering events begin to destroy the coherence (and vice versa). In the case of 2D materials where scattering is reduced due to their atomically sharp interfaces, proximity- effects are particularly robust allowing correlated states to persist over long distances. The discovery of graphene and other
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[111, 88, 801, 179]]<|/det|>
+2D materials, together with the technology enabling 2D heterostructures has led to the observation of strong proximity effects at the atomic limit including proximity induced superconductivity, magnetism<sup>8</sup> and spin-orbit effects<sup>9- 16</sup>.  
+
+<|ref|>text<|/ref|><|det|>[[111, 200, 861, 499]]<|/det|>
+The CDW state is yet another correlated electron state which is driven by electron phonon coupling<sup>17</sup> (EPC) or alternatively via electron correlations<sup>18</sup>. It has been observed in systems such as unconventional superconductors<sup>19</sup> and many 2D transition- metal dichalcogenide (TMDs)<sup>20</sup>. However, identifying a proximity effect between a CDW material and a normal metal has been elusive. This is primarily due to screening in 3D metals and interface defect scattering. The use of 2D materials that were stacked in an inert atmosphere to avoid interface damage and contamination, together with sensitive local probes providing direct access to the charge distribution in graphene, has made it possible to overcome these hurdles<sup>21,22</sup>.  
+
+<|ref|>text<|/ref|><|det|>[[111, 521, 870, 892]]<|/det|>
+In this work, we present evidence of the emergence of a CDW in graphene produced by its proximity to the commensurate CDW (CCDW) in a \(\mathrm{TaS_2}\) substrate. While there are several recent experiments which have studied vertical heterostructures of graphene and a TMD with STM<sup>22- 24</sup>, it has proven difficult to experimentally isolate the electronic states in the graphene layer from those belonging to the TMD substrates making it difficult to definitively prove a proximity induced CDW in graphene. In this work, by using the coexisting CDW and Mott gap in \(\mathrm{TaS_2}\) , we can isolate the electronic states in the graphene layer and establish the presence of the proximity induced CDW. Using scanning tunneling microscopy (STM) and spectroscopy (STS) we show that the charge density modulation of the CCDW in \(\mathrm{TaS_2}\) persists within the contacted graphene layer. Additionally, we find that the Mott gap in \(\mathrm{TaS_2}\) is reduced due to its close contact with the Dirac carriers in graphene. By comparing with first- principle
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[63, 88, 866, 285]]<|/det|>
+76 calculations based on density- functional theory (DFT), we find that in addition to a global 77 charge- transfer between the two surfaces caused by the work- function difference, the proximity 78 induced CDW in graphene which is characterized by local charge modulation and lattice 79 distortion is described by a novel mechanism of short- range exchange interactions mediated by 80 second- order local electron hopping, which is distinctly different from superconducting, 81 magnetic and spin- orbit proximity effects.  
+
+<|ref|>sub_title<|/ref|><|det|>[[65, 337, 386, 360]]<|/det|>
+## 82 Results and discussion  
+
+<|ref|>text<|/ref|><|det|>[[65, 386, 707, 409]]<|/det|>
+84 Scanning tunneling microscopy of graphene covered TaS2.  
+
+<|ref|>image<|/ref|><|det|>[[130, 435, 884, 850]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[112, 861, 877, 905]]<|/det|>
+<center>FIGURE 1 | Scanning tunneling microscopy of graphene covered TaS2 (a) Schematic of the device and STM measurement setup. (b, left) Cartoon showing a single star-of-David (red) in TaS2. The black arrows denote the displacement of Ta atoms towards a central Ta atom. (b, right) The transition temperatures between the incommensurate CDW (ICCDW), nearly </center>
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[113, 88, 875, 168]]<|/det|>
+commensurate CDW (NCCDW) and commensurate CDW (CCDW) phases of TaS2. (c) Cartoon showing a monolayer graphene layer on top of TaS2. Here, the angle between the graphene layer and TaS2 is \(13.9^{\circ}\) to form a commensurate unit cell (cyan) for DFT calculations. (d) An STM topography image of graphene covered TaS2 measured at \(77\mathrm{K}\) using a tunneling set point of \(\mathrm{I} = 150\mathrm{pA}\) , \(\mathrm{V}_{\mathrm{b}} = 250\mathrm{mV}\) . Each bright spot corresponds to a star of David with a measured CDW wavelength of \(1.2\mathrm{nm}\) . This can also be seen in the 2D-FFT of the topography data (inset). (e) An STM topography image and its 2D FFT (inset) of a bulk TaS2 sample measured at \(77\mathrm{K}\) using a tunneling set point of \(\mathrm{I} = 150\mathrm{pA}\) , \(\mathrm{V}_{\mathrm{b}} = 500\mathrm{mV}\) .  
+
+<|ref|>text<|/ref|><|det|>[[110, 199, 870, 682]]<|/det|>
+TaS2 is comprised of hexagonal layers of tantalum atoms coordinated octahedrally by sulfur atoms. Below 550 K a high temperature metallic phase transits into an incommensurate CDW phase, followed by a nearly commensurate CDW (NCCDW) below \(350\mathrm{K}\) , and a CCDW below \(180\mathrm{K}^{21}\) (Fig. 1b, right). The CDW unit cell consists of a 13- atom cluster where 12 atoms displace from their high- temperature equilibrium positions toward the central, \(13^{\mathrm{th}}\) Ta atom forming a \(\sqrt{13} \times \sqrt{13}\) reconstructed supercell (Fig. 1b, left) also known as a David star (DS) structure. The CDW in TaS2 is driven by strong electron- phonon coupling which critically suppresses an acoustic phonon mode along the \(\Gamma \rightarrow \mathrm{M}\) direction \(^{25,26}\) (Supplementary Fig. 4) leading to the static displacement of the lattice with the wavevector \(\mathrm{Q}_{\mathrm{CDW}}\) , corresponding to the \(\sqrt{13} \times \sqrt{13}\) CDW. This soft phonon mode at the Kohn anomaly wavevector, \(\mathrm{Q}_{\mathrm{CDW}}\) , consists primarily of longitudinal vibrations of Ta atoms with a minor contribution from transverse vibrations of S atoms relative to the phonon propagating direction \(\mathrm{Q}_{\mathrm{CDW}}\) . The DS atomic arrangement involves the local lattice contraction around the center of the star, in which the bond lengths between Ta ions are shorter than those between Ta ions outside the star.  
+
+<|ref|>text<|/ref|><|det|>[[110, 700, 860, 896]]<|/det|>
+We studied vertical heterostructures consisting of thin TaS2 flakes (7- 50 nm) covered by monolayer graphene (Fig. 1a). The samples were assembled inside a dry argon- filled glovebox (<0.1 ppm \(\mathrm{O}_{2}\) , \(\mathrm{H}_{2}\mathrm{O}\) ) \(^{21,27}\) to avoid oxidation of the air sensitive surface of TaS2 and subsequently cooled to \(77\mathrm{K}\) inside a home- built STM \(^{28,29}\) . At this temperature TaS2 is in the CCDW regime. The STM topography (Fig. 1d) measured on a graphene- covered sample using a tunneling set- point of \(\mathrm{V}_{\mathrm{b}} = 250\mathrm{mV}\) , \(\mathrm{I} = 150\mathrm{pA}\) shows a triangular array of tall spots. The
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[57, 87, 861, 428]]<|/det|>
+116 lattice spacing for this triangular array is about 1.2 nm as measured directly from the 117 topography image and confirmed with its 2D- FFT (Fig. 1d, inset). This spacing is equal to the 118 expected CDW wavelength (LCDW) in TaS2 indicating that each tall spot corresponds to one DS. 119 The topography of the graphene covered sample thus reflects the distinct arrangement of the 120 DS clusters which are the hallmark of the CDW in TaS2. The topography is identical to that 121 measured on a bulk TaS2 sample without a graphene cover under similar tunneling conditions 122 (Fig. 1e). This is also true for typical negative bias setpoints of \(\mathrm{V_b} < - 300 \mathrm{mV}\) . Therefore, for 123 sufficiently large bias voltages of either sign, there is no noticeable difference in the STM 124 topography of TaS2 with or without a graphene cover. This is not surprising given the strong 125 CDW in TaS2 and the relatively small density of states in graphene compared to TaS2.  
+
+<|ref|>text<|/ref|><|det|>[[60, 444, 820, 467]]<|/det|>
+126 Interfacial charge transfer and reduction of on- site Coulomb repulsion  
+
+<|ref|>image<|/ref|><|det|>[[130, 495, 936, 844]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[112, 857, 881, 900]]<|/det|>
+<center>FIGURE 1 | Effects of interaction between graphene and TaS2. (a) and (b) are the measured dI/dV curves on bulk TaS2 and graphene/TaS2 respectively. The positions of the Hubbard band peaks and the broad features originating from the CDW distortion are labelled. The dashed lines in (b) indicate the bias voltages at which topography scans (g-i) were measured. (c) The calculated </center>
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[113, 88, 882, 196]]<|/det|>
+DOS for bulk TaS2. (d) The calculated DOS for the heterostructure decomposed into the graphene DOS (blue curve) and TaS2(red curve) DOS respectively. The graphene DOS is multiplied by a factor (39) before adding the TaS2 DOS (black curve) for comparison with experiment. This accounts for the smaller tip-graphene separation. See SI for how the factor was estimated. I A map of dI/dV (Vb) vs position across 8 DSs in the graphene/TaS2 sample. The intensity of both the Hubbard bands is highest at the center of the DSs. (f) Three representative spectra from the colormI(e) demonstrate this intensity variation. (g-i) STM topography scans measured at bias voltages of -300 mV, -150 mV and +300 mV respectively. These energies are indicated by dashed lines in (b). The tunneling resistance (R) was kept constant at 1 GΩ for all scans. The small lateral mismatch between scans is a result of thermal drift of the sample. All scale bars are 2 nm.  
+
+<|ref|>text<|/ref|><|det|>[[113, 201, 847, 289]]<|/det|>
+The similarity between the topography scans of TaS2 with or without a graphene cover might suggest that there is little or no interaction between the two materials. Our spectroscopy data shows that this is not the case.  
+
+<|ref|>text<|/ref|><|det|>[[111, 310, 852, 576]]<|/det|>
+The band structure of bulk TaS2 consists of an isolated, half- filled, spin- degenerate, flat band at the Fermi level (EF) (Supplementary Figure 5). In the CCDW phase, the flat band splits into an occupied spin up lower Hubbard band (LHB) and empty spin down upper Hubbard band (UHB) with a Mott gap of \(\sim 0.5 \mathrm{eV}\) in between (ab initio calculation Fig. 2c). The other bands corresponding to the CDW distortion appear at energies below the LHB (CDW- VB) and above the UHB (CDW- CB) respectively. These spectroscopic features were observed in our experimentally measured dI/dV spectrum (Fig. 2a) on a bulk TaS2 sample and agree with previous reports<sup>20,31- 33</sup>.  
+
+<|ref|>text<|/ref|><|det|>[[111, 598, 864, 899]]<|/det|>
+The dI/dV spectrum measured on the graphene covered sample (Fig. 2b) similarly shows peaks at \(\sim - 270 \mathrm{meV}\) and \(+70 \mathrm{meV}\) which are identified as the LHB and UHB. This identification is supported by the intensity variation of these features as a function of distance from SD centers (Fig. 2e,f). However, the Mott gap between the two peaks appears to be absent. Instead, we observe a small but finite density of states. These states presumably belong to the graphene layer which is a gapless semi- metal whose low- energy dispersion consists of Dirac cones which touch at the charge neutrality point (CNP). Our ab initio calculations for monolayer graphene on monolayer TaS2 (Fig. 2d), decomposed into the graphene DOS and TaS2 DOS, reveal that the CNP of graphene is about \(0.3 \mathrm{eV}\) above \(\mathrm{E}_{\mathrm{F}}\) of the system, indicating that it is
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[111, 88, 824, 145]]<|/det|>
+hole doped. This kind of interfacial charge transfer is likely due to the differences in work function between the graphene \((\Phi_{\mathrm{G}} \sim 4.6 \mathrm{eV}^{34})\) and \(\mathrm{TaS_2(\Phi_{TaS_2} \sim 5.2 \mathrm{eV}^{35})}\) .  
+
+<|ref|>text<|/ref|><|det|>[[111, 167, 864, 186]]<|/det|>
+Another notable difference is that the separation between the Hubbard band peaks  
+
+<|ref|>text<|/ref|><|det|>[[111, 202, 860, 222]]<|/det|>
+( \(\sim 338 \mathrm{mV}\) ) is smaller by about \(31\%\) compared to the bare \(\mathrm{TaS_2}\) sample ( \(\sim 491 \mathrm{mV}\) ). We  
+
+<|ref|>text<|/ref|><|det|>[[111, 237, 840, 257]]<|/det|>
+attribute this to the reduction of the on- site Coulomb repulsion (U) in the graphene covered  
+
+<|ref|>text<|/ref|><|det|>[[111, 272, 864, 292]]<|/det|>
+\(\mathrm{TaS_2}\) . Graphene is known to be a semi- metal with high- mobility itinerant carriers. These highly  
+
+<|ref|>text<|/ref|><|det|>[[111, 308, 800, 327]]<|/det|>
+mobile electrons from graphene somewhat suppress the localized picture of the narrow  
+
+<|ref|>text<|/ref|><|det|>[[111, 342, 840, 362]]<|/det|>
+Hubbard d- bands in \(\mathrm{TaS_2}\) and thus reduce the on- site U value of Ta. Comparing the ab initio  
+
+<|ref|>text<|/ref|><|det|>[[111, 377, 850, 397]]<|/det|>
+calculated band structures (supplementary Fig. 6) for different U values with the STS results,  
+
+<|ref|>text<|/ref|><|det|>[[111, 412, 864, 432]]<|/det|>
+suggests that the graphene layer screens the Coulomb interaction in \(\mathrm{TaS_2}\) and the Hubbard U of  
+
+<|ref|>text<|/ref|><|det|>[[111, 447, 844, 467]]<|/det|>
+Ta is lowered from \(2.27 \mathrm{eV}\) to \(1.70 \mathrm{eV}\) . Therefore, we use a phenomenological value \(\mathrm{U} = 1.70\)  
+
+<|ref|>text<|/ref|><|det|>[[111, 482, 845, 501]]<|/det|>
+eV for our ab initio calculations (Fig. 2d) which reasonably reproduces the trend observed in  
+
+<|ref|>text<|/ref|><|det|>[[111, 517, 300, 535]]<|/det|>
+the STS measurements.
+
+<--- Page Split --->
+<|ref|>image<|/ref|><|det|>[[130, 108, 864, 584]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[113, 591, 878, 790]]<|/det|>
+<center>FIGURE 2 | Modelling co-tunneling and electrostatic screening (a) A schematic cross section of the tunneling junction. The mean graphene-TaS \(_2\) separation is \(\sim 0.55 \mathrm{nm}\) . Co-tunneling into the graphene and TaS2 layers was estimated by measuring the tunneling current decay constant \(z_0\) . (b) The integrated DOS of the graphene (blue curve) and the TaS2 (red curve) in the energy window \([\mathrm{E}_{\mathrm{F}}, \mathrm{E}_{\mathrm{F}} + \mathrm{eV}_{\mathrm{b}}]\) calculated using the DOS from DFT calculations. The integrated DOS for TaS2 is about an order of magnitude larger than that of graphene for large \(\mathrm{V}_{\mathrm{b}}\) . For \(\mathrm{V}_{\mathrm{b}}\) inside the Mott gap, the graphene DOS dominates. (c) A schematic which considers the screening of the CDW in TaS2 by the electrons in graphene. The bottom subplot shows the electron density (p) in the TaS2 as a function of position. The resulting electric potential (U(r)) denoted by the orange line, tunes the local charge density of the graphene layer. This is denoted by the shift of the charge neutrality point of graphene’s Dirac cones (blue) at two representative positions. The Fermi level is denoted by the dashed black line. The red squiggly lines denote the Hubbard bands of the TaS2. The hatched region shows the states within the energy window \([\mathrm{E}_{\mathrm{F}}, \mathrm{E}_{\mathrm{F}} + \mathrm{eV}_{\mathrm{b}}]\) for the two positions. There are more states available for tunneling in the graphene above the center of the DSs compared to the sides. This should result in a triangular CDW like pattern in topography measured at these tunneling conditions. This is at odds with the observed hexagonal honeycomb pattern. (d) A dI/dV spectrum measured on a bilayer graphene/TaS2 area which shows that the CNP is clearly around 0.3 eV. The Hubbard bands are greatly suppressed compared to the monolayer graphene/TaS2 owing to the increased separation of the topmost graphene layer from the TaS2, additional screening, and the lack of direct hybridization between the two materials. </center>  
+
+<|ref|>text<|/ref|><|det|>[[112, 822, 836, 842]]<|/det|>
+In addition to the local height variation in the sample, STM topography also depends on the  
+
+<|ref|>text<|/ref|><|det|>[[112, 858, 830, 878]]<|/det|>
+total number of states in the energy window \([\mathrm{E}_{\mathrm{F}}, \mathrm{E}_{\mathrm{F}} + \mathrm{eV}_{\mathrm{b}}]\) . For CDW materials, the local
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[111, 88, 867, 319]]<|/det|>
+density of states variation dominates the topography signal. Since the integrated density of states of \(\mathrm{TaS_2}\) is orders of magnitude larger than that of the graphene (Fig. 3b) for large \(\mathrm{V_b}\) , we estimate that there is significant tunneling current into both the graphene and \(\mathrm{TaS_2}\) , despite the larger z separation between the tip and \(\mathrm{TaS_2}\) layer (Fig. 3a). Consequently, the topography scans measured on the graphene covered sample resemble that of \(\mathrm{TaS_2}\) as shown for \(\mathrm{V_b} = - 300 \mathrm{mV}\) (Fig. 2g) and \(\mathrm{V_b} = +300 \mathrm{mV}\) (Fig. 2i). Note that these two energy windows include the LHB and UHB of the \(\mathrm{TaS_2}\) respectively.  
+
+<|ref|>text<|/ref|><|det|>[[111, 339, 866, 640]]<|/det|>
+Surprisingly, the topography pattern measured at \(\mathrm{V_b} = - 150 \mathrm{mV}\) (Fig. 2h), which is in the Mott gap of \(\mathrm{TaS_2}\) , is hexagonal instead of triangular, but with the same periodicity as the CDW. A hexagonal lattice is the anti- lattice of a triangular lattice. This energy window includes only the states belonging to graphene since the \(\mathrm{TaS_2}\) layer is gapped (Fig. 3b) and thus reflects the electronic density redistribution in graphene due to its proximity to \(\mathrm{TaS_2}\) . This observation suggests that the electrons in the graphene layer also form a CDW- like pattern which is out- of- phase with the underlying \(\mathrm{TaS_2}\) CDW. We call this the proximity induced CDW in graphene. The hexagonal pattern was observed for multiple energy windows inside the \(\mathrm{TaS_2}\) Mott gap but never outside it (see Supplementary Fig. 9).  
+
+<|ref|>text<|/ref|><|det|>[[111, 660, 863, 893]]<|/det|>
+Note that this out- of- phase CDW cannot be explained by a simple screening picture (Fig. 3c). To wit: if one models the CDW in \(\mathrm{TaS_2}\) as a periodic modulation of electron density \((\rho (r))\) which peaks at the center of the DSs (Fig. 3c, bottom), then the electrostatic potential \((U(r))\) near the surface of \(\mathrm{TaS_2}\) will also peak near the center of the DSs (orange line in Fig. 3c). A graphene layer placed on top of \(\mathrm{TaS_2}\) will experience this spatially varying potential leading to spatial doping variation, i.e., shifting of the local CNP. This CNP variation is in addition to the average shift of the CNP \((\sim 0.3 \mathrm{eV})\) due to work function difference between
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[111, 88, 861, 248]]<|/det|>
+the two materials as discussed previously. If we consider an energy window in the Mott gap of the \(\mathrm{TaS_2}\) , then there are more graphene states (hatched regions in Fig. 3c) near the center of the SD compared to the region between neighboring DSs. This should result in a triangular CDW pattern in STM topography, which is in- phase with the underlying \(\mathrm{TaS_2}\) CDW. This is contrary to our observation of a hexagonal pattern, thus ruling out this simple picture.  
+
+<|ref|>text<|/ref|><|det|>[[111, 270, 866, 465]]<|/det|>
+We also studied an area of the sample covered with bilayer graphene. In this area the topography showed almost no CDW pattern. \(\mathrm{dI / dV}\) spectra (Fig. 3d) showed a bilayer graphene- like V shape with a pronounced dip near 0.3 V, indicating the CNP. Very weak features corresponding to the position of the Hubbard bands were also seen. These observations indicate that the proximity induced CDW relies on very short- range coupling between the graphene and \(\mathrm{TaS_2}\) .  
+
+<|ref|>text<|/ref|><|det|>[[111, 487, 498, 508]]<|/det|>
+Modeling the proximity induced CDW.  
+
+<|ref|>image<|/ref|><|det|>[[127, 519, 876, 844]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[112, 856, 877, 901]]<|/det|>
+<center>FIGURE 3| Ab initio band structure calculations. (a) Top view of \(\mathrm{G / TaS_2}\) heterostructure. Blue, brown, and yellow spheres indicate C, Ta, and S atoms, respectively. Black, blue, and brown rhombuses show the \(5 \times 5\) graphene/ \(\sqrt{13} \times \sqrt{13}\) \(\mathrm{TaS_2}\) supercell, graphene \(1 \times 1\) unit cell, and 1T- \(\mathrm{TaS_2}\) \(1 \times 1\) unit cell, respectively. The graphene and \(\mathrm{TaS_2}\) layer are twisted by \(\sim 13.9^{\circ}\) in this CDW </center>
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[113, 88, 884, 223]]<|/det|>
+phase. (b) Side view of graphene/TaS₂ heterostructure overlaid with the charge density map (bubble-shaped color contour indicating the electron density) corresponding to the states at the two crossing points of the Dirac cone and Lower Hubbard band. The slight overlap between graphene and TaS₂ electron clouds gives rise to the interlayer coupling and proximity effect. Here, blue, red, and green spheres represent C, Ta, and S atoms, respectively. (c) GGA+U band structure of \(\sqrt{13} \times \sqrt{13}\) CDW reconstructed TaS₂ with U=2.27eV (d) GGA+U band structure of graphene/TaS₂ using a phenomenological value U = 1.70 eV. Owing to the charge transfer from graphene to TaS₂, the Fermi level (zero energy) moves from the lower Hubbard band to the upper Hubbard band, and the graphene-associated Dirac point at the K-point of the superstructure Brillouin zone is shifted to \(\sim\) 0.3 eV above the Fermi level (E_F) indicating hole doping. (e-g) The integrated DOS in the energy range from E_F to -300 mV, -200 mV and +300 mV respectively. DSs are overlaid for clarity. At -300 mV and +300 mV the DSs form a triangular lattice while at -200 mV they form a hexagonal lattice.  
+
+<|ref|>text<|/ref|><|det|>[[112, 254, 866, 765]]<|/det|>
+In addition to the global charge transfer between the graphene and TaS₂, we obtain local charge modulations and lattice distortions (see supplementary Fig. 8) mediated by the interfacial orbital hybridizations (Fig. 4b) through the crossing states of the graphene Dirac cone with the TaS₂ Hubbard bands (Fig 4d). Since different carbon atoms in the heterostructure unit- cell have a different registration with respect to the underlying CDW SD, it is natural to expect that the hybridization between graphene p₂- bands and TaS₂- d bands varies as a function of position within the supercell (Fig 4a). This can be seen in the calculated charge density map (Fig. 4b). Moreover, the local density of states is significantly affected by the energy- dependent interlayer hybridizations as can be seen in the calculated band structure (Fig. 4d), particularly within or outside the Mott gap of TaS₂. Thus, we calculate the total density of states in the energy windows [E_F, E_F + eV_b] for V_b=- 0.3 V, - 0.2 V and +0.3 V (Fig. 4e- g). They show triangular, hexagonal and triangular CDW patterns below, within, and above the Mott gap region, respectively, demonstrating the inverted behavior of the in- gap states. These results are in good agreement with our topography scans (Fig. 2g- i) and further support the out- of- phase proximity induced CDW in the graphene layer.
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[111, 87, 844, 108]]<|/det|>
+We believe that the proximity induced CDW is a novel effect driven by periodically varying  
+
+<|ref|>text<|/ref|><|det|>[[111, 121, 857, 142]]<|/det|>
+band hybridization. This can be modeled using a simplified Hamiltonian of the graphene/TaS2  
+
+<|ref|>text<|/ref|><|det|>[[111, 157, 250, 177]]<|/det|>
+system given by:  
+
+<|ref|>equation<|/ref|><|det|>[[115, 220, 863, 270]]<|/det|>
+\[H = H_{d} + H_{c} + H_{t}, \quad (2)\]  
+
+<|ref|>equation<|/ref|><|det|>[[115, 250, 863, 293]]<|/det|>
+\[H_{d} = \sum_{\langle i,j\rangle ,\sigma} - t_{i j}^{d}d_{i,\sigma}^{\dagger}d_{j,\sigma} + h.c. - \sum_{\langle i^{\prime},j^{\prime}\rangle ,\sigma}(\Delta_{i^{\prime},j^{\prime}}^{CDW}(i^{\prime},j^{\prime}))^{*}d_{i^{\prime},\sigma}^{\dagger}d_{j^{\prime},\sigma} + h.c. + \sum_{\langle i^{\prime},j^{\prime}\rangle}|\Delta_{i^{\prime},j^{\prime}}^{CDW}(i^{\prime},j^{\prime})|^{2}, \quad (2)\]  
+
+<|ref|>equation<|/ref|><|det|>[[279, 298, 703, 343]]<|/det|>
+\[H_{c} = \sum_{\langle i,j\rangle ,\sigma} - t_{i j}^{c}c_{i,\sigma}^{\dagger}c_{j,\sigma} + h.c. = \sum_{k,\sigma}(\epsilon_{k} - \mu)c_{k,\sigma}^{\dagger}c_{k,\sigma},\]  
+
+<|ref|>equation<|/ref|><|det|>[[378, 351, 603, 396]]<|/det|>
+\[H_{t} = -t\sum_{i,\sigma}c_{i,\sigma}^{\dagger}d_{i,\sigma} + h.c.,\]  
+
+<|ref|>text<|/ref|><|det|>[[111, 428, 789, 450]]<|/det|>
+where \(\mathrm{H}_{\mathrm{d}}\) \(\mathrm{(H_{c})}\) stands for the simplified Hamiltonian of the 1T- TaS2 (graphene) layer,  
+
+<|ref|>text<|/ref|><|det|>[[111, 463, 857, 485]]<|/det|>
+respectively, and \(\mathrm{H}_{\mathrm{t}}\) describes a weak charge transfer (hopping) term between these two layers  
+
+<|ref|>text<|/ref|><|det|>[[111, 500, 833, 522]]<|/det|>
+(we neglect a small mismatch in spatial locations between the nearest- neighbor sites of the  
+
+<|ref|>text<|/ref|><|det|>[[111, 535, 841, 558]]<|/det|>
+corresponding layer). The insulating \(\mathrm{TaS}_{2}\) layer shows CDW order with the order parameter  
+
+<|ref|>text<|/ref|><|det|>[[111, 570, 830, 595]]<|/det|>
+\(\Delta_{i,j}^{CDW}(i^{\prime},j^{\prime})\equiv \sum_{\sigma}\langle d_{i^{\prime},\sigma}^{\dagger}d_{j^{\prime},\sigma}\rangle\) where \(i^{\prime},j^{\prime}\) are sites within the CDW unit cell. The graphene  
+
+<|ref|>text<|/ref|><|det|>[[111, 608, 820, 630]]<|/det|>
+layer has the linear Dirac dispersion: \((\epsilon_{k} - \mu)\approx \hbar \nu_{F}|k - k_{F}|\) where \(\mathrm{v_{F}}\) \(\mathrm{k_{F}}\) are the Fermi  
+
+<|ref|>text<|/ref|><|det|>[[111, 644, 857, 666]]<|/det|>
+velocity and Fermi wavevector respectively. Here, i, j refer to the nearest- neighbor sites of the  
+
+<|ref|>text<|/ref|><|det|>[[111, 680, 844, 704]]<|/det|>
+corresponding lattices, and \(\mathrm{t_{ij}^{d(c)}}\) refers to the nearest- neighbor tight- binding hoping terms on  
+
+<|ref|>text<|/ref|><|det|>[[111, 719, 450, 740]]<|/det|>
+the 1T- TaS2 (graphene) layer, respectively.  
+
+<|ref|>text<|/ref|><|det|>[[111, 765, 839, 787]]<|/det|>
+Via the second order perturbation in the \(\mathrm{H}_{\mathrm{t}}\) term of the CDW unit cell, the following  
+
+<|ref|>text<|/ref|><|det|>[[111, 802, 377, 825]]<|/det|>
+exchange term \(\mathrm{H}_{\mathrm{t}}^{(2)}\) is generated:  
+
+<|ref|>equation<|/ref|><|det|>[[310, 847, 672, 899]]<|/det|>
+\[H_{t}^{(2)} = t^{2}\sum_{\langle i^{\prime},j^{\prime}\rangle ,\sigma ,\sigma^{\prime}}c_{i^{\prime},\sigma}^{\dagger}d_{i^{\prime},\sigma}d_{j^{\prime},\sigma^{\prime}}^{\dagger}c_{j^{\prime},\sigma^{\prime}} + h.c.\]
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[57, 88, 852, 155]]<|/det|>
+A simple mean- field decoupling of \(\mathrm{H}_{\mathrm{t}}^{(2)}\) in terms of \(\Delta_{\mathrm{d}}^{\mathrm{CDW}}(\mathrm{i}^{\prime},\mathrm{j}^{\prime})\) (considering only \(\sigma = \sigma^{\prime}\) and assuming spin- isotropic CDW order \(\langle d_{j^{\prime},\uparrow}^{\dagger}d_{j^{\prime},\uparrow}\rangle = \langle d_{j^{\prime},\downarrow}^{\dagger}d_{j^{\prime},\downarrow}\rangle\) gives \(H_{\mathrm{t}}^{(2)}\to H_{\mathrm{t}}^{M F}\) with:  
+
+<|ref|>equation<|/ref|><|det|>[[120, 177, 860, 227]]<|/det|>
+\[H_{\mathrm{t}}^{M F}\approx -t^{2} / 2\sum_{\langle i^{\prime},j^{\prime}\rangle ,\sigma}(\Delta_{d}^{C D W}(i^{\prime},j^{\prime}))^{*}c_{i^{\prime},\sigma}^{\dagger}c_{j^{\prime},\sigma} - t^{2} / 2\sum_{\langle i^{\prime},j^{\prime}\rangle ,\sigma}(\Delta_{d}^{C D W}(i^{\prime},j^{\prime}))^{*}\langle c_{i^{\prime},\sigma}^{\dagger}c_{j^{\prime},\sigma}\rangle +h.c.,\]  
+
+<|ref|>text<|/ref|><|det|>[[56, 247, 825, 401]]<|/det|>
+where the mean- field decoupling term \(\langle (c_{i^{\prime},\sigma}^{\dagger}c_{j^{\prime},\sigma})d_{i^{\prime},\sigma}^{\dagger}d_{j^{\prime},\sigma}\rangle\) in \(\mathrm{H}_{\mathrm{t}}^{(2)}\) is neglected since we expect \(\left|\langle c_{i^{\prime},\sigma}^{\dagger}c_{j^{\prime},\sigma}\rangle \right|\ll \left|\langle d_{i^{\prime},\sigma}^{\dagger}d_{j^{\prime},\sigma}\rangle \right|\) . The CDW proximity effect is manifested in \(\mathrm{H}_{\mathrm{t}}^{M F}\) as a weak CDW order \(\Sigma_{\sigma}\langle c_{i^{\prime},\sigma}^{\dagger}c_{j^{\prime},\sigma}\rangle\) is induced on the graphene layer by the second order charge transfer between the two layers with the following identification:  
+
+<|ref|>equation<|/ref|><|det|>[[232, 420, 749, 462]]<|/det|>
+\[\Delta_{c}^{C D W}(i^{\prime},j^{\prime})\equiv -1 / 2\sum_{\sigma}\langle c_{i^{\prime},\sigma}^{\dagger}c_{j^{\prime},\sigma}\rangle^{*} = -t^{2} / 2\left(\Delta_{d}^{C D W}(i^{\prime},j^{\prime})\right)^{*},\]  
+
+<|ref|>text<|/ref|><|det|>[[58, 477, 850, 545]]<|/det|>
+or equivalently, \(\Sigma_{\sigma}\langle c_{i^{\prime},\sigma}^{\dagger}c_{j^{\prime},\sigma}\rangle = \mathrm{t}^{2}\Delta_{\mathrm{d}}^{\mathrm{CDW}}(\mathrm{i}^{\prime},\mathrm{j}^{\prime})\) . Via the above identification, the Hamiltonian \(\mathrm{H}_{\mathrm{t}}^{M F}\) can be expressed as:  
+
+<|ref|>equation<|/ref|><|det|>[[247, 564, 733, 611]]<|/det|>
+\[H_{\mathrm{t}}^{M F} = \sum_{\langle i^{\prime},j^{\prime}\rangle ,\sigma}\Delta_{c}^{C D W}(i^{\prime},j^{\prime})c_{i^{\prime},\sigma}^{\dagger}c_{j^{\prime},\sigma} + h.c. + 2|\Delta_{c}^{C D W}(i^{\prime},j^{\prime})|^{2},\]  
+
+<|ref|>text<|/ref|><|det|>[[56, 632, 857, 888]]<|/det|>
+which leads to \(\Delta_{c}^{C D W}(i^{\prime},j^{\prime}) = - 1 / 2\sum_{\sigma}\langle c_{i^{\prime},\sigma}^{\dagger}c_{j^{\prime},\sigma}\rangle^{*}\) identified above via minimizing the free energy associated with \(\mathrm{H}_{\mathrm{t}}^{M F}\) with respect to \(\Delta_{\mathrm{c}}^{C D W}(\mathrm{i}^{\prime},\mathrm{j}^{\prime})\) . Note that from above derivations, we indeed find that \(\left|\langle c_{i^{\prime},\sigma}^{\dagger}c_{j^{\prime},\sigma}\rangle \right|\sim t^{2}\left|\langle d_{i^{\prime},\sigma}^{\dagger}d_{j^{\prime},\sigma}\rangle \right|\ll \left|\langle d_{i^{\prime},\sigma}^{\dagger}d_{j^{\prime},\sigma}^{\dagger}\rangle \right|\) , as expected. Note also that the CDW order parameters induced on graphene layer shows the opposite sign with respect to that on 1T- TaS \(_2\) layer, consistent with the hole- like (particle- like) CDW intensity on graphene (1T- TaS \(_2\) ) layer obtained from DFT calculations, respectively. Meanwhile, the opposite sign of the proximity induced CDW on the graphene layer with
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[110, 87, 867, 284]]<|/det|>
+respect to the CDW on the TaS2 layer as predicted within our mean- field theory is in perfect agreement with the "out- of- phase" relation between CDW patterns observed inside and outside of Mott gap by our STM measurement (see Fig. 2 g- i and corresponding text above). This agreement provides a strong case for CDW proximity in graphene. We emphasize here that the above mechanism based on charge transfer is distinct from all the previously realized proximity effects, including superconducting, magnetic, and spin- orbit proximity effects.  
+
+<|ref|>sub_title<|/ref|><|det|>[[115, 326, 261, 348]]<|/det|>
+## Conclusions  
+
+<|ref|>text<|/ref|><|det|>[[110, 375, 866, 814]]<|/det|>
+In summary, we demonstrate, by STM/STS measurements and DFT calculations, the existence of a proximity induced CDW in graphene. We propose a model based on the short- range exchange interaction between carriers in graphene and the CDW hosted by the TaS2 crystal which qualitatively captures the main features of the CDW proximity effect. Concomitant with the proximity induced CDW in graphene, which is out- of- phase with that in TaS2, we observe a substantial reduction in the Mott gap of the TaS2 crystal, indicating the presence of proximity- induced mid- gap carriers which screen the Mott- Hubbard interaction. These observations open intriguing possibilities for engineering correlations and manipulating charge carriers in heterostructures. They suggest the prospect of non- linear electronic devices based on sliding and pinning of the CDW induced within the graphene layer and low power switches based on controlling correlation gaps through contact. Additional measurements including spin- resolved STM/STS, Raman spectroscopy, and electronic transport will further help elucidate the effects proposed in this work.
+
+<--- Page Split --->
+<|ref|>sub_title<|/ref|><|det|>[[113, 91, 217, 113]]<|/det|>
+## Methods  
+
+<|ref|>text<|/ref|><|det|>[[112, 137, 563, 158]]<|/det|>
+Sample fabrication and STM measurements  
+
+<|ref|>text<|/ref|><|det|>[[112, 157, 857, 179]]<|/det|>
+Samples were fabricated by mechanical exfoliation of graphene and separately \(\mathrm{TaS_2}\) flakes  
+
+<|ref|>text<|/ref|><|det|>[[112, 191, 855, 211]]<|/det|>
+inside an argon- filled glovebox. \(\mathrm{TaS_2}\) flakes were exfoliated from a bulk 1T- TaS2 crystal (2D  
+
+<|ref|>text<|/ref|><|det|>[[112, 226, 857, 247]]<|/det|>
+semiconductor or grown by iodine chemical vapor transport) and transferred onto a passivated  
+
+<|ref|>text<|/ref|><|det|>[[112, 261, 830, 281]]<|/det|>
+SiO2- capped degenerately doped Si wafer. The graphene and 1T- TaS2 flakes were aligned  
+
+<|ref|>text<|/ref|><|det|>[[112, 295, 853, 315]]<|/det|>
+vertically and brought into close contact with micromanipulators under an optical microscope  
+
+<|ref|>text<|/ref|><|det|>[[112, 330, 808, 350]]<|/det|>
+and then heated to promote adhesion. Standard electron beam lithography and electrode  
+
+<|ref|>text<|/ref|><|det|>[[112, 364, 844, 384]]<|/det|>
+deposition (4- 5nm Ti/40- 50nm Au) were used to make electrical contact to the sample. After  
+
+<|ref|>text<|/ref|><|det|>[[112, 399, 793, 419]]<|/det|>
+removing the PMMA mask the resulting heterostructure was annealed (180- 220°C) in  
+
+<|ref|>text<|/ref|><|det|>[[112, 434, 824, 455]]<|/det|>
+hydrogen/argon (10% : 90%) to remove polymer residues followed by AFM tip sweeping.  
+
+<|ref|>text<|/ref|><|det|>[[112, 469, 864, 490]]<|/det|>
+STM and STS were performed using a homebuilt STM at 77 K in high- vacuum \(< 10^{- 5}\) Torr. To  
+
+<|ref|>text<|/ref|><|det|>[[112, 504, 860, 525]]<|/det|>
+locate the micron size samples we employed a technique using the STM tip (mechanically cut  
+
+<|ref|>text<|/ref|><|det|>[[112, 540, 816, 560]]<|/det|>
+Pt/Ir ) as a capacitive antenna29. Two such samples were measured by STM using several  
+
+<|ref|>text<|/ref|><|det|>[[112, 575, 864, 596]]<|/det|>
+mechanically cut Pt/Ir wire tips. Additionally, we also studied a \(\mathrm{TaS_2}\) flake transferred to a pre  
+
+<|ref|>text<|/ref|><|det|>[[112, 610, 866, 631]]<|/det|>
+patterned gold electrode using PDMS polymer. This sample was transferred to the STM system  
+
+<|ref|>text<|/ref|><|det|>[[112, 645, 483, 666]]<|/det|>
+with \(< 15\) mins of exposure to the atmosphere.  
+
+<|ref|>text<|/ref|><|det|>[[112, 690, 485, 710]]<|/det|>
+Ab initio calculation of band structure  
+
+<|ref|>text<|/ref|><|det|>[[112, 710, 840, 731]]<|/det|>
+We performed first- principle electronic structure calculations using the projector augmented  
+
+<|ref|>text<|/ref|><|det|>[[112, 745, 768, 766]]<|/det|>
+wave (PAW) approach within the framework of density functional theory (DFT) as  
+
+<|ref|>text<|/ref|><|det|>[[112, 780, 866, 800]]<|/det|>
+implemented in the Vienna ab initio Simulation Package (VASP)36- 38. The exchange correlation  
+
+<|ref|>text<|/ref|><|det|>[[112, 814, 866, 835]]<|/det|>
+is described in the Perdew- Burke- Ernzerhof (PBE) form of generalized gradient approximation  
+
+<|ref|>text<|/ref|><|det|>[[112, 849, 828, 870]]<|/det|>
+(GGA)39,40. To take the strong correlation of Ta d- electrons into consideration, we perform  
+
+<|ref|>text<|/ref|><|det|>[[112, 884, 852, 905]]<|/det|>
+generalized- gradient approximation plus on- site U (GGA+U) calculations with U=2.27eV for
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[111, 88, 866, 144]]<|/det|>
+bare, monolayer \(\mathrm{TaS_2}\) in accordance with previous DFT calculations \(^{30,41}\) . The lattice structure is theoretically optimized with the atomic forces converged within \(0.01 \mathrm{eV / Ang}\) .  
+
+<|ref|>text<|/ref|><|det|>[[111, 165, 867, 380]]<|/det|>
+Next, we calculated the band structure of a monolayer \(\mathrm{TaS_2}\) covered with graphene again using a GGA+U scheme (Fig. 2d). To enable this calculation, we considered a twist angle of 13.9 degrees between the graphene and \(\mathrm{TaS_2}\) layers. This forms a commensurate structure (Fig. 1d) because \(5 \times a_G \approx L_{CDW}\) , where \(a_G = 0.246 \mathrm{nm}\) is the lattice constant of graphene. We note however, that the main features of our results are insensitive to the choice of commensurate structures (Supplementary Figure 8). Furthermore, we chose a reduced value of \(\mathrm{U} = 1.7 \mathrm{eV}\) to reproduce the observed spacing between the Hubbard bands.  
+
+<|ref|>text<|/ref|><|det|>[[111, 418, 870, 716]]<|/det|>
+The calculated DOS of the heterostructure is shown in Fig. 2d and is decomposed into the states from graphene(blue) and \(\mathrm{TaS_2}\) (red) layers. However, we cannot directly compare their sum to the measured \(\mathrm{dI / dV}\) because the tunneling current has a greater contribution from the graphene than the \(\mathrm{TaS_2}\) due to the exponential decay of tunneling with tip- sample distance. We therefore calculate a weighted sum (black) of the projected DOS of graphene and \(\mathrm{TaS_2}\) which takes this into account (see Supplementary Fig. 11 for details). This is in good qualitative agreement with the measured \(\mathrm{dI / dV}\) curve (Fig. 2b). The transfer of electrons from the graphene layer to the \(\mathrm{TaS_2}\) layer also explains the shift of \(\mathrm{E_F}\) from the edge of the LHB in the \(\mathrm{TaS_2}\) to the edge of the UHB in graphene/\(\mathrm{TaS_2}\) .  
+
+<|ref|>sub_title<|/ref|><|det|>[[453, 739, 543, 757]]<|/det|>
+## References  
+
+<|ref|>text<|/ref|><|det|>[[111, 778, 880, 884]]<|/det|>
+Geim & Grigorieva. Van der Waals heterostructures. Nature 499, 419- 425 (2013). Li, G., Luican, A. & dos Santos, J. M. B. L. a. H. Castro Neto, A. Reina, J. Kong, and EY Andrei, Observation of Van Hove singularities in twisted graphene layers. Nature Physics 6, 109- 109 (2010). Levy et al. Strain- Induced Pseudo- Magnetic Fields Greater Than 300 Tesla in Graphene Nanobubbles. Science 329, 544- 547 (2010).
+
+<--- Page Split --->
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+367 4 Carrillo, B. et al. Strained fold-assisted transport in graphene systems. Physical Review B 368 94, 125422- 125422 (2016). 369 5 Mao, J. & Cao, Y. Evidence of Flat Bands and Correlated States in Buckled Graphene 370 Superlattices. Nature 584 (2020). 371 6 Jiang, Y. et al. Visualizing Strain- Induced Pseudomagnetic Fields in Graphene through an 372 hBN Magnifying Glass. Nano Letters 17, 2839- 2843 (2017). 373 https://doi.org:10.1021/acs.nanolett.6b05228 374 7 Meissner. Superconductivity of Contacts with Interposed Barriers. Physical Review 117, 375 672- 680 (1960). 376 8 Hauser. Magnetic Proximity Effect. Physical Review 187, 580- 583 (1969). 377 9 Zhao et al. Tuning Phase Transitions in 1T- TaS2 via the Substrate. Nano Letters 17, 378 3471- 3477 (2017). 379 10 Frano et al. Long- range charge- density- wave proximity effect at cuprate/manganate 380 interfaces. Nature Materials 15, 831- 834 (2016). 381 11 Li, Zhu, Stewart & Hebard. Bi- 2212/1T- TaS2 Van der Waals junctions: Interplay of 382 proximity induced high- Tcsuperconductivity and CDW order. Scientific Reports 7, 4639- 383 4639 (2017). 384 12 Dreher et al. Proximity Effects on the Charge Density Wave Order and Superconductivity 385 in Single- Layer NbSe2. ACS Nano (2021). 386 13 Du, Skachko & Andrei. Josephson current and multiple Andreev reflections in graphene 387 SNS junctions. Phys. Rev B 77 (2008). 388 14 Heersche, Jarillo, H., Oostinga, Vandersypen & Morpurgo. Bipolar supercurrent in 389 graphene. Nature 446, 56- 59 (2007). 390 15 Avsar et al. Spin- orbit proximity effect in graphene. Nat Commun 5, 4875- 4875 (2014). 391 16 Liang et al. The magnetic proximity effect and electrical field tunable valley degeneracy 392 in MoS2/EuS van der Waals heterojunctions. Nanoscale 9, 9502- 9509 (2017). 393 17 Johannes, M. D. & Mazin, I. I. Fermi surface nesting and the origin of charge density 394 waves in metals. Physical Review B 77, 165135- 165135 (2008). 395 18 Overhauser, A. W. Exchange and correlation instabilities of simple metals. Physical 396 Review 167, 691- 691 (1968). 397 19 Keimer, Kivelson, Norman, Uchida & Zaanen. From quantum matter to high- temperature 398 superconductivity in copper oxides. Nature 518, 179- 186 (2015). 399 20 Lin, H. et al. Scanning tunneling spectroscopic study of monolayer 1 T- TaS 2 and 1 T- 400 TaSe 2. Nano Research 13, 133- 137- 133- 137 (2020). 401 21 Altvater et al. Charge Density Wave Vortex Lattice Observed in Graphene- Passivated 1T- 402 TaS2 by Ambient Scanning Tunneling Microscopy. Nano Lett 21, 6132- 6138 (2021). 403 22 Zhang, Watanabe, Taniguchi & LeRoy. Local characterization and engineering of 404 proximitized correlated states in graphene/NbSe2 vertical heterostructures. Physical 405 Review B 102, 085429- 085429 (2020). 406 23 Rahnejat, K. C. et al. Charge density waves in the graphene sheets of the superconductor 407 CaC6. Nature communications 2, 558- 558 (2011). 408 24 Chen, Y. et al. Visualizing the anomalous charge density wave states in graphene/NbSe2 409 heterostructures. Advanced Materials 32, 2003746- 2003746 (2020). 410 25 Chen, Singh, Lin & Pereira. Reproduction of the Charge Density Wave Phase Diagram in 411 1T- TiSe2 Exposes its Excitonic Character. Physical Review Letters 121, 226602- 226602 412 (2018).
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[50, 90, 881, 775]]<|/det|>
+413 26 Zhang et al. Evidence for a Quasi- One- Dimensional Charge Density Wave in CuTe by 414 Angle- Resolved Photoemission Spectroscopy. Physical Review Letters 121, 206402- 415 206402 (2018). 416 27 Altvater et al. Observation of a topological defect lattice in the charge density wave of 417 1T- TaS2. Applied Physics Letters 119, 121601- 121601 (2021). 418 28 Li, Luican & Andrei. Scanning Tunneling Spectroscopy of Graphene on Graphite. 419 Physical Review Letters 102, 176804- 176804 (2009). 420 29 Li, Luican & Andrei. Self- navigation of a scanning tunneling microscope tip toward a 421 micron- sized graphene sample. Review of Scientific Instruments 82, 073701- 073701 422 (2011). 423 30 Qiao et al. Mottness Collapse in 1T- \$mathrm{TaS}_{2- x}\$ Se_ x\$\$ Transition- Metal 424 Dichalcogenide: An Interplay between Localized and Itinerant Orbitals. Physical Review 425 X 7 (2017). 426 31 Ma, L. et al. A metallic mosaic phase and the origin of Mott- insulating state in 1T- TaS2. 427 Nature communications 7, 1- 8- 1- 8 (2016). 428 Wang et al. Surface- Limited Superconducting Phase Transition on 1 T- TaS2. ACS Nano 429 12, 12619- 12628 (2018). 430 33 Cho, D. et al. Nanoscale manipulation of the Mott insulating state coupled to charge 431 order in 1 T- TaS2. Nature communications 7, 10453- 10453 (2016). 432 34 Yu, Y.- J. et al. Tuning the graphene work function by electric field effect. Nano letters 9, 433 3430- 3434- 3430- 3434 (2009). 434 35 Shimada, T., Ohuchi, F. S. & Parkinson, B. A. Work function and photothreshold of 435 layered metal dichalcogenides. Japanese journal of applied physics 33, 2696- 2698- 436 2696- 2698 (1994). 437 36 Kresse. Ab initio molecular dynamics for liquid metals. Journal of Non- Crystalline Solids 438 192- 193, 222- 229 (1995). 439 37 Kresse & Furthmüller. Efficiency of ab- initio total energy calculations for metals and 440 semiconductors using a plane- wave basis set. Computational Materials Science 6, 15- 50 441 (1996). 442 38 Kresse & Furthmüller. Efficient iterative schemes for ab initio total- energy calculations 443 using a plane- wave basis set. Physical Review B 54, 11169- 11186 (1996). 444 39 Perdew et al. Atoms, molecules, solids, and surfaces: Applications of the generalized 445 gradient approximation for exchange and correlation. Physical Review B 46, 6671- 6687 446 (1992). 447 40 Perdew & Wang. Pair- distribution function and its coupling- constant average for the spin- 448 polarized electron gas. Physical Review B 46, 12947- 12954 (1992). 449 41 Darancet, Millis & Marianetti. Three- dimensional metallic and two- dimensional 450 insulating behavior in octahedral tantalum dichalcogenides. Physical Review B 90, 451 045134- 045134 (2014).
+
+<--- Page Split --->
+<|ref|>sub_title<|/ref|><|det|>[[113, 90, 345, 113]]<|/det|>
+## Acknowledgements  
+
+<|ref|>text<|/ref|><|det|>[[110, 138, 850, 460]]<|/det|>
+NT and EYA acknowledge support from the Department of Energy grant DOE- FG02- 99ER45742 and the Gordon and Betty Moore Foundation EPiQS initiative grant GBMF9453; MAA was supported by the National Science Foundation grant EFRI 1433307; GL was supported by Rutgers University; CJW was supported by The National Research Foundation of Korea (NRF), grant No. 2016K1A4A4A01922028 and 2020M3H4A2084417; SWC was supported by the Betty Moore Foundation’s EPiQS grant GBMF6402 and Rutgers University, C.- H. C. was supported by MOST (Grant NO.: 107- 2112- M- 009- 010- MY3, 110- 2112- M- A49- 018- MY3) and the NCTS of Taiwan, R.O.C., J.H.T acknowledges support from the Ministry of Science and Technology, Taiwan under grant: MOST 109- 2112- M- 007 - 034 - MY3, and from NCHC, CINC- NTU, AS- iMATE- 109- 13, and CQT- NTHU- MOE, Taiwan.
+
+<--- Page Split --->
+<|ref|>sub_title<|/ref|><|det|>[[43, 42, 312, 70]]<|/det|>
+## Supplementary Files  
+
+<|ref|>text<|/ref|><|det|>[[43, 92, 768, 112]]<|/det|>
+This is a list of supplementary files associated with this preprint. Click to download.  
+
+<|ref|>text<|/ref|><|det|>[[60, 130, 229, 149]]<|/det|>
+SIGTaS2nmt.pdf
+
+<--- Page Split --->

preprint/preprint__1eb88b8e99ba8ca78ed896f518cb1347831354bf2216abcb3b0f8e3c72eb76f4/images_list.json

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+  {
+    "type": "image",
+    "img_path": "images/Figure_1.jpg",
+    "caption": "Fig. 1. C9ORF78 stably interacts with BRR2",
+    "footnote": [],
+    "bbox": [
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+    "page_idx": 38
+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_3.jpg",
+    "caption": "Fig. 3. Mutational disruption of the C9ORF78-BRR2<sup>HR</sup> interaction and comparison to DDX23 binding.",
+    "footnote": [],
+    "bbox": [],
+    "page_idx": 38
+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_4.jpg",
+    "caption": "Fig. 4. C9ORF78 regulates alternative splicing",
+    "footnote": [],
+    "bbox": [
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+      ]
+    ],
+    "page_idx": 38
+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_5.jpg",
+    "caption": "Fig. 5. Regulation of PTBP2 exon 10 depends on the BRR2-C9ORF78 interaction.",
+    "footnote": [],
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+    "caption": "Fig. 6. C9ORF78 interacts with additional spliceosomal components.",
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+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_7.jpg",
+    "caption": "Fig. 7. Putative C8ORF78 neighborhood in the C\\* complex.",
+    "footnote": [],
+    "bbox": [
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preprint/preprint__1ec0e20dfe1397bcc4c3b75dcfb56d1f19c1e910da1612eea66abbbed7b5cf35/images_list.json

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+[
+  {
+    "type": "image",
+    "img_path": "images/Figure_1.jpg",
+    "caption": "Fig. 1 | Schematic illustration of the synthesis process and detection effects of pFeSAN. The pFeSAN was synthesized by a two-step method using Hb@ZIF-8 as precursors, in which mesoporous structures with Fe-N₃ sites were formed.",
+    "footnote": [],
+    "bbox": [
+      [
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+    ],
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+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_2.jpg",
+    "caption": "Fig. 2 | Morphology and structure characterizations. a HAADF TEM image of pFeSAN. b Elemental mapping of C, Fe, N, and O elements of pFeSAN. c HAADF-STEM image of pFeSAN showing the atomically dispersed Fe single atom sites as bright dots (yellow cycles marked single atoms). d EELS spectra of pFeSAN. e Pore size distribution curves of C, FeSAN and pFeSAN. f Raman spectra of as-prepared C, FeSAN and pFeSAN. g EPR spectrum of FeSAN and pFeSAN. h The corresponding percentage of N configurations for FeSAN and pFeSAN.",
+    "footnote": [],
+    "bbox": [
+      [
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+      ]
+    ],
+    "page_idx": 8
+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_3.jpg",
+    "caption": "Fig. 3 | Atomic structure characterization and composition of pFeSAN. a The Fe K-edge XANES and b the Fourier-transformed magnitudes of the experimental Fe K-edge EXAFS spectra of pFeSAN and the reference samples of Fe foil, \\(\\mathrm{Fe_2O_3}\\) and FePc, respectively. c The experimental FT-EXAFS spectra and fitting curves of pFeSAN. d Wavelet transform for the Fe K-edge EXAFS signals of pFeSAN and the reference samples of Fe foil, \\(\\mathrm{Fe_2O_3}\\) and FePc, respectively.",
+    "footnote": [],
+    "bbox": [
+      [
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+        845,
+        360
+      ]
+    ],
+    "page_idx": 13
+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_4.jpg",
+    "caption": "Fig. 4 | Oxidase-like activity evaluation. a-b Comparison of oxidase-like activities among various nanozymes. The reaction conditions were \\(25^{\\circ}\\mathrm{C}\\) for \\(10\\mathrm{min}\\) in acetate buffer (pH 4.0). c-d Comparison of oxidase-like activities of pFeSAN at various temperature (10-70 \\(^\\circ \\mathrm{C}\\) ) and pH (2-12) conditions. e The durability of pFeSAN treated with acid or alkali for \\(24\\mathrm{h}\\) . f Comparison of relative oxidase-like catalytic activities of different nanozymes. g Steady-state kinetic assay of pFeSAN and FeSAN with TMB as substrate. h Comparison of kinetics for pFeSAN and FeSAN. \\(\\mathrm{K_m}\\) is the Michaelis-Menten constant. \\(\\mathrm{V_{max}}\\) is the maximal reaction velocity. \\(\\mathrm{K_{cat}}\\) is the catalytic rate constant.",
+    "footnote": [],
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+    ],
+    "page_idx": 15
+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_5.jpg",
+    "caption": "Fig. 5 | Oxidase-like catalytic mechanism of pFeSAN. a UV-vis absorption spectra of pFeSAN + TMB in air, \\(\\mathrm{O_2}\\) -saturated, and \\(\\mathrm{N_2}\\) -saturated acetate buffer (pH 4.0). b Schematic illustration of oxidase-like characteristics of pFeSAN-catalyzed TMB oxidation. c CV curves of pFeSAN and FeSAN in pH 4.0 acetate buffer containing TMB. d EIS spectra of pFeSAN and FeSAN. e Calculated electron transfer number derived from rotating ring-disk electrode and \\(\\mathrm{H_2O_2}\\) yields of the pFeSAN. f EPR spectra of pFeSAN in the presence of excess phenyloxoiodine at 77 K.",
+    "footnote": [],
+    "bbox": [
+      [
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+        848,
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+      ]
+    ],
+    "page_idx": 19
+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_6.jpg",
+    "caption": "Fig. 6 | DFT theoretical calculation of oxidase-like activity over pFeSAN. a Proposed reaction pathways of \\(\\mathrm{O_2}\\) reduction to \\(\\mathrm{H_2O}\\) with optimized adsorption configurations on pFeSAN. b Corresponding free energy diagram for oxidase-like reaction on FeSAN and pFeSAN.",
+    "footnote": [],
+    "bbox": [
+      [
+        150,
+        472,
+        844,
+        653
+      ]
+    ],
+    "page_idx": 21
+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_7.jpg",
+    "caption": "Fig. 7 | Analytical performance of the pFeSAN-based colorimetric system for GSH detection. a Schematic illustration of the pFeSAN-based GSH biosensing system. b The linear relationship between the relative activity and the GSH concentration ranges from \\(50~\\mathrm{nM}\\) to \\(1\\mathrm{mM}\\) . Inset shows the photograph of reaction solution in the different concentration of GSH. c Comparison of the performance of the detection of GSH with different methods. d Selectivity toward GSH of the pFeSAN-based colorimetric system against common interfering chemicals. The numbers from 1 to 15 are Ctrl, GSH, \\(\\mathrm{Na^{+}}\\) , \\(\\mathrm{Ca^{2 + }}\\) , \\(\\mathrm{Mg^{2 + }}\\) , \\(\\mathrm{K^{+}}\\) , glutamic acid, tryptophan, arginine, glycine, cysteine, ascorbic acid, glucose, glucose oxidase and BSA. e The linear relationship between the variation of relative activity and the number of cells. The inset: photographs of reaction solution with different cell numbers. f DAB staining was performed to evaluate the level of GSH in tumor-bearing liver tissue. Scale \\(\\mathrm{bar} = 200 \\mu \\mathrm{m}\\) . g Ki67 staining was performed to assess the proliferation of tumor cells in tumor-bearing liver tissue. Scale \\(\\mathrm{bar} = 200 \\mu \\mathrm{m}\\) . h Pearson's correlation coefficient of the oxidized DAB (brown)/Ki67 (red fluorescence), which appeared in tissue slices of tumor-bearing liver tissue (Pearson's coefficient \\(\\mathrm{r} = -0.76\\) ). Bars represent the mean \\(\\pm \\mathrm{SEM}\\) ; \\*\\*, \\(\\mathrm{p} < 0.01\\) .",
+    "footnote": [],
+    "bbox": [
+      [
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+    "page_idx": 23
+  }
+]

preprint/preprint__1ec0e20dfe1397bcc4c3b75dcfb56d1f19c1e910da1612eea66abbbed7b5cf35/preprint__1ec0e20dfe1397bcc4c3b75dcfb56d1f19c1e910da1612eea66abbbed7b5cf35.mmd

@@ -0,0 +1,490 @@
+
+# Bioinspired porous three-coordinated single-atom Fe nanozyme with oxidase-like activity for tumor visual identification via glutathione  
+
+Da Chen  Northwestern Polytechnical University  Zhaoming Xia  Tsinghua University  Zhixiong Guo  Northwestern Polytechnical University  Wangyan Gou  Northwestern Polytechnical University  Junlong Zhao  The Fourth Military Medical University  https://orcid.org/0000- 0002- 6148- 5641  Xuemei Zhou  Wenzhou University  Xiaoe Tan  Northwestern Polytechnical University  Wenbin Li  Northwestern Polytechnical University  Shoujie Zhao  Fourth Military Medical University  Zhimin Tian  Northwestern Polytechnical University  Yongquan Qu ( \(\square\) yongquan@mail.xjtu.edu.cn)  Northwestern Polytechnical University  https://orcid.org/0000- 0002- 6202- 1929  
+
+## Article  
+
+# Keywords:  
+
+Posted Date: May 19th, 2023  
+
+DOI: https://doi.org/10.21203/rs.3.rs- 2922979/v1
+
+<--- Page Split --->
+
+License: © ① This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License  
+
+Additional Declarations: There is NO Competing Interest.  
+
+Version of Record: A version of this preprint was published at Nature Communications on November 6th, 2023. See the published version at https://doi.org/10.1038/s41467-023-42889-w.
+
+<--- Page Split --->
+
+# Bioinspired porous three-coordinated single-atom Fe nanozyme with oxidase-like activity for tumor visual identification via glutathione  
+
+Da Chen \(^1\) , Zhaoming Xia \(^2\) , Zhixiong Guo \(^1\) , Wangyan Gou \(^1\) , Junlong Zhao \(^3\) , Xuemei Zhou \(^4\) , Xiaohé Tan \(^1\) , Wenbin Li \(^1\) , Shoujie Zhao \(^4\) , Zhimin Tian \(^{1,\ast}\) and Yongquan Qu \(^{1,\ast}\)  
+
+\(^1\) Key Laboratory of Special Functional and Smart Polymer Materials of Ministry of Industry and Information Technology, School of Chemistry and Chemical Engineering, Northwestern Polytechnical University, Xi'an 710072, China. E- mail: zhimintian@nwpu.edu.cn; yongquan@nwpu.edu.cn \(^2\) Department of Chemistry, Southern University of Science and Technology, Shenzhen 518055, China. \(^3\) State Key Laboratory of Cancer Biology, Department of Medical Genetics and Developmental Biology, Fourth Military Medical University, Xi'an 710032, China. \(^4\) Key Laboratory of Carbon Materials of Zhejiang Province, Wenzhou University, Wenzhou 325035, China.  
+
+e- mail: zhimintian@nwpu.edu.cn; yongquan@nwpu.edu.cn
+
+<--- Page Split --->
+
+## Abstract  
+
+Inspired by structures of natural metalloenzymes, a biomimetic synthetic strategy was developed for scalable synthesis of porous \(\mathrm{Fe - N_3}\) single atom nanozymes (pFeSAN) using hemoglobin as Fe- source and template. pFeSAN delivered 3.3- and 8791- fold higher oxidase- like activity than \(\mathrm{Fe - N_4}\) and \(\mathrm{Fe_3O_4}\) nanozymes. The high catalytic performance is attributed to (1) the suppressed aggregation of atomically dispersed Fe; (2) facilitated mass transfer and maximized exposure of active sites for the created mesopores by thermal removal of hemoglobin (2\~3 nm); and (3) unique electronic configuration of \(\mathrm{Fe - N_3}\) for the oxygen- to- water oxidation pathway (analogy with natural cytochrome c oxidase). The pFeSAN was successfully demonstrated for the rapid colorimetric detection of glutathione with a low limit of detection (2.4 nM) and wide range (50 nM–1 mM), and further developed as a real- time, facile, rapid (∼6 min) and precise visualization analysis methodology of tumors via glutathione level, showing its potentials for diagnostic and clinic applications.
+
+<--- Page Split --->
+
+Nanozymes, featured by low cost, high stability, tailorable surface properties and facile synthesis and storage, deliver enzyme- like activities for prosperous applications in the fields of disease diagnosis and treatments \(^{1 - 6}\) . However, nanozymes generally suffer from their lower activity and unsatisfactory specificity in comparison with their natural counterparts, which severely limited their practical applications \(^{7 - 9}\) . Fortunately, natural metalloenzymes (cytochrome c oxidase ( \(C c O\) ), superoxide dismutase, etc.) with the well- defined local coordination environments and electronic structures have provided us with ingenious blueprints for the rational design of nanozymes \(^{8,10 - 12}\) .  
+
+Among various nanozymes, the single- atom metal nanozymes with the tailorable chemical, geometric and electronic configurations of atomically dispersed metals bonding with nitrogen- doped carbon support (M- N- C) possess similar configurations of the active metal centers of natural metalloenzymes, being recognized as alternatives of natural enzymes. Unfortunately, majority of them were synthesized via pyrolysis at high temperatures, leading to structural collapse and part of the buried \(\mathrm{MN_x}\) units inaccessible to biomolecules \(^{11,13,14}\) . Also, the strong stacking of those N- doped graphite carbon in nanozymes generally induces the frustrated diffusion of bio- substrates to metal sites. Both mass transfer restriction and active site encapsulation severely decrease the overall activity of those single- atom nanozymes. Besides, pyrolysis can induce the considerable aggregation of single- atom metal due to the carbon loss at high temperatures \(^{15 - 17}\) . Hence, various methods including spatial confinement, defect/vacancy engineering and coordination modulations have been developed to solve those problems \(^{18 - 20}\) . However, only part of those inadequacies could be overcome.
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+Thus, seeking for a new synthetic strategy of single- atom metal nanozymes to simultaneously achieve the atomic metal dispersion, modulated electronic structure, elevated mass transport and tailorable coordination environment is still on high demands.  
+
+Inspired by the structural features of natural \(C_cO\) , herein, we demonstrated a biomimetic synthetic strategy for the massive and facile preparation of porous single- atom Fe nanozymes (pFeSAN) using hemoglobin (Hb) as Fe- source embedded inside zeolitic imidazolate framework (ZIF- 8), which solved the above- mentioned challenges (Fig. 1). Evenly distributed iron atoms in each Hb with a size of 2\~3 nm effectively avoided the agglomeration of active sites and created mesopores in the nanozymes during pyrolysis, thereby maximumly exposing the atomic Fe sites and significantly facilitating the mass transfer of reactants/products in catalysts. Structural characterizations demonstrated the atomically dispersed Fe with a Fe- N₃ coordination in pFeSAN. Impressively, pFeSAN delivered outstanding oxidase- like activity, which was 3.3- and 8791- times higher than those of four- coordinated Fe- N single- atom (Fe- N₄) and Fe₃O₄ nanozymes, respectively. Most importantly, the extensive mechanism investigations illustrated that pFeSAN underwent a catalytic pathway of the four- electron reduction of oxygen into H₂O, being identical to that of \(C_cO^{21- 24}\) . Abnormal high concentration of glutathione (GSH) in cells and tissues at millimolar level generally suggests high risk of many diseases²⁵-²⁷. Based on the color evolution between colorless 3,3',5,5'- tetramethylbenzidine (TMB) and blue oxTMB enabled by oxidase- like pFeSAN and reductive GSH, pFeSAN was developed as a highly selective and
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+sensitive probe for the rapid GSH detection with a low limit of detection (LOD) of 2.4 nM and a wide window (50 nM–1 mM). Then, the pFeSAN-GSH assay was developed for the accurate detection of intratumoral GSH at millimolar levels and as the facile, rapid and precise visualization methodology of tumor area.  
+
+![](images/Figure_1.jpg)
+
+<center>Fig. 1 | Schematic illustration of the synthesis process and detection effects of pFeSAN. The pFeSAN was synthesized by a two-step method using Hb@ZIF-8 as precursors, in which mesoporous structures with Fe-N₃ sites were formed. </center>
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+## Results  
+
+## Synthesis and characterization of pFeSAN  
+
+We proposed a two- step biomimetic synthesis strategy of pFeSAN by integrating Hb as Fe- resource and ZIF- 8 as the precursor of N- doped carbon (Fig. 1). Initially, a mixed methanol solution of \(\mathrm{Zn(NO_3)_2}\) , 2- methylimidazole and Hb reacted to produce uniform Hb@ZIF- 8 biohybrids with a rhombic dodecahedron shape and an average size of \(1081.4 \pm 15.4 \mathrm{nm}\) (Supplementary Fig. 1). Compared with the white color of ZIF- 8 and brownish red color of Hb, the pale brown color of Hb@ZIF- 8 disclosed the successful integration of ZIF- 8 and Hb (Supplementary Fig. 2). Consistent with the characterized absorption peak of Hb at \(\sim 405 \mathrm{nm}\) , the similar ultraviolet- visible (UV- vis) absorption of Hb@ZIF- 8 suggested the integrated Hb in ZIF- 8 (Supplementary Fig. 3) \(^{28}\) . X- ray powder diffraction (XRD) pattern showed that Hb@ZIF- 8 inherited the crystallographic structure of ZIF- 8, indicating the well- maintained crystal structure (Supplementary Fig. 4). The Infrared \(\mathrm{C} = \mathrm{O}\) bond stretching vibration of amide peak at \(1,660 \mathrm{cm^{- 1}}\) was observed for both Hb and Hb@ZIF- 8, again confirming the successful internalization of Hb with ZIF- 8 (Supplementary Fig. 5) \(^{28}\) . The thermogravimetric analysis suggested a Hb- loading of \(40.5 \mathrm{wt.\%}\) inside Hb@ZIF- 8 (Supplementary Fig. 6). \(\mathrm{N}_2\) adsorption isothermal analysis displayed an apparently lower surface area of Hb@ZIF- 8 ( \(663.8 \mathrm{m^2 / g}\) ) than \(850.3 \mathrm{m^2 / g}\) of ZIF- 8 alone, elucidating the partial occupation of ZIF- 8 pores by Hb (Supplementary Fig. 7) \(^{29}\) . Taken together, all characterizations demonstrated the successful immobilization of Hb within ZIF- 8.
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+<center>Fig. 2 | Morphology and structure characterizations. a HAADF TEM image of pFeSAN. b Elemental mapping of C, Fe, N, and O elements of pFeSAN. c HAADF-STEM image of pFeSAN showing the atomically dispersed Fe single atom sites as bright dots (yellow cycles marked single atoms). d EELS spectra of pFeSAN. e Pore size distribution curves of C, FeSAN and pFeSAN. f Raman spectra of as-prepared C, FeSAN and pFeSAN. g EPR spectrum of FeSAN and pFeSAN. h The corresponding percentage of N configurations for FeSAN and pFeSAN. </center>  
+
+Subsequently, Hb@ZIF- 8 was pyrolyzed at \(900^{\circ}\mathrm{C}\) under Ar to decompose Hb, remove \(\mathrm{Zn}^{2 + }\) and then give pFeSAN. Compared with Hb@ZIF- 8, characterizations of transmission electron microscopy (TEM), scanning electron microscopy (SEM) and high- angle annular dark field- scanning TEM (HAADF- STEM) demonstrated the preserved polyhedral structure of as- synthesized pFeSAN with a reduced size of \(685.1 \pm 7.3 \mathrm{nm}\) and the formation of porous structure (Fig. 2a and Supplementary Fig. 8). The typical C- N and C=N Infrared vibration peaks instead of C=O stretching
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+vibration of amide were observed at 1,200\~1,600 cm\(^{- 1}\) for pFeSAN, suggesting the thermal decomposition of Hb and the carbonization of Hb@ZIF- 8 to give the N- doped carbon (Supplementary Fig. 9) \(^{30}\) . The XRD pattern of pFeSAN displayed only two broad characteristic diffraction peaks of graphitic carbon at \(\sim 24^{\circ}\) and \(\sim 44^{\circ}\) and no peaks related to iron/iron oxides (Supplementary Fig. 10), indicating a high dispersion of iron \(^{11,31}\) . The energy- dispersive X- ray spectroscopy (EDS) mappings showed the homogeneous distribution of C, Fe, N and O elements in the entire structure of pFeSAN (Fig. 2b). The aberration- corrected HAADF- STEM with angstrom resolution exhibited many isolated and bright spots ( \(\sim 0.2 \mathrm{nm}\) ), demonstrating the presence of the atomically dispersed Fe in pFeSAN (Fig. 2c). As revealed from the electron energy- loss spectroscopy (EELS) of pFeSAN, the point signal at the bright spot (yellow circle) further illustrated the co- existence of the neighboring single Fe and N atoms to give the Fe- \(\mathrm{N}_{\mathrm{x}}\) configuration (Fig. 2d) \(^{32}\) . Comparatively, the absence of characteristic peak of Fe at the site of carbon alone (blue circle) again supported the atomic dispersion of Fe. Notably, the cohesive interaction between iron atoms and protein structure of Hb allowed a confinement effect to suppress the iron agglomeration, eventually yielding the atomically dispersed Fe sites.  
+
+The commercial \(\mathrm{Fe}_{3}\mathrm{O}_{4}\) nanoparticles, the carbon supports (C) synthesized via the similar process without Hb, the porous carbon supports (pC) obtained using bull serum albumin (BSA) as template and the heme- derived single- atom Fe- \(\mathrm{N}_{4}\) catalysts through pyrolysis (FeSAN) were synthesized as references (Supplementary Figs. 11- 16) \(^{33}\) . A significant number of mesopores (3\~4 nm) were observed for pFeSAN due to the Hb
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+pyrolysis in ZIF- 8 (Fig. 2e), which was also verified by the \(\mathrm{H_4}\) type hysteresis loop of the \(\mathrm{N}_2\) adsorption/desorption isotherm of pFeSAN (Supplementary Fig. 17a)31. Consequently, pFeSAN provided a high surface area of \(705.8\mathrm{m}^2 /\mathrm{g}\) than that of FeSAN (516.6 \(\mathrm{m}^2 /\mathrm{g}\) ) without mesopores (Supplementary Fig. 17b). Raman spectra exhibited a larger \(\mathrm{I_D / I_G}\) ratio of pFeSAN than those of C and FeSAN, indicating a higher degree of structural defects in pFeSAN (Fig. 2f)32. While, no characteristic Raman peaks of iron oxides for both pFeSAN and FeSAN further demonstrated the atomically dispersed Fe in two catalysts. Additionally, the electron paramagnetic resonance (EPR) spectrum of pFeSAN delivered a 2.4- fold higher sharp signal than that of FeSAN at \(\mathrm{g} = 2.003\) , corresponding to the coordinatively unsaturated iron atoms and abundant N defect sites in pFeSAN (Fig. 2g)34,35. Overall, pFeSAN possesses a higher degree of graphitization and abundant nitrogen defect, indicating that the designed biomimetic synthetic strategy enables the effective modulation of the local electronic structure of Fe in pFeSAN and thereby affect their enzyme- like activity.  
+
+Afterwards, the chemical states of pFeSAN were investigated by X- ray photoelectron spectroscopy (XPS)11,31,32. The N 1s XPS spectra of pFeSAN and FeSAN suggested the existence of five types of N: pyridinic- N (398.3 eV), pyrrole- N (400.5 eV), graphitic- N (401.3 eV), oxidized- N (403.7 eV), and Fe- N peak (399.5 eV, Figure S18a). Thus, the formation of Fe- N bonds in the N- doped carbon was verified for both catalysts. Compared with FeSAN, pFeSAN possessed a remarkably higher proportion of pyridinic N (Fig. 2h and Supplementary Fig. 18a), which was believed to be important for the oxidase- like activity of single- atom nanozymes31. The binding environments of
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+C in pFeSAN and FeSAN can be deconvoluted into three types of C- C/C=C, C- N/O and O=C- C, indicating the formation of the N- doped graphite- like C (Supplementary Fig. 18b). Compared with the C- N/O peak of FeSAN (285.7 eV), the slight blue shift of the C- N/O peak (286.1 eV) of pFeSAN indicated a stronger electron attraction capability of the Fe atom coordinated with N in pFeSAN than that in FeSAN (Supplementary Fig. 18c) \(^{36}\) . Thus, the local Fe electronic structure could be effectively modulated by the coordination environments, thereby potentially tailoring the electron transfer and affecting their oxidase- like activity. The O 1s XPS spectra suggested the presence of the absorbed \(\mathrm{O_2}\) (530.7 eV), \(\mathrm{C = O}\) (532.2 eV) and C- OH/C- O- OH groups (533.6 eV, Supplementary Fig. 18d). Especially, the strong and broad peak at 530.7eV for pFeSAN indicated its strong ability to adsorb and activate oxygen, which could potentially promote the oxidase- like activity \(^{37}\) . No significant signal of XPS Fe 2p was observed for two catalysts due to the low Fe loadings in pFeSAN (0.36 wt.%) and FeSAN (0.21 wt.%), which were determined by inductively coupled plasma mass spectrometry (Supplementary Fig. 18e).  
+
+## Atomic structure analysis of pFeSAN  
+
+Afterwards, the Fe K- edge X- ray absorption fine structure spectra were employed to probe the local chemical states and coordination environments of pFeSAN with iron phthalocyanine (FePc), \(\mathrm{Fe_2O_3}\) and Fe foil as references. The rising- edge position of the X- ray absorption near- edge structure spectra (XANES) of pFeSAN was between those of \(\mathrm{Fe_2O_3}\) and FePc, and very close to that of FePc, indicating that the atomically
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+dispersed Fe carried a positive charge with the oxidation state slightly over \(+2\) (Fig. 3a). Notably, the absorption edge of pFeSAN almost overlapped with that of FePc, indicating their similar electronic structures around Fe. Figure 3b showed the Fourier transform (FT) k3- weighted extended X- ray absorption fine structure (EXAFS) spectra. The main peak of pFeSAN at 1.47 Å corresponded to the Fe- N first coordination shell, similar to FePc. The local coordination environment of Fe was resolved by EXAFS fitting (Fig. 3c and Supplementary Table 1), giving a Fe- N distance of 1.91 Å and Fe- N coordination number of 2.8 for pFeSAN. The negligible Fe- Fe signal in 2- 3 Å compared with that of Fe foil indicates the atomically dispersed Fe in pFeSAN. This could be further confirmed by the wavelet transformed EXAFS without peaks corresponding to Fe- Fe bond (2- 3 Å in R- space and \(\sim 8.3 \AA^{- 1}\) in K- space, Fig. 3d) \(^{34}\) . The above X- ray absorption analysis demonstrated that the single- atom Fe in pFeSAN existed as the edge- hosted Fe- N₃ moieties. Previous studies have shown that the stronger adsorption of O₂ on Fe- N₃ than Fe- N₄, indicating the significantly larger oxygen affinity of pFeSAN with the unsaturated Fe- N₃ coordination than that of FeSAN with Fe- N₄ unit \(^{37,38}\) . Combining the O 1s XPS spectra (Supplementary Fig. 18d), the strong binding between O₂ and pFeSAN can effectively activate oxygen, being a decisive significance for its subsequently enhanced oxidase- like activity \(^{37,38}\) .
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+<center>Fig. 3 | Atomic structure characterization and composition of pFeSAN. a The Fe K-edge XANES and b the Fourier-transformed magnitudes of the experimental Fe K-edge EXAFS spectra of pFeSAN and the reference samples of Fe foil, \(\mathrm{Fe_2O_3}\) and FePc, respectively. c The experimental FT-EXAFS spectra and fitting curves of pFeSAN. d Wavelet transform for the Fe K-edge EXAFS signals of pFeSAN and the reference samples of Fe foil, \(\mathrm{Fe_2O_3}\) and FePc, respectively. </center>  
+
+Derived from the dynamic light- scattering (DLS) measurements, the average hydrodynamic diameter of pFeSAN in water was \(649.7 \pm 11.4 \mathrm{nm}\) , consistent with TEM (Supplementary Fig. 19). Moreover, the zeta potentials of pFeSAN in water and acetate buffer (pH 4.0) were - 24.3 and \(14.2 \mathrm{mV}\) , respectively, indicating the stable dispersion of pFeSAN under various environments (Supplementary Fig. 20). As evaluated, pFeSAN exhibited a long- term stability in water, acetate buffer (pH 4.0) and cell culture medium without the significant changes of the average hydrodynamic diameters during
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+one- week co- incubation (Supplementary Fig. 21), which benefits the subsequent biomedical applications<sup>39</sup>. Notably, the biomimetic synthesis strategy can be easily scaled up. 13.89 g of pFeSAN was prepared from a single batch reaction of 10 L (Supplementary Fig. 22). Importantly, the price of Hb (\$3.6/g, Shanghai Yuanye, S12021- 5g) is competitive to and even lower than that of FeCl<sub>3</sub>. Therefore, this biomimetic synthetic strategy is cost- effective and facile for massive production.  
+
+Overall, pFeSAN exhibits multiple advantages including (1) Pyrolysis of natural metalloprotein of Hb (2\~3 nm) forms abundant mesopores, leading to the maximum exposure of single metal sites and facilitated mass transfer during catalysis; (2) Benefiting from the protein structure of Hb containing only four Fe atoms, Hb as the Fe- precursor effectively suppresses the Fe aggregation during pyrolysis to give atomically dispersed Fe sites with the maximized utilization efficiency; (3) Unsymmetrically coordinated Fe- N<sub>3</sub> sites deliver a strong adsorption and activation of O<sub>2</sub>; (4) High stability under physiological conditions makes pFeSAN suitable for bio- utilizations; (5) Facile, low- cost and scalable synthesis of pFeSAN offers its potential for practical applications. All features suggested the promises of pFeSAN as artificial enzymes for biomedical applications.
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+<center>Fig. 4 | Oxidase-like activity evaluation. a-b Comparison of oxidase-like activities among various nanozymes. The reaction conditions were \(25^{\circ}\mathrm{C}\) for \(10\mathrm{min}\) in acetate buffer (pH 4.0). c-d Comparison of oxidase-like activities of pFeSAN at various temperature (10-70 \(^\circ \mathrm{C}\) ) and pH (2-12) conditions. e The durability of pFeSAN treated with acid or alkali for \(24\mathrm{h}\) . f Comparison of relative oxidase-like catalytic activities of different nanozymes. g Steady-state kinetic assay of pFeSAN and FeSAN with TMB as substrate. h Comparison of kinetics for pFeSAN and FeSAN. \(\mathrm{K_m}\) is the Michaelis-Menten constant. \(\mathrm{V_{max}}\) is the maximal reaction velocity. \(\mathrm{K_{cat}}\) is the catalytic rate constant. </center>  
+
+## Oxidase-like activity  
+
+Oxidase-like activity of pFeSAN was investigated with TMB as substrate in the acetate buffer (pH 4.0, Fig. 4a-b). pFeSAN rapidly catalyzed the oxidation of TMB (1 mM), yielding a blue-colored product with a maximum absorbance at \(652\mathrm{nm}\) . Comparatively, the ZIF- 8, Hb@ZIF- 8, C, BSA@ZIF and pC catalysts showed the bare capability for the TMB oxidation, demonstrating the atomically dispersed Fe sites as active centers.
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+Then, the synthetic parameters of pFeSAN were optimized to reach the highest activity. Initially, the pyrolysis temperatures were optimized from 700 to \(1000^{\circ}\mathrm{C}\) to give the catalysts donated as pFeSAN- T (T represented the pyrolysis temperatures). Their structural characterizations manifested that four pFeSAN catalysts preserved the dodecahedron- like morphology with the abundant mesopores and atomically dispersed Fe species (Fig. 2a- c, Supplementary Figs. 23,24). Among various catalysts, pFeSAN- 900 delivered the highest catalytic activity (Supplementary Fig. 25), in which the calcination temperature of \(900^{\circ}\mathrm{C}\) was employed for subsequent investigations. Besides, pFeSAN mentioned hereinafter referred to pFeSAN- 900, unless otherwise stated.  
+
+Simultaneously, the catalytic activity of pFeSAN was evaluated under various conditions, including environmental pH and temperatures. The catalysts exhibited a highly stabilized oxidase- like activity within a wide temperature window from 10 to \(70^{\circ}\mathrm{C}\) , suggesting its broad application scenarios under various temperatures (Fig. 4c). Analogy with the natural oxidase enzyme, pFeSAN showed a pH- depended catalytic performance, delivering the highest catalytic activity in weakly acidic media (pH=4, Fig. 4d) \(^{21,39}\) . The oxidase- like activity increased with the concentration of pFeSAN and delivered a catalytic activity exceeded \(50\%\) at \(20 \mu \mathrm{g / mL}\) (Supplementary Fig. 26). Therefore, the concentration of pFeSAN at \(20 \mu \mathrm{g / mL}\) , room temperature and pH=4 were chosen as the optimized reaction conditions for future studies. Also, pFeSAN after the pre- treatments with \(1.0 \mathrm{M} \mathrm{HCl}\) (pH = 0) or \(1.0 \mathrm{M} \mathrm{NaOH}\) (pH = 14) solutions displayed a satisfactory catalytic stability, which was attributed to the structural robustness of the Fe- \(\mathrm{N}_3\) conformation (Fig. 4e).
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+The oxidase- like activity of \(\mathrm{Fe_3O_4}\) and FeSAN as the comparative nanozymes were also evaluated at the same amount of Fe in pFeSAN (Supplementary Fig. 27). Quantitatively, the oxidase- like activity of pFeSAN was 8791- and 3.3- fold higher than that of \(\mathrm{Fe_3O_4}\) and FeSAN, respectively (Fig. 4f). Considering the same amount of the atomically dispersed Fe in pFeSAN and FeSAN, their dramatical difference in the oxidase- like activity could be only attributed to the well- regulated coordination environments and the constructed mesopores of pFeSAN. Then, the steady state kinetic analysis was performed on FeSAN and pFeSAN. The typical Michaelis- Menten curves were obtained by plotting the corresponding initial reaction rates and substrate concentrations (Fig. 4g). The derived \(\mathrm{K_m}\) (Michaelis constant) and \(\mathrm{K_{cat}}\) (catalytic constant), were used to assess the binding affinity between TMB substrate and nanozymes and the oxidase- like activity of nanozymes, respectively (Supplementary Fig. 28 and Table 2) \(^{21,32}\) . pFeSAN delivered a lower \(\mathrm{K_m}\) value (0.17 mmol/L) than FeSAN (0.29 mmol/L), suggesting a better affinity between TMB and pFeSAN. Meanwhile, the values of the maximum reaction velocity \(\mathrm{(V_{max})}\) and \(\mathrm{K_{cat}}\) of pFeSAN were both higher than those of FeSAN. \(\mathrm{V_{max}}\) of pFeSAN (1.67 \(\mu \mathrm{M / s}\) ) was 46.4- fold higher than that of FeSAN (0.036 \(\mu \mathrm{M / s}\) ). \(\mathrm{K_{cat}}\) of pFeSAN was \(2.6 \times 10^{6} / \mathrm{s}\) , which was 27.1- fold higher than that of FeSAN (9.6 \(\times 10^{4} / \mathrm{s}\) , Fig. 4h). Also, the mesoporous characteristics of pFeSAN delivered a 34.6% higher TMB adsorption capacity of pFeSAN than that of FeSAN in aqueous solutions (Supplementary Fig. 29). All results demonstrated that pFeSAN possessed a higher affinity towards TMB substrate and delivered a higher intrinsic catalytic activity than FeSAN, which could be attributed
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+to (1) the modulated local electronic structures for a strong substrate affinity and (2) the mesoporous structure of pFeSAN for the facilitated mass transfer and accessibility of Fe sites.  
+
+## Catalytic mechanism  
+
+Next, the catalytic mechanism of pFeSAN was explored. Initially, the \(\mathrm{O_2}\) levels were controlled for the catalytic TMB oxidation to identify the roles of oxygen<sup>21,39</sup>. pFeSAN showed negligible oxidase- like activity in the nitrogen- degassed environments, indicating that pFeSAN did not serve directly as the oxidants (Fig. 5a). Comparatively, pFeSAN delivered 2.2- fold higher oxidase- like activity in the \(\mathrm{O_2}\) - saturated solution than that under the air- saturated solution, illustrating \(\mathrm{O_2}\) indeed as the oxidants. Analogous with the majority of the previously reported oxidase- like nanozymes, pFeSAN as oxidase mimetics with oxygen as electron acceptor might generate reactive oxygen species (ROS, e.g. \(\cdot \mathrm{OH}\) , \(\mathrm{O_2}\) , \(\mathrm{O_2}\) ) to oxidize TMB<sup>1- 3</sup>. To examine this point, the \(\cdot \mathrm{O_2}\) inhibitor superoxide dismutase, \(\cdot \mathrm{OH}\) scavenger mannitol and \(\mathrm{O_2}\) quencher furfuryl alcohol were introduced into the catalytic reactions<sup>40,41</sup>. Surprisingly, the oxidase- like activity of pFeSAN was only slightly reduced in the presence of those quenchers, suggesting the barely generated free ROS herein (Supplementary Fig. 30a). Furthermore, the trapping agent 5,5- dimethyl- 1- pyrroline N- oxide was also employed to probe the possible involved active species by electron paramagnetic resonance (EPR)<sup>23,40</sup>. No apparently detectable signals of any ROS further proved that ROSs were not the main intermediates for the oxidase- like activity of pFeSAN (Supplementary Fig.
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+30b), which was very different from the majority of the previously reported nanozymes<sup>1- 3,39</sup>. These results imply that pFeSAN may mediate the complete \(\mathrm{O_2}\) - to- water reduction without the release of free ROS during oxidation, analogy with the catalytic pathways of natural \(\mathrm{CcO}\) (Fig. 5b)<sup>23,40</sup>.  
+
+![](images/Figure_5.jpg)
+
+<center>Fig. 5 | Oxidase-like catalytic mechanism of pFeSAN. a UV-vis absorption spectra of pFeSAN + TMB in air, \(\mathrm{O_2}\) -saturated, and \(\mathrm{N_2}\) -saturated acetate buffer (pH 4.0). b Schematic illustration of oxidase-like characteristics of pFeSAN-catalyzed TMB oxidation. c CV curves of pFeSAN and FeSAN in pH 4.0 acetate buffer containing TMB. d EIS spectra of pFeSAN and FeSAN. e Calculated electron transfer number derived from rotating ring-disk electrode and \(\mathrm{H_2O_2}\) yields of the pFeSAN. f EPR spectra of pFeSAN in the presence of excess phenyloxoiodine at 77 K. </center>
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+Afterwards, the electron transfer process analyzed by electrochemical methods was explored to understand the oxidase- like mechanisms of pFeSAN. Two pairs of oxidation- reduction peaks were observed in the cyclic voltammetry curve of TMB in the acetate buffer \(\mathrm{(pH = 4.0}\) , Fig. 5c) \(^{39,42}\) . These profiles indicated that the TMB electrooxidation catalyzed by pFeSAN and FeSAN proceeded via a two successive one-electron oxidation: (1) to yield an intermediate product TMB- free radical and then (2) to give the completely oxidized product quinonediimine. Compared with FeSAN, the redox peak intensity of TMB in the presence of pFeSAN was much stronger, indicating more available active sites of pFeSAN for more efficient TMB oxidation. The electrochemical impedance spectroscopy (EIS) measurements were conducted to reveal the charge- transfer kinetics between TMB and active sites. The semicircle diameter of pFeSAN was smaller than that of FeSAN, indicating a lower interfacial resistance and higher charge transfer efficiency of pFeSAN (Fig. 5d) \(^{43,44}\) .  
+
+To explore the electron transfer path during the oxidation, the rotating ring- disk electrode (RRDE) tests were performed. Figure 5e showed that the \(\mathrm{H}_2\mathrm{O}_2\) yield of pFeSAN remained below \(7.5\%\) over a wide potential range of 0.1–0.8 V. Derived from the RRDE test, the average electron transfer number (n) of pFeSAN was 3.7, indicating the oxygen activation on the atomically dispersed Fe through a four- electron oxygen reduction reaction pathway \(^{20,22,40,45}\) . This process requires four \(\mathrm{H}^+\) and four electrons \(\mathrm{(O_2 + 4H^+ + 4e^- \rightarrow 2H_2O)}\) for the complete \(\mathrm{O_2}\) - to- \(\mathrm{H}_2\mathrm{O}\) reduction. Thus, the electrochemical understandings of these stepwise proton and electron transfers reveal the essence of pFeSAN for its oxidase- like performance.
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+These results indicated that pFeSAN mediated the complete \(\mathrm{O_2}\) - to- \(\mathrm{H_2O}\) reduction without the ROS generation, consistent with EPR results (Supplementary Fig. 30b). Therefore, the intermediate of \(\mathrm{Fe(IV) = O}\) , usually present in the catalytic cycle of natural oxidases, is considered as the active transient state<sup>46</sup>. To verify the presence of the \(\mathrm{Fe(IV) = O}\) intermediate of pFeSAN, the EPR spectrum of the pFeSAN- enabled oxidation with excessive phenyloxoiodine was recorded at 77K. A typical diamond- shaped sign signal at \(\mathrm{g} = 2.03\) , consistent with \(\mathrm{n}^2\) - peroxo heme species, indicated the formation of \(\mathrm{Fe(IV) = O}\) intermediate in pFeSAN for oxidation (Fig. 5f)<sup>21,23</sup>. Therefore, the oxidase- like activity of pFeSAN proceeds through the \(\mathrm{O_2}\) - to- \(\mathrm{H_2O}\) pathway, analogy with that of natural \(\mathrm{CcO}\) .  
+
+![](images/Figure_6.jpg)
+
+<center>Fig. 6 | DFT theoretical calculation of oxidase-like activity over pFeSAN. a Proposed reaction pathways of \(\mathrm{O_2}\) reduction to \(\mathrm{H_2O}\) with optimized adsorption configurations on pFeSAN. b Corresponding free energy diagram for oxidase-like reaction on FeSAN and pFeSAN. </center>
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+To further reveal the origins of the oxidase- like activity of pFeSAN, density functional theory (DFT) calculations were performed on both Fe- N3 and Fe- N4 units. According to the proposed catalytic pathway of pFeSAN (Fig. 6a), the models of the isolated Fe sites with three- or four- coordinated pyridinic N incorporated within graphene matrix were built for calculations (Fig. 6b) \(^{21,23}\) . In acidic media, an O2 molecule underwent through (I) the initial adsorption on the isolated Fe sites to give \(*O_{2}\) ; (II) the subsequent protonation to form \(*OOH\) on the top of Fe atoms; (III) the dissociation of the \(*OOH\) intermediate associates with H+ to give the active \(*O\) intermediates and H2O; (IV) the protonation of \(*O\) to give \(*OH\) ; and (V) the recombination of \(*OH\) and H+ to generate H2O. The calculated adsorption energy of \(*O_{2}\) on Fe- N3 was 0.11 eV, which was larger than that of \(*O_{2}\) on Fe- N4 (0.03 eV), indicating a stronger O2 adsorption on pFeSAN and thereafter its effective activation with the weakened O- O bond strength and elongated O- O bond length (1.297 Å) in comparison with 1.23 Å of free O2 and 1.295 Å of \(*O_{2}\) on Fe- N4. DFT calculations indicate the step III ( \(*OOH + H^{+} + e^{-} \rightarrow *O + H_{2}O\) ) as the rate- determined step. In comparison with that of FeSAN (2.20 eV), the smaller \(\Delta G_{\mathrm{III}}\) of pFeSAN (1.92 eV) suggests its higher oxidase- like activity. Thus, the coordination- unsaturated Fe in Fe- N3 exhibits the strong adsorption and activation of O2, promotes the O- O cleavage of the adsorbed \(*OOH\) and generates the active \(*O\) intermediates, thereafter greatly improving the oxidase- like activity of pFeSAN.
+
+<--- Page Split --->
+![](images/Figure_7.jpg)
+
+<center>Fig. 7 | Analytical performance of the pFeSAN-based colorimetric system for GSH detection. a Schematic illustration of the pFeSAN-based GSH biosensing system. b The linear relationship between the relative activity and the GSH concentration ranges from \(50~\mathrm{nM}\) to \(1\mathrm{mM}\) . Inset shows the photograph of reaction solution in the different concentration of GSH. c Comparison of the performance of the detection of GSH with different methods. d Selectivity toward GSH of the pFeSAN-based colorimetric system against common interfering chemicals. The numbers from 1 to 15 are Ctrl, GSH, \(\mathrm{Na^{+}}\) , \(\mathrm{Ca^{2 + }}\) , \(\mathrm{Mg^{2 + }}\) , \(\mathrm{K^{+}}\) , glutamic acid, tryptophan, arginine, glycine, cysteine, ascorbic acid, glucose, glucose oxidase and BSA. e The linear relationship between the variation of relative activity and the number of cells. The inset: photographs of reaction solution with different cell numbers. f DAB staining was performed to evaluate the level of GSH in tumor-bearing liver tissue. Scale \(\mathrm{bar} = 200 \mu \mathrm{m}\) . g Ki67 staining was performed to assess the proliferation of tumor cells in tumor-bearing liver tissue. Scale \(\mathrm{bar} = 200 \mu \mathrm{m}\) . h Pearson's correlation coefficient of the oxidized DAB (brown)/Ki67 (red fluorescence), which appeared in tissue slices of tumor-bearing liver tissue (Pearson's coefficient \(\mathrm{r} = -0.76\) ). Bars represent the mean \(\pm \mathrm{SEM}\) ; \*\*, \(\mathrm{p} < 0.01\) . </center>
+
+<--- Page Split --->
+
+## GSH detection  
+
+GSH is an essential antioxidant to maintain the redox balance of biological systems, and its abnormal levels are commonly associated with various diseases including tumors, infections, and neurodegenerative disorders<sup>25- 27,47- 50</sup>. The current analytical techniques often require sophisticated equipments and time- consuming sample preparation processes<sup>25,47</sup>. Especially, the GSH levels of tumor cells (0.5–1.0 mM) are much higher than those normal cells. However, it’s lack of a facile yet accurate method to detect GSH at high concentrations in tumor tissue. GSH with the antioxidant capability can reduce blue oxTMB into colorless TMB, thus providing a quantitative methodology to probe the concentration of GSH and offering a visual methodology to monitor the GSH distribution in biological tissues (Supplementary Fig. 31). With the pre- mixture with GSH, the absorbance of the TMB- pFeSAN system at 652 nm insignificantly increased with time due to the reductive ability of GSH (Supplementary Fig. 32). Besides, the oxidase- like activity and FT- IR spectrum of the GSH- pretreated pFeSAN were analogous to that of the untreated pFeSAN, confirming that GSH did not affect the oxidase- like activity of pFeSAN but only reduced the blue ox- TMB into colorless TMB (Supplementary Figs. 33,34).  
+
+Inspired by the above results, the combination of the reductive GSH and the oxidase- like pFeSAN establishes a simply and rapidly colorimetric GSH assay system (Fig. 7a). As expected, the absorbance decreased with the increased concentrations of GSH and exhibited a linear relation within a broad GSH window (0.05 μM–1.0 mM, Fig. 7b, Supplementary Fig. 35). The detection systems were featured by a broad range of GSH
+
+<--- Page Split --->
+
+and a low derived LOD of \(2.4 \mathrm{nM}\) , which were superior to the majority of other previously reported GSH detection systems (Fig. 7c, Supplementary Fig. 35 and Table 3) \(^{26,49}\) . Moreover, the color changes of the corresponding measurements in the presence of GSH with various concentrations could be clearly distinguished by the visual inspection (Fig. 7b insert), giving the prerequisites for subsequent visualization of the tissue GSH assay. Furthermore, the pFeSAN- based GSH detection system could resist the interferences of various metal ions, amino acids, and proteins, validating well anti- interference and specificity of the pFeSAN- based GSH detection system (Fig. 7d). The recovery tests were also employed to evaluate the accuracy of this method by standard addition method. The recovery rates of the pFeSAN- based GSH detection system were in the range of \(98\%\) to \(106.1\%\) in compassion with the recovery rates of commercial GSH assay kit in the range of \(85\%\) to \(107.5\%\) , demonstrating the satisfactory feasibility in practical sample analysis (Supplementary Table 4). Besides, all relative standard deviations (RSD) were \(< 2.5\%\) , which illustrated the accuracy and practically of the pFeSAN- based bioanalysis system for the GSH detection, especially for those at high GSH levels.  
+
+The identification and analysis of tumor is particularly important for the survival of cancer patients. Therefore, it is highly desirable to develop sensitive, specific, and rapid methods to diagnose tumor. As a cancer biomarker, the high GSH levels (0.5–1.0 mM) play important roles in maintaining intracellular redox homeostasis in tumor cells. Fortunately, the exceptionally high detection upper limit of pFeSAN is well suitable to detect GSH with high levels. pFeSAN was applied to monitor the GSH levels in normal
+
+<--- Page Split --->
+
+liver cells (AML12) and liver tumor cells (Hepa 1- 6). The absorption of AML12 cells and Hepa 1- 6 cells at \(652~\mathrm{nm}\) gradually declined with the increased number of cells from \(1000\mathrm{to}8\times 10^{5}\) due to the effective reduction by the intracellular GSH. Comparatively, the inhibition efficiency of two types of cells on the chromogenic reaction was very different, which was apparently distinguished by the naked eye (Fig. 7e and Supplementary Fig. 36). The GSH level of Hepa 1- 6 cells was higher than that of normal AML12 cells at the same cell density. When the cell densities reached \(0.5\times 10^{5} / \mathrm{mL}\) , the absorbance of the two colorimetric systems was extremely significantly different ( \(^{**}p< 0.01\) ). The above results suggested that pFeSAN- based colorimetric systems could serve as a promising platform for the rapid and ultrasensitive GSH detection.  
+
+Analysis of the tumor area in clinical practice enables the identifications of the boundary between tumor and normal tissue and the site of surgical resection<sup>51</sup>. The tumor microenvironment usually expresses an excess of GSH in comparison with the health counterparts. Herein, a visualization analysis system was constructed for the real- time and specific localization of tumor regions by using the intratumorally overexpressed GSH in liver orthotopic transplantation tumors. Briefly, the slices from the tumor- bearing liver tissue were incubated with pFeSAN and chromogenic substrate (3,3'- Diaminobenzidine, DAB) for 6 min. After the incubation, the DAB substrate displayed brown oxidation product signal in the normal area of liver tissue section under the bright field microscope (Fig. 7f). However, due to the high GSH expression in tumor tissue, the oxidized DAB products were reduced by GSH into colorless DAB again,
+
+<--- Page Split --->
+
+leading to no obvious brown signal in tumor area. Therefore, a clear distinction between tumor cells and adjacent health cells was observed in the para- carcinoma liver tumor tissue area, demonstrating that this pFeSAN- GSH assay methodology successfully distinguished tumor tissue from normal tissue. Next, the immunofluorescence staining as the gold standard was performed to verify the accuracy of this methodology (Fig. 7g). The expression of Ki67 protein as a marker in tumor pathology is strongly associated with tumor growth and proliferation<sup>52,53</sup>. The above results demonstrated that the intensity of the oxidized DAB decreased with the increase of tumor proliferation. In addition, Pearson's correlation coefficients of oxidized DAB (brown) and Ki67 (red) was - 0.76, further indicating a high negative correlation (Fig. 7h). This was attributed to the oxidized DAB (brown) being reduced to a colorless product with the increase of GSH in tumor tissues. Compared with fluorescence images of classic tumor proliferation marker (Ki- 67) in adjacent sections of the same tumor- bearing liver tissue, the pFeSAN- GSH visualization detection exhibited a similar accuracy and a much shorter analysis time (6 min) than the conventional immunofluorescence diagnostic of tumor (>5h) and avoided complex operation process as well<sup>54</sup>. All results demonstrated the facile, accessibility and precision of the developed pFeSAN- GSH visualization analysis with great potentials for clinical tumor tissue detection.  
+
+## Conclusion  
+
+Inspired by the structure of natural \(C_cO\) , we demonstrated a biomimetic synthetic strategy for scalable synthesis of porous Fe- N<sub>3</sub> single- atom nanozymes using Hb as
+
+<--- Page Split --->
+
+both template and Fe- source, which delivered a high oxidase- like activity. The mesoporous features of pFeSAN significantly promoted mass transport and maximumly exposed active iron sites during reaction. The Fe- N₃ moiety enabled a strong oxygen adsorption/activation, which underwent the complete O₂- to- H₂O pathway for oxidation, analogy with that of the natural C₆O. Taking all advantages mentioned above, pFeSAN exhibited exceptional oxidase- like activity and durability. Based on the outstanding oxidase- like performance for TMB oxidation and the reduction capability of GSH for oxTMB, the pFeSAN- GSH system was constructed for the colorimetric detection of GSH with high sensitivity, wide detecting range and good specificity. Colorimetric analysis based on the pFeSAN assay was further developed for real- time and rapid detection of GSH in biological tissues and also demonstrated as the visualization methodology for the rapid and precise identifications of the tumor boundary. This work may open promising avenues for the design of single- atom nanozymes as alternatives of natural enzymes for biomedical applications.  
+
+## Methods  
+
+## Materials  
+
+Hemoglobin (Hb) and glucose oxidase (GOx) were purchased from Shanghai Yuanye Bio- Technology Co. Ltd. Fe₃O₄, bovine serum albumin, sodium acetate (NaAc, >99%), glacial acetic acid (HAC, 99.99%), anhydrous dimethylsulfoxide (DMSO), iodosylbenzene (97%) and acetonitrile (99.9%) were purchased from Aladdin Co., Ltd. Heme, 3,3',5,5'- tetramethylbenzidine, 2- methylimidazole, Zn(NO₃)₂·6H₂O, superoxide
+
+<--- Page Split --->
+
+dismutase, L- ascorbic acid, L- tryptophan, L- glycine, glutamic acid, mannitol, furfuryl alcohol, L- cysteine, L- arginine, glutathione, 5,5- dimethyl- 1- pyrroline N- oxide, nafion solution (99%), methanol (99.9%) and 4',6- diamidino- 2- phenylindole (DAPI) were purchased from Sigma- Aldrich. Glutathione assay kit and 3,3'- Diaminobenzidin was purchased from Shanghai Beyotime Biotechnology Co., Ltd. Dulbecco's Modified Eagle Medium (DMEM), Dulbecco's Modified Eagle Medium/Nutrient Mixture F- 12 (DMEM/F12), penicillin/streptomycin and fetal bovine serum (FBS) was obtained from Gibco. All aqueous solutions were prepared with deionized water (18.2 MΩ·cm, Millipore).  
+
+## Characterizations  
+
+Scanning electron microscope (SEM) images were obtained from a Verios G4 ultra high resolution field emission scanning electron microscope (FEI, USA). Transmission electron microscope images, elemental mapping, electron energy loss spectroscopy and high- angle annular dark- field scanning transmission electron microscope (HADDF- STEM) images were performed on a Titan Themis G2 transmission electron microscope (FEI, USA) operated at 300 kV. Nanoparticle size distribution and zeta potential were measured by a Zetasizer Nano ZS90 nanoparticle size analyzer (Malvern, U.K.). Fourier transform infrared spectroscopy were obtained from a Nicolet iS20 Fourier transform infrared spectrometer (Thermo Scientific, USA). Thermogravimetric analysis was performed on a TGA8000 (PerkinElmer, USA) instrument from 30 °C to 1000 °C at a ramping rate of 5 °C min<sup>- 1</sup> in the Ar. X- ray diffraction patterns were recorded by the D8 Advance X- ray diffractometer (Bruker, USA). X- ray photoelectron
+
+<--- Page Split --->
+
+spectroscopy was conducted on a Thermo Scientific™ K- Alpha™+ spectrometer equipped with a monochromatic Al Kα X- ray source (1486.6 eV) operating at 100 W. Samples were analyzed under vacuum (P < 10<sup>-8</sup> mbar) with a pass energy of 150 eV (survey scans) or 50eV (high- resolution scans). All peaks were calibrated with C1s peak binding energy at 284.6 eV for adventitious carbon. The Fe loadings were determined by inductively coupled plasma atomic emission spectrometry (Agilent 5110, USA). Raman spectra were explored with a Scientific LabRAM HR Evolution Raman spectrometer (HORIBA, Japan) equipped with a 532 nm laser. BET surface area, nitrogen adsorption and desorption isotherm linear plot and pore diameter distributions of various catalysts were observed by an automatic surface area and porosity analyzer (Micromeritics ASAP 2460, USA). The catalysts were degassed at 150 °C for 8 h and then tested for nitrogen adsorption and desorption. Ultraviolet and visible spectrophotometry absorption spectra were recorded using a TU- 1950 UV- vis spectrophotometer in the wavelength range of 200- 800 nm. The nitrogen vacancies were detected by an EMXplus 10/12 electron paramagnetic resonance spectrometer (Bruker, German). The electron paramagnetic resonance of pFeSAN was measured in acetonitrile/PhIO solution at 77 K to analyze its intermediate state, in which the experimental conditions were controlled at the frequency of 9.853 GHz, the microwave power of 20.0 mW, the center field of 3510 G, the modulation amplitude of 2.0 G and the time constant of 40.96 ms.  
+
+The extended X- ray absorption fine structure measurements of the catalysts were carried out the 21A X- ray nanodiffraction beamline of Taiwan Photon Source (TPS),
+
+<--- Page Split --->
+
+National Synchrotron Radiation Research Center (NSRRC). This beamline adopted the 4- bounce channel- cut Si (111) monochromator for mono- beam X- ray nanodiffraction and X- ray absorption spectroscopy. The end- station equipped with three ionization chambers and Lytle/SDD detector after the focusing position of KB mirror for transmission and fluorescence mode X- ray absorption spectroscopy. The photon flux on the sample was range from \(1 \times 10^{11} - 3 \times 10^{9}\) photon/sec for X- ray energy from 6- 27 keV.  
+
+## Preparation of catalysts  
+
+The catalyst precursors were prepared by mixing \(9\mathrm{mL}\) of methanol solution containing 2- methylimidazole (0.308 g) with \(1\mathrm{mL}\) of aqueous Hb (60 mg/mL) or \(1\mathrm{mL}\) of aqueous heme (2.2 mg/mL) or \(1\mathrm{mL}\) aqueous BSA (1 mg/mL) or \(1\mathrm{mL}\) of \(\mathrm{H}_2\mathrm{O}\) . Afterwards, 10 mL of methanol solution containing 0.279 g of \(\mathrm{Zn(NO_3)_2\cdot 6H_2O}\) was added. After the continuous stirring for \(6\mathrm{h}\) at room temperature, the solids were centrifuged (6000 rpm for \(5\mathrm{min}\) ), and washed with methanol three times. The collected solids were dried at \(60^{\circ}\mathrm{C}\) to yield Hb@ZIF- 8, Heme@ZIF- 8, BSA@ZIF- 8 and ZIF- 8 as the precursors of respective catalysts. For synthesis of porous Fe single atom nanozymes (pFeSAN), the Hb@ZIF- 8 precursors were placed in quartz boats in a tube furnace under a flowing of \(\mathrm{Ar}(50\mathrm{cm}^3 /\mathrm{min})\) , raised up to the desired temperatures with a ramping rate of \(5^{\circ}\mathrm{C}\mathrm{min}^{- 1}\) , and then kept at the target temperatures for \(3\mathrm{h}\) . The Fe single atom nanozymes (FeSAN), carbon supports (C) and porous carbon supports (pC) were also synthesized through the identical approach except with Heme@ZIF- 8, BSA@ZIF- 8 and ZIF- 8 as precursors, respectively.
+
+<--- Page Split --->
+
+## Oxidase-like activity  
+
+The oxidase- mimicking activity was evaluated by using TMB as substrate at \(25^{\circ}\mathrm{C}\) under acidic conditions (pH 4.0, 0.1 M HAc- NaAc buffer). The absorbance of oxTMB was recorded at \(652~\mathrm{nm}\) through TU- 1950 UV- vis spectrophotometer. In detail, \(20~\mu \mathrm{L}\) of various catalysts aqueous solution (1 mg/mL) and \(20~\mu \mathrm{L}\) of TMB (50 mM DMSO) were sequentially added into HAc- NaAc buffer (0.1 M, pH 4.0). The final volume was fixed to \(1\mathrm{mL}\) by adding deionized water (18.2 MΩ·cm). After 10 min, the catalytic oxidation of TMB was determined by their UV- vis absorption spectra.  
+
+To evaluate the activity of pFeSAN to reaction temperature and pH, its oxidase- like activity was measured at various temperatures (10, 20, 30, 40, 50, 60 and \(70^{\circ}\mathrm{C}\) ) or medias with various pH values (2, 4, 6, 8, 10 and 12).  
+
+To evaluate the stability of pFeSAN, the catalysts were treated with 1 M HCl or 1 M NaOH solutions for \(24\mathrm{h}\) , and then centrifuged off and washed with deionized water for 6 times before testing their oxidase- mimicking activity.  
+
+## Enzymatic kinetic analysis  
+
+Further kinetic experiments were performed for TMB oxidation at different concentrations by using various catalysts. The experiment conditions were similar to that of oxidase- like activity, in which \(1.0\mathrm{mL}\) HAc- NaAc buffer (0.1 M pH 4) containing \(20~\mu \mathrm{L}\) of catalysts (1.0 mg/mL) aqueous solution and different volumes of TMB solution (50 mM DMSO). The kinetic constants ( \(\mathrm{K_m}\) and \(\mathrm{V_{max}}\) ) were obtained according to Michaelis- Menten equation: \(\mathrm{V} = \mathrm{V_{max}}[\mathrm{S}] / (\mathrm{K_m} + [\mathrm{S}])\) , where \(\mathrm{V}\) was the initial velocity, [S] was the concentration of the substrate, \(\mathrm{K_m}\) was the
+
+<--- Page Split --->
+
+Michaelis- Menten constant, and \(\mathrm{V}_{\mathrm{max}}\) was the maximal reaction velocity. The value of \(\mathrm{K}_{\mathrm{m}}\) was equivalent to the substrate concentration at which the rate of conversion was half of \(\mathrm{V}_{\mathrm{max}}\) . \(\mathrm{V}_{\mathrm{max}}\) and \(\mathrm{K}_{\mathrm{m}}\) were calculated from the Lineweaver- Burk double- reciprocal plot (1/V and 1/[S]). The absorbance signal was converted to concentration by the Beer- Lambert law ( \(\mathrm{A} = \epsilon \mathrm{bc}\) ) where \(\epsilon = 39000 \mathrm{M}^{- 1}\mathrm{cm}^{- 1}\) at \(652 \mathrm{nm}\) for the oxidized TMB (oxTMB).  
+
+## Adsorption experiment  
+
+The relative difference in the TMB adsorption capability of pFeSAN and FeSAN was determined by the obvious UV- vis absorption peak of TMB at \(285 \mathrm{nm}\) . Firstly, the absorbance of different concentrations of TMB solutions in HAc- NaAc buffer (0.1 M, pH 7.0) were measured to plot a standard curve. Then \(1.0 \mathrm{mL}\) of NaAc solution (0.1 M HAc- NaAc buffer, pH 7.0) containing \(50 \mu \mathrm{M}\) TMB and \(5 \mu \mathrm{g} / \mathrm{mL}\) catalysts were stirred for \(10 \mathrm{min}\) . Then, the supernatants were centrifuged off to measure the absorbance.  
+
+## Catalytic mechanism of pFeSAN  
+
+To verify the dependence of the oxidase- like activity of pFeSAN on oxygen, taking it as a control in air, nitrogen or oxygen was introduced into the reaction flask for 30 minutes before measuring oxidase- like activity. Subsequently, the absorbance was measured after \(10 \mathrm{min}\) of sealing reaction.  
+
+The influences of various scavengers on the oxidase- like activity of pFeSAN were investigated by comparing the activity in the absence or presence of ROS scavengers. In the catalytic oxidation of TMB, the pFeSAN was added firstly into the HAc- NaAc
+
+<--- Page Split --->
+
+buffer (pH 4.0) containing SOD (0.5 mg/mL) or mannitol (50 mM) or FFA (5 mM) and TMB (1 mM). Then, the absorbance at 652 nm was measured respectively. The free radicals (such as \(\cdot \mathrm{OH}\) and \(\cdot \mathrm{O}_2\) ) in reaction systems were examined by a Bruker spectrometer in the HAc- NaAc (pH 4 or pH 7) buffer, using DMPO as the spin- trapping agent.  
+
+## Electrochemical analysis  
+
+The catalyst ink was prepared by dispersing the nanozymes (4 mg) through sonication in a mixed solvent (1 mL) containing \(32~\mu \mathrm{L}\) of a \(5.0\mathrm{wt}\%\) Nafion solution, \(200~\mu \mathrm{L}\) of ethanol and \(768~\mu \mathrm{L}\) of deionized water. The cyclic voltammetry (CV) curves were evaluated using a three- electrode system on an electrochemical workstation (CHI 760E, Shanghai Chenhua, China), including the carbon paper (CP) as the working electrode, a graphite rod as the counter electrode, and a saturated calomel electrode (SCE) as the reference electrode. Briefly, \(10~\mu \mathrm{L}\) of the ink was loaded onto CP with a catalyst loading of \(0.56\mathrm{mgcm^{- 2}}\) . The CV tests were performed in an \(\mathrm{O}_2\) - saturated NaAc solution (0.1 M, pH 4.0) with TMB (1mM). All potential values were calibrated to the reversible hydrogen potential \(\mathrm{(E_{RHE})}\) \(\mathrm{(E_{RHE} = E_{SCE} + 0.241 + 0.0592\times pH)}\) . The CV measurements were recorded at a scan rate of \(50\mathrm{mV}\mathrm{s}^{- 1}\) in a potential range of - 0.2- 1.2 V.  
+
+To verify the electron transfer number, an aliquot \((20~\mu \mathrm{L})\) of the ink was then dropped on a rotating ring disk electrode (RRDE) glassy carbon electrode with a diameter of 4
+
+<--- Page Split --->
+
+mm and a loading of \(0.64\mathrm{mgcm}^{- 2}\) . The RRDE measurements were carried out at a rotating speed of \(1200\mathrm{rpm}\) . The hydrogen peroxide \(\mathrm{(H_2O_2)}\) yield \((\%)\) and the electron transfer number (n) can be calculated based on the Equation:  
+
+\[\mathrm{H_2O_2(\%) = 200I_D/N(I_D + I_R/N)}\]  
+
+\[\mathrm{n = 4I_D / (I_D + I_R / N)}\]  
+
+where \(\mathrm{I_D}\) is the disk current, \(\mathrm{I_R}\) is the ring current, and N is the collection efficiency of the ring electrode (0.39 in this work) \(^{17}\) .  
+
+## Density functional theory (DFT) calculation  
+
+All the spin- polarized DFT calculations are performed by the Vienna Ab initio Simulation Package (VASP) with the projector augmented wave (PAW) method \(^{55}\) . The exchange- functional is treated using the generalized gradient approximation (GGA) with Perdew- Burke- Ernzerhof (PBE) functional. The energy cutoff for the plane wave basis expansion was set to \(400\mathrm{eV}\) . Partial occupancies of the Kohn–Sham orbitals were allowed using the Gaussian smearing method and a width of \(0.2\mathrm{eV}\) . The single layer graphene with the active center of \(\mathrm{Fe - N_4}\) was built, where one of the coordinated N was removed to build the structure of \(\mathrm{Fe - N_3}\) . The k- point of \(2\times 2\times 1\) was used in the Brillouin zone for all surface structure optimization. The self- consistent calculations apply a convergence energy threshold of \(10^{- 5}\mathrm{eV}\) , and the force convergency was set to \(0.05\mathrm{eV / \AA}\) . The reaction free energy was calculated following the computational hydrogen electrode (CHE) model. The free energy corrections were considered at the temperature of \(298\mathrm{K}\) , following:  
+
+\[\Delta \mathrm{G} = \Delta \mathrm{E} + \Delta \mathrm{GZPE} + \Delta \mathrm{GU} - \mathrm{T}\Delta \mathrm{S}\]
+
+<--- Page Split --->
+
+where \(\Delta \mathrm{E}\) , \(\Delta \mathrm{GZPE}\) , \(\Delta \mathrm{GU}\) , and \(\Delta \mathrm{S}\) refer to the DFT calculated energy change, the correction from zero- point energy, the correction from inner energy and the correction from entropy \(^{56}\) .  
+
+The solvent effect was considered due to the stabilization of adsorbate from the H- bond network in the. A stabilization of - 0.17, and - 0.20 eV were considered for \(\mathrm{OH^{*}}\) , and \(\mathrm{OO}\mathrm{H}^{*}\) according to previous study \(^{57}\) .  
+
+## Colorimetric detection of GSH based on the pFeSAN  
+
+In the standard procedure, the assays were performed in 1 mL HAc- NaAc buffer (0.1 M, pH 4.0) with different concentrations of GSH (0.05, 0.5, 2.5, 5, 12.5, 25, 50, 100, 150, 200, 250, 500 or \(1000~\mu \mathrm{M}\) ), where the TMB and pFeSAN concentrations were 1 mM and \(20~\mu \mathrm{g / mL}\) , respectively. After 10 min of incubation at room temperature, a standard curve was established by measuring the UV- vis absorbance of solutions.  
+
+## Anti-interference evaluation  
+
+To explore anti- interference of the GSH detection system, various ions and molecules were independently mixed and then the peak intensity at \(652~\mathrm{nm}\) was measured after 10 min of incubation. Specifically, \(20~\mu \mathrm{L}\) of pFeSAN (1 mg/mL) and \(20~\mu \mathrm{L}\) of TMB (50 mM) were added into HAc- NaAc buffer (0.1 M, pH 4.0), which contains \(0.1~\mathrm{mM}\) GSH, \(\mathrm{Na^{+}}\) , \(\mathrm{Ca^{2 + }}\) , \(\mathrm{Mg^{2 + }}\) , \(\mathrm{K^{+}}\) , Glu, Trp, Arg, Gly, Cys, L- AA, glucose, GOx or BSA. The final volume was fixed to \(1~\mathrm{mL}\) by adding deionized water. After reacting at room temperature for 10 min, the activity of pFeSAN were recorded in the presence of various ions or molecules according to the standard procedure as mention above.
+
+<--- Page Split --->
+
+## Effect of GSH on the pFeSAN  
+
+Effect of GSH on the pFeSANThe pFeSAN was incubated with GSH (1 mM) for 10 min and then collected by centrifugation and thorough washing by pure water for three times to obtain the GSH- pretreated pFeSAN. To analyze the effect of GSH on the activity of the pFeSAN nanozyme, 20 μL of pFeSAN or GSH- pretreated pFeSAN (1 mg/mL), 20 μL of TMB (50 mM) and 960 μL of HAc- NaAc buffer (pH 4.0) were mixed and incubated at 25°C for 10 min. The absorbance of each reaction solution at the wavelength of 500- 800 nm was recorded.  
+
+## Detection of GSH in cell samples  
+
+Hepa 1- 6 cells were cultured in DMEM medium (10% FBS and 1% penicillin/streptomycin) at \(37^{\circ}\mathrm{C}\) in a humidified atmosphere containing \(5\% \mathrm{CO}_2\) . AML- 12 cells were grown in DMEM/F12 medium with 10% FBS and 1% penicillin/streptomycin at \(37^{\circ}\mathrm{C}\) . To prepare Hepa 1- 6 and AML- 12 cell samples, the cells were digested by trypsin- ethylenediaminetetraacetic acid and then re- suspended in phosphate buffered saline (10 mM PBS, \(\mathrm{pH} = 7.4\) ).  
+
+Then, 20 μL of pFeSAN (1 mg/mL) and 20 μL of TMB (50 mM DMSO) were sequentially added into HAc- NaAc buffer (0.1 M pH 4) and the final volume was fixed to 1 mL, which contained different amounts of Hepa 1- 6 or AML- 12 (1000, 10000, 50000, 100000, 200000, 300000, 500000 or 800000). After 10 min, the catalytic oxidation of TMB was investigated by the UV- vis absorption spectra.
+
+<--- Page Split --->
+
+## Mice and tumor-bearing mouse model  
+
+C57BL/6 mice (7 weeks) were maintained in a specific pathogen- free facility. All animal experiments were approved by the Animal Experiment Administration Committee of the Fourth Military Medical University to ensure ethical and humane treatment of animals (KY2022593- 1). For orthotopic liver tumor models, the Hepa1- 6 cells \((5 \times 10^{6})\) were suspended in \(25 \mu \mathrm{L}\) of Matrigel (Sigma- Aldrich) and inoculated into the liver parenchyma of the left lobe for in situ liver tumor model. Whereafter, C57BL/6 mice were euthanized for three weeks after inoculation, and part of the tumor- bearing liver tissue for immunofluorescence staining and intrahepatic GSH detection.  
+
+## Intrahepatic GSH detection  
+
+In order to analyze the GSH distribution within the tumor- bearing liver tissue, the liver samples were subjected to the frozen section and then stained by pFeSAN and DAB for 5 min. Then, the stained tissues were monitored by using an inverted microscope.  
+
+## Immunofluorescence staining  
+
+The tumor- bearing liver tissues from C57BL/6 mice were frozen section, and then the slices were permeabilized with \(0.3\%\) Triton X- 100 solution for \(15 \mathrm{~min}\) at room temperature. After blocking the slices with \(5\%\) BSA for \(30 \mathrm{~min}\) at \(37^{\circ} \mathrm{C}\) , tumor- bearing liver tissue slices were incubated with Ki67 antibody (1:250, Abcam, ab16667) overnight at \(4^{\circ} \mathrm{C}\) . Afterwards, the slices were washed for three times with PBS and stained with secondary antibody Goat Anti- Rabbit IgG- Alexa Fluor 594 (1:200, Abcam, ab150080) for \(1 \mathrm{~h}\) at \(25^{\circ} \mathrm{C}\) . Finally, the tissue sections were washed thoroughly with
+
+<--- Page Split --->
+
+PBS, counterstained with DAPI and imaged under a laser scanning confocal microscope (FV- 1000, Olympus, Japan).  
+
+## Statistical analysis  
+
+The data were analyzed using Origin software (version 2018). All experiments were repeated at least 3 times and presented as mean \(\pm\) SD. Asterisks indicate significant differences ( \(^{**}p < 0.01\) ), analyzed by unpaired Student's two- sided t test.  
+
+## Data availability  
+
+The all data generated in this study are provided in the Supplementary Information/Source Data file, or from the corresponding authors upon reasonable request. Source data are provided with this paper.  
+
+## Acknowledgements  
+
+We acknowledge the financial support from the National Natural Science Foundation of China (52002314 and 21872109). Authors also acknowledge the support from Fundamental Research Funds for the Central Universities (D5000210635 and D5000210829). The calculations were supported by TianHe- 2 at Shanxi Supercomputing Center of China and Central for High Performance Computing of Northwestern Polytechnical University.  
+
+## Author contributions  
+
+Z.T. and Y.Q. conceived and designed the project. D.C., Z.X., Z.G., W.G., X.T., and W.L. performed the experiments and analyzed the data. Z.T. and Y.Q. supervised the
+
+<--- Page Split --->
+
+project. J.Z., X.Z. and S.Z analyzed data. D.C., Z.T. and Y.Q. wrote and editing of the manuscript. All authors discussed the results and contributed to the preparation.  
+
+## Competing interests  
+
+The authors declare that they have no conflict of interest.  
+
+## Additional information  
+
+Supplementary information The online version contains supplementary material available at  
+
+Correspondence and requests for materials should be addressed to Zhimin Tian and Yongquan Qu.  
+
+## References  
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+
+<--- Page Split --->
+
+## Supplementary Files  
+
+This is a list of supplementary files associated with this preprint. Click to download.  
+
+SupportingInformation.pdf
+
+<--- Page Split --->

preprint/preprint__1ec0e20dfe1397bcc4c3b75dcfb56d1f19c1e910da1612eea66abbbed7b5cf35/preprint__1ec0e20dfe1397bcc4c3b75dcfb56d1f19c1e910da1612eea66abbbed7b5cf35_det.mmd

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+<|ref|>title<|/ref|><|det|>[[44, 106, 911, 208]]<|/det|>
+# Bioinspired porous three-coordinated single-atom Fe nanozyme with oxidase-like activity for tumor visual identification via glutathione  
+
+<|ref|>text<|/ref|><|det|>[[42, 230, 744, 740]]<|/det|>
+Da Chen  Northwestern Polytechnical University  Zhaoming Xia  Tsinghua University  Zhixiong Guo  Northwestern Polytechnical University  Wangyan Gou  Northwestern Polytechnical University  Junlong Zhao  The Fourth Military Medical University  https://orcid.org/0000- 0002- 6148- 5641  Xuemei Zhou  Wenzhou University  Xiaoe Tan  Northwestern Polytechnical University  Wenbin Li  Northwestern Polytechnical University  Shoujie Zhao  Fourth Military Medical University  Zhimin Tian  Northwestern Polytechnical University  Yongquan Qu ( \(\square\) yongquan@mail.xjtu.edu.cn)  Northwestern Polytechnical University  https://orcid.org/0000- 0002- 6202- 1929  
+
+<|ref|>sub_title<|/ref|><|det|>[[45, 777, 101, 794]]<|/det|>
+## Article  
+
+<|ref|>title<|/ref|><|det|>[[45, 814, 135, 832]]<|/det|>
+# Keywords:  
+
+<|ref|>text<|/ref|><|det|>[[45, 852, 297, 871]]<|/det|>
+Posted Date: May 19th, 2023  
+
+<|ref|>text<|/ref|><|det|>[[44, 890, 473, 909]]<|/det|>
+DOI: https://doi.org/10.21203/rs.3.rs- 2922979/v1
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[42, 44, 910, 87]]<|/det|>
+License: © ① This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License  
+
+<|ref|>text<|/ref|><|det|>[[42, 105, 530, 125]]<|/det|>
+Additional Declarations: There is NO Competing Interest.  
+
+<|ref|>text<|/ref|><|det|>[[42, 160, 945, 204]]<|/det|>
+Version of Record: A version of this preprint was published at Nature Communications on November 6th, 2023. See the published version at https://doi.org/10.1038/s41467-023-42889-w.
+
+<--- Page Split --->
+<|ref|>title<|/ref|><|det|>[[147, 95, 849, 149]]<|/det|>
+# Bioinspired porous three-coordinated single-atom Fe nanozyme with oxidase-like activity for tumor visual identification via glutathione  
+
+<|ref|>text<|/ref|><|det|>[[147, 162, 850, 210]]<|/det|>
+Da Chen \(^1\) , Zhaoming Xia \(^2\) , Zhixiong Guo \(^1\) , Wangyan Gou \(^1\) , Junlong Zhao \(^3\) , Xuemei Zhou \(^4\) , Xiaohé Tan \(^1\) , Wenbin Li \(^1\) , Shoujie Zhao \(^4\) , Zhimin Tian \(^{1,\ast}\) and Yongquan Qu \(^{1,\ast}\)  
+
+<|ref|>text<|/ref|><|det|>[[147, 246, 852, 519]]<|/det|>
+\(^1\) Key Laboratory of Special Functional and Smart Polymer Materials of Ministry of Industry and Information Technology, School of Chemistry and Chemical Engineering, Northwestern Polytechnical University, Xi'an 710072, China. E- mail: zhimintian@nwpu.edu.cn; yongquan@nwpu.edu.cn \(^2\) Department of Chemistry, Southern University of Science and Technology, Shenzhen 518055, China. \(^3\) State Key Laboratory of Cancer Biology, Department of Medical Genetics and Developmental Biology, Fourth Military Medical University, Xi'an 710032, China. \(^4\) Key Laboratory of Carbon Materials of Zhejiang Province, Wenzhou University, Wenzhou 325035, China.  
+
+<|ref|>text<|/ref|><|det|>[[147, 558, 634, 576]]<|/det|>
+e- mail: zhimintian@nwpu.edu.cn; yongquan@nwpu.edu.cn
+
+<--- Page Split --->
+<|ref|>sub_title<|/ref|><|det|>[[148, 95, 227, 111]]<|/det|>
+## Abstract  
+
+<|ref|>text<|/ref|><|det|>[[146, 128, 853, 596]]<|/det|>
+Inspired by structures of natural metalloenzymes, a biomimetic synthetic strategy was developed for scalable synthesis of porous \(\mathrm{Fe - N_3}\) single atom nanozymes (pFeSAN) using hemoglobin as Fe- source and template. pFeSAN delivered 3.3- and 8791- fold higher oxidase- like activity than \(\mathrm{Fe - N_4}\) and \(\mathrm{Fe_3O_4}\) nanozymes. The high catalytic performance is attributed to (1) the suppressed aggregation of atomically dispersed Fe; (2) facilitated mass transfer and maximized exposure of active sites for the created mesopores by thermal removal of hemoglobin (2\~3 nm); and (3) unique electronic configuration of \(\mathrm{Fe - N_3}\) for the oxygen- to- water oxidation pathway (analogy with natural cytochrome c oxidase). The pFeSAN was successfully demonstrated for the rapid colorimetric detection of glutathione with a low limit of detection (2.4 nM) and wide range (50 nM–1 mM), and further developed as a real- time, facile, rapid (∼6 min) and precise visualization analysis methodology of tumors via glutathione level, showing its potentials for diagnostic and clinic applications.
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[147, 93, 852, 373]]<|/det|>
+Nanozymes, featured by low cost, high stability, tailorable surface properties and facile synthesis and storage, deliver enzyme- like activities for prosperous applications in the fields of disease diagnosis and treatments \(^{1 - 6}\) . However, nanozymes generally suffer from their lower activity and unsatisfactory specificity in comparison with their natural counterparts, which severely limited their practical applications \(^{7 - 9}\) . Fortunately, natural metalloenzymes (cytochrome c oxidase ( \(C c O\) ), superoxide dismutase, etc.) with the well- defined local coordination environments and electronic structures have provided us with ingenious blueprints for the rational design of nanozymes \(^{8,10 - 12}\) .  
+
+<|ref|>text<|/ref|><|det|>[[147, 393, 852, 896]]<|/det|>
+Among various nanozymes, the single- atom metal nanozymes with the tailorable chemical, geometric and electronic configurations of atomically dispersed metals bonding with nitrogen- doped carbon support (M- N- C) possess similar configurations of the active metal centers of natural metalloenzymes, being recognized as alternatives of natural enzymes. Unfortunately, majority of them were synthesized via pyrolysis at high temperatures, leading to structural collapse and part of the buried \(\mathrm{MN_x}\) units inaccessible to biomolecules \(^{11,13,14}\) . Also, the strong stacking of those N- doped graphite carbon in nanozymes generally induces the frustrated diffusion of bio- substrates to metal sites. Both mass transfer restriction and active site encapsulation severely decrease the overall activity of those single- atom nanozymes. Besides, pyrolysis can induce the considerable aggregation of single- atom metal due to the carbon loss at high temperatures \(^{15 - 17}\) . Hence, various methods including spatial confinement, defect/vacancy engineering and coordination modulations have been developed to solve those problems \(^{18 - 20}\) . However, only part of those inadequacies could be overcome.
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[147, 94, 851, 223]]<|/det|>
+Thus, seeking for a new synthetic strategy of single- atom metal nanozymes to simultaneously achieve the atomic metal dispersion, modulated electronic structure, elevated mass transport and tailorable coordination environment is still on high demands.  
+
+<|ref|>text<|/ref|><|det|>[[147, 245, 852, 897]]<|/det|>
+Inspired by the structural features of natural \(C_cO\) , herein, we demonstrated a biomimetic synthetic strategy for the massive and facile preparation of porous single- atom Fe nanozymes (pFeSAN) using hemoglobin (Hb) as Fe- source embedded inside zeolitic imidazolate framework (ZIF- 8), which solved the above- mentioned challenges (Fig. 1). Evenly distributed iron atoms in each Hb with a size of 2\~3 nm effectively avoided the agglomeration of active sites and created mesopores in the nanozymes during pyrolysis, thereby maximumly exposing the atomic Fe sites and significantly facilitating the mass transfer of reactants/products in catalysts. Structural characterizations demonstrated the atomically dispersed Fe with a Fe- N₃ coordination in pFeSAN. Impressively, pFeSAN delivered outstanding oxidase- like activity, which was 3.3- and 8791- times higher than those of four- coordinated Fe- N single- atom (Fe- N₄) and Fe₃O₄ nanozymes, respectively. Most importantly, the extensive mechanism investigations illustrated that pFeSAN underwent a catalytic pathway of the four- electron reduction of oxygen into H₂O, being identical to that of \(C_cO^{21- 24}\) . Abnormal high concentration of glutathione (GSH) in cells and tissues at millimolar level generally suggests high risk of many diseases²⁵-²⁷. Based on the color evolution between colorless 3,3',5,5'- tetramethylbenzidine (TMB) and blue oxTMB enabled by oxidase- like pFeSAN and reductive GSH, pFeSAN was developed as a highly selective and
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[147, 93, 852, 224]]<|/det|>
+sensitive probe for the rapid GSH detection with a low limit of detection (LOD) of 2.4 nM and a wide window (50 nM–1 mM). Then, the pFeSAN-GSH assay was developed for the accurate detection of intratumoral GSH at millimolar levels and as the facile, rapid and precise visualization methodology of tumor area.  
+
+<|ref|>image<|/ref|><|det|>[[150, 270, 845, 641]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[147, 670, 852, 766]]<|/det|>
+<center>Fig. 1 | Schematic illustration of the synthesis process and detection effects of pFeSAN. The pFeSAN was synthesized by a two-step method using Hb@ZIF-8 as precursors, in which mesoporous structures with Fe-N₃ sites were formed. </center>
+
+<--- Page Split --->
+<|ref|>sub_title<|/ref|><|det|>[[148, 95, 214, 111]]<|/det|>
+## Results  
+
+<|ref|>sub_title<|/ref|><|det|>[[148, 131, 515, 149]]<|/det|>
+## Synthesis and characterization of pFeSAN  
+
+<|ref|>text<|/ref|><|det|>[[147, 168, 852, 859]]<|/det|>
+We proposed a two- step biomimetic synthesis strategy of pFeSAN by integrating Hb as Fe- resource and ZIF- 8 as the precursor of N- doped carbon (Fig. 1). Initially, a mixed methanol solution of \(\mathrm{Zn(NO_3)_2}\) , 2- methylimidazole and Hb reacted to produce uniform Hb@ZIF- 8 biohybrids with a rhombic dodecahedron shape and an average size of \(1081.4 \pm 15.4 \mathrm{nm}\) (Supplementary Fig. 1). Compared with the white color of ZIF- 8 and brownish red color of Hb, the pale brown color of Hb@ZIF- 8 disclosed the successful integration of ZIF- 8 and Hb (Supplementary Fig. 2). Consistent with the characterized absorption peak of Hb at \(\sim 405 \mathrm{nm}\) , the similar ultraviolet- visible (UV- vis) absorption of Hb@ZIF- 8 suggested the integrated Hb in ZIF- 8 (Supplementary Fig. 3) \(^{28}\) . X- ray powder diffraction (XRD) pattern showed that Hb@ZIF- 8 inherited the crystallographic structure of ZIF- 8, indicating the well- maintained crystal structure (Supplementary Fig. 4). The Infrared \(\mathrm{C} = \mathrm{O}\) bond stretching vibration of amide peak at \(1,660 \mathrm{cm^{- 1}}\) was observed for both Hb and Hb@ZIF- 8, again confirming the successful internalization of Hb with ZIF- 8 (Supplementary Fig. 5) \(^{28}\) . The thermogravimetric analysis suggested a Hb- loading of \(40.5 \mathrm{wt.\%}\) inside Hb@ZIF- 8 (Supplementary Fig. 6). \(\mathrm{N}_2\) adsorption isothermal analysis displayed an apparently lower surface area of Hb@ZIF- 8 ( \(663.8 \mathrm{m^2 / g}\) ) than \(850.3 \mathrm{m^2 / g}\) of ZIF- 8 alone, elucidating the partial occupation of ZIF- 8 pores by Hb (Supplementary Fig. 7) \(^{29}\) . Taken together, all characterizations demonstrated the successful immobilization of Hb within ZIF- 8.
+
+<--- Page Split --->
+<|ref|>image<|/ref|><|det|>[[153, 90, 845, 333]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[147, 367, 852, 608]]<|/det|>
+<center>Fig. 2 | Morphology and structure characterizations. a HAADF TEM image of pFeSAN. b Elemental mapping of C, Fe, N, and O elements of pFeSAN. c HAADF-STEM image of pFeSAN showing the atomically dispersed Fe single atom sites as bright dots (yellow cycles marked single atoms). d EELS spectra of pFeSAN. e Pore size distribution curves of C, FeSAN and pFeSAN. f Raman spectra of as-prepared C, FeSAN and pFeSAN. g EPR spectrum of FeSAN and pFeSAN. h The corresponding percentage of N configurations for FeSAN and pFeSAN. </center>  
+
+<|ref|>text<|/ref|><|det|>[[147, 665, 852, 890]]<|/det|>
+Subsequently, Hb@ZIF- 8 was pyrolyzed at \(900^{\circ}\mathrm{C}\) under Ar to decompose Hb, remove \(\mathrm{Zn}^{2 + }\) and then give pFeSAN. Compared with Hb@ZIF- 8, characterizations of transmission electron microscopy (TEM), scanning electron microscopy (SEM) and high- angle annular dark field- scanning TEM (HAADF- STEM) demonstrated the preserved polyhedral structure of as- synthesized pFeSAN with a reduced size of \(685.1 \pm 7.3 \mathrm{nm}\) and the formation of porous structure (Fig. 2a and Supplementary Fig. 8). The typical C- N and C=N Infrared vibration peaks instead of C=O stretching
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[147, 87, 853, 710]]<|/det|>
+vibration of amide were observed at 1,200\~1,600 cm\(^{- 1}\) for pFeSAN, suggesting the thermal decomposition of Hb and the carbonization of Hb@ZIF- 8 to give the N- doped carbon (Supplementary Fig. 9) \(^{30}\) . The XRD pattern of pFeSAN displayed only two broad characteristic diffraction peaks of graphitic carbon at \(\sim 24^{\circ}\) and \(\sim 44^{\circ}\) and no peaks related to iron/iron oxides (Supplementary Fig. 10), indicating a high dispersion of iron \(^{11,31}\) . The energy- dispersive X- ray spectroscopy (EDS) mappings showed the homogeneous distribution of C, Fe, N and O elements in the entire structure of pFeSAN (Fig. 2b). The aberration- corrected HAADF- STEM with angstrom resolution exhibited many isolated and bright spots ( \(\sim 0.2 \mathrm{nm}\) ), demonstrating the presence of the atomically dispersed Fe in pFeSAN (Fig. 2c). As revealed from the electron energy- loss spectroscopy (EELS) of pFeSAN, the point signal at the bright spot (yellow circle) further illustrated the co- existence of the neighboring single Fe and N atoms to give the Fe- \(\mathrm{N}_{\mathrm{x}}\) configuration (Fig. 2d) \(^{32}\) . Comparatively, the absence of characteristic peak of Fe at the site of carbon alone (blue circle) again supported the atomic dispersion of Fe. Notably, the cohesive interaction between iron atoms and protein structure of Hb allowed a confinement effect to suppress the iron agglomeration, eventually yielding the atomically dispersed Fe sites.  
+
+<|ref|>text<|/ref|><|det|>[[147, 724, 852, 890]]<|/det|>
+The commercial \(\mathrm{Fe}_{3}\mathrm{O}_{4}\) nanoparticles, the carbon supports (C) synthesized via the similar process without Hb, the porous carbon supports (pC) obtained using bull serum albumin (BSA) as template and the heme- derived single- atom Fe- \(\mathrm{N}_{4}\) catalysts through pyrolysis (FeSAN) were synthesized as references (Supplementary Figs. 11- 16) \(^{33}\) . A significant number of mesopores (3\~4 nm) were observed for pFeSAN due to the Hb
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[147, 90, 853, 596]]<|/det|>
+pyrolysis in ZIF- 8 (Fig. 2e), which was also verified by the \(\mathrm{H_4}\) type hysteresis loop of the \(\mathrm{N}_2\) adsorption/desorption isotherm of pFeSAN (Supplementary Fig. 17a)31. Consequently, pFeSAN provided a high surface area of \(705.8\mathrm{m}^2 /\mathrm{g}\) than that of FeSAN (516.6 \(\mathrm{m}^2 /\mathrm{g}\) ) without mesopores (Supplementary Fig. 17b). Raman spectra exhibited a larger \(\mathrm{I_D / I_G}\) ratio of pFeSAN than those of C and FeSAN, indicating a higher degree of structural defects in pFeSAN (Fig. 2f)32. While, no characteristic Raman peaks of iron oxides for both pFeSAN and FeSAN further demonstrated the atomically dispersed Fe in two catalysts. Additionally, the electron paramagnetic resonance (EPR) spectrum of pFeSAN delivered a 2.4- fold higher sharp signal than that of FeSAN at \(\mathrm{g} = 2.003\) , corresponding to the coordinatively unsaturated iron atoms and abundant N defect sites in pFeSAN (Fig. 2g)34,35. Overall, pFeSAN possesses a higher degree of graphitization and abundant nitrogen defect, indicating that the designed biomimetic synthetic strategy enables the effective modulation of the local electronic structure of Fe in pFeSAN and thereby affect their enzyme- like activity.  
+
+<|ref|>text<|/ref|><|det|>[[147, 611, 852, 891]]<|/det|>
+Afterwards, the chemical states of pFeSAN were investigated by X- ray photoelectron spectroscopy (XPS)11,31,32. The N 1s XPS spectra of pFeSAN and FeSAN suggested the existence of five types of N: pyridinic- N (398.3 eV), pyrrole- N (400.5 eV), graphitic- N (401.3 eV), oxidized- N (403.7 eV), and Fe- N peak (399.5 eV, Figure S18a). Thus, the formation of Fe- N bonds in the N- doped carbon was verified for both catalysts. Compared with FeSAN, pFeSAN possessed a remarkably higher proportion of pyridinic N (Fig. 2h and Supplementary Fig. 18a), which was believed to be important for the oxidase- like activity of single- atom nanozymes31. The binding environments of
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[146, 91, 852, 636]]<|/det|>
+C in pFeSAN and FeSAN can be deconvoluted into three types of C- C/C=C, C- N/O and O=C- C, indicating the formation of the N- doped graphite- like C (Supplementary Fig. 18b). Compared with the C- N/O peak of FeSAN (285.7 eV), the slight blue shift of the C- N/O peak (286.1 eV) of pFeSAN indicated a stronger electron attraction capability of the Fe atom coordinated with N in pFeSAN than that in FeSAN (Supplementary Fig. 18c) \(^{36}\) . Thus, the local Fe electronic structure could be effectively modulated by the coordination environments, thereby potentially tailoring the electron transfer and affecting their oxidase- like activity. The O 1s XPS spectra suggested the presence of the absorbed \(\mathrm{O_2}\) (530.7 eV), \(\mathrm{C = O}\) (532.2 eV) and C- OH/C- O- OH groups (533.6 eV, Supplementary Fig. 18d). Especially, the strong and broad peak at 530.7eV for pFeSAN indicated its strong ability to adsorb and activate oxygen, which could potentially promote the oxidase- like activity \(^{37}\) . No significant signal of XPS Fe 2p was observed for two catalysts due to the low Fe loadings in pFeSAN (0.36 wt.%) and FeSAN (0.21 wt.%), which were determined by inductively coupled plasma mass spectrometry (Supplementary Fig. 18e).  
+
+<|ref|>sub_title<|/ref|><|det|>[[149, 664, 471, 682]]<|/det|>
+## Atomic structure analysis of pFeSAN  
+
+<|ref|>text<|/ref|><|det|>[[147, 714, 852, 882]]<|/det|>
+Afterwards, the Fe K- edge X- ray absorption fine structure spectra were employed to probe the local chemical states and coordination environments of pFeSAN with iron phthalocyanine (FePc), \(\mathrm{Fe_2O_3}\) and Fe foil as references. The rising- edge position of the X- ray absorption near- edge structure spectra (XANES) of pFeSAN was between those of \(\mathrm{Fe_2O_3}\) and FePc, and very close to that of FePc, indicating that the atomically
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[147, 90, 852, 748]]<|/det|>
+dispersed Fe carried a positive charge with the oxidation state slightly over \(+2\) (Fig. 3a). Notably, the absorption edge of pFeSAN almost overlapped with that of FePc, indicating their similar electronic structures around Fe. Figure 3b showed the Fourier transform (FT) k3- weighted extended X- ray absorption fine structure (EXAFS) spectra. The main peak of pFeSAN at 1.47 Å corresponded to the Fe- N first coordination shell, similar to FePc. The local coordination environment of Fe was resolved by EXAFS fitting (Fig. 3c and Supplementary Table 1), giving a Fe- N distance of 1.91 Å and Fe- N coordination number of 2.8 for pFeSAN. The negligible Fe- Fe signal in 2- 3 Å compared with that of Fe foil indicates the atomically dispersed Fe in pFeSAN. This could be further confirmed by the wavelet transformed EXAFS without peaks corresponding to Fe- Fe bond (2- 3 Å in R- space and \(\sim 8.3 \AA^{- 1}\) in K- space, Fig. 3d) \(^{34}\) . The above X- ray absorption analysis demonstrated that the single- atom Fe in pFeSAN existed as the edge- hosted Fe- N₃ moieties. Previous studies have shown that the stronger adsorption of O₂ on Fe- N₃ than Fe- N₄, indicating the significantly larger oxygen affinity of pFeSAN with the unsaturated Fe- N₃ coordination than that of FeSAN with Fe- N₄ unit \(^{37,38}\) . Combining the O 1s XPS spectra (Supplementary Fig. 18d), the strong binding between O₂ and pFeSAN can effectively activate oxygen, being a decisive significance for its subsequently enhanced oxidase- like activity \(^{37,38}\) .
+
+<--- Page Split --->
+<|ref|>image<|/ref|><|det|>[[147, 85, 845, 360]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[147, 386, 850, 590]]<|/det|>
+<center>Fig. 3 | Atomic structure characterization and composition of pFeSAN. a The Fe K-edge XANES and b the Fourier-transformed magnitudes of the experimental Fe K-edge EXAFS spectra of pFeSAN and the reference samples of Fe foil, \(\mathrm{Fe_2O_3}\) and FePc, respectively. c The experimental FT-EXAFS spectra and fitting curves of pFeSAN. d Wavelet transform for the Fe K-edge EXAFS signals of pFeSAN and the reference samples of Fe foil, \(\mathrm{Fe_2O_3}\) and FePc, respectively. </center>  
+
+<|ref|>text<|/ref|><|det|>[[147, 648, 852, 889]]<|/det|>
+Derived from the dynamic light- scattering (DLS) measurements, the average hydrodynamic diameter of pFeSAN in water was \(649.7 \pm 11.4 \mathrm{nm}\) , consistent with TEM (Supplementary Fig. 19). Moreover, the zeta potentials of pFeSAN in water and acetate buffer (pH 4.0) were - 24.3 and \(14.2 \mathrm{mV}\) , respectively, indicating the stable dispersion of pFeSAN under various environments (Supplementary Fig. 20). As evaluated, pFeSAN exhibited a long- term stability in water, acetate buffer (pH 4.0) and cell culture medium without the significant changes of the average hydrodynamic diameters during
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[147, 93, 852, 299]]<|/det|>
+one- week co- incubation (Supplementary Fig. 21), which benefits the subsequent biomedical applications<sup>39</sup>. Notably, the biomimetic synthesis strategy can be easily scaled up. 13.89 g of pFeSAN was prepared from a single batch reaction of 10 L (Supplementary Fig. 22). Importantly, the price of Hb (\$3.6/g, Shanghai Yuanye, S12021- 5g) is competitive to and even lower than that of FeCl<sub>3</sub>. Therefore, this biomimetic synthetic strategy is cost- effective and facile for massive production.  
+
+<|ref|>text<|/ref|><|det|>[[147, 316, 852, 706]]<|/det|>
+Overall, pFeSAN exhibits multiple advantages including (1) Pyrolysis of natural metalloprotein of Hb (2\~3 nm) forms abundant mesopores, leading to the maximum exposure of single metal sites and facilitated mass transfer during catalysis; (2) Benefiting from the protein structure of Hb containing only four Fe atoms, Hb as the Fe- precursor effectively suppresses the Fe aggregation during pyrolysis to give atomically dispersed Fe sites with the maximized utilization efficiency; (3) Unsymmetrically coordinated Fe- N<sub>3</sub> sites deliver a strong adsorption and activation of O<sub>2</sub>; (4) High stability under physiological conditions makes pFeSAN suitable for bio- utilizations; (5) Facile, low- cost and scalable synthesis of pFeSAN offers its potential for practical applications. All features suggested the promises of pFeSAN as artificial enzymes for biomedical applications.
+
+<--- Page Split --->
+<|ref|>image<|/ref|><|det|>[[152, 92, 845, 320]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[147, 353, 852, 668]]<|/det|>
+<center>Fig. 4 | Oxidase-like activity evaluation. a-b Comparison of oxidase-like activities among various nanozymes. The reaction conditions were \(25^{\circ}\mathrm{C}\) for \(10\mathrm{min}\) in acetate buffer (pH 4.0). c-d Comparison of oxidase-like activities of pFeSAN at various temperature (10-70 \(^\circ \mathrm{C}\) ) and pH (2-12) conditions. e The durability of pFeSAN treated with acid or alkali for \(24\mathrm{h}\) . f Comparison of relative oxidase-like catalytic activities of different nanozymes. g Steady-state kinetic assay of pFeSAN and FeSAN with TMB as substrate. h Comparison of kinetics for pFeSAN and FeSAN. \(\mathrm{K_m}\) is the Michaelis-Menten constant. \(\mathrm{V_{max}}\) is the maximal reaction velocity. \(\mathrm{K_{cat}}\) is the catalytic rate constant. </center>  
+
+<|ref|>sub_title<|/ref|><|det|>[[149, 708, 327, 725]]<|/det|>
+## Oxidase-like activity  
+
+<|ref|>text<|/ref|><|det|>[[147, 743, 853, 910]]<|/det|>
+Oxidase-like activity of pFeSAN was investigated with TMB as substrate in the acetate buffer (pH 4.0, Fig. 4a-b). pFeSAN rapidly catalyzed the oxidation of TMB (1 mM), yielding a blue-colored product with a maximum absorbance at \(652\mathrm{nm}\) . Comparatively, the ZIF- 8, Hb@ZIF- 8, C, BSA@ZIF and pC catalysts showed the bare capability for the TMB oxidation, demonstrating the atomically dispersed Fe sites as active centers.
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[147, 94, 852, 410]]<|/det|>
+Then, the synthetic parameters of pFeSAN were optimized to reach the highest activity. Initially, the pyrolysis temperatures were optimized from 700 to \(1000^{\circ}\mathrm{C}\) to give the catalysts donated as pFeSAN- T (T represented the pyrolysis temperatures). Their structural characterizations manifested that four pFeSAN catalysts preserved the dodecahedron- like morphology with the abundant mesopores and atomically dispersed Fe species (Fig. 2a- c, Supplementary Figs. 23,24). Among various catalysts, pFeSAN- 900 delivered the highest catalytic activity (Supplementary Fig. 25), in which the calcination temperature of \(900^{\circ}\mathrm{C}\) was employed for subsequent investigations. Besides, pFeSAN mentioned hereinafter referred to pFeSAN- 900, unless otherwise stated.  
+
+<|ref|>text<|/ref|><|det|>[[147, 427, 852, 891]]<|/det|>
+Simultaneously, the catalytic activity of pFeSAN was evaluated under various conditions, including environmental pH and temperatures. The catalysts exhibited a highly stabilized oxidase- like activity within a wide temperature window from 10 to \(70^{\circ}\mathrm{C}\) , suggesting its broad application scenarios under various temperatures (Fig. 4c). Analogy with the natural oxidase enzyme, pFeSAN showed a pH- depended catalytic performance, delivering the highest catalytic activity in weakly acidic media (pH=4, Fig. 4d) \(^{21,39}\) . The oxidase- like activity increased with the concentration of pFeSAN and delivered a catalytic activity exceeded \(50\%\) at \(20 \mu \mathrm{g / mL}\) (Supplementary Fig. 26). Therefore, the concentration of pFeSAN at \(20 \mu \mathrm{g / mL}\) , room temperature and pH=4 were chosen as the optimized reaction conditions for future studies. Also, pFeSAN after the pre- treatments with \(1.0 \mathrm{M} \mathrm{HCl}\) (pH = 0) or \(1.0 \mathrm{M} \mathrm{NaOH}\) (pH = 14) solutions displayed a satisfactory catalytic stability, which was attributed to the structural robustness of the Fe- \(\mathrm{N}_3\) conformation (Fig. 4e).
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[147, 90, 853, 895]]<|/det|>
+The oxidase- like activity of \(\mathrm{Fe_3O_4}\) and FeSAN as the comparative nanozymes were also evaluated at the same amount of Fe in pFeSAN (Supplementary Fig. 27). Quantitatively, the oxidase- like activity of pFeSAN was 8791- and 3.3- fold higher than that of \(\mathrm{Fe_3O_4}\) and FeSAN, respectively (Fig. 4f). Considering the same amount of the atomically dispersed Fe in pFeSAN and FeSAN, their dramatical difference in the oxidase- like activity could be only attributed to the well- regulated coordination environments and the constructed mesopores of pFeSAN. Then, the steady state kinetic analysis was performed on FeSAN and pFeSAN. The typical Michaelis- Menten curves were obtained by plotting the corresponding initial reaction rates and substrate concentrations (Fig. 4g). The derived \(\mathrm{K_m}\) (Michaelis constant) and \(\mathrm{K_{cat}}\) (catalytic constant), were used to assess the binding affinity between TMB substrate and nanozymes and the oxidase- like activity of nanozymes, respectively (Supplementary Fig. 28 and Table 2) \(^{21,32}\) . pFeSAN delivered a lower \(\mathrm{K_m}\) value (0.17 mmol/L) than FeSAN (0.29 mmol/L), suggesting a better affinity between TMB and pFeSAN. Meanwhile, the values of the maximum reaction velocity \(\mathrm{(V_{max})}\) and \(\mathrm{K_{cat}}\) of pFeSAN were both higher than those of FeSAN. \(\mathrm{V_{max}}\) of pFeSAN (1.67 \(\mu \mathrm{M / s}\) ) was 46.4- fold higher than that of FeSAN (0.036 \(\mu \mathrm{M / s}\) ). \(\mathrm{K_{cat}}\) of pFeSAN was \(2.6 \times 10^{6} / \mathrm{s}\) , which was 27.1- fold higher than that of FeSAN (9.6 \(\times 10^{4} / \mathrm{s}\) , Fig. 4h). Also, the mesoporous characteristics of pFeSAN delivered a 34.6% higher TMB adsorption capacity of pFeSAN than that of FeSAN in aqueous solutions (Supplementary Fig. 29). All results demonstrated that pFeSAN possessed a higher affinity towards TMB substrate and delivered a higher intrinsic catalytic activity than FeSAN, which could be attributed
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[147, 94, 851, 187]]<|/det|>
+to (1) the modulated local electronic structures for a strong substrate affinity and (2) the mesoporous structure of pFeSAN for the facilitated mass transfer and accessibility of Fe sites.  
+
+<|ref|>sub_title<|/ref|><|det|>[[149, 220, 332, 237]]<|/det|>
+## Catalytic mechanism  
+
+<|ref|>text<|/ref|><|det|>[[147, 264, 852, 888]]<|/det|>
+Next, the catalytic mechanism of pFeSAN was explored. Initially, the \(\mathrm{O_2}\) levels were controlled for the catalytic TMB oxidation to identify the roles of oxygen<sup>21,39</sup>. pFeSAN showed negligible oxidase- like activity in the nitrogen- degassed environments, indicating that pFeSAN did not serve directly as the oxidants (Fig. 5a). Comparatively, pFeSAN delivered 2.2- fold higher oxidase- like activity in the \(\mathrm{O_2}\) - saturated solution than that under the air- saturated solution, illustrating \(\mathrm{O_2}\) indeed as the oxidants. Analogous with the majority of the previously reported oxidase- like nanozymes, pFeSAN as oxidase mimetics with oxygen as electron acceptor might generate reactive oxygen species (ROS, e.g. \(\cdot \mathrm{OH}\) , \(\mathrm{O_2}\) , \(\mathrm{O_2}\) ) to oxidize TMB<sup>1- 3</sup>. To examine this point, the \(\cdot \mathrm{O_2}\) inhibitor superoxide dismutase, \(\cdot \mathrm{OH}\) scavenger mannitol and \(\mathrm{O_2}\) quencher furfuryl alcohol were introduced into the catalytic reactions<sup>40,41</sup>. Surprisingly, the oxidase- like activity of pFeSAN was only slightly reduced in the presence of those quenchers, suggesting the barely generated free ROS herein (Supplementary Fig. 30a). Furthermore, the trapping agent 5,5- dimethyl- 1- pyrroline N- oxide was also employed to probe the possible involved active species by electron paramagnetic resonance (EPR)<sup>23,40</sup>. No apparently detectable signals of any ROS further proved that ROSs were not the main intermediates for the oxidase- like activity of pFeSAN (Supplementary Fig.
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[147, 94, 850, 224]]<|/det|>
+30b), which was very different from the majority of the previously reported nanozymes<sup>1- 3,39</sup>. These results imply that pFeSAN may mediate the complete \(\mathrm{O_2}\) - to- water reduction without the release of free ROS during oxidation, analogy with the catalytic pathways of natural \(\mathrm{CcO}\) (Fig. 5b)<sup>23,40</sup>.  
+
+<|ref|>image<|/ref|><|det|>[[150, 258, 848, 551]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[147, 571, 852, 814]]<|/det|>
+<center>Fig. 5 | Oxidase-like catalytic mechanism of pFeSAN. a UV-vis absorption spectra of pFeSAN + TMB in air, \(\mathrm{O_2}\) -saturated, and \(\mathrm{N_2}\) -saturated acetate buffer (pH 4.0). b Schematic illustration of oxidase-like characteristics of pFeSAN-catalyzed TMB oxidation. c CV curves of pFeSAN and FeSAN in pH 4.0 acetate buffer containing TMB. d EIS spectra of pFeSAN and FeSAN. e Calculated electron transfer number derived from rotating ring-disk electrode and \(\mathrm{H_2O_2}\) yields of the pFeSAN. f EPR spectra of pFeSAN in the presence of excess phenyloxoiodine at 77 K. </center>
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[146, 94, 852, 560]]<|/det|>
+Afterwards, the electron transfer process analyzed by electrochemical methods was explored to understand the oxidase- like mechanisms of pFeSAN. Two pairs of oxidation- reduction peaks were observed in the cyclic voltammetry curve of TMB in the acetate buffer \(\mathrm{(pH = 4.0}\) , Fig. 5c) \(^{39,42}\) . These profiles indicated that the TMB electrooxidation catalyzed by pFeSAN and FeSAN proceeded via a two successive one-electron oxidation: (1) to yield an intermediate product TMB- free radical and then (2) to give the completely oxidized product quinonediimine. Compared with FeSAN, the redox peak intensity of TMB in the presence of pFeSAN was much stronger, indicating more available active sites of pFeSAN for more efficient TMB oxidation. The electrochemical impedance spectroscopy (EIS) measurements were conducted to reveal the charge- transfer kinetics between TMB and active sites. The semicircle diameter of pFeSAN was smaller than that of FeSAN, indicating a lower interfacial resistance and higher charge transfer efficiency of pFeSAN (Fig. 5d) \(^{43,44}\) .  
+
+<|ref|>text<|/ref|><|det|>[[147, 575, 852, 890]]<|/det|>
+To explore the electron transfer path during the oxidation, the rotating ring- disk electrode (RRDE) tests were performed. Figure 5e showed that the \(\mathrm{H}_2\mathrm{O}_2\) yield of pFeSAN remained below \(7.5\%\) over a wide potential range of 0.1–0.8 V. Derived from the RRDE test, the average electron transfer number (n) of pFeSAN was 3.7, indicating the oxygen activation on the atomically dispersed Fe through a four- electron oxygen reduction reaction pathway \(^{20,22,40,45}\) . This process requires four \(\mathrm{H}^+\) and four electrons \(\mathrm{(O_2 + 4H^+ + 4e^- \rightarrow 2H_2O)}\) for the complete \(\mathrm{O_2}\) - to- \(\mathrm{H}_2\mathrm{O}\) reduction. Thus, the electrochemical understandings of these stepwise proton and electron transfers reveal the essence of pFeSAN for its oxidase- like performance.
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[147, 93, 852, 448]]<|/det|>
+These results indicated that pFeSAN mediated the complete \(\mathrm{O_2}\) - to- \(\mathrm{H_2O}\) reduction without the ROS generation, consistent with EPR results (Supplementary Fig. 30b). Therefore, the intermediate of \(\mathrm{Fe(IV) = O}\) , usually present in the catalytic cycle of natural oxidases, is considered as the active transient state<sup>46</sup>. To verify the presence of the \(\mathrm{Fe(IV) = O}\) intermediate of pFeSAN, the EPR spectrum of the pFeSAN- enabled oxidation with excessive phenyloxoiodine was recorded at 77K. A typical diamond- shaped sign signal at \(\mathrm{g} = 2.03\) , consistent with \(\mathrm{n}^2\) - peroxo heme species, indicated the formation of \(\mathrm{Fe(IV) = O}\) intermediate in pFeSAN for oxidation (Fig. 5f)<sup>21,23</sup>. Therefore, the oxidase- like activity of pFeSAN proceeds through the \(\mathrm{O_2}\) - to- \(\mathrm{H_2O}\) pathway, analogy with that of natural \(\mathrm{CcO}\) .  
+
+<|ref|>image<|/ref|><|det|>[[150, 472, 844, 653]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[147, 679, 850, 810]]<|/det|>
+<center>Fig. 6 | DFT theoretical calculation of oxidase-like activity over pFeSAN. a Proposed reaction pathways of \(\mathrm{O_2}\) reduction to \(\mathrm{H_2O}\) with optimized adsorption configurations on pFeSAN. b Corresponding free energy diagram for oxidase-like reaction on FeSAN and pFeSAN. </center>
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[147, 90, 853, 820]]<|/det|>
+To further reveal the origins of the oxidase- like activity of pFeSAN, density functional theory (DFT) calculations were performed on both Fe- N3 and Fe- N4 units. According to the proposed catalytic pathway of pFeSAN (Fig. 6a), the models of the isolated Fe sites with three- or four- coordinated pyridinic N incorporated within graphene matrix were built for calculations (Fig. 6b) \(^{21,23}\) . In acidic media, an O2 molecule underwent through (I) the initial adsorption on the isolated Fe sites to give \(*O_{2}\) ; (II) the subsequent protonation to form \(*OOH\) on the top of Fe atoms; (III) the dissociation of the \(*OOH\) intermediate associates with H+ to give the active \(*O\) intermediates and H2O; (IV) the protonation of \(*O\) to give \(*OH\) ; and (V) the recombination of \(*OH\) and H+ to generate H2O. The calculated adsorption energy of \(*O_{2}\) on Fe- N3 was 0.11 eV, which was larger than that of \(*O_{2}\) on Fe- N4 (0.03 eV), indicating a stronger O2 adsorption on pFeSAN and thereafter its effective activation with the weakened O- O bond strength and elongated O- O bond length (1.297 Å) in comparison with 1.23 Å of free O2 and 1.295 Å of \(*O_{2}\) on Fe- N4. DFT calculations indicate the step III ( \(*OOH + H^{+} + e^{-} \rightarrow *O + H_{2}O\) ) as the rate- determined step. In comparison with that of FeSAN (2.20 eV), the smaller \(\Delta G_{\mathrm{III}}\) of pFeSAN (1.92 eV) suggests its higher oxidase- like activity. Thus, the coordination- unsaturated Fe in Fe- N3 exhibits the strong adsorption and activation of O2, promotes the O- O cleavage of the adsorbed \(*OOH\) and generates the active \(*O\) intermediates, thereafter greatly improving the oxidase- like activity of pFeSAN.
+
+<--- Page Split --->
+<|ref|>image<|/ref|><|det|>[[145, 90, 850, 338]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[147, 351, 850, 867]]<|/det|>
+<center>Fig. 7 | Analytical performance of the pFeSAN-based colorimetric system for GSH detection. a Schematic illustration of the pFeSAN-based GSH biosensing system. b The linear relationship between the relative activity and the GSH concentration ranges from \(50~\mathrm{nM}\) to \(1\mathrm{mM}\) . Inset shows the photograph of reaction solution in the different concentration of GSH. c Comparison of the performance of the detection of GSH with different methods. d Selectivity toward GSH of the pFeSAN-based colorimetric system against common interfering chemicals. The numbers from 1 to 15 are Ctrl, GSH, \(\mathrm{Na^{+}}\) , \(\mathrm{Ca^{2 + }}\) , \(\mathrm{Mg^{2 + }}\) , \(\mathrm{K^{+}}\) , glutamic acid, tryptophan, arginine, glycine, cysteine, ascorbic acid, glucose, glucose oxidase and BSA. e The linear relationship between the variation of relative activity and the number of cells. The inset: photographs of reaction solution with different cell numbers. f DAB staining was performed to evaluate the level of GSH in tumor-bearing liver tissue. Scale \(\mathrm{bar} = 200 \mu \mathrm{m}\) . g Ki67 staining was performed to assess the proliferation of tumor cells in tumor-bearing liver tissue. Scale \(\mathrm{bar} = 200 \mu \mathrm{m}\) . h Pearson's correlation coefficient of the oxidized DAB (brown)/Ki67 (red fluorescence), which appeared in tissue slices of tumor-bearing liver tissue (Pearson's coefficient \(\mathrm{r} = -0.76\) ). Bars represent the mean \(\pm \mathrm{SEM}\) ; \*\*, \(\mathrm{p} < 0.01\) . </center>
+
+<--- Page Split --->
+<|ref|>sub_title<|/ref|><|det|>[[148, 95, 277, 111]]<|/det|>
+## GSH detection  
+
+<|ref|>text<|/ref|><|det|>[[147, 125, 853, 710]]<|/det|>
+GSH is an essential antioxidant to maintain the redox balance of biological systems, and its abnormal levels are commonly associated with various diseases including tumors, infections, and neurodegenerative disorders<sup>25- 27,47- 50</sup>. The current analytical techniques often require sophisticated equipments and time- consuming sample preparation processes<sup>25,47</sup>. Especially, the GSH levels of tumor cells (0.5–1.0 mM) are much higher than those normal cells. However, it’s lack of a facile yet accurate method to detect GSH at high concentrations in tumor tissue. GSH with the antioxidant capability can reduce blue oxTMB into colorless TMB, thus providing a quantitative methodology to probe the concentration of GSH and offering a visual methodology to monitor the GSH distribution in biological tissues (Supplementary Fig. 31). With the pre- mixture with GSH, the absorbance of the TMB- pFeSAN system at 652 nm insignificantly increased with time due to the reductive ability of GSH (Supplementary Fig. 32). Besides, the oxidase- like activity and FT- IR spectrum of the GSH- pretreated pFeSAN were analogous to that of the untreated pFeSAN, confirming that GSH did not affect the oxidase- like activity of pFeSAN but only reduced the blue ox- TMB into colorless TMB (Supplementary Figs. 33,34).  
+
+<|ref|>text<|/ref|><|det|>[[147, 724, 851, 890]]<|/det|>
+Inspired by the above results, the combination of the reductive GSH and the oxidase- like pFeSAN establishes a simply and rapidly colorimetric GSH assay system (Fig. 7a). As expected, the absorbance decreased with the increased concentrations of GSH and exhibited a linear relation within a broad GSH window (0.05 μM–1.0 mM, Fig. 7b, Supplementary Fig. 35). The detection systems were featured by a broad range of GSH
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[147, 90, 853, 670]]<|/det|>
+and a low derived LOD of \(2.4 \mathrm{nM}\) , which were superior to the majority of other previously reported GSH detection systems (Fig. 7c, Supplementary Fig. 35 and Table 3) \(^{26,49}\) . Moreover, the color changes of the corresponding measurements in the presence of GSH with various concentrations could be clearly distinguished by the visual inspection (Fig. 7b insert), giving the prerequisites for subsequent visualization of the tissue GSH assay. Furthermore, the pFeSAN- based GSH detection system could resist the interferences of various metal ions, amino acids, and proteins, validating well anti- interference and specificity of the pFeSAN- based GSH detection system (Fig. 7d). The recovery tests were also employed to evaluate the accuracy of this method by standard addition method. The recovery rates of the pFeSAN- based GSH detection system were in the range of \(98\%\) to \(106.1\%\) in compassion with the recovery rates of commercial GSH assay kit in the range of \(85\%\) to \(107.5\%\) , demonstrating the satisfactory feasibility in practical sample analysis (Supplementary Table 4). Besides, all relative standard deviations (RSD) were \(< 2.5\%\) , which illustrated the accuracy and practically of the pFeSAN- based bioanalysis system for the GSH detection, especially for those at high GSH levels.  
+
+<|ref|>text<|/ref|><|det|>[[147, 687, 852, 890]]<|/det|>
+The identification and analysis of tumor is particularly important for the survival of cancer patients. Therefore, it is highly desirable to develop sensitive, specific, and rapid methods to diagnose tumor. As a cancer biomarker, the high GSH levels (0.5–1.0 mM) play important roles in maintaining intracellular redox homeostasis in tumor cells. Fortunately, the exceptionally high detection upper limit of pFeSAN is well suitable to detect GSH with high levels. pFeSAN was applied to monitor the GSH levels in normal
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[147, 93, 852, 484]]<|/det|>
+liver cells (AML12) and liver tumor cells (Hepa 1- 6). The absorption of AML12 cells and Hepa 1- 6 cells at \(652~\mathrm{nm}\) gradually declined with the increased number of cells from \(1000\mathrm{to}8\times 10^{5}\) due to the effective reduction by the intracellular GSH. Comparatively, the inhibition efficiency of two types of cells on the chromogenic reaction was very different, which was apparently distinguished by the naked eye (Fig. 7e and Supplementary Fig. 36). The GSH level of Hepa 1- 6 cells was higher than that of normal AML12 cells at the same cell density. When the cell densities reached \(0.5\times 10^{5} / \mathrm{mL}\) , the absorbance of the two colorimetric systems was extremely significantly different ( \(^{**}p< 0.01\) ). The above results suggested that pFeSAN- based colorimetric systems could serve as a promising platform for the rapid and ultrasensitive GSH detection.  
+
+<|ref|>text<|/ref|><|det|>[[147, 501, 852, 891]]<|/det|>
+Analysis of the tumor area in clinical practice enables the identifications of the boundary between tumor and normal tissue and the site of surgical resection<sup>51</sup>. The tumor microenvironment usually expresses an excess of GSH in comparison with the health counterparts. Herein, a visualization analysis system was constructed for the real- time and specific localization of tumor regions by using the intratumorally overexpressed GSH in liver orthotopic transplantation tumors. Briefly, the slices from the tumor- bearing liver tissue were incubated with pFeSAN and chromogenic substrate (3,3'- Diaminobenzidine, DAB) for 6 min. After the incubation, the DAB substrate displayed brown oxidation product signal in the normal area of liver tissue section under the bright field microscope (Fig. 7f). However, due to the high GSH expression in tumor tissue, the oxidized DAB products were reduced by GSH into colorless DAB again,
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[146, 90, 852, 748]]<|/det|>
+leading to no obvious brown signal in tumor area. Therefore, a clear distinction between tumor cells and adjacent health cells was observed in the para- carcinoma liver tumor tissue area, demonstrating that this pFeSAN- GSH assay methodology successfully distinguished tumor tissue from normal tissue. Next, the immunofluorescence staining as the gold standard was performed to verify the accuracy of this methodology (Fig. 7g). The expression of Ki67 protein as a marker in tumor pathology is strongly associated with tumor growth and proliferation<sup>52,53</sup>. The above results demonstrated that the intensity of the oxidized DAB decreased with the increase of tumor proliferation. In addition, Pearson's correlation coefficients of oxidized DAB (brown) and Ki67 (red) was - 0.76, further indicating a high negative correlation (Fig. 7h). This was attributed to the oxidized DAB (brown) being reduced to a colorless product with the increase of GSH in tumor tissues. Compared with fluorescence images of classic tumor proliferation marker (Ki- 67) in adjacent sections of the same tumor- bearing liver tissue, the pFeSAN- GSH visualization detection exhibited a similar accuracy and a much shorter analysis time (6 min) than the conventional immunofluorescence diagnostic of tumor (>5h) and avoided complex operation process as well<sup>54</sup>. All results demonstrated the facile, accessibility and precision of the developed pFeSAN- GSH visualization analysis with great potentials for clinical tumor tissue detection.  
+
+<|ref|>sub_title<|/ref|><|det|>[[148, 777, 247, 793]]<|/det|>
+## Conclusion  
+
+<|ref|>text<|/ref|><|det|>[[148, 827, 850, 882]]<|/det|>
+Inspired by the structure of natural \(C_cO\) , we demonstrated a biomimetic synthetic strategy for scalable synthesis of porous Fe- N<sub>3</sub> single- atom nanozymes using Hb as
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[146, 92, 853, 596]]<|/det|>
+both template and Fe- source, which delivered a high oxidase- like activity. The mesoporous features of pFeSAN significantly promoted mass transport and maximumly exposed active iron sites during reaction. The Fe- N₃ moiety enabled a strong oxygen adsorption/activation, which underwent the complete O₂- to- H₂O pathway for oxidation, analogy with that of the natural C₆O. Taking all advantages mentioned above, pFeSAN exhibited exceptional oxidase- like activity and durability. Based on the outstanding oxidase- like performance for TMB oxidation and the reduction capability of GSH for oxTMB, the pFeSAN- GSH system was constructed for the colorimetric detection of GSH with high sensitivity, wide detecting range and good specificity. Colorimetric analysis based on the pFeSAN assay was further developed for real- time and rapid detection of GSH in biological tissues and also demonstrated as the visualization methodology for the rapid and precise identifications of the tumor boundary. This work may open promising avenues for the design of single- atom nanozymes as alternatives of natural enzymes for biomedical applications.  
+
+<|ref|>sub_title<|/ref|><|det|>[[148, 669, 226, 685]]<|/det|>
+## Methods  
+
+<|ref|>sub_title<|/ref|><|det|>[[148, 707, 234, 722]]<|/det|>
+## Materials  
+
+<|ref|>text<|/ref|><|det|>[[147, 741, 852, 910]]<|/det|>
+Hemoglobin (Hb) and glucose oxidase (GOx) were purchased from Shanghai Yuanye Bio- Technology Co. Ltd. Fe₃O₄, bovine serum albumin, sodium acetate (NaAc, >99%), glacial acetic acid (HAC, 99.99%), anhydrous dimethylsulfoxide (DMSO), iodosylbenzene (97%) and acetonitrile (99.9%) were purchased from Aladdin Co., Ltd. Heme, 3,3',5,5'- tetramethylbenzidine, 2- methylimidazole, Zn(NO₃)₂·6H₂O, superoxide
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[147, 93, 852, 410]]<|/det|>
+dismutase, L- ascorbic acid, L- tryptophan, L- glycine, glutamic acid, mannitol, furfuryl alcohol, L- cysteine, L- arginine, glutathione, 5,5- dimethyl- 1- pyrroline N- oxide, nafion solution (99%), methanol (99.9%) and 4',6- diamidino- 2- phenylindole (DAPI) were purchased from Sigma- Aldrich. Glutathione assay kit and 3,3'- Diaminobenzidin was purchased from Shanghai Beyotime Biotechnology Co., Ltd. Dulbecco's Modified Eagle Medium (DMEM), Dulbecco's Modified Eagle Medium/Nutrient Mixture F- 12 (DMEM/F12), penicillin/streptomycin and fetal bovine serum (FBS) was obtained from Gibco. All aqueous solutions were prepared with deionized water (18.2 MΩ·cm, Millipore).  
+
+<|ref|>sub_title<|/ref|><|det|>[[149, 428, 306, 445]]<|/det|>
+## Characterizations  
+
+<|ref|>text<|/ref|><|det|>[[147, 464, 853, 891]]<|/det|>
+Scanning electron microscope (SEM) images were obtained from a Verios G4 ultra high resolution field emission scanning electron microscope (FEI, USA). Transmission electron microscope images, elemental mapping, electron energy loss spectroscopy and high- angle annular dark- field scanning transmission electron microscope (HADDF- STEM) images were performed on a Titan Themis G2 transmission electron microscope (FEI, USA) operated at 300 kV. Nanoparticle size distribution and zeta potential were measured by a Zetasizer Nano ZS90 nanoparticle size analyzer (Malvern, U.K.). Fourier transform infrared spectroscopy were obtained from a Nicolet iS20 Fourier transform infrared spectrometer (Thermo Scientific, USA). Thermogravimetric analysis was performed on a TGA8000 (PerkinElmer, USA) instrument from 30 °C to 1000 °C at a ramping rate of 5 °C min<sup>- 1</sup> in the Ar. X- ray diffraction patterns were recorded by the D8 Advance X- ray diffractometer (Bruker, USA). X- ray photoelectron
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[147, 90, 852, 821]]<|/det|>
+spectroscopy was conducted on a Thermo Scientific™ K- Alpha™+ spectrometer equipped with a monochromatic Al Kα X- ray source (1486.6 eV) operating at 100 W. Samples were analyzed under vacuum (P < 10<sup>-8</sup> mbar) with a pass energy of 150 eV (survey scans) or 50eV (high- resolution scans). All peaks were calibrated with C1s peak binding energy at 284.6 eV for adventitious carbon. The Fe loadings were determined by inductively coupled plasma atomic emission spectrometry (Agilent 5110, USA). Raman spectra were explored with a Scientific LabRAM HR Evolution Raman spectrometer (HORIBA, Japan) equipped with a 532 nm laser. BET surface area, nitrogen adsorption and desorption isotherm linear plot and pore diameter distributions of various catalysts were observed by an automatic surface area and porosity analyzer (Micromeritics ASAP 2460, USA). The catalysts were degassed at 150 °C for 8 h and then tested for nitrogen adsorption and desorption. Ultraviolet and visible spectrophotometry absorption spectra were recorded using a TU- 1950 UV- vis spectrophotometer in the wavelength range of 200- 800 nm. The nitrogen vacancies were detected by an EMXplus 10/12 electron paramagnetic resonance spectrometer (Bruker, German). The electron paramagnetic resonance of pFeSAN was measured in acetonitrile/PhIO solution at 77 K to analyze its intermediate state, in which the experimental conditions were controlled at the frequency of 9.853 GHz, the microwave power of 20.0 mW, the center field of 3510 G, the modulation amplitude of 2.0 G and the time constant of 40.96 ms.  
+
+<|ref|>text<|/ref|><|det|>[[148, 835, 850, 889]]<|/det|>
+The extended X- ray absorption fine structure measurements of the catalysts were carried out the 21A X- ray nanodiffraction beamline of Taiwan Photon Source (TPS),
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[147, 93, 852, 333]]<|/det|>
+National Synchrotron Radiation Research Center (NSRRC). This beamline adopted the 4- bounce channel- cut Si (111) monochromator for mono- beam X- ray nanodiffraction and X- ray absorption spectroscopy. The end- station equipped with three ionization chambers and Lytle/SDD detector after the focusing position of KB mirror for transmission and fluorescence mode X- ray absorption spectroscopy. The photon flux on the sample was range from \(1 \times 10^{11} - 3 \times 10^{9}\) photon/sec for X- ray energy from 6- 27 keV.  
+
+<|ref|>sub_title<|/ref|><|det|>[[149, 354, 356, 371]]<|/det|>
+## Preparation of catalysts  
+
+<|ref|>text<|/ref|><|det|>[[147, 388, 853, 891]]<|/det|>
+The catalyst precursors were prepared by mixing \(9\mathrm{mL}\) of methanol solution containing 2- methylimidazole (0.308 g) with \(1\mathrm{mL}\) of aqueous Hb (60 mg/mL) or \(1\mathrm{mL}\) of aqueous heme (2.2 mg/mL) or \(1\mathrm{mL}\) aqueous BSA (1 mg/mL) or \(1\mathrm{mL}\) of \(\mathrm{H}_2\mathrm{O}\) . Afterwards, 10 mL of methanol solution containing 0.279 g of \(\mathrm{Zn(NO_3)_2\cdot 6H_2O}\) was added. After the continuous stirring for \(6\mathrm{h}\) at room temperature, the solids were centrifuged (6000 rpm for \(5\mathrm{min}\) ), and washed with methanol three times. The collected solids were dried at \(60^{\circ}\mathrm{C}\) to yield Hb@ZIF- 8, Heme@ZIF- 8, BSA@ZIF- 8 and ZIF- 8 as the precursors of respective catalysts. For synthesis of porous Fe single atom nanozymes (pFeSAN), the Hb@ZIF- 8 precursors were placed in quartz boats in a tube furnace under a flowing of \(\mathrm{Ar}(50\mathrm{cm}^3 /\mathrm{min})\) , raised up to the desired temperatures with a ramping rate of \(5^{\circ}\mathrm{C}\mathrm{min}^{- 1}\) , and then kept at the target temperatures for \(3\mathrm{h}\) . The Fe single atom nanozymes (FeSAN), carbon supports (C) and porous carbon supports (pC) were also synthesized through the identical approach except with Heme@ZIF- 8, BSA@ZIF- 8 and ZIF- 8 as precursors, respectively.
+
+<--- Page Split --->
+<|ref|>sub_title<|/ref|><|det|>[[149, 95, 327, 111]]<|/det|>
+## Oxidase-like activity  
+
+<|ref|>text<|/ref|><|det|>[[147, 130, 852, 372]]<|/det|>
+The oxidase- mimicking activity was evaluated by using TMB as substrate at \(25^{\circ}\mathrm{C}\) under acidic conditions (pH 4.0, 0.1 M HAc- NaAc buffer). The absorbance of oxTMB was recorded at \(652~\mathrm{nm}\) through TU- 1950 UV- vis spectrophotometer. In detail, \(20~\mu \mathrm{L}\) of various catalysts aqueous solution (1 mg/mL) and \(20~\mu \mathrm{L}\) of TMB (50 mM DMSO) were sequentially added into HAc- NaAc buffer (0.1 M, pH 4.0). The final volume was fixed to \(1\mathrm{mL}\) by adding deionized water (18.2 MΩ·cm). After 10 min, the catalytic oxidation of TMB was determined by their UV- vis absorption spectra.  
+
+<|ref|>text<|/ref|><|det|>[[147, 390, 851, 483]]<|/det|>
+To evaluate the activity of pFeSAN to reaction temperature and pH, its oxidase- like activity was measured at various temperatures (10, 20, 30, 40, 50, 60 and \(70^{\circ}\mathrm{C}\) ) or medias with various pH values (2, 4, 6, 8, 10 and 12).  
+
+<|ref|>text<|/ref|><|det|>[[147, 500, 850, 593]]<|/det|>
+To evaluate the stability of pFeSAN, the catalysts were treated with 1 M HCl or 1 M NaOH solutions for \(24\mathrm{h}\) , and then centrifuged off and washed with deionized water for 6 times before testing their oxidase- mimicking activity.  
+
+<|ref|>sub_title<|/ref|><|det|>[[149, 612, 380, 630]]<|/det|>
+## Enzymatic kinetic analysis  
+
+<|ref|>text<|/ref|><|det|>[[147, 648, 852, 890]]<|/det|>
+Further kinetic experiments were performed for TMB oxidation at different concentrations by using various catalysts. The experiment conditions were similar to that of oxidase- like activity, in which \(1.0\mathrm{mL}\) HAc- NaAc buffer (0.1 M pH 4) containing \(20~\mu \mathrm{L}\) of catalysts (1.0 mg/mL) aqueous solution and different volumes of TMB solution (50 mM DMSO). The kinetic constants ( \(\mathrm{K_m}\) and \(\mathrm{V_{max}}\) ) were obtained according to Michaelis- Menten equation: \(\mathrm{V} = \mathrm{V_{max}}[\mathrm{S}] / (\mathrm{K_m} + [\mathrm{S}])\) , where \(\mathrm{V}\) was the initial velocity, [S] was the concentration of the substrate, \(\mathrm{K_m}\) was the
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[147, 93, 852, 299]]<|/det|>
+Michaelis- Menten constant, and \(\mathrm{V}_{\mathrm{max}}\) was the maximal reaction velocity. The value of \(\mathrm{K}_{\mathrm{m}}\) was equivalent to the substrate concentration at which the rate of conversion was half of \(\mathrm{V}_{\mathrm{max}}\) . \(\mathrm{V}_{\mathrm{max}}\) and \(\mathrm{K}_{\mathrm{m}}\) were calculated from the Lineweaver- Burk double- reciprocal plot (1/V and 1/[S]). The absorbance signal was converted to concentration by the Beer- Lambert law ( \(\mathrm{A} = \epsilon \mathrm{bc}\) ) where \(\epsilon = 39000 \mathrm{M}^{- 1}\mathrm{cm}^{- 1}\) at \(652 \mathrm{nm}\) for the oxidized TMB (oxTMB).  
+
+<|ref|>sub_title<|/ref|><|det|>[[149, 317, 352, 335]]<|/det|>
+## Adsorption experiment  
+
+<|ref|>text<|/ref|><|det|>[[147, 353, 852, 558]]<|/det|>
+The relative difference in the TMB adsorption capability of pFeSAN and FeSAN was determined by the obvious UV- vis absorption peak of TMB at \(285 \mathrm{nm}\) . Firstly, the absorbance of different concentrations of TMB solutions in HAc- NaAc buffer (0.1 M, pH 7.0) were measured to plot a standard curve. Then \(1.0 \mathrm{mL}\) of NaAc solution (0.1 M HAc- NaAc buffer, pH 7.0) containing \(50 \mu \mathrm{M}\) TMB and \(5 \mu \mathrm{g} / \mathrm{mL}\) catalysts were stirred for \(10 \mathrm{min}\) . Then, the supernatants were centrifuged off to measure the absorbance.  
+
+<|ref|>sub_title<|/ref|><|det|>[[150, 576, 432, 594]]<|/det|>
+## Catalytic mechanism of pFeSAN  
+
+<|ref|>text<|/ref|><|det|>[[147, 612, 852, 742]]<|/det|>
+To verify the dependence of the oxidase- like activity of pFeSAN on oxygen, taking it as a control in air, nitrogen or oxygen was introduced into the reaction flask for 30 minutes before measuring oxidase- like activity. Subsequently, the absorbance was measured after \(10 \mathrm{min}\) of sealing reaction.  
+
+<|ref|>text<|/ref|><|det|>[[147, 760, 850, 854]]<|/det|>
+The influences of various scavengers on the oxidase- like activity of pFeSAN were investigated by comparing the activity in the absence or presence of ROS scavengers. In the catalytic oxidation of TMB, the pFeSAN was added firstly into the HAc- NaAc
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[147, 93, 851, 255]]<|/det|>
+buffer (pH 4.0) containing SOD (0.5 mg/mL) or mannitol (50 mM) or FFA (5 mM) and TMB (1 mM). Then, the absorbance at 652 nm was measured respectively. The free radicals (such as \(\cdot \mathrm{OH}\) and \(\cdot \mathrm{O}_2\) ) in reaction systems were examined by a Bruker spectrometer in the HAc- NaAc (pH 4 or pH 7) buffer, using DMPO as the spin- trapping agent.  
+
+<|ref|>sub_title<|/ref|><|det|>[[149, 279, 364, 297]]<|/det|>
+## Electrochemical analysis  
+
+<|ref|>text<|/ref|><|det|>[[145, 312, 853, 803]]<|/det|>
+The catalyst ink was prepared by dispersing the nanozymes (4 mg) through sonication in a mixed solvent (1 mL) containing \(32~\mu \mathrm{L}\) of a \(5.0\mathrm{wt}\%\) Nafion solution, \(200~\mu \mathrm{L}\) of ethanol and \(768~\mu \mathrm{L}\) of deionized water. The cyclic voltammetry (CV) curves were evaluated using a three- electrode system on an electrochemical workstation (CHI 760E, Shanghai Chenhua, China), including the carbon paper (CP) as the working electrode, a graphite rod as the counter electrode, and a saturated calomel electrode (SCE) as the reference electrode. Briefly, \(10~\mu \mathrm{L}\) of the ink was loaded onto CP with a catalyst loading of \(0.56\mathrm{mgcm^{- 2}}\) . The CV tests were performed in an \(\mathrm{O}_2\) - saturated NaAc solution (0.1 M, pH 4.0) with TMB (1mM). All potential values were calibrated to the reversible hydrogen potential \(\mathrm{(E_{RHE})}\) \(\mathrm{(E_{RHE} = E_{SCE} + 0.241 + 0.0592\times pH)}\) . The CV measurements were recorded at a scan rate of \(50\mathrm{mV}\mathrm{s}^{- 1}\) in a potential range of - 0.2- 1.2 V.  
+
+<|ref|>text<|/ref|><|det|>[[147, 835, 850, 890]]<|/det|>
+To verify the electron transfer number, an aliquot \((20~\mu \mathrm{L})\) of the ink was then dropped on a rotating ring disk electrode (RRDE) glassy carbon electrode with a diameter of 4
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[147, 92, 851, 187]]<|/det|>
+mm and a loading of \(0.64\mathrm{mgcm}^{- 2}\) . The RRDE measurements were carried out at a rotating speed of \(1200\mathrm{rpm}\) . The hydrogen peroxide \(\mathrm{(H_2O_2)}\) yield \((\%)\) and the electron transfer number (n) can be calculated based on the Equation:  
+
+<|ref|>equation<|/ref|><|det|>[[373, 205, 623, 223]]<|/det|>
+\[\mathrm{H_2O_2(\%) = 200I_D/N(I_D + I_R/N)}\]  
+
+<|ref|>equation<|/ref|><|det|>[[425, 241, 572, 260]]<|/det|>
+\[\mathrm{n = 4I_D / (I_D + I_R / N)}\]  
+
+<|ref|>text<|/ref|><|det|>[[147, 278, 851, 335]]<|/det|>
+where \(\mathrm{I_D}\) is the disk current, \(\mathrm{I_R}\) is the ring current, and N is the collection efficiency of the ring electrode (0.39 in this work) \(^{17}\) .  
+
+<|ref|>sub_title<|/ref|><|det|>[[147, 353, 528, 372]]<|/det|>
+## Density functional theory (DFT) calculation  
+
+<|ref|>text<|/ref|><|det|>[[147, 388, 852, 856]]<|/det|>
+All the spin- polarized DFT calculations are performed by the Vienna Ab initio Simulation Package (VASP) with the projector augmented wave (PAW) method \(^{55}\) . The exchange- functional is treated using the generalized gradient approximation (GGA) with Perdew- Burke- Ernzerhof (PBE) functional. The energy cutoff for the plane wave basis expansion was set to \(400\mathrm{eV}\) . Partial occupancies of the Kohn–Sham orbitals were allowed using the Gaussian smearing method and a width of \(0.2\mathrm{eV}\) . The single layer graphene with the active center of \(\mathrm{Fe - N_4}\) was built, where one of the coordinated N was removed to build the structure of \(\mathrm{Fe - N_3}\) . The k- point of \(2\times 2\times 1\) was used in the Brillouin zone for all surface structure optimization. The self- consistent calculations apply a convergence energy threshold of \(10^{- 5}\mathrm{eV}\) , and the force convergency was set to \(0.05\mathrm{eV / \AA}\) . The reaction free energy was calculated following the computational hydrogen electrode (CHE) model. The free energy corrections were considered at the temperature of \(298\mathrm{K}\) , following:  
+
+<|ref|>equation<|/ref|><|det|>[[355, 872, 640, 889]]<|/det|>
+\[\Delta \mathrm{G} = \Delta \mathrm{E} + \Delta \mathrm{GZPE} + \Delta \mathrm{GU} - \mathrm{T}\Delta \mathrm{S}\]
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[147, 94, 850, 188]]<|/det|>
+where \(\Delta \mathrm{E}\) , \(\Delta \mathrm{GZPE}\) , \(\Delta \mathrm{GU}\) , and \(\Delta \mathrm{S}\) refer to the DFT calculated energy change, the correction from zero- point energy, the correction from inner energy and the correction from entropy \(^{56}\) .  
+
+<|ref|>text<|/ref|><|det|>[[147, 205, 850, 299]]<|/det|>
+The solvent effect was considered due to the stabilization of adsorbate from the H- bond network in the. A stabilization of - 0.17, and - 0.20 eV were considered for \(\mathrm{OH^{*}}\) , and \(\mathrm{OO}\mathrm{H}^{*}\) according to previous study \(^{57}\) .  
+
+<|ref|>sub_title<|/ref|><|det|>[[148, 316, 606, 335]]<|/det|>
+## Colorimetric detection of GSH based on the pFeSAN  
+
+<|ref|>text<|/ref|><|det|>[[147, 353, 851, 521]]<|/det|>
+In the standard procedure, the assays were performed in 1 mL HAc- NaAc buffer (0.1 M, pH 4.0) with different concentrations of GSH (0.05, 0.5, 2.5, 5, 12.5, 25, 50, 100, 150, 200, 250, 500 or \(1000~\mu \mathrm{M}\) ), where the TMB and pFeSAN concentrations were 1 mM and \(20~\mu \mathrm{g / mL}\) , respectively. After 10 min of incubation at room temperature, a standard curve was established by measuring the UV- vis absorbance of solutions.  
+
+<|ref|>sub_title<|/ref|><|det|>[[149, 540, 395, 557]]<|/det|>
+## Anti-interference evaluation  
+
+<|ref|>text<|/ref|><|det|>[[147, 575, 852, 855]]<|/det|>
+To explore anti- interference of the GSH detection system, various ions and molecules were independently mixed and then the peak intensity at \(652~\mathrm{nm}\) was measured after 10 min of incubation. Specifically, \(20~\mu \mathrm{L}\) of pFeSAN (1 mg/mL) and \(20~\mu \mathrm{L}\) of TMB (50 mM) were added into HAc- NaAc buffer (0.1 M, pH 4.0), which contains \(0.1~\mathrm{mM}\) GSH, \(\mathrm{Na^{+}}\) , \(\mathrm{Ca^{2 + }}\) , \(\mathrm{Mg^{2 + }}\) , \(\mathrm{K^{+}}\) , Glu, Trp, Arg, Gly, Cys, L- AA, glucose, GOx or BSA. The final volume was fixed to \(1~\mathrm{mL}\) by adding deionized water. After reacting at room temperature for 10 min, the activity of pFeSAN were recorded in the presence of various ions or molecules according to the standard procedure as mention above.
+
+<--- Page Split --->
+<|ref|>sub_title<|/ref|><|det|>[[148, 95, 409, 112]]<|/det|>
+## Effect of GSH on the pFeSAN  
+
+<|ref|>text<|/ref|><|det|>[[147, 130, 852, 372]]<|/det|>
+Effect of GSH on the pFeSANThe pFeSAN was incubated with GSH (1 mM) for 10 min and then collected by centrifugation and thorough washing by pure water for three times to obtain the GSH- pretreated pFeSAN. To analyze the effect of GSH on the activity of the pFeSAN nanozyme, 20 μL of pFeSAN or GSH- pretreated pFeSAN (1 mg/mL), 20 μL of TMB (50 mM) and 960 μL of HAc- NaAc buffer (pH 4.0) were mixed and incubated at 25°C for 10 min. The absorbance of each reaction solution at the wavelength of 500- 800 nm was recorded.  
+
+<|ref|>sub_title<|/ref|><|det|>[[149, 390, 432, 408]]<|/det|>
+## Detection of GSH in cell samples  
+
+<|ref|>text<|/ref|><|det|>[[147, 426, 852, 630]]<|/det|>
+Hepa 1- 6 cells were cultured in DMEM medium (10% FBS and 1% penicillin/streptomycin) at \(37^{\circ}\mathrm{C}\) in a humidified atmosphere containing \(5\% \mathrm{CO}_2\) . AML- 12 cells were grown in DMEM/F12 medium with 10% FBS and 1% penicillin/streptomycin at \(37^{\circ}\mathrm{C}\) . To prepare Hepa 1- 6 and AML- 12 cell samples, the cells were digested by trypsin- ethylenediaminetetraacetic acid and then re- suspended in phosphate buffered saline (10 mM PBS, \(\mathrm{pH} = 7.4\) ).  
+
+<|ref|>text<|/ref|><|det|>[[147, 648, 852, 817]]<|/det|>
+Then, 20 μL of pFeSAN (1 mg/mL) and 20 μL of TMB (50 mM DMSO) were sequentially added into HAc- NaAc buffer (0.1 M pH 4) and the final volume was fixed to 1 mL, which contained different amounts of Hepa 1- 6 or AML- 12 (1000, 10000, 50000, 100000, 200000, 300000, 500000 or 800000). After 10 min, the catalytic oxidation of TMB was investigated by the UV- vis absorption spectra.
+
+<--- Page Split --->
+<|ref|>sub_title<|/ref|><|det|>[[148, 95, 480, 112]]<|/det|>
+## Mice and tumor-bearing mouse model  
+
+<|ref|>text<|/ref|><|det|>[[147, 130, 852, 410]]<|/det|>
+C57BL/6 mice (7 weeks) were maintained in a specific pathogen- free facility. All animal experiments were approved by the Animal Experiment Administration Committee of the Fourth Military Medical University to ensure ethical and humane treatment of animals (KY2022593- 1). For orthotopic liver tumor models, the Hepa1- 6 cells \((5 \times 10^{6})\) were suspended in \(25 \mu \mathrm{L}\) of Matrigel (Sigma- Aldrich) and inoculated into the liver parenchyma of the left lobe for in situ liver tumor model. Whereafter, C57BL/6 mice were euthanized for three weeks after inoculation, and part of the tumor- bearing liver tissue for immunofluorescence staining and intrahepatic GSH detection.  
+
+<|ref|>sub_title<|/ref|><|det|>[[149, 428, 390, 446]]<|/det|>
+## Intrahepatic GSH detection  
+
+<|ref|>text<|/ref|><|det|>[[147, 464, 852, 558]]<|/det|>
+In order to analyze the GSH distribution within the tumor- bearing liver tissue, the liver samples were subjected to the frozen section and then stained by pFeSAN and DAB for 5 min. Then, the stained tissues were monitored by using an inverted microscope.  
+
+<|ref|>sub_title<|/ref|><|det|>[[150, 576, 406, 593]]<|/det|>
+## Immunofluorescence staining  
+
+<|ref|>text<|/ref|><|det|>[[147, 611, 853, 854]]<|/det|>
+The tumor- bearing liver tissues from C57BL/6 mice were frozen section, and then the slices were permeabilized with \(0.3\%\) Triton X- 100 solution for \(15 \mathrm{~min}\) at room temperature. After blocking the slices with \(5\%\) BSA for \(30 \mathrm{~min}\) at \(37^{\circ} \mathrm{C}\) , tumor- bearing liver tissue slices were incubated with Ki67 antibody (1:250, Abcam, ab16667) overnight at \(4^{\circ} \mathrm{C}\) . Afterwards, the slices were washed for three times with PBS and stained with secondary antibody Goat Anti- Rabbit IgG- Alexa Fluor 594 (1:200, Abcam, ab150080) for \(1 \mathrm{~h}\) at \(25^{\circ} \mathrm{C}\) . Finally, the tissue sections were washed thoroughly with
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[148, 94, 850, 149]]<|/det|>
+PBS, counterstained with DAPI and imaged under a laser scanning confocal microscope (FV- 1000, Olympus, Japan).  
+
+<|ref|>sub_title<|/ref|><|det|>[[148, 169, 310, 186]]<|/det|>
+## Statistical analysis  
+
+<|ref|>text<|/ref|><|det|>[[147, 205, 851, 298]]<|/det|>
+The data were analyzed using Origin software (version 2018). All experiments were repeated at least 3 times and presented as mean \(\pm\) SD. Asterisks indicate significant differences ( \(^{**}p < 0.01\) ), analyzed by unpaired Student's two- sided t test.  
+
+<|ref|>sub_title<|/ref|><|det|>[[148, 372, 293, 389]]<|/det|>
+## Data availability  
+
+<|ref|>text<|/ref|><|det|>[[148, 408, 850, 502]]<|/det|>
+The all data generated in this study are provided in the Supplementary Information/Source Data file, or from the corresponding authors upon reasonable request. Source data are provided with this paper.  
+
+<|ref|>sub_title<|/ref|><|det|>[[149, 529, 318, 546]]<|/det|>
+## Acknowledgements  
+
+<|ref|>text<|/ref|><|det|>[[147, 571, 852, 776]]<|/det|>
+We acknowledge the financial support from the National Natural Science Foundation of China (52002314 and 21872109). Authors also acknowledge the support from Fundamental Research Funds for the Central Universities (D5000210635 and D5000210829). The calculations were supported by TianHe- 2 at Shanxi Supercomputing Center of China and Central for High Performance Computing of Northwestern Polytechnical University.  
+
+<|ref|>sub_title<|/ref|><|det|>[[149, 802, 334, 819]]<|/det|>
+## Author contributions  
+
+<|ref|>text<|/ref|><|det|>[[148, 845, 850, 900]]<|/det|>
+Z.T. and Y.Q. conceived and designed the project. D.C., Z.X., Z.G., W.G., X.T., and W.L. performed the experiments and analyzed the data. Z.T. and Y.Q. supervised the
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[147, 93, 850, 149]]<|/det|>
+project. J.Z., X.Z. and S.Z analyzed data. D.C., Z.T. and Y.Q. wrote and editing of the manuscript. All authors discussed the results and contributed to the preparation.  
+
+<|ref|>sub_title<|/ref|><|det|>[[148, 176, 323, 194]]<|/det|>
+## Competing interests  
+
+<|ref|>text<|/ref|><|det|>[[148, 219, 610, 237]]<|/det|>
+The authors declare that they have no conflict of interest.  
+
+<|ref|>sub_title<|/ref|><|det|>[[148, 264, 351, 282]]<|/det|>
+## Additional information  
+
+<|ref|>text<|/ref|><|det|>[[147, 297, 816, 351]]<|/det|>
+Supplementary information The online version contains supplementary material available at  
+
+<|ref|>text<|/ref|><|det|>[[147, 363, 836, 415]]<|/det|>
+Correspondence and requests for materials should be addressed to Zhimin Tian and Yongquan Qu.  
+
+<|ref|>sub_title<|/ref|><|det|>[[147, 440, 245, 457]]<|/det|>
+## References  
+
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+
+<--- Page Split --->
+<|ref|>sub_title<|/ref|><|det|>[[44, 42, 312, 70]]<|/det|>
+## Supplementary Files  
+
+<|ref|>text<|/ref|><|det|>[[44, 93, 765, 113]]<|/det|>
+This is a list of supplementary files associated with this preprint. Click to download.  
+
+<|ref|>text<|/ref|><|det|>[[61, 130, 318, 150]]<|/det|>
+SupportingInformation.pdf
+
+<--- Page Split --->

preprint/preprint__1ec8dcef6c217679fce963799555f6fa1fd9203dcfd67afbab1b5e42b94e9f72/images_list.json

@@ -0,0 +1,77 @@
+[
+  {
+    "type": "image",
+    "img_path": "images/Figure_1.jpg",
+    "caption": "Fig. 1 Design principle of rotaxane 1. The known binding preference and selective emission quenching of CB8-dye complexes with electron-rich aromatic analytes was exploited for the molecular engineering of a biofluid-applicable chemosensor that can detect the health-relevant amino acid Trp in human blood serum and urine. The hydrophilic and bulky \\(\\beta\\) -cyclodextrin moieties not only act as stopper groups that prevent CB8-dye disintegration in biofluids, but also protect 1 from adverse interactions with serum proteins and enable its covalent anchoring to surfaces for sensor chip preparation.",
+    "footnote": [],
+    "bbox": [
+      [
+        110,
+        424,
+        884,
+        590
+      ]
+    ],
+    "page_idx": 2
+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_2.jpg",
+    "caption": "Fig. 2 Formation and characterization of rotaxane 1. a Schematic representation of the synthetic route yielding rotaxane 1. b \\(^1\\mathrm{H}\\) NMR of rotaxane 1 in deuterium oxide (D2O). c Analytical HPLC of 1 (30% acetonitrile in water, 0.1% trifluoroacetic acid). d ESI-MS of 1 (1:1 acetonitrile/H2O, 1% formic acid).",
+    "footnote": [],
+    "bbox": [
+      [
+        123,
+        247,
+        875,
+        563
+      ]
+    ],
+    "page_idx": 4
+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_3.jpg",
+    "caption": "Fig. 3 Host-guest binding with rotaxane 1. a Schematic representation of analyte binding by rotaxane 1. b Absorbance (solid line) and normalized emission (dotted line) spectra of 1 in the absence (green) and presence of Trp (red) in water. c Binding isotherms of 1 (2.5 \\(\\mu \\mathrm{M}\\) ) and Trp in water (stars) and 1X PBS (dots) and the corresponding fit (red line) of the obtained data. d Emission quenching of 1 (1.0 \\(\\mu \\mathrm{M}\\) ) upon addition of bioactive analytes in 1X PBS. e Chemical structures of bioactive analytes tested in this study. f-g Electronic circular dichroism (ECD) spectra of 1 (100 \\(\\mu \\mathrm{M}\\) ) in the presence of f L-Trp or D-Trp and g L-Phe or D-Phe. The ECD spectra of the enantiomeric amino acids are shown for comparison. h Possible mechanism for chirality-induction through interaction of \\(\\beta\\) -cyclodextrin stopper groups with the DAP reporter dye and the CB8 portal area.",
+    "footnote": [],
+    "bbox": [
+      [
+        140,
+        78,
+        857,
+        576
+      ]
+    ],
+    "page_idx": 5
+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_4.jpg",
+    "caption": "Fig. 4 Tryptophan detection in serum samples with rotaxane 1. a Schematic representation of the state-of-the-art workflow for Trp quantification in blood serum by HPLC including the mandatory deproteinization step. b The Trp concentration levels of deproteinized serum samples (1:1 diluted with \\(624~\\mathrm{mM}\\) perchloric acid) were obtained by a HPLC assay. c Schematic workflow for emission-based sensing of Trp with 1 in untreated serum samples in a plate reader format. d and e Bar graphs of the averaged emission intensity (from 8 replica measurements, maximum errors were generously estimated) of the serum samples prior and after addition of 1, after spiking with Trp and subsequent indole addition. To assist the comparison, Trp concentration values obtained by HPLC are depicted as labels. See Supplementary Information for details on the assay protocols and error estimation.",
+    "footnote": [],
+    "bbox": [
+      [
+        147,
+        78,
+        848,
+        600
+      ]
+    ],
+    "page_idx": 7
+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_5.jpg",
+    "caption": "Fig. 5 Immobilization of the rotaxane 1 on glass surfaces by \\(\\mu \\mathrm{CS}\\) . a Schematic representation of microarray printing of 1 via microchannel cantilever spotting \\((\\mu \\mathrm{CS})\\) on an isocyanate-covered surface and analyte detection by microarrays. b Fluorescence microscopy images (DAPI filter, 10 s exposure) and c mean emission intensity for a sensor chip prepared by \\(\\mu \\mathrm{CS}\\) of 1. Incubation with \\(10\\mu \\mathrm{M}\\) Trp solution led to a strong emission decrease. 1X PBS used for all measurements.",
+    "footnote": [],
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preprint/preprint__1ec8dcef6c217679fce963799555f6fa1fd9203dcfd67afbab1b5e42b94e9f72/preprint__1ec8dcef6c217679fce963799555f6fa1fd9203dcfd67afbab1b5e42b94e9f72.mmd

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+
+# A molecular-engineered rotaxane overcomes longstanding biofluid-challenges and enables optical tryptophan detection in human serum  
+
+Joana Kramer  Karlsruhe Institute of Technology  Laura Grimm  Karlsruhe Institute of Technology  https://orcid.org/0000- 0002- 1808- 2206  Chunting Zhong  Karlsruhe Institute of Technology  Michael Hirtz  Karlsruhe Institute of Technology  https://orcid.org/0000- 0002- 2647- 5317  Frank Biedermann ( frank.biedermann@kit.edu )  Karlsruhe Institute of Technology  https://orcid.org/0000- 0002- 1077- 6529  
+
+## Article  
+
+Keywords:  
+
+Posted Date: February 28th, 2022  
+
+DOI: https://doi.org/10.21203/rs.3.rs- 1380544/v1  
+
+License: © © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License
+
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+
+# A molecular-engineered rotaxane overcomes longstanding biofluid-challenges and enables optical tryptophan detection in human serum  
+
+Joana Krämer<sup>a</sup>, Laura M. Grimm<sup>a</sup>, Chunting Zhong<sup>a,b</sup>, Michael Hirtz<sup>a,b</sup>, and Frank Biedermann<sup>a</sup>\*  
+
+<sup>a</sup> Institute of Nanotechnology (INT), Karlsruhe Institute of Technology (KIT), Hermann-von-Helmholtz Platz 1, 76344 Eggenstein-Leopoldshafen, Germany  <sup>b</sup> Karlsruhe Nano Micro Facility (KNMFi), Karlsruhe Institute of Technology (KIT), Hermann-von-Helmholtz Platz 1, 76344 Eggenstein-Leopoldshafen, Germany  
+
+## Abstract  
+
+Despite excelling in simple solvent mixtures, almost all known supramolecular chemosensors cannot be applied in real biofluids due to their adverse interactions with salts, proteins, and other biomolecules. Instead of following the established strategy of searching for alternative synthetic binders with improved affinity and selectivity parameters, we herein report a molecular engineering approach that specifically addresses this biofluid challenge. The developed dual- macrocycle rotaxane is the first supramolecular system that can sense the biomarker tryptophan in biofluids at physiological relevant concentrations, even in protein- and lipid- containing blood serum. Moreover, this chemosensor is used for emission- based high- throughput screening in a microplate format, in label- free enzymatic reaction monitoring, and in chirality sensing through induced circular dichroism. Printed sensor chips with surface- immobilized microarrays of this rotaxane were used for fluorescence microscopy imaging of tryptophan. Our chemosensor achieves a long- awaited applicability in complex biomedica and will foster future applications of supramolecular sensors for molecular diagnostics.  
+
+## Introduction  
+
+Sensing of biomolecules, such as amino acids and their derivatives, has gained importance in modern molecular diagnostics.<sup>1- 4</sup> However, it is so far mostly limited to clinical labs that are equipped with a high- performance liquid chromatography coupled with mass spectrometry (HPLC- MS) or nuclear magnetic resonance (NMR) capacities.<sup>5- 7</sup> Simple- to- use, fast- responding, and inexpensive chemosensors operating through molecular- recognition principles are thus highly sought- after alternatives.<sup>8- 12</sup> Indeed, optical chemosensor- based devices for routine metal cation sensing and for glucose monitoring have already reached clinics and personal homes.<sup>1,13,14</sup> However, these successes are the exception rather than the rule and benefit from the comparably high concentration (> millimolar) of these target analytes. In general, most metabolites of biological and medical interest cannot be detected in biofluids by so far reported chemosensors due to deficiencies with respect to affinity, selectivity, signal transduction, and stability.<sup>1,15,16</sup> In particular, the many
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+different bioactive small molecules present in biofluids, combined with high concentrations of proteins and salts, result in a completely different matrix environment than that of solvent mixtures, deionized water, or low salinity buffers. Unfortunately, the latter are predominantly employed in proof- of- concept reports of synthetic chemosensors in the hope that the extension to biofluids is afterwards a relatively straightforward matter. However, exactly this very perspective may have been the decisive bottleneck that has prevented molecular recognition- based systems from reaching general practical utility.  
+
+The amino acid tryptophan (Trp) is an important metabolite as it is an essential building block in protein biosynthesis, a precursor for serotonin and melatonin, and the basis of the kynurenine pathway. Moreover, the metabolism of tryptophan is associated with aging and produces metabolites that control inflammation, regulate energy homeostasis and modulate behaviour. Consequently, Trp levels in blood and urine are interesting biomarkers and therapeutic targets as they correlate, e.g., with cardiovascular and neurodegenerative diseases as well as with the risk of sepsis progression (see Supplementary Table 1 for selected examples). Unfortunately, state- of- the- art HPLC methods for the measurement of Trp levels in biofluids require additional treatments, e.g., deproteinization of blood serum, and have no prospects for future routine use in point- of- care units, ambulances, or general medical practices.  
+
+![](images/Figure_1.jpg)
+
+<center>Fig. 1 Design principle of rotaxane 1. The known binding preference and selective emission quenching of CB8-dye complexes with electron-rich aromatic analytes was exploited for the molecular engineering of a biofluid-applicable chemosensor that can detect the health-relevant amino acid Trp in human blood serum and urine. The hydrophilic and bulky \(\beta\) -cyclodextrin moieties not only act as stopper groups that prevent CB8-dye disintegration in biofluids, but also protect 1 from adverse interactions with serum proteins and enable its covalent anchoring to surfaces for sensor chip preparation. </center>  
+
+In the pursuit of a molecular recognition- based chemosensor for tryptophan that is both stable and operational in biofluids, we were inspired by supramolecular rotaxanes which combine novel functionalities with a superior tolerance to complex media. Rotaxanes have received attention for building molecular machines, supramolecular catalyst, and as binders for inorganic anions. In contrast to most rotaxanes, in which the axis component almost completely fills the host cavity, we developed a rotaxane design that allows for Trp binding alongside a chromophoric and fluorescent dye axis component in a hydrophobic cavity host (see Fig. 1). This topology enables molecular interactions between the host and dye- axle, thereby fostering affinity, selectivity, and signal transduction.
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+The macrocyclic cucurbit[8]url (CB8) can simultaneously bind two aromatic compounds in aqueous media. \(^{34 - }\) \(^{38}\) Thus, self-assembled CB8·dye complexes have been used for the complexation and detection of aryl- functional metabolites, taking advantage of new optical features in ternary complex formation. \(^{15,37 - 39}\) However, both the disintegration of CB8·dye complexes in saline biofluids due to metal cation binding to CB8, \(^{40}\) and the competitive displacement of the dye molecule from the host cavity by hydrophobic guests (such as steroids, peptides, or biogenic amines) has typically restricted their use to deionized water or low saline buffers. \(^{15}\) Moreover, the known intercalation of CB8·dye complexes into the binding pockets of serum albumin proteins \(^{41}\) could be a serious drawback for an intended use in blood serum.  
+
+Herein, we demonstrate that rotaxanation of CB8 with a reporter dye and cyclodextrin as stopper groups provides the first precedence of a fluorescent chemosensor that can detect tryptophan in biofluids, i.e., human blood serum and urine, in the physiological concentration range. Furthermore, the utility of the chemosensor rotaxane for chirality sensing, enzymatic reaction monitoring, and for sensor chip preparation is showcased.  
+
+## Results  
+
+Rotaxane design, synthesis, and characterization. Many fascinating supramolecular studies of cucurbit[n]url- based rotaxanes and pseudorotaxanes have appeared since the early days of CBn (re)discovery. \(^{42 - 44}\) In 2011, the first CB8- based rotaxane was presented that can complex additional aromatic guests in aqueous and organic media. \(^{25}\) Unfortunately, its axle component – viologen – is non- emissive and provides only weak absorbance changes in the presence of Trp. This rotaxane has thus been employed for fundamental binding studies but has no prospects for sensing applications. First, we attempted to develop analogous chemosensing rotaxanes with a fluorescent dicationic reporter dye such as di- alkylated 2,7- diazapyrene (DAP) as the axle component. The functionalization of DAP proved to be more challenging than that of viologens as copper- accelerated alkyne- azide click conditions were not tolerated, whereas copper- free click reactions with strained alkynes yielded almost insoluble materials (see also Supplementary Information). Consequently, we investigated alternative conjugation strategies that overcome the susceptibility of DAP dyes to strong bases and reducing agents. Indeed, fluorescent cucurbit[8]url- rotaxane 1 can be prepared by a convergent synthetic route through a thiol- maleimide click coupling (Fig. 2). \(\beta\) - Cyclodextrin ( \(\beta\) - CD) was identified as a suitable stopper ensuring the aqueous solubility of the rotaxane and offering possibilities for its further covalent (through OH bonds) and non- covalent (through its hydrophobic cavity) modification. Moreover, based on the available literature data on the interaction of cyclodextrin- drug formulations with serum albumin, \(^{45}\) we expected that the installation of cyclodextrin stopper groups as part of the chemosensor will effectively prevent the undesirable encapsulation of the CB8·dye moiety into the hydrophobic binding pockets of albumin, \(^{41}\) and thus will support the applicability of the resulting rotaxane in untreated blood serum. Therefore, a maleimide- functionalized tetraethylene glycol (TEG) linker was coupled through its N- hydroxycucinimide- functionality to 3A- Amino- 3A- deoxy- (2AS,3AS)- \(\beta\) - cyclodextrin to yield a thiol- reactive stopper group. Furthermore, the aromatic DAP core was functionalized with thio- butyl linkers.
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+Finally, the pseudorotaxane which was formed by self-assembling CB8 with the DAP- axle was covalently linked to two stopper groups via a Michael thiol- ene reaction. Rotaxane 1 was then obtained after purification by preparative HPLC and characterized by \(^1\mathrm{H}\) and diffusion ordered spectroscopy (DOSY) NMR, ESI- MS, dynamic light scattering (DLS) (Fig. 2 and Supplementary Fig. 1 and 2), as well as optical spectroscopy (Fig 3). As expected, DLS measurements revealed self- aggregation of the chemosensing system upon heating from 20 to \(50^{\circ}\mathrm{C}\) , which is a common behavior of cyclodextrins in water. \(^{46}\) This design feature enables the utilization of \(\beta\) - CD typical characteristics such as chirality induction within the rotaxane assembly, see below.  
+
+![](images/Figure_2.jpg)
+
+<center>Fig. 2 Formation and characterization of rotaxane 1. a Schematic representation of the synthetic route yielding rotaxane 1. b \(^1\mathrm{H}\) NMR of rotaxane 1 in deuterium oxide (D2O). c Analytical HPLC of 1 (30% acetonitrile in water, 0.1% trifluoroacetic acid). d ESI-MS of 1 (1:1 acetonitrile/H2O, 1% formic acid). </center>  
+
+Host- guest binding properties. Firstly, the analyte- sensing functionality of rotaxane 1 was tested by an emission- based analyte assay with indole and adamantanol (see Supplementary Fig. 3). The response of rotaxane 1 towards indole (i.e., the emission quenching of the rotaxane 1) remained similarly strong in 1X phosphate buffered saline (1X PBS) as in water. Competitive binders cannot displace the dye independently of their stronger binding affinity towards CB8 due to the installed stopper groups. In a second step, the host- guest binding properties of rotaxane 1 with Trp and its analogues, i.e., indole and tryptamine, were investigated in detail by emission- based titration experiments in water, 1X PBS and in human urine (Fig. 3 and Supplementary Fig. 4- 6). The observed emission intensity decrease indicated analyte binding in close proximity to the DAP dye in the CB8 cavity. Binding affinities ( \(K_{\mathrm{a}}\) , see Table 1) were determined by fitting the titration curves to a 1:1 binding model (Fig. 3c). Indole, tryptamine, and Trp caused a strong emission quenching of rotaxane 1 in aqueous media and human urine.
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+![](images/Figure_3.jpg)
+
+<center>Fig. 3 Host-guest binding with rotaxane 1. a Schematic representation of analyte binding by rotaxane 1. b Absorbance (solid line) and normalized emission (dotted line) spectra of 1 in the absence (green) and presence of Trp (red) in water. c Binding isotherms of 1 (2.5 \(\mu \mathrm{M}\) ) and Trp in water (stars) and 1X PBS (dots) and the corresponding fit (red line) of the obtained data. d Emission quenching of 1 (1.0 \(\mu \mathrm{M}\) ) upon addition of bioactive analytes in 1X PBS. e Chemical structures of bioactive analytes tested in this study. f-g Electronic circular dichroism (ECD) spectra of 1 (100 \(\mu \mathrm{M}\) ) in the presence of f L-Trp or D-Trp and g L-Phe or D-Phe. The ECD spectra of the enantiomeric amino acids are shown for comparison. h Possible mechanism for chirality-induction through interaction of \(\beta\) -cyclodextrin stopper groups with the DAP reporter dye and the CB8 portal area. </center>  
+
+Importantly, as the \(K_{\mathrm{a}}\) values were almost identical in water and 1X PBS, the desired salt tolerance was indeed achieved through the rotaxanation strategy. Besides, the emission response selectivity of 1 was evaluated in a plate reader format by exposing its saline buffered solutions to different types of biorelevant analytes, such as aliphatic amino acids, amino acid derivatives and polyamines (Fig. 3d- e and Supplementary Information for experimental details). Particularly indole, tryptamine, and Trp caused a strong emission quenching of 1 in 1X PBS. In contrast, dopamine, indole- 3- acetic acid (IAA), and phenylalanine (Phe) only showed significant emission quenching at non- physiologically high concentrations ( \(>500 \mu \mathrm{M}\) ). The rotaxane was almost unresponsive to aliphatic amino acids and polyamines.
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+Chirality sensing. Chirality plays an essential role in biology, and the occurrence of D- amino acids in living organisms is a useful indicator of various changes including aging, diseases, or disorders.47,48 Several molecular probes and supramolecular systems for chirality sensing of bioactive molecules have been devised in order to achieve higher assay throughputs with simpler handling and lower costs than contemporary chiral chromatographic methods.4,49- 52 Akin to self- assembled CB8•dye complexes that have been reported for chirality sensing of aromatic amino acids,51,53 we expected that rotaxane 1 can be used for the differentiation between enantiomeric aromatic amino acids in combination with electronic chirality dichroism (ECD) spectroscopy. Indeed, L- Trp can be distinguished from D- Trp and L- Phe from D- Phe upon complexation by 1 through the emerging induced chiroptical fingerprints in the visible region of the ECD spectrum (Fig. 3f- g). Note that the slight chiroptical response of 1, and the asymmetry between the ECD spectra of 1•L- Phe versus 1•D- Phe can be attributed to the chirality induction caused by the chiral \(\beta\) - cyclodextrin stopper groups. This suggests that the \(\beta\) - cyclodextrin moieties can come in close proximity to the CB8- dye complex, as is schematically depicted in Fig. 3h, and may present an additional mechanism by which the binding pocket of the chemosensor is protected from interferents present in biofluids. In addition to that, the \(\beta\) - cyclodextrin stopper groups offer the possibility to further tune the distinct supramolecular architecture of the chemosensor. Future efforts will be directed towards the development of stimuli- responsive hydrogels of the rotaxane by adding additives functionalized with multiple cyclodextrin- binding guests.  
+
+Tryptophan detection in biological environments. The excellent salt stability and good emission- response selectivity of rotaxane 1 for indoyl- type compounds prompted us to investigate the sensing of L- Trp in blood specimens (Fig. 4 and Supporting Information). Therefore, untreated human and bovine blood serum samples, i.e., serum that still contains proteins, were pipetted into microplate wells and the emission upon addition of 1 \((I_{1})\) , and the emission reduction upon subsequent spiking with Trp \((I_{2})\) were recorded. Finally, excess of indole was added to fully saturate the binding pocket and thereby to quench the chemosensor emission to its maximum \((I_{3})\) . As an additional control the autofluorescence \((I_{0})\) of each of the serum sample was measured. The emission data was converted into quenching efficiencies (EQ) which are plotted in Fig. 4d and e.
+
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+![](images/Figure_4.jpg)
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+<center>Fig. 4 Tryptophan detection in serum samples with rotaxane 1. a Schematic representation of the state-of-the-art workflow for Trp quantification in blood serum by HPLC including the mandatory deproteinization step. b The Trp concentration levels of deproteinized serum samples (1:1 diluted with \(624~\mathrm{mM}\) perchloric acid) were obtained by a HPLC assay. c Schematic workflow for emission-based sensing of Trp with 1 in untreated serum samples in a plate reader format. d and e Bar graphs of the averaged emission intensity (from 8 replica measurements, maximum errors were generously estimated) of the serum samples prior and after addition of 1, after spiking with Trp and subsequent indole addition. To assist the comparison, Trp concentration values obtained by HPLC are depicted as labels. See Supplementary Information for details on the assay protocols and error estimation. </center>  
+
+In order to correlate the emission quenching values to Trp concentrations by established HPLC protocols, serum samples were deproteinized according to standard clinical lab procedures (Fig. 4a and b and Supplementary Figure 7). Pleasingly, there is a correspondence of the quenching efficiency trends obtained from our rotaxane- based optical assay with the HPLC- derived Trp levels. Moreover, Trp concentrations within the typical concentration ranges of healthy (approx. \(50 - 70~\mu \mathrm{M}\) ) and diseased patients (approx. \(20 - 45~\mu \mathrm{M}\) , see Supplementary Table 1 and 2) are distinguishable from each other. Compared to HPLC routines, our
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+chemosensor assay is both much faster ( \(< 1\) min per sample) and parallelizable. Additionally, the possibility to use untreated blood serum further simplifies and shortens the assay protocol.  
+
+Moreover, we found that rotaxane 1 can be used for label- free enzymatic reaction monitoring in real time, as demonstrated for the pepsin- catalyzed hydrolysis of the Trp- rich protein bovine serum albumin in a biological saline buffer (Supplementary Fig. 8). For comparison, this is a great practical challenge with existing technologies that provide only discontinuous data points and require time- consuming sample pre- and post- treatment steps. Other supramolecular proof- of- concept reports that inspired our use of rotaxane 1 have so far been limited to minimal buffers. \(^{51,54,55}\)  
+
+Rotaxane microarrays for analyte detection. The inherent functional groups of the designed rotaxane provide a synthetic handle for immobilization on functionalized glass surfaces. \(^{56}\) Here, scanning probe lithography methods offer a flexible route to create micro- and even nanoscaled surface patterns of immobilized functional molecules. \(^{57}\) To create microarrays of rotaxane 1, we chose microchannel cantilever spotting \((\mu \mathrm{CS})\) , which allows for deposition of femtoliter- sized droplets on surfaces that can act as vessels for coupling reactions. \(^{58,59}\) Therefore, an isocyanate- functionalized glass substrate was prepared and reacted with the HO- groups of the \(\beta\) - cyclodextrin stopper (Fig. 5 and Supplementary Information). We thereby fabricated a microstructured and washable sensor chip allowing for Trp detection down to submicromolar concentrations, as is shown by fluorescence microscopy images and emission intensity quenching effects displayed in Fig. 5b- c (see also Supplementary Fig. 9 and Supplementary Table 3). The remarkable sensitivity increase of rotaxane 1 by surface immobilization corresponds to our recent findings for indicator displacement chemosensors. \(^{60}\) Importantly, the rotaxane- functionalized microarrays are more selective than printed microarrays with the binary CB8•MDAP chemosensor complex. While both allow for detection of tryptophan or indole, the hydrophobic and strongly CB8- binding guest meantime does not alter the emission of the rotaxane 1 microarray. Memantine is not an electron- rich aromatic compound and thus cannot bind next to the rotaxane- protected reporter dye DAP. In contrast, the CB8•MDAP sensor chips respond to the presence of meantime though Methyl- DAP (MDAP) displacement, and thus share the undesirable cross- reactivity to other metabolites with that of bimolecular CBn•dye reporter pairs (Supplementary Fig. 10 and Supplementary Table 4). \(^{1,15,61}\) Finally, the immobilization also allows for printing reference dyes next to the chemosensor array to normalize the emission signal (Supplementary Fig. 11 and Supplementary Table 5). By encapsulation of such sensor chips into microfluidic channels, they can act as convenient and low volume sensing platform. \(^{60}\) The possibility to immobilize the Trp- binding rotaxane on surfaces opens up its potential for the future integration into lab- on- a- chip designs with plasmonic biosensors. \(^{62}\)
+
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+![](images/Figure_5.jpg)
+
+<center>Fig. 5 Immobilization of the rotaxane 1 on glass surfaces by \(\mu \mathrm{CS}\) . a Schematic representation of microarray printing of 1 via microchannel cantilever spotting \((\mu \mathrm{CS})\) on an isocyanate-covered surface and analyte detection by microarrays. b Fluorescence microscopy images (DAPI filter, 10 s exposure) and c mean emission intensity for a sensor chip prepared by \(\mu \mathrm{CS}\) of 1. Incubation with \(10\mu \mathrm{M}\) Trp solution led to a strong emission decrease. 1X PBS used for all measurements. </center>  
+
+## Discussion  
+
+Due to its ubiquity in many biological processes and associated diseases, \(^{20,21}\) the amino acid tryptophan is an attractive target for the development of synthetic receptors. As Trp lacks a chemically reactive side chain besides the common amino and carboxylate moieties present in all amino acids, its selective molecular recognition by a synthetic receptor requires the installation of non- covalent binding motifs that promote cation- \(\pi\) or \(\pi\) - \(\pi\) stacking interactions with the indole ring of Trp. \(^{63,64}\) In fact, several synthetic binders for Trp and its derivatives are already known, e.g., see Table 2 for selected examples. \(^{25,51,65 - 67}\) However, none of these systems are suitable for optical sensing of Trp in biofluids as they lack the required selectivity and salt tolerance.  
+
+Instead of following a common strategy in supramolecular chemistry – the search for alternative macrocyclic hosts that may bind the target analyte with (even) higher affinity or selectivity – we adopted here a different design concept. By comparing the key parameters of Trp- binding by synthetic receptors with expected diagnostic needs, i.e., typical Trp concentration levels found in biofluids (see Supplementary Table 1), we came to the conclusion that self- assembled CB8 \(\cdot\) dye complexes may already offer sufficient affinities to distinguish Trp level differences in blood serum between healthy and diseased patients. Moreover, the concomitant strong dye emission quenching upon Trp binding appeared sufficient to develop sensitive fluorescence- based assays for Trp detection at practically relevant concentration levels. These outstanding characteristics in terms of binding strength and signal transduction capabilities of CB8 \(\cdot\) dye complexes are likely the result of the interplay of a peculiar and powerful version of the hydrophobic effect, i.e., high- energy cavity water release, \(^{68}\) and the perfect parallel arrangement of the indoyl moiety next to an electron deficient, dicationic fluorescent reporter dye, whose high emission quantum yield is quenched when electron- rich moieties are present in its vicinity. \(^{69}\) By pursuing the molecular engineering approach we aimed to receive a
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+chemosensor which remains functional in the presence of typical interferents occurring in biofluids such as salts, competitively binding metabolites and proteins<sup>14,39,40,69</sup> and is therefore superior over self-assembled CB8•dye chemosensor complexes.  
+
+We herein report the first chemosensor for emission- based detection of the health- relevant amino acid tryptophan that is operational in both urine and blood serum. Our rotaxanation strategy ensures both superior stability and functionality in biofluids and targets the physiological Trp concentration range, while preserving important advantages of supramolecular chemosensors such as their fast response time, as well as their utility for label- free reaction monitoring and chirality sensing. Our concept provides additional opportunities for chemosensor immobilization into microarrays on sensor chips that can facilitate the transfer of synthetic receptors and chemosensors into clinical or diagnostic applications.  
+
+## Methods  
+
+Chemicals and solvents. Ultrapure deionized water was obtained from a SATORIUS ARIUM® PRO DI water purification system with ASTM Type 1 water quality. Buffer solutions were prepared following standard protocols. All chemosensor and analyte stock solutions were prepared in ultrapure water or 1X PBS and stored at \(+4^{\circ}\mathrm{C}\) . Details on chemicals, suppliers, and descriptions on synthetic procedures are given in the Supplementary Information.  
+
+NMR spectroscopy. NMR spectra were recorded on a BRUKER Avance 500 (1H NMR: 500 MHz; 13C NMR: 126 MHz) at room temperature. The chemical shift \(\delta\) is expressed in parts per million (ppm) whereas the residual signal of the solvent was used as secondary reference. 1H NMR spectra were analyzed using FT- processing software, 13C NMR spectra were 1H- decoupled and characterization of the 13C NMR spectra was ensued through the DEPT- technique.  
+
+Diffusion ordered spectroscopy (DOSY). DOSY data were obtained on a Bruker AM 400 (400 MHz) at 298 K using a LED- bipolar gradient paired with 2 spoil gradients (ledbpgp2s), with 16 incremental steps (with 16 scans each) in the gradient strength, ramped from 2 % to 98 % of the maximum gradient strength. The gradient pulse length \(\delta /2\) (p30) and diffusion delay \(\Delta\) (d20) were specifically optimised and set to 1.8 ms \(\delta = 3.6 \mathrm{ms}\) ) and \(\Delta = 79.9 \mathrm{ms}\) , respectively. Chemical shifts \(\delta\) are expressed in parts per million (ppm) and calibrated with respect to \(\mathrm{D}_2\mathrm{O}\) as internal standard. The spectra were calibrated and phased using TopSpin. The pseudo- 2D DOSY plots were computed using Bruker Dynamic Center, fitting the intensity decay with the following equation: \(I(g) = I_0 \exp \left(-D g^2 \gamma^2 \delta^2 (\Delta - \frac{\delta}{3})\right)\) .  
+
+High performance liquid chromatography (HPLC). Analytical HPLC experiments were carried out on a LC- 2000Plus HPLC system equipped with an UV- 2075 UV- Vis detector as well as a FP- 920 fluorescence detector and a KROMASIL 100 C18 5 \(\mu \mathrm{m}\) LC column (250×4.6 mm, AGELA) at a flow rate of 1.3 mL/min for rotaxane purification and 1.2 mL/min for the quantitative Trp concentration determination. Preparative HPLC
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+experiments were performed on the same system but equipped with a KROMASIL 100 C18 5 \(\mu \mathrm{m}\) LC precoll- umn \((50\times 20\mathrm{mm}\) , AGELA) and a KROMASIL 100 C18 5 \(\mu \mathrm{m}\) LC preparative column \((250\times 50\mathrm{mm}\) , AGELA). All crude samples were dissolved in a mixture of water and ACN \((\mathrm{v / v} = 4 / 1)\) and applied as solutions. A flow rate of \(10\mathrm{mL / min}\) was used for preparative column runs. Note: At higher concentrations of rotaxane 1 we noticed a small peak shoulder in the chromatogram which we assign to the formation of aggregates, however, when the main peak fraction was collected and re- subjected to HPLC analysis, a similar HPLC chromatogram was obtained, suggesting that the small peak shoulder is indeed not due to impurities.  
+
+HPLC- based quantification of L- Trp in serum samples. The quantification of the L- tryptophan concentration of three different blood serum samples (human serum, steroid depleted human serum, and bovine calf serum) was done using common HPLC methods according to literature. Further details on the calibration curve and the L- Trp concentration determination of each serum sample can be found in the Supplementary Information.  
+
+Electrospray ionization mass spectrometry (ESI- MS). ESI- MS experiments were carried out on a BRUKER micrOTOF- Q (208 - 320 Vac, 50/60 Hz, 1800 VA) mass spectrometer equipped with an On- line NanoElectrospray ion source. The spectra were interpreted by molecular peaks \([\mathrm{M}]^{n + }\) or peaks of protonated molecules \([\mathrm{M} + \mathrm{H}]^{n + }\) and are shown with their mass- to- charge ratio (m/z). Solvents used were \(\mathrm{H}_2\mathrm{O}\) , MeOH, and \(\mathrm{H}_2\mathrm{O}:1\%\) formic acid.  
+
+Optical spectroscopy. Absorbance spectra were measured on a JASCO V- 730 double- beam UV- Vis spectrophotometer with an automatic stirring unit and were baseline corrected. Steady- state emission spectra were recorded on a JASCO FP- 8300 fluorescence spectrometer equipped with a 450 W xenon arc lamp, double- grating excitation, emission monochromators, and a water- thermostated cell holder (STR- 812). Fluorescence- based titration experiments were performed manually or by an ATS- 827 automatic titration unit and the normalized emission was fitted according to a 1:1 binding model by a least square fit to determine the binding affinities (see also Supplementary information). All cuvettes were equipped with a stirrer allowing rapid mixing. Emission and excitation spectra were corrected for source intensity (lamp and grating).  
+
+Electronic circular dichroism (ECD) spectroscopy. ECD spectra were recorded on a JASCO J- 1500 CD spectrometer equipped with a Peltier- thermostated cell holder and an automatic stirring unit. The ECD spectra reported were baseline corrected for water. All spectra were recorded at \(25^{\circ}\mathrm{C}\) .  
+
+Dynamic light scattering (DLS). DLS measurements were performed on a MALVERN ZetaSizer Nano instrument with disposable acryl cuvettes. The chemosensor solution (150 \(\mu \mathrm{M}\) in ultrapure water) was heated up from 20 to \(55^{\circ}\mathrm{C}\) using the automated heating cycle provided by the software. The intensity of the scattered light was measured at a fixed angle \((173^{\circ})\) . The wavelength of the laser light used for the light scattering experiments was set to 633 nm. Data analysis was performed according to standard procedures using the Malvern software. The values viscosity and the refractive index were adapted from the provided software.
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+Microwell plate- based serum experiments. An EnSight™ multimode plate reader and black opaque OptiPlate™- 96 polystyrene microplates (both PERKIN ELMER) were used for all microwell plate- based assays. The instrument was equipped with monochromatic fluorescence intensity detection (top- and bottom- reading) as well as filter- and monochromator- based absorbance detection and temperature control. The fluorescence emission measurements (100 flashes) were performed as single wavelength measurements ( \(\lambda_{\mathrm{ex}} = 393 \mathrm{nm}\) , \(\lambda_{\mathrm{em}} = 450 \mathrm{nm}\) ) at \(25^{\circ}\mathrm{C}\) . A detailed procedure of the Trp detection in untreated serum samples with rotaxane 1 can be found in the Supplementary Information.  
+
+Immobilization of rotaxane 1 on glass surfaces. A suitable isocyanate for immobilization was produced by first activating a pre- cleaned \(\mathrm{SiO_2}\) - glass surface with oxygen plasma (10 sccm \(\mathrm{O_2}\) , 0.2 mbar, 100 W, 2 min, ATTO system, DIENER ELECTRONICS, Germany) to receive a hydroxylated \(\mathrm{SiO_2}\) - OH layer. Then, the \(\mathrm{SiO_2}\) - OH surface was immersed in \(1 \mathrm{mg / mL} 4,4'\) - diisocyanate methylendibenzol (MDI) containing \(1 \mu \mathrm{L} / \mathrm{mL}\) dibutyltin dilaurate (TDL) as catalyst in anhydrous DMSO at \(80^{\circ}\mathrm{C}\) for \(24 \mathrm{h}\) . Finally, the substrate ( \(\mathrm{SiO_2}\) - NCO) was rinsed with acetone for 2 min and dried with \(\mathrm{N_2}\) . Then \(0.5 \mu \mathrm{L}\) of rotaxane ink ( \(3 \mathrm{mg / mL}\) in DMSO containing \(10\%\) (v/v) TDL and \(10\%\) (v/v) PEG 600) were applied to the reservoir of a microchannel cantilever \(^{70}\) (SPT- SC10S, BIOFORCE NANOSCIENCES), mounted to the lithography setup (NLP2000, NANOINK), and spotted for defined durations ( \(\sim 0.5 - 1 \mathrm{s}\) ) at a controlled humidity of \(40\%\) . After printing, the substrate was heated to \(80^{\circ}\mathrm{C}\) for \(24 \mathrm{h}\) , then washed with ethanol, and dried with \(\mathrm{N_2}\) . Control experiments were performed according to previous work, \(^{60}\) see also Supplementary Information.  
+
+Analyte detection with rotaxane microarrays. Analyte solutions were prepared in 1X PBS or \(10 \mathrm{mM}\) HEPES buffer, pH 7.0. The rotaxane- patterned substrates were covered with \(20 \mu \mathrm{L}\) analyte solution for \(5 \mathrm{min}\) , washed with water, and dried with \(\mathrm{N_2}\) . The fluorescence imaging was performed on a NIKON ECLIPSE Ti2 inverted fluorescence microscope (NIKON, Germany) equipped with an Intensilight illumination, a NIKON DS Q12 camera, and a DAPI filter set (DAPI- U HQ, NIKON, Germany). All data is expressed as mean \(\pm\) standard deviation of at least three independent measurements.  
+
+## Data Availability  
+
+All data are available from the corresponding author upon reasonable request and are digitally stored on the severs of the home institution. Furthermore, binding parameters can be found on suprabank.org; DOI: 10.34804/supra.20220128415.  
+
+## References  
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+
+<--- Page Split --->
+
+## Acknowledgements  
+
+AcknowledgementsJ.K. thanks the Evonik foundation for financial support. L.M.G. and F.B. acknowledge the DFG (Emmy Noether Grant and SPP 1807) and C.Z. acknowledges the CSC fellowship (No. 201808440323). This work was partly carried out with the support of the Karlsruhe Nano Micro Facility (KNMF, www.knmf.kit.edu), a Helmholtz Research Infrastructure at Karlsruhe Institute of Technology (KIT, www.kit.edu). We thank Dr. Pierre Picchetti for carrying out DLS measurements and Joël Monti for performing DOSY NMR measurements.  
+
+## Ethics declarations  
+
+Ethics declarationsAll procedures performed in studies involving human participants were in accordance with the formal statement of ethical principles published by the World Medical Association in the declaration of Helsinki in 1964 and its later amendments or comparable ethical standards. Informed consent was obtained from all individual participants included in the study.  
+
+## Competing interests  
+
+The authors declare no competing financial interests.  
+
+## Contributions  
+
+ContributionsJ.K. and L.M.G. conceptualized the study, designed and performed the experiments, analyzed data, and prepared the manuscript. C.Z. performed printing and imaging experiments and analyzed the data. M.H. conceptualized the study and provided expertise. F.B. conceptualized the study, provided expertise, and prepared the manuscript. All authors have contributed to, commented, and agreed on the manuscript preparation.  
+
+## Corresponding author  
+
+Corresponding authorCorrespondence and requests for materials should be addressed to Frank Biedermann (frank.biedermann@kit.edu, https://orcid.org/0000- 0002- 1077- 6529).  
+
+## Additional Information  
+
+Supplementary information is available for this paper.  
+
+Table 1 Binding affinities (given as log \(K_{a}\) ) of rotaxane 1 for indoyl-based analytes in aqueous media at \(25^{\circ}\mathrm{C}\) Estimated error of \(\log K_{\mathrm{a}} = 0.2\)   
+
+<table><tr><td>analyte / medium</td><td>water</td><td>1X PBS</td><td>urine (1:2 in 1X PBS)</td></tr><tr><td>indole</td><td>5.42</td><td>5.48</td><td>~ 4.18</td></tr><tr><td>L-Trp</td><td>3.93</td><td>3.95</td><td>3.37</td></tr><tr><td>tryptamine</td><td>3.69</td><td>4.14</td><td>-</td></tr></table>
+
+<--- Page Split --->
+
+
+Table 2 Comparison of representative synthetic binders for tryptophan. See Supplementary Fig. 12 for the corresponding chemical structures.   
+
+<table><tr><td>Trp</td><td>optical signal</td><td>reported medium</td><td>ref.</td></tr><tr><td>crown ether-type receptor*</td><td>absorbance</td><td>Chloroform</td><td>66</td></tr><tr><td>pillar[5]arene-type receptor</td><td>emission</td><td>H2O:DMSO (7:3)</td><td>65</td></tr><tr><td>binary CB8*dye complexes</td><td>emission</td><td>H2O,<br>low saline buffers</td><td>51,67</td></tr><tr><td>CB8-viologen rotaxane</td><td>-</td><td>low saline buffer, acetonitrile</td><td>25</td></tr><tr><td>rotaxane 1</td><td>emission</td><td>saline buffers, biofluids</td><td>this work</td></tr></table>
+
+\\* TrpOMe binding was investigated.
+
+<--- Page Split --->
+
+## Supplementary Files  
+
+This is a list of supplementary files associated with this preprint. Click to download.  
+
+SupplementaryInformationKraemeretal.pdf
+
+<--- Page Split --->

preprint/preprint__1ec8dcef6c217679fce963799555f6fa1fd9203dcfd67afbab1b5e42b94e9f72/preprint__1ec8dcef6c217679fce963799555f6fa1fd9203dcfd67afbab1b5e42b94e9f72_det.mmd

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+<|ref|>title<|/ref|><|det|>[[44, 108, 838, 208]]<|/det|>
+# A molecular-engineered rotaxane overcomes longstanding biofluid-challenges and enables optical tryptophan detection in human serum  
+
+<|ref|>text<|/ref|><|det|>[[44, 230, 700, 460]]<|/det|>
+Joana Kramer  Karlsruhe Institute of Technology  Laura Grimm  Karlsruhe Institute of Technology  https://orcid.org/0000- 0002- 1808- 2206  Chunting Zhong  Karlsruhe Institute of Technology  Michael Hirtz  Karlsruhe Institute of Technology  https://orcid.org/0000- 0002- 2647- 5317  Frank Biedermann ( frank.biedermann@kit.edu )  Karlsruhe Institute of Technology  https://orcid.org/0000- 0002- 1077- 6529  
+
+<|ref|>sub_title<|/ref|><|det|>[[44, 499, 102, 515]]<|/det|>
+## Article  
+
+<|ref|>text<|/ref|><|det|>[[44, 536, 137, 553]]<|/det|>
+Keywords:  
+
+<|ref|>text<|/ref|><|det|>[[44, 573, 336, 592]]<|/det|>
+Posted Date: February 28th, 2022  
+
+<|ref|>text<|/ref|><|det|>[[44, 611, 474, 630]]<|/det|>
+DOI: https://doi.org/10.21203/rs.3.rs- 1380544/v1  
+
+<|ref|>text<|/ref|><|det|>[[44, 649, 909, 690]]<|/det|>
+License: © © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License
+
+<--- Page Split --->
+<|ref|>title<|/ref|><|det|>[[87, 81, 910, 135]]<|/det|>
+# A molecular-engineered rotaxane overcomes longstanding biofluid-challenges and enables optical tryptophan detection in human serum  
+
+<|ref|>text<|/ref|><|det|>[[87, 150, 803, 167]]<|/det|>
+Joana Krämer<sup>a</sup>, Laura M. Grimm<sup>a</sup>, Chunting Zhong<sup>a,b</sup>, Michael Hirtz<sup>a,b</sup>, and Frank Biedermann<sup>a</sup>\*  
+
+<|ref|>text<|/ref|><|det|>[[86, 209, 911, 296]]<|/det|>
+<sup>a</sup> Institute of Nanotechnology (INT), Karlsruhe Institute of Technology (KIT), Hermann-von-Helmholtz Platz 1, 76344 Eggenstein-Leopoldshafen, Germany  <sup>b</sup> Karlsruhe Nano Micro Facility (KNMFi), Karlsruhe Institute of Technology (KIT), Hermann-von-Helmholtz Platz 1, 76344 Eggenstein-Leopoldshafen, Germany  
+
+<|ref|>sub_title<|/ref|><|det|>[[88, 317, 179, 335]]<|/det|>
+## Abstract  
+
+<|ref|>text<|/ref|><|det|>[[86, 360, 913, 603]]<|/det|>
+Despite excelling in simple solvent mixtures, almost all known supramolecular chemosensors cannot be applied in real biofluids due to their adverse interactions with salts, proteins, and other biomolecules. Instead of following the established strategy of searching for alternative synthetic binders with improved affinity and selectivity parameters, we herein report a molecular engineering approach that specifically addresses this biofluid challenge. The developed dual- macrocycle rotaxane is the first supramolecular system that can sense the biomarker tryptophan in biofluids at physiological relevant concentrations, even in protein- and lipid- containing blood serum. Moreover, this chemosensor is used for emission- based high- throughput screening in a microplate format, in label- free enzymatic reaction monitoring, and in chirality sensing through induced circular dichroism. Printed sensor chips with surface- immobilized microarrays of this rotaxane were used for fluorescence microscopy imaging of tryptophan. Our chemosensor achieves a long- awaited applicability in complex biomedica and will foster future applications of supramolecular sensors for molecular diagnostics.  
+
+<|ref|>sub_title<|/ref|><|det|>[[88, 631, 218, 650]]<|/det|>
+## Introduction  
+
+<|ref|>text<|/ref|><|det|>[[86, 674, 913, 895]]<|/det|>
+Sensing of biomolecules, such as amino acids and their derivatives, has gained importance in modern molecular diagnostics.<sup>1- 4</sup> However, it is so far mostly limited to clinical labs that are equipped with a high- performance liquid chromatography coupled with mass spectrometry (HPLC- MS) or nuclear magnetic resonance (NMR) capacities.<sup>5- 7</sup> Simple- to- use, fast- responding, and inexpensive chemosensors operating through molecular- recognition principles are thus highly sought- after alternatives.<sup>8- 12</sup> Indeed, optical chemosensor- based devices for routine metal cation sensing and for glucose monitoring have already reached clinics and personal homes.<sup>1,13,14</sup> However, these successes are the exception rather than the rule and benefit from the comparably high concentration (> millimolar) of these target analytes. In general, most metabolites of biological and medical interest cannot be detected in biofluids by so far reported chemosensors due to deficiencies with respect to affinity, selectivity, signal transduction, and stability.<sup>1,15,16</sup> In particular, the many
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[87, 78, 912, 209]]<|/det|>
+different bioactive small molecules present in biofluids, combined with high concentrations of proteins and salts, result in a completely different matrix environment than that of solvent mixtures, deionized water, or low salinity buffers. Unfortunately, the latter are predominantly employed in proof- of- concept reports of synthetic chemosensors in the hope that the extension to biofluids is afterwards a relatively straightforward matter. However, exactly this very perspective may have been the decisive bottleneck that has prevented molecular recognition- based systems from reaching general practical utility.  
+
+<|ref|>text<|/ref|><|det|>[[87, 213, 912, 411]]<|/det|>
+The amino acid tryptophan (Trp) is an important metabolite as it is an essential building block in protein biosynthesis, a precursor for serotonin and melatonin, and the basis of the kynurenine pathway. Moreover, the metabolism of tryptophan is associated with aging and produces metabolites that control inflammation, regulate energy homeostasis and modulate behaviour. Consequently, Trp levels in blood and urine are interesting biomarkers and therapeutic targets as they correlate, e.g., with cardiovascular and neurodegenerative diseases as well as with the risk of sepsis progression (see Supplementary Table 1 for selected examples). Unfortunately, state- of- the- art HPLC methods for the measurement of Trp levels in biofluids require additional treatments, e.g., deproteinization of blood serum, and have no prospects for future routine use in point- of- care units, ambulances, or general medical practices.  
+
+<|ref|>image<|/ref|><|det|>[[110, 424, 884, 590]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[87, 610, 911, 696]]<|/det|>
+<center>Fig. 1 Design principle of rotaxane 1. The known binding preference and selective emission quenching of CB8-dye complexes with electron-rich aromatic analytes was exploited for the molecular engineering of a biofluid-applicable chemosensor that can detect the health-relevant amino acid Trp in human blood serum and urine. The hydrophilic and bulky \(\beta\) -cyclodextrin moieties not only act as stopper groups that prevent CB8-dye disintegration in biofluids, but also protect 1 from adverse interactions with serum proteins and enable its covalent anchoring to surfaces for sensor chip preparation. </center>  
+
+<|ref|>text<|/ref|><|det|>[[87, 714, 912, 890]]<|/det|>
+In the pursuit of a molecular recognition- based chemosensor for tryptophan that is both stable and operational in biofluids, we were inspired by supramolecular rotaxanes which combine novel functionalities with a superior tolerance to complex media. Rotaxanes have received attention for building molecular machines, supramolecular catalyst, and as binders for inorganic anions. In contrast to most rotaxanes, in which the axis component almost completely fills the host cavity, we developed a rotaxane design that allows for Trp binding alongside a chromophoric and fluorescent dye axis component in a hydrophobic cavity host (see Fig. 1). This topology enables molecular interactions between the host and dye- axle, thereby fostering affinity, selectivity, and signal transduction.
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[86, 78, 912, 255]]<|/det|>
+The macrocyclic cucurbit[8]url (CB8) can simultaneously bind two aromatic compounds in aqueous media. \(^{34 - }\) \(^{38}\) Thus, self-assembled CB8·dye complexes have been used for the complexation and detection of aryl- functional metabolites, taking advantage of new optical features in ternary complex formation. \(^{15,37 - 39}\) However, both the disintegration of CB8·dye complexes in saline biofluids due to metal cation binding to CB8, \(^{40}\) and the competitive displacement of the dye molecule from the host cavity by hydrophobic guests (such as steroids, peptides, or biogenic amines) has typically restricted their use to deionized water or low saline buffers. \(^{15}\) Moreover, the known intercalation of CB8·dye complexes into the binding pockets of serum albumin proteins \(^{41}\) could be a serious drawback for an intended use in blood serum.  
+
+<|ref|>text<|/ref|><|det|>[[87, 259, 912, 345]]<|/det|>
+Herein, we demonstrate that rotaxanation of CB8 with a reporter dye and cyclodextrin as stopper groups provides the first precedence of a fluorescent chemosensor that can detect tryptophan in biofluids, i.e., human blood serum and urine, in the physiological concentration range. Furthermore, the utility of the chemosensor rotaxane for chirality sensing, enzymatic reaction monitoring, and for sensor chip preparation is showcased.  
+
+<|ref|>sub_title<|/ref|><|det|>[[88, 365, 163, 383]]<|/det|>
+## Results  
+
+<|ref|>text<|/ref|><|det|>[[85, 404, 912, 900]]<|/det|>
+Rotaxane design, synthesis, and characterization. Many fascinating supramolecular studies of cucurbit[n]url- based rotaxanes and pseudorotaxanes have appeared since the early days of CBn (re)discovery. \(^{42 - 44}\) In 2011, the first CB8- based rotaxane was presented that can complex additional aromatic guests in aqueous and organic media. \(^{25}\) Unfortunately, its axle component – viologen – is non- emissive and provides only weak absorbance changes in the presence of Trp. This rotaxane has thus been employed for fundamental binding studies but has no prospects for sensing applications. First, we attempted to develop analogous chemosensing rotaxanes with a fluorescent dicationic reporter dye such as di- alkylated 2,7- diazapyrene (DAP) as the axle component. The functionalization of DAP proved to be more challenging than that of viologens as copper- accelerated alkyne- azide click conditions were not tolerated, whereas copper- free click reactions with strained alkynes yielded almost insoluble materials (see also Supplementary Information). Consequently, we investigated alternative conjugation strategies that overcome the susceptibility of DAP dyes to strong bases and reducing agents. Indeed, fluorescent cucurbit[8]url- rotaxane 1 can be prepared by a convergent synthetic route through a thiol- maleimide click coupling (Fig. 2). \(\beta\) - Cyclodextrin ( \(\beta\) - CD) was identified as a suitable stopper ensuring the aqueous solubility of the rotaxane and offering possibilities for its further covalent (through OH bonds) and non- covalent (through its hydrophobic cavity) modification. Moreover, based on the available literature data on the interaction of cyclodextrin- drug formulations with serum albumin, \(^{45}\) we expected that the installation of cyclodextrin stopper groups as part of the chemosensor will effectively prevent the undesirable encapsulation of the CB8·dye moiety into the hydrophobic binding pockets of albumin, \(^{41}\) and thus will support the applicability of the resulting rotaxane in untreated blood serum. Therefore, a maleimide- functionalized tetraethylene glycol (TEG) linker was coupled through its N- hydroxycucinimide- functionality to 3A- Amino- 3A- deoxy- (2AS,3AS)- \(\beta\) - cyclodextrin to yield a thiol- reactive stopper group. Furthermore, the aromatic DAP core was functionalized with thio- butyl linkers.
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[86, 78, 913, 232]]<|/det|>
+Finally, the pseudorotaxane which was formed by self-assembling CB8 with the DAP- axle was covalently linked to two stopper groups via a Michael thiol- ene reaction. Rotaxane 1 was then obtained after purification by preparative HPLC and characterized by \(^1\mathrm{H}\) and diffusion ordered spectroscopy (DOSY) NMR, ESI- MS, dynamic light scattering (DLS) (Fig. 2 and Supplementary Fig. 1 and 2), as well as optical spectroscopy (Fig 3). As expected, DLS measurements revealed self- aggregation of the chemosensing system upon heating from 20 to \(50^{\circ}\mathrm{C}\) , which is a common behavior of cyclodextrins in water. \(^{46}\) This design feature enables the utilization of \(\beta\) - CD typical characteristics such as chirality induction within the rotaxane assembly, see below.  
+
+<|ref|>image<|/ref|><|det|>[[123, 247, 875, 563]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[86, 574, 911, 617]]<|/det|>
+<center>Fig. 2 Formation and characterization of rotaxane 1. a Schematic representation of the synthetic route yielding rotaxane 1. b \(^1\mathrm{H}\) NMR of rotaxane 1 in deuterium oxide (D2O). c Analytical HPLC of 1 (30% acetonitrile in water, 0.1% trifluoroacetic acid). d ESI-MS of 1 (1:1 acetonitrile/H2O, 1% formic acid). </center>  
+
+<|ref|>text<|/ref|><|det|>[[86, 635, 913, 879]]<|/det|>
+Host- guest binding properties. Firstly, the analyte- sensing functionality of rotaxane 1 was tested by an emission- based analyte assay with indole and adamantanol (see Supplementary Fig. 3). The response of rotaxane 1 towards indole (i.e., the emission quenching of the rotaxane 1) remained similarly strong in 1X phosphate buffered saline (1X PBS) as in water. Competitive binders cannot displace the dye independently of their stronger binding affinity towards CB8 due to the installed stopper groups. In a second step, the host- guest binding properties of rotaxane 1 with Trp and its analogues, i.e., indole and tryptamine, were investigated in detail by emission- based titration experiments in water, 1X PBS and in human urine (Fig. 3 and Supplementary Fig. 4- 6). The observed emission intensity decrease indicated analyte binding in close proximity to the DAP dye in the CB8 cavity. Binding affinities ( \(K_{\mathrm{a}}\) , see Table 1) were determined by fitting the titration curves to a 1:1 binding model (Fig. 3c). Indole, tryptamine, and Trp caused a strong emission quenching of rotaxane 1 in aqueous media and human urine.
+
+<--- Page Split --->
+<|ref|>image<|/ref|><|det|>[[140, 78, 857, 576]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[86, 585, 911, 697]]<|/det|>
+<center>Fig. 3 Host-guest binding with rotaxane 1. a Schematic representation of analyte binding by rotaxane 1. b Absorbance (solid line) and normalized emission (dotted line) spectra of 1 in the absence (green) and presence of Trp (red) in water. c Binding isotherms of 1 (2.5 \(\mu \mathrm{M}\) ) and Trp in water (stars) and 1X PBS (dots) and the corresponding fit (red line) of the obtained data. d Emission quenching of 1 (1.0 \(\mu \mathrm{M}\) ) upon addition of bioactive analytes in 1X PBS. e Chemical structures of bioactive analytes tested in this study. f-g Electronic circular dichroism (ECD) spectra of 1 (100 \(\mu \mathrm{M}\) ) in the presence of f L-Trp or D-Trp and g L-Phe or D-Phe. The ECD spectra of the enantiomeric amino acids are shown for comparison. h Possible mechanism for chirality-induction through interaction of \(\beta\) -cyclodextrin stopper groups with the DAP reporter dye and the CB8 portal area. </center>  
+
+<|ref|>text<|/ref|><|det|>[[87, 716, 912, 891]]<|/det|>
+Importantly, as the \(K_{\mathrm{a}}\) values were almost identical in water and 1X PBS, the desired salt tolerance was indeed achieved through the rotaxanation strategy. Besides, the emission response selectivity of 1 was evaluated in a plate reader format by exposing its saline buffered solutions to different types of biorelevant analytes, such as aliphatic amino acids, amino acid derivatives and polyamines (Fig. 3d- e and Supplementary Information for experimental details). Particularly indole, tryptamine, and Trp caused a strong emission quenching of 1 in 1X PBS. In contrast, dopamine, indole- 3- acetic acid (IAA), and phenylalanine (Phe) only showed significant emission quenching at non- physiologically high concentrations ( \(>500 \mu \mathrm{M}\) ). The rotaxane was almost unresponsive to aliphatic amino acids and polyamines.
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[86, 78, 913, 458]]<|/det|>
+Chirality sensing. Chirality plays an essential role in biology, and the occurrence of D- amino acids in living organisms is a useful indicator of various changes including aging, diseases, or disorders.47,48 Several molecular probes and supramolecular systems for chirality sensing of bioactive molecules have been devised in order to achieve higher assay throughputs with simpler handling and lower costs than contemporary chiral chromatographic methods.4,49- 52 Akin to self- assembled CB8•dye complexes that have been reported for chirality sensing of aromatic amino acids,51,53 we expected that rotaxane 1 can be used for the differentiation between enantiomeric aromatic amino acids in combination with electronic chirality dichroism (ECD) spectroscopy. Indeed, L- Trp can be distinguished from D- Trp and L- Phe from D- Phe upon complexation by 1 through the emerging induced chiroptical fingerprints in the visible region of the ECD spectrum (Fig. 3f- g). Note that the slight chiroptical response of 1, and the asymmetry between the ECD spectra of 1•L- Phe versus 1•D- Phe can be attributed to the chirality induction caused by the chiral \(\beta\) - cyclodextrin stopper groups. This suggests that the \(\beta\) - cyclodextrin moieties can come in close proximity to the CB8- dye complex, as is schematically depicted in Fig. 3h, and may present an additional mechanism by which the binding pocket of the chemosensor is protected from interferents present in biofluids. In addition to that, the \(\beta\) - cyclodextrin stopper groups offer the possibility to further tune the distinct supramolecular architecture of the chemosensor. Future efforts will be directed towards the development of stimuli- responsive hydrogels of the rotaxane by adding additives functionalized with multiple cyclodextrin- binding guests.  
+
+<|ref|>text<|/ref|><|det|>[[86, 482, 913, 659]]<|/det|>
+Tryptophan detection in biological environments. The excellent salt stability and good emission- response selectivity of rotaxane 1 for indoyl- type compounds prompted us to investigate the sensing of L- Trp in blood specimens (Fig. 4 and Supporting Information). Therefore, untreated human and bovine blood serum samples, i.e., serum that still contains proteins, were pipetted into microplate wells and the emission upon addition of 1 \((I_{1})\) , and the emission reduction upon subsequent spiking with Trp \((I_{2})\) were recorded. Finally, excess of indole was added to fully saturate the binding pocket and thereby to quench the chemosensor emission to its maximum \((I_{3})\) . As an additional control the autofluorescence \((I_{0})\) of each of the serum sample was measured. The emission data was converted into quenching efficiencies (EQ) which are plotted in Fig. 4d and e.
+
+<--- Page Split --->
+<|ref|>image<|/ref|><|det|>[[147, 78, 848, 600]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[86, 605, 911, 715]]<|/det|>
+<center>Fig. 4 Tryptophan detection in serum samples with rotaxane 1. a Schematic representation of the state-of-the-art workflow for Trp quantification in blood serum by HPLC including the mandatory deproteinization step. b The Trp concentration levels of deproteinized serum samples (1:1 diluted with \(624~\mathrm{mM}\) perchloric acid) were obtained by a HPLC assay. c Schematic workflow for emission-based sensing of Trp with 1 in untreated serum samples in a plate reader format. d and e Bar graphs of the averaged emission intensity (from 8 replica measurements, maximum errors were generously estimated) of the serum samples prior and after addition of 1, after spiking with Trp and subsequent indole addition. To assist the comparison, Trp concentration values obtained by HPLC are depicted as labels. See Supplementary Information for details on the assay protocols and error estimation. </center>  
+
+<|ref|>text<|/ref|><|det|>[[87, 733, 911, 864]]<|/det|>
+In order to correlate the emission quenching values to Trp concentrations by established HPLC protocols, serum samples were deproteinized according to standard clinical lab procedures (Fig. 4a and b and Supplementary Figure 7). Pleasingly, there is a correspondence of the quenching efficiency trends obtained from our rotaxane- based optical assay with the HPLC- derived Trp levels. Moreover, Trp concentrations within the typical concentration ranges of healthy (approx. \(50 - 70~\mu \mathrm{M}\) ) and diseased patients (approx. \(20 - 45~\mu \mathrm{M}\) , see Supplementary Table 1 and 2) are distinguishable from each other. Compared to HPLC routines, our
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[87, 78, 911, 119]]<|/det|>
+chemosensor assay is both much faster ( \(< 1\) min per sample) and parallelizable. Additionally, the possibility to use untreated blood serum further simplifies and shortens the assay protocol.  
+
+<|ref|>text<|/ref|><|det|>[[86, 123, 912, 253]]<|/det|>
+Moreover, we found that rotaxane 1 can be used for label- free enzymatic reaction monitoring in real time, as demonstrated for the pepsin- catalyzed hydrolysis of the Trp- rich protein bovine serum albumin in a biological saline buffer (Supplementary Fig. 8). For comparison, this is a great practical challenge with existing technologies that provide only discontinuous data points and require time- consuming sample pre- and post- treatment steps. Other supramolecular proof- of- concept reports that inspired our use of rotaxane 1 have so far been limited to minimal buffers. \(^{51,54,55}\)  
+
+<|ref|>text<|/ref|><|det|>[[85, 270, 912, 789]]<|/det|>
+Rotaxane microarrays for analyte detection. The inherent functional groups of the designed rotaxane provide a synthetic handle for immobilization on functionalized glass surfaces. \(^{56}\) Here, scanning probe lithography methods offer a flexible route to create micro- and even nanoscaled surface patterns of immobilized functional molecules. \(^{57}\) To create microarrays of rotaxane 1, we chose microchannel cantilever spotting \((\mu \mathrm{CS})\) , which allows for deposition of femtoliter- sized droplets on surfaces that can act as vessels for coupling reactions. \(^{58,59}\) Therefore, an isocyanate- functionalized glass substrate was prepared and reacted with the HO- groups of the \(\beta\) - cyclodextrin stopper (Fig. 5 and Supplementary Information). We thereby fabricated a microstructured and washable sensor chip allowing for Trp detection down to submicromolar concentrations, as is shown by fluorescence microscopy images and emission intensity quenching effects displayed in Fig. 5b- c (see also Supplementary Fig. 9 and Supplementary Table 3). The remarkable sensitivity increase of rotaxane 1 by surface immobilization corresponds to our recent findings for indicator displacement chemosensors. \(^{60}\) Importantly, the rotaxane- functionalized microarrays are more selective than printed microarrays with the binary CB8•MDAP chemosensor complex. While both allow for detection of tryptophan or indole, the hydrophobic and strongly CB8- binding guest meantime does not alter the emission of the rotaxane 1 microarray. Memantine is not an electron- rich aromatic compound and thus cannot bind next to the rotaxane- protected reporter dye DAP. In contrast, the CB8•MDAP sensor chips respond to the presence of meantime though Methyl- DAP (MDAP) displacement, and thus share the undesirable cross- reactivity to other metabolites with that of bimolecular CBn•dye reporter pairs (Supplementary Fig. 10 and Supplementary Table 4). \(^{1,15,61}\) Finally, the immobilization also allows for printing reference dyes next to the chemosensor array to normalize the emission signal (Supplementary Fig. 11 and Supplementary Table 5). By encapsulation of such sensor chips into microfluidic channels, they can act as convenient and low volume sensing platform. \(^{60}\) The possibility to immobilize the Trp- binding rotaxane on surfaces opens up its potential for the future integration into lab- on- a- chip designs with plasmonic biosensors. \(^{62}\)
+
+<--- Page Split --->
+<|ref|>image<|/ref|><|det|>[[92, 80, 900, 308]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[87, 315, 911, 372]]<|/det|>
+<center>Fig. 5 Immobilization of the rotaxane 1 on glass surfaces by \(\mu \mathrm{CS}\) . a Schematic representation of microarray printing of 1 via microchannel cantilever spotting \((\mu \mathrm{CS})\) on an isocyanate-covered surface and analyte detection by microarrays. b Fluorescence microscopy images (DAPI filter, 10 s exposure) and c mean emission intensity for a sensor chip prepared by \(\mu \mathrm{CS}\) of 1. Incubation with \(10\mu \mathrm{M}\) Trp solution led to a strong emission decrease. 1X PBS used for all measurements. </center>  
+
+<|ref|>sub_title<|/ref|><|det|>[[88, 392, 196, 411]]<|/det|>
+## Discussion  
+
+<|ref|>text<|/ref|><|det|>[[87, 432, 912, 589]]<|/det|>
+Due to its ubiquity in many biological processes and associated diseases, \(^{20,21}\) the amino acid tryptophan is an attractive target for the development of synthetic receptors. As Trp lacks a chemically reactive side chain besides the common amino and carboxylate moieties present in all amino acids, its selective molecular recognition by a synthetic receptor requires the installation of non- covalent binding motifs that promote cation- \(\pi\) or \(\pi\) - \(\pi\) stacking interactions with the indole ring of Trp. \(^{63,64}\) In fact, several synthetic binders for Trp and its derivatives are already known, e.g., see Table 2 for selected examples. \(^{25,51,65 - 67}\) However, none of these systems are suitable for optical sensing of Trp in biofluids as they lack the required selectivity and salt tolerance.  
+
+<|ref|>text<|/ref|><|det|>[[87, 606, 912, 896]]<|/det|>
+Instead of following a common strategy in supramolecular chemistry – the search for alternative macrocyclic hosts that may bind the target analyte with (even) higher affinity or selectivity – we adopted here a different design concept. By comparing the key parameters of Trp- binding by synthetic receptors with expected diagnostic needs, i.e., typical Trp concentration levels found in biofluids (see Supplementary Table 1), we came to the conclusion that self- assembled CB8 \(\cdot\) dye complexes may already offer sufficient affinities to distinguish Trp level differences in blood serum between healthy and diseased patients. Moreover, the concomitant strong dye emission quenching upon Trp binding appeared sufficient to develop sensitive fluorescence- based assays for Trp detection at practically relevant concentration levels. These outstanding characteristics in terms of binding strength and signal transduction capabilities of CB8 \(\cdot\) dye complexes are likely the result of the interplay of a peculiar and powerful version of the hydrophobic effect, i.e., high- energy cavity water release, \(^{68}\) and the perfect parallel arrangement of the indoyl moiety next to an electron deficient, dicationic fluorescent reporter dye, whose high emission quantum yield is quenched when electron- rich moieties are present in its vicinity. \(^{69}\) By pursuing the molecular engineering approach we aimed to receive a
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[87, 78, 911, 141]]<|/det|>
+chemosensor which remains functional in the presence of typical interferents occurring in biofluids such as salts, competitively binding metabolites and proteins<sup>14,39,40,69</sup> and is therefore superior over self-assembled CB8•dye chemosensor complexes.  
+
+<|ref|>text<|/ref|><|det|>[[86, 160, 912, 313]]<|/det|>
+We herein report the first chemosensor for emission- based detection of the health- relevant amino acid tryptophan that is operational in both urine and blood serum. Our rotaxanation strategy ensures both superior stability and functionality in biofluids and targets the physiological Trp concentration range, while preserving important advantages of supramolecular chemosensors such as their fast response time, as well as their utility for label- free reaction monitoring and chirality sensing. Our concept provides additional opportunities for chemosensor immobilization into microarrays on sensor chips that can facilitate the transfer of synthetic receptors and chemosensors into clinical or diagnostic applications.  
+
+<|ref|>sub_title<|/ref|><|det|>[[87, 334, 178, 352]]<|/det|>
+## Methods  
+
+<|ref|>text<|/ref|><|det|>[[86, 369, 911, 478]]<|/det|>
+Chemicals and solvents. Ultrapure deionized water was obtained from a SATORIUS ARIUM® PRO DI water purification system with ASTM Type 1 water quality. Buffer solutions were prepared following standard protocols. All chemosensor and analyte stock solutions were prepared in ultrapure water or 1X PBS and stored at \(+4^{\circ}\mathrm{C}\) . Details on chemicals, suppliers, and descriptions on synthetic procedures are given in the Supplementary Information.  
+
+<|ref|>text<|/ref|><|det|>[[86, 489, 912, 599]]<|/det|>
+NMR spectroscopy. NMR spectra were recorded on a BRUKER Avance 500 (1H NMR: 500 MHz; 13C NMR: 126 MHz) at room temperature. The chemical shift \(\delta\) is expressed in parts per million (ppm) whereas the residual signal of the solvent was used as secondary reference. 1H NMR spectra were analyzed using FT- processing software, 13C NMR spectra were 1H- decoupled and characterization of the 13C NMR spectra was ensued through the DEPT- technique.  
+
+<|ref|>text<|/ref|><|det|>[[86, 609, 912, 794]]<|/det|>
+Diffusion ordered spectroscopy (DOSY). DOSY data were obtained on a Bruker AM 400 (400 MHz) at 298 K using a LED- bipolar gradient paired with 2 spoil gradients (ledbpgp2s), with 16 incremental steps (with 16 scans each) in the gradient strength, ramped from 2 % to 98 % of the maximum gradient strength. The gradient pulse length \(\delta /2\) (p30) and diffusion delay \(\Delta\) (d20) were specifically optimised and set to 1.8 ms \(\delta = 3.6 \mathrm{ms}\) ) and \(\Delta = 79.9 \mathrm{ms}\) , respectively. Chemical shifts \(\delta\) are expressed in parts per million (ppm) and calibrated with respect to \(\mathrm{D}_2\mathrm{O}\) as internal standard. The spectra were calibrated and phased using TopSpin. The pseudo- 2D DOSY plots were computed using Bruker Dynamic Center, fitting the intensity decay with the following equation: \(I(g) = I_0 \exp \left(-D g^2 \gamma^2 \delta^2 (\Delta - \frac{\delta}{3})\right)\) .  
+
+<|ref|>text<|/ref|><|det|>[[86, 806, 912, 892]]<|/det|>
+High performance liquid chromatography (HPLC). Analytical HPLC experiments were carried out on a LC- 2000Plus HPLC system equipped with an UV- 2075 UV- Vis detector as well as a FP- 920 fluorescence detector and a KROMASIL 100 C18 5 \(\mu \mathrm{m}\) LC column (250×4.6 mm, AGELA) at a flow rate of 1.3 mL/min for rotaxane purification and 1.2 mL/min for the quantitative Trp concentration determination. Preparative HPLC
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[87, 78, 912, 232]]<|/det|>
+experiments were performed on the same system but equipped with a KROMASIL 100 C18 5 \(\mu \mathrm{m}\) LC precoll- umn \((50\times 20\mathrm{mm}\) , AGELA) and a KROMASIL 100 C18 5 \(\mu \mathrm{m}\) LC preparative column \((250\times 50\mathrm{mm}\) , AGELA). All crude samples were dissolved in a mixture of water and ACN \((\mathrm{v / v} = 4 / 1)\) and applied as solutions. A flow rate of \(10\mathrm{mL / min}\) was used for preparative column runs. Note: At higher concentrations of rotaxane 1 we noticed a small peak shoulder in the chromatogram which we assign to the formation of aggregates, however, when the main peak fraction was collected and re- subjected to HPLC analysis, a similar HPLC chromatogram was obtained, suggesting that the small peak shoulder is indeed not due to impurities.  
+
+<|ref|>text<|/ref|><|det|>[[87, 244, 912, 351]]<|/det|>
+HPLC- based quantification of L- Trp in serum samples. The quantification of the L- tryptophan concentration of three different blood serum samples (human serum, steroid depleted human serum, and bovine calf serum) was done using common HPLC methods according to literature. Further details on the calibration curve and the L- Trp concentration determination of each serum sample can be found in the Supplementary Information.  
+
+<|ref|>text<|/ref|><|det|>[[87, 363, 912, 471]]<|/det|>
+Electrospray ionization mass spectrometry (ESI- MS). ESI- MS experiments were carried out on a BRUKER micrOTOF- Q (208 - 320 Vac, 50/60 Hz, 1800 VA) mass spectrometer equipped with an On- line NanoElectrospray ion source. The spectra were interpreted by molecular peaks \([\mathrm{M}]^{n + }\) or peaks of protonated molecules \([\mathrm{M} + \mathrm{H}]^{n + }\) and are shown with their mass- to- charge ratio (m/z). Solvents used were \(\mathrm{H}_2\mathrm{O}\) , MeOH, and \(\mathrm{H}_2\mathrm{O}:1\%\) formic acid.  
+
+<|ref|>text<|/ref|><|det|>[[87, 483, 912, 659]]<|/det|>
+Optical spectroscopy. Absorbance spectra were measured on a JASCO V- 730 double- beam UV- Vis spectrophotometer with an automatic stirring unit and were baseline corrected. Steady- state emission spectra were recorded on a JASCO FP- 8300 fluorescence spectrometer equipped with a 450 W xenon arc lamp, double- grating excitation, emission monochromators, and a water- thermostated cell holder (STR- 812). Fluorescence- based titration experiments were performed manually or by an ATS- 827 automatic titration unit and the normalized emission was fitted according to a 1:1 binding model by a least square fit to determine the binding affinities (see also Supplementary information). All cuvettes were equipped with a stirrer allowing rapid mixing. Emission and excitation spectra were corrected for source intensity (lamp and grating).  
+
+<|ref|>text<|/ref|><|det|>[[87, 670, 911, 733]]<|/det|>
+Electronic circular dichroism (ECD) spectroscopy. ECD spectra were recorded on a JASCO J- 1500 CD spectrometer equipped with a Peltier- thermostated cell holder and an automatic stirring unit. The ECD spectra reported were baseline corrected for water. All spectra were recorded at \(25^{\circ}\mathrm{C}\) .  
+
+<|ref|>text<|/ref|><|det|>[[87, 745, 912, 875]]<|/det|>
+Dynamic light scattering (DLS). DLS measurements were performed on a MALVERN ZetaSizer Nano instrument with disposable acryl cuvettes. The chemosensor solution (150 \(\mu \mathrm{M}\) in ultrapure water) was heated up from 20 to \(55^{\circ}\mathrm{C}\) using the automated heating cycle provided by the software. The intensity of the scattered light was measured at a fixed angle \((173^{\circ})\) . The wavelength of the laser light used for the light scattering experiments was set to 633 nm. Data analysis was performed according to standard procedures using the Malvern software. The values viscosity and the refractive index were adapted from the provided software.
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[86, 78, 912, 232]]<|/det|>
+Microwell plate- based serum experiments. An EnSight™ multimode plate reader and black opaque OptiPlate™- 96 polystyrene microplates (both PERKIN ELMER) were used for all microwell plate- based assays. The instrument was equipped with monochromatic fluorescence intensity detection (top- and bottom- reading) as well as filter- and monochromator- based absorbance detection and temperature control. The fluorescence emission measurements (100 flashes) were performed as single wavelength measurements ( \(\lambda_{\mathrm{ex}} = 393 \mathrm{nm}\) , \(\lambda_{\mathrm{em}} = 450 \mathrm{nm}\) ) at \(25^{\circ}\mathrm{C}\) . A detailed procedure of the Trp detection in untreated serum samples with rotaxane 1 can be found in the Supplementary Information.  
+
+<|ref|>text<|/ref|><|det|>[[86, 243, 912, 488]]<|/det|>
+Immobilization of rotaxane 1 on glass surfaces. A suitable isocyanate for immobilization was produced by first activating a pre- cleaned \(\mathrm{SiO_2}\) - glass surface with oxygen plasma (10 sccm \(\mathrm{O_2}\) , 0.2 mbar, 100 W, 2 min, ATTO system, DIENER ELECTRONICS, Germany) to receive a hydroxylated \(\mathrm{SiO_2}\) - OH layer. Then, the \(\mathrm{SiO_2}\) - OH surface was immersed in \(1 \mathrm{mg / mL} 4,4'\) - diisocyanate methylendibenzol (MDI) containing \(1 \mu \mathrm{L} / \mathrm{mL}\) dibutyltin dilaurate (TDL) as catalyst in anhydrous DMSO at \(80^{\circ}\mathrm{C}\) for \(24 \mathrm{h}\) . Finally, the substrate ( \(\mathrm{SiO_2}\) - NCO) was rinsed with acetone for 2 min and dried with \(\mathrm{N_2}\) . Then \(0.5 \mu \mathrm{L}\) of rotaxane ink ( \(3 \mathrm{mg / mL}\) in DMSO containing \(10\%\) (v/v) TDL and \(10\%\) (v/v) PEG 600) were applied to the reservoir of a microchannel cantilever \(^{70}\) (SPT- SC10S, BIOFORCE NANOSCIENCES), mounted to the lithography setup (NLP2000, NANOINK), and spotted for defined durations ( \(\sim 0.5 - 1 \mathrm{s}\) ) at a controlled humidity of \(40\%\) . After printing, the substrate was heated to \(80^{\circ}\mathrm{C}\) for \(24 \mathrm{h}\) , then washed with ethanol, and dried with \(\mathrm{N_2}\) . Control experiments were performed according to previous work, \(^{60}\) see also Supplementary Information.  
+
+<|ref|>text<|/ref|><|det|>[[87, 498, 912, 628]]<|/det|>
+Analyte detection with rotaxane microarrays. Analyte solutions were prepared in 1X PBS or \(10 \mathrm{mM}\) HEPES buffer, pH 7.0. The rotaxane- patterned substrates were covered with \(20 \mu \mathrm{L}\) analyte solution for \(5 \mathrm{min}\) , washed with water, and dried with \(\mathrm{N_2}\) . The fluorescence imaging was performed on a NIKON ECLIPSE Ti2 inverted fluorescence microscope (NIKON, Germany) equipped with an Intensilight illumination, a NIKON DS Q12 camera, and a DAPI filter set (DAPI- U HQ, NIKON, Germany). All data is expressed as mean \(\pm\) standard deviation of at least three independent measurements.  
+
+<|ref|>sub_title<|/ref|><|det|>[[88, 650, 262, 670]]<|/det|>
+## Data Availability  
+
+<|ref|>text<|/ref|><|det|>[[87, 686, 911, 750]]<|/det|>
+All data are available from the corresponding author upon reasonable request and are digitally stored on the severs of the home institution. Furthermore, binding parameters can be found on suprabank.org; DOI: 10.34804/supra.20220128415.  
+
+<|ref|>sub_title<|/ref|><|det|>[[88, 770, 200, 789]]<|/det|>
+## References  
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+
+<--- Page Split --->
+<|ref|>sub_title<|/ref|><|det|>[[88, 81, 285, 100]]<|/det|>
+## Acknowledgements  
+
+<|ref|>text<|/ref|><|det|>[[86, 116, 912, 245]]<|/det|>
+AcknowledgementsJ.K. thanks the Evonik foundation for financial support. L.M.G. and F.B. acknowledge the DFG (Emmy Noether Grant and SPP 1807) and C.Z. acknowledges the CSC fellowship (No. 201808440323). This work was partly carried out with the support of the Karlsruhe Nano Micro Facility (KNMF, www.knmf.kit.edu), a Helmholtz Research Infrastructure at Karlsruhe Institute of Technology (KIT, www.kit.edu). We thank Dr. Pierre Picchetti for carrying out DLS measurements and Joël Monti for performing DOSY NMR measurements.  
+
+<|ref|>sub_title<|/ref|><|det|>[[88, 268, 281, 287]]<|/det|>
+## Ethics declarations  
+
+<|ref|>text<|/ref|><|det|>[[87, 303, 911, 390]]<|/det|>
+Ethics declarationsAll procedures performed in studies involving human participants were in accordance with the formal statement of ethical principles published by the World Medical Association in the declaration of Helsinki in 1964 and its later amendments or comparable ethical standards. Informed consent was obtained from all individual participants included in the study.  
+
+<|ref|>sub_title<|/ref|><|det|>[[88, 410, 291, 429]]<|/det|>
+## Competing interests  
+
+<|ref|>text<|/ref|><|det|>[[88, 447, 481, 464]]<|/det|>
+The authors declare no competing financial interests.  
+
+<|ref|>sub_title<|/ref|><|det|>[[88, 485, 231, 503]]<|/det|>
+## Contributions  
+
+<|ref|>text<|/ref|><|det|>[[87, 520, 911, 606]]<|/det|>
+ContributionsJ.K. and L.M.G. conceptualized the study, designed and performed the experiments, analyzed data, and prepared the manuscript. C.Z. performed printing and imaging experiments and analyzed the data. M.H. conceptualized the study and provided expertise. F.B. conceptualized the study, provided expertise, and prepared the manuscript. All authors have contributed to, commented, and agreed on the manuscript preparation.  
+
+<|ref|>sub_title<|/ref|><|det|>[[88, 628, 315, 647]]<|/det|>
+## Corresponding author  
+
+<|ref|>text<|/ref|><|det|>[[87, 663, 910, 697]]<|/det|>
+Corresponding authorCorrespondence and requests for materials should be addressed to Frank Biedermann (frank.biedermann@kit.edu, https://orcid.org/0000- 0002- 1077- 6529).  
+
+<|ref|>sub_title<|/ref|><|det|>[[88, 714, 326, 732]]<|/det|>
+## Additional Information  
+
+<|ref|>text<|/ref|><|det|>[[87, 750, 490, 767]]<|/det|>
+Supplementary information is available for this paper.  
+
+<|ref|>table<|/ref|><|det|>[[77, 819, 920, 904]]<|/det|>
+<|ref|>table_caption<|/ref|><|det|>[[87, 787, 910, 816]]<|/det|>
+Table 1 Binding affinities (given as log \(K_{a}\) ) of rotaxane 1 for indoyl-based analytes in aqueous media at \(25^{\circ}\mathrm{C}\) Estimated error of \(\log K_{\mathrm{a}} = 0.2\)   
+
+<table><tr><td>analyte / medium</td><td>water</td><td>1X PBS</td><td>urine (1:2 in 1X PBS)</td></tr><tr><td>indole</td><td>5.42</td><td>5.48</td><td>~ 4.18</td></tr><tr><td>L-Trp</td><td>3.93</td><td>3.95</td><td>3.37</td></tr><tr><td>tryptamine</td><td>3.69</td><td>4.14</td><td>-</td></tr></table>
+
+<--- Page Split --->
+<|ref|>table<|/ref|><|det|>[[78, 112, 917, 336]]<|/det|>
+<|ref|>table_caption<|/ref|><|det|>[[85, 80, 910, 109]]<|/det|>
+Table 2 Comparison of representative synthetic binders for tryptophan. See Supplementary Fig. 12 for the corresponding chemical structures.   
+
+<table><tr><td>Trp</td><td>optical signal</td><td>reported medium</td><td>ref.</td></tr><tr><td>crown ether-type receptor*</td><td>absorbance</td><td>Chloroform</td><td>66</td></tr><tr><td>pillar[5]arene-type receptor</td><td>emission</td><td>H2O:DMSO (7:3)</td><td>65</td></tr><tr><td>binary CB8*dye complexes</td><td>emission</td><td>H2O,<br>low saline buffers</td><td>51,67</td></tr><tr><td>CB8-viologen rotaxane</td><td>-</td><td>low saline buffer, acetonitrile</td><td>25</td></tr><tr><td>rotaxane 1</td><td>emission</td><td>saline buffers, biofluids</td><td>this work</td></tr></table>
+
+<|ref|>table_footnote<|/ref|><|det|>[[88, 336, 366, 351]]<|/det|>
+\\* TrpOMe binding was investigated.
+
+<--- Page Split --->
+<|ref|>sub_title<|/ref|><|det|>[[44, 42, 311, 70]]<|/det|>
+## Supplementary Files  
+
+<|ref|>text<|/ref|><|det|>[[44, 93, 765, 113]]<|/det|>
+This is a list of supplementary files associated with this preprint. Click to download.  
+
+<|ref|>text<|/ref|><|det|>[[60, 130, 457, 150]]<|/det|>
+SupplementaryInformationKraemeretal.pdf
+
+<--- Page Split --->

preprint/preprint__1ece1594cf2761e8f8a995e2dd0b6df5e94d30bd1976cd3d120d2774427e9690/images_list.json

@@ -0,0 +1,92 @@
+[
+  {
+    "type": "image",
+    "img_path": "images/Figure_1.jpg",
+    "caption": "Fig. 1: Development of carbon pricing schemes in selected regions of the world. Not only are the carbon prices picking up but their global coverage is also increasing. As the carbon pricing roll-out is getting wider spread the question of optimal pricing becomes more urgent.",
+    "footnote": [],
+    "bbox": [
+      [
+        112,
+        250,
+        884,
+        570
+      ]
+    ],
+    "page_idx": 3
+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_2.jpg",
+    "caption": "Fig. 2: Schematic representation of the underlying economic model. Agents that attempt to optimize an internal metric are colored red. Note that individual households will only maintain specific connections to a limited set of commodity producers not to all at once [38].",
+    "footnote": [],
+    "bbox": [
+      [
+        125,
+        270,
+        870,
+        553
+      ]
+    ],
+    "page_idx": 5
+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_3.jpg",
+    "caption": "Fig. 3: (SF5), (SF12) Cross correlations between lagged real GDP and consumption (C), investment (I), R&D (RD), total debt (TotDebt), prices, energy demand (EnDem), emissions (Em), unemployment rate (U), labor income share (LIS), nominal wages, real wages, markups, average labor productivity (LP) and the ratio of inventories and sales.",
+    "footnote": [],
+    "bbox": [
+      [
+        115,
+        78,
+        881,
+        644
+      ]
+    ],
+    "page_idx": 7
+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_4.jpg",
+    "caption": "Fig. 4: Analyzing the effects of carbon tax on emissions and GDP, this study contrasts two green capacity scenarios. We vary carbon tax ( \\(\\tau_{c}\\) ) from 0 to 0.8, observing emissions and GDP in high (80%) and low (20%) green capacity setups. Results, averaged over the last 20 time-steps and five runs, highlight the differential economic responses to varying carbon tax levels.",
+    "footnote": [],
+    "bbox": [
+      [
+        113,
+        81,
+        856,
+        419
+      ]
+    ],
+    "page_idx": 10
+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_5.jpg",
+    "caption": "Fig. 5: We compare the consequences of a fuel price shock ( \\(\\pm 50\\%\\) ) on the share of income and wealth for each bracket to an unperturbed economy with identical seeds for the simulation. The shock period is demarcated in gray. The poor prove to be more vulnerable to shocks in the short term and feel the effects of the shock the longest.",
+    "footnote": [],
+    "bbox": [
+      [
+        115,
+        80,
+        880,
+        414
+      ]
+    ],
+    "page_idx": 13
+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_6.jpg",
+    "caption": "Fig. 6: Following the KC of various economic observables over time. The end of the gray area demarcates the end of the warm-up period. Now we introduce repeated \"shocks\" to the economy by incrementally increasing the carbon tax. The vertical line demarcates the \"critical transition\", note the reduction of normalized KC building up to this point and the recovery of some shortly after.",
+    "footnote": [],
+    "bbox": [
+      [
+        120,
+        82,
+        880,
+        420
+      ]
+    ],
+    "page_idx": 15
+  }
+]

preprint/preprint__1ece1594cf2761e8f8a995e2dd0b6df5e94d30bd1976cd3d120d2774427e9690/preprint__1ece1594cf2761e8f8a995e2dd0b6df5e94d30bd1976cd3d120d2774427e9690.mmd

@@ -0,0 +1,611 @@
+
+# Carbon Pricing Drives Critical Transition to Green Growth  
+
+Isaak Mengesha  
+
+i.a.mengesha@uva.nl  
+
+University of Amsterdam  Debraj Roy  Univeristy of Amsterdam  
+
+## Article  
+
+Keywords: Green Transition, Tipping points, Inequality, Criticality Measures  
+
+Posted Date: June 17th, 2024  
+
+DOI: https://doi.org/10.21203/rs.3. rs- 3725851/v2  
+
+License: © © This work is licensed under a Creative Commons Attribution 4.0 International License.  Read Full License  
+
+Additional Declarations: The authors declare no competing interests.  
+
+Version of Record: A version of this preprint was published at Nature Communications on February 3rd, 2025. See the published version at https://doi.org/10.1038/s41467- 025- 56540- 3.
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+# Carbon Pricing Drives Critical Transition to Green Growth  
+
+Isaak Mengesha \(^{1*}\) , Joos Akkerman \(^{2\dagger}\) and Debraj Roy \(^{1\dagger}\) \(^{1*}\) Computational Science Lab, Informatics Institute, University of Amsterdam, Netherlands, Noord- Holland, Amsterdam. \(^{2}\) Centre for Social Complexity of Climate Change, Technical University of Delft, Netherlands, Noord- Holland, Delft.  
+
+\*Corresponding author(s). E- mail(s): i.a.mengesha@uva.nl; Contributing authors: J.Akkerman@tudelft.nl; d.roy@uva.nl; †These authors contributed equally to this work.  
+
+## Abstract  
+
+Carbon pricing is a crucial tool in the efforts to address and mitigate climate change. In 2014, only \(12\%\) of carbon emissions fell under carbon pricing at USD7 per tonne; now, about \(23\%\) of greenhouse gas emissions are priced at USD32 per tonne. However, the regressive nature of carbon pricing can disproportionately affect low- income populations and potentially reduce political support and public awareness. This raises questions about the dynamics of increasing carbon pricing rates and the optimal balance between inequality, emissions, and economic growth. We find that a critical level of carbon taxation can induce tipping points incentivizing technological adoption and rapidly reducing emissions. By combining taxes with redistributive measures at these tipping points, we demonstrate that emissions can be rapidly reduced while decreasing inequality. We also introduce real- time metrics for detecting sector- specific tipping points, without requiring counterfactual analysis. Our research has important implications for the ongoing debate around the relationship between economic growth, inequality, and environmental sustainability.  
+
+Keywords: Green Transition, Tipping points, Inequality, Criticality Measures
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+## Main  
+
+As carbon emissions continue to rise to unprecedented levels, largely driven by the burning of fossil fuels, the sixth assessment report from the Intergovernmental Panel on Climate Change (IPCC) serves to alleviate uncertainties surrounding the risks associated with current emission pathways [1, 2]. This comprehensive report reveals the existence of multiple critical thresholds within the climate system. If these thresholds are surpassed, they have the potential to trigger abrupt and irreversible changes in the Earth's climate [3, 4]. Examples of these tipping points include the melting of the Greenland ice sheet and the thawing of permafrost. As emission levels surpass those outlined in the Paris Agreement, the likelihood of encountering these tipping points significantly increases, leading to further temperature increases and imposing direct and potentially catastrophic economic costs. Consequently, there is an urgent need for coordinated efforts by countries, businesses, and individuals to limit global warming well below the 2 degrees Celsius threshold. Therefore, it is crucial to evaluate the effective implementation of socioeconomic interventions [5]. Among the promising policy approaches, carbon pricing stands out as it incentivizes low- carbon practices, stimulates innovation, and reduces costs [6- 9]. Carbon pricing, whether through mechanisms like cap- and- trade systems such as the EU Emissions Trading System (EU ETS) or carbon taxes, is widely regarded as the primary policy tool for addressing the climate crisis. By incorporating the costs associated with negative impacts on health, the environment, and future generations, carbon pricing provides strong incentives to reduce fossil fuel consumption and ultimately achieve net- zero emissions. For instance, the introduction of carbon taxation can drive decision- making shifts across sectors, a critical aspect of achieving a sustainable transformation [10]. While some nations have embraced such a tax, others frequently resort to different strategies to control carbon emissions [11, 12] (see figure 1). Therefore, it is essential to consider the potential regressive impacts of such policies on vulnerable communities and industries reliant on fossil fuels. To mitigate this, combining carbon pricing with redistribution and targeted support becomes a solution that addresses equity concerns while minimizing adverse effects on economic growth and employment [13].  
+
+The objective of setting an optimal carbon price (CP) is to reduce greenhouse gas emissions and transition to a low- carbon economy [14]. There's no universal value for the optimal CP; it varies based on economic environments, time frames, and discount rates used in calculations. Region- specific factors like climate and economic structures also play a role in determining the optimal CP [15]. For example, areas vulnerable to extreme weather may incur a higher social cost for emissions [16]. Policy objectives of governments or organizations can further influence the CP, as some may aim for quick emissions reductions, while others might focus on economic stability [17]. Ultimately, determining the optimal CP is a complex process that requires careful consideration of the factors mentioned above. Here we argue that it is important to understand the nature of the relationship between carbon price and environmental, social,
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+and macroeconomic outcomes. Do the outcomes vary linearly with increasing carbon price, or do we observe a non- linear critical relationship? Such an understanding is crucial and should precede discussions on optimal carbon prices. Our study has two main goals: first, to understand how CP affects environmental, social, and macroeconomic outcomes; second, to explore scenarios where emissions can be rapidly reduced with minimal economic disruption. We are particularly interested in the economic regime that emerges after applying CP.  
+
+![](images/Figure_1.jpg)
+
+<center>Fig. 1: Development of carbon pricing schemes in selected regions of the world. Not only are the carbon prices picking up but their global coverage is also increasing. As the carbon pricing roll-out is getting wider spread the question of optimal pricing becomes more urgent. </center>  
+
+Recent empirical work suggests a neutral to mild positive effect of carbon taxation on GDP growth [18, 19]. Metcalf et al. further highlight the importance of careful design in carbon taxation systems, considering factors like revenue recycling, distributional impacts, and complementary policies [20]. However, challenges persist with the methodology used to analyze the relationship between carbon taxes and GDP, including concerns related to endogeneity, omitted variables, time frame, and model specification [21]. These problems limit the accurate assessment of the causal relationship between carbon taxes and GDP growth, underestimating the long- term impact, and creating ambiguity in estimating effects. All of this makes it challenging for policymakers to design optimal carbon taxation systems and achieve desired environmental and economic outcomes.
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+Economic models, such as the Solow- Swan model, Dynamic Stochastic General Equilibrium Modeling (DSGE), and Integrated Assessment Models (IAM), play a key role in explaining policy differences and their implications [22, 23]. Within these models, General Equilibrium models have been particularly influential [24]. These models also effectively capture the sector- specific effects of carbon pricing, revealing both challenges and growth opportunities across different industries [25, 26]. Furthermore, they address distributional impacts, emphasizing the need for equitable outcomes through revenue redistribution mechanisms [27, 28]. However, it is crucial to acknowledge the challenges these models face. Real- world economies are intricate systems characterized by complexity and feedback mechanisms, which can be oversimplified in these models [24, 29]. Consequently, there is a risk of limited representation of economic dynamics and potential mischaracterization or omission of feedback mechanisms. Additionally, the assumption of homogeneity across economic agents in General Equilibrium models overlooks the heterogeneity present in the real economy. These limitations can compromise the model's ability to capture different agents' diverse behaviors and responses to policy interventions [30]. Furthermore, the reliance of these models on static equilibrium assumptions inhibits their capacity to effectively capture time- dependent effects and the dynamics associated with policy changes, as they assume a steady state without accounting for dynamic adjustments and transitional periods [31].  
+
+A promising approach to address these limitations is agent- based computational economics (ACE), which is the intersection between the agent- based modeling paradigm and economics. Here, economic processes are presented as the product of the dynamic system of interacting economic agents [32]. ACE models allow for greater heterogeneity in agent attributes and behaviors, with agents typically exhibiting non- fully rational behavior. This modeling paradigm is well- suited for "micro- founded" macroeconomic models, where agents' behavior is shaped by a diverse range of behavioral and expectation rules of varying complexity and rationality and in response to the environment or policies in place [33- 35]. When it comes to carbon pricing, it is crucial to consider the diverse behaviors, groups, and interactions involved in the broader economy - especially when considering inequality [36]. Partly because public support for climate mitigation policies depends on the careful balance and distribution of costs within the population and achieved emission- reductions [37]. Here, we present an Agent- based model (ABM) which integrates the energy sector, refined household behavior rooted in both rational and bounded rationality as per behavioral economics literature, and nuanced firm and labor market dynamics. The inclusion of a heterogeneous household sector with bounded rational agents is a notable extension, providing a rich platform to explore the interplay between carbon taxation, technological adoption rates, and their ripple effects across different income brackets within the economy. The model incorporate behavioural responses of individuals and businesses to carbon pricing, including changes in consumption patterns, investment decisions, and innovation. Our model's capacity to reflect non- linear technological
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+adoption rates and the potential re- distributive impacts of carbon taxation, serves as a bridge to align broader climate policy discourse with micro- level economic dynamics, fostering a nuanced understanding essential for effective climate policy design amid global warming mitigation endeavors.  
+
+## Integrated Framework for Carbon Policy Experiments  
+
+![](images/Figure_2.jpg)
+
+<center>Fig. 2: Schematic representation of the underlying economic model. Agents that attempt to optimize an internal metric are colored red. Note that individual households will only maintain specific connections to a limited set of commodity producers not to all at once [38]. </center>  
+
+The backbone of our ABM is the Keynes+Schumpeter (KS) family of models [39], renowned for their capacity to mirror innovation- driven economic growth. Furthermore, we introduce an energy- producing sector from an IAM by Lamperti et al. as well as the integration of nuanced labor market dynamics, as proposed by Dosi et al. [40, 41]. Consumption behaviour of households is taken from Lengnick [38]. The interactions are specified in Fig. 2. Note that entities colored grey act as mediators to redistribute capital gains or provide necessary liquidity. They neither change the total money supply nor influence production capacities directly (see section 2).  
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+Here, we present the validation of our model using "stylized facts". Employing stylized facts as benchmarks for model validation is justified due to their empirical robustness, generalizability and their ability to encapsulate complex
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+Table 1: Stylized facts are roughly equivalent to Lamperti et al. (2018) and Dosi et al. (2017) [40, 41].   
+
+<table><tr><td>SF</td><td>Description</td></tr><tr><td>SF1</td><td>Endogenous and self-sustained growth of output, with persistent fluctuations.</td></tr><tr><td>SF2</td><td>The distribution of GDP growth rates is fat-tailed.</td></tr><tr><td>SF3</td><td>The duration of recessions is exponentially distributed.</td></tr><tr><td>SF4</td><td>Aggregate consumption is less volatile than GDP, and aggregate investments are more volatile than GDP.</td></tr><tr><td>SF5</td><td>Cross-correlations of macro-variables:<br>  - Consumption, net investments, productivity, R&amp;amp;D investments, and energy demand are pro-cyclical.<br>  - Unemployment, markups, and the inventories-sales ratio are counter-cyclical.</td></tr><tr><td>SF6</td><td>Synchronization of the business cycle and emission dynamics.</td></tr><tr><td>SF7</td><td>Firm (log) size distribution is right-skewed.</td></tr><tr><td>SF8</td><td>The distribution of firm growth rates is fat-tailed.</td></tr><tr><td>SF9</td><td>Labor productivity heterogeneous across firms and persistent productivity differentials between firms. Persistent energy and carbon efficiency heterogeneity across firms.</td></tr><tr><td>SF10</td><td>Lumpy investment rates at firm level.</td></tr><tr><td>SF11</td><td>Both the wealth and income distributions are right-skewed, but the wealth is more concentrated at the top than income [42].</td></tr><tr><td>SF12</td><td>The labor income share (LIS) behaves anticyclically [43].</td></tr></table>  
+
+macroeconomic dynamics. Specifically, we leverage a set of stylized facts outlined in Table 1, which parallel those identified by Lamperti et al. (2018) and Dosi et al. (2017) [40, 41] and expand upon them with SF11 and SF12. These facts were chosen for their relevance in scrutinizing the research questions at hand: They encompass critical aspects of economic behavior, such as endogenous growth, fat- tailed GDP growth rates, and business cycle synchronization with emission dynamics, that are inherently linked to the effects of carbon pricing. By aligning our model with these empirical regularities, we aim to ensure not only its fidelity to real- world phenomena but also its applicability in answering questions central to the effective design and implementation of carbon pricing mechanisms.
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+<center>Fig. 3: (SF5), (SF12) Cross correlations between lagged real GDP and consumption (C), investment (I), R&D (RD), total debt (TotDebt), prices, energy demand (EnDem), emissions (Em), unemployment rate (U), labor income share (LIS), nominal wages, real wages, markups, average labor productivity (LP) and the ratio of inventories and sales. </center>  
+
+Our model aligns closely with the cross- correlations of macro- variables in SF5, as shown in Fig. 3. Pro- cyclical variables include consumption, investments, labor productivity (LP), RD investments, energy demand, and emissions. In contrast, unemployment, markups, and inventories- sales ratios are counter- cyclical [44, 45]. Moreover, figure 3 also validates SF12's anticyclical behavior of labor income share. Lagged cross- correlations within the model indicate a positive correlation between past GDP growth and current
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+debt levels, whereas higher current debt levels negatively correlate with future growth rates.  
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+After de- trending time series data using a band pass filter, output, investment, and consumption exhibit the well- known "roller- coaster" dynamics [44- 46]. Consistent with empirical data, consumption shows less volatility compared to GDP, while investment fluctuations are notably more volatile, see Fig. S3. Here we can also see the endogenous growth of output with persistent fluctuations (SF1) and the synchronization of the business cycle and emission dynamics (SF6). To account for distributional effects, the model needs to produce endogenous income and wealth inequality. In Figure S8 we see the final distribution of wealth and incomes. These distributions remain (roughly) stable and exhibit inequality as emergent from heterogeneous preferences regarding the faced inter- temporal optimization problem. This replicates the in SF11 formulated right- skewed but for wealth more concentrated distribution of assets [42].  
+
+The supplementary information (SI) presents the validation of the remaining stylized facts. Utilizing default parameters, we observe endogenous output growth with persistent fluctuations (SF1), differential consumption and investment volatility (SF4), and synchronous business and emission cycles (SF6), as shown in Figure S3. The model's reproduction of fat- tailed GDP growth rates (SF2) and the exponential distribution of recession durations (SF3), as depicted in Figure S4, signals its capability to capture real- world economic extremes and cycles. Additionally, significant skewness and kurtosis in firm size and growth distributions (SF7 and SF8), along with the non- normality of these distributions confirmed by statistical tests in Tab. 5, Fig. S5. Additionally, we also observe heterogeneous labor productivity across firms (SF9) as shown in Fig. S6, which all together suggests realistic heterogeneity in firm dynamics.  
+
+Collectively, these outcomes underscore the model's proficiency in reflecting complex economic phenomena. This validation is essential for accurately assessing the impact of carbon pricing on key macroeconomic performance metrics. A more thorough analysis of the model behavior (e.g. effect of price shocks on producers or the influence of progressive taxation) can be found in the SI. Additionally, a global sensitivity analysis (GSA) has been conducted to explore the combinatorial impacts of various model parameters, following the common practice for computational modeling approaches as detailed in the Supplementary Material [47]. Here we utilize the PAWN algorithm [48, 49].
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+## Mapping Effective Energy Policies  
+
+Here we investigate the potential effects of aggressive carbon pricing on emissions and economic growth. Specifically, we have examined two complementary scenarios in our analysis. First, we have considered a scenario of "low green capacity" - representing economies that have a high barrier to green energy transition due to cost, availability of technology, resources and land. The second scenario ("high green capacity") represents advanced economies where fuel switching is comparably easier. Of course any degree in between can be chosen as well. The green capacity is controlled using parameter \(g_{\mathrm{limit}}\) (see section iv) in Methods). A value of \(g_{\mathrm{limit}} = 0\) indicates no green capacity by limiting the maximum share of green energy plants to zero while \(g_{\mathrm{limit}} = 1\) indicates maximal green capacity. For each of the two scenarios, we vary \(\tau_{c}\) (carbon tax) from 0 to 0.8 and measure indexed emissions and real GDP.  
+
+We initiate the model with heterogeneous agents, assigning households varied discount rates and firms' divergent initial capital and technological capabilities. A warm- up period of 300- time steps (equivalent to 25 years) is implemented and empirically verified to offer adequate system relaxation across all parameter settings. This phase is integral for mitigating initial condition biases and ensuring statistical robustness in Agent- Based Models. Following the warm- up we scrutinize the economy's response to varying levels of carbon taxation, denoted by \(\tau_{c}\) . In the following experiment we investigate the effects of a temporary carbon shock lasting roughly 2 years or 24 time steps (Fig. 4). The depicted values are averaged over the last 20 time steps and across ten simulation runs to ascertain data robustness. Two technological scenarios are distinguished: one permitting an \(80\%\) green power- plant exchange and another allowing only a \(20\%\) replacement. These serve as proxies for the divergent energy transition capabilities among countries, such as Germany and Norway. No limits were placed on the technological efficiency of consumer or capital goods producers. In the majority scenario, a strong decoupling arises between GDP and emissions, whereas the minority scenario shows a substantial emission reduction only when both GDP and emissions simultaneously decline. To capture this behavior, we introduce an order parameter—emissions per unit of economic output, demarcating the critical transition (See fig. 6 c) below).
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+<center>Fig. 4: Analyzing the effects of carbon tax on emissions and GDP, this study contrasts two green capacity scenarios. We vary carbon tax ( \(\tau_{c}\) ) from 0 to 0.8, observing emissions and GDP in high (80%) and low (20%) green capacity setups. Results, averaged over the last 20 time-steps and five runs, highlight the differential economic responses to varying carbon tax levels. </center>  
+
+## Non-Linear Dynamics in Economic Response  
+
+Our study highlights the non- linear impact of carbon pricing on emissions, pinpointing a critical carbon price as a key factor. This critical price level is high enough to significantly diminish the appeal of investing in new coal- fired power plants, simultaneously making renewable energy sources more economically viable. Given the decision processes of energy producers in our model we can explicitly state the tax frontier at which tipping occurs (see section XII in Supplementary Material). This point demarcates when energy producers strictly prefer investing in green power plants, which require more capital investment. This pattern of non- linear response to carbon pricing has been observed in various countries, notably in the context of the European Union's Emissions Trading Scheme (ETS) implemented in 2005. Initially, the ETS failed to produce a substantial impact due to relatively low carbon prices that did not incentivize significant investment in lower- carbon technologies [50]. Recognizing this shortcoming, Great Britain introduced the Carbon Price Support policy in 2013. This policy mandated a supplementary fee over a set Carbon Price Floor for the power sector, effectively raising the cost of carbon emissions. The significant emission reductions witnessed in 2015- 2016 can be
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+largely attributed to this heightened carbon pricing [51]. Further substantiation of our thesis comes from the observed increase in technology diffusion within the energy sector and the interaction effects it entails [52]. Empirical models predicting the energy transition’s acceleration lend further credence to the concept of a critical transition, indicative of a tipping point in the electricity market [53, 54]. Such tipping points, while previously theoretical, are now increasingly observable and necessitate a clear demarcation, as indicated by early warning models and studies [55]. These findings collectively suggest that the carbon pricing mechanism, when reaching a critical threshold, can trigger a substantial and rapid shift towards renewable energy sources, marking a pivotal moment in the global effort to mitigate climate change [56].  
+
+Second, we show that carbon pricing is not a blanket policy that will be successful everywhere. In countries that lack the potential for renewable energy, raising the carbon price would represent a blanket tax on energy and may not drive the economy to a low- carbon regime [57]. Phasing out coal power plants, the single largest source of global greenhouse gas emissions (20%), is another major challenge as retiring or re- purposing them requires large amounts of private investment and public support [58]. Meanwhile, though a growing number of investment funds prioritize sustainability, investment decisions are increasingly being based on environmental, social, and corporate governance factors that don’t necessarily focus on climate issues [59, 60] In the longer term, a careful balance is needed to redirect how existing infrastructure could be used without going so far as to incentivize building new infrastructure and avoidable carbon lock- in [61]. The potential for rapid and material global emissions reductions appears to have gone unnoticed thus far; it is about time that the benefits of fuel switching received greater attention.  
+
+## Fueling the Economy Through Carbon Pricing  
+
+The observation of GDP growth in the context of increasing carbon taxation merits examination. Our model employs a progressive tax system, as illustrated in Fig. 4 (c), which manifests effects on GDP through varying degrees of progressivity. Specifically, a \(prog\) value of \(- 1\) allocates tax revenue to households in proportion to their wealth. A \(prog\) value less than \(- 1\) yields regressive outcomes, while a value greater than \(- 1\) enhances progressivity (see Eq. 24). In our experiments, we set \(prog = - 0.5\) . The data reveals elevated GDP attributable to increased consumption, notably in lower- income brackets. This consumption- driven GDP growth is further amplified when redistribution from producers to households is incorporated, an effect uniquely accentuated under the regime of carbon taxation. The collapse of the economy at elevated tax levels manifests as a surge in monthly bankruptcies, from under 1% to over 10%. This sharp transition accentuates the non- linear effects of carbon taxation, undermining its redistributive advantages by destabilizing the corporate landscape.  
+
+The efficacy of carbon pricing, specifically through a carbon tax, has been a subject of considerable debate, primarily due to apprehensions regarding its
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+impact on economic growth, income distribution, and international competitiveness [62]. However, emerging evidence suggests that these adverse effects can be substantially mitigated. A pivotal strategy in this regard is the efficient recycling of carbon tax revenues, coupled with the adoption of redistributive measures, which can effectively cushion the impact on economic growth [63]. The economic costs associated with carbon taxes are generally evaluated in terms of the change in future GDP relative to a baseline scenario devoid of such a tax. This evaluation often involves a dichotomy of economic models: the 'top- down' models, which utilize aggregate economic data and underscore sectoral interactions, and the 'bottom- up' models, which focus on technological solutions for energy efficiency and fuel substitution. Top- down models typically predict GDP loss under emissions stabilization, while bottom- up models suggest minimal or positive impacts on GDP growth from carbon taxes [64]. The IPCC report echoes this, presenting a range of outcomes from net benefits (1.2%) to marginal losses (0.5%). The efficacy of carbon taxes hinges on factors like economic context and model assumptions. Notably, GDP doesn't fully represent welfare; a dip in GDP growth could coincide with welfare gains due to environmental benefits. The key lies in the effective use of carbon tax revenues and the responsiveness of economic agents. Properly recycled revenues could lead to positive economic growth effects, addressing initial concerns about carbon pricing.  
+
+## Green Growth, Fair Gains  
+
+Here we investigate the distributional impact of price shocks on income and wealth groups. Shocks of \(- 50\%\) and \(+50\%\) in fossil fuel prices are applied for two years, reverting to a baseline price thereafter. Lower- income and wealth groups are more affected by these shocks, with effects being more persistent for wealth than income and in line with recent empirical observations [65]. For higher income groups, real income reductions are offset by increases in capital income, explaining their quicker recovery. The experiment also reveals that consumer goods producers exhibit asymmetric responses to price shocks, capturing more benefits from price decreases than losses from increases, suggesting low market competition. This information is vital for designing policies to mitigate the adverse welfare effects of such shocks. Similarly, we observe the impact of price shocks due to taxation. Notably regardless of the direction of the price shock low and middle- income groups are disadvantaged more (see fig. S9 in SI).
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+<center>Fig. 5: We compare the consequences of a fuel price shock ( \(\pm 50\%\) ) on the share of income and wealth for each bracket to an unperturbed economy with identical seeds for the simulation. The shock period is demarcated in gray. The poor prove to be more vulnerable to shocks in the short term and feel the effects of the shock the longest. </center>  
+
+Carbon pricing, as a means to internalize the external costs of carbon emissions, invariably raises the production costs of carbon- intensive goods, consequently increasing household expenses. This escalation in costs can have broad implications, notably affecting factor prices such as wages and capital returns. Such shifts in the economic landscape disproportionately impact lower- income groups, who are more vulnerable to price shocks. However, the strategic redistribution of carbon tax revenues presents a twofold advantage. Firstly, it has the potential to spur economic growth, and secondly, it can effectively mitigate the adverse effects of these price shocks on poorer communities. Furthermore, this redistribution mechanism could garner increased support for climate mitigation measures, as it demonstrates tangible benefits for broader society [13]. Despite these promising avenues, there remain open questions regarding the optimal implementation and long- term impacts of such policies, necessitating further research and careful policy design.  
+
+## Implications for Policy  
+
+Climate change poses significant risks to the global economy. To combat this, countries are expected to expedite the transition to cleaner energy sources, aiming to reduce emissions by over \(40\%\) in the next seven years as a crucial
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+step towards achieving net- zero emissions by 2050. This shift towards net- zero emissions signifies a fundamental structural change in the global economy, necessitating a radical transformation of the energy sector and other key economic domains. The concept of a carbon tax generating revenue to enhance economic activity or efficiency has been a central theme in the field of environmental economics [66]. In this study, we illustrate how an optimal carbon price can facilitate the transition to a low- carbon economy while sustaining economic growth, a concept known as the 'double dividend'. Utilizing a computational model, we reveal that implementing an optimal carbon tax offers two key advantages: a reduction in carbon emissions and an increase in GDP and economic efficiency through the effective use of tax revenue. However, our work remains stylized, meaning we do not provide a more sophisticated definition of the "low green capacity" scenario, which depicts economies with significant barriers to transitioning to green energy, and the "high green capacity" scenario, representing advanced economies where fuel switching is more feasible. While we show that this is a relevant distinction that should influence decision- makers (DM), heuristics for the "green capacity" of a given economy are needed to effectively guide policy [67- 69]. This also hints at the broader problem of leveraging the observed non- linear dynamics in real- time.  
+
+## Early Warning Signs  
+
+While the identification of optimal taxation levels across different parameter regimes could offer valuable insights, it does not address the immediate challenge of identifying the current parameter regime of the real- world economy in question. This ranges from parameters governing firm behaviors to the previously mentioned "green capacity" of energy producers. Moreover, conducting counterfactual analyses on real- world economies is impossible. DM's have to act under time- constraint and without the luxury of running policy experiments at economic scale. Therefore, a real- time metric that can detect critical economic transitions irrespective of the parameter regime would proof useful for assessing the impact of additional taxation. Allowing DM's to gauge the immanent economic response will aid choice for optimal trade- offs. In this study, we monitor various observable to assess their predictive power. The observable that ended up as the strongest predictors are the markup of capital goods producers, debt levels of consumer goods producers, total emission index, and energy producers' emission index. KC proves particularly effective in measuring the phenomenon known as "critical slowing down" a precursor to critical transitions in complex systems [70]. This is because KC can discern the variations in complexity and the corresponding diminishing resilience within a system—key indicators of an impending critical threshold [71]. Figure 6 illustrates this selection of observable, showcasing their potential for predictive power within our model.
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+<center>Fig. 6: Following the KC of various economic observables over time. The end of the gray area demarcates the end of the warm-up period. Now we introduce repeated "shocks" to the economy by incrementally increasing the carbon tax. The vertical line demarcates the "critical transition", note the reduction of normalized KC building up to this point and the recovery of some shortly after. </center>  
+
+Although obtaining the necessary data may pose challenges, proxy measurements, and qualitative interviews could offer invaluable insights when coupled with appropriate analytical methods, similar to methodological developments in the ecologic literature [72]. While criticality- based estimates may not translate to actionable policy decisions, they can help interpret high- dimensional data and provide essential context for decision- makers (DMs) [73]. Proximity to a criticality could inspire DMs to move more cautiously. Of course further work is needed to better understand these non- linear responses of industries and the broader economy, specifically in the context of energy transition and carbon pricing. More concretely we aim to validate the predictive power of these observables with empirical data. However, the advancement of both, the prediction of the economic response as well as the distance to a potential criticality, could provide value for DMs.  
+
+## Conclusion  
+
+We use a validated agent- based model to demonstrate that implementing an "optimal" carbon tax can yield two distinct benefits: foremost, a decrease in carbon emissions, and secondly, an increase in GDP and economic efficiency
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+resulting from the utilization of the revenue generated by the carbon tax - often referred to as the 'double dividend' concept. Securing a future with reduced carbon emissions is not just essential but also beneficial for the economy. Shifting towards a low- carbon economy may initially pose challenges, such as decreased demand due to higher carbon prices and energy expenses. However, these challenges can be mitigated by reinvesting carbon revenues in government initiatives and reducing employment taxes. Crucially, the reduction in emissions will also alleviate the physical consequences of climate change, thereby decreasing overall macroeconomic expenses. Our findings have important implications for the ongoing debate around the relationship between economic growth, inequality, and environmental sustainability. By recognizing the existence of a robust criticality (tipping point) and the trade- off between the above three factors, decision- makers can make more informed and effective decisions that promote long- term sustainability and prosperity. Tipping points can significantly impact cost- benefit analysis by introducing non- linearities and uncertainties that can affect the overall outcomes of a project, policy, or decision. Incorporating the potential for tipping points in cost- benefit analysis for carbon pricing requires a more nuanced and dynamic approach such as using micro- simulation models to assess their impact on emissions reduction, economic efficiency, and social equity.  
+
+While no single measure can fully deliver the climate goals, carbon pricing is necessary but not always sufficient to reduce emissions, as also noted by William Nordhaus [74, 75]. Moving towards a net- zero economy demands significant investments in green electricity and energy storage. The way economies navigate these investments involves policy trade offs. Successful experiences from countries at various stages of development, such as Chile, Singapore, and Sweden, show that political hurdles associated with carbon pricing can be overcome. We show that carbon pricing should be complemented by other mitigation instruments to address market failures and promote innovation and deployment of low- carbon technologies [76]. A pragmatic and equitable proposal calls for an international carbon price floor, differentiated across countries at different levels of economic development [77]. The associated carbon revenues could be partly shared across countries to facilitate the green transition. A just transition should also include robust fiscal transfers to vulnerable households, workers, and communities. A better understanding of carbon pricing is essential for countering misinformation related to climate change and climate policy. It empowers individuals to engage in informed discussions, correct misconceptions, support evidence- based policies, and promote responsible media coverage. This can ultimately contribute to more effective climate action and a reduction in the spread of climate- related misinformation.  
+
+Our works paves the way for future research on exploring innovative strategies to enhance the effectiveness of carbon pricing mechanisms. Specifically, we can explore policy synergies - how carbon pricing can be integrated with other climate policies, such as renewable energy incentives, energy efficiency programs, and regulations on emissions from specific sectors. Further, the model
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+can be extended to assess the specific challenges and opportunities associated with implementing carbon pricing in different sectors, such as agriculture, transportation, and heavy industry is paramount. As the global community intensifies efforts to address climate change, research in these areas will be crucial for developing more effective and equitable carbon pricing policies. Additionally, computational models that integrate insights from economics, political science, sociology, and other fields will likely play a key role in advancing our understanding of the complex dynamics associated with carbon pricing.
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+## Methods  
+
+In this section, we detail the methodology employed in our study, centered around an agent- based modeling approach. The core of our model consists of four main types of agents: households, capital- good producers, consumer- good producers and one energy producer. Each with distinct roles and behaviors that are crucial to understanding the phenomena under study. In the following subsections, we provide an in- depth description of each agent type, including their attributes, decision- making processes, and interactions with other agents.  
+
+## i) Households  
+
+In our economic model, households are depicted as dynamic, heterogeneous agents, pivotal in shaping the economy's dynamics through their unique job skills \(\pi_{i}\) and wealth levels. These skills, reflecting the gross income distribution in the Netherlands and modeled using a log- normal distribution (detailed in appendix XIII), determine their market attractiveness and labor income. Their economic behavior, influenced by diverse income sources such as labor \((Y_{i,t}^{L})\) , investments \((Y_{i,t}^{K})\) , and social benefits \((Y_{i,t}^{S})\) , shapes their consumption, savings decisions, and overall liquid wealth \((W_{i,t})\) . Actively engaging in both labor and goods markets, households make strategic consumption choices based on price, availability, and adjust market relationships in response to demand fulfillment. This adaptive behavior not only reflects their individual economic circumstances but also significantly contributes to the broader market dynamics, affecting the supply- demand equilibrium in the consumer goods and labor markets. At each time step \(t\) , households make decisions: (1) the amount of goods to consume and their suppliers, (2) their employer. The consumption amount depends on their wealth level \(W_{i,t}\) , interpreting \(W_{i,t}\) as liquid wealth excluding assets like real estate (as in [38]). The wealth level adjusts through various income sources, with labor income \(Y_{i,t}^{L}\) defined as:  
+
+\[Y_{i,t}^{L} = \left\{ \begin{array}{ll}w_{i,t}L_{i} & \mathrm{if~employed}\\ UB_{t} & \mathrm{if~unemployed} \end{array} \right. \quad (1)\]  
+
+where \(w_{i,t}\) is the wage for \(L_{i}\) labor units provided, and \(U B_{t}\) are unemployment benefits. Total wealth for consumption at \(t\) is \(W_{i,t} = L A_{i,t - 1} + Y_{i,t}^{L} + Y_{i,t}^{K} + Y_{i,t}^{S}\) , where \(L A_{i,t}\) is the liquid asset level. Households choose goods based on price and availability, adjusting market relationships in response to demand fulfillment. In case of unmet demand, they buy from other producers until no supply remains, avoiding high involuntary savings due to market mismatches (appendix XI). After transactions, they know their true consumption level \(C_{i,t}^{\mathrm{true}}\) and liquid assets \(L A_{i,t + 1}\) for the next period. Households may break trade connections with unsatisfactory suppliers (probability \(\psi_{Q}\) ) or seek lower prices (probability \(\psi_{P}\) ), applying pressure on producers to meet demand and reduce prices ([38]). Again, they are only bounded rational. In our case, this means, instead of analytically solving their explicit
+
+<--- Page Split --->
+
+inter- temporal choice problem they approximate it by comparing a finite set of actions (i.e. they lack the ability to compute perfect optima). These households have consumption and investment preferences, based on their wealth levels and heterogeneous time preferences. Each household \(i\) inter- temporal optimization problem is defined as:  
+
+\[\alpha_{i}^{*} = \max_{\alpha_{i}\in [0,1]}\mathbb{E}_{t}\left[\sum_{j = t}^{T}\beta_{i}^{j - t}U(\alpha_{i}W_{i,t})\right], \quad (2)\]  
+
+where \(\alpha_{i}\) is the absolute propensity to consume, as a portion of wealth level \(W_{i,t}\) . \(\beta_{i}^{j - t}\) is the household's discount factor with \(j - t\) as index for current and future time- steps, and \(U(\cdot)\) is the utility function, given as \(U(C_{i,t}) = C_{i,t}^{1 - \rho} / (1 - \rho)\) . This optimization problem is constrained by:  
+
+\[W_{i,t + 1} = (1 + r_{t})[W_{i,t} + Y_{i,t} - C_{i,t}],\]  
+
+Here, \(Y_{i,t}\) is the household's income level at time t, which can come from labor, capital or government subsidies. \(C_{i,t}\) is the consumption level and \(r_{t}\) the real interest rate. In order to solve the optimization problem in equation 2, households form expectations of their future income- and wealth level and the interest rate. These expectations are formed using simple adaptive updating rules:  
+
+\[W_{i,t + 1}^{e} = \omega W_{i,t} + (1 - \omega)W_{i,t}^{e},\]  
+
+where \(W_{i,t}^{e}\) is the expected value and \(\omega\) is a weighting factor. Based on the optimization problem, heterogeneous time preferences and adjusting changing expectations, households have heterogeneous saving rates on both the cross- sectional and inter- temporal level. Furthermore, these savings are precautionary, meaning that savings are motivated by the desire to keep a stable consumption level in case of a negative income shock.  
+
+## ii) Capital Good Producers  
+
+The model consists of two classes of producers: capital goods producers and consumer goods producers. Capital goods producers supply the capital inputs used by the consumer good producer. In other words, they provide the machines/infrastructure for consumer goods producers to operate. These 'machines' have heterogeneous technological levels of labor productivity \((LP)\) , energy efficiency \((EE)\) , and environmental friendliness \((EF)\) . These productivity levels are represented as \(A_{i,v}^{k}\) (productivity of machines for the consumption- good industry) and \(B_{i,v}^{k}\) (productivity of the capital producer itself) for producer \(i\) , version \(v\) of the machine and technology type \(k = \{LP, EE, EF\}\) [40]. The cost of production \(c_{i,t}\) for one machine unit is then given as:
+
+<--- Page Split --->
+
+\[c_{i,t} = \frac{w_{i,t}}{B_{i,v}^{LP}} +\frac{(\tau^{E} + c_{t}^{E})}{B_{i,v}^{EE}} +\tau^{C}B_{i,v}^{EF}. \quad (3)\]  
+
+Here, \(c_{t}^{E}\) is the cost per unit of energy (described in section iv)) in period \(t\) , and with labor skill level \(\omega_{i,t}\) . \(\tau^{E}\) and \(\tau^{C}\) are the energy tax and carbon tax, respectively. The producer has to pay for using a unit of energy or emitting a unit of carbon (described in section vii)). Once the cost is determined, the capital producer's price is determined by a fixed mark- up rate \(\mu_{1} > 0\) , giving the price \(p_{i,t} = (1 + \mu_{1})c_{i,t}\) .  
+
+Capital good producers are the main driver of technological growth. They can improve their technology levels in two ways: (1) technological innovation through a stochastic R&D process, and (2) adopting technologies owned by others through a stochastic process of imitation. For both ways, a R&D budget is used to hire researchers from the labor force. This budget is given as a fraction \(\nu\) of the previous sales \(S_{i,t - 1}\) , which gives \(R D_{i,t} = \nu S_{i,t - 1}\) . If \(S_{i,t - 1} = 0\) , the R&D budget is based off the stock of liquid assets \(N W_{i,t}\) , which gives \(R D_{i,t} = \nu N W_{i,t}\) . Here, \(S_{i,t - 1}\) are the sales in the past period and \(\nu \in [0,1]\) is a global parameter that determines the extent of research. The R&D budget is then split between an innovation \((I N_{i,t})\) and an imitation \((I M_{i,t})\) budget using a global parameter \(\xi \in [0,1]\) . The budgets are then given as:  
+
+\[I N_{i,t} = \xi R D_{i,t},\qquad I M_{i,t} = (1 - \xi)R D_{i,t}.\]  
+
+The innovation and imitation process is then modelled in two steps. Firstly, the probability that a firm gets access to a candidate technology through innovation and/or imitation is given by a Bernoulli distribution with parameter \(\theta_{i,t}^{I N}\) and \(\theta_{i,t}^{I M}\) .  
+
+\[\theta_{i,t}^{I N} = 1 - e^{\zeta_{1}I N_{i,t}},\qquad \theta_{i,t}^{I M} = 1 - e^{\zeta_{2}I M_{i,t}}, \quad (4)\]  
+
+where \(\zeta_{1},\zeta_{2}< 0\) are global parameters, representing the firm's search capabilities [39]. The Bernoulli draws then determine if innovation and/or imitation are successful.  
+
+In the case of a successful innovation, the candidate technologies are acquired through a stochastic shock \(\chi \sim \mathrm{Beta}(\alpha_{1},\beta_{1})\) to the incumbent technology level. \(\chi\) is drawn independently for all \(k\in \{L P,E E,E F\}\) and \(A\) and \(B\) over a support \([\chi_{1},\bar{\chi}_{1}],\chi_{1}\in [- 1,0]\) and \(\bar{\chi}_{1}\in [0,1]\) . The new technology levels are then given by:  
+
+\[A_{i,t}^{L P,I N} = A_{i,t}^{L P}\left(1 + \chi_{i,t}^{A,L P}\right), B_{i,t}^{I N,L P} = B_{i,t}^{L P}\left(1 + \chi_{i,t}^{B,L P}\right),\] \[A_{i,t}^{E E,I N} = A_{i,t}^{E E}\left(1 + \chi_{i,t}^{A,E E}\right), B_{i,t}^{I N,E E} = B_{i,t}^{E E}\left(1 + \chi_{i,t}^{B,E E}\right),\] \[A_{i,t}^{L P,I N} = A_{i,t}^{L P}\left(1 - \chi_{i,t}^{A,E F}\right), B_{i,t}^{I N,E F} = B_{i,t}^{E F}\left(1 - \chi_{i,t}^{B,E F}\right).\]
+
+<--- Page Split --->
+
+Similarly, given a successful imitation, firm \(i\) can use the technology set \(\{A_{j,t}^{k,IM},B_{j,t}^{k,IM}\}\) of a firm \(j\) as a candidate set. Firm \(j\) is chosen randomly, weighted inversely by the Euclidean distance of its technology set to that of firm \(i\) . This means firms are more likely to imitate firms that are more like themselves.  
+
+Capital producers then choose the most competitive from the innovated and imitated candidate sets and the incumbent technology \(\{A_{i,t - 1}^{k},B_{i,t - 1}^{k}\}\) . For this, capital good producers estimate which of their technology sets would be most attractive to consumer good producers, which we will describe later in equation 11. Once the new technology set is known, the producer sends a 'brochure' to consumer good producers showcasing the price and technology levels of the capital goods it produces. The brochure is sent to all historical clients \(HC_{i,t}\) and a random set of new clients \(NC_{i,t}\) of size \(\gamma |HC_{i,t}|\) with \(\gamma \in [0,1]\) [39].  
+
+The production of capital goods requires the input of labor. It is assumed the production of capital goods takes the whole time period. Machines are thus ordered at the start of the period and delivered at the end of the period. A complication that follows from the inclusion of households is that producers no longer get a portion of the labor force 'assigned' (as in [39]). Instead, they have to attract employees through the labor market process (described in section v)). This means that producers that fire employees, and want to hire them back a period later are likely to find they labor demand can no longer be met. In order to avoid this, consumer good producers smooth their demand for labor. This is done through the global smoothing parameter \(\lambda \in [0,1]\) . Using this and based on the amount of ordered machines \(O_{i,t}\) , the capital good producer sets its demand for labor \(L_{i,t}^{d}\) as follows:  
+
+\[L_{i,t}^{d} = \lambda L_{i,t - 1} + (1 - \lambda)\left(\frac{O_{i,t}}{B_{i,t}^{LP}} +\frac{RD_{i,t}}{\bar{w}_{i,t}}\right), \quad (5)\]  
+
+where \(L_{i,t - 1}\) is the labor stock of the producer at time \(t - 1\) and \(\bar{w}_{i,t}\) is the average wage paid to employees. Due to the labor supply smoothing, the desired labor stock can thus be larger than strictly needed for the desired production level, as to avoid labor shortages in later periods.  
+
+## iii) Consumer Good Producers  
+
+Consumer good producers use their stock of capital and labor to produce consumer goods. The production is given as a Leontief production function, meaning production is limited by the absolute size of either the capital stock or the productivity- weighted labor stock. The maximum possible production amount \(Q_{i,t}^{\mathrm{max}}\) for consumer good producer \(i\) at time \(t\) is thus given as:  
+
+\[Q_{i,t}^{\mathrm{max}} = \min \left\{\bar{A}_{i,t}^{LP}L_{i,t},K_{i,t}\right\} , \quad (6)\]  
+
+where \(\bar{A}_{i,t}^{LP}\) is the average of labor productivity of the machines owned by the firm, weighted by the frequency of the machines. Production is thus constrained
+
+<--- Page Split --->
+
+by the labor supply, the average productivity level and the absolute stock of capital.  
+
+In order to acquire sufficient quantities of each of the production factors, consumer good producer \(i\) has to make an estimate of the true demand \(D_{i,t}\) it will face at time \(t\) . The true demand of households for consumer goods is only revealed after production has already taken place. Producers therefore need to have sufficient goods in stock, or the household will buy its goods elsewhere. Estimating this demand is made more difficult by the fact that, unlike in [39], the market share is no longer a direct function of the producer's 'competitiveness' level. Instead, it results from the interactions with households in the consumer market. Consumer good producers employ an adaptive scheme to form expectations of the demand that will follow from this consumer market process. The scheme for expected demand \(D_{i,t}^{\mathrm{exp}}\) is defined as:  
+
+\[D_{i,t}^{\mathrm{exp}} = \omega D_{i,t - 1}^{\mathrm{exp}} + (1 - \omega)(D_{i,t - 1} + D_{i,t - 1}^{U}). \quad (7)\]  
+
+Here, \(\omega \in [0,1]\) is the memory parameter, which determines to which extent the most recent observations are weighted when determining the new expectations. \(D_{i,t - 1}\) is the total amount of sold goods at time \(t - 1\) and \(D_{i,t - 1}^{U}\) is the total demand for goods producer \(i\) was unable to supply at time \(t - 1\)  
+
+Using the expected demand, the desired short- term production levels can be determines, denoted \(Q_{i,t}^{d,ST}\) . Unlike [39], this model makes a distinction between short- run \((Q_{i,t}^{d,ST})\) and long- run \((Q_{i,t}^{d,LT})\) production targets. Given that the capital stock can only be augmented over a longer time frame, the short- term production targets must be met by changing the labor stock. The desired short- term production quantity is defined as:  
+
+\[Q_{i,t}^{d,ST} = D_{i,t}^{e} + N_{i,t}^{d} - N_{i,t - 1}. \quad (8)\]  
+
+Here, \(N_{i,t}^{d}\) is the desired amount of products in inventory, given by \(\iota D_{i,t - 1}\) with global parameter \(\iota \leq 0\) governing the desired inventory size. \(N_{i,t - 1}\) is the amount of products in inventory in the last period. From the desired short- term production level, the desired labor stock can be determined. This desired labor stock \(L_{i,t}^{d}\) is again smoothed using \(\lambda\) , and is defined as follows:  
+
+\[L_{i,t}^{d} = \lambda L_{i,t - 1} + (1 - \lambda)\min \left\{\frac{Q_{i,t}^{d,ST}}{\overline{A}_{i,t}^{LP}},\frac{K_{i,t}}{\overline{A}_{i,t}^{LP}}\right\} . \quad (9)\]  
+
+Here, \(K_{i,t}\) is the company's total capital stock, measured in the number of machine units. The second term in equation 9 is an augmented version of the Leontief production function in equation 6. It sets the desired labor supply such that is not more than desired, but also not more than can be produced with.  
+
+The long- term desired production determines investments in the by the capital stock. \(Q_{i,t}^{d,LT}\) is equal to the expected production level \(Q_{i,t}^{\mathrm{exp}}\) , which is obtained using an adaptive expectation scheme:  
+
+\[Q_{i,t}^{d,LT} = Q_{i,t}^{\mathrm{exp}} = \omega Q_{i,t - 1} + (1 - \omega)((1 + \iota)D_{i,t}^{\mathrm{exp}}). \quad (10)\]
+
+<--- Page Split --->
+
+The desired capital stock \(K_{i,t}^{d}\) is then equal to the expected long-term production quantity \(Q_{i,t}^{d,LT}\) , which sets the period's desired expansionary investment equal to \(E I_{i,t}^{d} = K_{i,t}^{d} - K_{i,t}\) [39]. Consumer good producers also make a choice for replacing capital goods, either because they exceed their economic lifetime \(\eta\) , or because buying a new machine is cheaper than running the current one. For this, producers compare the cost of running the machine with the cost of buying a new machine from the known producers:  
+
+\[\min_{h\in \{C u r r,I N,I M\}}\left[p_{i,t}^{h} + b c^{h}(A_{i}^{h},t)\right]. \quad (11)\]  
+
+Here, \(b\) is the payback period, \(p^{h}\) the price of a machine of type \(h\) and \(c^{h}\) the cost of running it. This cost includes energy costs and taxes on energy use and carbon emissions:  
+
+\[c_{i,t} = \frac{w_{i,t}}{A_{i,t}^{LP}} +\frac{(\tau^{E} + c_{t}^{E})}{A_{i,t}^{EE}} +\tau^{C}A_{i,t}^{EF}. \quad (12)\]  
+
+When assessing the replacement investments, producers assess all the machine versions in their machine stock \(\Xi_{i,t}\) and replace if \(\frac{p^{*}}{c_{A} - c^{*}} \leq b\) , where \(p^{*}, c^{*}\) are the price and cost of the newly offered machine, or when the machine's age exceeds its economic age \(\eta\) .  
+
+Once the production levels are set, the consumer good producer must set a price for its goods. For this, it first determines the cost of production it will face using equation 12 and the weighted average of the productivity levels of the machines that are used. The price is then determined using a dynamic markup rate \(\mu_{i,t}\) , and given as \(p_{i,t} = (1 + \mu_{i,t}) c_{i,t}\) , where the markup rate is given as:  
+
+\[\mu_{i,t} = 1 + \epsilon_{i,t}^{\mu} \times \mathrm{sign}(\delta \mu_{i,t - 1}) \times \mathrm{sign}(\delta \Pi_{i,t - 1}), \quad (13)\]  
+
+with \(\epsilon_{i,t}^{\mu} \sim \mathcal{U}(0, \bar{\epsilon}^{\mu})\) being a stochastic shock, \(\delta \mu_{i,t} = \mu_{i,t - 1} - \mu_{i,t - 2}\) and \(\delta \Pi_{i,t - 1} = \Pi_{i,t - 1} - \Pi_{i,t - 2}\) . This rule is different from the one used by [39], and serves as a simple dynamic optimization rule. If the producer increased the price in the last period and the profits increased as a result (i.e. \(\mathrm{sign}(\delta \mu_{i,t - 1}) = \mathrm{sign}(\delta \Pi_{i,t - 1}) = 1\) ), the producer will perform another price increase. Inversely, if the price was reduced and this resulted in a decrease in profit, the price is increased \((\mathrm{sign}(\delta \mu_{i,t - 1}) = \mathrm{sign}(\delta \Pi_{i,t - 1}) = - 1\) , so the product is 1). A price increase with a subsequent profit decrease, or a price decrease with a profit increase will both lead to a price decrease. So, consumer good producers continue strategies that pay off, and otherwise change strategies. This simulates a more realistic optimizing behavior and better allows for an adjustment of markups to general market conditions.  
+
+The number of capital- and consumer good producers in the model is static, but individual firms can go bankrupt, and are then replaced by a new entrant. Firms with zero market share or negative assets are declared bankrupt and
+
+<--- Page Split --->
+
+removed from the model. The replacing firms acquire a fraction of the average level of capital. Capital good producers may select a technology that is a fraction \(\chi^{NE}\) of the best available technologies \(A_{t}^{\mathrm{best}}\) and \(B_{t}^{\mathrm{best}}\) , with \(\chi^{NE} \sim \mathrm{Beta}(\alpha_{2}, \beta_{2})\) , \(\chi^{NE} \in (0, 1)\) . New consumer good producers can choose capital goods through the aforementioned brochure mechanism [39].  
+
+## iv) Energy Producer  
+
+The energy producer is a profit- maximizing monopolist which produces energy units to meet energy demand \(D_{t}^{E}\) . Households do not directly buy energy, and thus only affected by energy prices through the prices of producers. Both consumer- and capital good producers require energy for their production process, but the supply for energy is never constrained. The number of units these producers require is equal to their average energy efficiency technology levels, \(A_{t,t}^{EE}\) and \(B_{t,t}^{EE}\) , times their total production levels. Based on the total energy demand, the energy producer can determine its profit function as:  
+
+\[\Pi_{t}^{E} = p_{t}^{E}D_{t}^{E} - PC_{t}^{E} - IC_{t}^{E} - RD_{t}^{E}. \quad (14)\]  
+
+In this equation, \(p_{t}^{E}\) is the energy price at time \(t\) , \(PC_{t}^{E}\) the total cost of producing \(D_{t}^{E}\) units of energy, \(IC_{t}^{E}\) the total replacement and expansion investments and \(RD_{t}^{E}\) the R&D expenditures.  
+
+The energy producer has a portfolio of power plants, consisting of 'green' and 'dirty' plants. Green plants use renewable sources which have zero marginal costs of production (so cost of production \(c_{t}^{GE} = 0\) ). The production capacity \(Q_{t}^{GE}\) is equal to the capacity \(K_{t}^{GE}\) , because no fuels are required. Accounting for the varying degrees of green energy capacity of different regions we regulate the maximum share of green power plants in the portfolio through the parameter \(g_{\mathrm{limit}} \in [0, 1]\) . Dirty plants use fossil fuels, meaning their cost of production is dependent on the cost of fossil fuels \(p_{t}^{f}\) and the thermal efficiency of the plant \(A_{v}^{DE}\) for each vintage \(v\) of dirty power plant. This gives the cost function:  
+
+\[c_{v,t}^{DE} = \frac{p_{t}^{f}}{A_{v}^{DE}} + \tau^{C}em_{v}^{DE}. \quad (15)\]  
+
+Because of their use of fossil fuels, dirty technologies have a positive carbon footprint \(em_{v}^{DE}\) for each produced unit of energy over which they have to pay a carbon tax \(\tau^{C}\) .  
+
+The energy producer will use power plants in order of least marginal costs, meaning it will always first use its full stock of green technologies. Let \(\Upsilon\) be the set of power plants required to meet demand. For \(D_{t}^{E} > K_{t}^{GE}\) , the energy producer will have a positive cost of production, given as:  
+
+\[P C_{t}^{E} = \sum_{v\in \Upsilon}g_{v,t}^{D E}c_{v,t}^{D E}A_{v}^{D E}, \quad (16)\]  
+
+with \(g_{v,t}^{D E}\) being the number of produced energy units of the power plant. The energy producer asks for a fixed mark- up rate \(\mu^{E} \geq 0\) on top of its cost price.
+
+<--- Page Split --->
+
+This cost price is determined as the average marginal production cost of dirty plants \(\bar{c}_{t}^{DE}\) multiplied by the fraction of dirty energy in the last period. This gives the following energy price:  
+
+\[p_{t}^{e} = \left\{ \begin{array}{ll}\mu^{e} & \mathrm{If~}D_{t}^{E}\leq K_{t}^{GE}\\ \mu^{e} + \bar{c}_{\tau ,t}^{DE}\frac{Q_{t}^{DE}}{Q_{t - 1}^{E}} & \mathrm{If~}D_{t}^{E} > K_{t}^{GE} \end{array} \right. \quad (17)\]  
+
+Energy producers do not use labor to produce, and their only production constrained is thus the stock of power plants. The producer can replace plants or expand the production capacity through investment. In order to avoid unrealistic energy shortages, it is assumed these investments can be realized instantly. The maximum production capacity is defined as:  
+
+\[\bar{Q}_{t}^{E} = \sum_{v}g_{v,t}^{DE}A_{v}^{DE} + \sum_{v}g_{v,t}^{GE}. \quad (18)\]  
+
+Using this maximum capacity, the producer can decide to expand its plant stock based on the gap between the actual capital stock and the desired amount of capital, \(K_{t}^{E,d}\) . If the maximum capacity does not match the demand (i.e. \(\bar{Q}_{t}^{E}< D_{t}^{E}\) ), the planned expansionary investment will be \(E I_{t}^{E} = K_{t}^{E,d} - K_{t}^{E}\) . In order to choose which technology to buy (more of), producers decide whether the implementation cost of green technologies ( \(I C_{v}^{G E}\) for current best vintage \(v\) ) is less than the estimated cost of operation of dirty technologies (as dirty technologies are assumed to have zero cost of implementation). Hence, if \(I C_{v}^{G E}\leq b^{E}c_{v,t}^{D E}\) , the complete additional stock of power plants will be green, where \(b^{E}\) is the discount factor. Here, the comparison is made between the cheapest green and dirty technologies. If the green technology is chosen, the cost of expansion is given as \(E C_{t}^{E} = I C_{v}^{G E}E I_{t}^{E}\) , and \(E C_{t}^{E} = 0\) if the dirty technology is chosen [40].  
+
+The process of innovation is similar to that of the capital good producers. The energy producer invests a fraction \(\nu^{E}\in (0,1)\) of its previous revenue in R&D, which is split in a budget for investment in green energies ( \(I N_{t}^{G E}\) ) and dirty energies ( \(I N_{t}^{D E}\) ). \(\xi^{G E}\in (0,1)\) is then the fraction of investments going to green technologies. Like for capital good producers, these innovations are successful with the probabilities \(\theta_{t}^{G E}\) and \(\theta_{t}^{D E}\) :  
+
+\[\theta_{t}^{G E} = 1 - e^{\eta^{G E}I N_{t}^{G E}},\qquad \theta_{t}^{D E} = 1 - e^{\eta^{D E}I N_{t}^{D E}}, \quad (19)\]  
+
+with \(\eta^{G E},\eta^{D E}\in (0,1)\) . When successful, the installment cost of green technologies will be reduced with a factor \(\chi^{G E}\sim \mathrm{Beta}(\alpha_{1},\beta_{1})\) , where \(\chi^{G E}\in (0,1)\) (i.e. \(I C_{v}^{G E} = I C_{v - 1}^{G E}\chi^{G E}\) ). Successful innovation for dirty technologies mean its thermal efficiency may improve and/or its emissions may reduce:  
+
+\[A_{v}^{D E} = A_{v - 1}^{D E}(1 + \chi^{D E,A}),\qquad e m_{v}^{D E} = e m_{v - 1}^{D E}(1 - \chi^{D E,e m}), \quad (20)\]  
+
+where \(\chi^{D E,A},\chi^{D E,e m}\sim \mathrm{Beta}(\alpha_{1},\beta_{1})\) are independently drawn. The energy producer will then choose the technology set which is cheapest to produce with.
+
+<--- Page Split --->
+
+This mean that a new technology set that improves the emissions technology may be worse in terms of thermal efficiency, and vice versa, as long as it has lower operational costs than the incumbent technology set [40].  
+
+## v) Labor Market  
+
+The labor market mechanisms are largely based of those introduced in [41] \(^{1}\) , but diverts with the important addition of the heterogeneous labor productivity of households. The employment of households by producers is determined through the labor market matching process. Before this process starts, the total amount of labor demand and supply are established. Producers set their desired change in their labor stock ( \(\Delta L_{i,t}^{d}\) ) while planning their production amount. Producers with positive \(\Delta L_{i,t}^{d}\) will try to hire workers, whereas the producers with negative \(\Delta L_{i,t}^{d}\) will fire excessive workers in their labor stock. After excessive workers were fired, a pool of job- seeking households is formed from unemployed households, and employed households that look for a better paying job. The employees that were fired by firms that went bankrupt at the end of last period are also included in this pool.  
+
+Households have a requested wage level \(w_{i,t}^{r}\) , which is based on their (recent) wage history, and if the household is currently employed. The requested wage is determined as follows:  
+
+\[w_{i,t}^{r} = \left\{ \begin{array}{ll}\max \{w_{i}^{\min},w_{i,t}^{s}\} & \mathrm{If~unemployed}\\ (1 + \epsilon_{w})w_{i,t - 1} & \mathrm{If~employed} \end{array} \right. \quad (21)\]  
+
+The requested wage for an employed jobseeker is thus by a fraction \(\epsilon_{w} \geq 0\) higher than its current wage. Unemployed jobseekers demand a lower satisfying wage \(w_{i,t}^{s}\) . This wage is adapted according to the following updating rule:  
+
+\[w_{i,t}^{s} = \left\{ \begin{array}{ll}(1 - \epsilon_{w})w_{i,t}^{s} & \mathrm{If~unemployed}\\ \max \{w_{i}^{\min},w_{i,t}\} & \mathrm{If~employed} \end{array} \right. \quad (22)\]  
+
+where \(w_{i}^{\min}\) is the minimum wage set by the government. This mechanism allows for wages to adapt to market conditions. In the case of high unemployment, more households will revise their requested wage down, leading to a drop in the average wage level.  
+
+In the case of low unemployment, producers will hire more employed job seekers, which will create an upward pressure on the average wage level. Furthermore, each firm \(j\) sets a maximum offered wage \(w_{j,t}^{O}\) .  
+
+This is equal to the highest wage for which the producer does not make a loss based on the average level of labor productivity and the selling price. So, \(w_{j,t}^{O} = p_{i,t}\bar{A}_{j,t}^{LP}\) for consumer good producers and \(w_{j,t}^{O} = p_{i,t}\bar{B}_{j,t}^{LP}\) for capital good producers.  
+
+The labor market matching is then started with all these job- seeking households and labor- seeking producers. For each labor- seeking producer, a job- seeking queue is generated consisting of a random sample of job- seeking
+
+<--- Page Split --->
+
+households. The effective labor amount each household offers is given by its productivity level and its labor amount as \(\pi_{i}L_{i}\) . The producer will then go down the queue and hire the household \(i\) if \(\pi_{i}L_{i} \leq \Delta L^{d}\) until its labor demand is satisfied or the queue has run out of households. If \(\pi_{i,t}L_{i,t} > \Delta L^{d}\) but \(\Delta L^{d} > 0\) , the employer will look further in the queue for an employee with less offered labor supply.  
+
+Note that employers pay \(w_{i,t}L_{i}\) to employee \(i\) , which does not take into account the employee's productivity level \(\pi_{i}\) . Hence, employees effectively pay \(w_{i,t} / \pi_{i}\) per unit of labor, which is taken into account by employers when hiring and firing employees. Firstly, the offered wage \(w_{j,t}^{O}\) is compared to the effective requested wage \(w_{i,t}^{r} / \pi_{i}\) . Secondly, employers will fire employees by order of their relative wage (i.e. highest to lowest \(w_{i,t} / \pi_{i}\) ), instead of based on the nominal wage. This has the important implication that highly productive employees can request and maintain a higher wage than employees with low productivity. As such, the addition of labor productivity heterogeneity allows for a wage distribution that is endogenously unequal. This will be further discussed in section viii).  
+
+## vi) Index Fund  
+
+This model introduces an index fund through which the capital ownership of households is represented, and capital income and losses are added to the model. Furthermore, the index fund functions as a capital market where firms and the government can receive capital investments, which will be further explained in section viii). The three main functions of the index fund can be summarized as: (1) collect dividends and redistribute these to households, or take wealth from households in the case of capital loss (2) provide 'venture capital' investments to newly entering firms, and (3) provide the government with funding in case of a budget deficit.  
+
+The inflow of money of the index fund originates from the consumer good-, capital good- and energy producers. At the end of each period, these producers determine their new level of liquid assets \(NW_{i,t}\) . They then determine the maximum level of liquid assets needed for their operational expenses, \(NW_{i,t}^{\max}\) , by applying the ratio \(\Lambda^{NW} > 0\) to their total amount of expenses in the last period. When \(NW_{i,t} > NW_{i,t}^{\max}\) , the excess of liquid assets is paid out to the index fund as dividends.  
+
+Using these dividends, the index fund will then firstly supply newly entering producers with 'venture capital'. For this, the index fund first examines the total amount of desired capital of new entrants, and finances this amount completely or partially, depending on the available funds. As such, newly entering firms do not have to finance (all) new capital using debt, and therefore do not have an unreasonably large debt burden. The firms that have gone bankrupt may still have equity and debt on their balance. The index fund receives the net equity of these firms, meaning \(EQ_{i,t} - deb_{i,t}\) of bankrupt firm \(i\) . So, if debts cannot be paid back with the remaining equity, the index fund incurs a capital loss.
+
+<--- Page Split --->
+
+After all the new entrants have been initiated, the remaining dividends are paid out to the households as capital income \(Y_{i,t}^{K}\) . Households do not explicitly own stocks, but their exposure to the financial market is proportional their wealth level. For household \(i\) , this gives the following capital income:  
+
+\[Y_{i,t}^{K} = (1 - \tau^{K})Div_{t}\left(\frac{W_{i,t}}{\sum_{j}^{HH}W_{j,t}}\right), \quad (23)\]  
+
+where \(\tau^{K}\) is the capital gains tax (discussed in section vii)) paid out to the government by the index fund, \(Div_{t}\) the total amount of dividends paid out to households and \(HH\) the total amount of households. Wealthier households thus receive a higher capital income. As such, this additional source of income allows for household to not only follow from labor income, but also from capital income, which gives a more complete overview of wealth buildup.  
+
+## vii) Government  
+
+The government in this model is significantly expanded from the one in [39] with a more elaborate set of policy instruments. These added policy instruments consists of more types of taxation and social spending, and have a broader impact on the economy. The 5 tax types are as follows:  
+
+- \(\tau^{L}\) : Labor income tax, which is the tax households pay over their labor income.  
+- \(\tau^{K}\) : Capital tax, which is the tax households pay over the capital income received from the index fund.  
+- \(\tau^{\Pi}\) : Profit tax, which is levied on (positive) profits made by producers.  
+- \(\tau^{E}\) : Energy tax, which is the additional fee producers have to pay for every unit of energy they use during production.  
+- \(\tau^{C}\) : Carbon tax, which is the additional fee producers have to pay for every unit of carbon that is emitted during production.  
+
+In the baseline scenario, \(\tau^{L}\) , \(\tau^{K}\) and \(\tau^{\Pi}\) will be positive. These taxes are markups, so the tax is determined as a percentage of each of the respective tax bases. \(\tau^{E}\) and \(\tau^{C}\) are only levied during the policy experiments. These tax rates are not markups, but a fee that has to be paid for each unit of emissions or energy use.  
+
+Tax revenues are firstly used for paying unemployment benefits \(UB_{t}\) to unemployed households. The level of \(UB_{t}\) is set as a fraction of the minimum wage \(w_{t}^{\min}\) , which is also set by the government. Secondly, the remaining government budget is used for social benefits. Thus, the government does not run budget surpluses, but instead redistributes excess tax revenues back to the households. These social benefits can be interpreted as all the subsidies and services governments provide, apart from unemployment benefits. Here, each household \(i\) receives social benefits \(Y_{i,t}^{S}\) based on their total income level in the last period \(Y_{i,t - 1}\) . Therefore, each household receives the following amount of social benefits, as a share of the government surplus \(GS_{t}\) :
+
+<--- Page Split --->
+
+\[Y_{i,t}^{S} = G S_{t}\left(\frac{Y_{i,t - 1}^{-p r o g}}{\sum_{j = 1}^{H H}Y_{j,t - 1}^{-p r o g}}\right). \quad (24)\]  
+
+Here, the \(p r o g\) variable is of critical importance. This parameters determines how the social benefits are redistributed, and thus set the 'progressivity' of government spending. For \(p r o g = 0\) , all households will receive an equal share of \(G S_{t}\) . In practice, this means the taxes are redistributed progressively, because higher incomes (nominally) pay a larger amount of taxes. For \(p r o g = - 1\) , each household will receive a share of \(G S_{t}\) proportional to their income level. Then, the progressive or regressive impact of the redistribution of \(G S_{t}\) depends on the relative size of the tax rates. For example, if \(\tau^{L} > \tau^{K}\) , higher wealth households will pay a lower effective tax rate because capital income forms a larger share of their total income. This means the redistribution under \(p r o g = - 1\) would be regressive. To avoid these complications, \(\tau^{L}\) and \(\tau^{K}\) will be set to equal size in the experiments that follow. Then, all \(p r o g > - 1\) are net progressive and all \(p r o g < - 1\) are net regressive. The inclusion of this \(p r o g\) parameter, together with the larger range of tax types, allows for a more detailed analysis of fiscal policy packages.  
+
+## viii) The Money Supply  
+
+The last extension presented in this model is that the money supply is held constant. Because a fluctuating money supply has real effect on the overall economy, this would distort the experimental results. Therefore, all transactions made by the agents must end up in the money stock of other agents (except for debt financing, discussed below). Firstly, figure 2 shows the money flows that follow from productive activities, or from the government taxing and redistributing tax revenues. All these money flows follow from the mechanisms described above.  
+
+When the activities of producers or the government cannot be funded with their own funds, they seek outside financing. The source of financing can be from within the system, which typically means the firm receives an investment from the index fund. This is meant for newly entering companies, and for the government if it has a budget deficit but must still pay \(U B_{t}\) (note that the government does not borrow for the other social benefits). When companies cannot finance their operational expenses, they can take a loan from the international money market (IMM). This is the only exception where money from outside the system is allowed to flow in. The debt flows out again as the debt is paid off \(^{2}\) . The allowed amount of debt per producer is limited to a factor \(\Lambda^{\mathrm{Deb}}\) times last period's sales. In case a producer needs debt to finance its production and investments, but its debt exceeds the allowed level, it will first reduce investments and then production. If the debt level then still exceeds the maximum, the company is declared bankrupt [39]. Figure 2 shows the money flows involved in financing. Here, it can be seen that the index fund
+
+<--- Page Split --->
+
+(IF) plays a central role in distributing dividends and capital losses throughout the economy.
+
+<--- Page Split --->
+
+## Acknowledgments  
+
+AcknowledgmentsThe authors acknowledge the support from Ministry of Education, Culture and Science in the Netherlands under the Sector plans for scientific research.  
+
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+
+<--- Page Split --->
+
+## Supplementary Files  
+
+This is a list of supplementary files associated with this preprint. Click to download.  
+
+CPSI.pdf
+
+<--- Page Split --->

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+    "type": "image",
+    "img_path": "images/Figure_6.jpg",
+    "caption": "FIG. 6. Zero-field specific heat \\(C / T\\) of the \\(\\mathrm{NbH_2}\\) single crystal used in this study showing a sharp superconducting transition centered at \\(T_{\\mathrm{c}} = 5.5 \\mathrm{K}\\) .",
+    "footnote": [],
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+  {
+    "type": "image",
+    "img_path": "images/Figure_2.jpg",
+    "caption": "Figure 2 | Magnetic torque data in parallel fields of NbS₂. a Magnetic torque measured at fixed temperatures as a function of the magnetic field applied strictly parallel to the basal plane ( \\(\\theta = 0^{\\circ}\\) ) near the onset upper critical field \\(H_{c2}\\) (open circles). The solid lines represent data measured at increasing field, while the dotted lines are data measured at decreasing field (overlapping parts of the data for low fields have been omitted and offsets have been added for reasons of clarity). b Magnetic torque data at 0.35 K where \\(H_{c2}\\) exceeded the highest field in our magnet cryostat. c Magnetic torque measured in fixed parallel fields as a function of temperature. The data was normalized and a weak linear normal state background was removed for clarity. Open circles mark the onset critical temperatures.",
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+    "img_path": "images/Figure_3.jpg",
+    "caption": "Figure 3 | Thermodynamic bulk probes displaying the upper critical field \\(H_{c2}\\) transition of NbS2 in high parallel magnetic fields. a Specific heat \\(C / T\\) (circles) and linear thermal expansion coefficient \\(\\alpha = (1 / L_0) dL(T) / dT\\) (squares) of NbS2 showing the superconducting transition in a magnetic field of 15 T applied strictly parallel to the layer structure. b Specific heat of NbS2 measured with an ac technique with a small 1 mK temperature modulation during field sweeps at different fixed temperatures. The stars mark the fields in which the constant normal state specific heat value (dotted lines) is reached.",
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+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_4.jpg",
+    "caption": "Figure 4 | Thermodynamic bulk probes displaying the phase transition between the BCS low-field SC phase and the FLLO state in NbS₂. a Magnetic field derivative \\(dC(T)/dH\\) of the specific heat, measured at \\(300 \\mathrm{mK}\\) during a field sweep (see b for the corresponding \\(C/T\\) data) together with the linear magnetostriction coefficient \\(\\lambda = (1 / L_0) dL(H) / dH\\) for magnetic fields applied strictly parallel to the layer structure. Both quantities reproducibly show a small transition anomaly at \\(\\sim 10 \\mathrm{T}\\) . b Field derivative \\(d\\tau (H) / dH\\) of the magnetic torque for selected temperatures showing a small dip-like anomaly near \\(10 \\mathrm{T}\\) .",
+    "footnote": [],
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+    ],
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+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_5.jpg",
+    "caption": "Figure 5 | Magnetic field vs. temperature phase diagram of NbS2 in magnetic field applied parallel to the layer structure. Filled black circles represent the upper critical field \\(H_{c2}(T)\\) as measured by torque magnetometry during field scans at constant temperature, red open circles represent the critical temperature \\(T_{c}(H)\\) measured by torque during temperature scans. Blue triangles are \\(H_{c2}(T)\\) data obtained from ac calorimetry measured during field scans. The orange star and green filled circle mark the critical temperature in \\(C_{p}(T)\\) and \\(\\alpha (T)\\) data measured in 15 T. The violet star marks a small additional transition anomaly in the magnetostriction and filled magenta circle marks a similar anomaly visible in the field derivative of the specific heat near the Pauli limit due to the transition to the FFLO state (Fig. 2c). In addition, the dark cyan circles correspond to a small anomaly in the magnetic torque attributed to the same phase transition within the superconducting state (Fig. 2d). Additional lines mark \\(H_{c2}\\) data obtained from torque magnetometry in field scans measured with a small misalignment angle of 1 degree. The orange and dark cyan lines are theoretical predictions of the low temperature \\(H_{c2}\\) line for superconductivity with the FFLO state [32] and for Ising superconductivity, respectively. We have also added a scaled \\(H_{c2}\\) curve of the organic superconductor \\(\\kappa\\) -(BEDT-TTF)₂Cu(NCS)₂, which shows a very similar upturn of the \\(H_{c2}\\) line due to the formation of an FFLO state [15].",
+    "footnote": [],
+    "bbox": [
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+    "type": "image",
+    "img_path": "images/Figure_1.jpg",
+    "caption": "Figure 1",
+    "footnote": [],
+    "bbox": [
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+    "page_idx": 15
+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_2.jpg",
+    "caption": "Figure 2",
+    "footnote": [],
+    "bbox": [
+      [
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+        60,
+        930,
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+    ],
+    "page_idx": 16
+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_3.jpg",
+    "caption": "Figure 3",
+    "footnote": [],
+    "bbox": [
+      [
+        80,
+        70,
+        940,
+        481
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+    ],
+    "page_idx": 17
+  },
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+    "type": "image",
+    "img_path": "images/Figure_4.jpg",
+    "caption": "Figure 4",
+    "footnote": [],
+    "bbox": [
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+        933,
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+    ],
+    "page_idx": 18
+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_5.jpg",
+    "caption": "Figure 5",
+    "footnote": [],
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preprint/preprint__1f0e1a3d63db588f869f132183e57a3969bf75e622f40af39e54d489d558c4e2/preprint__1f0e1a3d63db588f869f132183e57a3969bf75e622f40af39e54d489d558c4e2.mmd

@@ -0,0 +1,220 @@
+
+# Superconductivity beyond Pauli's limit in bulk NbS2: Evidence for the Fulde-Ferrell-Larkin-Ovchinnikov state  
+
+Chang- woo Cho Hong Kong University of Science and Technology  Jian Lyu  
+
+Department of Physics, The Hong Kong University of Science and Technology https://orcid.org/0000- 0002- 0633- 3282  
+
+Cheuk Ng Hong Kong University of Science and Technology  
+
+James Jun He RIKEN Center for Emergent Matter Science (CEMS), Wako, Saitama 351- 0198, Japan https://orcid.org/0000- 0002- 6245- 1021  
+
+Tarob Abdel- Baset Taibah University  
+
+Mahmoud Abdel- Hafiez Uppsala University  
+
+Rolf Lortz ( \(\square\) lortz@ust.hk) Hong Kong University of Science and Technology  
+
+## Article  
+
+Keywords: Bulk Transition Metal Dichalcogenide Superconductor, Ising Spin Orbit Coupling, Upper Critical Field, Magnetic Field Driven Phase Transition  
+
+Posted Date: December 2nd, 2020  
+
+DOI: https://doi.org/10.21203/rs.3.rs- 105803/v1  
+
+License: \(\circledcirc\) This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License  
+
+Version of Record: A version of this preprint was published at Nature Communications on June 16th, 2021. See the published version at https://doi.org/10.1038/s41467- 021- 23976- 2.
+
+<--- Page Split --->
+
+# Superconductivity beyond Pauli's limit in bulk NbS₂: Evidence for the Fulde-Ferrell-Larkin-Ovchinnikov state  
+
+Chang- woo Cho \(^{1}\) , Jian Lyu \(^{1,2}\) , Cheuk Yin Ng \(^{1}\) , James Jun He \(^{3}\) , Tarob A. Abdel- Baset \(^{4,5}\) , Mahmoud Abdel- Hafiez \(^{6}\) and Rolf Lortz \(^{1,*}\)  
+
+\(^{1}\) Department of Physics, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong.  
+
+\(^{2}\) Department of Physics, Southern University of Science and Technology, 1088 Xueyuan Road, Nanshan District, Shenzhen, Guangdong Province, China.  
+
+\(^{3}\) RIKEN Center for Emergent Matter Science (CEMS), Saitama, Wako 351- 0198, Japan.  
+
+\(^{4}\) Department of Physics, Faculty of Science, Taibah University, Yanbu, 46423, Saudi Arabia;  
+
+\(^{5}\) Department of Physics, Faculty of Science, Fayoum University, Fayoum 63514, Egypt;  
+
+\(^{6}\) Department of Physics and Astronomy, Uppsala University, Uppsala SE- 75120, Sweden.  
+
+We present magnetic torque, specific heat and thermal expansion measurements combined with a piezo rotary positioner of the bulk transition metal dichalcogenide (TMD) superconductor NbS₂ in high magnetic fields applied strictly parallel to its layer structure. The upper critical field of superconducting TMDs in the 2D form is known to be dramatically enhanced by a special form of Ising spin orbit coupling. This Ising superconductivity is very robust against the Pauli limit for superconductivity. We find that superconductivity beyond the Pauli limit still exists in bulk single crystals of NbS₂. However, the comparison of our upper critical field transition line with numerical simulations rather points to the development of a Fulde- Ferrell- Larkin- Ovchinnikov state above the Pauli limit as a cause. This is also consistent with the observation of a magnetic field driven phase transition in the thermodynamic quantities within the superconducting state near the Pauli limit.  
+
+Transition metal dichalcogenides have been the focus of recent research due to their wide range of unique electronic properties in their 2D form with high potential for technological applications [1- 8]. Among them are intrinsic superconductors with 2H- NbSe₂ and 2H- NbS₂ as representatives of the highest critical temperatures. In their 2D form, they have aroused great interest due to the discovery of Ising superconductivity, which allows them to withstand the Pauli limit of superconductivity [4,5]. While the upper critical field (Hc₂) of a spin- singlet type- II superconductor is normally determined by the orbital limit for superconductivity [9], there are rare cases where the orbital limit is particularly high. Possible reasons are heavy effective masses of the quasiparticles [10- 12], or a highly anisotropic structure that suppresses the orbital limit due to the open nature of the Fermi surface [13- 24]. In this case, superconductivity is in principle abruptly destroyed at the Pauli limit [25,26] in the form of a first- order phase transition. There are two ways in which a superconductor can maintain its superconducting (SC) state above the Pauli limit. The first possibility is the formation of a Fulde- Ferrell- Larkin- Ovchinnikov (FFLO) state [27,28], in which the Cooper pairs obtain a finite center- of- mass momentum, resulting in a spatially modulated order parameter in a wide range of their magnetic- field vs. temperature phase diagram,
+
+<--- Page Split --->
+
+which can extend far beyond the Pauli limit. Such spatial modulation of superconductivity has been described as a pair density wave state and is also used to explain the pseudogap phase in cuprates [29]. Prominent examples of the FFLO state have been found in some layered organic superconductors [13- 23], but also in form of the prominent Q- phase the heavy- fermion compound CeCoIn \(_5\) , where a spatially modulated order parameter coexists with a magnetic spin density wave order [10- 12], and most recently in the iron- based superconductor KFe \(_2\) As \(_2\) [24]. For 2D dichalcogenides, Ising superconductivity is another possibility [4,5]. Here, the breaking of the mirror symmetry in the plane leads to a very strong pinning of the electron spins out of the plane due to the Ising- Spin- Orbit interaction (ISOI), resulting in opposite spin directions in the adjacent K and K' electron pockets. This ISOI effectively protects the Cooper pairs when an in- plane magnetic field is applied to the 2D layer, resulting in enormous enhancements of the critical field. This phase is of particular interest due to the recent theoretical prediction of a topological SC state with Majorana zero modes in high parallel applied fields in monolayer NbSe \(_2\) [30,31].  
+
+While reports on Ising superconductivity usually focus on monolayers, it is also clear that \(H_{\mathrm{c2}}\) in 2D materials with multiple layers still exceeds the Pauli limit [5]. It is not clear whether ISOI could still have an influence in the bulk form of these layered materials with very weak interlayer coupling. In this letter, we present magnetic torque, specific heat and thermal expansion measurements on a bulk single crystal of NbS \(_2\) , where we align the layered structure parallel to the applied magnetic field using a piezo rotary stage with millidegree accuracy. We find that \(H_{\mathrm{c2}}\) significantly exceeds the Pauli limit of 10 T at low temperatures and shows a pronounced characteristic upswing towards low temperatures. This is a clear indication that an unusual SC state is formed [14,15,24,32]. Thermal expansion measurements as a bulk thermodynamic method, which are closely related to the specific heat, indicate a small transition anomaly near the Pauli limit indicating a phase transition within the SC state. Using theoretical simulations [33], we show that Ising superconductivity is not accountable for such a strong \(H_{\mathrm{c2}}\) upturn in bulk NbS \(_2\) . The existence of the additional phase transition within the SC state points to a formation of the FFLO state.  
+
+Figure 1a shows magnetic torque data measured at various fixed temperatures with a small \(1^{\circ}\) misalignment of the field with respect to the basal plane of NbS \(_2\) . At lower temperatures, when \(H_{\mathrm{c2}}\) reaches higher fields, it is obvious that the transition becomes sharper and more step- like, which may be an indication for Pauli- limited first- order behavior [15,24]. Only a small tail persists above the step- shaped transition, which indicates a certain persistence of superconductivity. \(H_{\mathrm{c2}}\) reaches a maximum at \(1.15\mathrm{K}\) and then decreases again slightly towards \(0.35\mathrm{K}\) .  
+
+To achieve a strictly parallel field orientation, we aligned the layered structure of the NbS \(_2\) single crystal by first minimizing \(\tau\) at a fixed field and temperature by gradually rotating the sample through the parallel orientation [24]. This allowed us to determine the approximate parallel orientation. Subsequently, we repeated field scans at tiny variations of orientation of \(0.1^{\circ}\) or less until we found torque data with minimum amplitude and minimal opening of the hysteresis loop. This corresponded to the parallel orientation. A sequence of measurements at small angular variation is shown in Fig. 1b. As the field decreases, the torque builds up much more continuously below \(H_{\mathrm{c2}}\) , suggesting that the sharp jumps in Fig. 1a are due to screening currents that build up
+
+<--- Page Split --->
+
+continuously during the field sweep in the SC state and decay abruptly when approaching the Pauli limit in slightly tilted fields.  
+
+In Fig. 2a, we show \(\tau\) data measured at different fixed temperatures during field sweeps for the field applied parallel to the \(\mathrm{NbS_2}\) basal plane. Here we identify the \(H_{\mathrm{c2}}\) transition from the onset point where the data starts to deviate from zero, as marked by the additional open circles. In the same field, a hysteretic opening of the two branches, which was recorded when the field was swept up and down, confirms the onset of superconductivity. At \(350\mathrm{mK}H_{\mathrm{c2}}\) exceeds our highest field of \(15\mathrm{T}\) , and a hysteresis exists up to \(15\mathrm{T}\) (Fig. 2b).  
+
+In Fig. 2c we present torque data measured in constant, strictly parallel fields as a function of temperature. The SC transition can be identified as a step- like transition. The data were normalized by the jump size at \(T_{\mathrm{c}}(H)\) for better clarity. We take the upper onset of the transition to identify \(T_{\mathrm{c}}(H)\) as marked by the additional open circles. Note that other criteria to define of \(T_{\mathrm{c}}(H)\) or \(H_{\mathrm{c2}}(T)\) provide qualitatively similar phase diagrams.  
+
+In Fig. 3 and Fig. 4 we summarize our specific heat and linear thermal expansion data. To achieve the parallel field orientation, we maximized the \(T_{\mathrm{c}}\) in a fixed field of a few Tesla by slightly rotating the sample in repeated measurements [24]. Fig 3a illustrates the specific heat \(C / T\) and the linear thermal expansion coefficient \(\alpha = (1 / L_{0})dL(T) / dT\) in a parallel field of \(15\mathrm{T}\) . Both thermodynamic quantities, which are closely related via the thermodynamic Ehrenfest relationship, show a SC transition, which demonstrates that \(15\mathrm{T}\) is not sufficient to suppress superconductivity. In the inset (Fig. 3b) we show specific heat data measured with our \(ac\) modulated temperature technique measured during field sweeps at constant temperature of the thermal bath. A standard BCS superconductor would show the characteristic step- like transition at \(H_{\mathrm{c2}}\) [34]. However, the \(\mathrm{NbS_2}\) data only display a broad bump centered at relatively low fields, while the signal fades very gradually towards higher fields to approach the normal state. There are no additional features to be seen here that could indicate additional phase transitions within the SC state. However, in Fig. 4a we illustrate the magnetic field derivative of the \(300\mathrm{- mK}\) specific heat data together with the linear magnetostriction coefficient \(\lambda = (1 / L_{0})dL(H) / dH\) . For both quantities, a small anomaly indicates a phase transition within the SC state at \(\sim 10\mathrm{T}\) , which occurs at the theoretical Pauli limit. The transition is reproducible when the field is swept up and down. In the following we will attribute it to the transition that separates the ordinary low- field SC state from a high- field FFLO state. In the magnetic torque \(\pi (H)\) in the temperature range of \(0.3\) to \(2.5\mathrm{K}\) tiny anomalies are hidden in the large slope near \(10\mathrm{T}\) , but become visible after subtraction of a linear background in the form of a downward step, or as a dip- like structure in the field derivative \(d\pi (H) / dH\) (Fig. 4b). We summarize our results in the \(H / T\) phase diagram in Fig. 5. At high temperatures, the \(H_{\mathrm{c2}}\) transition line begins to rise with a non- vertical slope. This is in contrast to the behavior found in 2D samples and shows that there are significant orbital effects on the upper critical transition, which are naturally absent in 2D samples. We also include scaled \(H_{\mathrm{c2}}\) data of the organic superconductor \(\kappa\) -(BEDT- TTF) \(_2\) Cu(NCS) \(_2\) [15], for which numerous studies have demonstrated the existence of an FFLO state [13- 15,18- 21]. It is obvious that the initial slope of \(\mathrm{NbS_2}\) is much weaker than for \(\kappa\) -(BEDT- TTF) \(_2\) Cu(NCS) \(_2\) , which proves a smaller Maki parameter \(\alpha_{m} = \sqrt{2}\frac{H_{\mathrm{orb}}(0)}{H_{p}(0)}\) . A large Maki parameter in the range between 1.7- 3.4 [35,36] is considered one of the decisive prerequisites for the formation of an FFLO state. From a fit with the Werthamer
+
+<--- Page Split --->
+
+Helfand- Hohenberg (WHH) model of the initial \(H_{\mathrm{c2}}\) slope of \(\mathrm{NbS_2}\) we obtain an orbital limit \(H_{\mathrm{orb}}\) \(\sim 23\mathrm{T}\) , which gives \(\alpha_{m} = 3.25\) , which is certainly large enough to support an FFLO state. At \(3.6\mathrm{K}\) the \(H_{\mathrm{c2}}\) line rises above the BCS Pauli limit at \(\sim 10\mathrm{T}\) and then begins to saturate down to \(1.5\mathrm{K}\) . At lower temperatures it shows a characteristic upswing and raises above \(15\mathrm{T}\) , where it leaves the field region accessible in our experiment. The upswing is strikingly similar to that observed in \(\kappa\) (BEDT- TTF) \(_2\) Cu(NCS) \(_2\) , with both data being almost congruent in the high field region. This upswing is suppressed if the \(\mathrm{NbS_2}\) sample is misaligned by only one degree. Such an upswing indicates a certain change in the SC properties making it more robust against the strong Zeeman fields. It is generally interpreted as an indication of the development of an FFLO state. In fact, the phase diagram looks remarkably similar to other FFLO systems [10- 24], including the field- induced transition at \(\sim 10\mathrm{T}\) , which probably indicates the phase change between the ordinary SC state at low fields and the high- field FFLO state. However, in most other FFLO systems, the \(H_{\mathrm{c2}}\) transition typically sharpens and becomes of first order as one approaches the Pauli limit [11,14,24]. In \(\mathrm{NbS_2}\) , however, the \(H_{\mathrm{c2}}\) transition in high parallel fields remains very continuous, indicating a very gradual decay of superconductivity towards the normal state in the form of a second order transition. In Ref. 37 it was shown that the first- order nature of the \(H_{\mathrm{c2}}\) transition in high fields occurs only in the absence of the orbital effect, while in its presence it remains of second order anywhere. With orbital effect it is expected that only the transition between the FFLO state and the BCS state should be of first- order nature. This is fully consistent with our observation: The finite \(H_{\mathrm{c2}}\) slope in the high temperature range is clearly due to the orbital effect, and the additional transition at \(10\mathrm{T}\) , observed as a spike in the magnetostriction coefficient, indicates a first- order nature of the FFLO to BCS transition. However, a small misalignment of only one degree sharpens the transition observed in torque to a step- like more first- order- like transition and indicates that superconductivity becomes Pauli limited. The fact that the \(H_{\mathrm{c2}}\) transition line then goes through a maximum is indeed expected for the Pauli limited case in tilted fields [37].  
+
+It is often claimed that ISOI is a specialty of monolayers with their broken inversion symmetry, which leads to a spin splitting of the Fermi's surface, while it is spin degenerate in bilayers and the bulk [5]. In reality bilayers have \(H_{\mathrm{c2}}\) values, which still exceed the Pauli limit by a factor of \(\sim 4\) [5]. This suggests that ISOI can be weakened by the intra- layer coupling but is not completely quenched because of weak intra- layer coupling in TMD materials. Therefore, one concern regarding the FFLO scenario as an explanation of the phase diagram is that the ISOI should still have a considerable influence in the bulk. ISOI is not compatible with a finite- momentum pair density wave state in the FFLO phase because it suppresses the effect of the in- plane Zeeman field on the Fermi surface. However, \(\mathrm{NbS_2}\) has multiple Fermi pockets at the \(\Gamma\) and \(\mathrm{K / K'}\) points. While the K pockets are protected by ISOI, the spin- orbit interaction at the \(\Gamma\) pocket is weak and therefore compatible with the FFLO state. To test whether the phase diagram could alternatively be explained by Ising superconductivity, we performed numerical simulations using a three- band tight- binding model [38] with a spin- orbit coupling strength of \(\beta_{\mathrm{SO}} = 87\mathrm{meV}\) . In order to simulate a bulk sample, we used a thickness of 10 layers, which proved to be sufficient since we found that increasing the thickness further does not affect the critical field much for greater thicknesses. The obtained low- temperature \(H_{\mathrm{c2}}\) line is included in Fig. 5. While for samples in the 2D limit it could be shown that Ising superconductivity can cause very pronounced \(H_{\mathrm{c2}}\) upturns at low temperature
+
+<--- Page Split --->
+
+similar to FFLO states [6,33,39], it is obvious that for reasonable values of the spin- orbit coupling strength the upturn of bulk NbS \(_2\) is much too weak to explain our phase diagram.  
+
+To conclude, with the additional evidence of a field- driven phase transition within the SC state near the Pauli limit and the good agreement with theoretical simulations, it can be concluded that the phase diagram we observed for a bulk NbS \(_2\) single crystal in magnetic fields strictly parallel to its layer structure, with its pronounced upswing of the \(H_{\mathrm{c2}}\) line far above the Pauli limit for superconductivity, is most plausibly explained by an FFLO state that forms in magnetic fields above 10 T. This high- field phase requires further systematic experiments including nuclear magnetic resonance to directly monitor the nature of the FFLO pair density wave state.  
+
+## References  
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+
+<--- Page Split --->
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+
+## Acknowledgments  
+
+We thank U. Lampe for technical support. This work was supported by grants from the Research Grants Council of the Hong Kong Special Administrative Region, China (GRF- 16302018, GRF- 16300717, C6025- 19G, SBI17SC14).  
+
+## Author contributions  
+
+This work was initiated by R.L.; C.w.C., J.L. and C.Y.N. carried out the magnetic torque experiments. C.Y.N., J.L. and R.L. carried out the specific heat experiments; the thermal expansion experiments were conducted by C.Y.N. and R.L.; the single crystal sample was provided by M.A.H. and T.A.A.B.; J.J.H. provided the numerical simulations. The manuscript was prepared by R.L. and all authors were involved in discussions and contributed to the manuscript.  
+
+## Competing financial interests  
+
+The authors declare no competing financial interests.  
+
+## Data availability  
+
+The data that support the findings of this study are available from the corresponding author upon reasonable request.
+
+<--- Page Split --->
+
+## Methods  
+
+## Sample Preparation  
+
+The high- quality single crystal of 2H- NbS \(_2\) was grown by a solvent evaporation technique, which is described in detail in Ref. 40. The sample represented a small square shaped platelet, which was completely flat on the macroscopic scale. The zero- field specific heat (Fig. 6) displays a sharp superconducting transition jump centered at \(T_{\mathrm{c}} = 5.5 \mathrm{K}\) indicative for a large superconducting volume fraction and good quality of the single crystal.  
+
+![](images/Figure_6.jpg)
+
+<center>FIG. 6. Zero-field specific heat \(C / T\) of the \(\mathrm{NbH_2}\) single crystal used in this study showing a sharp superconducting transition centered at \(T_{\mathrm{c}} = 5.5 \mathrm{K}\) . </center>  
+
+## Experimental techniques  
+
+All our experimental sensors fit on the same piezo rotary stage mounted on our \(^3\mathrm{He}\) probe in a 15 T magnet cryostat. The rotator allows the sample to be aligned in relation to the field direction with millidegree accuracy. Field sweeps were conducted at a rate of \(0.1 - 0.5 \mathrm{T / min}\) . Temperature dependent measurements at different fixed fields were performed during sweeps at \(0.04 \mathrm{K / min}\) .  
+
+The magnetic torque \(\tau\) was measured using a capacitive cantilever technique [24]. The cantilever leg is insulated from the counterplate of the capacitor by a thin sapphire sheet. This allows reversible measurements of the torque both as a function of the field or temperature. The capacitance is measured with a General Radio 1615- A capacitance bridge in combination with a SR850 lock- in amplifier. The torque \(\tau\) is directly related to the anisotropic DC magnetization by the relation \(\tau = \mathbf{M} \times \mathbf{H}\) , where \(\mathbf{H}\) is the applied magnetic field. Since for such layered superconductors the DC magnetization is expected to be greatest in the out- of- plane direction, this relationship suggests that \(\tau\) vanishes in parallel fields. In reality, it reaches a minimum, but does not disappear completely during a complete field sweep due to higher- order quadrupole
+
+<--- Page Split --->
+
+components [15,24]. The ultra- high resolution allows us to detect the tiny signal with a very good signal- to- noise ratio.  
+
+The thermal expansion was measured with a miniature capacitance dilatometer [41], in which the sample is pressed with a screw mechanism against one of the plates of a parallel- plate capacitor suspended by a firm spring mechanism. A change in the sample length, induced either by a change in temperature (thermal expansion) or by the field (magnetostriction), changes the distance between the plates and thus causes small changes in the capacitance, which is measured in the same way as described for the torque. Thermal expansion is a bulk thermodynamic quantity closely related to specific heat and allows us to detect small changes in the sample length that occur during phase transitions.  
+
+The specific heat \(C_{\mathrm{p}}\) was measured using an alternating temperature \((ac)\) technique with a mK temperature modulation amplitude [24]. To account for the relatively flat temperature dependence of the \(H_{c2}\) line in the low temperature / high magnetic field \((H / T)\) regime, we performed the experiments at fixed base temperature during field sweeps. Note that the data measured in this somewhat unusual way still represent the thermal response of the sample with respect to a small temperature change, although here we probe the variation of the specific heat with respect to the field. The reason for is that near the Pauli limit the slope of the \(H_{c2}\) line in the magnetic field vs. temperature \((H - T)\) phase diagram becomes very small and measurements during field sweep sampling reveal much more details in the important part of the phase diagram. The calorimeter platform was supported by thin nylon wires that become stiff at low temperature and serve as a support to prevent magnetic torque effects.
+
+<--- Page Split --->
+![](images/Figure_unknown_0.jpg)
+
+<center>Figures </center>  
+
+Figure 1 | Magnetic torque data in a slightly tilted field of \(\mathbf{NbS}_2\) . a Magnetic torque \(\tau (H)\) of \(\mathrm{NbS}_2\) , measured at fixed temperatures as a function of the applied magnetic field \(H\) , oriented with a small angle \(\theta = 1^{\circ}\) to the basal plane. The data were measured at increasing field. Circles mark the points where the steepest slope occurs near \(H_{c2}\) . b Magnetic torque measured at \(T = 0.35 \mathrm{K}\) at various angles near \(\theta = 0^{\circ}\) (parallel field). A weak linear normal state contribution was removed from all data and offsets were added for better clarity.
+
+<--- Page Split --->
+![](images/Figure_2.jpg)
+
+<center>Figure 2 | Magnetic torque data in parallel fields of NbS₂. a Magnetic torque measured at fixed temperatures as a function of the magnetic field applied strictly parallel to the basal plane ( \(\theta = 0^{\circ}\) ) near the onset upper critical field \(H_{c2}\) (open circles). The solid lines represent data measured at increasing field, while the dotted lines are data measured at decreasing field (overlapping parts of the data for low fields have been omitted and offsets have been added for reasons of clarity). b Magnetic torque data at 0.35 K where \(H_{c2}\) exceeded the highest field in our magnet cryostat. c Magnetic torque measured in fixed parallel fields as a function of temperature. The data was normalized and a weak linear normal state background was removed for clarity. Open circles mark the onset critical temperatures. </center>
+
+<--- Page Split --->
+![](images/Figure_3.jpg)
+
+<center>Figure 3 | Thermodynamic bulk probes displaying the upper critical field \(H_{c2}\) transition of NbS2 in high parallel magnetic fields. a Specific heat \(C / T\) (circles) and linear thermal expansion coefficient \(\alpha = (1 / L_0) dL(T) / dT\) (squares) of NbS2 showing the superconducting transition in a magnetic field of 15 T applied strictly parallel to the layer structure. b Specific heat of NbS2 measured with an ac technique with a small 1 mK temperature modulation during field sweeps at different fixed temperatures. The stars mark the fields in which the constant normal state specific heat value (dotted lines) is reached. </center>
+
+<--- Page Split --->
+![](images/Figure_4.jpg)
+
+<center>Figure 4 | Thermodynamic bulk probes displaying the phase transition between the BCS low-field SC phase and the FLLO state in NbS₂. a Magnetic field derivative \(dC(T)/dH\) of the specific heat, measured at \(300 \mathrm{mK}\) during a field sweep (see b for the corresponding \(C/T\) data) together with the linear magnetostriction coefficient \(\lambda = (1 / L_0) dL(H) / dH\) for magnetic fields applied strictly parallel to the layer structure. Both quantities reproducibly show a small transition anomaly at \(\sim 10 \mathrm{T}\) . b Field derivative \(d\tau (H) / dH\) of the magnetic torque for selected temperatures showing a small dip-like anomaly near \(10 \mathrm{T}\) . </center>
+
+<--- Page Split --->
+![](images/Figure_5.jpg)
+
+<center>Figure 5 | Magnetic field vs. temperature phase diagram of NbS2 in magnetic field applied parallel to the layer structure. Filled black circles represent the upper critical field \(H_{c2}(T)\) as measured by torque magnetometry during field scans at constant temperature, red open circles represent the critical temperature \(T_{c}(H)\) measured by torque during temperature scans. Blue triangles are \(H_{c2}(T)\) data obtained from ac calorimetry measured during field scans. The orange star and green filled circle mark the critical temperature in \(C_{p}(T)\) and \(\alpha (T)\) data measured in 15 T. The violet star marks a small additional transition anomaly in the magnetostriction and filled magenta circle marks a similar anomaly visible in the field derivative of the specific heat near the Pauli limit due to the transition to the FFLO state (Fig. 2c). In addition, the dark cyan circles correspond to a small anomaly in the magnetic torque attributed to the same phase transition within the superconducting state (Fig. 2d). Additional lines mark \(H_{c2}\) data obtained from torque magnetometry in field scans measured with a small misalignment angle of 1 degree. The orange and dark cyan lines are theoretical predictions of the low temperature \(H_{c2}\) line for superconductivity with the FFLO state [32] and for Ising superconductivity, respectively. We have also added a scaled \(H_{c2}\) curve of the organic superconductor \(\kappa\) -(BEDT-TTF)₂Cu(NCS)₂, which shows a very similar upturn of the \(H_{c2}\) line due to the formation of an FFLO state [15]. </center>
+
+<--- Page Split --->
+![](images/Figure_1.jpg)
+
+<center>Figure 1 </center>  
+
+Magnetic torque data in a slightly tilted field of NbS2. a Magnetic torque \(\tau\) (H) of NbS2, measured at fixed temperatures as a function of the applied magnetic field H, oriented with a small angle \(\theta = 1^{\circ}\) to the basal plane. The data were measured at increasing field. Circles mark the points where the steepest slope occurs near Hc2. b Magnetic torque measured at \(\mathrm{T} = 0.35\mathrm{K}\) at various angles near \(\theta = 0^{\circ}\) (parallel field). A weak linear normal state contribution was removed from all data and offsets were added for better clarity.
+
+<--- Page Split --->
+![](images/Figure_2.jpg)
+
+<center>Figure 2 </center>  
+
+Magnetic torque data in parallel fields of NbS2. a Magnetic torque measured at fixed temperatures as a function of the magnetic field applied strictly parallel to the basal plane ( \(\theta = 0^{\circ}\) ) near the onset upper critical field Hc2 (open circles). The solid lines represent data measured at increasing field, while the dotted lines are data measured at decreasing field (overlapping parts of the data for low fields have been omitted and offsets have been added for reasons of clarity). b Magnetic torque data at 0.35 K where Hc2 exceeded the highest field in our magnet cryostat. c Magnetic torque measured in fixed parallel fields as a function of temperature. The data was normalized and a weak linear normal state background was removed for clarity. Open circles mark the onset critical temperatures.
+
+<--- Page Split --->
+![](images/Figure_3.jpg)
+
+<center>Figure 3 </center>  
+
+Thermodynamic bulk probes displaying the upper critical field Hc2 transition of NbS2 in high parallel magnetic fields. a Specific heat C/T (circles) and linear thermal expansion coefficient \(\alpha = (1 / L0) \mathrm{dL(T)} / \mathrm{dT}\) (squares) of NbS2 showing the superconducting transition in a magnetic field of 15 T applied strictly parallel to the layer structure. b Specific heat of NbS2 measured with an ac technique with a small 1 mK temperature modulation during field sweeps at different fixed temperatures. The stars mark the fields in which the constant normal state specific heat value (dotted lines) is reached.
+
+<--- Page Split --->
+![](images/Figure_4.jpg)
+
+<center>Figure 4 </center>  
+
+Thermodynamic bulk probes displaying the phase transition between the BCS low- field SC phase and the FLLO state in NbS2. a Magnetic field derivative \(\mathrm{dC(T)} / \mathrm{dH}\) of the specific heat, measured at \(300 \mathrm{mK}\) during a field sweep (see b for the corresponding C/T data) together with the linear magnetostriction coefficient \(\lambda = (1 / \mathrm{L0}) \mathrm{dL(H)} / \mathrm{dH}\) for magnetic fields applied strictly parallel to the layer structure. Both quantities reproducibly show a small transition anomaly at \(\sim 10 \mathrm{T}\) . b Field derivative \(\mathrm{dT(H)} / \mathrm{dH}\) of the magnetic torque for selected temperatures showing a small dip- like anomaly near \(10 \mathrm{T}\) .
+
+<--- Page Split --->
+![](images/Figure_5.jpg)
+
+<center>Figure 5 </center>  
+
+Magnetic field vs. temperature phase diagram of NbS2 in magnetic field applied parallel to the layer structure. Filled black circles represent the upper critical field Hc2(T) as measured by torque magnetometry during field scans at constant temperature, red open circles represent the critical temperature Tc(H) measured by torque during temperature scans. Blue triangles are Hc2(T) data obtained from ac calorimetry measured during field scans. The orange star and green filled circle mark the critical temperature in Cp(T) and α(T) data measured in 15 T. The violet star marks a small additional transition anomaly in the magnetostriction and filled magenta circle marks a similar anomaly visible in the field derivative of the specific heat near the Pauli limit due to the transition to the FFLO state (Fig. 2c). In addition, the dark cyan circles correspond to a small anomaly in the magnetic torque attributed to the same phase transition within the superconducting state (Fig. 2d). Additional lines mark Hc2 data obtained from torque magnetometry in field scans measured with a small misalignment angle of 1
+
+<--- Page Split --->
+
+degree. The orange and dark cyan lines are theoretical predictions of the low temperature Hc2 line for superconductivity with the FFLO state [32] and for Ising superconductivity, respectively. We have also added a scaled Hc2 curve of the organic superconductor \(\kappa\) - (BEDT- TTF)2Cu(NCS)2, which shows a very similar upturn of the Hc2 line due to the formation of an FFLO state [15].
+
+<--- Page Split --->