| NTCP | ATGCTGAGGCAAGGATGTTC
AGCAGCAGCACGACAGAGTA |
| G6PC | GTGTCCGTGATCGCAAGCC
GACGAGGTTGAGCCAGTCTC |
| CYP3A4 | TTCCTCCCTGAAAGATTCAGC
GTTGAAGAAGTCTCCTAAGCT |
| PEPCK | AAGAAGTGCTTTGCTCTCAG
CCTTAAATGACCTTGTCGT |
| CYP2D6 | CATACCTGCCCTACTACCAAA
TGTCTGCCTGGTCCTC |
| PGC1α | CCTTCGAGCACAAGAAAACA
TGCTTCGTCGTCAAAAACAG |
| HNF6 | AAATCACCATTTCCCAGCAG
ACTCCTCCTTCTTGCGTCA |
| ALB | ATGCTGAGGCAAGGATGTTC
AGCAGCAGCACGACAGAGTA |
| AAT | AGGGGCTGAAGCTAGTGGAT
TCCTCGGTGTCCTTGACTTC |
+
+Histology. Samples were fixed with \(4\%\) (w/v) paraformaldehyde (PFA, Sigma- Aldrich) overnight at \(4^{\circ}\mathrm{C}\) washed 3 times with PBS and submerged in PBS- sodium azide ( \(0.01\%\) v/v) solution at \(4^{\circ}\mathrm{C}\) until embedded in paraffin. Hydrogel sections (5 \(\mu \mathrm{m}\) ) were prepared using a microtome (Microm HM 360, Marshall Scientific.) For Hematoxylin and Eosin (H&E) staining, sections were treated with xylene solution to remove the paraffin, and gradually rehydrated in ethanol (100 to \(70\%\) , v/v). H&E staining was performed by submerging rehydrated hydrogel sections in Harris Hematoxylin solution, acid alcohol, bluing reagent and Eosin- Y solution by order. Stained samples were dehydrated with ascending alcohol series, washed in xylene solution, and mounted with DPX mountant (Sigma- Aldrich).
+
+Immunofluorescence analysis of liver tissue samples. Following deparaffinization in xylene and rehydration in descending alcohol series, heat- mediated antigen retrieval was performed by incubating hydrogel sections in Dako antigen retrieval solution (Dako) for 20 min at \(98^{\circ}\mathrm{C}\) . This step was followed by cell permeabilization with \(0.01\%\) (v/v) Triton- X (Sigma- Aldrich) solution in PBS, for 20 minutes. Samples
+
+<--- Page Split --->
+
+were then incubated with \(5\%\) (v/v) Goat or Donkey Serum (Dako) for 30 min. Primary antibodies diluted in Dako antibody diluent solution, were incubated overnight at \(4^{\circ}C\) , followed by washing steps and incubation with Alexa- coupled secondary antibody (1:500) and Hoechst 33412 (1:500) solution for 1 hour at room temperature. Finally, samples were washed in PBS, and mounted with Vectashield antifade mounting medium (Vector Laboratories). Stained sections were imaged using laser scanning confocal microscope (LSM 880, Zeiss, Germany), and image processing were performed on ZEN Blue software (Zeiss, Germany).
+
+Image acquisition and analysis of cerebral tissue samples. Fluorescence images were obtained using a confocal microscope (Leica SP8 DIVE, Leica Microsystems) equipped with 10x NA0.4 dry objective. Acquisition parameters were kept constant for all samples obtained in the same experiment. Image processing was performed using Fiji/ImageJ (NIH) and custom- written ImageJ Java plugin. At the data preprocessing stage, image stacks were collapsed by maximum intensity projection. Resulting images were translated to 8- bit lookup table representation, manually cleaned from grid structure elements and thresholded by replacing all sub- threshold pixels with black (zero value) pixels. Lookup tables of the images were then remapped from [Threshold..255] back to full [0..255] range. Threshold values were chosen manually for Hoechst and marker channels for each image in order to remove background from the images. For all markers except HIF1α, total protein expression levels in a tissue were quantified as a relative number of cells expressing the protein, i.e.
+
+\[Protein expression = \frac{Marker^{+}cellnumber}{Totalcellnumber} \times 100\%\]
+
+On the assumption that cell size is constant across the samples, the relative number of cells was estimated as a ratio of areas occupied by cells on marker and Hoechst channels. Corresponding areas were calculated by counting all non- zero pixels on marker and Hoechst channels. For statistical comparison, results for each marker were pooled from all experiments.
+
+As HIF1α marker is constitutively expressed in cells under normoxic conditions, its expression level in a tissue was estimated as the average fluorescence intensity on the marker channel. Threshold values were kept constant for organoids, non- perfused and perfused samples obtained in the same experiment;
+
+<--- Page Split --->
+
+expression values obtained in the experiment were normalized to organoid value and normalized data from different experiments were averaged.
+
+All results are expressed as mean \(\pm\) SEM. Statistical comparisons between two data groups were analyzed using unpaired two- tailed Student's \(t\) - test with a \(95\%\) confidence interval (Origin Pro v9.5, OriginLab Inc.). Significance was marked on plots by \*, \*\* and \*\*\* for \(p< 0.05\) , \(p< 0.01\) , and \(p< 0.001\) correspondingly. If not stated otherwise, the statistical significance was assessed for comparisons of perfused and non- perfused samples to organoid control samples and denoted by "pControl".
+
+Organoid dissociation and single- cell RNA sequencing. For dissociation, the microfluidic grids of non- perfused and perfused samples were removed from the perfusion chips and, along with the control organoids, placed temporarily in DMEM/F12 media in independent \(15 \text{mL}\) Falcon tubes until all 3 samples were ready for the next step (time \(< 5 \text{min}\) ). The DMEM was then replaced with \(1 \text{mL}\) of pre- warmed TrypLE Express (12605010, Gibco) at \(37^{\circ} \text{C}\) . The samples were transferred to a warm water bath at \(37^{\circ} \text{C}\) for 7.5 minutes with gentle agitation every 1 minute after the first 3 minutes. A visual inspection was performed using an inverted microscope to ensure complete dissociation of tissues and the presence of single cells. The \(1 \text{mL}\) solution was introduced to \(9 \text{mL}\) of DMEM supplemented with \(20\%\) FBS for TrypLE Express neutralization, and centrifuged at \(500 \text{rcf}\) for 5 minutes. The pellet was resuspended in \(200 \text{uL}\) of N2B27 media and put on ice. This dissociation protocol yielded an average viability of above \(80\%\) across all samples.
+
+Library preparations for the scRNAseq was performed using 10X Genomics Chromium Single Cell 3' Kit, v3 (10X Genomics, Pleasanton, CA, USA). The cell count and the viability of the samples were accessed using LUNA dual fluorescence cell counter (Logos Biosystems) and a targeted cell recovery of 6000 cells was aimed for all the samples. Post cell count and QC, the samples were immediately loaded onto the Chromium Controller. Single cell RNAseq libraries were prepared using manufacturers recommendations (Single cell 3' reagent kits v3 user guide; CG00052 Rev B), and at the different check points, the library quality was accessed using Qubit (ThermoFisher) and Bioanalyzer (Agilent). Single cell libraries were sequenced using paired- end sequencing workflow and with recommended 10X; v3 read
+
+<--- Page Split --->
+
+parameters (28- 8- 0- 91 cycles). The data generated from sequencing was de- multiplexed and mapped against human genome reference using CellRanger v3.0.2.
+
+Single Cell RNA sequencing Data Processing. We have sequenced 4,818, 5,453, and 2,804 cells for non- perfused, perfused and control organoids samples respectively for a total of 13,075 cells, which were reduced after QC steps to 8,625 cells with an average of 1,852 detected genes per cell. Data processing and subsequent steps were performed using the Seurat72 tool for single cell genomics version 3 in R version 4.0.3. A filtering step was performed to ensure the quality of the data, where the counts of mitochondrial reads and total genes reads were assessed. Cells with more than \(15\%\) of identifiable genes rising from the mitochondrial genome were filtered out. Similarly, cells having fewer identifiable genes than 200 (low quality) and above 7,500 (probable doublets) were filtered out. Data normalization was performed, followed by the identification of 2,000 highly variable genes using the FindVariableFeatures(). S- phase and G2M- phase cell cycle regression was performed to allow cell clustering purely on cell identity and fate, which otherwise was biased by cell cycle phases. Auto scaling of the data was performed and described using principal component analysis (PCA) using the RunPCA() function.
+
+Correlation analysis. Correlation heatmap between samples was performed in R using the top 100 marker genes for each sample using the FindAllMarkers() function followed by the selection of the top 100 marker genes using the top_n() function with \(n = 100\) and wt = avg.logFC. The correlation analysis was generated using the cor() function using the Pearson method and the heatmap using the heatmap.2() function in R.
+
+Data Clustering. Graph- based clustering using the FindNeighbors() function (using top 15 principal components (PCs)) and FindCluster() function (resolution = 0.5) was performed to group cells based on their transcriptional profiles. No batch correction was performed on the data set. Data visualization was performed using the Uniform Manifold Approximation and Projection (UMAP) dimensionality reduction technique using the RunUMAP package while employing the top 15 PCs identified in the previous PCA step. Cluster annotation was based on hallmark genes to identify Neuroepithelial cluster (PAX6, PAX7, PAX3), Pluripotent- Neuroepithelial transitioning (P- NE) cells (WNT4, IRX1, IRX2), Proliferating cells
+
+<--- Page Split --->
+
+(CDC6, PTN, MK167, TOP2A), medium mitochondrial gene content and low glycolysis pluripotent cells (MK167, TOP2A, with the expression of NANOG, POU5F1, DPPA4), Pluripotent cells (NANOG, POU5F1, DPPA4), highly glycolytic pluripotent cells that link to the Cycling pluripotent and Pluripotent cells but include the expressions (LDHA, ENO1, HK2), a cluster undergoing EMT/migration with hypoxic markers (EPCAM, VIM, TWIST2, VEGFA) and finally a stressed cluster (FOS, HIF1A, JUN). One cluster was manually removed due to very low unique molecular identifier (UMI) count, after which the data was reprocessed to account for the removal of the cluster.
+
+Pseudotime trajectories. Trajectory analysis was performed using Monocle \(3^{73}\) on R by employing the monocole3 and SeuratWrappers libraries. The Seurat obtained object containing all combined samples was passed to the Monocle 3 pipeline using the as.cell_data_set() function followed by processing using the cluster_cells() function. The pseudotime trajectory was inferred by first using the subset function on the only partition detected, followed by the learn_graph() function. Cells were colored in their inferred pseudotime using the which.max() function with the FetchData() (AVP) function, followed by the order_cells() function with the root_cell = max.avp. The UMAP superimposed with inferred pseudotime trajectory was generated using the plot_cells() function.
+
+Gene- set analysis and hierarchical clustering. Gene- sets were created using the AddModuleScore() function in R. three gene- set were created to explore the dataset, pluripotency gene- set (POU5F1, NANOG, CDH1), glycolysis gene- set (ALDOA, BPGM, ENO1, ENO2, GPI, HK1, HK2, PFKL, PFKM, PGAM1, PGK1, PKM, TP11), and a neural progenitor gene- set (PAX3, PAX6, PAX7, OTX2, CDH2). The % mitochondrial genes were obtain as evaluated by the Seurat pipeline. Each cell was evaluated and scored and its expression plotted on UMAPS. Cluster level hierarchical clustering of the gene- set scores and % mitochondrial genes was employed using R package heatmap.2 after scaling the expression across rows (clusters).
+
+Design and fabrication of soft micro- fluidic grids. The microfluidic grids were designed as a multitude of parallel capillaries stemming from a common reservoir. The dimensions of the grid (2.6x2.6x1.5mm) were
+
+<--- Page Split --->
+
+chosen as a compromise between size and the fabrication time. The grids were micro- fabricated on custom baseplates using high- resolution 3D printer (Photonic Professional GT2, Nanoscribe GmbH) equipped with 2- photon femtosecond laser. CAD model of the capillary grid was pre- processed by DeScribe software (Nanoscribe GmbH) to produce a printing job with defined printing parameters. A custom- formulation photopolymerizable resin was used to print microfluidic grids which contained \(2\%\) 2- Benzyl- 2- (dimethylamino)- 4'- morpholinobutyrophenone (lrgacure 369), \(7\%\) propylene glycol methyl ether acetate (PGMEA), \(36\%\) poly(ethylene glycol) diacrylate, MW700 (PEGDA 700), \(25\%\) pentaerythritol triacrylate (PETA), \(20\%\) Triton X- 100 and \(10\%\) water. lrgacure 369 was purchased from Tokyo Chemical Industry Ltd, the other components were from Sigma- Aldrich.
+
+The microfluidic grids were printed on custom plates 3D printed on Formlabs Form 2 printer from a biocompatible resin (Dental SG, Formlabs Inc), post- cured by UV light (Formcure station, Formlabs Inc) for 2h at \(80^{\circ}C\) and washed in 2- propanol for 3- 4 days with daily change of the solvent. The plates had 1mm thickness and 10mm diameter, with 0.5mm perfusion holes. The grid printing process was set up such that the inlets in the microfluidic grid were aligned with the perfusion holes in a baseplate. After fabrication, baseplate- grid assemblies were washed during 2 days in PGMEA, 12 days in 2- propanol and then kept in PBS until use. The solvents were refreshed every 1- 2 days and PBS was refreshed daily for the first week and every 2- 3 days afterwards.
+
+Fabrication of perfusion chips and setting up perfusion. The perfusion chips were implemented as an assembly as shown in Supplementary Fig. 1. Two microfluidic grids were sandwiched between two polydimethylsiloxane (PDMS) custom- profiled blocks. Two metal plates clamped with screws provided a tight junction between PDMS blocks and cover slips, thereby forming fluidic channels within the chip. The chips were designed to only allow contact of the medium with PDMS blocks and cover slips, thereby isolating the flow from contact with other materials of the chip. Guiding frames were used to simplify the alignment of the PDMS blocks during the chip assembly process.
+
+The PDMS blocks were made by casting liquid PDMS compound into custom- designed plastic molds and curing overnight at \(80^{\circ}C\) . The plastic molds were fabricated by stereo- lithography (SLA) 3D printer Form 2 (Formlabs Inc.) using a bio- compatible Formlabs Dental SG resin. The printed molds were
+
+<--- Page Split --->
+
+post- processed by thorough washing in 2- propanol and post- cured for 90 min at \(80^{\circ}C\) (Formcure, Formlabs Inc.). Metal plates were made from 1mm stainless steel sheet by laser cutting and stainless steel screws were used for clamping the assembly. The guiding frames were 3D printed and cover slips were glued to the metal plates with a bio- compatible UV/heat curable epoxy (NOA 86H, Norland) to provide a final two- part setup shown in Supplementary Fig. 1d.
+
+Two chambers of the chip, containing perfused and non- perfused grids, a peristaltic pump and a medium reservoir (50ml Falcon tube) were connected by flexible PVC tubing (ID/OD 0.8/1.6mm) in series (Supplementary Fig. 1e). The medium was taken from the reservoir by the pump and fed through chambers containing the non- perfused and then the perfused grid, after which the medium was sent back to the reservoir for \(\mathrm{CO_2 / O_2}\) equilibration. A 0.22um filter was connected between the two chambers of the chip to protect capillaries of the perfused grid from potential clogging with tissue debris. The medium reservoir and the chip were kept in a CO2 incubator, while the peristaltic pump remained outside to protect electronics from humidity and to ensure proper cooling. The medium reservoir and the chip were installed on a custom made 3D printed holder (Supplementary Fig. 1f) to simplify transitions between a CO2 incubator and a laminar hood. At day 0 of an experiment, all parts of the perfusion were sterilized by ethanol and UV light in a laminar hood, connected together and filled with equilibrated medium. The microfluidic grids, seeded with hPSC spheroids, were installed in the chip (Supplementary Fig. 1d) and the chip was clamped by screws (Supplementary Fig. 1f- i). Air bubbles were removed from the system and the perfusion started at approx. \(400\mu \mathrm{L} / \mathrm{min}\) flow rate (Supplementary Fig. 1, f- iii).
+
+Multi- grid perfusion chips were designed to provide an equal multiplexed perfusion for all microfluidic grids. The fluidic inlet of the chip, via three steps of bifurcations, was split into eight fluidic channels feeding eight wells. All fluidic channels had equal length and cross- section and, therefore, equal hydraulic resistance. The multi- grid chip were fabricated by stereo- lithography (SLA) 3D printer Form 2 (Formlabs Inc.) using Formlabs Dental SG resin. The printed chips were post- processed by thorough washing in 2- propanol and post- cured for 90 min at \(60^{\circ}C\) . 7- 8mm pieces of syringe needles (0.9mm) were cut by hand drill/cutter (Precision drill, Proxxon Inc.) and glued into chip inlets by a biocompatible UV/heat curable epoxy glue to serve as connectors for flexible tubing. This assembly was again post- cured in FormCure station for 120 min at \(80^{\circ}C\) to achieve complete hardening of the glue. To integrate microfluidic
+
+<--- Page Split --->
+
+grids, a custom profile gaskets, cut from 0.5mm PDMS sheet by a laser cutter, were placed under the grid substrates to provide a liquid- tight contact and the substrates were fixed in place by 3D printed fasteners.
+
+Data availability. All raw sequencing data, and the combined processed and metadata files generated in this study are available at GEO. The accession number for the reported data is GSE181290. This study did not generate any unique code. For review purposes please use the following token (ybchyyqofpejzgv) to access the sequencing data at: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE181290. The ImageJ Java code used for analysis of the image data are available upon request.
+
+<--- Page Split --->
+
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+62. Wang, M., Tan, J., Miao, Y., Li, M. & Zhang, Q. Role of \(\mathrm{Ca^{2 + }}\) and ion channels in the regulation of apoptosis under hypoxia. Histol Histopathol 33, 237-246 (2018).
+
+63. Punovuori, K. et al. N-cadherin stabilises neural identity by dampening anti-neural signals. Development 146, dev183269 (2019).
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+64. Tietz, P. S. & Larusso, N. F. Cholangiocyte biology. Curr Opin Gastroenterol 22, 279-287 (2006).
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+65. Ruebner, B. H., Blankenberg, T. A., Burrows, D. A., Soohoo, W. & Lund, J. K. Development and Transformation of the Ductal Plate in the Developing Human Liver. Pediatric Pathology 10, 55-68 (1990).
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+66. Limaye, P. B. et al. Expression of specific hepatocyte and cholangiocyte transcription factors in human liver disease and embryonic development. Lab Invest 88, 865-872 (2008).
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+67. Ranga, A. et al. Neural tube morphogenesis in synthetic 3D microenvironments. Proc Natl Acad Sci USA 113, E6831-E6839 (2016).
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+68. Medina, J. D. et al. Functionalization of Alginate with Extracellular Matrix Peptides Enhances Viability and Function of Encapsulated Porcine Islets. Adv. Healthcare Mater. 9, 2000102 (2020).
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+69. Lancaster, M. A. & Knoblich, J. A. Generation of cerebral organoids from human pluripotent stem cells. Nature Protocols 9, 2329-2340 (2014).
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+70. Boon, R. et al. Amino acid levels determine metabolism and CYP450 function of hepatocytes and hepatoma cell lines. Nat Commun 11, 1393 (2020).
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+71. Roelandt, P., Vanhove, J. & Verfaillie, C. Directed Differentiation of Pluripotent Stem Cells to Functional Hepatocytes. in Pluripotent Stem Cells (eds. Lakshmipathy, U. & Vemuri, M. C.) vol. 997 141-147 (Humana Press, 2013).
+
+72. Stuart, T. et al. Comprehensive Integration of Single-Cell Data. Cell 177, 1888-1902.e21 (2019).
+
+<--- Page Split --->
+
+73. Cao, J. et al. The single-cell transcriptional landscape of mammalian organogenesis. Nature 566, 496–502 (2019).
+
+74. Kumar, M. et al. A fully defined matrix to support a pluripotent stem cell derived multi-cell-liver steatohepatitis and fibrosis model. Biomaterials 276, 121006 (2021).
+
+<--- Page Split --->
+
+Figures
+
+<--- Page Split --->
+
+
+| NTCP | ATGCTGAGGCAAGGATGTTC
AGCAGCAGCACGACAGAGTA |
| G6PC | GTGTCCGTGATCGCAAGCC
GACGAGGTTGAGCCAGTCTC |
| CYP3A4 | TTCCTCCCTGAAAGATTCAGC
GTTGAAGAAGTCTCCTAAGCT |
| PEPCK | AAGAAGTGCTTTGCTCTCAG
CCTTAAATGACCTTGTCGT |
| CYP2D6 | CATACCTGCCCTACTACCAAA
TGTCTGCCTGGTCCTC |
| PGC1α | CCTTCGAGCACAAGAAAACA
TGCTTCGTCGTCAAAAACAG |
| HNF6 | AAATCACCATTTCCCAGCAG
ACTCCTCCTTCTTGCGTCA |
| ALB | ATGCTGAGGCAAGGATGTTC
AGCAGCAGCACGACAGAGTA |
| AAT | AGGGGCTGAAGCTAGTGGAT
TCCTCGGTGTCCTTGACTTC |
+
+<|ref|>text<|/ref|><|det|>[[113, 529, 886, 754]]<|/det|>
+Histology. Samples were fixed with \(4\%\) (w/v) paraformaldehyde (PFA, Sigma- Aldrich) overnight at \(4^{\circ}\mathrm{C}\) washed 3 times with PBS and submerged in PBS- sodium azide ( \(0.01\%\) v/v) solution at \(4^{\circ}\mathrm{C}\) until embedded in paraffin. Hydrogel sections (5 \(\mu \mathrm{m}\) ) were prepared using a microtome (Microm HM 360, Marshall Scientific.) For Hematoxylin and Eosin (H&E) staining, sections were treated with xylene solution to remove the paraffin, and gradually rehydrated in ethanol (100 to \(70\%\) , v/v). H&E staining was performed by submerging rehydrated hydrogel sections in Harris Hematoxylin solution, acid alcohol, bluing reagent and Eosin- Y solution by order. Stained samples were dehydrated with ascending alcohol series, washed in xylene solution, and mounted with DPX mountant (Sigma- Aldrich).
+
+<|ref|>text<|/ref|><|det|>[[113, 792, 884, 900]]<|/det|>
+Immunofluorescence analysis of liver tissue samples. Following deparaffinization in xylene and rehydration in descending alcohol series, heat- mediated antigen retrieval was performed by incubating hydrogel sections in Dako antigen retrieval solution (Dako) for 20 min at \(98^{\circ}\mathrm{C}\) . This step was followed by cell permeabilization with \(0.01\%\) (v/v) Triton- X (Sigma- Aldrich) solution in PBS, for 20 minutes. Samples
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[113, 87, 884, 282]]<|/det|>
+were then incubated with \(5\%\) (v/v) Goat or Donkey Serum (Dako) for 30 min. Primary antibodies diluted in Dako antibody diluent solution, were incubated overnight at \(4^{\circ}C\) , followed by washing steps and incubation with Alexa- coupled secondary antibody (1:500) and Hoechst 33412 (1:500) solution for 1 hour at room temperature. Finally, samples were washed in PBS, and mounted with Vectashield antifade mounting medium (Vector Laboratories). Stained sections were imaged using laser scanning confocal microscope (LSM 880, Zeiss, Germany), and image processing were performed on ZEN Blue software (Zeiss, Germany).
+
+<|ref|>text<|/ref|><|det|>[[112, 320, 885, 630]]<|/det|>
+Image acquisition and analysis of cerebral tissue samples. Fluorescence images were obtained using a confocal microscope (Leica SP8 DIVE, Leica Microsystems) equipped with 10x NA0.4 dry objective. Acquisition parameters were kept constant for all samples obtained in the same experiment. Image processing was performed using Fiji/ImageJ (NIH) and custom- written ImageJ Java plugin. At the data preprocessing stage, image stacks were collapsed by maximum intensity projection. Resulting images were translated to 8- bit lookup table representation, manually cleaned from grid structure elements and thresholded by replacing all sub- threshold pixels with black (zero value) pixels. Lookup tables of the images were then remapped from [Threshold..255] back to full [0..255] range. Threshold values were chosen manually for Hoechst and marker channels for each image in order to remove background from the images. For all markers except HIF1α, total protein expression levels in a tissue were quantified as a relative number of cells expressing the protein, i.e.
+
+<|ref|>equation<|/ref|><|det|>[[310, 655, 685, 688]]<|/det|>
+\[Protein expression = \frac{Marker^{+}cellnumber}{Totalcellnumber} \times 100\%\]
+
+<|ref|>text<|/ref|><|det|>[[113, 713, 884, 820]]<|/det|>
+On the assumption that cell size is constant across the samples, the relative number of cells was estimated as a ratio of areas occupied by cells on marker and Hoechst channels. Corresponding areas were calculated by counting all non- zero pixels on marker and Hoechst channels. For statistical comparison, results for each marker were pooled from all experiments.
+
+<|ref|>text<|/ref|><|det|>[[113, 830, 884, 907]]<|/det|>
+As HIF1α marker is constitutively expressed in cells under normoxic conditions, its expression level in a tissue was estimated as the average fluorescence intensity on the marker channel. Threshold values were kept constant for organoids, non- perfused and perfused samples obtained in the same experiment;
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[114, 88, 883, 135]]<|/det|>
+expression values obtained in the experiment were normalized to organoid value and normalized data from different experiments were averaged.
+
+<|ref|>text<|/ref|><|det|>[[113, 146, 884, 282]]<|/det|>
+All results are expressed as mean \(\pm\) SEM. Statistical comparisons between two data groups were analyzed using unpaired two- tailed Student's \(t\) - test with a \(95\%\) confidence interval (Origin Pro v9.5, OriginLab Inc.). Significance was marked on plots by \*, \*\* and \*\*\* for \(p< 0.05\) , \(p< 0.01\) , and \(p< 0.001\) correspondingly. If not stated otherwise, the statistical significance was assessed for comparisons of perfused and non- perfused samples to organoid control samples and denoted by "pControl".
+
+<|ref|>text<|/ref|><|det|>[[112, 319, 885, 629]]<|/det|>
+Organoid dissociation and single- cell RNA sequencing. For dissociation, the microfluidic grids of non- perfused and perfused samples were removed from the perfusion chips and, along with the control organoids, placed temporarily in DMEM/F12 media in independent \(15 \text{mL}\) Falcon tubes until all 3 samples were ready for the next step (time \(< 5 \text{min}\) ). The DMEM was then replaced with \(1 \text{mL}\) of pre- warmed TrypLE Express (12605010, Gibco) at \(37^{\circ} \text{C}\) . The samples were transferred to a warm water bath at \(37^{\circ} \text{C}\) for 7.5 minutes with gentle agitation every 1 minute after the first 3 minutes. A visual inspection was performed using an inverted microscope to ensure complete dissociation of tissues and the presence of single cells. The \(1 \text{mL}\) solution was introduced to \(9 \text{mL}\) of DMEM supplemented with \(20\%\) FBS for TrypLE Express neutralization, and centrifuged at \(500 \text{rcf}\) for 5 minutes. The pellet was resuspended in \(200 \text{uL}\) of N2B27 media and put on ice. This dissociation protocol yielded an average viability of above \(80\%\) across all samples.
+
+<|ref|>text<|/ref|><|det|>[[112, 639, 885, 861]]<|/det|>
+Library preparations for the scRNAseq was performed using 10X Genomics Chromium Single Cell 3' Kit, v3 (10X Genomics, Pleasanton, CA, USA). The cell count and the viability of the samples were accessed using LUNA dual fluorescence cell counter (Logos Biosystems) and a targeted cell recovery of 6000 cells was aimed for all the samples. Post cell count and QC, the samples were immediately loaded onto the Chromium Controller. Single cell RNAseq libraries were prepared using manufacturers recommendations (Single cell 3' reagent kits v3 user guide; CG00052 Rev B), and at the different check points, the library quality was accessed using Qubit (ThermoFisher) and Bioanalyzer (Agilent). Single cell libraries were sequenced using paired- end sequencing workflow and with recommended 10X; v3 read
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[114, 88, 883, 136]]<|/det|>
+parameters (28- 8- 0- 91 cycles). The data generated from sequencing was de- multiplexed and mapped against human genome reference using CellRanger v3.0.2.
+
+<|ref|>text<|/ref|><|det|>[[112, 174, 885, 516]]<|/det|>
+Single Cell RNA sequencing Data Processing. We have sequenced 4,818, 5,453, and 2,804 cells for non- perfused, perfused and control organoids samples respectively for a total of 13,075 cells, which were reduced after QC steps to 8,625 cells with an average of 1,852 detected genes per cell. Data processing and subsequent steps were performed using the Seurat72 tool for single cell genomics version 3 in R version 4.0.3. A filtering step was performed to ensure the quality of the data, where the counts of mitochondrial reads and total genes reads were assessed. Cells with more than \(15\%\) of identifiable genes rising from the mitochondrial genome were filtered out. Similarly, cells having fewer identifiable genes than 200 (low quality) and above 7,500 (probable doublets) were filtered out. Data normalization was performed, followed by the identification of 2,000 highly variable genes using the FindVariableFeatures(). S- phase and G2M- phase cell cycle regression was performed to allow cell clustering purely on cell identity and fate, which otherwise was biased by cell cycle phases. Auto scaling of the data was performed and described using principal component analysis (PCA) using the RunPCA() function.
+
+<|ref|>text<|/ref|><|det|>[[113, 551, 884, 660]]<|/det|>
+Correlation analysis. Correlation heatmap between samples was performed in R using the top 100 marker genes for each sample using the FindAllMarkers() function followed by the selection of the top 100 marker genes using the top_n() function with \(n = 100\) and wt = avg.logFC. The correlation analysis was generated using the cor() function using the Pearson method and the heatmap using the heatmap.2() function in R.
+
+<|ref|>text<|/ref|><|det|>[[113, 697, 885, 891]]<|/det|>
+Data Clustering. Graph- based clustering using the FindNeighbors() function (using top 15 principal components (PCs)) and FindCluster() function (resolution = 0.5) was performed to group cells based on their transcriptional profiles. No batch correction was performed on the data set. Data visualization was performed using the Uniform Manifold Approximation and Projection (UMAP) dimensionality reduction technique using the RunUMAP package while employing the top 15 PCs identified in the previous PCA step. Cluster annotation was based on hallmark genes to identify Neuroepithelial cluster (PAX6, PAX7, PAX3), Pluripotent- Neuroepithelial transitioning (P- NE) cells (WNT4, IRX1, IRX2), Proliferating cells
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[113, 87, 885, 280]]<|/det|>
+(CDC6, PTN, MK167, TOP2A), medium mitochondrial gene content and low glycolysis pluripotent cells (MK167, TOP2A, with the expression of NANOG, POU5F1, DPPA4), Pluripotent cells (NANOG, POU5F1, DPPA4), highly glycolytic pluripotent cells that link to the Cycling pluripotent and Pluripotent cells but include the expressions (LDHA, ENO1, HK2), a cluster undergoing EMT/migration with hypoxic markers (EPCAM, VIM, TWIST2, VEGFA) and finally a stressed cluster (FOS, HIF1A, JUN). One cluster was manually removed due to very low unique molecular identifier (UMI) count, after which the data was reprocessed to account for the removal of the cluster.
+
+<|ref|>text<|/ref|><|det|>[[113, 320, 885, 543]]<|/det|>
+Pseudotime trajectories. Trajectory analysis was performed using Monocle \(3^{73}\) on R by employing the monocole3 and SeuratWrappers libraries. The Seurat obtained object containing all combined samples was passed to the Monocle 3 pipeline using the as.cell_data_set() function followed by processing using the cluster_cells() function. The pseudotime trajectory was inferred by first using the subset function on the only partition detected, followed by the learn_graph() function. Cells were colored in their inferred pseudotime using the which.max() function with the FetchData() (AVP) function, followed by the order_cells() function with the root_cell = max.avp. The UMAP superimposed with inferred pseudotime trajectory was generated using the plot_cells() function.
+
+<|ref|>text<|/ref|><|det|>[[113, 580, 885, 802]]<|/det|>
+Gene- set analysis and hierarchical clustering. Gene- sets were created using the AddModuleScore() function in R. three gene- set were created to explore the dataset, pluripotency gene- set (POU5F1, NANOG, CDH1), glycolysis gene- set (ALDOA, BPGM, ENO1, ENO2, GPI, HK1, HK2, PFKL, PFKM, PGAM1, PGK1, PKM, TP11), and a neural progenitor gene- set (PAX3, PAX6, PAX7, OTX2, CDH2). The % mitochondrial genes were obtain as evaluated by the Seurat pipeline. Each cell was evaluated and scored and its expression plotted on UMAPS. Cluster level hierarchical clustering of the gene- set scores and % mitochondrial genes was employed using R package heatmap.2 after scaling the expression across rows (clusters).
+
+<|ref|>text<|/ref|><|det|>[[113, 842, 883, 890]]<|/det|>
+Design and fabrication of soft micro- fluidic grids. The microfluidic grids were designed as a multitude of parallel capillaries stemming from a common reservoir. The dimensions of the grid (2.6x2.6x1.5mm) were
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[113, 87, 885, 339]]<|/det|>
+chosen as a compromise between size and the fabrication time. The grids were micro- fabricated on custom baseplates using high- resolution 3D printer (Photonic Professional GT2, Nanoscribe GmbH) equipped with 2- photon femtosecond laser. CAD model of the capillary grid was pre- processed by DeScribe software (Nanoscribe GmbH) to produce a printing job with defined printing parameters. A custom- formulation photopolymerizable resin was used to print microfluidic grids which contained \(2\%\) 2- Benzyl- 2- (dimethylamino)- 4'- morpholinobutyrophenone (lrgacure 369), \(7\%\) propylene glycol methyl ether acetate (PGMEA), \(36\%\) poly(ethylene glycol) diacrylate, MW700 (PEGDA 700), \(25\%\) pentaerythritol triacrylate (PETA), \(20\%\) Triton X- 100 and \(10\%\) water. lrgacure 369 was purchased from Tokyo Chemical Industry Ltd, the other components were from Sigma- Aldrich.
+
+<|ref|>text<|/ref|><|det|>[[113, 349, 886, 570]]<|/det|>
+The microfluidic grids were printed on custom plates 3D printed on Formlabs Form 2 printer from a biocompatible resin (Dental SG, Formlabs Inc), post- cured by UV light (Formcure station, Formlabs Inc) for 2h at \(80^{\circ}C\) and washed in 2- propanol for 3- 4 days with daily change of the solvent. The plates had 1mm thickness and 10mm diameter, with 0.5mm perfusion holes. The grid printing process was set up such that the inlets in the microfluidic grid were aligned with the perfusion holes in a baseplate. After fabrication, baseplate- grid assemblies were washed during 2 days in PGMEA, 12 days in 2- propanol and then kept in PBS until use. The solvents were refreshed every 1- 2 days and PBS was refreshed daily for the first week and every 2- 3 days afterwards.
+
+<|ref|>text<|/ref|><|det|>[[113, 608, 885, 803]]<|/det|>
+Fabrication of perfusion chips and setting up perfusion. The perfusion chips were implemented as an assembly as shown in Supplementary Fig. 1. Two microfluidic grids were sandwiched between two polydimethylsiloxane (PDMS) custom- profiled blocks. Two metal plates clamped with screws provided a tight junction between PDMS blocks and cover slips, thereby forming fluidic channels within the chip. The chips were designed to only allow contact of the medium with PDMS blocks and cover slips, thereby isolating the flow from contact with other materials of the chip. Guiding frames were used to simplify the alignment of the PDMS blocks during the chip assembly process.
+
+<|ref|>text<|/ref|><|det|>[[113, 813, 884, 890]]<|/det|>
+The PDMS blocks were made by casting liquid PDMS compound into custom- designed plastic molds and curing overnight at \(80^{\circ}C\) . The plastic molds were fabricated by stereo- lithography (SLA) 3D printer Form 2 (Formlabs Inc.) using a bio- compatible Formlabs Dental SG resin. The printed molds were
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[113, 88, 884, 223]]<|/det|>
+post- processed by thorough washing in 2- propanol and post- cured for 90 min at \(80^{\circ}C\) (Formcure, Formlabs Inc.). Metal plates were made from 1mm stainless steel sheet by laser cutting and stainless steel screws were used for clamping the assembly. The guiding frames were 3D printed and cover slips were glued to the metal plates with a bio- compatible UV/heat curable epoxy (NOA 86H, Norland) to provide a final two- part setup shown in Supplementary Fig. 1d.
+
+<|ref|>text<|/ref|><|det|>[[112, 233, 885, 630]]<|/det|>
+Two chambers of the chip, containing perfused and non- perfused grids, a peristaltic pump and a medium reservoir (50ml Falcon tube) were connected by flexible PVC tubing (ID/OD 0.8/1.6mm) in series (Supplementary Fig. 1e). The medium was taken from the reservoir by the pump and fed through chambers containing the non- perfused and then the perfused grid, after which the medium was sent back to the reservoir for \(\mathrm{CO_2 / O_2}\) equilibration. A 0.22um filter was connected between the two chambers of the chip to protect capillaries of the perfused grid from potential clogging with tissue debris. The medium reservoir and the chip were kept in a CO2 incubator, while the peristaltic pump remained outside to protect electronics from humidity and to ensure proper cooling. The medium reservoir and the chip were installed on a custom made 3D printed holder (Supplementary Fig. 1f) to simplify transitions between a CO2 incubator and a laminar hood. At day 0 of an experiment, all parts of the perfusion were sterilized by ethanol and UV light in a laminar hood, connected together and filled with equilibrated medium. The microfluidic grids, seeded with hPSC spheroids, were installed in the chip (Supplementary Fig. 1d) and the chip was clamped by screws (Supplementary Fig. 1f- i). Air bubbles were removed from the system and the perfusion started at approx. \(400\mu \mathrm{L} / \mathrm{min}\) flow rate (Supplementary Fig. 1, f- iii).
+
+<|ref|>text<|/ref|><|det|>[[113, 639, 885, 890]]<|/det|>
+Multi- grid perfusion chips were designed to provide an equal multiplexed perfusion for all microfluidic grids. The fluidic inlet of the chip, via three steps of bifurcations, was split into eight fluidic channels feeding eight wells. All fluidic channels had equal length and cross- section and, therefore, equal hydraulic resistance. The multi- grid chip were fabricated by stereo- lithography (SLA) 3D printer Form 2 (Formlabs Inc.) using Formlabs Dental SG resin. The printed chips were post- processed by thorough washing in 2- propanol and post- cured for 90 min at \(60^{\circ}C\) . 7- 8mm pieces of syringe needles (0.9mm) were cut by hand drill/cutter (Precision drill, Proxxon Inc.) and glued into chip inlets by a biocompatible UV/heat curable epoxy glue to serve as connectors for flexible tubing. This assembly was again post- cured in FormCure station for 120 min at \(80^{\circ}C\) to achieve complete hardening of the glue. To integrate microfluidic
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[113, 88, 883, 136]]<|/det|>
+grids, a custom profile gaskets, cut from 0.5mm PDMS sheet by a laser cutter, were placed under the grid substrates to provide a liquid- tight contact and the substrates were fixed in place by 3D printed fasteners.
+
+<|ref|>text<|/ref|><|det|>[[113, 175, 884, 309]]<|/det|>
+Data availability. All raw sequencing data, and the combined processed and metadata files generated in this study are available at GEO. The accession number for the reported data is GSE181290. This study did not generate any unique code. For review purposes please use the following token (ybchyyqofpejzgv) to access the sequencing data at: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE181290. The ImageJ Java code used for analysis of the image data are available upon request.
+
+<--- Page Split --->
+<|ref|>sub_title<|/ref|><|det|>[[115, 89, 206, 105]]<|/det|>
+## References
+
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+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[114, 395, 177, 411]]<|/det|>
+Figures
+
+<--- Page Split --->
+<|ref|>image<|/ref|><|det|>[[171, 110, 825, 744]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[114, 770, 883, 904]]<|/det|>
+| Step / Parameter | Reference | Value | Equation | Result | Uncertainty |
| Chinstrap penguin annual Fe input in the Southern Ocean | Present study | 514 tonnes Fe yr-1 | | | 266 - 762 tonnes Fe yr-1 |
| Fraction of Fe retained in the photic zone | 10 | 0.25/0.5*/0.75 | 5.14x108 g Fe yr-1 x 0.5 | 2.57x108 g Fe yr-1 retained in the photic zone | 1.33 - 3.81 x108 g Fe yr-1 |
| Fraction of Fe bioavailable for phytoplankton | 10 | 0.25/0.5*/0.75 | 2.57x108 g Fe yr-1 x 0.5 | 1.28 x108 g Fe yr-1 bioavailable for phyto. | 0.66 - 1.9 x108 g Fe yr-1 |
| Fe : C ratio of phytoplankton in the Southern Ocean | 38,52,53 | 3 μmol Fe : mol C | | | 1 - 6 μmol Fe : mol C |
| Fe molecular weight | | 55.845 g mol-1 | | | |
| g of carbon incorporated into phytoplankton biomass (NPP) | Present study | | (1.28 x108 g Fe yr-1 x 0.018 mol Fe x 106 μmol Fe x mol C x 12.01 g C) / (g Fe x mol Fe x 3 μmol Fe x mol C) | 9.22 x1012 g C yr-1 incorporated into phyto. | 4.75 - 13.7 x1012 g C yr-1 |
| Southern Ocean area (South of 60°S) | | 2 x1013 m2 | | | |
| Rate of NPP stimulation by Cp guano input | Present study | | (9.22 x1012 g C yr-1 incorporated into phyto.) / (2 x1013 m2) | 0.46 g C m-2 yr-1 incorporated into phyto. | 0.24 - 0.68 g C m-2 yr-1 |
+
+<--- Page Split --->
+
+## Supplementary Files
+
+This is a list of supplementary files associated with this preprint. Click to download.
+
+NCOMMS2226937Trs.pdf
+
+<--- Page Split --->
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@@ -0,0 +1,603 @@
+<|ref|>title<|/ref|><|det|>[[44, 106, 927, 175]]<|/det|>
+# The contribution of penguin guano to the Southern Ocean iron pool
+
+<|ref|>text<|/ref|><|det|>[[44, 195, 940, 260]]<|/det|>
+Oleg Korolev ( \(\boxed{\infty}\) o.belyaev@csic.es) Institute of Marine Sciences of Andalusia, Spanish National Research Council https://orcid.org/0000- 0002- 8851- 2996
+
+<|ref|>text<|/ref|><|det|>[[44, 265, 930, 450]]<|/det|>
+Erica Sparaventi Institute of Marine Sciences of Andalusia, Spanish National Research Council Gabriel Navarro Consejo Superior de Investigaciones Cientificas (CSIC) https://orcid.org/0000- 0002- 8919- 0060
+
+<|ref|>text<|/ref|><|det|>[[44, 487, 101, 504]]<|/det|>
+Article
+
+<|ref|>text<|/ref|><|det|>[[44, 524, 137, 542]]<|/det|>
+Keywords:
+
+<|ref|>text<|/ref|><|det|>[[44, 561, 300, 580]]<|/det|>
+Posted Date: July 22nd, 2022
+
+<|ref|>text<|/ref|><|det|>[[44, 600, 475, 619]]<|/det|>
+DOI: https://doi.org/10.21203/rs.3.rs- 1804836/v1
+
+<|ref|>text<|/ref|><|det|>[[42, 636, 910, 680]]<|/det|>
+License: © \(\circledast\) This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License
+
+<--- Page Split --->
+<|ref|>sub_title<|/ref|><|det|>[[85, 85, 770, 106]]<|/det|>
+## The contribution of penguin guano to the Southern Ocean iron pool
+
+<|ref|>text<|/ref|><|det|>[[85, 132, 910, 184]]<|/det|>
+Oleg B. Korolev\*,1, Erica Sparaventi1, Gabriel Navarro1, Araceli Rodríguez-Romero2, Antonio Tovar- Sánchez1
+
+<|ref|>text<|/ref|><|det|>[[85, 207, 853, 228]]<|/det|>
+1 Department of Ecology and Coastal Management, Institute of Marine Sciences of Andalusia
+
+<|ref|>text<|/ref|><|det|>[[85, 240, 808, 260]]<|/det|>
+(ICMAN), Spanish National Research Council (CSIC), 11510 Puerto Real, Cádiz, Spain.
+
+<|ref|>text<|/ref|><|det|>[[85, 283, 860, 303]]<|/det|>
+2 Department of Analytical Chemistry. Faculty of Marine and Environmental Sciences, Marine
+
+<|ref|>text<|/ref|><|det|>[[85, 315, 856, 335]]<|/det|>
+Research Institute (INMAR), University of Cádiz, Campus Río San Pedro, 11510 Puerto Real,
+
+<|ref|>text<|/ref|><|det|>[[85, 348, 198, 367]]<|/det|>
+Cádiz, Spain.
+
+<|ref|>text<|/ref|><|det|>[[82, 390, 913, 870]]<|/det|>
+Iron plays a crucial role in the high- nutrient, low- chlorophyll Southern Ocean regions, promoting phytoplankton growth and enhancing atmospheric carbon sequestration1- 5. In this area, the recycling of biogenic iron is partially driven by the iron- rich Antarctic krill (Euphausia superba), and baleen whale species as one of their main predators6- 10. However, although oceanic iron fertilization by Antarctic seabirds has been addressed by previous studies11- 13, penguins have received limited attention, despite their representing the largest seabird biomass in the southern polar region14. Here, we use breeding site guano volumes estimated from drone images and cross- validated with deep learning- powered penguin census, in combination with guano chemical composition to assess the relative iron export to the Antarctic waters from one the most abundant penguin species, the Chinstrap penguin (Pygoscelis antarcticus). Our results show that these seabirds are a relevant but previously ignored contributor to the iron remobilization pool in the Southern Ocean. With an average guano concentration of 3 mg iron g- 1, we estimate that the Chinstrap penguin population is recycling 514 tonnes iron yr- 1. The current iron contribution represents half of the input these penguins were able to recycle four decades ago, as they have declined in more than 50% since then15. Our results evidence the need for in- depth
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[85, 84, 911, 135]]<|/det|>
+understanding how these seabirds' population dynamics impact the surrounding Antarctic marine ecosystems.
+
+<|ref|>text<|/ref|><|det|>[[83, 156, 913, 406]]<|/det|>
+Areas of the Southern Ocean are considered high- nutrient, low- chlorophyll regions| Step / Parameter | Reference | Value | Equation | Result | Uncertainty |
| Chinstrap penguin annual Fe input in the Southern Ocean | Present study | 514 tonnes Fe yr-1 | | | 266 - 762 tonnes Fe yr-1 |
| Fraction of Fe retained in the photic zone | 10 | 0.25/0.5*/0.75 | 5.14x108 g Fe yr-1 x 0.5 | 2.57x108 g Fe yr-1 retained in the photic zone | 1.33 - 3.81 x108 g Fe yr-1 |
| Fraction of Fe bioavailable for phytoplankton | 10 | 0.25/0.5*/0.75 | 2.57x108 g Fe yr-1 x 0.5 | 1.28 x108 g Fe yr-1 bioavailable for phyto. | 0.66 - 1.9 x108 g Fe yr-1 |
| Fe : C ratio of phytoplankton in the Southern Ocean | 38,52,53 | 3 μmol Fe : mol C | | | 1 - 6 μmol Fe : mol C |
| Fe molecular weight | | 55.845 g mol-1 | | | |
| g of carbon incorporated into phytoplankton biomass (NPP) | Present study | | (1.28 x108 g Fe yr-1 x 0.018 mol Fe x 106 μmol Fe x mol C x 12.01 g C) / (g Fe x mol Fe x 3 μmol Fe x mol C) | 9.22 x1012 g C yr-1 incorporated into phyto. | 4.75 - 13.7 x1012 g C yr-1 |
| Southern Ocean area (South of 60°S) | | 2 x1013 m2 | | | |
| Rate of NPP stimulation by Cp guano input | Present study | | (9.22 x1012 g C yr-1 incorporated into phyto.) / (2 x1013 m2) | 0.46 g C m-2 yr-1 incorporated into phyto. | 0.24 - 0.68 g C m-2 yr-1 |
+
+<--- 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|>[[61, 130, 311, 150]]<|/det|>
+NCOMMS2226937Trs.pdf
+
+<--- Page Split --->
diff --git a/preprint/preprint__0207534b48d5dc7f202715e97467615fe845cf6d2005bff553f5b155b496fd65/images_list.json b/preprint/preprint__0207534b48d5dc7f202715e97467615fe845cf6d2005bff553f5b155b496fd65/images_list.json
new file mode 100644
index 0000000000000000000000000000000000000000..eadc805d004186658e720963b85599b80615cb44
--- /dev/null
+++ b/preprint/preprint__0207534b48d5dc7f202715e97467615fe845cf6d2005bff553f5b155b496fd65/images_list.json
@@ -0,0 +1,62 @@
+[
+ {
+ "type": "image",
+ "img_path": "images/Figure_1.jpg",
+ "caption": "Figure 1: (a) Log-scale global map of grid cell mean ‘average edge distance’ (AED, m), as used as model input in this study and interpretable as the average Euclidian distance from an average patch edge to its interior and calculated as described (Methods, Fig. S1) to remove the urban proportion of road area. (b) System diagram of fragmentation-fire relations implemented within the ORCHIDEE model structure, where fragmentation is affecting fire size, spread, number and propensity/rate of spread (ROS)/intensity, with counteracting effects with respect to BA. Green boxes denote where AED impinges on individual fire variables (blue boxes), given modulating factors (orange) that result in emergent grid-scale fire tendencies (red). Arrows denote positive (red) and negative (blue) relationships. FDI=Fire Danger Index; ECO2=Fire CO2 emissions.",
+ "footnote": [],
+ "bbox": [
+ [
+ 120,
+ 149,
+ 545,
+ 555
+ ]
+ ],
+ "page_idx": 3
+ },
+ {
+ "type": "image",
+ "img_path": "images/Figure_2.jpg",
+ "caption": "Figure 2: (a,b): Gross fractional increase (a) and decrease (b) in simulated mean annual BA \\((f(\\Delta BA_{Frag.}))\\) versus a control simulation without fragmentation (log-scale). Grid cells where the absolute change in area burned \\(< 0.2\\%\\) of a grid cell \\((-5\\mathrm{km}^2\\mathrm{yr}^{-1})\\) were masked out. Aggregate annual increases and decreases in BA (Ha \\(\\mathrm{yr}^{-1}\\) ) due to fragmentation are included in million hectares (Mha). (c) Regression of logit link-transformed monthly mean BA against the square root of RD \\((\\mathrm{mkm}^2)\\) . Dashed black line: Observation-based regression model between BA and road density from ref. (Haas et al., 2022 71). Grey line: all simulated grid cells. Blue line and circles: only the simulated grids where fragmentation explicitly decreases mean fire size, plotted against the original road density data used in Haas et al. Red line and circles: same but plotted against the road density used in our simulations where the urban fraction of roads is removed. (d) Frequency counts of grid cells across bins of mean fragmented vegetation patch size (Ha) aggregated from the global AED map in Fig. 1a (red bars), with observed maximum (blue/purple), mean (green) and minimum (orange) observed fire patch size frequencies averaged from 2001-2020 for each grid cell, across the observed range of fire patch size using data from refs (85-88). The overlap between maximum obs. fire size (blue) and fragment size (red) is shaded purple to highlight their statistical overlap. This Figure facilitates interpretation of the fire size range and grid cell quantity affected (C,B) or unaffected (A,D) by fragmentation (see Fig. 2c).",
+ "footnote": [],
+ "bbox": [
+ [
+ 120,
+ 355,
+ 868,
+ 652
+ ]
+ ],
+ "page_idx": 4
+ },
+ {
+ "type": "image",
+ "img_path": "images/Figure_3.jpg",
+ "caption": "Figure 3: (a) Simulated fractional changes in BA due to fragmentation in northern S. America \\((f(\\Delta BA_{Frag.}),\\) colour-bar), overlaid with BA and fragmentation-proxy trend data from Rosan et al. (2022)102, which were",
+ "footnote": [],
+ "bbox": [
+ [
+ 125,
+ 75,
+ 744,
+ 860
+ ]
+ ],
+ "page_idx": 7
+ },
+ {
+ "type": "image",
+ "img_path": "images/Figure_4.jpg",
+ "caption": "Figure 4: (a) Time-averaged map showing where fragmentation relative to the control simulation without fragmentation leads to coupled (increasing or decreasing in the same direction) changes in BA \\((\\Delta \\mathrm{BA}_{\\mathrm{Frag}}\\) , Ha yr \\(^{-1}\\) ) and fire \\(\\mathrm{CO}_2\\) emissions intensity \\((\\Delta \\mathrm{ECO}_{2\\mathrm{Frag}}\\) , gC \\(\\mathrm{m}^{-2}\\) yr \\(^{-1}\\) ), shown in light colours, or decoupled changes (one increasing, the other decreasing), in dark colours. Blue depicts areas where \\(\\Delta \\mathrm{BA}_{\\mathrm{Frag}}\\) is negative, and red where it is positive. (b) Global spatial sensitivity of fire to hypothetical fragmentation levels: \\(\\mathrm{AED}_{\\mathrm{F2}}\\) simulation ensemble-averaged spatial distribution of fragmentation-doubling effects on fire burned area (unitless colour scale). All grid cells show a mean decrease in BA over the 9 simulations, but because of differential direction of change between simulations, we show in dark colours those grid cells where the average \\(\\mathrm{AED}_{\\mathrm{F2}}\\) impact is most likely to increase BA (highest fire susceptibility) and in light colours where it is most likely to decrease BA.",
+ "footnote": [],
+ "bbox": [
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+ ],
+ "page_idx": 9
+ }
+]
\ No newline at end of file
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@@ -0,0 +1,639 @@
+<|ref|>title<|/ref|><|det|>[[44, 107, 883, 175]]<|/det|>
+# Human land fragmentation drives tropical forest fires but dampens global burned area
+
+<|ref|>text<|/ref|><|det|>[[44, 195, 178, 214]]<|/det|>
+Simon Bowring
+
+<|ref|>text<|/ref|><|det|>[[52, 222, 333, 240]]<|/det|>
+simon_bowring@hotmail.com
+
+<|ref|>text<|/ref|><|det|>[[44, 268, 940, 308]]<|/det|>
+Laboratoire des Sciences du Climat et de l'Environnement (LSCE), https://orcid.org/0000- 0002- 0041- 0937
+
+<|ref|>text<|/ref|><|det|>[[44, 316, 590, 356]]<|/det|>
+Wei Li Tsinghua University https://orcid.org/0000- 0003- 2543- 2558
+
+<|ref|>text<|/ref|><|det|>[[44, 362, 330, 402]]<|/det|>
+Florent Mouillot CEFE, Université de Montpellier
+
+<|ref|>text<|/ref|><|det|>[[44, 408, 950, 470]]<|/det|>
+Thais Rosan Faculty of Environment, Science and Economy, University of Exeter https://orcid.org/0000- 0003- 0155- 1739
+
+<|ref|>text<|/ref|><|det|>[[44, 477, 916, 520]]<|/det|>
+Philippe Ciais Laboratoire des Sciences du Climat et de l'Environnement https://orcid.org/0000- 0001- 8560- 4943
+
+<|ref|>sub_title<|/ref|><|det|>[[44, 560, 103, 577]]<|/det|>
+## Article
+
+<|ref|>text<|/ref|><|det|>[[44, 597, 137, 616]]<|/det|>
+Keywords:
+
+<|ref|>text<|/ref|><|det|>[[44, 635, 318, 654]]<|/det|>
+Posted Date: October 3rd, 2023
+
+<|ref|>text<|/ref|><|det|>[[44, 674, 475, 693]]<|/det|>
+DOI: https://doi.org/10.21203/rs.3.rs- 3337266/v1
+
+<|ref|>text<|/ref|><|det|>[[44, 711, 914, 753]]<|/det|>
+License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License
+
+<|ref|>text<|/ref|><|det|>[[44, 772, 534, 791]]<|/det|>
+Additional Declarations: There is NO Competing Interest.
+
+<|ref|>text<|/ref|><|det|>[[42, 827, 941, 870]]<|/det|>
+Version of Record: A version of this preprint was published at Nature Communications on October 24th, 2024. See the published version at https://doi.org/10.1038/s41467- 024- 53460- 6.
+
+<--- Page Split --->
+<|ref|>title<|/ref|><|det|>[[117, 83, 804, 99]]<|/det|>
+# Human land fragmentation drives tropical forest fires but dampens global burned area
+
+<|ref|>text<|/ref|><|det|>[[116, 125, 758, 143]]<|/det|>
+\(^{1,2}\) Simon P.K. Bowring\*, \(^{3}\) Wei Li, \(^{4}\) Florent Mouillot, \(^{5}\) Thais M. Rosan, \(^{1}\) Philippe Ciais.
+
+<|ref|>text<|/ref|><|det|>[[115, 157, 881, 293]]<|/det|>
+\(^{1}\) Laboratoire des Sciences du Climat et de l'Environnement (LSCE), IPSL- CEA- CNRS- UVSQ, Université Paris- Saclay, Gif- sur- Yvette, France. \(^{2}\) Laboratoire de Géologie, Département de Géosciences, Ecole normale supérieure (ENS), 24 rue Lhomond, 75231 Paris Cedex 05, France \(^{3}\) Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University, Beijing, China. \(^{4}\) UMR 5175 CEFE, Université de Montpellier, CNRS, IRD, 1919 Route de Mende, 34293 Montpellier, France \(^{5}\) Faculty of Environment, Science and Economy, University of Exeter, Exeter, United Kingdom
+
+<|ref|>text<|/ref|><|det|>[[117, 308, 504, 323]]<|/det|>
+\*Corresponding author: simon.bowring@lsec.ipsl.fr
+
+<|ref|>sub_title<|/ref|><|det|>[[117, 354, 187, 367]]<|/det|>
+## Abstract
+
+<|ref|>text<|/ref|><|det|>[[116, 368, 881, 549]]<|/det|>
+Landscape fragmentation has been correlated with either increases or decreases in burned area (BA), but their causal mechanisms remain elusive. Here, road density, a fragmentation proxy, is implemented in a CMIP6 coupled land- fire model, enabling dynamic representation of bottom- up processes affecting fragment edges. Over 2000- 2013, fragmentation altered BA by \(>10\%\) in \(16\%\) of burned \([0.5^{\circ}]\) grid- cells and caused gross changes of \(- 6.5\%\) to \(+5.5\%\) in global BA. Model output mimicked the global satellite- observed negative relationship between fragmentation and BA, although some regional BA decreases were matched by fire intensity increases. In recently- deforested tropical areas, however, fragmentation drove significant, observationally- consistent increases in BA \((- 1 / 4\) of Brazilian, Indonesian total BA). Fragmentation BA's relationship with population density is negative globally- averaged, but hump- shaped and largely positive in tropical and temperate forests. We suggest fragmentation could 'tip' toward net BA- amplification with future tropical forest degradation and fire- activity, providing policymakers a first quantification of fragmentation- fire risks.
+
+<|ref|>sub_title<|/ref|><|det|>[[117, 593, 217, 608]]<|/det|>
+## Introduction
+
+<|ref|>text<|/ref|><|det|>[[116, 622, 881, 834]]<|/det|>
+Human land use change (LUC) affects a third of the terrestrial surface \(^{1}\) , and the fragmentation of natural land \(^{2}\) results in large- scale biodiversity loss \(^{3,4}\) , habitat degradation \(^{5}\) , changes to the surface energy balance \(^{6 - 10}\) and biogeochemical cycling \(^{11}\) , leading to around one- third of global carbon (C) emissions \(^{12,13}\) . LUC is forecast to increase substantially by 2100, with expansions in agricultural and settlement area across all future climate- SSP scenarios of \(+12 - 83\%\) \(^{14}\) and \(+54 - 111\%\) \(^{15}\) , respectively forecast over a 2015 baseline. Concurrently, C emissions and attendant increases in global temperatures will perturb atmospheric and hydrologic circulations, combining to increase the future frequency and severity of fire events \(^{16 - 21}\) , the global area prone to frequent fire \((+ - 30\%)^{22}\) , and population- exposure to their immense socioeconomic cost \(^{23}\) . Context- specific studies have demonstrated both negative and positive interactions of LUC with fire probability without determining their drivers \(^{18,24 - 27}\) . Yet to date, no mechanistic representation of the link between the two has been developed. This restricts the capacity of a sustainable economic and infrastructural policy to consider the implications of LUC and fragmentation \(^{28}\) for fire risk, and hampers understanding and forecasting of fire behaviour, the role of human in altering prehistoric fire regimes \(^{29}\) .
+
+<|ref|>text<|/ref|><|det|>[[117, 848, 880, 909]]<|/det|>
+Fire and LUC interact via weather and vegetation through landscape fragmentation, the natural or manmade spatial discontinuity of vegetation due to ecological and economic transitions such as roads, breaks in topography and parent material, disease outbreaks, and fire itself \(^{30}\) . The fragment concept conceives of isolated vegetation patches, each with an interior and an 'edge' that is subject to a diverse range of
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[117, 83, 881, 203]]<|/det|>
+'edge effects' due to contact with a non- vegetated space. The 'edge limit' represents the point where the thinning of interior vegetation is at a maximum, whereas the 'edge area' represents the area between fragment limit and interior that is subject to a gradient of 'edge effects' such as soil and fuel drying. This conception of fragmentation has been deployed for studying edge impacts on ecology, land use dynamics and spatial planning| Variable | Value | Description | Rationale |
| auAF | 2.68*104 | Urban area RL removal regression coefficient (RL/Urban Area Fraction). | High RL urban areas unlikely to have significant BA removed to isolate 'fragmentation' versus 'urban' effect. |
| n pat forest | 1 | Parameter multiplier allowing individual forest fire size to exceed patch size by this factor, otherwise limited by it. | No data to suggest that this size can or cannot be exceeded given patch size unless extreme/crown fire |
| n pat grass | 1.25 | Parameter multiplier allowing individual grass fire size to exceed patch size by this factor, otherwise limited by it. | Value over unity based on assumption that some proportion of fragmented grasslands may allow spread beyond patch due to proportion of fine fuel |
| FSTTREE | conditional, empirically derived | Tree fire spread threshold, a flame height, tree height, canopy width dependent 'crown fire' condition | Allows fire to spread beyond patch size when fuel dryness, wind speed and allow flame height to exceed canopy |
| FSTGRASS | conditional, empirical | Grass fire spread threshold, based on areal fuel density | Allows fire to spread beyond patch size when fuel density exceeds threshold. |
| EDwind | 16m | Edge depth through which wind infiltration is altered by fragment edge | Assumes wind comes from a 1 direction in a given patch, patch average edge depth is approx. to 4m (16/4) in a given fire. |
| EDMoisture | 20m | Edge depth through which fuel moisture affected by fragment edge | Empirically-derived (Methods), assumes linear gradient of drying, and fuel drying itself is scaled quadratically downward with fuel type to reflect radial thickness of model fuel classes. |
| EDhumid. | 1m | Average depth through which human activities there affect ignition probability | This is assumed because although effects may be deeper, the time averaged edge depth along fragment edges is likely small. |
+
+<|ref|>table_caption<|/ref|><|det|>[[115, 442, 881, 468]]<|/det|>
+Table 1: Key components of the fragmentation module, their value, description and rationale. See Methods for detailed description and calibration of each parameter.
+
+<|ref|>title<|/ref|><|det|>[[115, 491, 281, 502]]<|/det|>
+# Competing Interests
+
+<|ref|>text<|/ref|><|det|>[[115, 506, 441, 518]]<|/det|>
+The authors declare no competing interests.
+
+<|ref|>title<|/ref|><|det|>[[115, 536, 255, 547]]<|/det|>
+# Data Availability
+
+<|ref|>text<|/ref|><|det|>[[115, 551, 768, 563]]<|/det|>
+The data presented in this study are available on request from the corresponding author.
+
+<|ref|>title<|/ref|><|det|>[[115, 581, 258, 592]]<|/det|>
+# Code Availability
+
+<|ref|>text<|/ref|><|det|>[[115, 596, 881, 623]]<|/det|>
+The version of ORCHIDEE-MICT-SPITFIRE developed here is published (DOI: ) and can be downloaded at the request of the corresponding author.
+
+<|ref|>title<|/ref|><|det|>[[115, 642, 273, 652]]<|/det|>
+# Acknowledgements
+
+<|ref|>text<|/ref|><|det|>[[115, 657, 881, 684]]<|/det|>
+SPKB was funded by research project FirEUrisk, a European Union Horizon 2020 research and innovation program under Grant Agreement No. 101003890.
+
+<|ref|>title<|/ref|><|det|>[[115, 732, 206, 742]]<|/det|>
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+
+<--- Page Split --->
+<|ref|>sub_title<|/ref|><|det|>[[44, 42, 312, 70]]<|/det|>
+## Supplementary Files
+
+<|ref|>text<|/ref|><|det|>[[44, 92, 768, 113]]<|/det|>
+This is a list of supplementary files associated with this preprint. Click to download.
+
+<|ref|>text<|/ref|><|det|>[[60, 130, 253, 150]]<|/det|>
+MSSupplement.pdf
+
+<--- Page Split --->
diff --git a/preprint/preprint__022065845b0149dd1bde836e14da3448f70c4603767a53533c6448baf134c8c3/images_list.json b/preprint/preprint__022065845b0149dd1bde836e14da3448f70c4603767a53533c6448baf134c8c3/images_list.json
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@@ -0,0 +1,305 @@
+
+# Crowding results from optimal integration of visual targets with contextual information
+
+Guido Marco CicchiniConsiglio Nazionale delle Ricerche https://orcid.org/0000- 0002- 3303- 0420
+
+Giovanni D'ErricoCNR Neuroscience Institute https://orcid.org/0000- 0002- 0491- 581XDavid Burr (Dave@in.cnr.it)University of Florence https://orcid.org/0000- 0003- 1541- 8832
+
+## Article
+
+# Keywords:
+
+Posted Date: March 1st, 2022
+
+DOI: https://doi.org/10.21203/rs.3.rs- 1296243/v1
+
+License: © 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 September 30th, 2022. See the published version at https://doi.org/10.1038/s41467- 022- 33508- 1.
+
+<--- Page Split --->
+
+# Crowding results from optimal integration of visual targets with contextual information
+
+Guido Marco Cicchini| Variable name | Acronym | Units | How does the
variable
determine
population
density? | Cross-validated
Deviance
explained.
Mean [95% CI] | Full-dataset
Deviance
explained |
| Effective Temperature* | ET | C | Energy availability | 0.774
[0.8-0.826] | 0.792 |
Potential
Evapotranspiration** | PET | mm/yr | Energy availability | 0.751
[0.8-0.81] | 0.774 |
Mean Annual
Temperature | MAT | C | Energy availability | 0.733
[0.7-0.777] | 0.757 |
Mean temperature of the
Coldest Month | MCM | C | Extreme Events | 0.798
[0.8-0.839] | 0.812 |
Mean temperature of the
Warmest Month | MWM | C | Extreme Events | 0.705
[0.7-0.762] | 0.737 |
| Temperature Seasonality | TSeson | C | Annual Variability | 0.777
[0.8-0.839] | 0.811 |
Spring Mean
Temperature | SpMT | C | Seasonal trends | 0.789
[0.8-0.828] | 0.804 |
Summer Mean
Temperature | SmMT | C | Seasonal trends | 0.773
[0.8-0.817] | 0.786 |
| Fall Mean Temperature | FMT | C | Seasonal trends | 0.725
[0.7-0.786] | 0.750 |
Winter Mean
Temperature | WMT | C | Seasonal trends | 0.765
[0.8-0.808] | 0.782 |
| Annual precipitation | PREC | mm/yr | Energy availability | 0.701
[0.7-0.757] | 0.712 |
Precipitation of the
Driest Month | PDM | mm/m
onth | Extreme Events | 0.746
[0.7-0.793] | 0.760 |
Precipitation of the
Wettest Month | PDM | mm/m
onth | Extreme Events | 0.77
[0.8-0.804] | 0.784 |
| Precipitation Seasonality | PSeson | mm/m
onth | Annual Variability | 0.737
[0.7-0.772] | 0.748 |
| Spring Precipitation | SpPREC | mm/m
onth | Seasonal trends | 0.773
[0.8-0.814] | 0.788 |
| Summer Precipitation | SmPREC | mm/m
onth | Seasonal trends | 0.753
[0.8-0.8] | 0.779 |
| Fall Precipitation | FPREC | mm/m
onth | Seasonal trends | 0.7
[0.7-0.772] | 0.711 |
| Winter Precipitation | TPREC | mm/m
onth | Seasonal trends | 0.774
[0.8-0.826] | 0.792 |
Topographic
Ruggedness Index*** | | m | Habitat
Heterogeneity | 0.751
[0.8-0.81] | 0.774 |
| 654 | * Calculated following 25 |
| 655 | ** Calculated following on 93. |
| 656 | *** Calculated following 94 |
+
+<--- Page Split --->
+
+
+| Target | Cat. No. | IgG type, source |
| h/m STING | NBP2-24683 | Rabbit polyclonal
Novus Biologicals, Denver, CO |
| h/m AIM2 | 201708-T10 | Rabbit polyclonal
Sino Biological, Eschborn, Germany |
| h/m DDX41 | 102459-T32 | Rabbit polyclonal
Sino Biological, Eschborn, Germany |
| h/m p204 (IFI16) | NBP2-27153 | Rabbit Polyclonal
Novus Biologicals, Denver, CO |
| h/m ZBP1 | 207744-T08 | Rabbit polyclonal
Sino Biological, Eschborn, Germany |
| h/m LC3 | L8918 | Rabbit polyclonal, Merck Sigma-Aldrich, St. Louis, MO, Darmstadt, Germany |
| h/m UCP1 | PA1-24894 | Rabbit polyclonal
ThermoFisher Scientific, Rockford, IL |
| m NPFF | ab10352 | Rabbit polyclonal
Abcam, Cambridge, UK |
| β-actin | NB600-532SS | Rabbit polyclonal
Novus Biologicals, Denver, CO |
| h/m DDX41 | 102459-T32 | Rabbit polyclonal
Invitrogen, Carlsbad, CA |
| h/m Tmem150b | PA5-71527 | Rabbit polyclonal
Invitrogen, Carlsbad, CA |
| J2 (dsRNA) | Anti-dsRNA [J2] | Mouse monoclonal
Absolute Antibody, Wilton, UK |
| m IRF7 | 12-5829-82 | PE-conjugated monoclonal IgG, and matching isotype IgG, ThermoFisher, Waltham, MA |
| m F4/80 antigen | sc-377009 | F4/80 APC, CD45 PerCy5.5, CD11b APC or PE or AF700 (FACS analysis), eBioscience,
ThermoFisher, Waltham, MA, Santa Cruz Biotech (for IHC) |
| m CD11b | E-AB-F1081E | H+L, cross-Adsorbed, FITC, polyclonal, secondary antibody, Invitrogen, Carlsbad, CA |
| Rabbit anti-goat IgG | F-2765 | Goat anti-Rabbit IgG (H+L), HRP-conjugated
Invitrogen, Carlsbad, CA |
| Goat anti-rabbit IgG | A16096 | |
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+## Supplemental Methods
+
+## Activation and inhibition of cytosolic DNA/RNA sensors
+
+Activation and inhibition of cytosolic DNA/RNA sensorsTo activate STING, we treated adipocytes or 3T3- L1 cells with cGAMP (InvivoGene, Toulouse, France) for 6- 18 h, or overexpressed the pCMV6 plasmid (OriGene Technologies, Rockville, MD). In the latter case, \(1 \mu \mathrm{g}\) of DNA was transfected into 300,000 cells using TurboFect Transfection Reagent (Fisher Scientific, Hampton, NH). Control cells received transfection reagent only. Analyses were performed 18- h after transfection. To stimulate RIG- IMDA5, we transfected 3T3- L1 cells at \(80\%\) confluency with high molecular weight polyinosine- polycytidylic acid (p(I:C)) or poly(deoxaydenylic- deoxythymidylic) acid (p(dA:dT)) using the LyoVec cationic lipid- based transfection reagent (InvivoGene, Toulouse, France). Control cells were treated with LyoVec transfection reagent only. We used \(2.5 - 5 \mu \mathrm{g / ml}\) p(dA:dT) or p(I:C), and cells were analyzed 2- 24 h after transfection. IFI16/p204 was activated with \(1 \mu \mathrm{g / ml}\) VACV- 70 conjugated to LyoVec transfection reagent (InvivoGene; 18 h) (41). Treatments are summarized in the table below.
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+