Resolving the fibrotic niche of human liver cirrhosis at single-cell level

Abstract

Liver cirrhosis is a major cause of death worldwide and is characterized by extensive fibrosis. There are currently no effective antifibrotic therapies available. To obtain a better understanding of the cellular and molecular mechanisms involved in disease pathogenesis and enable the discovery of therapeutic targets, here we profile the transcriptomes of more than 100,000 single human cells, yielding molecular definitions for non-parenchymal cell types that are found in healthy and cirrhotic human liver. We identify a scar-associated TREM2+CD9+ subpopulation of macrophages, which expands in liver fibrosis, differentiates from circulating monocytes and is pro-fibrogenic. We also define ACKR1+ and PLVAP+ endothelial cells that expand in cirrhosis, are topographically restricted to the fibrotic niche and enhance the transmigration of leucocytes. Multi-lineage modelling of ligand and receptor interactions between the scar-associated macrophages, endothelial cells and PDGFRα+ collagen-producing mesenchymal cells reveals intra-scar activity of several pro-fibrogenic pathways including TNFRSF12A, PDGFR and NOTCH signalling. Our work dissects unanticipated aspects of the cellular and molecular basis of human organ fibrosis at a single-cell level, and provides a conceptual framework for the discovery of rational therapeutic targets in liver cirrhosis.

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Fig. 1: Single-cell atlas of human liver NPCs.
Fig. 2: Identifying SAMac subpopulations.
Fig. 3: Pro-fibrogenic phenotype of SAMacs.
Fig. 4: Identifying scar-associated endothelial subpopulations.
Fig. 5: Identifying a SAMes subpopulation.
Fig. 6: Multi-lineage interactions in the fibrotic niche.

Data availability

Our expression data are freely available for user-friendly interactive browsing online at http://www.livercellatlas.mvm.ed.ac.uk. CellPhoneDB is available at www.CellPhoneDB.org. All raw sequencing data have been deposited in the Gene Expression Omnibus (GEO) under accession GSE136103.

Code availability

R scripts enabling the main steps of the analysis are available from the corresponding authors on reasonable request.

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Acknowledgements

This work was supported by an MRC Clinician Scientist Fellowship (MR/N008340/1) to P.R., a Wellcome Trust Senior Research Fellowship in Clinical Science (ref. 103749) to N.C.H., an AbbVie Future Therapeutics and Technologies Division grant to N.C.H., a Guts UK–Children’s Liver Disease Foundation grant (ref. R43927) to N.C.H., a Tenovus Scotland grant (ref. E18/05) to R.D. and N.C.H. and British Heart Foundation grants (RM/17/3/33381; RE/18/5/34216) to N.C.H. R.V.-T. was funded by EMBO and Human Frontiers long-term fellowships. C.J.W. was funded by a BBSRC New Investigator Award (BB/N018869/1). P.N.N., C.J.W. and N.T.L. are funded by the NIHR Birmingham Biomedical Research Centre at the University Hospitals Birmingham NHS Foundation Trust and the University of Birmingham. This paper presents independent research supported by the NIHR Birmingham Biomedical Research Centre at the University Hospitals Birmingham NHS Foundation Trust and the University of Birmingham. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care. J.P.I. is funded by the NIHR Bristol Biomedical Research Centre, University Hospitals Bristol Foundation Trust and the University of Bristol. C.P.P. was funded by the UK Medical Research Council, MC_UU_00007/15. This work was also supported by Wellcome Sanger core funding (WT206194). We thank the patients who donated liver tissue and blood for this study. We thank J. Davidson, C. Ibbotson, J. Black and A. Baird of the Scottish Liver Transplant Unit and the research nurses of the Wellcome Trust Clinical Research Facility for assistance with consenting patients for this study. We thank the liver transplant coordinators and surgeons of the Scottish Liver Transplant Unit and the surgeons and staff of the Hepatobiliary Surgical Unit, Royal Infirmary of Edinburgh for assistance in procuring human liver samples. We thank S. Johnston, W. Ramsay and M. Pattison for technical assistance with FACS and flow cytometry. We thank J. Henderson for technical support and G. Muirhead for assistance with isolation of liver endothelial cells. This publication is part of the Human Cell Atlas (www.humancellatlas.org/publications).

Author information

Affiliations

Authors

Contributions

P.R. performed experimental design, tissue procurement, data generation, data analysis and interpretation, and manuscript preparation; R.D. performed experimental design, data generation and data analysis; E.F.D., K.P.M., B.E.P.H., M.B., J.A.M. and N.T.L. performed data generation and analysis; J.R.P. generated the interactive online browser; M.E. and R.V.-T. assisted with CellPhoneDB analyses and critically appraised the manuscript; T.J.K. performed pathological assessments and provided intellectual contribution; N.O.C., J.A.F. and P.N.N. provided intellectual contribution; C.J.W. performed tissue procurement, data generation, interpretation and intellectual contribution; J.R.W.-K. performed computational analysis with assistance from J.R.P. and R.S.T. and advice from C.P.P., J.C.M. and S.A.T.; J.R.W.-K. also helped with manuscript preparation, and C.P.P., J.C.M. and S.A.T. critically appraised the manuscript; E.M.H., D.J.M. and S.J.W. procured human liver tissue and critically appraised the manuscript. J.P.I., F.T. and J.W.P. provided intellectual contribution and critically appraised the manuscript; N.C.H. conceived the study, designed experiments, interpreted data and prepared the manuscript.

Corresponding authors

Correspondence to P. Ramachandran or N. C. Henderson.

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The authors declare no competing interests.

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Extended data figures and tables

Extended Data Fig. 1 Strategy for isolation of human liver non-parenchymal cells.

a, Patient demographics and clinical information. Data are mean ± s.e.m. b, Flow cytometry gating strategy for isolation of leucocytes (CD45+) and other non-parenchymal cells (CD45) from human liver; representative plots from ten livers. c, Flow cytometry gating strategy for isolation of PBMCs; representative plots from four patients. d, Clustering 103,568 cells from healthy (n = 5) and cirrhotic (n = 5) livers, healthy PBMCs (n = 1) and cirrhotic PBMCs (n = 4) (left), annotating the source (PBMC versus liver; middle) and cell lineage inferred from known marker gene signatures (right). e, Dot plot annotating PBMC and liver clusters by lineage signatures. Circle size indicates cell fraction expressing signature greater than mean; colour indicates mean signature expression (red, high; blue, low). f, CXCR4 gene expression in single cells derived from blood or liver tissue, divided by cell lineage. Bottom right, representative immunofluorescence image (n ≥ 3) of CXCR4 (green) and DAPI (blue) in human liver; arrows denote CXCR4 cells in the lumen of a blood vessel. Scale bar, 50 μm. g, Violin plots showing the number of unique genes (nGene), number of total unique molecular identifiers (nUMI) and mitochondrial gene fraction expressed in five PBMC samples. Black lines denote the median. h, Pie charts showing the proportion of cell lineages per PBMC sample. i, Box and whisker plots showing the agreement in expression profiles across five PBMC samples. Pearson correlation coefficients between average expression profiles for cells in each lineage, across all pairs of samples. Black bars denote the median; box edges denote the twenty-fifth and seventy-fifth percentiles; whiskers denote the full range.

Extended Data Fig. 2 Quality control and annotation of human liver-resident cells.

a, Lineage signature expression across 66,135 liver-resident cells from healthy (n = 5) and cirrhotic (n = 5) human livers (red, high; blue, low). b, Dot plot annotating liver-resident cell clusters by lineage signature. Circle size indicates cell fraction expressing signature greater than mean; colour indicates mean signature expression (red, high; blue, low). c, Violin plots of the number of unique genes (left), number of total UMIs (middle) and mitochondrial gene fraction (right) across 66,135 liver-resident cells from healthy (n = 5) and cirrhotic (n = 5) livers. Black lines denote the median. d, Pie charts of the proportion of cell lineage per liver sample. e, Box and whisker plots of the agreement in expression profiles across healthy (n = 5) and cirrhotic (n = 5) liver samples, as in Extended Data Fig. 1i. f, t-SNE visualization of liver-resident cells per liver sample, with cirrhotic samples annotated by aetiology of underlying liver disease. ALD, alcohol-related liver disease; PBC, primary biliary cholangitis.

Extended Data Fig. 3 Annotating human liver lymphoid cells.

a, Clustering of 36,900 T cells and ILCs (left) from healthy (n = 5) and cirrhotic (n = 5) human livers, annotating the injury condition (right). NK, natural killer cell; cNK, cytotoxic NK cell. b, Fractions of T cell and ILC subpopulations in healthy (n = 5) and cirrhotic (n = 5) livers. c, Selected gene expression in 36,900 T cells and ILCs. d, Heat map of T cell and ILC cluster marker genes (colour-coded by cluster and condition), with exemplar genes labelled (right). Columns denote cells; rows denote genes. e, t-SNE visualizations of downsampled T cell and ILC dataset (7,380 cells from healthy (n = 5) and cirrhotic (n = 5) human livers) before and after imputation (scImpute); annotating data used for visualization and clustering, inferred lineage and injury condition. No additional heterogeneity was observed after imputation. f, Clustering 2,746 B cells and plasma cells (left) from healthy (n = 5) and cirrhotic (n = 5) human livers, annotating the injury condition (right). g, Heat map of B cell and plasma cell cluster marker genes (colour-coded by cluster and condition), with exemplar genes labelled (right). Columns denote cells; rows denote genes. h, Fractions of B cell and plasma cell subpopulations in healthy (n = 5) and cirrhotic (n = 5) livers. Data are mean ± s.e.m. P values determined by Wald test (b). Source data

Extended Data Fig. 4 Annotating human liver MPs.

a, Clustering and selected genes expressed in 10,737 MPs from healthy (n = 5) and cirrhotic (n = 5) human livers. b, Scaled gene expression of KC cluster markers across MP cells from healthy (n = 5) and cirrhotic (n = 5) livers. c, Representative immunofluorescence images (n ≥ 3) of TIMD4 (red), CD163 (white), MARCO (green) and DAPI (blue) in healthy and cirrhotic liver; arrows denote CD163+MARCO+TIMD4 cells. d, Immunohistochemistry (left) and cell counts (right) of TIMD4 expression in healthy (n = 12) and cirrhotic (n = 9) human liver. e, Immunohistochemistry (left) and cell counts (right) of MARCO expression in healthy (n = 8) and cirrhotic (n = 8) liver. f, Flow cytometry gating strategy for identifying KCs, TMs and SAMacs in healthy (n = 2) and cirrhotic (n = 3) liver. SAMacs are detected as TREM2+CD9+ cells within the TM and SAMac gate (see Fig. 2f). g, Representative immunofluorescence images (n ≥ 3) of TREM2 (red), MNDA (white), collagen 1 (green) and DAPI (blue) in cirrhotic liver. h, Representative images (n = 2) of TREM2 (smFISH; red), MNDA (immunofluorescence; green) and DAPI (blue) in cirrhotic liver. i, Representative immunofluorescence images (n ≥ 3) of CD9 (red), MNDA (white), collagen 1 (green) and DAPI (blue) in cirrhotic liver. j, Immunohistochemistry (top) and cell counts (bottom) of TREM2 expression in healthy (n = 10) and cirrhotic (n = 9) liver. k, Immunohistochemistry (top) and cell counts (bottom) of CD9 expression in healthy (n = 12) and cirrhotic (n = 10) liver. l, Top, exemplar tissue segmentation of cirrhotic liver section into fibrotic septae (orange) and parenchymal nodules (purple). Bottom, cell counts based on immunohistochemistry analysis of TREM2 (n = 9), CD9 (n = 11), TIMD4 (n = 9) and MARCO (n = 7) in parenchymal nodules and fibrotic septae. m, Top, clustering and annotation of 208 cycling MP cells from healthy (n = 5) and cirrhotic (n = 5) livers, with scaled gene expression of MP subpopulation markers across four clusters of cycling MP cells. Bottom, fractions of cycling MP subpopulations in healthy (n = 5) and cirrhotic (n = 5) livers. All scale bars, 50 μm. Data are mean ± s.e.m. P values determined by two-tailed Mann–Whitney (e, j, k), two-tailed Wilcoxon test (l) or Wald test (m). Source data

Extended Data Fig. 5 Phenotypic characterization of mononuclear phagocytes in healthy and cirrhotic human livers.

a, Top, self-organizing map (60 × 60 grid) of smoothed scaled metagene expression of 10,737 MPs from healthy (n = 5) and cirrhotic (n = 5) livers. In total, 20,952 genes, 3,600 metagenes and 44 signatures were identified. A–F denote metagene signatures overexpressed in one or more MP subpopulations. Bottom, smoothed mean metagene expression profile for each MP subpopulation. b, Radar plots (left), exemplar genes (middle) and selected GO enrichment (right) of metagene signatures A–F showing distribution of signature expression across MP subpopulations from 10,737 MP cells. c, Diffusion map (DM) visualization of blood monocytes and liver-resident MP lineages (23,075 cells from healthy (n = 5) and cirrhotic (n = 5) liver samples and PBMCs (n = 5)), annotating monocle pseudotemporal dynamics (purple to yellow). Top, RNA velocity field (red arrows) visualized using Gaussian smoothing on regular grid. Bottom, annotation of MPs by subpopulation and injury condition. d, Unspliced–spliced phase portraits (top); 23,075 cells coloured and visualized as in Fig. 3a; monocyte (MNDA), SAMac (CD9) and KC (TIMD4) marker genes. Cells plotted above or below the steady-state (black dashed line) indicate increasing or decreasing expression of gene, respectively. Spliced expression profile for stated genes (middle row; red, high, blue, low). Unspliced residuals for stated genes (bottom row), positive (red) indicating expected upregulation, negative (blue) indicating expected downregulation. MNDA displays negative velocity in SAMacs; CD9 displays positive velocity in monocytes and SAMacs; TIMD4 velocity is restricted to KCs. e, Cubic smoothing spline curve fitted to averaged expression of all genes in module 2 from the blood monocyte-to-SAMac pseudotemporal trajectory (see Fig. 3c), with selected GO enrichment (right). f, Cubic smoothing spline curve fitted to averaged expression of all genes in module 3 from the blood monocyte-to-cDC pseudotemporal trajectory (see Fig. 3c), with selected GO enrichment (right). g, Luminex assay showing quantification of levels of stated proteins in culture medium from FACS-isolated SAMacs (n = 3), TMs (n = 2) and KCs (n = 2). Control denotes medium alone (n = 2). Data are mean ± s.e.m. h, Heat map of transcription factor regulons across MP pseudotemporal trajectory and in KCs (colour-coded by MP cluster, condition and pseudotime), with selected regulons labelled (right). Columns denote cells; rows denote genes. i, Scaled regulon expression of selected regulons across MP clusters from healthy (n = 5) and cirrhotic (n = 5) livers. All P values determined by Fisher’s exact test. Source data

Extended Data Fig. 6 Characterization of macrophages in mouse liver fibrosis.

a, Clustering and annotating 3,250 mouse (m)MPs from healthy (n = 3) and fibrotic (4 weeks CCl4 treatment; n = 3) livers. b, Annotating mouse MP cells by injury condition. c, Heat map of mouse MP cluster marker genes (top; colour-coded by cluster and condition), with exemplar genes labelled (right). Columns denote cells; rows denote genes. d, Selected genes expressed in 3,250 mouse MPs. e, Representative flow cytometry plots of the gating strategy (n = 8 from two independent experiments) for identifying mouse KCs, CD9 TMs and CD9+ SAMacs in fibrotic mice. f, Quantifying mouse macrophage subpopulations by flow cytometry in healthy (n = 6) and fibrotic (n = 8) mouse livers from two independent experiments. The macrophage subpopulation (x axis) is shown as a percentage of total viable CD45+ cells (y axis). Data are mean ± s.e.m. P values determined by two-tailed Mann–Whitney test. g, Co-culture of primary mouse HSCs from uninjured livers and either FACS-isolated CD9 mouse TMs or CD9+ mouse SAMacs from fibrotic livers (n = 8 mice; two independent experiments). Right, qPCR of Col3a1 expression in HSCs; expression relative to mean expression of quiescent HSC. P value determined by two-tailed Wilcoxon test. h, Clustering 3,250 mouse MPs and 10,737 human (h)MPs into five clusters using canonical correlation analysis. Annotation of cross-species clusters (identity). i, Annotation of human and mouse macrophage subpopulations from 3,250 mouse MPs and 10,737 human MPs. j, Selected genes expressed in 3,250 mouse MPs and 10,737 human MPs. Source data

Extended Data Fig. 7 SAMac expansion in human NASH.

ad, Deconvolution of publicly available whole liver microarray data (n = 73) assessed for frequency of SAMacs, KCs and TMs using the Cibersort algorithm. a, Macrophage composition. GEO accession numbers are shown on the x axis; the fraction of monocyte–macrophages is shown on the y axis. Liver phenotypes are annotated at the top. b, Frequency of SAMacs in control (n = 14), heathy obese (n = 27), steatosis (n = 14) and NASH (n = 18) livers. c, Left, frequency of SAMacs in patients with histological NAFLD activity scores (NAS) of 0 (n = 37), 1–3 (n = 19) and 4–7 (n = 17). Right, frequency of SAMacs in patients with histological fibrosis scores of 0 (n = 46), 1 (n = 20) and 2–4 (n = 5). d, Left, frequency of SAMacs in female (n = 58) and male (n = 15) patients. Middle, frequency of SAMacs in patients aged 23–39 (n = 22), 40–49 (n = 29) and 50–80 (n = 22). Right, frequency of SAMacs in patients with a body mass index (BMI) of 17–30 (n = 18), 31–45 (n = 28) and 46–70 (n = 27). e, Left, immunohistochemistry of CD9 and TREM2 expression in NAFLD liver biopsy sections. Scale bars, 50 μm. Right, cell counts of CD9 and TREM2 expression. CD9: NAS 1–3 (n = 13), NAS 4–8 (n = 21). TREM2: NAS 1–3 (n = 12), NAS 4–8 (n = 16). f, Correlation of cell counts with picrosirius red (PSR) digital morphometric pixel quantification in NAFLD liver biopsy tissue with CD9 staining (top; n = 39) or TREM2 staining (bottom; n = 32). Data are mean ± s.e.m. P values determined by Kruskal–Wallis and Dunn test (b, c), two-tailed Mann–Whitney test (e) or Pearson’s correlation and linear regression (f). Source data

Extended Data Fig. 8 Phenotypic characterization of endothelial cells in healthy and cirrhotic human livers.

a, Clustering and selected genes expressed in 8,020 endothelial cells from healthy (n = 4) and cirrhotic (n = 3) human livers. b, Scaled gene expression of endothelial cluster markers across endothelial cells from healthy (n = 4) and cirrhotic (n = 3) livers. c, Top, digital pixel quantification of PLVAP immunostaining of cirrhotic liver sections (n = 10) in parenchymal nodules and fibrotic septae. Bottom, ACKR1 immunostaining of cirrhotic liver sections (n = 10) in parenchymal nodules and fibrotic septae. d, Flow cytometry analysis of PLVAP, CD34 and ACKR1 in endothelial cells from healthy (n = 3, grey) or cirrhotic (n = 7, red) livers. Top, representative histograms; bottom, MFI values. e, Flow-based adhesion assay. Peripheral blood monocytes assessed for adhesion to primary human liver endothelial cells (top) and the percentage of adherent monocytes that transmigrate (bottom); endothelial cells isolated from healthy (n = 5) or cirrhotic (n = 4) livers. f, Endothelial cell gene knockdown. Cirrhotic endothelial cells were treated with siRNA against PLVAP (n = 6) or ACKR1 (n = 5) or with control siRNA (n = 6). Top, representative flow cytometry histograms for stated markers, with comparison to isotype control antibody. Bottom, flow-based adhesion assay, with PBMCs assessed for adhesion (bottom left) and the percentage of adherent cells that transmigrate (bottom right) after siRNA treatment of endothelial cells. g, Top left, self-organizing map (60 × 60 grid) of smoothed scaled metagene expression of endothelia lineage. In total, 21,237 genes, 3,600 metagenes and 45 signatures were identified. A–E denote metagene signatures overexpressed in one or more endothelial subpopulations. Bottom left, smoothed mean metagene expression profile for each endothelial subpopulation. Middle, radar plots of metagene signatures A–E showing distribution of signature expression across endothelial subpopulations, exemplar genes (middle) and Gene Ontology enrichment (right). h, Heat map of endothelial subpopulation transcription factor regulon expression (colour-coded by cluster and condition) across 8,020 endothelial cells from healthy (n = 4) and cirrhotic (n = 3) human livers. Exemplar regulons are labelled (right). Columns denote cells; rows denote regulons. i, t-SNE visualization of endothelial lineage (8,020 cells from healthy (n = 4) and cirrhotic (n = 3) livers), annotating monocle pseudotemporal dynamics (purple to yellow; grey indicates lack of inferred trajectory). RNA velocities (red arrows) visualized using Gaussian smoothing on regular grid. j, Representative immunofluorescence images (n ≥ 3) of RSPO3, PDPN, AIF1L, VWA1 or ACKR1 (red), CD34 (white), PLVAP (green) and DAPI (blue) in healthy and cirrhotic liver. Scale bars, 50 μm. k, Annotation of 8,020 endothelial cells by subpopulation and injury condition. LSEC, liver sinusoidal endothelial cells. Data are mean ± s.e.m. P values determined by two-tailed Wilcoxon test (c), two-tailed Mann–Whitney test (d, e), Kruskal–Wallis and Dunn test (f), or Fisher’s exact test (g). Source data

Extended Data Fig. 9 Characterization of mesenchymal cells in healthy and cirrhotic human livers.

a, Selected genes expressed in 2,318 mesenchymal cells from healthy (n = 4) and cirrhotic (n = 3) human livers. b, Clustering 319 SAMes into two further subclusters. c, Heat map of SAMes subcluster marker genes (colour-coded by cluster and condition), with exemplar genes labelled (right). Columns denote cells; rows denote genes. d, Fractions of SAMes subpopulations in healthy (n = 4) and cirrhotic (n = 3) livers. e, f, Representative immunofluorescence images (n ≥ 3) of OSR1 (red), collagen 1 (green) and DAPI (blue) in portal region of healthy liver (e) or fibrotic niche of cirrhotic liver (f). Scale bars, 50 μm. g, Scaled gene expression of selected genes across 2,318 mesenchymal cells from healthy (n = 4) and cirrhotic (n = 3) livers. h, t-SNE visualization of 1,178 HSCs and SAMes from healthy (n = 4) and cirrhotic (n = 3) livers annotated by monocle pseudotemporal dynamics (purple to yellow). RNA velocity field (red arrows) visualized using Gaussian smoothing on regular grid. i, Heat map of cubic smoothing spline curves fitted to genes differentially expressed across HSC-to-SAMes pseudotemporal trajectories, grouped by hierarchical clustering (k = 2); colour-coded by pseudotime and condition (top). Gene co-expression modules (colour) and exemplar genes are labelled (right). Data are mean ± s.e.m. P values determined by Wald test (d). Source data

Extended Data Fig. 10 Characterization of the cellular interactome in the fibrotic niche.

a, b, Representative immunofluorescence images (n ≥ 3) of fibrotic niche in cirrhotic liver. a, TREM2 (red), PLVAP (white), PDGFRα (green) and DAPI (blue). b, TREM2 (red), ACKR1 (white), PDGFRα (green) and DAPI (blue). c, Proliferation assay. Human HSCs were treated with conditioned medium from primary hepatic macrophage subpopulations SAMac (n = 2), TMs (n = 2), KCs (n = 2) or control medium (n = 2). The AUC of the percentage change in HSC number over time (hours) is shown on the y axis. Data are mean ± s.e.m. d, Circle plot showing potential interaction magnitude from ligands expressed by SAMacs and SAEndos to receptors expressed on SAMes. e, Circle plot showing potential interaction magnitude from ligands expressed by SAMes to receptors expressed on SAMacs and SAEndos. f, Dot plot of ligand–receptor interactions between SAMes (n = 7 human livers), SAMacs (n = 10 human livers) and SAEndos (n = 7 human livers). Ligand (red) and cognate receptor (blue) shown on the x axis; populations that express ligand (red) and receptor (blue) are shown on the y axis; circle size denotes P value (permutation test); colour (red, high; yellow, low) denotes average ligand and receptor expression levels in interacting subpopulations. g, Top, representative immunofluorescence image (n ≥ 3) of CCL2 (red), CCR2 (white), PDGFRα (green) and DAPI (blue) in fibrotic niche in cirrhotic liver; arrows denote CCL2+PDGFRα+ cells. Bottom, representative immunofluorescence image (n ≥ 3) of ANGPT1 (red), TEK (white), PDGFRα (green) and DAPI (blue) in fibrotic niche in cirrhotic liver; arrows denote ANGPT1+PDGFRα+ cells. h, Circle plot denotes potential interaction magnitude from ligands expressed by SAMacs to receptors expressed on SAEndos. i, Dot plot of ligand–receptor interactions between SAMacs (n = 10 human livers) and SAEndos (n = 7 human livers) as in f. j, Representative immunofluorescence image (n ≥ 3) of TREM2 (red), FLT1 (white), VEGFA (green) and DAPI (blue) in fibrotic niche in cirrhotic liver; arrows denote TREM2+VEGFA+ cells. k, Circle plot of the potential interaction magnitude from ligands expressed by SAEndos to receptors expressed on SAMacs. l, Dot plot of ligand–receptor interactions between SAEndo (n = 7 human livers) and SAMacs (n = 10 human livers) as in f. m, Top, representative immunofluorescence image (n ≥ 3) of TREM2 (red), CD200 (white), CD200R (green) and DAPI (blue) in fibrotic niche in cirrhotic liver; arrows denote TREM2+CD200R+ cells. Bottom, representative immunofluorescence image (n ≥ 3) of TREM2 (red), DLL4 (white), NOTCH2 (green) and DAPI (blue) in fibrotic niche in cirrhotic liver; arrows denote TREM2+NOTCH2+ cells. All scale bars, 50 μm. Source data

Supplementary information

Supplementary Information

This file contains Supplementary Note 1 (Annotation of hepatic lymphoid cells), Supplementary Note 2 (Annotation of liver mononuclear phagocytes), Supplementary Note 3: Annotation of liver endothelial cells) and Supplementary References

Reporting Summary

Supplementary Table 1

| Cell lineage signature genes for supervised clustering This table provides the list of genes used as lineage signatures for signature analysis (Extended Data Figure 1e, 2b)

Supplementary Table 2

| Quality metrics for single-cell RNA-seq datasets This table provides a summary of mean±SEM of number of genes (nGene), number of UMIs (nUMI) and mitochondrial gene fraction (fraction.mito) for each human scRNA-seq sample presented in the manuscript (n=5 healthy liver, n=5 cirrhotic liver, n=4 cirrhotic PBMC). Pre-QC (prior to removal of poor quality cells) and Post-QC (after removal of poor quality cells) data are shown. The fraction of cells removed from each dataset as poor quality (nGene<300 or fraction.mito>0.3) are listed

Supplementary Table 3

| Marker genes for unsupervised clustering of liver-resident cells This table provides the list of marker genes specific to each of the identified clusters from 66,135 liver-resident cells isolated from 5 healthy and 5 cirrhotic human livers, AUC classifier (Figure 1e)

Supplementary Table 4

| Marker genes for supervised clustering of liver-resident cell lineages This table provides the list of marker genes specific to each of the identified lineages from 66,135 liver-resident cells isolated from 5 healthy and 5 cirrhotic human livers, AUC classifier (Figure 1e)

Supplementary Table 5

| Marker genes for unsupervised clustering of T cells and ILCs This table provides the list of marker genes specific to each of the identified clusters from 36,900 T cells and innate lymphoid cells (ILC) isolated from 5 healthy and 5 cirrhotic human livers, AUC classifier (Extended Data Figure 3d)

Supplementary Table 6

| Marker genes for unsupervised clustering of imputed T cells and ILCs This table provides the list of marker genes specific to each of the identified clusters from 7,380 T cells and innate lymphoid cells (ILC) isolated from 5 healthy and 5 cirrhotic human livers, after scImpute processing, AUC classifier (Extended Data Figure 3e)

Supplementary Table 7

| Marker genes for unsupervised clustering of B cells and plasma cells This table provides the list of marker genes specific to each of the identified clusters from 2,746 B cells and plasma cells isolated from 5 healthy and 5 cirrhotic human livers, AUC classifier (Extended Data Figure 3g)

Supplementary Table 8

| Marker genes for unsupervised clustering of mononuclear phagocytes This table provides the list of marker genes specific to each of the identified clusters from 10,737 mononuclear phagocytes isolated from 5 healthy and 5 cirrhotic human livers, AUC classifer (Figure 2d)

Supplementary Table 9

| Distinguishing metagene signatures and associated ontology terms for MP SOM This table provides the list of genes associated with each SOM metagene signature identified as distinguishing subpopulations within the mononuclear phagocyte lineage dataset, scrat, and their associated gene ontology terms, Fisher’s exact test (Extended Data Figure 5a, b)

Supplementary Table 10

| Differential gene modules and associated ontology terms expressed over MP pseudotemporal trajectory This table provides the list of genes associated with each module of differentially-expressed genes over the MP pseudotemporal trajectory, monocle::differentialGeneTest, and their associated gene ontology terms, Fisher’s exact test (Figure 3c, d, Extended Data Figure 5e, f)

Supplementary Table 11

| Marker genes for unsupervised clustering of mouse MP This table provides the list of marker genes specific to each of the identified clusters from 3,250 mouse mononuclear phagocytes (mMP) isolated from healthy (n=3) and fibrotic (n=3) mouse livers, AUC classifier (Extended Data Figure 6c)

Supplementary Table 12

| Transcription factor regulons differentially expressed over MP pseudotemporal trajectory and in KC This table provides the list of transcription factor regulons differentially expressed over the MP pseudotime trajectory, monocle::differentialGeneTest, and in KC (Extended Data Figure 5h, i)

Supplementary Table 13

| Marker genes for unsupervised clustering of endothelial cells This table provides the list of marker genes specific to each of the identified clusters from 8,020 liver endothelial cells isolated from 4 healthy and 3 cirrhotic human livers, AUC classifier (Figure 4c)

Supplementary Table 14

| Distinguishing metagene signatures and associated ontology terms for endothelia SOM This table provides the list of genes associated with each SOM metagene signature identified as distinguishing subpopulations within the endothelia lineage dataset (8,020 cells), scrat, and their associated gene ontology terms, Fisher’s exact test (Extended Data Figure 8g)

Supplementary Table 15

| Marker transcription factor regulons for unsupervised clustering of endothelial cells This table provides the list of marker regulons specific to each of the identified clusters in the 8,020 endothelia cell dataset, AUC classifier (Extended Data Figure 8h)

Supplementary Table 16

| Marker genes for unsupervised clustering of mesenchymal cells This table provides the list of marker genes specific to each of the identified clusters from 2,318 mesenchymal cells isolated from 4 healthy and 3 cirrhotic human livers, AUC classifier (Figure 5b)

Supplementary Table 17

| Marker genes for unsupervised sub-clustering of scar-associated mesenchymal cells This table provides the list of marker genes specific to each of the 2 identified clusters from 319 scar-associated mesenchymal cells isolated from 4 healthy and 3 cirrhotic human livers, AUC classifier (Extended Data Figure 9c)

Supplementary Table 18

| Ligand-receptor interactions identified as significant in the fibrotic niche This table provides the list of ligand-receptor interactions identified as significant by CellPhoneDB between pairs of cellular subpopulations within the fibrotic niche of a cirrhotic human liver, permutation test (Figure 6a, e, Extended Data Figure 10f, i, l)

Supplementary Table 19

| Antibodies used in the study This table provides a list of commercial antibodies and conditions used in this study (Methods)

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Ramachandran, P., Dobie, R., Wilson-Kanamori, J.R. et al. Resolving the fibrotic niche of human liver cirrhosis at single-cell level. Nature 575, 512–518 (2019). https://doi.org/10.1038/s41586-019-1631-3

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