A human liver cell atlas reveals heterogeneity and epithelial progenitors

Article metrics

Abstract

The human liver is an essential multifunctional organ. The incidence of liver diseases is rising and there are limited treatment options. However, the cellular composition of the liver remains poorly understood. Here we performed single-cell RNA sequencing of about 10,000 cells from normal liver tissue from nine human donors to construct a human liver cell atlas. Our analysis identified previously unknown subtypes of endothelial cells, Kupffer cells, and hepatocytes, with transcriptome-wide zonation of some of these populations. We show that the EPCAM+ population is heterogeneous, comprising hepatocyte-biased and cholangiocyte populations as well as a TROP2int progenitor population with strong potential to form bipotent liver organoids. As a proof-of-principle, we used our atlas to unravel the phenotypic changes that occur in hepatocellular carcinoma cells and in human hepatocytes and liver endothelial cells engrafted into a mouse liver. Our human liver cell atlas provides a powerful resource to enable the discovery of previously unknown cell types in normal and diseased livers.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: scRNA-seq reveals cell types in the adult human liver.
Fig. 2: Heterogeneity and zonation of hepatocytes and endothelial cells.
Fig. 3: Identification of a putative progenitor population in the adult human liver.
Fig. 4: TROP2int cells are a source of liver organoid formation.
Fig. 5: scRNA-seq of patient-derived HCC reveals cancer-specific gene signatures and perturbed cellular phenotypes.
Fig. 6: Exploring the gene expression signature of human liver cells in a humanized mouse model.

Data availability

Data generated during this study have been deposited in the Gene Expression Omnibus (GEO) with the accession code GSE124395. The human liver cell atlas can be interactively explored at http://human-liver-cell-atlas.ie-freiburg.mpg.de/.

Change history

  • 09 September 2019

    In this Article, the issue paginated PDF only included the paginated pages, this has now been updated to include the online-only sections again. The HTML was unaffected.

References

  1. 1.

    Michalopoulos, G. K. & DeFrances, M. C. Liver regeneration. Science 276, 60–66 (1997).

  2. 2.

    Ryerson, A. B. et al. Annual report to the nation on the status of cancer, 1975–2012, featuring the increasing incidence of liver cancer. Cancer 122, 1312–1337 (2016).

  3. 3.

    Grün, D. & van Oudenaarden, A. Design and analysis of single-cell sequencing experiments. Cell 163, 799–810 (2015).

  4. 4.

    Herman, J. S., Sagar & Grün, D. FateID infers cell fate bias in multipotent progenitors from single-cell RNA-seq data. Nat. Methods 15, 379–386 (2018).

  5. 5.

    Grün, D. et al. Single-cell messenger RNA sequencing reveals rare intestinal cell types. Nature 525, 251–255 (2015).

  6. 6.

    Jungermann, K. & Kietzmann, T. Zonation of parenchymal and nonparenchymal metabolism in liver. Annu. Rev. Nutr. 16, 179–203 (1996).

  7. 7.

    Gebhardt, R. Metabolic zonation of the liver: regulation and implications for liver function. Pharmacol. Ther. 53, 275–354 (1992).

  8. 8.

    Kietzmann, T. Metabolic zonation of the liver: the oxygen gradient revisited. Redox Biol. 11, 622–630 (2017).

  9. 9.

    Halpern, K. B. et al. Single-cell spatial reconstruction reveals global division of labour in the mammalian liver. Nature 542, 352–356 (2017).

  10. 10.

    MacParland, S. A. et al. Single cell RNA sequencing of human liver reveals distinct intrahepatic macrophage populations. Nat. Commun. 9, 4383 (2018).

  11. 11.

    Haghverdi, L., Büttner, M., Wolf, F. A., Buettner, F. & Theis, F. J. Diffusion pseudotime robustly reconstructs lineage branching. Nat. Methods 13, 845–848 (2016).

  12. 12.

    Strauss, O., Phillips, A., Ruggiero, K., Bartlett, A. & Dunbar, P. R. Immunofluorescence identifies distinct subsets of endothelial cells in the human liver. Sci. Rep. 7, 44356 (2017).

  13. 13.

    Halpern, K. B. et al. Paired-cell sequencing enables spatial gene expression mapping of liver endothelial cells. Nat. Biotechnol. 36, 962–970 (2018).

  14. 14.

    Raven, A. et al. Cholangiocytes act as facultative liver stem cells during impaired hepatocyte regeneration. Nature 547, 350–354 (2017).

  15. 15.

    Michalopoulos, G. K., Barua, L. & Bowen, W. C. Transdifferentiation of rat hepatocytes into biliary cells after bile duct ligation and toxic biliary injury. Hepatology 41, 535–544 (2005).

  16. 16.

    Schmelzer, E. et al. Human hepatic stem cells from fetal and postnatal donors. J. Exp. Med. 204, 1973–1987 (2007).

  17. 17.

    Turner, R. et al. Human hepatic stem cell and maturational liver lineage biology. Hepatology 53, 1035–1045 (2011).

  18. 18.

    Grün, D. et al. De novo prediction of stem cell identity using single-cell transcriptome data. Cell Stem Cell 19, 266–277 (2016).

  19. 19.

    Okabe, M. et al. Potential hepatic stem cells reside in EpCAM+ cells of normal and injured mouse liver. Development 136, 1951–1960 (2009).

  20. 20.

    Cardinale, V. et al. Multipotent stem/progenitor cells in human biliary tree give rise to hepatocytes, cholangiocytes, and pancreatic islets. Hepatology 54, 2159–2172 (2011).

  21. 21.

    Kodama, Y. et al. Hes1 is required for the development of intrahepatic bile ducts. Gastroenterology 124, A123 (2003).

  22. 22.

    Sosa-Pineda, B., Wigle, J. T. & Oliver, G. Hepatocyte migration during liver development requires Prox1. Nat. Genet. 25, 254–255 (2000).

  23. 23.

    Huch, M. et al. Long-term culture of genome-stable bipotent stem cells from adult human liver. Cell 160, 299–312 (2015).

  24. 24.

    Betge, J. et al. MUC1, MUC2, MUC5AC, and MUC6 in colorectal cancer: expression profiles and clinical significance. Virchows Arch. 469, 255–265 (2016).

  25. 25.

    Park, S.-W. et al. The protein disulfide isomerase AGR2 is essential for production of intestinal mucus. Proc. Natl Acad. Sci. USA 106, 6950–6955 (2009).

  26. 26.

    Forner, A., Reig, M. & Bruix, J. Hepatocellular carcinoma. Lancet 391, 1301–1314 (2018).

  27. 27.

    Matkowskyj, K. A. et al. Aldoketoreductase family 1B10 (AKR1B10) as a biomarker to distinguish hepatocellular carcinoma from benign liver lesions. Hum. Pathol. 45, 834–843 (2014).

  28. 28.

    Rantakari, P. et al. The endothelial protein PLVAP in lymphatics controls the entry of lymphocytes and antigens into lymph nodes. Nat. Immunol. 16, 386–396 (2015).

  29. 29.

    Grompe, M. & Strom, S. Mice with human livers. Gastroenterology 145, 1209–1214 (2013).

  30. 30.

    Azuma, H. et al. Robust expansion of human hepatocytes in Fah–/–/Rag2–/–/Il2rg–/– mice. Nat. Biotechnol. 25, 903–910 (2007).

  31. 31.

    Uhlén, M. et al. Proteomics. Tissue-based map of the human proteome. Science 347, 1260419 (2015).

  32. 32.

    Krieger, S. E. et al. Inhibition of hepatitis C virus infection by anti-claudin-1 antibodies is mediated by neutralization of E2-CD81-claudin-1 associations. Hepatology 51, 1144–1157 (2010).

  33. 33.

    Lieber, A., Peeters, M. J., Gown, A., Perkins, J. & Kay, M. A. A modified urokinase plasminogen activator induces liver regeneration without bleeding. Hum. Gene Ther. 6, 1029–1037 (1995).

  34. 34.

    Mailly, L. et al. Clearance of persistent hepatitis C virus infection in humanized mice using a claudin-1-targeting monoclonal antibody. Nat. Biotechnol. 33, 549–554 (2015).

  35. 35.

    Hashimshony, T. et al. CEL-Seq2: sensitive highly-multiplexed single-cell RNA-seq. Genome Biol. 17, 77 (2016).

  36. 36.

    Li, H. & Durbin, R. Fast and accurate long-read alignment with Burrows-Wheeler transform. Bioinformatics 26, 589–595 (2010).

  37. 37.

    Grün, D., Kester, L. & van Oudenaarden, A. Validation of noise models for single-cell transcriptomics. Nat. Methods 11, 637–640 (2014).

  38. 38.

    Anders, S. & Huber, W. Differential expression analysis for sequence count data. Genome Biol. 11, R106 (2010).

  39. 39.

    Yu, G., Wang, L.-G., Han, Y. & He, Q.-Y. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 16, 284–287 (2012).

  40. 40.

    Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).

  41. 41.

    Mootha, V. K. et al. PGC-1α-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat. Genet. 34, 267–273 (2003).

  42. 42.

    Yu, G. & He, Q. Y. ReactomePA: an R/Bioconductor package for reactome pathway analysis and visualization. Mol. Biosyst. 12, 477–479 (2016).

  43. 43.

    Broutier, L. et al. Culture and establishment of self-renewing human and mouse adult liver and pancreas 3D organoids and their genetic manipulation. Nat. Protocols 11, 1724–1743 (2016).

  44. 44.

    Aizarani, N. et al. Protocol for single-cell RNA-sequencing of cryopreserved human liver cells. Protoc. Exch. https://doi.org/10.21203/rs.2.9620/v1 (2019).

Download references

Acknowledgements

This study was supported by the Max Planck Society, the German Research Foundation (DFG) (SPP1937 GR4980/1-1, GR4980/3-1, and GRK2344 MeInBio), the DFG under Germany’s Excellence Strategy (CIBSS – EXC-2189 – Project ID 390939984), and the Behrens-Weise-Foundation (all to D.G.); and by ARC, Paris and Institut Hospital-Universitaire, Strasbourg (TheraHCC, TheraHCC2.0 IHUARC IHU201301187 and IHUARC2019 IHU201901299 to T.F.B.), the European Union (ERC-AdG-2014-671231-HEPCIR, EU H2020-667273-HEPCAR and ERC-PoC-2016-PRELICAN to T.F.B), Agence nationale de recherches sur le sida et les hépatites virales (ANRS, ECTZ35076) and the Foundation of the University of Strasbourg. This work was done under the framework of the LABEX ANR-10-LABX-0028_HEPSYS and Inserm Plan Cancer (Plan Cancer 2014-2019, Action 13.1, appel à projets 2018) and benefits from funding from the state managed by the French National Research Agency as part of the Investments for the future. Institut Universitaire France (IUF) to T.F.B. We thank S. Hobitz, K. Schuldes (MPI-IE FACS facility) and U. Bönisch (MPI-IE Deep Sequencing facility); the CRB (Centre de Ressources Biologiques-Biological Resource Centre of the Strasbourg University Hospitals) for the management of regulatory requirements of patient-derived liver tissue; C. Fauvelle and L. Heydmann for their contributions to the initial single-cell isolations; F. Juehling, F. H. T. Duong and C. Schuster for helpful discussions; the patients for providing informed consent to participate in the study; and the nurses, technicians and medical doctors of the hepatobiliary surgery and pathology services of Strasbourg University Hospitals for their support. This publication is part of the Human Cell Atlas (https://www.humancellatlas.org/publications).

Peer review information

Nature thanks Meritxell Huch, Shalev Itzkovitz and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Author information

T.F.B. and D.G. conceived the study. N.A. designed, optimized and performed cell sorting, scRNA-seq, organoid culture and immunofluorescence, provided validation using the Human Protein Atlas, and performed computational analysis and interpretation of the data. A.S. managed the supply of patient material, isolated single cells from patient tissue, performed animal experiments and carried out immunofluorescence. S. contributed to scRNA-seq analyses and performed scRNA-seq experiments. L.M. performed animal experiments. J.S.H. created the web interface. S.D. isolated single cells from patient tissues. P.P. performed liver resections and provided patient liver samples. T.F.B. established the liver tissue supply pipeline and supervised the animal experiments. D.G. analysed and interpreted the data and supervised experiments and analysis by N.A., S. and J.S.H. D.G., N.A. and T.F.B. coordinated and led the study. N.A. and D.G. wrote the manuscript with input from S., A.S. and T.F.B.

Correspondence to Thomas F. Baumert or Dominic Grün.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data figures and tables

Extended Data Fig. 1 scRNA-seq analysis of normal liver resection specimens from nine adult patients.

a, FACS plot for CD45 and ASGR1 staining from a mixed fraction (hepatocyte and non-parenchymal cells). b, FACS plot for PECAM1 and CD34 staining from a mixed fraction. c, FACS plot for CLEC4G staining from a mixed fraction. ac, n = 6 independent experiments. d, t-SNE map showing the IDs of the nine patients from whom the cells were sequenced. Cells were sequenced from freshly prepared single-cell suspensions for patients 301, 304, 325 and BP1, and from cryopreserved single-cell suspensions for patients 301, 304, 308, 309, 310, 311, 315 and 325. Cells were sorted and sequenced mainly in an unbiased fashion from non-parenchymal cell, hepatocyte and mixed fractions for patients 301 and 304. Non-parenchymal and mixed fractions were used to sort specific populations on the basis of markers. CD45 and CD45+ cells were sorted from all patients. CLEC4G+ LSECs were sorted by FACS from patients 308, 310, 315 and 325. EPCAM+ cells were sorted by FACS from patients 308, 309, 310, 311, 315 and 325. e, t-SNE map highlighting data for fresh and cryopreserved cells from patients 301, 304 and 325. Although minor shifts in frequencies within cell populations are visible, transcriptomes of fresh and cryopreserved cells co-cluster. Differential gene expression analysis of fresh versus cryopreserved cells, for example, for endothelial cells of patient 325 in cluster 10 (f), did not reveal any differentially expressed genes. d, e, n = 10,372 cells. f, Bar plot showing the number of differentially expressed genes (Benjamini–Hochberg corrected P < 0.01; see Methods) between fresh and cryopreserved cells within each cluster for patient 325 (top; n = 2,248 cells) and patients 304 (n = 959 cells) and 301 (n = 1,329 cells) (bottom). Only clusters with more than five cells from fresh and cryopreserved samples were included for the computation. g, Scatter plot of mean normalized expression across fresh and cryopreserved cells from patient 325 in endothelial cells of cluster 10 (no differentially expressed genes, left; n = 101 cells) and cluster 11 (maximal number of differentially expressed genes across all clusters, right; n = 272 cells). Red dots indicate differentially expressed genes (Benjamini–Hochberg corrected P < 0.01; see Methods). Diagonal (solid black line) and log2 fold changes of 4 (broken black lines) are indicated. Almost all differentially expressed genes for cluster 11 exhibit log2 fold changes of less than 4. h, Bar plot showing the fraction of sorted cells which passed quality filtering (see Methods) after scRNA-seq. Error bars are derived from the sampling error assuming binomial counting statistics. F, fresh samples; C, cryopreserved samples. i, t-SNE map highlighting cells sequenced from mixed plates representing unbiased samples for patients 301 and 304. Without any enrichment strategy, hepatocytes and immune cells strongly dominate and endothelial cells and EPCAM+ cells are rarely sequenced. j, Table of patient information. CCM, colon cancer metastasis; ICC, intrahepatic cholangiocarcinoma; LR, liver resection.

Extended Data Fig. 2 The endothelial cell compartment is a heterogeneous mixture of subpopulations.

a, Expression heat map of genes upregulated in endothelial cell clusters (Benjamini–Hochberg corrected P < 0.01; n = 1,830 cells; see Methods). For each cluster the top ten upregulated genes were extracted and expression of the joint set is shown in the heat map across all endothelial cell clusters. Genes were ordered by hierarchical clustering. b, Expression t-SNE maps for the LSEC and MaVEC marker genes PECAM1, CLEC4G, CD34, CLEC4M and FLT1. c, Expression t-SNE maps for VWF, AQP1, CCL21, TFF3, UNC5B and IGFBP5. d, Expression t-SNE maps for CPE and CLU. e, Expression t-SNE map for H19. be, Colour bars indicate log2 normalized expression. n = 10,372 cells. f, Immunostaining of CD34, CLEC4G, PECAM1 and AQP1 in normal liver tissue from the Human Protein Atlas. The portal area for AQP1 is enlarged to show positive staining of both bile duct cells and portal MaVECs (black arrows).

Extended Data Fig. 3 Evolutionary conservation of zonation profiles.

a, Diffusion maps highlighting inferred dpt and Alb expression (left), and a self-organizing map for mouse hepatocyte single-cell RNA-seq data9 (right; see Methods). See Fig. 2 for details. b, Heat map showing the spatial hepatocyte zonation profiles (nine zones) inferred by Halpern et al.9 using the same ordering of genes as in a. c, Pearson’s correlation coefficient of zonation profiles inferred by Halpern et al.9 and our dpt approach after discretizing dpt-inferred zonation profiles into nine equally sized bins. We found that 1,347 out of 1,684 genes (80%) above the expression cutoff exhibited a positive correlation between the two methods. d, Pearson’s correlation coefficient as a function of average normalized expression. Negative correlations are enriched at low expression, and Pearson’s correlation of zonation profiles positively correlates with expression (Spearman’s R = 0.25; n = 1,684 genes). e, t-SNE map of single-cell transcriptomes highlighting the clusters generated by RaceID3, run separately on hepatocytes (clusters 11, 14, and 17 in Fig. 1c). The map reveals a major group of hepatocyte clusters and a number of small clusters that co-express T cell-related genes, B cell-related genes or progenitor genes. f, t-SNE maps highlighting the expression of ALB, the immune cell marker gene PTPRC, the B cell marker gene IGKC, and the progenitor marker gene EPCAM. The colour bar indicates log2 normalized expression. Co-expression of hepatocyte and immune cell markers could either indicate the presence of doublets or be due to spillover of highly expressed genes such as ALB between cells during library preparation. For the zonation analysis (Fig. 2), only cells in clusters 3, 7, 19, 4, 2, 9, 8 and 11 from the map in e were included. e, f, n = 3,040 cells. g, Immunostaining for the periportal markers CPS1, PCK1, MTHFS, and GATM from the Human Protein Atlas31. The zonation module containing each gene in the SOM (Fig. 2a) is indicated in parentheses. P, portal tracts; C, central veins. h, Pathways enriched for genes in hepatocyte central/mid modules 24 and 33 (top; n = 659 genes) and periportal modules 1 and 3 (bottom; n = 422 genes) (compare with Fig. 2a). i, Immunostaining of the central marker ENG from the Human Protein Atlas31. The zonation module in the SOM (Fig. 2b) is indicated in parentheses. j, Pathways enriched for genes in endothelial central/mid modules 1 and 3 (top; n = 422 genes) and periportal module 20 (bottom; n = 73 genes) (compare with Fig. 2b). h, j, P values in the pathway enrichment analysis were calculated using a hypergeometric model and adjusted using the Benjamini–Hochberg method (see Methods). k, Pearson’s correlation coefficient of hepatocyte zonation profiles of orthologue pairs of human and mouse genes. Mouse data are from Halpern et al.9 (n = 967 genes) l, Pearson’s correlation coefficient of endothelial cell (including MVECs and LSECs) zonation profiles of orthologue pairs of human and mouse genes (n = 977 genes). Mouse data are from Halpern et al.13. See Methods for details.

Extended Data Fig. 4 The human liver contains different Kupffer cell populations.

a, Expression t-SNE maps of marker genes for Kupffer cell subtypes. The colour bar indicates log2 normalized expression (n = 10,372 cells). b, Major pathways upregulated in the CD1C+ antigen-presenting (n = 12 genes) and LILRB5+ metabolic/immunoregulatory (n = 35 genes) Kupffer cell subsets as revealed by Reactome pathway analysis. The number of genes in each pathway is shown on the x axis. P values were calculated using a hypergeometric model and adjusted using the Benjamini–Hochberg method. c, Expression heat map of genes upregulated in Kupffer cell clusters (Benjamini–Hochberg corrected P < 0.01, see Methods). For each cluster, the top ten upregulated genes were extracted and expression of the joint set is shown in the heat map across all Kupffer cell clusters. Genes were ordered by hierarchical clustering.

Extended Data Fig. 5 The human liver contains different B cell populations.

Expression t-SNE maps of the markers for the B cell subtypes. The colour bars indicate log2 normalized expression (n = 10,372 cells).

Extended Data Fig. 6 Heterogeneity of NK and NKT cells in the human liver.

a–c, Expression t-SNE maps of inferred markers of cluster 5 (a), cluster 1 (b) and cluster 3 (c). Cluster 5 comprises mainly CD56+CD8A NK cells, some of which show upregulated CCL4. Cluster 1 comprises CD56CD8A+ NKT cells, which show upregulated CCL5. Cluster 3 consists of both CD56+ and CD56CD8A+ NKT cells. Clusters 1 and 3 express T cell receptor components exemplified by CD3D co-receptor expression. d, Differential expression of killer cell lectin-like receptor genes across the different populations shown in ac. e, Differential expression of granzyme genes across the different populations shown in ac. Colour bars indicate log2 normalized expression. ae, n = 10,372 cells.

Extended Data Fig. 7 scRNA-seq identifies marker genes expressed by EPCAM+ cells.

a, Expression t-SNE maps (left) for EPCAM, CD24, FGFR2, TACSTD2, CLDN1, TM4SF4, WWTR1 and ANXA4 (n = 10,372 cells) and immunohistochemistry from the Human Protein Atlas (right) for CLDN1, TM4SF4, WWTR1, and ANXA4. b, Expression t-SNE maps for ASGR1 and CFTR (n = 10,372 cells). c, t-SNE maps showing expression of KRT19, ALB, TACSTD2 and MUC6 in the EPCAM+ compartment (n = 1,087 cells). ac, Colour bars indicate log2 normalized expression. d, Expression heat map of proliferation marker genes (MKI67, PCNA), AFP, and identified markers of the EPCAM+ compartment. Genes highlighted in red correspond to newly identified markers of the EPCAM+ compartment. The heat map comprises all clusters to show the specificity of the markers for the progenitor compartment. The expression analysis confirms the absence of proliferating and AFP+ cells. e, Immunofluorescence labelling of EPCAM and KRT19 on human liver tissue. EPCAM+KRT19low/– cells (solid arrow) in the canals of Hering (asterisk) and EPCAM+KRT19+ cells (broken arrow) in the bile duct (arrowhead) are indicated. Nuclei are stained with DAPI. Scale bar, 10 μm (n = 3 independent experiments).

Extended Data Fig. 8 The EPCAM+ compartment segregates into different major subpopulations.

a, Separate RaceID3 and StemID2 analyses of the EPCAM+ and hepatocyte populations reveal a lineage tree connecting EPCAM+ cells to mature hepatocytes via an EPCAM+ hepatocyte progenitor cluster (part of the EPCAM+ population in Fig. 3b). Shown are links with StemID2 P < 0.05. The node colour highlights transcriptome entropy. b, Two-dimensional diffusion map representation of the population shown in a, highlighting expression of the hepatocyte marker ALB (left), EPCAM (centre), and the mature cholangiocyte marker CFTR (right). The maps suggest continuous transitions from the EPCAM+ compartment towards hepatocytes and mature cholangiocytes. c, Expression t-SNE map of EPCAM (top) and the hepatocyte marker ASGR1 (bottom) for the population shown in a. Colour bars indicate log2 normalized expression. b, c, n = 3,877 cells. d, Expression heat map of de novo identified markers of the EPCAM+ compartment, highlighting the expression distribution within clusters of this population only (Fig. 3). e, Expression heat map of all genes that were differentially expressed in the more mature clusters, belonging to the groups denoted as ‘hepatocyte fate’ and ‘cholangiocyte fate’. For each of these clusters, the top ten upregulated genes (Benjamini–Hochberg corrected P < 0.01) were selected, and the joint set of these genes is shown in the figure. f, Expression t-SNE maps of CXCL8, MMP7 and HP. Colour bars indicate log2 normalized expression. df, n = 1,087 cells. g, Normalized expression counts of ALB, KRT19 and TACSTD2 in cells sequenced from the gates in Fig. 4a (n = 293 cells). Centre line, mean; boxes, interquartile range; whiskers, 5% and 95% quantiles; data points, outliers. h, t-SNE map of RaceID3 clusters for organoid cells and EPCAM+ cells from patients (Fig. 3), including cells sorted from the gates in a. i, Expression t-SNE maps of EPCAM, CD24 and AQP1 in organoid cells and EPCAM+ cells from patients. j, Expression t-SNE maps of SFRP5, ALB, AGR2 and MKI67. Colour bars indicate log2 normalized expression. hjn = 2,870 cells. k, GSEA of genes that are differentially expressed between organoid and EPCAM+ liver cells from patients (Benjamini–Hochberg corrected P < 0.01, n = 11,610 genes; see Methods).

Extended Data Fig. 9 Cell types from patient-derived HCC exhibit perturbed gene expression signatures.

a, FACS plot of CD45 and ASGR1 staining on cells from HCC samples (n = 3 independent experiments). b, Symbol t-SNE map showing the IDs of HCC patients (n = 11,654 cells). c, t-SNE map showing RaceID3 clusters for normal liver cells co-analysed with cells from HCC tissues (n = 3 patients). d, Expression heat map of genes that are differentially expressed between cancer cells from HCC and normal hepatocytes (Benjamini–Hochberg corrected P < 0.05 and log2 fold change >1.6; n = 256 cells; see Methods). Genes highlighted in red correspond to differentially expressed genes validated by immunohistochemistry. e, Immunostaining of CPS1 and CYP2C8 in normal liver and HCC tissues from the Human Protein Atlas. f, Expression heat map of genes that are differentially expressed between endothelial cells from HCC and normal endothelial cells from MaVEC and LSEC clusters. Benjamini–Hochberg corrected P < 0.05; log2 fold change >1.5; n = 1,936 cells; see Methods). Genes highlighted in red correspond to differentially expressed genes validated by immunohistochemistry. g, Immunostaining of CD34, LAMB1, AQP1 and PLVAP in normal liver and HCC tissues from the Human Protein Atlas. h, Heat map of genes that are differentially expressed between normal and HCC-resident NK and NKT cells (Benjamini–Hochberg corrected P < 0.05; n = 2,754 cells; see Methods). i, Heat map of genes that are differentially expressed between normal and HCC-resident Kupffer cells (Benjamini–Hochberg corrected P < 0.05; n = 991 cells; see Methods). j, GSEA of genes that are differentially expressed between normal and HCC-resident NK and NKT cells (n = 15,442 genes). k, GSEA of genes that are differentially expressed between normal and HCC-resident Kupffer cells (n = 15,442 genes).

Extended Data Fig. 10 Transplanted human liver cells in a humanized mouse model exhibit a distinct gene signature compared to cells within the human liver.

a, t-SNE map of RaceID3 clusters of liver cells from patients co-analysed with cells from the humanized mouse liver model. b, Expression t-SNE maps of the hepatocyte marker gene ALB. c, Expression t-SNE maps of the endothelial marker CLEC4G. d, Expression t-SNE maps of HP, PCK1 and CCND1. e, Expression t-SNE maps of the liver endothelial cell zonated genes LYVE1, FCN3 and CD14. f, Expression t-SNE maps of PECAM1, CD34 and AQP1. af, Colour bars indicate log2 normalized expression. n = 10,683 cells. g, Heat maps of genes that are differentially expressed between hepatocytes (n = 3,175 cells) and endothelial cells (n = 1,710 cells) from patients (human hepatocytes and human endothelial cells) and from the humanized mouse model (HMouse hepatocytes and HMouse endothelial cells). Benjamini–Hochberg corrected P < 0.05; see Methods.

Supplementary information

Supplementary Information

This file contains Supplementary Notes 1-5 and Supplementary References.

Reporting Summary

Supplementary Table 1 Gene expression signatures of all clusters. Official gene symbol, mean expression across all genes not contained within a cluster (mean.ncl), mean expression across all genes contained within a cluster (mean.cl), fold change between cluster and non-cluster cells (fc), differential expression p-value (pv, see Methods), and Benjamini-Hochberg corrected p-value are given. Only genes with P<0.01 are shown for each cluster. (n= 10,372 cells).

Supplementary Table 2 Human hepatocyte zonation. Official gene symbol, SOM module and Benjamini-Hochberg corrected ANOVA p-value of zonated genes in human hepatocytes (n=3 bins; 2,534 cells; Methods).

Supplementary Table 3 Human hepatocyte zonation data. Official gene symbol, SOM module and loess-smoothed expression z-scores (see Methods) of DPT-ordered hepatocytes. These data were used to create the zonation map in Fig. 2a.

Supplementary Table 4 Human endothelial cell zonation. Official gene symbol, SOM module and Benjamini-Hochberg corrected ANOVA p-value of zonated genes in human liver endothelial cells (n=3 bins; 1,361 cells; Methods).

Supplementary Table 5 Human endothelial cell zonation data. Official gene symbol, SOM module and loess-smoothed expression z-scores (see Methods) of DPT-ordered endothelial cells. These data were used to create the zonation map in Fig. 2b.

Supplementary Table 6 Conservation of hepatocyte zonation between mouse and human. Official gene symbol, SOM module and Benjamini-Hochberg corrected ANOVA p-value of zonated genes in human and mouse hepatocytes (Methods), and Pearson’s correlation coefficient between mouse and human zonation profiles (n=9 bins).

Supplementary Table 7 Conservation of liver endothelial cell zonation between mouse and human. Official gene symbol, SOM module and Benjamini-Hochberg corrected ANOVA p-value of zonated genes in human liver endothelial cells (Methods) and Pearson’s correlation coefficient between mouse and human zonation profiles. Mouse zonation p-values are taken from Halpern et al. 13 (n=4 bins).

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Further reading

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.