Liver disease is a major global health-care problem, affecting an estimated 844 million people worldwide. Despite this substantial burden, therapeutic options for liver disease remain limited, in part owing to a paucity of detailed analyses defining the cellular and molecular mechanisms that drive these conditions in humans. Single-cell transcriptomic technologies are transforming our understanding of cellular diversity and function in health and disease. In this Review, we discuss how these technologies have been applied in hepatology, advancing our understanding of cellular heterogeneity and providing novel insights into fundamental liver biology such as the metabolic zonation of hepatocytes, endothelial cells and hepatic stellate cells, and the cellular mechanisms underpinning liver regeneration. Application of these methodologies is also uncovering critical pathophysiological changes driving disease states such as hepatic fibrosis, where distinct populations of macrophages, endothelial cells and mesenchymal cells reside within a spatially distinct fibrotic niche and interact to promote scar formation. In addition, single-cell approaches are starting to dissect key cellular and molecular functions in liver cancer. In the near future, new techniques such as spatial transcriptomics and multiomic approaches will further deepen our understanding of disease pathogenesis, enabling the identification of novel therapeutic targets for patients across the spectrum of liver diseases.
Single-cell RNA sequencing coupled with spatial mapping have demonstrated previously unknown molecular patterns of metabolic zonation of hepatocytes, endothelial cells and hepatic stellate cells across the human and mouse liver lobule.
Single-cell analysis of the adaptive immune compartment, specifically T cells, in hepatocellular carcinoma and intrahepatic cholangiocarcinoma has highlighted potential new approaches to prognostication and the development of immunotherapy strategies for affected patients.
The study of human liver macrophage subpopulations at the single-cell level has identified a distinct population of monocyte-derived macrophages that expand during liver fibrosis; these cells reside in the fibrotic niche and promote mesenchymal cell activation and scar deposition.
Distinct populations of endothelial cells and mesenchymal cells also expand in liver fibrosis and are topographically located in the fibrotic niche.
Interactome modelling of ligand–receptor pairs between subpopulations of scar-associated macrophages, and endothelial and mesenchymal cells, in liver fibrosis provides a molecular framework for the therapeutic targeting of key pathogenic cell populations.
This is a preview of subscription content, access via your institution
Open Access articles citing this article.
An integrated gene-to-outcome multimodal database for metabolic dysfunction-associated steatotic liver disease
Nature Medicine Open Access 30 October 2023
Signal Transduction and Targeted Therapy Open Access 12 September 2023
GepLiver: an integrative liver expression atlas spanning developmental stages and liver disease phases
Scientific Data Open Access 10 June 2023
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 print issues and online access
$209.00 per year
only $17.42 per issue
Rent or buy this article
Prices vary by article type
Prices may be subject to local taxes which are calculated during checkout
Marcellin, P. & Kutala, B. K. Liver diseases: a major, neglected global public health problem requiring urgent actions and large-scale screening. Liver Int. 38 (Suppl. 1), 2–6 (2018).
Hernandez-Gea, V. & Friedman, S. L. Pathogenesis of liver fibrosis. Annu. Rev. Pathol. Mech. Dis. 6, 425–456 (2011).
Asrani, S. K., Devarbhavi, H., Eaton, J. & Kamath, P. S. Burden of liver diseases in the world. J. Hepatol. 70, 151–171 (2019).
Angulo, P. et al. Liver fibrosis, but no other histologic features, is associated with long-term outcomes of patients with nonalcoholic fatty liver disease. Gastroenterology 149, 389–397 (2015).
Stubbington, M. J. T., Rozenblatt-Rosen, O., Regev, A. & Teichmann, S. A. Single cell transcriptomics to explore the immune system in health and disease. Science 358, 58–63 (2017).
Giladi, A. & Amit, I. Single-cell genomics: a stepping stone for future immunology discoveries. Cell 172, 14–21 (2018).
Regev, A. et al. The Human Cell Atlas. Elife 6, e27041 (2017).
Schulze, R. J., Schott, M. B., Casey, C. A., Tuma, P. L. & McNiven, M. A. The cell biology of the hepatocyte: a membrane trafficking machine. J. Cell Biol. 218, 2096–2112 (2019).
Banales, J. M. et al. Cholangiocyte pathobiology. Nat. Rev. Gastroenterol. Hepatol. 16, 269–281 (2019).
Gebhardt, R. Metabolic zonation of the liver: regulation and implications for liver function. Pharmacol. Ther. 53, 275–354 (1992).
Ben-Moshe, S. & Itzkovitz, S. Spatial heterogeneity in the mammalian liver. Nat. Rev. Gastroenterol. Hepatol. 16, 395–410 (2019).
Halpern, K. B. et al. Single-cell spatial reconstruction reveals global division of labour in the mammalian liver. Nature 542, 352–356 (2017).
Halpern, K. B. et al. Paired-cell sequencing enables spatial gene expression mapping of liver endothelial cells. Nat. Biotechnol. 36, 962–970 (2018).
MacParland, S. A. et al. Single cell RNA sequencing of human liver reveals distinct intrahepatic macrophage populations. Nat. Commun. 9, 4383 (2018).
Aizarani, N. et al. A human liver cell atlas reveals heterogeneity and epithelial progenitors. Nature 572, 199–204 (2019).
Ben-Moshe, S. et al. Spatial sorting enables comprehensive characterization of liver zonation. Nat. Metab. 1, 899–911 (2019).
Camp, J. G. et al. Multilineage communication regulates human liver bud development from pluripotency. Nature 546, 533–538 (2017).
Font-Burgada, J. et al. Hybrid periportal hepatocytes regenerate the injured liver without giving rise to cancer. Cell 162, 766–779 (2015).
Wang, B., Zhao, L., Fish, M., Logan, C. Y. & Nusse, R. Self-renewing diploid Axin2+ cells fuel homeostatic renewal of the liver. Nature 524, 180–185 (2015).
Lin, S. et al. Distributed hepatocytes expressing telomerase repopulate the liver in homeostasis and injury. Nature 556, 244–248 (2018).
Autexier, C. & Lue, N. F. The structure and function of telomerase reverse transcriptase. Annu. Rev. Biochem. 75, 493–517 (2006).
Marshall, A. et al. Relation between hepatocyte G1 arrest, impaired hepatic regeneration, and fibrosis in chronic hepatitis C virus infection. Gastroenterology 128, 33–42 (2005).
Wiemann, S. U. et al. Hepatocyte telomere shortening and senescence are general markers of human liver cirrhosis. FASEB J. 16, 935–942 (2002).
Bird, T. G. et al. TGFβ inhibition restores a regenerative response in acute liver injury by suppressing paracrine senescence. Sci. Transl. Med. 10, eaan1230 (2018).
Boulter, L., Lu, W.-Y. & Forbes, S. J. Differentiation of progenitors in the liver: a matter of local choice. J. Clin. Invest. 123, 1867–1873 (2013).
Deng, X. et al. Chronic liver injury induces conversion of biliary epithelial cells into hepatocytes. Cell Stem Cell 23, 114–122 (2018).
Lu, W.-Y. et al. Hepatic progenitor cells of biliary origin with liver repopulation capacity. Nat. Cell Biol. 17, 971–983 (2015).
Raven, A. et al. Cholangiocytes act as facultative liver stem cells during impaired hepatocyte regeneration. Nature 547, 350–354 (2017).
Pepe-Mooney, B. J. et al. Single-cell analysis of the liver epithelium reveals dynamic heterogeneity and an essential role for YAP in homeostasis and regeneration. Cell Stem Cell 25, 23–38 (2019).
Planas-Paz, L. et al. YAP, but not RSPO-LGR4/5, signaling in biliary epithelial cells promotes a ductular reaction in response to liver injury. Cell Stem Cell 25, 39–53 (2019).
Ramachandran, P. et al. Resolving the fibrotic niche of human liver cirrhosis at single-cell level. Nature 575, 512–518 (2019).
Heymann, F. & Tacke, F. Immunology in the liver — from homeostasis to disease. Nat. Rev. Gastroenterol. Hepatol. 13, 88–110 (2016).
Robinson, M. W., Harmon, C. & O’Farrelly, C. Liver immunology and its role in inflammation and homeostasis. Cell. Mol. Immunol. 13, 267–276 (2016).
Pellicoro, A., Ramachandran, P., Iredale, J. P. & Fallowfield, J. A. Liver fibrosis and repair: immune regulation of wound healing in a solid organ. Nat. Rev. Immunol. 14, 181–194 (2014).
Nishida, N. & Kudo, M. Immunological microenvironment of hepatocellular carcinoma and its clinical implication. Oncology 92, 40–49 (2017).
van Furth, R. in Methods for Studying Mononuclear Phagocytes Ch. 27 (eds Adams, D. O., Edelson, P. J. & Koren, H. S.) 243–251 (Elsevier, 1981).
Guilliams, M. et al. Dendritic cells, monocytes and macrophages: a unified nomenclature based on ontogeny. Nat. Rev. Immunol. 14, 571–578 (2014).
Gomez Perdiguero, E. et al. Tissue-resident macrophages originate from yolk-sac-derived erythro-myeloid progenitors. Nature 518, 547–551 (2015).
Schulz, C. et al. A lineage of myeloid cells independent of Myb and hematopoietic stem cells. Science 336, 86–90 (2012).
Hoeffel, G. et al. C-Myb+ erythro-myeloid progenitor-derived fetal monocytes give rise to adult tissue-resident macrophages. Immunity 42, 665–678 (2015).
Yona, S. et al. Fate mapping reveals origins and dynamics of monocytes and tissue macrophages under homeostasis. Immunity 38, 79–91 (2013).
Bain, C. C. et al. Long-lived self-renewing bone marrow-derived macrophages displace embryo-derived cells to inhabit adult serous cavities. Nat. Commun. 7, ncomms11852 (2016).
Hashimoto, D. et al. Tissue-resident macrophages self-maintain locally throughout adult life with minimal contribution from circulating monocytes. Immunity 38, 792–804 (2013).
Bajpai, G. et al. The human heart contains distinct macrophage subsets with divergent origins and functions. Nat. Med. 24, 1234–1245 (2018).
Mass, E. et al. Specification of tissue-resident macrophages during organogenesis. Science 353, aaf4238 (2016).
Schraml, B. U. et al. Genetic tracing via DNGR-1 expression history defines dendritic cells as a hematopoietic lineage. Cell 154, 843–858 (2013).
Karlmark, K. R. et al. Hepatic recruitment of the inflammatory Gr1+ monocyte subset upon liver injury promotes hepatic fibrosis. Hepatology 50, 261–274 (2009).
Ramachandran, P. et al. Differential Ly-6C expression identifies the recruited macrophage phenotype, which orchestrates the regression of murine liver fibrosis. Proc. Natl Acad. Sci. USA 109, E3186–E3195 (2012).
Yang, L. et al. Vascular endothelial growth factor promotes fibrosis resolution and repair in mice. Gastroenterology 146, 1339–1350 (2014).
Scott, C. L. et al. Bone marrow-derived monocytes give rise to self-renewing and fully differentiated Kupffer cells. Nat. Commun. 7, 10321 (2016).
Blériot, C. et al. Liver-resident macrophage necroptosis orchestrates type 1 microbicidal inflammation and type-2-mediated tissue repair during bacterial infection. Immunity 42, 145–158 (2015).
Krenkel, O. & Tacke, F. Liver macrophages in tissue homeostasis and disease. Nat. Rev. Immunol. 17, 306–321 (2017).
Scott, C. L. & Guilliams, M. The role of Kupffer cells in hepatic iron and lipid metabolism. J. Hepatol. 69, 1197–1199 (2018).
You, Q., Cheng, L., Kedl, R. M. & Ju, C. Mechanism of T cell tolerance induction by murine hepatic Kupffer cells. Hepatology 48, 978–990 (2008).
Scott, C. L. et al. The transcription factor ZEB2 is required to maintain the tissue-specific identities of macrophages. Immunity 49, 312–325.e5 (2018).
Aibar, S. et al. SCENIC: single-cell regulatory network inference and clustering. Nat. Methods 14, 1083–1086 (2017).
Theurl, I. et al. On-demand erythrocyte disposal and iron recycling requires transient macrophages in the liver. Nat. Med. 22, 945–951 (2016).
Wu, R., Nakatsu, G., Zhang, X. & Yu, J. Pathophysiological mechanisms and therapeutic potentials of macrophages in non-alcoholic steatohepatitis. Expert. Opin. Ther. Targets 20, 615–626 (2016).
Krenkel, O. et al. Therapeutic inhibition of inflammatory monocyte recruitment reduces steatohepatitis and liver fibrosis. Hepatology 67, 1270–1283 (2018).
Pradere, J.-P. et al. Hepatic macrophages but not dendritic cells contribute to liver fibrosis by promoting the survival of activated hepatic stellate cells in mice. Hepatology 58, 1461–1473 (2013).
Ohkubo, H. et al. VEGFR1-positive macrophages facilitate liver repair and sinusoidal reconstruction after hepatic ischemia/reperfusion injury. PLoS ONE 9, e105533 (2014).
Zigmond, E. et al. Infiltrating monocyte-derived macrophages and resident Kupffer cells display different ontogeny and functions in acute liver injury. J. Immunol. 193, 344–353 (2014).
Liaskou, E. et al. Monocyte subsets in human liver disease show distinct phenotypic and functional characteristics. Hepatology 57, 385–398 (2013).
Zimmermann, H. W. et al. Functional contribution of elevated circulating and hepatic non-classical CD14+CD16+ monocytes to inflammation and human liver fibrosis. PLoS ONE 5, 1–15 (2010).
Stoeckius, M. et al. Simultaneous epitope and transcriptome measurement in single cells. Nat. Methods 14, 865–868 (2017).
Peterson, V. M. et al. Multiplexed quantification of proteins and transcripts in single cells. Nat. Biotechnol. 35, 936–939 (2017).
Xiong, X. et al. Landscape of intercellular crosstalk in healthy and NASH liver revealed by single-cell secretome gene analysis. Mol. Cell 75, 644–660 (2019).
Krenkel, O. et al. Myeloid cells in liver and bone marrow acquire a functionally distinct inflammatory phenotype during obesity-related steatohepatitis. Gut 69, 551–563 (2020).
Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36, 411–420 (2018).
La Manno, G. et al. RNA velocity of single cells. Nature 560, 494–498 (2018).
Trapnell, C. et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat. Biotechnol. 32, 381–386 (2014).
Wang, X., Park, J., Susztak, K., Zhang, N. R. & Li, M. Bulk tissue cell type deconvolution with multi-subject single-cell expression reference. Nat. Commun. 10, 380 (2019).
Schelker, M. et al. Estimation of immune cell content in tumour tissue using single-cell RNA-seq data. Nat. Commun. 8, 2032 (2017).
Newman, A. M. et al. Robust enumeration of cell subsets from tissue expression profiles. Nat. Methods 12, 453–457 (2015).
Baron, M. et al. A single-cell transcriptomic map of the human and mouse pancreas reveals inter- and intra-cell population structure. Cell Syst. 3, 346–360 (2016).
Ahrens, M. et al. DNA methylation analysis in nonalcoholic fatty liver disease suggests distinct disease-specific and remodeling signatures after bariatric surgery. Cell Metab. 18, 296–302 (2013).
Villani, A.-C. et al. Single-cell RNA-seq reveals new types of human blood dendritic cells, monocytes, and progenitors. Science 356, eaah4573 (2017).
Dutertre, C.-A. et al. Single-cell analysis of human mononuclear phagocytes reveals subset-defining markers and identifies circulating inflammatory dendritic cells. Immunity 51, 573–589 (2019).
See, P. et al. Mapping the human DC lineage through the integration of high-dimensional techniques. Science 356, eaag3009 (2017).
O’Keeffe, M., Mok, W. H. & Radford, K. J. Human dendritic cell subsets and function in health and disease. Cell. Mol. Life Sci. 72, 4309–4325 (2015).
Bachem, A. et al. Superior antigen cross-presentation and XCR1 expression define human CD11c+CD141+ cells as homologues of mouse CD8+ dendritic cells. J. Exp. Med. 207, 1273–1281 (2010).
Yang, W. et al. Neutrophils promote the development of reparative macrophages mediated by ROS to orchestrate liver repair. Nat. Commun. 10, 1076 (2019).
Calvente, C. J. et al. Neutrophils contribute to spontaneous resolution of liver inflammation and fibrosis via microRNA-223. J. Clin. Invest. 129, 4091–4109 (2019).
Goh, Y. P. S. et al. Eosinophils secrete IL-4 to facilitate liver regeneration. Proc. Natl Acad. Sci. USA 110, 9914–9919 (2013).
Jarido, V. et al. The emerging role of mast cells in liver disease. Am. J. Physiol. Liver Physiol. 313, G89–G101 (2017).
Zhang, Q. et al. Landscape and dynamics of single immune cells in hepatocellular carcinoma. Cell 179, 829–845 (2019).
Cohen, M. et al. Lung single-cell signaling interaction map reveals basophil role in macrophage imprinting. Cell 175, 1031–1044 (2018).
Norris, S. et al. Resident human hepatitis lymphocytes are phenotypically different from circulating lymphocytes. J. Hepatol. 28, 84–90 (1998).
Zheng, C. et al. Landscape of infiltrating T cells in liver cancer revealed by single-cell sequencing. Cell 169, 1342–1356 (2017).
Ma, L. et al. Tumor cell biodiversity drives microenvironmental reprogramming in liver cancer. Cancer Cell 36, 418–430 (2019).
Stubbington, M. J. T. et al. T cell fate and clonality inference from single-cell transcriptomes. Nat. Methods 13, 329–332 (2016).
Jiang, N., Schonnesen, A. A. & Ma, K.-Y. Ushering in integrated T cell repertoire profiling in cancer. Trends Cancer 5, 85–94 (2019).
Lanier, L. L. Plastic fantastic innate lymphoid cells. J. Exp. Med. 216, 1726–1727 (2019).
Björklund, Å. K. et al. The heterogeneity of human CD127+ innate lymphoid cells revealed by single-cell RNA sequencing. Nat. Immunol. 17, 451–460 (2016).
Peters, A. L. et al. Single cell RNA sequencing reveals regional heterogeneity of hepatobiliary innate lymphoid cells in a tissue-enriched fashion. PLoS ONE 14, e0215481 (2019).
Fasbender, F., Widera, A., Hengstler, J. G. & Watzl, C. Natural killer cells and liver fibrosis. Front. Immunol. 7, 19 (2016).
Ochel, A., Tiegs, G. & Neumann, K. Type 2 innate lymphoid cells in liver and gut: from current knowledge to future perspectives. Int. J. Mol. Sci. 20, 1896 (2019).
Luci, C., Vieira, E., Perchet, T., Gual, P. & Golub, R. Natural killer cells and type 1 innate lymphoid cells are new actors in non-alcoholic fatty liver disease. Front. Immunol. 10, 1192 (2019).
Faggioli, F. et al. B lymphocytes limit senescence-driven fibrosis resolution and favor hepatocarcinogenesis in mouse liver injury. Hepatology 67, 1970–1985 (2018).
Novobrantseva, T. I. Attenuated liver fibrosis in the absence of B cells. J. Clin. Invest. 115, 3072–3082 (2005).
Doi, H. et al. Dysfunctional B-cell activation in cirrhosis resulting from hepatitis C infection associated with disappearance of CD27-positive B-cell population. Hepatology 55, 709–719 (2012).
Goldstein, L. D. et al. Massively parallel single-cell B-cell receptor sequencing enables rapid discovery of diverse antigen-reactive antibodies. Commun. Biol. 2, 304 (2019).
Hu, Q. et al. An atlas of infiltrated B-lymphocytes in breast cancer revealed by paired single-cell RNA-sequencing and antigen receptor profiling. Preprint at https://www.biorxiv.org/content/10.1101/695601v1 (2019).
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).
Lalor, P. Human hepatic sinusoidal endothelial cells can be distinguished by expression of phenotypic markers related to their specialised functions in vivo. World J. Gastroenterol. 12, 5429–5439 (2006).
Poisson, J. et al. Liver sinusoidal endothelial cells: physiology and role in liver diseases. J. Hepatol. 66, 212–227 (2017).
Géraud, C. et al. GATA4-dependent organ-specific endothelial differentiation controls liver development and embryonic hematopoiesis. J. Clin. Invest. 127, 1099–1114 (2017).
Ding, B. S. et al. Divergent angiocrine signals from vascular niche balance liver regeneration and fibrosis. Nature 505, 97–102 (2014).
Xie, G. et al. Role of differentiation of liver sinusoidal endothelial cells in progression and regression of hepatic fibrosis in rats. Gastroenterology 142, 918–927 (2012).
Schwager, S. & Detmar, M. Inflammation and lymphatic function. Front. Immunol. 10, 308 (2019).
Tamburini, B. A. J. et al. Chronic liver disease in humans causes expansion and differentiation of liver lymphatic endothelial cells. Front. Immunol. 10, 1036 (2019).
Wells, R. G. The portal fibroblast: not just a poor man’s stellate cell. Gastroenterology 147, 41–47 (2014).
Friedman, S. L. Hepatic stellate cells: protean, multifunctional, and enigmatic cells of the liver. Physiol. Rev. 88, 125–172 (2008).
Ramadori, G. & Saile, B. Mesenchymal cells in the liver – one cell type or two? Liver Int. 22, 283–294 (2002).
Weiskirchen, R. & Tacke, F. Cellular and molecular functions of hepatic stellate cells in inflammatory responses and liver immunology. Hepatobiliary Surg. Nutr. 3, 344–363 (2014).
Bataller, R. & Brenner, D. A. Liver fibrosis. J. Clin. Invest. 115, 209–218 (2005).
Ramachandran, P. & Henderson, N. C. Antifibrotics in chronic liver disease: tractable targets and translational challenges. Lancet Gastroenterol. Hepatol. 1, 328–340 (2016).
Dobie, R. & Henderson, N. C. Homing in on the hepatic scar: recent advances in cell-specific targeting of liver fibrosis. F1000Research 5, 1749 (2016).
Friedman, S. L. Hepatic fibrosis: emerging therapies. Dig. Dis. 33, 504–507 (2015).
Hinz, B. et al. Recent developments in myofibroblast biology. Am. J. Pathol. 180, 1340–1355 (2012).
Mederacke, I., Dapito, D. H., Affò, S., Uchinami, H. & Schwabe, R. F. High-yield and high-purity isolation of hepatic stellate cells from normal and fibrotic mouse livers. Nat. Protoc. 10, 305–315 (2015).
Weiskirchen, S., Tag, C. G., Sauer-Lehnen, S., Tacke, F. & Weiskirchen, R. in Fibrosis. Methods in Molecular Biology Vol. 1627 (ed. Rittié, L.) 165–191 (Humana Press, 2017).
Dobie, R. et al. Single-cell transcriptomics uncovers zonation of function in the mesenchyme during liver fibrosis. Cell Rep. 29, 1832–1847 (2019).
Wake, K. & Sato, T. Intralobular heterogeneity of perisinusoidal stellate cells in porcine liver. Cell Tissue Res. 273, 227–237 (1993).
Mederacke, I. et al. Fate tracing reveals hepatic stellate cells as dominant contributors to liver fibrosis independent of its aetiology. Nat. Commun. 4, 2823 (2013).
Iwaisako, K. et al. Origin of myofibroblasts in the fibrotic liver in mice. Proc. Natl Acad. Sci. USA 111, E3297–E3305 (2014).
Krenkel, O., Hundertmark, J., Ritz, T. P., Weiskirchen, R. & Tacke, F. Single cell RNA sequencing identifies subsets of hepatic stellate cells and myofibroblastsin liver fibrosis. Cells 8, 503 (2019).
Browaeys, R., Saelens, W. & Saeys, Y. NicheNet: modeling intercellular communication by linking ligands to target genes. Nat. Methods 17, 159–162 (2020).
Vento-Tormo, R. et al. Single-cell reconstruction of the early maternal–fetal interface in humans. Nature 563, 347–353 (2018).
Minutti, C. M. et al. A macrophage-pericyte axis directs tissue restoration via amphiregulin-induced transforming growth factor beta activation. Immunity 50, 645–654 (2019).
McKee, C. et al. Amphiregulin activates human hepatic stellate cells and is upregulated in non alcoholic steatohepatitis. Sci. Rep. 5, 8812 (2015).
Chen, J. et al. EGFR signaling promotes TGF β-dependent renal fibrosis. J. Am. Soc. Nephrol. 23, 215–224 (2012).
Wilhelm, A. et al. Interaction of TWEAK with Fn14 leads to the progression of fibrotic liver disease by directly modulating hepatic stellate cell proliferation. J. Pathol. 239, 109–121 (2016).
Makino, K. et al. Blockade of PDGF receptors by crenolanib has therapeutic effect in patient fibroblasts and in preclinical models of systemic sclerosis. J. Invest. Dermatol. 137, 1671–1681 (2017).
Ni, M. et al. Novel insights on Notch signaling pathways in liver fibrosis. Eur. J. Pharmacol. 826, 66–74 (2018).
Rodriques, S. G. et al. Slide-seq: a scalable technology for measuring genome-wide expression at high spatial resolution. Science 363, 1463–1467 (2019).
Vickovic, S. et al. High-definition spatial transcriptomics for in situ tissue profiling. Nat. Methods 16, 987–990 (2019).
Eng, C.-H. L. et al. Transcriptome-scale super-resolved imaging in tissues by RNA seqFISH+. Nature 568, 235–239 (2019).
Arazi, A. et al. The immune cell landscape in kidneys of patients with lupus nephritis. Nat. Immunol. 20, 902–914 (2019).
Lake, B. B. et al. A single-nucleus RNA-sequencing pipeline to decipher the molecular anatomy and pathophysiology of human kidneys. Nat. Commun. 10, 1–15 (2019).
Jäkel, S. et al. Altered human oligodendrocyte heterogeneity in multiple sclerosis. Nature 566, 543–547 (2019).
Budnik, B., Levy, E., Harmange, G. & Slavov, N. SCoPE-MS: mass spectrometry of single mammalian cells quantifies proteome heterogeneity during cell differentiation. Genome Biol. 19, 161 (2018).
Shema, E., Bernstein, B. E. & Buenrostro, J. D. Single-cell and single-molecule epigenomics to uncover genome regulation at unprecedented resolution. Nat. Genet. 51, 19–25 (2019).
Cao, J. et al. Joint profiling of chromatin accessibility and gene expression in thousands of single cells. Science 361, 1380–1385 (2018).
Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902 (2019).
Zheng, G. X. Y. et al. Massively parallel digital transcriptional profiling of single cells. Nat. Commun. 8, 14049 (2017).
Klein, A. M. et al. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell 161, 1187–1201 (2015).
Parekh, S., Ziegenhain, C., Vieth, B., Enard, W. & Hellmann, I. zUMIs - a fast and flexible pipeline to process RNA sequencing data with UMIs. Gigascience 7, giy059 (2018).
McCarthy, D. J., Campbell, K. R., Lun, A. T. L. & Wills, Q. F. Scater: pre-processing, quality control, normalization and visualization of single-cell RNA-seq data in R. Bioinformatics 33, 1179–1186 (2017).
Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 19, 15 (2018).
Levine, J. H. et al. Data-driven phenotypic dissection of AML reveals progenitor-like cells that correlate with prognosis. Cell 162, 184–197 (2015).
Wang, B. et al. SIMLR: a tool for large-scale genomic analyses by multi-kernel learning. Proteomics 18, 1700232 (2018).
Kiselev, V. Y., Yiu, A. & Hemberg, M. scmap: projection of single-cell RNA-seq data across data sets. Nat. Methods 15, 359–362 (2018).
Aran, D. et al. Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage. Nat. Immunol. 20, 163–172 (2019).
Bendall, S. C. et al. Single-cell trajectory detection uncovers progression and regulatory coordination in human B cell development. Cell 157, 714–725 (2014).
Street, K. et al. Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics. BMC Genomics 19, 477 (2018).
Wolf, F. A. et al. PAGA: graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells. Genome Biol. 20, 59 (2019).
Bergen, V., Lange, M., Peidli, S., Wolf, F. A. & Theis, F. J. Generalizing RNA velocity to transient cell states through dynamical modeling. Preprint at https://www.biorxiv.org/content/10.1101/820936v1 (2019).
Matsumoto, H. et al. SCODE: an efficient regulatory network inference algorithm from single-cell RNA-Seq during differentiation. Bioinformatics 33, 2314–2321 (2017).
Van Den Brink, S. C. et al. Single-cell sequencing reveals dissociation-induced gene expression in tissue subpopulations. Nat. Methods 14, 935–936 (2017).
Ding, J. et al. Systematic comparison of single-cell and single-nucleus RNA-sequencing methods. Nat. Biotechnol. https://doi.org/10.1038/s41587-020-0465-8 (2020).
Picelli, S. et al. Full-length RNA-seq from single cells using Smart-seq2. Nat. Protoc. 9, 171–181 (2014).
Wu, A. R. et al. Quantitative assessment of single-cell RNA-sequencing methods. Nat. Methods 11, 41–46 (2014).
Xin, Y. et al. Use of the Fluidigm C1 platform for RNA sequencing of single mouse pancreatic islet cells. Proc. Natl Acad. Sci. USA 113, 3293–3298 (2016).
Jaitin, D. A. et al. Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types. Science 343, 776–779 (2014).
Keren-Shaul, H. et al. MARS-seq2.0: an experimental and analytical pipeline for indexed sorting combined with single-cell RNA sequencing. Nat. Protoc. 14, 1841–1862 (2019).
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).
Gierahn, T. M. et al. Seq-Well: portable, low-cost RNA sequencing of single cells at high throughput. Nat. Methods 14, 395–398 (2017).
Macosko, E. Z. et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202–1214 (2015).
Durand, M. et al. Human lymphoid organ cDC2 and macrophages play complementary roles in T follicular helper responses. J. Exp. Med. 216, 1561–1581 (2019).
Jackson, H. W. et al. The single-cell pathology landscape of breast cancer. Nature 578, 615–620 (2020).
David, B. A. et al. Combination of mass cytometry and imaging analysis reveals origin, location, and functional repopulation of liver myeloid cells in mice. Gastroenterology 151, 1176–1191 (2016).
Sankowski, R. et al. Mapping microglia states in the human brain through the integration of high-dimensional techniques. Nat. Neurosci. 22, 2098–2110 (2019).
Paul, F. et al. Transcriptional heterogeneity and lineage commitment in myeloid progenitors. Cell 163, 1663–1677 (2015).
The authors acknowledge the support of the Wellcome Trust, Medical Research Council and the Chan Zuckerberg Initiative.
The authors declare no competing interests.
Peer review information
Nature Reviews Gastroenterology & Hepatology thanks S. Friedman, D. Grun and M. Guillams for their contribution to the peer review of this work.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
A human liver cell atlas reveals heterogeneity and epithelial progenitors: http://human-liver-cell-atlas.ie-freiburg.mpg.de
Landscape and dynamics of single immune cells in hepatocellular carcinoma: http://cancer-pku.cn:3838/HCC/
Landscape of infiltrating T cells in liver cancer revealed by single-cell sequencing: http://hcc.cancer-pku.cn
Resolving the fibrotic niche of human liver cirrhosis at the single-cell level: http://www.livercellatlas.mvm.ed.ac.uk
Single-cell RNA sequencing of human liver reveals distinct intrahepatic macrophage populations: https://github.com/BaderLab/HumanLiver
Single-cell transcriptomics uncovers zonation of function in the mesenchyme during liver fibrosis: http://livermesenchyme.hendersonlab.mvm.ed.ac.uk
- Functional zonation
The functional specialization of different spatial regions of liver cells, distinguished by their zonal distribution.
- Trajectory analysis
By considering each single cell as representative of a snapshot along a continuous process, trajectory analysis attempts to reconstruct this path through cellular space by minimizing transcriptional changes between neighbouring cells.
- Pathway enrichment analysis
Given a list of genes, for example, differentially expressed markers of a given cellular population, pathway enrichment analysis identifies biological pathways that are more enriched than would be expected by chance.
- Fate-mapping computational algorithms
Fate-mapping algorithms quantify fate biases for progenitor cells along branching differentiation trajectories, to provide insight into cell fate choices and their regulation.
The set of all RNA molecules from one cell or group of cells.
- Gene regulatory network reconstruction
A computational approach that attempts to uncover the complex interplay of regulatory interactions that ultimately determine the expression level of a given gene, using such measurements as correlation.
- Antibody-tagging techniques
Methodologies in which antibodies are tagged with a unique molecular barcode. Barcoded antibodies bind to target epitopes on cells and the unique molecular barcodes are sequenced alongside the cellular transcriptome, providing an indication of the degree of antibody binding and the level of the target antigen on each single cell.
The analysis of heterogeneous populations of cells for the purpose of identifying the presence and proportions of the various populations of interest.
- Canonical correlation analysis
A data integration methodology that attempts to identify shared correlation structures across datasets, which can then be used to align multiple datasets to one another in a way that minimizes batch effect.
- RNA velocity analysis
This analysis uses the ratio of unspliced to spliced mRNA to infer directionality in single-cell data, by predicting the future state of individual cells and superimposing this prediction onto other cells in the dataset.
- Deconvolution algorithms
Using reference gene expression profiles of cell types of interest, these algorithms estimate cell type composition within a bulk RNA sample containing an unknown mixture of cells.
- Gene set enrichment
From a list of genes, for example, differentially expressed markers of a cellular population, gene set enrichment analysis identifies biological features such as pathways or functions that are more enriched than would be expected by chance.
The molecular interactions between biological entities such as proteins within cells and organisms.
This resource is a publicly available repository of curated receptors, ligands, and their interactions, integrated with a statistical framework that enables prediction of enriched cellular interactions between cell types from single-cell transcriptomic data.
The set of molecules secreted by a cell or organism.
About this article
Cite this article
Ramachandran, P., Matchett, K.P., Dobie, R. et al. Single-cell technologies in hepatology: new insights into liver biology and disease pathogenesis. Nat Rev Gastroenterol Hepatol 17, 457–472 (2020). https://doi.org/10.1038/s41575-020-0304-x
This article is cited by
Single-cell RNA sequencing deciphers the mechanism of sepsis-induced liver injury and the therapeutic effects of artesunate
Acta Pharmacologica Sinica (2023)
GepLiver: an integrative liver expression atlas spanning developmental stages and liver disease phases
Scientific Data (2023)
An integrated gene-to-outcome multimodal database for metabolic dysfunction-associated steatotic liver disease
Nature Medicine (2023)
Signal Transduction and Targeted Therapy (2023)
Integrated analysis of single-cell and bulk RNA-sequencing identifies a signature based on NK cell marker genes to predict prognosis and immunotherapy response in hepatocellular carcinoma
Journal of Cancer Research and Clinical Oncology (2023)