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Opposing roles of hepatic stellate cell subpopulations in hepatocarcinogenesis

Nature volume 610, pages 356–365 (2022)Cite this article

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Abstract

Hepatocellular carcinoma (HCC), the fourth leading cause of cancer mortality worldwide, develops almost exclusively in patients with chronic liver disease and advanced fibrosis1,2. Here we interrogated functions of hepatic stellate cells (HSCs), the main source of liver fibroblasts3, during hepatocarcinogenesis. Genetic depletion, activation or inhibition of HSCs in mouse models of HCC revealed their overall tumour-promoting role. HSCs were enriched in the preneoplastic environment, where they closely interacted with hepatocytes and modulated hepatocarcinogenesis by regulating hepatocyte proliferation and death. Analyses of mouse and human HSC subpopulations by single-cell RNA sequencing together with genetic ablation of subpopulation-enriched mediators revealed dual functions of HSCs in hepatocarcinogenesis. Hepatocyte growth factor, enriched in quiescent and cytokine-producing HSCs, protected against hepatocyte death and HCC development. By contrast, type I collagen, enriched in activated myofibroblastic HSCs, promoted proliferation and tumour development through increased stiffness and TAZ activation in pretumoural hepatocytes and through activation of discoidin domain receptor 1 in established tumours. An increased HSC imbalance between cytokine-producing HSCs and myofibroblastic HSCs during liver disease progression was associated with increased HCC risk in patients. In summary, the dynamic shift in HSC subpopulations and their mediators during chronic liver disease is associated with a switch from HCC protection to HCC promotion.

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Fig. 1: Promotion of HCC development by HSCs.
Fig. 2: HSC accumulation and activation occurs predominantly in the NT environment.
Fig. 3: scRNA-seq analysis reveals subsets of HSCs with differential expression of Hgf and Col1a1 in the fibrotic liver.
Fig. 4: Promotion of HCC by Col I and stiffness-associated accumulation of TAZ in hepatocytes.
Fig. 5: Promotion of hepatocarcinogenesis by DDR1.
Fig. 6: cyHSC-enriched HGF protects hepatocytes against death and reduces HCC development.

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Data availability

The microarray, RNA-seq, scRNA-seq and snRNA-seq datasets reported in this study have been deposited in the GEO database under the accession numbers GSE174748 and GSE212047. In addition, we analysed previously published whole liver or isolated HSC scRNA-seq datasets from GSE172492 and GSE158183, normal human liver snRNA-seq data from GSE185477 and the microarray datasets from GSE15654, GSE49541 and GSE10140. Source data are provided with this paper.

Code availability

R markdown scripts enabling the main steps of the analysis have been deposited into GitHub (https://github.com/Schwabelab/HSC_in_HCC). Survival of patients with HCC from TCGA dataset was determined using http://gepia2.cancer-pku.cn/#survival.

References

  1. Villanueva, A. Hepatocellular carcinoma. N. Engl. J. Med. 380, 1450–1462 (2019).

    Article  CAS  PubMed  Google Scholar 

  2. Affo, S., Yu, L. X. & Schwabe, R. F. The role of cancer-associated fibroblasts and fibrosis in liver cancer. Annu. Rev. Pathol. 12, 153–186 (2017).

    Article  CAS  PubMed  Google Scholar 

  3. 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).

    Article  ADS  PubMed  Google Scholar 

  4. 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).

    Article  PubMed  Google Scholar 

  5. Jemal, A. et al. Annual report to the nation on the status of cancer, 1975–2014, featuring survival. J. Natl Cancer Inst. 109, djx030 (2017).

    Article  PubMed Central  Google Scholar 

  6. Llovet, J. M. et al. Immunotherapies for hepatocellular carcinoma. Nat. Rev. Clin. Oncol. 19, 151–172 (2022).

    Article  CAS  PubMed  Google Scholar 

  7. Schwabe, R. F. & Luedde, T. Apoptosis and necroptosis in the liver: a matter of life and death. Nat. Rev. Gastroenterol. Hepatol. 15, 738–752 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  8. Dvorak, H. F. Tumors: wounds that do not heal. Similarities between tumor stroma generation and wound healing. N. Engl. J. Med. 315, 1650–1659 (1986).

  9. Paradis, V. et al. Hepatocellular carcinomas in patients with metabolic syndrome often develop without significant liver fibrosis: a pathological analysis. Hepatology 49, 851–859 (2009).

    Article  PubMed  Google Scholar 

  10. Lujambio, A. et al. Non-cell-autonomous tumor suppression by p53. Cell 153, 449–460 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Kocabayoglu, P. et al. β-PDGF receptor expressed by hepatic stellate cells regulates fibrosis in murine liver injury, but not carcinogenesis. J. Hepatol. 63, 141–147 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Duran, A. et al. p62/SQSTM1 by binding to vitamin D receptor inhibits hepatic stellate cell activity, fibrosis, and liver cancer. Cancer Cell 30, 595–609 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Yoshimoto, S. et al. Obesity-induced gut microbial metabolite promotes liver cancer through senescence secretome. Nature 499, 97–101 (2013).

    Article  ADS  CAS  PubMed  Google Scholar 

  14. Krizhanovsky, V. et al. Senescence of activated stellate cells limits liver fibrosis. Cell 134, 657–667 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Loo, T. M. et al. Gut microbiota promotes obesity-associated liver cancer through PGE2-mediated suppression of antitumor immunity. Cancer Discov. 7, 522–538 (2017).

    Article  CAS  PubMed  Google Scholar 

  16. Omori, S. et al. Generation of a p16 reporter mouse and its use to characterize and target p16high cells in vivo. Cell Metab. 32, 814–828.e6 (2020).

    Article  CAS  PubMed  Google Scholar 

  17. Wandzioch, E., Kolterud, A., Jacobsson, M., Friedman, S. L. & Carlsson, L. Lhx2–/– mice develop liver fibrosis. Proc. Natl Acad. Sci. USA 101, 16549–16554 (2004).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  18. Anstee, Q. M., Reeves, H. L., Kotsiliti, E., Govaere, O. & Heikenwalder, M. From NASH to HCC: current concepts and future challenges. Nat. Rev. Gastroenterol. Hepatol. 16, 411–428 (2019).

    Article  PubMed  Google Scholar 

  19. Wang, X. et al. Hepatocyte TAZ/WWTR1 promotes inflammation and fibrosis in nonalcoholic steatohepatitis. Cell Metab. 24, 848–862 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Hoshida, Y. et al. Gene expression in fixed tissues and outcome in hepatocellular carcinoma. N. Engl. J. Med. 359, 1995–2004 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Ji, J. et al. Hepatic stellate cell and monocyte interaction contributes to poor prognosis in hepatocellular carcinoma. Hepatology 62, 481–495 (2015).

    Article  CAS  PubMed  Google Scholar 

  22. Mu, X. et al. Hepatocellular carcinoma originates from hepatocytes and not from the progenitor/biliary compartment. J. Clin. Invest. 125, 3891–3903 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  23. Puche, J. E. et al. A novel murine model to deplete hepatic stellate cells uncovers their role in amplifying liver damage in mice. Hepatology 57, 339–350 (2013).

    Article  PubMed  Google Scholar 

  24. Affo, S. et al. Promotion of cholangiocarcinoma growth by diverse cancer-associated fibroblast subpopulations. Cancer Cell 39, 866–882.e11 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Bhattacharjee, S. et al. Tumor restriction by type I collagen opposes tumor-promoting effects of cancer-associated fibroblasts. J. Clin. Invest. 131, e146987 (2021).

    Article  CAS  PubMed Central  Google Scholar 

  26. 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.e5 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Hayward, M. K., Muncie, J. M. & Weaver, V. M. Tissue mechanics in stem cell fate, development, and cancer. Dev. Cell 56, 1833–1847 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Masuzaki, R. et al. Prospective risk assessment for hepatocellular carcinoma development in patients with chronic hepatitis C by transient elastography. Hepatology 49, 1954–1961 (2009).

    Article  PubMed  Google Scholar 

  29. Poynard, T. et al. Staging chronic hepatitis C in seven categories using fibrosis biomarker (FibroTest) and transient elastography (FibroScan(R)). J. Hepatol. 60, 706–714 (2014).

    Article  PubMed  Google Scholar 

  30. Wang, H. M. et al. Liver stiffness measurement as an alternative to fibrotic stage in risk assessment of hepatocellular carcinoma incidence for chronic hepatitis C patients. Liver Int. 33, 756–761 (2013).

    Article  CAS  PubMed  Google Scholar 

  31. Baglieri, J. et al. Nondegradable collagen increases liver fibrosis but not hepatocellular carcinoma in mice. Am. J. Pathol. 191, 1564–1579 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Su, H. et al. Collagenolysis-dependent DDR1 signalling dictates pancreatic cancer outcome. Nature https://doi.org/10.1038/s41586-022-05169-z (2022).

  33. Dhar, D. et al. Liver cancer initiation requires p53 inhibition by CD44-enhanced growth factor signaling. Cancer Cell 33, 1061–1077.e6 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Goyal, L., Muzumdar, M. D. & Zhu, A. X. Targeting the HGF/c-MET pathway in hepatocellular carcinoma. Clin. Cancer Res. 19, 2310–2318 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Takami, T. et al. Loss of hepatocyte growth factor/c-Met signaling pathway accelerates early stages of N-nitrosodiethylamine induced hepatocarcinogenesis. Cancer Res. 67, 9844–9851 (2007).

    Article  CAS  PubMed  Google Scholar 

  36. Ueki, T. et al. Hepatocyte growth factor gene therapy of liver cirrhosis in rats. Nat. Med. 5, 226–230 (1999).

    Article  CAS  PubMed  Google Scholar 

  37. Santoni-Rugiu, E. et al. Inhibition of neoplastic development in the liver by hepatocyte growth factor in a transgenic mouse model. Proc. Natl Acad. Sci. USA 93, 9577–9582 (1996).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  38. Suzuki, A. et al. Hepatocyte growth factor promotes cell survival from Fas-mediated cell death in hepatocellular carcinoma cells via Akt activation and Fas-death-inducing signaling complex suppression. Hepatology 32, 796–802 (2000).

    Article  CAS  PubMed  Google Scholar 

  39. Stoker, M. G., Shearer, M. & O'Neill, C. Growth inhibition of polyoma-transformed cells by contact with static normal fibroblasts. J. Cell Sci. 1, 297–310 (1966).

    Article  CAS  PubMed  Google Scholar 

  40. Alkasalias, T. et al. Inhibition of tumor cell proliferation and motility by fibroblasts is both contact and soluble factor dependent. Proc. Natl Acad. Sci. USA 111, 17188–17193 (2014).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  41. Sun, X. et al. Tumour DDR1 promotes collagen fibre alignment to instigate immune exclusion. Nature 599, 673–678 (2021).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  42. Chen, Y. et al. Type I collagen deletion in αSMA+ myofibroblasts augments immune suppression and accelerates progression of pancreatic cancer. Cancer Cell 39, 548–565.e6 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Nault, J. C. et al. Clinical impact of genomic diversity from early to advanced hepatocellular carcinoma. Hepatology 71, 164–182 (2020).

    Article  CAS  PubMed  Google Scholar 

  44. Krempen, K. et al. Far upstream regulatory elements enhance position-independent and uterus-specific expression of the murine α1(I) collagen promoter in transgenic mice. Gene Expr. 8, 151–163 (1999).

    CAS  PubMed  Google Scholar 

  45. Mangale, V. S. et al. Lhx2 selector activity specifies cortical identity and suppresses hippocampal organizer fate. Science 319, 304–309 (2008).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  46. Geisler, F., Algul, H., Paxian, S. & Schmid, R. M. Genetic inactivation of RelA/p65 sensitizes adult mouse hepatocytes to TNF-induced apoptosis in vivo and in vitro. Gastroenterology 132, 2489–2503 (2007).

    Article  CAS  PubMed  Google Scholar 

  47. Buchtler, S. et al. Cellular origin and functional relevance of collagen I production in the kidney. J. Am. Soc. Nephrol. 29, 1859–1873 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Affo, S. et al. Promotion of cholangiocarcinoma growth by diverse cancer-associated fibroblast subpopulations. Cancer Cell 39, 866–882.e11 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Phaneuf, D., Moscioni, A. D., LeClair, C., Raper, S. E. & Wilson, J. M. Generation of a mouse expressing a conditional knockout of the hepatocyte growth factor gene: demonstration of impaired liver regeneration. DNA Cell Biol. 23, 592–603 (2004).

    Article  CAS  PubMed  Google Scholar 

  50. Matsumoto, K. et al. Conditional inactivation of Has2 reveals a crucial role for hyaluronan in skeletal growth, patterning, chondrocyte maturation and joint formation in the developing limb. Development 136, 2825–2835 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Murtaugh, L. C., Stanger, B. Z., Kwan, K. M. & Melton, D. A. Notch signaling controls multiple steps of pancreatic differentiation. Proc. Natl Acad. Sci. USA 100, 14920–14925 (2003).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  52. Ozdemir, B. C. et al. Depletion of carcinoma-associated fibroblasts and fibrosis induces immunosuppression and accelerates pancreas cancer with reduced survival. Cancer Cell 25, 719–734 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Mitchell, C. & Willenbring, H. A reproducible and well-tolerated method for 2/3 partial hepatectomy in mice. Nat. Protoc. 3, 1167–1170 (2008).

    Article  CAS  PubMed  Google Scholar 

  54. Zhang, S. et al. The Hippo effector transcriptional coactivator with PDZ-binding motif cooperates with oncogenic β-catenin to induce hepatoblastoma development in mice and humans. Am. J. Pathol. 190, 1397–1413 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Tao, J. et al. Modeling a human hepatocellular carcinoma subset in mice through coexpression of met and point-mutant β-catenin. Hepatology 64, 1587–1605 (2016).

    Article  CAS  PubMed  Google Scholar 

  56. Mederacke, I., Dapito, D. H., Affo, 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. 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).

    Article  CAS  PubMed  Google Scholar 

  58. Chung, A. S. et al. Immune checkpoint inhibition is safe and effective for liver cancer prevention in a mouse model of hepatocellular carcinoma. Cancer Prev. Res. 13, 911–922 (2020).

    Article  CAS  Google Scholar 

  59. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  60. Zhou, Y. et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat. Commun. 10, 1523 (2019).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  61. Hao, Y. et al. Integrated analysis of multimodal single-cell data. Cell 184, 3573–3587.e29 (2021).

  62. Slyper, M. et al. A single-cell and single-nucleus RNA-seq toolbox for fresh and frozen human tumors. Nat. Med. 26, 792–802 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Andrews, T. S. et al. Single-cell, single-nucleus, and spatial RNA sequencing of the human liver identifies cholangiocyte and mesenchymal heterogeneity. Hepatol. Commun. 6, 821–840 (2022).

    Article  CAS  PubMed  Google Scholar 

  64. Fleming, S. J., Marioni, J. C. & Babadi, M. CellBender remove-background: a deep generative model for unsupervised removal of background noise from scRNA-seq datasets. Preprint at bioRxiv https://doi.org/10.1101/791699 (2022).

  65. Franzen, O., Gan, L. M. & Bjorkegren, J. L. M. PanglaoDB: a web server for exploration of mouse and human single-cell RNA sequencing data. Database 2019, baz046 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  66. Cao, J. et al. The single-cell transcriptional landscape of mammalian organogenesis. Nature 566, 496–502 (2019).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  67. Hoshida, Y. Nearest template prediction: a single-sample-based flexible class prediction with confidence assessment. PLoS ONE 5, e15543 (2010).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  68. Kuleshov, M. V. et al. Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res. 44, W90–W97 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Vento-Tormo, R. et al. Single-cell reconstruction of the early maternal–fetal interface in humans. Nature 563, 347–353 (2018).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  70. Efremova, M., Vento-Tormo, M., Teichmann, S. A. & Vento-Tormo, R. CellPhoneDB: inferring cell–cell communication from combined expression of multi-subunit ligand–receptor complexes. Nat. Protoc. 15, 1484–1506 (2020).

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

This work was supported by grants R01CA190844 and R01CA228483 (to R.F.S.) and R01DK116620 (to R.F.S. and I.T.) and the Columbia University Digestive and Liver Disease Research Center (1P30DK132710) and its Bioinformatics and Single Cell Analysis Core. J.Z.-R. was supported by the Ligue Nationale contre le Cancer (Equipe Labellisée) and Labex OncoImmunology (Investissement d’avenir). N.C.H. is supported by a Wellcome Trust Senior Research Fellowship in Clinical Science (ref. 219542/Z/19/Z), the Medical Research Council and a Chan Zuckerberg Initiative Seed Network Grant. Y.H. was supported by NIH grant CA233794 and Cancer Prevention and Research Institute of Texas grant RR180016. B.I. was supported by NIH grants R37CA258829 and R21CA263381. These studies used the resources of the Herbert Irving Comprehensive Cancer Center at Columbia University. The Flow Core, Molecular Pathology and Confocal and Specialized Microscopy shared resources are funded in part through NIH grants P30CA013696 and S10OD020056. A.F. was funded by a Foundation pour la Recherche Medicale postdoctoral fellowship (SPE20170336778), an American Liver Foundation Postdoctoral Research Award, an International Liver Cancer Association’s Fellowship and the Mandl Connective Tissue Research Fellowship. Y.S. is supported by the Uehara Memorial Foundation and the Naomi Berrie Diabetes Center Russell Berrie Foundation. D.D. is supported by F31 DK091980. S.B. is funded by Deutsche Forschungsgemeinschaft grant GZ:BH 155/1-1. S.A. was funded by an American Liver Foundation Postdoctoral Research Fellowship Award, a Cholangiocarcinoma Foundation’s Innovation Award and a Research Scholar Award from the American Gastroenterological Association. We thank E. Monuki (University of California, Irvine) for the Lhx2-floxed mice; M. Mack (University of Regensburg, Germany) for the Col1a1-floxed mice; R. Kalluri for the αSMA-TK mice; Y. Yamaguchi (Stamford Burnham Prebys Medical Discovery Institute, La Jolla) for the Has2-floxed mice; and E. Seki (University of California, Los Angeles), C. Hernandez (The University of Birmingham, UK) and C. Kuntzen (Columbia University) for scientific support and discussions.

Author information

Author notes

  1. Aashreya Ravichandra

    Present address: Klinikum Rechts der Isar, Technical University of Munich (TUM), Munich, Germany

  2. Silvia Affo

    Present address: Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain

  3. Jorge M. Caviglia

    Present address: Department of Health and Nutrition Sciences, Brooklyn College, City University of New York, New York, NY, USA

  4. LiKang Chin

    Present address: Department of Biomedical Engineering, Widener University, Chester, PA, USA

  5. Chuan Yin

    Present address: Department of Gastroenterology, Changzheng Hospital, Shanghai, China

  6. These authors contributed equally: Yoshinobu Saito, Ajay Nair, Dianne Dapito, Le-Xing Yu

Authors and Affiliations

  1. Department of Medicine, Columbia University, New York, NY, USA

    Aveline Filliol, Yoshinobu Saito, Ajay Nair, Dianne H. Dapito, Le-Xing Yu, Aashreya Ravichandra, Sonakshi Bhattacharjee, Silvia Affo, Qiuyan Sun, Jorge M. Caviglia, Xiaobo Wang, Jin Ku Kang, Amit Dipak Amin, Deqi Yin, Oscar M. Rodriguez-Fiallos, Chuan Yin, Adam Mehal, Benjamin Izar, Utpal B. Pajvani, Ira Tabas & Robert F. Schwabe

  2. Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA

    Ajay Nair

  3. Liver Tumor Translational Research Program, Harold C. Simmons Comprehensive Cancer Center, Division of Digestive and Liver Diseases, University of Texas Southwestern Medical Center, Dallas, TX, USA

    Naoto Fujiwara & Yujin Hoshida

  4. Department of Pharmacology, School of Medicine, University of California, San Diego, San Diego, CA, USA

    Hua Su & Michael Karin

  5. Department of Microbiology and Immunology, Columbia University Irving Medical Center, New York, NY, USA

    Thomas M. Savage & Nicholas Arpaia

  6. Centre for Inflammation Research, The Queen’s Medical Research Institute, Edinburgh BioQuarter, University of Edinburgh, Edinburgh, UK

    John R. Wilson-Kanamori, Sebastian Wallace, Ross Dobie & Neil C. Henderson

  7. Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    LiKang Chin, Dongning Chen & Rebecca G. Wells

  8. Functional Genomics of Solid Tumors Laboratory, Centre de Recherche des Cordeliers, INSERM, Sorbonne Université, Université de Paris, Paris, France

    Stefano Caruso & Jessica Zucman-Rossi

  9. Institute of Human Nutrition, Columbia University, New York, NY, USA

    Jin Ku Kang, Utpal B. Pajvani, Ira Tabas & Robert F. Schwabe

  10. Biomedical Informatics Shared Resource, Herbert Irving Comprehensive Cancer Center, and Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA

    Richard A. Friedman

  11. Department of Pathology, Columbia University Irving Medical Center, New York, NY, USA

    Helen E. Remotti & Ira Tabas

  12. MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK

    Neil C. Henderson

  13. Department of Physiology, Columbia University, New York, NY, USA

    Ira Tabas

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  1. Aveline Filliol
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  2. Yoshinobu Saito
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  35. Michael Karin
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  36. Neil C. Henderson
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A.F. designed experiments, generated, analysed and interpreted data and computational data and drafted the manuscript. Y.S. designed experiments, generated, analysed and interpreted data related to TAZ. A.N. designed and performed computational analyses of scRNA-seq and snRNA-seq data, including CellPhoneDB and cell trajectories. D.D. designed experiments, generated, analysed and interpreted data related to Lhx2. L.-X.Y., A.R., S.B., S.A. and Q.S. generated and analysed data. N.F. and Y.H. analysed myHSC/cyHSC imbalances and survival in human cohorts. H.S. and M.K. provided conceptual input and data on DDR1 activation and degradation. T.M.S. performed and assisted in the flow cytometry analysis (supervised by N.A.). J.M.C. generated RNA-seq data. D.C. and L.C. performed and analysed the stiffness experiments (supervised by R.G.W.). X.W. and I.T. contributed to studies of TAZ-driven HCC. S.C. and J.Z.-R. analysed mRNA expression and survival in human cohorts. J.K.K. measured lipid content in the liver (supervised by U.B.P.). A.D.A., S.W. and R.D. performed snRNA-seq. J.R.W.-K. performed computational analysis of human snRNA-seq. N.C.H. and B.I. oversaw snRNA-seq. D.Y., O.M.R.-F. and A.M. provided technical assistance. C.Y. generated, analysed and interpreted data related to the partial hepatectomy model. R.A.F. assisted with microarray analysis. H.R. contributed to histopathological tumour evaluation. R.F.S. conceived and oversaw the study, designed experiments, drafted and edited the manuscript.

Corresponding author

Correspondence to Robert F. Schwabe.

Ethics declarations

Competing interests

B.I. has received honoraria from consulting with Merck, Johnson & Johnson/Janssen Pharmaceuticals, AstraZeneca and Volastra Therapeutics. M.K. is a founder and SAB member of Elgia Pharma and received research support from Merck and Janssen Gossamer Bio. The other authors declare no competing interests.

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Nature thanks Scott Friedman and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data figures and tables

Extended Data Fig. 1 Analysis of hepatic stellate cell senescence during liver fibrosis and HCC development.

a, qPCR showing Trp53 mRNA in FACS-sorted HSC isolated from p53f/f (n = 2 mice) and p53ΔHSCmice (n = 4 mice). b–d, HCC was induced in p53f/f (n = 11 mice) andp53ΔHSC (n = 14 mice) mice by injection of DEN (i.p. 25 mg/kg at 2 weeks old) followed by 14 injections of CCl4 (i.p. 0.5 µL/g, 1x/week) starting one month after DEN. HSC activation and fibrogenesis were assessed in by qPCR for fibrogenic genes Acta2, Col1a1 and Lox in the liver (b). Fibrosis was evaluated by Sirius Red staining (c). HCC is shown by representative liver pictures and the tumour burden measured by liver/body weight ratio (LBR), tumour number and tumour size (d). e, qPCR showing Rela mRNA in FACS-sorted HSC from Relaf/fand RelaΔHSCmice (n = 3 mice/group). f–h, HCC was induced in Relaf/f(n = 9 mice) and RelaΔHSC(n = 10 mice) mice by injection of DEN (i.p. 25 mg/kg at 2 weeks old) followed by 17 injections of CCl4 (i.p. 0.5 µL/g, 1x/week). HSC activation and fibrogenesis was assessed by qPCR for the fibrogenic genes Acta2, Col1a1 and Lox in the liver (f). Fibrosis was evaluated by Sirius Red staining (g). HCC is shown by representative liver pictures and tumour burden measured by LBR, tumour number and tumour size (h). i–k, representative images showing senescence in specific cell types by senescence associated beta-galactosidase (SA-Gal) staining and co-staining for markers or lineage tracers of HSC (Lrat-Cre x TdTom), macrophages (anti-macrophage antibody), endothelial cells (endomucin antibody), cholangiocytes (CK19 antibody) and hepatocytes (AAV8-TGB-Cre x TdTom) in the CCl4 (n = 3 mice) (i), HF-CDAA diet (n = 1 mouse) (j) and Mdr2KO(n = 1 mouse) (k) mouse models of fibrosis. l–m, representative images showing senescence in specific cellular compartments by p21 IHC in combination with lineage markers for HSC (Lrat-Cre x TdTom) and hepatocytes (AAV8-TGB-Cre x TdTom) in the CCl4 (l) and HF-CDAA diet (m) mouse models of fibrosis (from n = 1 mouse per model). Data are shown as mean ± SEM, each data point represents one individual. Scale bars: 400 µm (c,g) and 100 µm (i–m). LBR: liver/body weight ratio. Statistics: data in b, c, d, e, g, Acta2 mRNA and Col1a1 mRNA in f, and LBR and tumour size in h were analysed by two-tailed Student’s t-test. Lox mRNA in d and tumour number in h were analysed by two-tailed Mann-Whitney test. Raw data are given in Source Data.

Source Data

Extended Data Fig. 2 Genetic strategies to manipulate HSC during hepatocarcinogenesis.

a, Lhx2 mRNA in isolated HSC (n = 5 mice), Kupffer cells (KC), endothelial cells (LSEC) and hepatocytes (n = 3 mice each). b, Lhx2 mRNA by scRNAseq from normal mouse liver (n = 1 mouse). c, qPCR showing deletion of Lhx2 by Lrat-Cre in whole liver: Lhx2f/f: n = 8 mice, Lhx2ΔHSC: n = 6 mice or FACS-sorted HSC (n = 2 mice/group). d–f, deletion of Lhx2, achieved via Mx1-Cre and poly I:C injections, increased liver fibrosis, shown by Sirius Red staining: Lhx2f/f: n = 11 mice, Lhx2del: n = 9 mice in non-tumour areas (d), HSC activation measured by qPCR: Lhx2f/f: n = 11 mice, Lhx2del: n = 8 mice (e); and promoted HCC development Lhx2f/f: n = 11 mice, Lhx2del: n = 9 mice (f) compared to Lhx2f/f littermates. g, Lrat-Cre-mediated Yap1 deletion (YapΔHSC) was confirmed in FACS-sorted HSC by qPCR: Yapf/f: n = 2 mice, YapΔHSC: n = 3 mice, and western blot (n = 2 mice/group). h, YapΔHSCmice showed reduced fibrosis, evaluated by Sirius Red (n = 15 mice/group) and HSC markers, measured by qPCR (Yapf/f: n = 14 mice, Yap ΔHSC: n = 15 mice), in non-tumour liver tissue from mice treated with DEN+CCl4. i, HSC depletion via Lrat-Cre-induced DTR significantly reduced Lrat mRNA in the DEN+CCl4 model (DTR neg: n = 15 mice, DTR pos: n = 16 mice). j-k, αSMA staining (n = 13 mice/group) and qPCR for Acta2 and Col1a1 (n = 12 mice/group) showed depletion of αSMA+ cells in non-tumour areas in αSMA-TKposmice compared to αSMA-TKneg littermates after ganciclovir (GCV) injections in DEN+CCl4-induced HCC (j) and αSMA-TKpos mice developed fewer tumours (n = 13 mice/group) (k). l, Liver fibrosis and deletion of Pdgfrb were determined by Sirius red staining and qPCR for Col1a1 and Pdgfrb in 4 month-old Mdr2KO PdgfrbΔHSC (n = 13 mice) and Mdr2KO Pdgfrbfl/fl (n = 13 mice) female mice. m, Tumour development was determined in 15 month-old Mdr2KO PdgfrbΔHSC (n = 8 mice) and Mdr2KO Pdgfrbfl/fl (n = 6 mice) female mice as described above. n, HCC development in mice overexpressing TAZS89A in hepatocytes receiving a NASH-FPC (n = 13 mice) or chow diet (n = 11 mice). o–q, DTRpos mice displayed efficient HSC depletion in the TAZ+FPC NASH-HCC model compared to DTRneg mice: DTRneg: n = 10 mice, DTRpos: n = 14 mice (o) as well as reduced tumour development: DTRneg: n = 10 mice, DTRpos: n = 14 mice (p), but no reduction of cholesterol and triglycerides measurement in non-tumour liver tissue (untreated: n = 3 mice, TAZ+FPC in DTRneg: n = 6 mice, TAZ+FPC in DTRpos: n = 7 mice) (q). r–s, Lrat-Crepos DTRpos or DTRneg mice were subjected to DEN+HF-CDAA-induced spontaneous hepatocarcinogenesis, revealing efficient HSC depletion by diphtheria toxin (DT) injections (n = 4 mice/group) (r) as well as reduced tumour development in DTRpos mice (n = 8 mice) compared to DTRneg mice (n = 6 mice) (s). t–u, αSMA-TKpos or αSMA-TKneg mice were subjected to NICD+HF-CDAA-induced hepatocarcinogenesis, revealing efficient fibroblast depletion after ganciclovir (GCV) injections: αSMA-TKpos (n = 8 mice) vs αSMA-TKneg mice (n = 7 mice) (t) as well as reduced tumour development in αSMA-TKpos (n = 8 mice) vs αSMA-TKneg mice (n = 9 mice) (u). Data are shown as mean ± SEM, each data point represents one individual, all scale bars: 200 µm. Statistics: data in d, all data in e besides Lox mRNA, Sirius Red in h, i, tumour number and tumour size in k, data in l besides Col1a1 mRNA, o, p, r, s, t and data in u besides tumour number were analysed by two-tailed Student’s t-test. The following data: Lox mRNA in e, f, all data in h besides Sirius Red, j, LBR in k, Col1a1 mRNA in l, m, n, and tumour number in u were analysed by two-tailed Mann-Whitney test. Data in q were analysed by one-way ANOVA (p < 0.001) followed by Tukey’s multiple comparison. Raw data are given in Source Data and uncropped western blots gels in Supplementary Fig. 7c

Source Data

Extended Data Fig. 3 Hepatic stellate cell accumulation occurs predominantly in the PME and affect genes and pathways relevant for tumourigenesis and fibrosis.

a, co-localization of Col1a1-GFP+ and Lrat-Cre-induced TdTom was quantified in non-tumour (NT) and tumour (Tu) areas of DEN+CCl4- and TAZ-FPC-induced HCC (n = 3 mice/HCC model – data related to Fig. 2a). b, Lrat-Cre-TdTom+ HSC and Col1a1-GFP+ fibroblasts were visualized, and co-localization of Col1a1-GFP+ and Lrat-Cre-induced TdTom and the Col1a1-GFP/TdTom double-positive area were quantified in 15 month-old Mdr2KO-induced (n = 3 mice) and HF-CDAA-diet-induced (n = 2 mice) HCC. c, Fibrosis was visualized and quantified by Sirius Red in 15 month-old Mdr2KO-induced and HF-CDAA-diet-induced HCC (n = 6 mice/group/HCC model). d, αSMA and Sirius Red quantification of paired non-tumour and human HCC developing in non-fibrotic livers (n = 20 cases - data related to Fig. 2b). e. UMAP visualization of cell populations from snRNA-seq of matching human non-tumour cirrhotic (n = 2) and tumour (n = 2) liver tissue pairs as well as the proportion of HSC/fibroblasts in both compartments. f–j, Bulk RNAseq of liver tissue from DEN+CCl4-treated Yapf/f (n = 5), YapΔHSC mice (n = 5) and normal liver (n = 8 mice). The heatmap displays up- and downregulated differentially expressed genes (DEG) in non-tumour tissues based on DESeq2 analysis from bulk RNAseq (compared to the normal liver, adjusted p-value <0.1 and log2FC>0.5 or < −0.5) (f). Comparison of genes expression in Yapfl/fl and Yap∆HSC tumour and non-tumour areas in DEN+CCl4-induced HCC (n = 5 mice/group) data are displayed as volcano plot before and after removal of HSC genes, identified by scRNA-seq analysis, or filtering on hepatocyte genes, identified by scRNA-seq analysis (n = 5 mice/group) (g). Metascape enrichment analysis of down-regulated DEG genes in non-tumour tissues of YapΔHSC compared to Yapf/f, all the pathway related to fibrosis and HSC-activation are marked in bold (n = 5 mice/group) (h). Gene set enrichment of RNA-seq data revealed apoptosis and G2/M checkpoint as enriched in Yapfl/fl vs Yap∆HSC non-tumour tissue (i). Displayed is the gene set enrichment analysis of the collection “Hallmark gene set” from the MSigDB with a FDRqval <0.25 in non-tumour tissues of Yap ΔHSC compared to Yapf/f (n = 5 mice/group) (j). k–l, hepatocyte death (k) and proliferation (l) were determined by TUNEL assay: YAP f/f: n = 15 mice, YAPΔHSC: n = 16 mice, αSMA-TKneg, n = 10 mice, αSMA-TKpos: n = 5 mice and serum ALT measurement: Yapf/f: n = 6 mice, YapΔHSC: n = 6 mice, αSMA-Tk neg, n = 10 mice, αSMA-TKpos: n = 5 mice (k) and Ki67 IHC: αSMA-TKneg, n = 8 mice, αSMA-TKpos: n = 4 mice (i) in non-tumoural tissues. m–n, proliferation was determined by Ki67 IHC in the tumor compartment of YapΔHSC (n = 14) and Yapfl/fl (n = 13) (m) as well as DTRpos (n = 9) and DTRneg (n = 5) mice (n). Data are shown as mean ± SEM, each data point represents one individual (a-d,k-n)), one cell (e) or one gene (g), all scale bars: 100 mm NT: Non-tumour, Tu: Tumour. Data are shown as mean ± SEM, each data point (a-d) as well as the HSC-fibroblast percentage quantification (e) represents one individual, in d each dot represent one gen, all scale bars: 100 mm. Statistics: all data displayed in graph dotplots besides ALT measured in αSMA-TKneg and αSMA-TKpos in k were analysed by two-tailed Student’s t-test. ALT in αSMA-TKneg and αSMA-TKpos in k were analysed by two-tailed Mann-Whitney test. Raw data are given in Source Data

Source Data

Extended Data Fig. 4 Determination of the role of HSC/fibroblasts within tumours and in a non-fibrotic HCC model and their effect on the hepatocyte, immune and endothelial cell compartments.

a–b, Ganciclovir (GCV) injections into DEN+CCl4-treated αSMA-TKpos and αSMA-TKneg littermates at late time points, when large tumours were established and when CCl4 injections had ceased for 1.5 weeks, resulted in a strong reduction of αSMA+ cells in tumour but not in non-tumour areas (IHC: αSMA-TKneg, n = 12 mice, αSMA-Tkpos: n = 10 mice; Acta2 RNA: NT: αSMA-Tkneg, n = 13 mice, αSMA-TKpos: n = 9 mice; Tu: αSMA-TKneg, n = 13 mice, αSMA-TKpos: n = 11 mice) (a), and did not affect tumour progression (αSMA-TKneg, n = 13 mice, αSMA-TKpos: n = 11 mice) (b). c–d, HSC depletion via Lrat-Cre-induced DTR in a non-fibrotic HCC model, induced by hydrodynamic tail vein injection of sleeping beauty-mediated expression of MET + CTNNB1-Myc-tag, was highly efficient, as determined by TdTom fluorescence (c) but did not affect liver/body weight ratio, tumour size, tumour number or tumour area assessed by Myc-tag staining (DTRneg: n = 11 mice, DTRpos: n = 7 mice) (d). e–f analysis of cell-cell interactions by CellPhoneDB in snRNA-seq data from cirrhotic liver patients (n = 4 cases) revealed HSC as a major cell type interacting with hepatocytes (e) or between different hepatocytes clusters and liver cells populations in the mouse fibrotic liver induced by 8xCCl4 (n = 3 mice) (f) as well as UMAP visualization of the proliferation marker Mki67 (n = 3 mice) (f). g–h, FACS analysis of total CD45+ leukocytes (g), and lymphocytes and myeloid cells (h) in the PME and TME after αSMA+ cell depletion during HCC development induced by DEN+17xCCl4 (NT: n = 8 mice per group, Tu: αSMA-TKneg, n = 4 mice, αSMA-TKpos: n = 5 mice - related to experiments in Extended Data Fig. 2 j, k) shows increased neutrophil and Ly6Chigh macrophage infiltration into non-tumour areas in αSMA-TK pos mice. i–j, CD45 IHC staining of the non-tumour and tumour tissue in DEN+CCl4 treated Yapfl/fl (n = 10 mice) and YapΔHSC mice (n = 12 mice) (i), and in HSC-depleted DTRpos mice compared to DTRneg mice (NT: DTR neg: n = 10 mice, DTRpos: n = 12 mice; Tu: DTReg: n = 9 mice, DTRpos: n = 11 mice) during HCC development induced by TAZ+FPC (j). k–l, endothelial cell analysis after HSC depletion induced by diphtheria toxin in DTRpos and DTRneg mice during HCC development induced by DEN+CCl4 or TAZ+FPC diet, related to experiments in Fig. 1e,f and Extended Data Fig. 2o–q. evaluated by endomucin staining (k) and qPCR for the endothelial cell markers Pecam1 (encoding for CD31) and Kdr (DEN+CCl4: NT: DTR neg: n = 15 mice, DTR pos: n = 14 mice; Tu: 16 mice per group; Kdr mRNA: NT: 14 mice per group; Tu: 16 mice per group; Pecam and Kdr mRNA: TAZ+FPC: NT: DTRneg: n = 10 mice, DTRpos: n = 14 mice; Tu: DTRneg: n = 10 mice, DTRpos: n = 13 mice) (l). Data are shown as mean ± SEM, each data point represents one individual (a–d and g–l) or one cell (f), scale bars: 100 µm (a-j) or 200 µm (k). GCV: ganciclovir; LBR: liver/body weight ratio; NK: Natural Killer, KC: Kupffer Cell, DC: Dendritic cell, NT: Non-tumour, Tu: Tumour. Statistics: all data in a besides Acta2 in Tu, data in b besides LBR, d, g, all data in h besides B-cells, CD4+, CD8+, KC and Ly6ClowLy6Gneg in NT, CD45 in tumour in i, all data in l besides Kdr mRNA in DEN+CCl4 and TAZ FPC in tumour were analysed by two-tailed Student’s t-test. Data in a: Acta2 mRNA in Tu, LBR in b, data in c, B-cells, CD4+, CD8+, KC and Ly6ClowLy6Gneg in NT, CD45 in tumour in h, CD45 in NT in I, data in j and Kdr mRNA in tumour in l were analysed by two-tailed Mann-Whitney test. Raw data are given in Source Data and gating strategy for h in Supplementary Fig. 2

Source Data

Extended Data Fig. 5 Characterization of genetic mutations, inflammation, immune cell and tumour markers in mice with genetic HSC depletion, activation of inhibition.

a, Mutations detected by PCR and sequencing for Hras Q61K/R/L, Braf V584E and Egfr F254I in Lhx2fl/fl (1 tumour/n; n = 19 mice) vs Lhx2ΔHSC (1 tumour/n; n = 7 mice) and Lhx2 del (1 tumour/n; n = 19 mice) mice treatead with DEN; Yapfl/fl (2 tumours/n; n = 14 mice) vs YapΔHSC (2 tumours/n; n = 15 mice) mice and DTR neg (1 tumour/n; n = 14 mice) vs DTR pos (1 tumour/n; n = 15 mice) mice treated with DEN+CCl4. b, Tumour grading represents average of 3 tumours per mouse in Lhx2fl/fl (n = 8 mice) and Lhx2ΔHSC (n = 7 mice) mice subjected to the DEN model; in Yapfl/fl (n = 14 mice) and YapΔHSC (n = 16 mice) mice subjected to the DEN+CCl4 model and DTRneg (n = 9 mice) and DTRpos (n = 14 mice) mice subjected to the TAZ+FPC HCC model. c–e, Characterization of inflammation, immune cells and tumour severity by qPCR for T cell markers, inflammatory genes and tumour-relevant marker genes in tumours from Lhx2ΔHSC mice with HSC activation in comparison to mice to Lhx2 f/f littermates (n = 7 mice per group) (c); YapΔHSC mice with HSC inhibition compared to Yapfl/fl mice (Cd3e, Cd4, Cd8, Ccl2, Ccl5, Tnfa, Ifng, Krt19, and Epcam mRNA: n = 15 mice per group; Afp, Gpc3, Prom1, and Sox9 mRNA: YAP f/f: n = 15 mice, YAPΔHSC: n = 14 mice). (d); and DTRpos (n = 16 mice) mice with HSC depletion compared to DTRneg (n = 16 mice) mice (e). Data are shown as mean ± SEM, each data point represents one individual. Data are shown as mean ± SEM, each data point represents one individual. Statistics: a: Mutation frequency were analysed by Chi-square test. b: tumour grading data were analysed by two-tailed Student’s t-test. All data in c besides Afp and Sox9 mRNA, Afp, Gpc3 and Prom1 mRNA in d, and Prom1 and Sox9 mRNA in e were analysed by two-tailed Mann-Whitney test. Raw data are given in Source Data

Source Data

Extended Data Fig. 6 Analysis of the myHSC and cyHSC subpopulations in mouse and human fibrotic livers.

a, UMAPs of myHSC and cyHSC signatures by scRNA-seq, each visualized specific signatures as well as the correlation of cyHSC and myHSC signature score in HSC isolated from mice fed with HF-CDAA NASH diet for 12 weeks (n = 1 mouse), from a 3 month old Mdr2KO mice (n = 1 mouse) or a mouse treated with TAZ+FPC diet (n = 1 mouse). b, visualization and quantification of myHSC and cyHSC populations in HSC from healthy (n = 4 individuals) and cirrhotic (n = 4 individuals) human livers by snRNAseq. c, analysis of microarray data from isolated HSC shows that genetic HSC activation via Mx1-Cre-mediated Lhx2 deletion (n = 4 Lhx2fl/fl, n = 4 Lhx2del) resulted in enriched myHSC signature and decreased cyHSC signature; and that genetic inhibition induced by Lrat-Cre-induced Yap1 deletion (n = 5 Yapfl/fl, n = 4 YapΔHSC) exerted the opposite effect with enriched cyHSC and decreased myHSC signature in isolated HSC after treatment with 6xCCl4. d, pseudotime analysis (of HSC from TAZ+FPC and Mdr2KO) as well as cyHSC, myHSC, Col1a1 and Hgf mRNA expression in HSC using the same dataset as in a. e, In situ analysis of Hgf mRNA, visualized by RNAscope, Col1a1-GFP and Lrat-Cre-induced TdTom and subsequent quantification shows separate populations of Col1a1-GFP high Hgf low myHSC, and Col1a1-GFPlow Hgfhigh cyHSC in Mdr2 KO (n = 3 mice) and TAZ+FPC-treated livers (n = 1 mouse). f–g, analysis of Hgf and Col1a1 expression by bulk RNA-seq (Untreated (Ctrl) and 12xCCl4: n = 4 mice, FPC: n = 5 mice) (f) or scRNA-seq (n = 1 per condition) (g) of FACS-sorted HSC from untreated mice, or from CCl4-treated or TAZ-FPC-treated mice. Dot plot bar graphs are shown as mean ± SEM. In the violin plots, box plots represent the interquartile range (IQR), Q1, median and Q3, whiskers as minimum (Q1-1.5xIQR) and maximum (Q3+1.5xIQR), and outlier data as individual dots. Each data point represents one cell (a,b,d) individual (f and Mdr2KO model in e) or quantification of one field of the same liver (TAZ+FPC model in e). Scale bar 50 µm. Statistics: a: P-values, coefficient of determination (R2) and statistical significance (P value) were determined using Pearson’s. Data in d were analysed by two-tailed Mann-Whitney test. Data in b, d and g were analysed by two-tailed Mann-Whitney test. Data in f were analysed by two-tailed Student’s t-test. Raw data are given in Source Data

Source Data

Extended Data Fig. 7 Determination of the role of HSC in hepatocarcinogenesis at different time points.

a, The long-term effects of Lrat-Cre-induced DTR-mediated HSC depletion in the first half of DEN+CCl4-induced hepatocarcinogenesis was tested by sacrificing mice 1.5 days (n = 3 mice per group), 28 days (DTRneg: n = 5 mice, DTRpos: n = 4 mice mice per group) and 42 days (DTRneg: n = 6 mice, DTRpos: n = 4 mice) after the last diphtheria toxin (DT) injection and determining Lrat-Cre-induced TdTom expression. b, Lrat-Crepos DTRpos (n = 9 mice) mice with HSC depletion in the first half of DEN+CCl4-induced hepatocarcinogenesis retained significant HSC depletion at time of sacrifice, as determined by qPCR for HSC markers Lrat and Des, but did not show significant changes in liver/body weight ratio (LBR), tumour number and tumour size compared to Lrat-Crepos DTRneg littermates. c–d, Effects of poly I:C-induced Lhx2 deletion at early time points (c, n = 9 mice per group) and late time points (d, (Lhx2f/f: n = 10 mice, Lhx2del: n = 12 mice) of DEN-induced HCC, as shown by representative liver pictures, liver/body ratio (LBR), tumour number and tumour size. Data are shown as mean ± SEM, each data point represents one individual, scale bars: 200 µm. Statistics: Data in a: one way ANOVA (p < 0.001) followed by Bonferroni’s multiple comparison (comparison between samples sac at the same time point). Data in b, LBR in c, tumour number and tumour size in d, were analysed by two-tailed Student’s t-test. Tumour number and tumour size in c and LBR in d: were analysed by two-tailed Mann-Whitney test. Raw data are given in Source Data

Source Data

Extended Data Fig. 8 Collagen type 1 deletion reduces liver stiffness and HCC development in DEN+CCl4-, NASH diet- and Mdr2KO-induced HCC.

a–b, UMAP visualization showing that Col1a1 is mainly expressed in the HSC population in the 8xCCl4 injured mouse liver by scRNAseq (n = 3 mice) (a) and in the HSC/fibroblast cluster in the human normal (n = 4 individuals), cirrhotic (n = 4 individuals) and HCC (n = 2 individuals) livers by snRNAseq (b). c, Col1a1 mRNA expression, measured by qPCR, in untreated liver (Ctrl, n = 5 mice, left panel) and the non-tumour tissue (n = 8 mice) from DEN+14xCCl4 treated mice; COL1A1 mRNA expression in normal (n = 84 individuals) and adjacent non-tumour tissue from patients with F0-F1 (n = 143 individuals), F2-F3 (n = 59 individuals) and F4 (n = 76 individuals) fibrosis (right panel). d–e, Mx1-Crepos Col1a1f/f (Col1a1del) showed significant reduction of fibrosis in non-tumour tissue, determined by Sirius Red staining and Col1a1 mRNA, compared to Col1a1f/f littermates in a profound fibrosis DEN+44xCCl4 model (Col1a1 f/f: n = 13 mice, Col1a1del: n = 14 mice - related to experiments in Fig. 4a-b) (d), and reduction of liver stiffness, determined by rheometry (control: n = 1 mouse; DEN+19xCCl4: n = 4 mice/group) (e). f, Sirius red staining shows a strong increase of fibrosis, in the non-tumour tissue, induced by the extensive fibrosis DEN+19xCCl4 regimen (0.5 to 1.5 μl of CCl4 per gram 2-3 injections /week; n = 5 mice) compared to a moderate fibrosis model induced by DEN+10xCCl4 (0.5 μl of CCl4 per gram, 1 injection /week; n = 5 mice; untreated controls n = 3 mice). g, Col1a1del mice treated with the moderate fibrosis DEN+15xCCl4 (0.5 μl of CCl4 per gram, 1injection /week as in f) reduced liver fibrosis (Col1a1f/f: n = 10 mice, Col1a1del: n = 14 mice) and Col1a1 mRNA (Col1a1f/f: n = 11 mice, Col1a1del: n = 8 mice) (g), but did not alter HCC development in comparison to Col1a1fl/fl mice (Col1a1f/f: n = 17 mice, Col1a1del: n = 14 mice) (h) or i, liver stiffness assessed by rheometry in the non-tumour tissue from mice treated with DEN+10xCCl4 (one injection per week as in f-h, n = 3 mice for untreated and n = 5 mice/group for DEN+CCl4). j–k, Mx1-Cre-mediated Col1a1 deletion in Mdr2KO female mice (Mdr2KO Col1a1 del, n = 11 mice and n = 7 mice for Col1a1 mRNA) efficiently reduced liver fibrosis (j) and HCC development (k), compared to Mx1-Creneg Mdr2KO littermates (Mdr2KO Col1a1fl/fl, n = 18 and n = 7 mice for Col1a1 mRNA). l, Liver stiffness assessed by rheometry was reduced in 22 week old Mdr2KO Col1a1del male mice (n = 2 mice) compared to their. Col1a1fl/fl Mdr2KO (n = 3 mice) littermates, livers from 8 week old untreated mice were used as control (n = 3). m–o, HSC-selective ablation of Col1a1 in Lrat-Crepos Col1a1f/f (Col1a1ΔHSC) efficiently reduced liver fibrosis (Sirius Red: Col1a1f/f: n = 16 mice, Col1a1ΔHSC: n = 14 mice; Col1a1 mRNA: Col1a1f/f: n = 15 mice, Col1a1ΔHSC: n = 13 mice) (m), HCC development (Col1a1f/f: n = 16 mice, Col1a1ΔHSC: n = 14 mice), (n) as well as hepatocyte proliferation in the non-tumour liver, as determined by Ki67 IHC and quantification of proliferating hepatocytes (Col1a1f/f: n = 13 mice, Col1a1ΔHSC: n = 14 mice; Ki67+ tumour cells: n = 9 mice) (o), compared to Lrat-Creneg littermate controls (Col1a1fl/fl) in the DEN + CCl4 profound fibrosis model (as in d). p–r, Col1a1ΔHSC mice displayed strong reductions of liver fibrosis (p), HCC development (q), but not in liver triglycerides and cholesterol content (r) compared to Col1a1fl/fl control mice in a model of NASH-associated HCC induced by 8 months of HF-CDAA diet (Col1a1fl/fl :n = 4 mice for Sirius red and triglycerides, n = 11 mice for Col1a1 mRNA and HCC, Col1a1ΔHSC: n = 7 mice for Sirius red and triglycerides, n = 10 mice for Col1a1 mRNA and HCC). Data are shown as mean ± SEM, box plots represent the interquartile Range (IQR), Q1, median and Q3, each data point represents one cell (a-b) or one individual (c, d, f, g, h, j, k, m–r), scale bars: 200 µm (d, f, g, j, m and p), 50 µm (o), Red arrows show the Ki67+ proliferating hepatocytes or Ki67+ tumour cells. Statistics: Col1a1 mRNA in mouse liver in c, data in d, f, g, LBR and tumour number in h, j, LBR and tumour size in k, m, LBR and tumour size in n, o, p, q and cholesterol in r were analysed by two-tailed Student’s t-test. Tumour size in h, tumour number in k and in n, and triglycerides in r were analysed by two-tailed Mann-Whitney test. Data in c: COL1A1 mRNA in human liver was analysed by one-way ANOVA (Kruskal-Wallis’s test) followed by Dunn's multiple comparison test with normal liver group. Asterisk indicates significance comparisons between Mdr2KOxCol1a1fl/fl and Mdr2KOxCol1a1del mice and hash symbol indicates significance comparisons between Mdr2KOxCol1a1fl/fl mice and untreated mice. # P<0.05, **,## P<0.001, ### P<0.001. Data in e and l: were analysed by two-way ANOVA followed by Tukey’s multiple comparison test. Raw data are given in Source Data

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Extended Data Fig. 9 Characterization of Col1a1 on hepatocyte death, HSC activation, inflammation and immune cell infiltration and YAP activation and effects of hepatocyte-specific Yap or Itgb1 deletion on hepatocarcinogenesis.

a–b, Col1a1f/f and Col1a1del mice were treated with the extensive fibrosis DEN+44xCCl4 regimen. Hepatocyte death was determined by TUNEL staining (n = 6 mice per group) in non-tumour tissues at the end of the DEN+44xCCl4 regimen 1 week after the last CCl4 injection, as well as by ALT measured 2 days after 20xCCl4 (n = 6 per group) (a). HSC activation and inflammation were assessed by αSMA staining (n = 8 per group) and qPCR for HSC activation and inflammatory markers in non-tumour tissue two days after 44xCCl4 (n = 8 mice per group) (b). c–d, HSC activation and Il1b mRNA were assessed in Col1a1f/f (n = 11 mice) and Col1a1ΔHSC (n = 1 mice) mice fed eight months with HF-CDAA diet, related to experiments in Extended Data Fig. 8p-r (c) and in 14 months-old Mdr2KO x Col1a1f/f and Mdr2KO x Col1a1del mice (n = 7 mice per group) - related to experiments in Extended Data Fig. 8 j-k (d). e-g, Determination of CD45+ lymphocytes (e), myeloid subpopulations) (f) and lymphoid (g) subpopulations by FACS in tumour and non-tumour areas of Lrat-Creneg Col1a1fl/fl mice (n = 5 mice) and Lrat-Crepos Col1a1fl/fl mice (Col1a1ΔHSC, n = 5 mice) with the extensive fibrosis DEN+44xCCl4 regimen. h, YAP staining in Col1a1fl/fl and Col1a1del mice treated with the extensive fibrosis DEN+19xCCl4 showed YAP expression mostly in non-parenchymal cells rather than in hepatocytes. i, Yap was efficiently deleted by injection of AAV8-TBG-Cre (YapΔHep, n = 10 mice) compared to AAV-TGB-empty (Yapfl/fl, n = 14 mice) but did not affect HCC development induced by the extensive fibrosis DEN+CCl4 HCC model, determined by liver/body weight ratio (LBR), tumour number and tumour size. j, Itgb1fl/fl mice were injected with either AAV8-TGB-empty (Itgb1fl/fl, n = 13) or AAV8-TBG-Cre (Itgb1ΔHep, n = 11) and effects of hepatocyte-specific Itgb1 deletion on HCC development were determined in the DEN+CCl4 extensive fibrosis HCC model by the liver/body weight ratio (LBR), tumour number and tumour size. Data are shown as mean ± SEM, each data point represents one individual. NT: non-tumour, Tum: tumour NK: Natural killer, Treg: T regulatory cells, GZB: Granzyme B, DC: Dendritic cells, KC: Kupffer cells, LBR: liver/body weight ratio. Scale bars: 100 µm (a and h), 200 µm (b). Statistics: TUNEL in a, data in b and d besides Il1b mRNA, Col1a2 mRNA in c, f, data in g besides B cells, NKT cells, CD8 and Tregs in non-tumour area, i, and data in j besides LBR were analysed by two-tailed Student’s t-test. ALT in a, Il1b mRNA in b and d, all data in c besides Col1a2, e, B cells, NKT cells, CD8 and Tregs in non-tumour area in g and LBR in j were analysed by two-tailed Mann-Whitney test. Raw data are given in Source Data and gating strategy for e–g in Supplementary Fig. 4

Source Data

Extended Data Fig. 10 Regulation and role of DDR1 in the fibrotic liver and HCC development.

a, UMAPs of scRNA-seq data showing hepatocyte Ddr1 expression in normal (n = 1 mouse) and 8xCCl4 fibrotic mouse livers (n = 3 mice – top panel) and of snRNA-seq from patients showing DDR1 expression in the hepatocytes/HCC cluster from healthy patients (n = 4 individuals), non-tumour (n = 2 individuals) and cirrhotic (n = 2 individuals) or HCC tumour (n = 2 individuals) liver tissues (bottom panel). b, Huh7, Hepa1-6, HepG2 HCC cells and mouse primary hepatocytes were treated with different cytokines for 24h, followed by immunoblotting for DDR1 and GAPDH (each n = 1 well, n = 1 experiment). c, Confirmation of Ddr1 deletion by qPCR in non-tumour liver and tumour liver from AAV8-TGB-empty treated Ddr1fl/fl and AAV8-TGB-Cre treated Ddr1ΔHep mice treated with DEN+44xCCl4 (in NT: Ddr1f/f: n = 9 mice, Ddr1ΔHep n = 8 mice; in Tu: Ddr1f/f: n = 10 mice, Ddr1ΔHep n = 8 mice - related to experiments in Fig. 5c). d, Fibrosis, αSMA+ myofibroblast accumulation and Ddr1 mRNA expression within tumours were quantified by Sirius Red staining, IHC and qPCR, respectively, in the ”regular fibrosis” DEN + 14xCCl4 HCC model (αSMA and Sirius Red: n = 10 mice; Ddr1 mRNA n = 11 mice) and in the “extensive fibrosis” DEN+44xCCl4 HCC model (αSMA and Sirius Red: n = 9 mice; Ddr1 mRNA n = 10 mice). e–f, Huh7 HCC cells (n = 3 biological replicate; n = 2 experiments) (e) and primary mouse hepatocyte (n = 1 well; n = 1 experiment) (f) were plated on either plastic or fibroblast ECM, samples were harvested at 24h or at the indicated times after plating, followed by western blot for pDDR1, DDR1, pAKT, AKT and b-actin or GAPDH. g, Huh7 cells were plated on ECM from wild-type (WT) fibroblasts or from MMP-resistant Col I (RR) fibroblasts, followed by western blot for pDDR1, DDR1, pAKT, AKT and -actin (n = 1 well, n = 3 experiments). h, expression of collagen-degrading MMPs, determined by qPCR, in the extensive fibrosis DEN+CCl4 model (n = 4 normal liver controls [Ctrl], n = 9 mice for non-tumour [NT] and tumour [Tu] samples). i, survival analysis in the TCGA dataset based on the expression of a collagen-degrading MMP signature (MMP1, MMP2, MMP8, MMP9, MMP13 and MMP14). j, YAP, TAZ, GAPDH, pAKT and AKT expression were determined by western blot in non-tumour tissue from Ddr1fl/fl and Ddr1ΔHep mice (n = 6 mice per group). k, Infiltration of CD3+ T cells was determined by IHC and quantified in both non-tumour and tumour tissue (CD3 IHC, Cd3e and Ptprc mRNA in non-tumour area: Ddr1f/f: n = 9 mice, Ddr1ΔHep n = 8 mice; CD3 IHC, Cd3e and Ptprc mRNA in non-tumour area: Ddr1f/f: n = 10 mice, Ddr1ΔHep n = 8 mice); expression of Cd3e and Ptprc (encoding CD45) was determined by qPCR. Data are shown as mean ± SEM, each data point represents one cell (a), one well (b,e,f,g) or one individual (c,h,k); scale bars: 200 µm (d), 50 µm (k). nd: non-detected. Immunoblots in j were performed on two different gels using the same samples: one gel for YAP/TAZ and GAPDH and a second gel for pAKT and AKT. Data are shown as mean ± SEM. Statistics: Ddr1 mRNA in NT in c, data in d, CD3+ IHC and Cd3e mRNA in non-tumour and Ptprc mRNA in tumour in g were analysed by two-tailed Student’s t-test. CD3+ IHC and Cd3e mRNA in tumour and Ptprc mRNA in non-tumour in g were analysed by two-tailed Mann-Whitney test. Data in h were analysed by one way ANOVA (Kruskal Wallis) for Mmp2 (p < 0.001), Mmp8 (p = 0.004) and Mmp9 mRNA (p = 0.004) followed by Dunn’s multiple comparison or for Mmp13 (p = 0.001) and Mmp14 mRNA one-way one-way ANOVA (p < 0.001) followed by Tukey’s multiple comparison. Raw data are given in Source Data and raw western blot gels in Supplementary Figs. 8-10

Source Data

Extended Data Fig. 11 Expression and role of myHSC-enriched hyaluronan and cyHSC-expressed HHIP and CXCL12 in hepatocarcinogenesis.

a, UMAP visualization and dotplot show predominant expression of Has2 in myHSC in the extensive fibrosis regimen, induced by 19xCCl4 (n = 1 mouse). b, bulk RNA sequencing in FACS-sorted HSC showed upregulation of Has2 in HSC from 12xCCl4-injured liver (n = 4 mice) compared to quiescent HSC (n = 4 mice). c–d, HCC was induced by DEN (i.p. 25 mg/kg at 2 weeks old) followed by 14 injections of CCl4 (i.p. 0.5 µl/g, 1x/week) and hyaluronan (HA) staining showed strong reduction of HA deposition in Has2ΔHSC mice compared to Has2f/f littermates (n = 13 mice per group) (c). HCC evaluation showed a slight reduction of HCC development in Has2ΔHSC (n = 20 mice) mice compared to Has2f/f littermates (n = 21 mice) (d). e, UMAPs of Hhip and Cxcl12 mRNA in normal (n = 1 mouse) and in 8xCCl4-treated (n = 3 mice) mouse livers. f, Mice with Lrat-Cre-induced conditional deletion of Hhip (HhipΔHSC) show efficient deletion in healthy control liver (n = 4 mice), non-tumour tissue and tumour tissue (Hhipf/f: n = 17 mice, HhipΔHSC n = 10 mice) as well as upregulation of Hedgehog target gene Gli1 in comparison to Hhipf/f controls. g, HhipΔHSC (n = 11 mice) and Hhipfl/fl (n = 17 mice) mice were subjected to DEN-induced hepatocarcinogenesis (i.p. 5 mg/kg at 2 weeks old), followed by evaluation of the liver/body weight ratio (LBR), tumour size and tumour number. h, Deletion of Cxcl12 was determined by qPCR in HSC isolated from mice with Lrat-Cre-induced deletion (Cxcl12ΔHSC) in comparison to Cxcl12fl/fl HSC (n = 2 mice per group). i, Cxcl12ΔHSC (n = 12 mice) and Cxcl12fl/fl (n = 14 mice) mice were subjected to DEN (i.p. 25 mg/kg at 2 weeks old) followed by 14 injections of CCl4 (i.p. 0.5 µl/g, 1x/week)4 induced HCC followed by evaluation of the liver/body weight ratio, tumour size and tumour number. Data are shown as mean ± SEM. In the dotplot graphs, each data point represent one cell (a, e) or one individual (b,c,d,g,h,i), scale bars: 400 µm. Statistics: Data in a, LBR in d, Gli1 mRNA in non-tumour in f, LBR in g and i were analysed by two-tailed Mann-Whitney test. Data in b, c, all data in d besides Gli1 mRNA, LBR in g and i, were analysed by two-tailed Student’s t-test. Raw data are given in Source Data.

Source Data

Extended Data Fig. 12 HGF deletion in HSC promotes fibrosis and inflammation in CCl4-induced liver injury.

a–c, UMAP visualization of Hgf and/or Met in the 8xCCl4 injured (n = 3 mice) (a) or normal mouse livers (n = 1) by scRNAseq (c) and in the human normal (n = 4 cases), cirrhotic (n = 4 cases) and HCC (n = 2 cases) livers by snRNAseq (b). d, significant reduction of Hgf mRNA in normal (n = 4 mice/group) and CCl4 liver (Untreated (UT): n = 3 mice, Hgf fl/fl: n = 8 mice, Hgf ΔHSC: n = 7 mice) as well as of HGF protein in livers (UT: n = 4 mice, CCl4: n = 5 mice/group) e, Fibrosis was assessed by Sirius Red staining in the non-tumour tissue from Hgf ΔHSC mice (n = 10 mice) compared to Hgf f/f littermates (n = 11 mice) during HCC development induced by DEN (i.p. 25 mg/kg at 2 weeks old) followed by 12 injections of CCl4 (i.p. 0.5 µL/g, 1x/week)4. f, HSC activation was determined by qPCR for the fibrogenic genes Col1a1 and Lox in livers from Hgf ΔHSC (n = 4 mice) and Hgf f/f mice (n = 7 mice) treated with injections of 6xCCl4. g, flow cytometric analysis of CD45+ cells as well as myeloid and lymphocyte populations in livers from Hgf ΔHSC (n = 7 mice) compared to Hgf f/f mice (n = 8 mice) treated with injections of 6xCCl4. h, qPCR for inflammatory genes from Hgf ΔHSC (n = 7 mice) and Hgf f/f (n = 8 mice) livers after treatment with 6xCCl4 injections. i, Liver/body ratio (LBR) in Hgf fl/fl (Ctrl: n = 10 mice, pHx: n = 6 mice) and Hgf ΔHSC (Ctrl: n = 8 mice, pHx: n = 7 mice) mice in untreated mice or after 48h partial hepatectomy (pHx) (left panel). Hepatocyte proliferation assessed by Ki67 staining 48h after PHx (right panel - Hgf f/f: n = 6 mice, Hgf ΔHSC: n = 7 mice). Data are shown as mean ± SEM, each data point represents one cell (a-c) or one individual (d-i), scale bars: 200 µm. GZB, granzyme B. Statistics: Data in d-g and i were analysed by two-tailed Student’s t-test. Data in h were analysed by two-tailed Mann-Whitney test. Raw data are given in Source Data and gating strategy for g in Supplementary Fig. 5

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Extended Data Fig. 13 Dysbalance between myHSC and cyHSC occurs in advanced liver disease and elevates HCC risk in patients.

a, UMAP visualization showing that the genes from the myHSC and cyHSC signatures, encoding secreted mediators, are strongly enriched in myHSC or cyHSC, and weakly or not expressed in other liver cell populations in 8xCCl4 injured mouse liver (n = 3 mice) analysed by scRNAseq. b, myHSC and cyHSC secreted gene signatures were applied to bulk RNA-seq data from cohorts of patients with HCV-induced liver disease (GSE10140) or NAFLD/NASH-induced liver disease (GSE49541), and the proportion of patients with high cyHSC/myHSC dysbalance was determined as described in the methods. c, HCC incidence was compared between patients with high cyHSC/myHSC dysbalance (i.e. a status with higher myHSC and lower cyHSC) and low cyHSC/myHSC dysbalance (i.e. a status lower myHSC and higher cyHSC) in a HCV-induced liver disease cohort (GSE15654). Statistics: data in b were analysed by two-sided Fisher’s exact test. Survival curves in c were represented using the Kaplan-Meier method and compared with log-rank statistics.

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Filliol, A., Saito, Y., Nair, A. et al. Opposing roles of hepatic stellate cell subpopulations in hepatocarcinogenesis. Nature 610, 356–365 (2022). https://doi.org/10.1038/s41586-022-05289-6

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