Opposing roles of hepatic stellate cell subpopulations in hepatocarcinogenesis

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. Access through your institution Buy or subscribe This is a preview of subscription content, access via

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subscriptions * Read our FAQs * Contact customer support SIMILAR CONTENT BEING VIEWED BY OTHERS FRIEND OR FOE? THE ELUSIVE ROLE OF HEPATIC STELLATE CELLS IN LIVER CANCER Article 07 August

2023 ACTIVATED HEPATIC STELLATE CELL-DERIVED BMP-1 INDUCES LIVER FIBROSIS VIA MEDIATING HEPATOCYTE EPITHELIAL-MESENCHYMAL TRANSITION Article Open access 12 January 2024 INITIATION OF HEPATIC

STELLATE CELL ACTIVATION EXTENDS INTO CHRONIC LIVER DISEASE Article Open access 27 November 2021 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 * Villanueva, A. Hepatocellular carcinoma. _N. Engl. J. Med._ 380, 1450–1462 (2019). Article  CAS 

PubMed  Google Scholar  * 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  * 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  * 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  * 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  * Llovet, J. M. et al. Immunotherapies for hepatocellular carcinoma. _Nat. Rev. Clin. Oncol._ 19, 151–172 (2022). Article

  CAS  PubMed  Google Scholar  * 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  * 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). * 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  * Lujambio, A. et al. Non-cell-autonomous tumor suppression by p53. _Cell_ 153, 449–460 (2013). Article  CAS  PubMed  PubMed Central 

Google Scholar  * 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  * 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  * 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  * Krizhanovsky, V. et al. Senescence of activated stellate cells limits liver fibrosis. _Cell_

134, 657–667 (2008). Article  CAS  PubMed  PubMed Central  Google Scholar  * 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  * 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  * 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  * 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  * 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  *

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  *

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

  * 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  * 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  * 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  * 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  * 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  * 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  * 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  * 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  * 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  * 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  * Su, H. et al. Collagenolysis-dependent DDR1 signalling dictates pancreatic cancer outcome. _Nature_

https://doi.org/10.1038/s41586-022-05169-z (2022). * 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  * 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  * 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  * 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  * 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  * 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  * 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  * 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  * 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  * 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  *

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  * 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  * 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  * 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  * 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  * 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  * 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  * 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  * 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  * 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  * 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  * 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  * 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  * 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  * 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  * 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  *

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  * 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  * Hao, Y. et al. Integrated analysis of multimodal single-cell data. _Cell_ 184, 3573–3587.e29 (2021). * 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  * 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  * 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). * 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  * 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  * 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  * 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  * 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  * 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 * Aashreya Ravichandra Present address: Klinikum Rechts der Isar, Technical University of Munich (TUM), Munich, Germany * Silvia Affo Present address: Institut d’Investigacions

Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain * Jorge M. Caviglia Present address: Department of Health and Nutrition Sciences, Brooklyn College, City University of New York,

New York, NY, USA * LiKang Chin Present address: Department of Biomedical Engineering, Widener University, Chester, PA, USA * Chuan Yin Present address: Department of Gastroenterology,

Changzheng Hospital, Shanghai, China * These authors contributed equally: Yoshinobu Saito, Ajay Nair, Dianne Dapito, Le-Xing Yu AUTHORS AND AFFILIATIONS * 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 *

Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA Ajay Nair * 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 * Department of Pharmacology, School of

Medicine, University of California, San Diego, San Diego, CA, USA Hua Su & Michael Karin * Department of Microbiology and Immunology, Columbia University Irving Medical Center, New York,

NY, USA Thomas M. Savage & Nicholas Arpaia * 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 * Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA LiKang Chin, Dongning Chen & 

Rebecca G. Wells * Functional Genomics of Solid Tumors Laboratory, Centre de Recherche des Cordeliers, INSERM, Sorbonne Université, Université de Paris, Paris, France Stefano Caruso & 

Jessica Zucman-Rossi * Institute of Human Nutrition, Columbia University, New York, NY, USA Jin Ku Kang, Utpal B. Pajvani, Ira Tabas & Robert F. Schwabe * 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 * Department

of Pathology, Columbia University Irving Medical Center, New York, NY, USA Helen E. Remotti & Ira Tabas * MRC Human Genetics Unit, Institute of Genetics and Cancer, University of

Edinburgh, Edinburgh, UK Neil C. Henderson * Department of Physiology, Columbia University, New York, NY, USA Ira Tabas Authors * Aveline Filliol View author publications You can also search

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View author publications You can also search for this author inPubMed Google Scholar CONTRIBUTIONS 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. PEER REVIEW PEER REVIEW

INFORMATION _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. ADDITIONAL

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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 _p53_f/f (n = 2 mice) and

_p53_ΔHSCmice (n = 4 mice). B–D, HCC was induced in _p53_f/f (n = 11 mice) and_p53_ΔHSC (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 _Rela_f/fand _Rela_ΔHSCmice (n = 3 mice/group). F–H, HCC was induced in _Rela_f/f(n = 9 mice) and _Rela_ΔHSC(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 _Mdr2_KO(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. _Lo_x 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: _Lhx2__f/f_: n = 8 mice, _Lhx2_ΔHSC_:_ 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: _Lhx2_f/f: n = 11 mice, _Lhx2_del: n = 9 mice in non-tumour areas (D), HSC activation measured by qPCR:

_Lhx2_f/f: n = 11 mice, _Lhx2_del: n = 8 mice (E); and promoted HCC development _Lhx2_f/f: n = 11 mice, _Lhx2_del: n = 9 mice (F) compared to _Lhx2_f/f littermates. G, _Lrat-Cre_-mediated

_Yap1_ deletion (_Yap_ΔHSC) was confirmed in FACS-sorted HSC by qPCR: _Yap_f/f: n = 2 mice, _Yap_ΔHSC: n = 3 mice, and western blot (n = 2 mice/group). H, _Yap_ΔHSCmice showed reduced

fibrosis, evaluated by Sirius Red (n = 15 mice/group) and HSC markers, measured by qPCR (_Yap_f/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 _Mdr2_KO _Pdgfrb_ΔHSC (n = 13 mice) and _Mdr2_KO _Pdgfrb_fl/fl (n = 13 mice) female mice. M, Tumour development was determined in

15 month-old _Mdr2_KO _Pdgfrb_ΔHSC (n = 8 mice) and _Mdr2_KO _Pdgfrb_fl/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-Cre_pos 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

_Mdr2_KO-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 _Mdr2_KO-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 _Yap_f/f (n = 5), _Yap_ΔHSC 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 _Yap_fl/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 _Yap_ΔHSC compared to _Yap_f/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 _Yap_fl/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 _Yap_f/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, _YAP_ΔHSC: n = 16 mice, αSMA-TKneg, n = 10 mice, αSMA-TKpos: n = 5 mice and serum ALT measurement: _Yap_f/f: n = 6

mice, _Yap_ΔHSC: 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 _Yap_ΔHSC (n = 14) and _Yap_fl/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 _Yap_fl/fl (n = 10 mice) and _Yap_ΔHSC 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

_Lhx2_fl/fl (1 tumour/n; n = 19 mice) vs _Lhx2_ΔHSC (1 tumour/n; n = 7 mice) and _Lhx2_ del (1 tumour/n; n = 19 mice) mice treatead with DEN; _Yap_fl/fl (2 tumours/n; n = 14 mice) vs

_Yap_ΔHSC (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 _Lhx2_fl/fl (n = 8 mice) and _Lhx2_ΔHSC (n = 7 mice) mice subjected to the DEN model; in _Yap_fl/fl (n = 14 mice) and _Yap_ΔHSC (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 _Lhx2_ΔHSC mice with HSC activation in comparison to mice to _Lhx2_ f/f littermates (n = 7 mice per group)

(C); _Yap_ΔHSC mice with HSC inhibition compared to _Yap_fl/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, _YAP_ΔHSC: 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 _Mdr2_KO 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 _Lhx2_fl/fl, n = 4 _Lhx2_del) resulted in enriched myHSC signature and decreased cyHSC signature; and that genetic

inhibition induced by _Lrat-Cre_-induced _Yap1_ deletion (n = 5 _Yap_fl/fl, n = 4 _Yap_ΔHSC) 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 _Mdr2_KO) 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_ _l_ow

myHSC, and _Col1a1_-GFPlow _Hgf__high_ 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 _Mdr2_KO 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, (_Lhx2_f/f: n = 10 mice, _Lhx2_del: 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 _MDR2_KO-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-Cre_pos _Col1a1_f/f (_Col1a1_del) showed significant reduction of fibrosis in non-tumour tissue,

determined by Sirius Red staining and _Col1a1 mRNA_, compared to _Col1a1_f/f littermates in a profound fibrosis DEN+44xCCl4 model (_Col1a1_ f/f: n = 13 mice, _Col1a1_del: 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, _Col1a1_del mice treated with the moderate fibrosis

DEN+15xCCl4 (0.5 μl of CCl4 per gram, 1injection /week as in f) reduced liver fibrosis (_Col1a1_f/f: n = 10 mice, _Col1a1_del: n = 14 mice) and _Col1a1_ mRNA (_Col1a1_f/f: n = 11 mice,

_Col1a1_del: n = 8 mice) (G), but did not alter HCC development in comparison to _Col1a1_fl/fl mice (_Col1a1_f/f: n = 17 mice, _Col1a1_del: 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 _Mdr2_KO female mice (_Mdr2_KO _Col1a1_ del, n = 11 mice and n = 7 mice for _Col1a1_ mRNA) efficiently reduced liver fibrosis (J) and HCC development (K), compared to

_Mx1-Cre_neg _Mdr2_KO littermates (_Mdr2_KO _Col1a1_fl/fl, n = 18 and n = 7 mice for _Col1a1_ mRNA). L, Liver stiffness assessed by rheometry was reduced in 22 week old _Mdr2_KO _Col1a1_del

male mice (n = 2 mice) compared to their. _Col1a1_fl/fl _Mdr2_KO (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 _Col1a1_f/f (_Col1a1_ΔHSC) efficiently reduced liver fibrosis (Sirius Red: _Col1a1_f/f: n = 16 mice, _Col1a1_ΔHSC: n = 14 mice; _Col1a1_ mRNA: _Col1a1_f/f: n = 15

mice, _Col1a1_ΔHSC: n = 13 mice) (M), HCC development (_Col1a1_f/f: n = 16 mice, _Col1a1_ΔHSC: n = 14 mice), (N) as well as hepatocyte proliferation in the non-tumour liver, as determined by

Ki67 IHC and quantification of proliferating hepatocytes (_Col1a1_f/f: n = 13 mice, _Col1a1_ΔHSC: n = 14 mice; Ki67+ tumour cells: n = 9 mice) (O), compared to _Lrat_-_Cre_neg littermate

controls (_Col1a1_fl/fl) in the DEN + CCl4 profound fibrosis model (as in d). P–R, _Col1a1_ΔHSC mice displayed strong reductions of liver fibrosis (P), HCC development (Q), but not in liver

triglycerides and cholesterol content (R) compared to _Col1a1_fl/fl control mice in a model of NASH-associated HCC induced by 8 months of HF-CDAA diet (_Col1a1_fl/fl :n = 4 mice for Sirius

red and triglycerides, n = 11 mice for _Col1a1_ mRNA and HCC, _Col1a1_ΔHSC: 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 _Mdr2__KO_x_Col1a1__fl/fl_ and _Mdr2__KO_x_Col1a1__del_ mice and hash symbol indicates

significance comparisons between _Mdr2__KO_x_Col1a1__fl/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 Source Data 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, _Col1a1_f/f and _Col1a1_del 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 _Col1a1_f/f (n = 11 mice) and

_Col1a1_ΔHSC (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 _Mdr2_KO _x Col1a1_f/f and _Mdr2_KO _x

Col1a1_del 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-Cre_neg _Col1a1_fl/fl mice (n = 5 mice) and _Lrat-Cre_pos _Col1a1_fl/fl mice (_Col1a1_ΔHSC, n = 5 mice) with the extensive

fibrosis DEN+44xCCl4 regimen. H, YAP staining in _Col1a1_fl/fl and _Col1a1_del 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 (_Yap_ΔHep, n = 10 mice) compared to AAV-TGB-empty (_Yap_fl/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, _Itgb1_fl/fl mice were injected with either

AAV8-TGB-empty (_Itgb1_fl/fl, n = 13) or AAV8-TBG-Cre (_Itgb1_ΔHep, 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 _Ddr1_fl/fl

and AAV8-TGB-Cre treated _Ddr1_ΔHep mice treated with DEN+44xCCl4 (in NT: _Ddr1_f/f: n = 9 mice, _Ddr1_ΔHep n = 8 mice; in Tu: _Ddr1_f/f: n = 10 mice, _Ddr1_ΔHep 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 _Ddr1_fl/fl and _Ddr1_ΔHep 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: _Ddr1_f/f: n = 9 mice, _Ddr1_ΔHep n = 8 mice; CD3 IHC, _Cd3e_ and _Ptprc_ mRNA

in non-tumour area: _Ddr1_f/f: n = 10 mice, _Ddr1_ΔHep 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 _Has2_ΔHSC mice compared to _Has2_f/f littermates (n = 13 mice per group) (C). HCC

evaluation showed a slight reduction of HCC development in _Has2_ΔHSC (n = 20 mice) mice compared to _Has2_f/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_ (_Hhip_ΔHSC) show efficient deletion in healthy control liver (n

 = 4 mice), non-tumour tissue and tumour tissue (_Hhip_f/f: n = 17 mice, _Hhip_ΔHSC n = 10 mice) as well as upregulation of Hedgehog target gene _Gli1_ in comparison to _Hhip_f/f controls.

G, _Hhip_ΔHSC (n = 11 mice) and _Hhip_fl/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 (_Cxcl12_ΔHSC) in comparison to

_Cxcl12_fl/fl HSC (n = 2 mice per group). I, _Cxcl12_ΔHSC (n = 12 mice) and _Cxcl12_fl/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 Source Data 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. SUPPLEMENTARY INFORMATION SUPPLEMENTARY INFORMATION This file includes Table of Contents, Supplementary Figs. 1–10 and

Supplementary Tables 1–8. REPORTING SUMMARY PEER REVIEW FILE SOURCE DATA SOURCE DATA FIGS. 1–6 AND EXTENDED DATA FIGS. 1–13 RIGHTS AND PERMISSIONS Springer Nature or its licensor holds

exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely

governed by the terms of such publishing agreement and applicable law. Reprints and permissions ABOUT THIS ARTICLE CITE THIS ARTICLE 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 Download citation * Received: 26 April 2021 *

Accepted: 30 August 2022 * Published: 05 October 2022 * Issue Date: 13 October 2022 * DOI: https://doi.org/10.1038/s41586-022-05289-6 SHARE THIS ARTICLE Anyone you share the following link

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