Trajectory analysis of hepatic stellate cell differentiation reveals metabolic regulation of cell commitment and fibrosis

Article https://doi.org/10.1038/s41467-025-56024-4 Trajectory analysis of hepatic stellate cell differentiation reveals metabolic regulation of cell commitment and fibrosis Raquel A. Martínez García de la Torre 1,2 , Julia Vallverdú 1 , Zhenqing Xu 1,2 , Silvia Ariño 1,2,3 , Raquel Ferrer-Lorente 1 , Laura Zanatto 1,2 , Maria Mercado-Gómez 1 ,BeatrizAguilar-Bravo 1 ,PalomaRuiz-Blázquez 1,2,3,4 , Maria FernandezFernandez 1,2,3,4 , Artur Navarro-Gascon 5,6 , Albert Blasco-Roset 5,6 , Paula Sànchez-Fernàndez-de-Landa 5,7 ,JoanPera 8 , Damia Romero-Moya 8 , Paula Ayuso Garcia 9 , Celia Martínez Sánchez 1,2,3 , Laura Sererols Viñas 1,2 , Paula Cantallops Vilà 1,2 ,CarmenI.CárcamoGiráldez 1 , Andrew McQuillin 10 ,MarshaY.Morgan 11 , Daniel Moya-Rull 12 , Núria Montserrat 12,13 ,DelphineEberlé 14 ,BartStaels 14 , Bénédicte Antoine 15 , Mikel Azkargorta 3,16 , Juan-José Lozano 3 , Maria L. Martínez-Chantar 3,17 , Alessandra Giorgetti 8,18 ,FélixElortza 3,16 , Anna Planavila 5,6 , Marta Varela-Rey 9,18 , Ashwin Woodhoo 9,19,20 , Antonio Zorzano 5,6,7,21 , Isabel Graupera 1,3,22 , Anna Moles 1,3,4 ,MarColl 1,2,23 ,SilviaAffo 1 & Pau Sancho-Bru 1,3,23 Defining the trajectory of cells during differentiation and disease is key for uncovering the mechanisms driving cell fate and identity. However, trajectories of human cells remain largely unexplored due to the challenges of studying them with human samples. In this study, we investigate the proteome trajectory of iPSCs differentiation to hepatic stellate cells (diHSCs) and identify RORA as a key transcription factor governing the metabolic reprogramming of HSCs necessary for diHSCs’commitment, identity, and activation. Using RORA deficientiPSCsandpharmacologicinterventions, we show that RORA is required for early differentiation and prevents diHSCs activation by reducing the high energetic state of the cells. While RORA knockout mice have enhanced fibrosis, RORA agonists rescue multi-organ fibrosis in in vivo models. Notably, RORA expression correlates negatively with liver fibrosis and HSCs activation markers in patients with liver disease. This study reveals that RORA regulates cell metabolic plasticity, important for mesoderm differentiation, pericyte quiescence, and fibrosis, influencing cell commitment and disease. During embryonic development cells undergo a transcriptomic and metabolic reprogramming controlled by pathways tightly regulated in a time, space, and cell-dependent manner. When the embryonic development ends, homeostasis takes over. Nonetheless, developmental signaling pathways can be reactivated for the maintenance and repair of adult tissues1. Therefore, it is fundamental to investigate the mechanisms governing cell identity and their fate upon development and disease to better define the differentiation paths and Received: 5 January 2024 Accepted: 7 January 2025 Check for updates A full list of affiliations appears at the end of the paper. e-mail: [email protected];[email protected] Nature Communications | (2025) 16:1489 1 1234567890():,; 1234567890():,; understand cell trajectories in space and time. In this context, human induced pluripotent stem cells (iPSCs) are a promising and useful tool, as they can differentiate into any cell type, and can be shaped in vitro to mimic a disease condition, offering the possibility to study cell physiological and pathophysiological trajectory2,3. Fibrosis is an evolutionary conserved mechanism involved in multiple chronic injury conditions and tissues and is responsible for nearly45%of all death in industrialized countries, being the liver one of the principal organs affected4.Liverfibrosis is the common outbreak of most liver diseases andis associated with the risk of mortality and liverrelated morbidity5. The main cell type responsible for liver fibrosis are hepatic stellate cells (HSCs)6. These cells are specialized liver pericytes that maintain extracellular matrix (ECM) homeostasis of the organ and store vitamin A in cytoplasmic lipid droplets7. During liver homeostasis, HSCs are in a quiescent state, but in response to injury, they activate and acquire a myofibroblastic-like phenotype, participating in the wound-healing response to injury and in tissue regeneration. In chronic liver injury, activation of HSCs persists, and they become the main fibrogenic cell type8. Cell metabolism is crucial for the activation of HSCs, which, to fulfil the high metabolic requirements during activation, increases glycolysis9, glutaminolysis10 and de novo lipogenesis11. The maintenance of the HSCs quiescent phenotype is tightly regulated by transcription factors (TF).Some of these TF are expressed during liver development and become downregulated during HSCs activation12–14. Restoration of their expression in activated HSCs, reestablish, in part, the quiescent state, indicating a parallelism between both processes of maturation and fibrogenesis12–14. Thus, suggesting that understanding the trajectory of HSCs in development and disease may help to identify molecular pathways suitable for preventing HSCs activation or promoting the regression to a quiescent phenotype, thus mitigating fibrogenesis in chronic liver diseases. RORA (Retinoic Acid Related Oprhan Receptor Alpha) is a member of the steroid/thyroid hormone receptor superfamily of TFs expressed in several tissues, including the liver, where it is known to regulate genes involved in hepatocyte lipid, cholesterol and glucose metabolism. RORA plays a major role in cellular development and differentiation of multiple tissues15,16, but its role in HSCs physiology remains poorly explored. Recent studies suggest that RORA may also play a role in the activation process of HSCs17,18. In this study, we performed a time-resolving proteome characterization of iPSCs to functional HSCs (diHSCs), which mimic phenotypic and functional characteristics of primary human HSCs. Furthermore, we identified RORA as a TF regulating both diHSCs differentiation and favouring the maintenance of a quiescent phenotype by modulating the metabolic state of the cells. In both, mesoderm commitment and diHSCs activation, downregulation of RORA mediates a metabolic switch, increasing glycolysis and mitochondrial oxidative phosphorylation system (OXPHOS). Moreover, we confirmed the anti-fibrogenic role of RORA in hepatic and extrahepatic pericytes, thus positioning RORA as a potential antifibrogenic multiorgan target. Overall, this work shows the potential of studying cell trajectories along differentiation and disease progression. Results Time-resolving proteomic analysis of diHSCs differentiation To construct a comprehensive proteomic profile of the iPSC differentiation towards functional iPSC-differentiated HSCs (diHSCs), we obtained 7 different timepoints across differentiation (day 0, day 2, day 4, day 6, day 8, day 10 and day 12) as previously described19–21. Samples from four independent differentiations were collected and assessed using mass spectrometry (MS)-based profiling (Fig. 1A). We identified 3064 proteins, of which 2475 were quantified, providing a detailed proteomic roadmap of the differentiation process from iPSCs to diHSCs. As expected, during the differentiation, HSC markers (DCN, LUM, PTN, MMP2, PCOLCE, LAMA5, IGFBP3, LGALS1, IGFBP5, and collagens) increased, accompanied by a decrease in the pluripotency proteins (RIF1, POU5F1, DPPA4, KPNA2, DNMT3B) (Fig. 1B, C). As expected at day 0, we found proteins enriched in gene ontologies (GOs) related to cell proliferation, translation, cell division, and pluripotency. Conversely, at day 12, we observed an enrichment in proteins related to ECM organization, collagen metabolism, wound healing, and TGFβ signaling (Fig. 1B; Supplementary Data 1). Gene Set Enrichment Analysis (GSEA) of proteome signatures of human liver cell types22 showed that the diHSCs proteome profile was significantly enriched in the HSC proteome signature, closely resembling the human primary HSCs proteome. (Fig. 1D). Moreover, based on the dynamic proteomic profile, we identified five expression clusters (Supplementary Fig. 1 and Supplementary Data 1); 1) A matrisome-enriched cluster, arising on day 8 and including proteins related to ECM organization and collagen metabolism (Fig. 1E and Supplementary Fig. 1B); 2) A metabolic cluster containing proteins involved in vesicular processes and cell commitment which was upregulated on days 4-8 and hereafter referred as “met-proteins”(Fig. 1F and Supplementary Fig. 1B); 3) a proliferationand 4) a pluripotencycluster, which both contained proteins down-regulated during differentiation (Fig. 1G, H, Supplementary Fig. 1B); and 5) a basal-proteins cluster, containing proteins showing minimal changes at the different stages of the differentiation, and involved in homeostatic cellular processes (Fig. 1I, Supplementary Fig. 1B). Altogether, this broad characterization enabled the construction of a proteomic roadmap detailing the differentiation of diHSCs from iPSCs. diHSCs resemble human primary HSCs proteome identity We next evaluated the similarity between diHSCs and primary human healthy HSCs (pHSCs) isolated from liver donors. When comparing the proteomesofdiHSCsandpHSCs,wefoundthatover60%oftheir profiles were shared (Fig. 1J). Notably, several proteins associated with the activated phenotype were enriched in pHSCs compared to the diHSCs, including EHD2, TIMP3, THY1, TIMP1, MYL9, and FBLN2. These markers suggest that primary HSCs exhibit a more activated state than diHSCs. Interestingly, proteins expressed by diHSCs showed a more proliferative state, reflecting increased cell division and chromatin remodeling, typical of iPSC-derived cells (Fig. 1J). Most importantly, pHSC and diHSCs shared proteins related to ECM remodeling, such as FSTL1, MMP14, SFRP2, PALLD, LOXL2, and RAB13 (Fig. 1J). Finally, by interrogating our proteomic data with published gene signatures of quiescent, activated, and stellate cell phenotypes (Zhang et al.)23, we observed that during differentiation, diHSCs gained expression of pathways related to quiescence, activation, as well as HSC identity phenotype (Fig. 1K–M). Overall, these data confirm that iPSCs progressively acquire the stellate proteomic phenotype along differentiation, thus enabling the use of diHSCs to further investigate their role in cell commitment and disease. Proteome-driven cell trajectory analysis identify RORA as a potential driver of diHSCs differentiation Principal component analysis (PCA) of the proteome differentiation profile showed a time-dependent distribution of samples along the differentiation process (Fig. 2A). Proteins comprised in PC1 explained stellate cell commitment across time, ascells gained in proteins related to collagen metabolism, ECM organization, or wound healing processes. To further dissect the progression of the differentiation, we performed Pearson’s correlation analysis showing that proteome data is divided into three phases (Fig. 2B). Phase 1, including days 0 to 2 and characterized by the expression of pluripotencyand proliferation proteins, such as POU5F1 and RIF1 (Fig. 2C), as well as early mesoderm proteins, such as CALB1 and MFGE8. In Phase 2, extended from day 4 to Article https://doi.org/10.1038/s41467-025-56024-4 Nature Communications | (2025) 16:1489 2 day 6, cells started to express met-proteins together with mesenchymal (FN1, ANXA6, PEF1, and VIM), fetal liver mesothelial proteins (FLNC, KRT18, CGN, and ALCAM), and fetal HSC markers such as COL6A3, NID2, FBLN, and PTN (Fig. 2C). Finally, phase3, from days 8 to day 12, was characterized by the final mature population of diHSCs, showing enrichment of matrisome-related proteins and mature HSCs proteins including aSMA, COL1A1, DES, and MMP2 (Fig. 2C). When comparing the proteomic profiles between phases, we observed thatproteins differentiallyexpressed from Phase 1toPhase 2 were related to characteristics of mesenchymal and mesothelial cells such as desmosomes and tight junction formation (DESP, DSG2, PRDX4) (Fig. 2D). Proteins differentially expressed between phases 2 and 3 were related to collagen and ECM organization (COL5A1, LUM, TNC, TGFBI, and NID2), which are typically expressed in HSCs, Article https://doi.org/10.1038/s41467-025-56024-4 Nature Communications | (2025) 16:1489 3 reinforcing the notion that mature HSCs appear in phase 3 of the differentiation (Fig. 2E). These results indicate that the diHSCs differentiation takes place in three main phases, which mimic key stages of embryonic development. Next, we assessed which TF were involved in the trajectory of HSCs from cell differentiation to cell identity, focusing on those regulating the transition between the identified phases. Our analysis identified RORA as the most significant TF predicted to be regulating the transition between phases 1 and 2, indicating a possible role of RORA in mesoderm commitment (Fig. 2D and Supplementary Data 2). Meis homeobox 1 (MEIS1) was predicted to regulate the transition from phases 2 to 3 (Fig. 2E and Supplementary Data 2). To further explore the role of TFs in stellate cell identity, we compared Phase 3 cells to pHSCs (Fig. 2F and Supplementary Data 2). To ensure the relevance of the selected TF to the pHSCs, we interrogated deposited and publicly available transcriptomic data from quiescent and activated primary human HSCs (GSE90525 & GSE67664)19. This analysis confirmed that RORA was the only TF upregulated in both quiescent and activated pHSCs when compared to diHSCs, thus suggesting its potential role in the acquisition the HSCs phenotype (Fig. 2G). RORA facilitates the diHSCs mature phenotype by improving mesoderm commitment During diHSCs differentiation, the gene expression of RORA showed a significant increase at day 4 of differentiation and progressively declined at days 6, 8, 10, lowering the pHSCs expression at day 12 (Fig. 3A). To assess the role of RORA across the differentiation, we activated RORA using the agonist SR1078 from day 2 until the end of the differentiation (day 12) (Fig. 3B). While the RORA agonist did not change the morphology of diHSCs (Supplementary Fig. 2A), we found that it increased the expression of mature HSC markers such as PCDH7,LRAT, LHX2 and RELN (Fig. 3C). At transcriptome level, the GSEA analysis of gene signatures of quiescent, activated and hepatic stellate phenotype (Zhang et al.23), revealed that RORA agonism promoted the quiescent gene signature (NES = 1.82; p-value = 0.024) (Fig. 3D), while preserving the cells’responsiveness to TGFβ(Supplementary Fig. 2B). Moreover, as shown by differential clustering in the PCA (Fig. 3E), RORA agonism induced transcriptomic changes in diHSCs, also reflected by the enrichment in biological processes and markers related to mesoderm (i.e. EOMES,SRF or GJA1) and mesenchyme development (i.e. GREM1, HAS2 or FGF8)(Fig.3F–H) and reduced enrichment in the biological processes related to cell proliferation (Fig. 3F and Supplementary Data 3). Functional transcriptomic analysis of differentially expressed genes in diHSCs treated with RORA agonist, identified enriched Reactome pathways such as FGFs signalling and a reduction of betaoxidation, tricarboxylic acid (TCA) cycle and mitotic cycle (Fig. 3Iand Supplementary Data 3). These findings suggest that RORA plays a pivotal role in regulating cellular metabolism during diHSCs differentiation. Overall, these data indicate that RORA might act as a transcriptional regulator ofthe mesoderm andmesenchymal specification, facilitating diHSCs differentiation. Functional role of RORA in mesoderm and mesenchymal specification To further dissect the role of RORA in diHSCs differentiation, we generated three heterozygous RORA-knockout iPSCs clones (RORA+/−) using CRISPR-Cas9 technology thus, mimicking in one allele the mutation presented in staggerer mice (sg/sg), spontaneous KO mice for the RORA gene24. Generation of iPSCs RORA+/−was confirmed by Sanger sequencing (Supplementary Fig. 2C). Cells showed a reduction in protein expression of RORA preserving pluripotency markers such as SOX2,OCT4 and NANOG at both gene and protein expression (Supplementary Fig. 2D-F). Moreover, all the clones generated presented a normal karyotype, thus confirming the genome stability of the newly generated cell lines (Supplementary Fig. 2G). Interestingly, during the differentiation of RORA+/−iPSCs we observed in all the clones tested with nearly 80% of the cells dying after day 4 (Fig. 3J). To address the cell mortality observed in RORA+/−iPSCs, we treated the cells with the RORA agonist SR1078 from day 2 until the end of differentiation. This treatment successfully reduced mortality in the RORA+/−differentiating cells and allowed cells to progress to the end of the differentiation, showing an HSCs phenotype but with differences in the expression of LRAT, LHX2, RELN and ACTA2 compared to the control WT (Fig. 3J, K). Moreover, RORA+/−diHSCs, showed a higher activation when plated on plastic (Fig. 3L, M). To bypass the lethality associated with RORA during the early stages of differentiation, we established an inducible doxycycline dependent (iCas9) system, to facilitate downregulation of RORA after reaching the mesoderm phase. To initiate Cas9 expression at the start of stage 3 of differentiation (day 8), we treated the cells with doxycycline starting at day 4 of the differentiation. By day 12, the differentiated cells showed a substantial increase in CAS9 expression, accompanied by significant upregulation of key stellate cell activation markers, particularly ACTA2 and COL1A1. In contrast, we noted a marked downregulation of LRAT, RELN,andLHX2 (Supplementary Fig. 2H), suggesting the acquisition of a more activated phenotype than control WT cells. These results were confirmed by using a chemical antagonist of RORA (SR1001) in the differentiation of wild type (WT) iPSCs. SR1001 treated cells showed a spindle morphology with increased ECM production at the end of differentiation (Supplementary Fig. 2I-J), while reducing gene expression of the HSCs genes RELN and LHX2 and increasing COL1A1 and ACTA2 (Supplementary Fig. 2K), together with increased collagen deposition, as assessed by Picrosirius staining (Supplementary Fig. 2K). Overall, these results position RORA as a potential transcriptional regulator of mesoderm and mesenchymal differentiation and key for the acquisition of the quiescent phenotype of diHSCs. RORA promotes a quiescent diHSCs phenotype and prevents diHSCs activation Next, we evaluated the effect of RORA on diHSCs activation and fibrosis using two in vitro models of diHSCs activation, one based on passage and the other based on TGFβstimuli. Previously we have shown that passaged diHSCs are a good model to induce cell activation Fig. 1 | Time-resolved proteomic characterization of the iPSCs differentiation to diHSCs shows that the stellate cell phenotype is acquired gradually. ASchematic representation of the experimental design used to characterize the differentiation process of iPSCs into diHSCs (differentiated hepatic stellate cells) at the proteome level, with a comparison to primary human HSCs. This characterization was performed in four independent differentiations and 4 primary HSCs. Created in https://BioRender.com.BChord diagram illustrating the enriched Gene Ontologies (GOs) at day 0 and day 12 of the differentiation protocol, alongside a volcano plot showing differentially expressed proteins between these two timepoints. CImmunoflourescence images at day 0 and day 12, showing the expression of LUM and OCT4. Scale bars: 200 µm for day 0, and 100 µm for day 12. DGene Set Enrichment Analysis (GSEA) of proteomic signatures from different humanliver cell types, including LSEC (Liver Sinusoidal Endothelial Cells), KC (Kupffer Cells), HSC (Hepatic Stellate Cells), and HEP (Hepatocytes) obtained from Ölander, M. et al.22. E–IProtein expression profiles of the five identified clusters during differentiation. The time points are labeled as D0 (day 0), D2 (day 2), D4 (day 4), D6 (day 6), D8 (day 8), D10 (day 10), and D12 (day 12). JVenn diagram showing the overlap in proteome profiles between primary HSCs and diHSCs, highlighting the most highly expressed proteins in each cell type including EHD2, TIMP3, THY1, TIMP1, MYL9 andFBLN2for pHSCs; HDAC2, SET, NASP, FUBP1, MCM3 and MCM4 for diHSCs and as common proteins FSTL1, MMP14, SFRP2, PALLD, LOXL2 and RAB13. (K-M) diHSCs exhibit pathways related to both quiescent and activated phenotypes, as well as key stellate cell pathways identified from the literature (Zhang et al.23). Article https://doi.org/10.1038/s41467-025-56024-4 Nature Communications | (2025) 16:1489 4 promoting a fibrogenic response14. diHSCs derived from WT iPSCs treated with the RORA agonistSR1078 for 24 h, prevented activation of passaged diHSCs and decreased the expression of activation markers ACTA2 and COL1A1, at both gene and protein levels (Fig. 3N, O) while increased the expression of quiescent markers LHX2 and LRAT (Fig. 3O). On the contrary, cells treated with the RORA antagonist (SR1001) after passage, increased their activation profile (Supplementary Fig. 3A). Transcriptomic analysis of the passaged diHSCs treated with SR1078 showed a reduction in GOs related to de novo lipogenesis and mitochondrial fatty acid β-oxidation (Fig. 3P and Supplementary Data 3). In addition, as shown in Fig. 3P, Q and Supplementary data 3, Article https://doi.org/10.1038/s41467-025-56024-4 Nature Communications | (2025) 16:1489 5 Reactome analysis showed changes in metabolic pathways, thereby suggesting a remodeling of the metabolic state in the diHSCs to maintain the quiescence phenotype. To evaluate which pathways may be regulated by RORA we investigated which genes were predicted to be direct targets of RORA in the GSEA database. Predicted genes contain at least one occurrence of the motif NWAWNNAGGTCAN within 4 kb of their transcription start sites [-2kb, +2 kb], corresponding to the RORA transcription factor binding site (V$RORA1_01, v7.4 TRANSFAC). Notably, we identified several genes related to differentiation (ITGAX, NRP1,andTSPAN7) and cytokines or growth factors involved in mesoderm and mesenchymal differentiation (CALCA, CMTM6, CNTF, CX3CL1, FGF9, IL17, IL22, IL7, NTF3, SEMA3F). Interestingly, most of these genes were transcription factors, suggesting that RORA functions as a master regulator, integrating diverse biological pathways to coordinate processes such as development, circadian rhythms, cell cycle regulation, stress responses, and differentiation (Supplementary Fig. 3B). Differentiated cells treated with the RORA agonist SR1078 showed enrichment in the signature of predicted genes. Next, we evaluated the effect of RORA on diHSCs response to TGFβ, as a second activation model20. diHSCs were treated with TGFβ for 24 h or 7 days while the SR1078 RORA agonist was added forthe last 24 h. diHSCs treated with SR1078 reduced both short and long-term TGFβ-activation by decreasing ACTA2,COL1A1 and LOX (Fig. 3R). To study the effect of RORA in a complex in vitro system, we evaluated the effect of SR1078 in 3D liver spheroids of HepG2 and diHSCs. Incubation ofliver spheroids with SR1078 for 24 h reduced the expression of COL1A1,ACTA2 and LOX (Fig. 3S). Moreover, liver spheroids incubated with TGFβand treated with SR1078 also showed a reduced expression of activation markers (COL1A1 and ACTA2) (Fig. 3T) as well. Overall, these results suggest that RORA plays a role in the deactivation of diHSCs and the maintenance of the quiescent diHSCs phenotype, which can be mediated by blocking metabolic adaptations required for HSCs activation. RORA regulates cell metabolism during mesoderm differentiation and diHSCs activation Since our data showed that RORA may be responsible for the modulation of the metabolic state of the cells along diHSCs differentiation and activation, we evaluated diHSCs metabolic reprogramming in the presence and absence of RORA, both during differentiation and activation. Previous reports showed that cells exiting pluripotency and differentiating to mesoderm undergo a metabolic switch increasing oxidative phosphorylation (OXPHOS) while reducing glycolytic flux25. Similarly, activation of HSCs is dependent upon induction of glycolysis9, glutaminolysis10 and de novo lipogenesis11. Using the Seahorse real-time cell metabolic analyzer, we found that cells from iPSC-RORA+/- differentiation have increased mitochondrial respiration at day 4, when mesoderm specification occurs just before cell death (basal, ATP-coupled and maximal respiration) (Fig. 4A, B). In addition, adenosine triphosphate (ATP) production from glycolysis was also increased in these cells, (Fig. 4A–C), and have higher intracellular glucose levels (Fig. 4D), suggesting that cells derived from iPSC-RORA+/- retained a metabolic profile more characteristic of undifferentiated cells. Accordingly, iPSC-RORA+/- cells expressed higher levels of genes related to glycolytic flux (ALDOB and GS6P)andadecreasedexpressionofACACA (Fig. 4E). Moreover, the expression of mesoderm (EOMES) and mesenchymal (VIM) markers wasreducedatbothproteinandgeneexpressioniniPSC-RORA +/- cells at day 4 of the differentiation (Fig. 4E, F) when compared to WT iPSCs. However, when iPSC-RORA+/- cells were treated with RORA agonist, they showed a reduction in intracellular glucose and glycolysisdependent ATP production (Fig. 4C, D), glycolytic gene expression (Fig. 4E) and glucose consumption from the media (Fig. 4D), thus confirming that iPSC-RORA+/- cells fail to initiate the differentiation toward mesoderm during diHSCs differentiations and retain a glycolytic profile of undifferentiated cells. To explore the role of RORA in mesoderm commitment, we differentiated iPSC-RORA+/- cells into both non-directed mesoderm, and renal mesoderm using established protocols. Non-directed mesoderm differentiation protocol showed a reduction in αSMA-GATA4 double positive cells after 21-days differentiation protocol indicating a poor mesoderm commitment (Supplementary Fig. 3C). However, the directed differentiation of iPSC-RORA+/- into renal organoids, did not show significant differences in comparison to control WT iPSCs in terms of protein expression (LTL, PDOXL and ECAD) (Supplementary Fig. 3D), therefore suggesting that RORA’sinfluence on mesoderm differentiation may be context-dependent. In diHSCs activation models, treatment with the RORA agonist (SR1078) reduced mitochondrial respiration, OXPHOS-dependent ATP production, glycolysis-dependent ATP production as well as intracellular glucose (Fig. 4G–J, L–O). Moreover, genes associated with glycolysis and lipid synthesis that were increased with activation, were found to be reduced after treatment with SR1078 (Fig. 4K, P). Altogether, these results indicate that RORA acts as a metabolic modulator in diHSCs trajectory which impacts the phenotype of diHSCs at different stages, including initial cell commitment and the quiescent cell identity at the end of differentiation. Given the previously established role of RORA in regulating lipid metabolism26, we further examined the lipidomic profile of passaged diHSCs treated with SR1078. Our findings indicate a shift towards a quiescent-like lipidomic phenotype, by reducing the number of lipids with higher carbon numbers and degree of saturation (Supplementary Fig. 4A). According to bibliography27 early HSC activation is marked by a reduction in phosphatidylcholines (PCs) and sphingomyelins containing saturated or monounsaturated fatty acids followed by an increase in PCs and dihexosylceramides (Hex2Cer) at later stages. In our analysis, SR1078-treated diHSCs showed a reduction in PCs and triglycerides (TGs), though no changes in HexCer weredetected, likely due to the short treatment duration (Supplementary Fig. 4B). We also observed a shift in the PC2 profile, driven by PCs and sphingomyelins with shorter, less unsaturated fatty acid chains (Supplementary Fig. 2 | Proteome trajectory analysis of diHSCs differentiation discovers three stages of maturation and identifies RORA as a potential driver of stellate cell differentiation. A Principal component analysis (PCA) of the proteome during differentiation reveals a time-dependent separation of data, indicating distinct stages of the differentiation process. BPearson correlation analysis identifies three phases of differentiation towards diHSCs: Phase 1 from day 0 (D0) to day 4 (D4), Phase 2 from day 4 (D4) to day 8 (D8),and the final maturation phase (Phase 3) from day 8 (D8) to day 12 (D12). CDotplotshowingproteinenrichmentthroughoutthe differentiation process. In Phase 1 (D0 to D4), pluripotency and mesoderm markers (POU5F1, RIF1, MFGE8, CALB1) are enriched. During Phase 2 (D4 to D8), mesenchymal and mesothelial proteins, along with fetal HSC markers (VIM, PEF1, KRT18, FN1, FBLN1, COL6A3, CGN, ANXA6, ALCAM), begin to dominate. Finally, in Phase 3 (D8 to D12), markers of mature HSCs are enriched (VCAN, PTN, NES, MMP2, MMP14, LAMA5, FLNC, FBLN2, DES, DCN, COL4A1, COL3A1, COL1A2, COL1A1, ACTA2). DVolcano plot comparing phases 1 and 2 highlights the enrichment of mesothelial markers (DESP, DSG2, PRDX4, WLS) and transcription factors (TFs) predicted in silico to regulate this transition. EVolcano Plot showing the comparison between phases 2 and 3 shows an increase in collagen and extracellular matrix (ECM) proteins (COL5A1, LUM, TNC, TGFBI, NID2), along with predicted TFs modulating this transition. FVolcano plot displaying the differentially expressed proteins between phase 3 (diHSCs) and primary HSCs, as well as the in silico predicted TFs involved in regulating the adult hepatic stellate cell phenotype. GTranscriptomic comparison of primary human activated and quiescent stellate cells with diHSCs, focusing on the predicted TFs modulating the adult stellate cell phenotype, using data from GSE90525 and GSE67664. Fold change (FC) between diHSCs vs. qHSCs = 3.20; FC diHSCs vs. aHSCs =1.60 for RORA expression. Article https://doi.org/10.1038/s41467-025-56024-4 Nature Communications | (2025) 16:1489 6 Fig. 4D-E). These findings suggest that RORA agonist treatment promotes a lipidomic profile consistent with quiescent stellate cells. RORA regulates fibrosis in vivo To evaluate the role of RORA in HSCs activation and liver fibrosis, we used a RORA-deficient strain, known as staggerer mice, hereafter sg/sg. We found no significant differences in baseline fibrosis of wildtype (WT)andsg/sgliversasshownbythehydroxyprolineassay,thoughsg/ sg livers exhibited a slight increase in the expression of Acta2 and Timp1 activation markers (Supplementary Fig. 5A and B). Next, we induced liver fibrosis using CCl 4 for 4 weeks (Fig. 5A).The livers of the sg/sg group presented an increased fibrotic content (Picrosirius) coupled with an increased in alpha smooth muscle actin (αSMA) staining as compared to their littermate WT counterparts Article https://doi.org/10.1038/s41467-025-56024-4 Nature Communications | (2025) 16:1489 7 (Fig. 5B). In agreement with previous reports showing increased hepatocyte injury in RORA-KO mice fed with a high-fat diet26,by hematoxylin and Eosin (H&E) staining we observed an increased tissue damage in the pericentral hepatocytes (Fig. 5B) in the sg/sg group. This was further supported by elevated alanine transaminase (ALT), aspartate transaminase (AST), phosphatase alkaline (AP) and lactate dehydrogenase (LDH) in sg/sg (Supplementary Fig. 5C). Moreover, gene expression of fibrogenic markers such as Acta2,Col1a1,Col1a2, Timp1 and Fn1 were significantly increased in the mutant group (Fig. 5C), with an increased neutrophil content (Supplementary Fig. 3D), and no significant differences in macrophages (F4/80) or lymphocytes (CD3) nor in proliferation were observed between groups (Supplementary Fig. 5D). As Rora expression is not limited to HSCs and to clarify RORA’s specific involvement in HSC pathophysiology in in vivo context, we generated mice with Rora deficient HSCs. We crossed the Rorafl/fl mice28 with the LratCre mice29, generating the heterozygous LratCreRorawt/floffspring, which was used for the experiments. Both LratCrepositive Rorawt/fl(Cre+), and LratCre-negative Rorawt/fl(Cre−) mice, were administered I.P. with CCl4 for 4 weeks. Despite not observing any significant differences in the liver-body weight ratio between the Cre+and Cre−groups (Fig. 5D), we detected a significant downregulation of Rora gene expression while maintenance of Lhx2 (Fig. 5G). Moreover, we observed an increase in collagen deposition in the Cre+vs Cre-group as detected by increased hydroxyproline levels (p= 0.05) (Fig. 5E) and enhanced collagen deposition as assessed by Picrosirius staining (Fig. 5F). These changes were accompanied by increased αSMA protein expression (Fig. 5F). Despite these changes in pro-fibrogenic genes and proteins, no significant differences were observed in terms of cell proliferation, as indicated by Mki67 protein expression, nor in the amount of F4/80 positive, and MPO positive cells (Fig. 5X). Altogether, these results confirm the role of RORA during fibrosis in vivo, therefore supporting our in vitro findings and confirming its specific impact on HSCdriven fibrosis. The RORA agonist reduces liver fibrosis in multiorgan fibrosis models To explore the translational potential of targeting RORA in fibrogenic diseases, we evaluated fibrotic content and gene expression of Rora in different liver disease models (Supplementary Fig. 5E). RORA agonist SR1078 was administered to CCl 4 -treated mice (Fig. 6A). SR1078 treatment led to an increase in Rora gene expression (Fig. 6B). H&E staining showed reduced tissue damage coupled with reduced collagen deposition and αSMA staining (Fig. 6C). Likewise, we observed a reduced gene expression of fibrogenic and HSCs activation markers such as Acta2,Col1a1, Timp1,Fn1,Col1a2,Pparg,Mmp2,Mmp9,Mmp12 and Timp1 (Fig. 6D and Supplementary Fig. 5A). Immunohistochemistry for F4/80, MPO and CD3 showed a non-significant reduction of inflammatory cell populations in mice treated with SR1078 (Supplementary Fig. 5C). Accordingly, Il1b,Tnfa and Il6 inflammatory cytokines gene expression was not significantly reduced (Supplementary Fig. 6A). Moreover, we also detected reduced parenchyma liver damage in SR1078 treated mice, as shown by reduced ALT, AST and LDH serological levels in the SR1078 treated group, compared to the vehicle one (Supplementary Fig. 6B). We also found reduced proliferative activity in the liver parenchyma as shown by mKi67 staining upon SR1078 treatment (Supplementary Fig. 6C). These results suggest that the RORA agonist mitigates liver damage and fibrosis by reducing HSCs activation. Since HSCs are liver pericytes showing important phenotypic and functional similarities with pericytes from other organs and participating in wound healing response, we also evaluated the effect of RORA agonism in other organs, in extrahepatic wound healing response models. First, we tested the effect of the SR1078 RORA agonist in an isoproterenol-induced cardiac hypertrophy mice model (Fig. 6E). Upon SR1078 treatment, mice showed reduced cardiac expression of Rora, accompanied by increased cardiac fibrosis (Fig. 6G). Treatment with the RORA agonist (SR1078) promoted the expression of Rora and reduced cardiac fibrogenic genes (Col3a1 and Acta2)(Fig.6H) as well as ECM deposition as assessed by Masson’s trichrome and picrosirius staining (Fig. 6F). Echography analysis showed no differences in cardiac structure and function between the vehicle and thetreated group (Table 1). Moreover, RORA treatment did not alter cardiomyocyte hypertrophy as shown by no differences in either the expression levels of the hypertrophy marker Acta1 nor in the cardiomyocyte area (Supplementary Fig. 6D), suggesting a positive effect of RORA agonism against cardiac fibrosis independently of hypertrophy development. The antifibrogenic role of RORA was further confirmed using primary culture of neonatal cardiomyocytes (NCMs) and fibroblasts (NCFs). Cultured NCFs to passage 3 reduced Rora expression while increasing Acta2, while RORA treatment reduced this expression, confirming its anti-fibrogenic effect (Fig. 6I). By contrast, in NCMs, the RORA agonist did notalter cardiomyocyte area orthe expression levels Fig. 3 | RAR-related orphan receptor A is a potential driver of the diHSCs phenotype by improving mesoderm commitment and modulating diHSCs activation state. A RORA gene expression during diHSC differentiation compared to primary HSCs (pHSCs). Data represent three independent differentiations and three pHSC samples. Significant differences are indicated as *p<0.05.BSchematic of the experimental design for SR1078 treatment during differentiation. Cells were treated with the RORA agonist SR1078 (0.1 mM) from day 2 onwards. CGene expression analysis of key HSC markers (PCDH7, LRAT, LHX2, RELN)inthreeindependent differentiations treated with SR1078 compared to the vehicle control. DGSEAs of reported gene signatures for quiescent, panand activated stellate cells. Obtained from Zhang et al.23.EPrincipal Component Analysis (PCA) showing transcriptomic differences between cells treated with the RORA agonist SR1078 from day 2 compared to the untreated group, n=3.FGOs upregulated (red) and downregulated (blue) in cells treated with the RORA agonist. GHeatmap of mesoderm markers in treated and untreated cells along differentiation with SR1078. HHeatmap of mesenchymal markers in treated and untreated cells along differentiation with SR1078. IReactome pathways upregulated (red) and downregulated (blue) in cells treated with the RORA agonist. JRepresentative microscopy images of diHSCs from WT, iPSCRORA+/- and treated iPSCRORA+/- with the RORA agonist (SR1078) at day 12. Scale bars represent 100μm. KGene expression of stellate cell markers (LRAT, LHX2, RELN and ACTA2)ofWTandtreatediPSCRORA+/- with the RORA agonist (SR1078) at day 12. n= 3 independent differentiations with two replicates. LRepresentative microscopy images of passaged (P1) diHSCs from WT and iPSCRORA+/- SR1078 treated. Scale bar represents 100 μm. MGene expression of activated stellate cell markers (ACTA2, COL1A1 and LOX). n= 3 independent differentiations with two replicates. NImmunoflourescence images for COLLAGEN and aSMA in WT passaged cells treated and untreated with RORA agonist SR1078 for 24 h. Scale bars represent 100 μm. OGene expression of quiescent (LRAT and LHX2) and activated (ACTA2 and COL1A1) markers in cells treated with the RORA agonist at passage. n= 3 independent differentiations with two replicates. PGOs upregulated (red) and downregulated (blue) in treated cells with the RORA agonist at passage. QReactome pathways upregulated (red) and downregulated (blue) in cells treated with the RORA agonist at passage. RGene expression of activated (markers in cells treated with the RORA agonist at passage, after TGFβstimulation (10 ng/μL) during 24 h and 7 days. n= 3 independent passaged cells with two replicates. SGene expression of activated markers (ACTA2, COL1A1 and LOX) in liver spheroids after SR1078 3 mM treatment during 24 h. n=3;Significant differences are indicated as *p<0.05. TGene expression of activated markers(ACTA2, COL1A1 and LOX) in liver spheroids after TGFβstimuli during 24 h and SR1078 3 mM treatment during 24 h more. n=3 independent spheroids experiments with 10 biological pool replicates each; All data is presented as mean ± SEM, no significance or * p<0.05,**p<0.01was determined by One sample t and Wilcoxon test. Article https://doi.org/10.1038/s41467-025-56024-4 Nature Communications | (2025) 16:1489 8 of the Acta1 hypertrophy marker in the presence of the hypertrophic agent phenylephrine (PE) confirming the absence of anti-hypertrophic effects in vitro (Fig. 6J). Second, we explored the antifibrogenic potential of RORA in a kidney damage model based ona unilateral ureteral obstruction (UUO) intervention (Supplementary Fig. 6D). The UUO model reduced the expression of Rora and promoted kidney fibrosis (Supplementary Fig. 6E). The RORA agonist-treated group showed a reduction in hydroxyproline content, indicating a reduction in collagen deposition (Supplementary Fig. 6F), however, we did not observe any significant reduction of αSMA expression in the 2 groups (Supplementary Fig. 6G). Consistently, SR1078 treated mice, showed a non-significant reduction in the gene expression of Acta2 and Col1a1 and an increased expression of Rora (Supplementary Fig. 6H). Article https://doi.org/10.1038/s41467-025-56024-4 Nature Communications | (2025) 16:1489 9 RNA extraction, cDNA synthesis, qRT-PCR analysis and Bulk RNA sequencing RNA from cells was obtained using the Quick RNA-Microprep Kit (Zymo, Cat#R1050), following the manufacturer’s instructions. For 3D cultures, total RNA from 6 pooled spheroids were extracted using the same kit. Total RNA was extracted from mouse whole liver tissue using Trizol (Life Technologies, Carlsbad, CA, Cat#15596026). The total RNA extracted underwent quality control by Bioanalyzer 2100 (Agilent Technologies, Santa Clara, CA). RNA concentrations were measured using the Nanodrop 1000 (ThermoFisher). cDNA synthesis was performed with the MultiScrib (Applied biosystems, Cat#4368813) followed by a quantitative reverse transcription polymerase chain reaction (qRT-PCR) on an ABI 7900HT cycler (Applied Biosystems) and SYBR green master mix (Life Technologies, Cat#11733046). Gene expression was normalized to GAPDH for in vitro systems and Actb for in vivo experiments. Expression values were calculated based on the ΔΔCt method. Primers used for gene expression analysis are referenced in Supplementary Table 2. For bulk-RNA sequencing, the quality of RNA was evaluated using Agilent RNA 6000 Nano Chips (Agilent Technologies, Santa Clara, CA, USA). Sequencing libraries were prepared following the TruSeq Stranded mRNA Sample Preparation with the corresponding kit starting from 125 ng of total RNA per sample. Sequencing of the mRNA libraries was performed in a HiSeq2500 (Illumina Inc, San Diego, CA, USA) to obtain at least 30 million 51ntsingle-end reads per sample. GO biological processes term enrichemnet, KEGG pathway, Reactome and gene set enrichment analysis were performed using DAVID39,andGSEAdatabase 40. Lipidomics The lipidomics assay was conducted in collaboration with Olobion. In brief, cells were harvested using trypsin and subsequently washed with PBS to eliminate residual media and contaminants. We added 275 µLof methanol directly to the cells along with a ball for homogenization, which was carried out for one minute. Following this, 1 mL of MTBE was incorporated into the mixture, which was then shaken and centrifuged. For lipidomic profiling, we collected 100 µL of the upper organic phase, evaporated it, and resuspended the residue in 100 µLof methanol containing an internal standard. This mixture was shaken, centrifuged, and prepared for LC-MS analysis. The lipids were separated using an ACQUITY UPLC BEH C18 column maintained at 65 °C and analyzed on a ZenoTOF 7600 system (SCIEX). A sample volume of 4μL was injected in both ESI positive and negative modes, and the lipidomics data were processed using oloMAP 2.0. Intracellular glucose level testing Intracellular glucose levels from day 4 differentiated cells and diHSCs passaged cells were assessed using the Glucose-Glo™Assay, following manufacturer instructions (Promega, Cat#TM494). Briefly, the assay was applied to cells in a 96-well plate. Before beginning the assay, the medium was removed, and the cells were washed with 100 μlofDPBS. To initiate glucose uptake, 50 μl of 2DG in DPBS wereused. The uptake reaction was stopped, and the samples were processed following the manufacturer’s instructions. Commercial kits used within this manuscript have been collected in Supplementary Table 3. XF24 extracellular flux The oxygen consumption ratio (OCR) was determined using the Seahorse XF Cell Mito Stress Test (Agilent, Santa Clara, CA, US). Briefly, iPSC-RORA +/- and their WT counterparts, treated and untreated with the RORA agonist SR1078 at 0.1 mM at day 4 of the differentiation, were sequentially treated with 1 μM oligomycin; 2 μM carbonyl cyanide m-chlorophenyl hydrazone (CCCP); and a 1 μM mixture including rotenone and antimycin A according to manufacturer’sinstructions. Seahorse XFe Wave Software (Agilent) was applied to analyze the data. Similarly, cells after passage were treated with RORA agonist and/or TGFb for 24 h. Finally, the cell mass was normalized quantified total protein using the BCA protein assay kit (ThermoFisher, Cat# 23225). Commercial kits used within this manuscript have been collected in Supplementary Table 3. Immunofluorescence and immunohistochemistry Cells were fixed using 4% Neutral Buffered Formalin for 15 min and washed with PBS for three times before permeabilization at room temperature during 30 min. with 0.2% Triton X-100 in PBS. Afterwards, the cells were blocked with 3% of donkey serum at room temperature. Cells were incubated overnight at 4 °C with primary antibodies in DAKO REALTM Anti-body diluent (Agilent, Cat#S2022). Secondary antibodies (1:500 dilution) were incubated for 30 min. To ensure nuclear staining, samples were mounted with Mounting Medium with 4’,6-diamidino-2-phenylindole (DAPI) (Vector Laboratories, Cat# H1200-10). Antibodies used within this manuscript have been collected in Supplementary Table 4. RORA immunofluorescence in human tissue Human liver sections were included in OCT and maintained at -80 °C. Slides were fixed at 4% PFA for 2 min. They were then washed in PBS for a few seconds and blocked in 100 µL 5% BSA in PBS + 0,3% TritonX100 per tissue section during 1 h. Tissue sections were incubated overnight with primary antibodies (RORA 1:50, Abcam, Cat#ab70061) in 5%BSA/PBS/ Triton at 4 °C in a dark moist chamber. Then, the sections were incubated 1 h with secondary antibodies in 5%BSA/PBS/Triton at room temperature (RT) in a dark moist chamber. Tissue sections were mounted using the Vectashield Mounting medium for fluorescence with DAPI. In vivo test in carbon tetrachloride-induced liver fibrosis model in homozygous staggerer mice To test the physiological role of RORA upon liver fibrosis in vivo, we established a carbon tetrachloride (CCl 4 )modelbasedonintraperitoneal (I.P.) injection of 0,5 ml/kg of CCl 4 diluted in corn oil. Mice were injected two days a week during four 4 weeks. Blood and liver tissues were collected for further analysis. Generation of RORA Knockout (KO) HSC-specificanimals To examine the impact of RORA KO specifically in HSCs in the context of liver fibrosis, we developed a new animal model. This was accomplished by crossing RORA floxed (fl/fl) mice with Lrat-Cre mice. The presence of the floxed allele and the Cre transgene in the offspring was confirmed through genotyping. n=5Lrat-Cre +Rorafl/−and n=5LratCre-Rorafl/−mice were injected i.p. with 0.5 mL/kg of CCl 4 (0.5 mL/kg of CCl 4 diluted in corn oil) twice a week for four weeks. Animals were sacrificed 24 h after the last CCl 4 injection and blood, and liver tissues were collected and analyzed. In vivo test of RORA as an antifibrogenic target in fibrogenic models For the liver fibrosis model, 14 C57BL/6 J mice were injected twice a week with CCl4 0,5 ml/kg (25% corn oil) during 4 weeks. During the last 2 weeks,mice were divided into two groups: 1) n= 7 mice were injected ip with SR1078 (10 mg/kg) twice a week; 2) n= 7 mice were injected ip with vehicle DMSO at the same concentration. SR1078 was diluted in 5% DMSO to a final concentration of 2 mg/ml. For the heart fibrotic model, n= 10 C57BL/6 J mice were administered isoproterenol (Sigma Aldrich, Cat#SLC62971) by continuous infusion of 60 mg/kg/day using minipumps (Model 1007D, Alzet). After 24 h, animals were divided into two groups: 1) n= 5 treated with SR1078 (10 mg/kg) every 48 h IP and 2) n= 5 injected IP with the vehicle during 1. Animals were sacrificed after one week. For the kidney fibrotic model, n= 19 C57BL/6 J mice underwent ligation of the left ureter and starting 3 days after surgery, the mice were divided into two groups: 1) n=10treatedwithSR1078 (10 mg/kg) from day 3 after surgery until day 15.; 2) n= 9 treated with Article https://doi.org/10.1038/s41467-025-56024-4 Nature Communications | (2025) 16:1489 16 vehicle fromday 3 aftersurgery for 15 Bloodandliver,heart and kidney tissues were collected for further analysis. Collagen tissue determination The amount of collagen in liver tissues was determined by measuring the content of hydroxyproline using a Hydroxyproline assay kit (Abcam, Cat#MAK008-1KT) as described by the manufacturer. Absorbance was measured at 540 nm using a FLUOstar OPTIMA FL reader (BMG LABTECH). Biochemical determination by Echevarne laboratories (Barcelona, Spain) Serum from mice included in the experimental studies was collected and analyzed for alanine transaminase (ALT), aspartate transaminase (AST), phosphatase alkaline (AP) and lactate dehydrogenase (LDH) values. Statistical analysis GraphPad Prism v8.0 was used for the statistical analyses. The D’Agostino-Pearson omnibus normality test, Anderson-Darling test and Shapiro-Wilk normality test were performed to assess data distribution. For statistical analysis of parametric data, the two-tailed unpaired Student’s t test was used for groups of two; one-way ANOVA followed by Sidak multiple comparison posthoc tests were used for comparison of more than two groups. For non-parametric data, the Mann-Whitney U test was used for groups of two while the KruskalWallis test followed by the Dunn multiple comparison posthoctestwas used for comparison of more than two groups. Reporting summary Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article. Data availability The proteomic data generated in this study have been deposited in ProteomeXchange Consortium via the PRIDE partner repository under accession code PXD058290. The transcriptomic data generated in this study have been deposited in Gene Expression Omnibus (GEO) database under accession code GSE282539. 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Gene set enrichment analysis: A knowledgebased approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. Usa. 102,15545–15550 (2005). Acknowledgements P.S-B. is supported by Instituto de Salud Carlos III (ISCIII) “PI20/00765”, PI23/00724, “FORT23/00002”,“DTS22/00032”,AGAUR,“2021 PROD 00035”, 23-0356; Dissecting the role of the 14q32 region in hepatoblastoma “Hblast14”Worldwide Cancer Research; PRYCO223102ARME; Scientific Foundation of the Spanish Association Against Cancer (AECC); Q6922, (B-ORG) Plan Complementario de Biotecnología aplicada a la Salud del Plan de Recuperación, Transformación y Resiliencia. Ministerio de Ciencia e Innovación and co-founded the European research and innovation program Horizon HORIZON-HLTH−2022-STAYHLTH-02 under agreement no. 101095679. R.A M-G is funded by Instituto de Salud Carlos III “FI18/00215”,S.A.receivedagrantfromtheMinisterio de Educación, Cultura y Deporte “FPU17/04992”, C.M is funded by Centro de Investigación en Red Enfermedades Hepáticas y Digestivas (CIBERehd).A.G.is supported by Competitiveness (MINECO PID2020-15591RB-100), La Maratode TV3 (202001-32) and CERCA Programme/ Generalitat de Catalunya for institutional support. J.P. is supported by La Maratode TV3 (20200132). D.R-M. is supported by Deutsche José Carreras Leukämie-Stiftung (DJCLS 13 R/2022). A.M. was funded by MCIN/AEI/10.13039/ 501100011033/FEDER, UE through the project grants PID2021-123652OBI00 and RTI2018-097475-A-100; by MCIN/AEI/10.13039/501100011033 and El FSE invest in your future through the contract RYC 2016-19731; by Pfizer grant #77131383; P.R.B. was funded by Ministerio de Universidades fellowships FPU19/05357; M.F.F. was funded by FPU20/01367. M.V.-R. is supported by Proyecto PID2020-119486RB-100 (funded by MCIN/ AEI/ 10.13039/501100011033), Proyecto LABAECC2024 (funded by Asociación Española Contra el Cáncer, AECC), HORIZON-TMA-MSCA-Doctoral Networks 2021 (101073094), and Redes de Investigación 2022 (RED2022134485-T). This study was funded by Instituto de Salud Carlos III through the Biobanks and Biomodels Platform and co‐funded by the European Union (PTC20/00013, PT20/00130, PT23/00009 and PTC23/00002 to N.M.) and by Instituto de Salud Carlos III and European Union—Next Generation EU, Plan de Recuperación Transformación y Resiliencia (TERAV/ISCIII RD21/0017/0018) to N.M; M.C. was funded by Ramon y Cajal programme from the Ministerio de Ciencia e Innovación RYC2019026662-I. S.A. is funded by la Caixa Foundation “100010434”and the European Union’s Horizon 2020 under the Marie Skłodowska-Curie “847648”, PID2021-124694OA-I00, MCIN/AEI/ 10.13039/501100011033 and FEDER Una manera de hacer Europa, The European Union grant agreement 101077312*. Ramon y Cajal program RYC2022-036321-I. (* Views and opinions expressed are, however, those of the author(s) only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them”). Author contributions R.A.M.G.T. conceived the study, designed and performed the experiments, interpreted the results and wrote the manuscript. J.V., Z.X., S.A., R.F-L., L.Z., M.M-G., B.A-B., P.R-B., M.F-F., A.N-G., A.B.-R., P.S-F., J.P., D.RM., P.A.G, C.M.S., L.S.V., P.C.V., C.I.C.C., D.M-R., D.E., B.S., B.A., M.A., J.J.L., M.L.M-C., A.G., F.E, performed experiments. M.A. and F.E. performed the proteomic analysis. J.J.L. performed computational analysis. A.M.,M.Y.M.,performedtheSNPsanalysis.N.M.,D.E.,B.S.,B.A.,M.M-C., A.G., A.P., A.Z., M.V.-R., A.W., I.G., M.C., provided insights. S.A. and P.SB., conceived the study, interpreted the results, supervised the experiments, and wrote the manuscript. Competing interests M.C. and P.S-B. have a patent (EP2016/079464) regarding the hepatic stellate cell differentiation. The remaining authors declare no competing interests. Additional information Supplementary information The online version contains supplementary material available at https://doi.org/10.1038/s41467-025-56024-4. Correspondence and requests for materials should be addressed to Silvia Affo or Pau Sancho-Bru. Peer review information Nature Communications thanks Scott Friedman, Hans Clevers and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. A peer review file is available. 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To view a copy of this licence, visit http:// creativecommons.org/licenses/by-nc-nd/4.0/. © The Author(s) 2025 Article https://doi.org/10.1038/s41467-025-56024-4 Nature Communications | (2025) 16:1489 18 1 Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain. 2 University of Barcelona, Barcelona, Spain. 3 Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd), Barcelona, Spain. 4 Department of Experimental Pathology, Institute of Biomedical Research of Barcelona, Spanish National Research Council (IIBB-CSIC), Barcelona, Spain. 5 Departament de Bioquímica i Biomedicina Molecular, Facultat de Biologia, Institut de Biomedicina de la Universitat de Barcelona, Universitat de Barcelona, Barcelona, Spain. 6 CIBER Fisitopatologiade laObesidad y Nutrición, Instituto de Salud Carlos III, Barcelona, Spain. 7 Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Spain. 8 Regenerative Medicine Program, Institut d’Investigació Biomèdica de Bellvitge (IDIBELL), Barcelona, Spain. 9 Gene Regulatory Control in Disease Laboratory, Center for Research in Molecular Medicine and ChronicDiseases (CIMUS), Instituto de Investigación Sanitaria de Santiago de Compostela (IDIS), University of Santiago de Compostela, Santiago de Compostela, Spain. 10 Molecular Psychiatry Laboratory, Division of Psychiatry, University College London, London WC1E 6DE, UK. 11 UCL Institute for Liver & Digestive Health,Division of Medicine, Royal Free Campus, University College London, London NW3 2PF, UK. 12 Pluripotency for Organ Regeneration, Institute for Bioengineering of Catalonia (IBEC), Barcelona Institute of Science and Technology (BIST), Carrer de Baldiri i Reixac, 15-21, Barcelona, Spain. 13 Catalan Institute for Research and Advanced Studies (ICREA), Passeig de Lluís Companys 23, Barcelona, Spain. 14 Univ. Lille, Inserm, CHU Lille, Institut Pasteur de Lille, U1011-EGID, 59000 Lille, France. 15 Sorbonne Université, Inserm, Centre de Recherche Saint-Antoine, CRSA, F-75012 Paris, France. 16 Proteomics Platform, CIC BioGUNE, Basque Research and Technology Alliance (BRTA), Bizkaia Science and Technology Park, Derio, Spain. 17 Liver Disease Lab, Center for Cooperative Research in Biosciences (CIC bioGUNE), Basque Research and Technology Alliance (BRTA), Bizkaia Technology Park, Building 801A, Derio, Spain. 18 Department of Pathology and Experimental Therapeutics, Faculty of Medicine and Health Sciences, Barcelona University, Barcelona, Spain. 19 Department of Biochemistry and Molecular Biology, University of Santiago de Compostela,Plaza do Obradoiros/n, Santiago de Compostela, Spain. 20 Oportunius Research Professor at CIMUS/USC, Galician Agency of Innovation (GAIN), Xunta de Galicia, Santiago de Compostela, A Coruña, Spain. 21 Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Instituto de Salud Carlos III, 28029Madrid,Spain. 22 Liver Unit, Hospital Clínic, Facultyof Medicine, University of Barcelona, Barcelona, Spain. 23 Medicine Department, Faculty of Medicine, University of Barcelona, Barcelona, Spain. e-mail: [email protected];[email protected]nic.cat Article https://doi.org/10.1038/s41467-025-56024-4 Nature Communications | (2025) 16:1489 19