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Single-cell atlas of human liver development reveals pathways directing hepatic cell fates

Nature Cell Biology volume 24, pages 1487–1498 (2022)Cite this article

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Abstract

The liver has been studied extensively due to the broad number of diseases affecting its vital functions. However, therapeutic advances have been hampered by the lack of knowledge concerning human hepatic development. Here, we addressed this limitation by describing the developmental trajectories of different cell types that make up the human liver at single-cell resolution. These transcriptomic analyses revealed that sequential cell-to-cell interactions direct functional maturation of hepatocytes, with non-parenchymal cells playing essential roles during organogenesis. We utilized this information to derive bipotential hepatoblast organoids and then exploited this model system to validate the importance of signalling pathways in hepatocyte and cholangiocyte specification. Further insights into hepatic maturation also enabled the identification of stage-specific transcription factors to improve the functionality of hepatocyte-like cells generated from human pluripotent stem cells. Thus, our study establishes a platform to investigate the basic mechanisms directing human liver development and to produce cell types for clinical applications.

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Fig. 1: Single-cell transcriptomic map of human liver development.
Fig. 2: Mapping non-parenchymal cell identity during human liver development.
Fig. 3: Identification of a hepatic stellate and endothelial cell progenitor in the early foetal liver.
Fig. 4: Modelling early hepatic development in vitro using HBOs.
Fig. 5: Cell-to-cell interaction networks during human liver development.
Fig. 6: Characterization of the differentiation capacity of HBOs into both hepatocyte and cholangiocyte mature lineages.
Fig. 7: Temporal overexpression of key transcription factors in hPSC-derived hepatocytes increases their similarity to adult primary hepatocytes.

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Data availability

Sequencing data that support the findings of this study have been deposited in ArrayExpress under accession code E-MTAB-8210. Foetal liver sequencing data have been deposited to ArrayExpress under accession E-MTAB-7189. Previously published foetal liver scRNA-seq data from Popescu et al. have been deposited in ArrayExpress under accession E-MTAB-7407. Previously published adult liver scRNA-seq data from MacParland et al. and Ramachandran et al. have been deposited in NCBI GEO under accession GSE115469 and GSE136103, respectively. All data sources are described in the Supplementary Tables. All additional raw numerical source data presented in plots and graphs in this study are found in the source data files. Any additional data are available upon reasonable request. Source data are provided with this paper.

Code availability

All Python and R scripts supporting the findings of this paper are available upon reasonable request.

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Acknowledgements

We thank K. Nayak (University Department of Paediatrics, Cambridge) for help with maintenance of cell lines and technical support and C. Usher for the illustrations in Figs. 1a,e and 4a. We thank the Cellular Genetics department at Cambridge University Hospital (I. Simonic) for performing comparative genomic hybridization, the Core Biochemical Assay Laboratory at Cambridge University Hospitals (K. Burling) for ELISA analysis of culture media, R. Barker and X. He (John Van Geest Centre for Brain Repair, University of Cambridge) for their help accessing tissue, F. Johansson (University of Washington) for surgical and animal support and the Tietze Foundation for funding support. C.A. and D.G. received funding from the Open Targets consortium (OTAR026 project) and the Wellcome Sanger core funding (WT206194). We acknowledge the Cambridge Biorepository for Translational Medicine for the provision of human tissue used in the study. We acknowledge the NHS Addenbrooke’s Hospital Tissue Bank for sectioning samples for histology and the Histopathology and Cytology service for immunohistochemistry staining of primary liver tissue. We thank the Cambridge Stem Cell Institute and the Imaging facility. B.T.W. was supported by the Gates Cambridge funding programme. A.D.B.R. was supported by Wellcome Translational Medicine and Therapeutics Clinician PhD programme. The L.V. lab is funded by the ERC Proof of Concept grant Relieve-Chol, by the ERC advanced grant New-Chol, the Cambridge University Hospitals National Institute for Health Biomedical Research Centre and the core support grant from the Wellcome Trust and Medical Research Council of the Wellcome–Medical Research Council Cambridge Stem Cell Institute. C.M.M. is funded by the NC3Rs Training Fellowship. F.S., L.V. and K.S.-P. gratefully acknowledge support from the Rosetrees Trust (REAG/240 and NMZG/233).

Author information

Author notes

  1. These authors contributed equally: Brandon T. Wesley, Alexander D. B. Ross.

  2. These authors contributed equally: Sarah A. Teichmann, Ludovic Vallier.

Authors and Affiliations

  1. Wellcome—MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK

    Brandon T. Wesley, Alexander D. B. Ross, Daniele Muraro, Rute A. Tomaz, Carola M. Morell, Katherine Ridley, Ekaterini D. Zacharis, Stephanie Brown, Olivia C. Tysoe, Daniel Ortmann, Fotios Sampaziotis, Matthias Zilbauer & Ludovic Vallier

  2. Department of Surgery, University of Cambridge, Cambridge, UK

    Brandon T. Wesley, Alexander D. B. Ross, Daniele Muraro, Rute A. Tomaz, Carola M. Morell, Ekaterini D. Zacharis, Sandra Petrus-Reurer, Krishnaa T. Mahbubani, Stephanie Brown, Olivia C. Tysoe, Daniel Ortmann, Fotios Sampaziotis, Kourosh Saeb-Parsy & Ludovic Vallier

  3. Department of Paediatrics, University of Cambridge, Cambridge, UK

    Alexander D. B. Ross, Katherine Ridley, Judith Kraiczy & Matthias Zilbauer

  4. Wellcome Sanger Institute, Hinxton, UK

    Daniele Muraro, Zhichao Miao, Jose Garcia-Bernardo, Clara Alsinet, Daniel Gaffney, Muzlifah Haniffa & Sarah A. Teichmann

  5. European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridge, UK

    Zhichao Miao

  6. Departments of Bioengineering and Pathology, University of Washington, Seattle, WA, USA

    Sarah Saxton & Kelly R. Stevens

  7. Institute for Stem Cell and Regenerative Medicine, University of Washington, Seattle, WA, USA

    Sarah Saxton & Kelly R. Stevens

  8. NIHR Cambridge Biomedical Research Centre, Cambridge, UK

    Sandra Petrus-Reurer & Kourosh Saeb-Parsy

  9. Digital Institute, Newcastle University, Newcastle upon Tyne, UK

    Dave Horsfall

  10. Biosciences Institute, Newcastle University, Newcastle upon Tyne, UK

    Rachel A. Botting, Emily Stephenson, Dorin-Mirel Popescu & Muzlifah Haniffa

  11. University of Toronto, Toronto, Ontario, Canada

    Sonya MacParland & Gary Bader

  12. Multi-Organ Transplant Program, Toronto General Hospital Research Institute, Toronto, Ontario, Canada

    Ian D. McGilvray

  13. Department of Dermatology and NIHR Newcastle Biomedical Research Centre, Newcastle Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK

    Muzlifah Haniffa

  14. Theory of Condensed Matter Group, Cavendish Laboratory, University of Cambridge, Cambridge, UK

    Sarah A. Teichmann

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Contributions

B.T.W. performed experimental design, data generation of primary human liver and hPSC differentiation scRNA-seq, tissue processing and analyses, data analysis and biological interpretation, and manuscript preparation; A.D.B.R. performed experimental design, generation of organoid systems and their differentiation, data generation, tissue dissection, processing, and analyses, data analysis and biological interpretation, and manuscript preparation; D.M. performed computational analyses and interpretation; S.S. and K.R.S. conceived of and performed the in vivo experiments, contributed to manuscript preparation and generated figures; J.K. performed experiments and provided analytical support and supervision; Z.M. performed computational analyses and interpretation; R.A.T. performed experimental design, tissue processing, critically appraised the manuscript and provided intellectual contributions; C.M.M. performed tissue dissociation protocol development, immunofluorescence analysis and provided intellectual contributions; K.R. performed smFISH immunostainings and imaging on primary tissue; K.T.M. provided primary adult human liver tissue directly from deceased donors in clinic; J.G.-B. provided technical support while experiments were planned and performed; R.A.B. provided published data to include in the study and critically appraised the manuscript with assistance from E.S. and D.-M.P.; C.A. provided data and intellectual contributions with advice from D.G.; S.M. and G.B. provided data and intellectual contributions with support from I.D.M.; D.O. provided intellectual contributions, performed flow cytometry analysis and critically appraised the manuscript; E.D.Z. contributed to in vitro experiments; S.B. contributed to experimental planning; F.S. performed tissue processing, provided data, provided intellectual contributions and critically appraised the manuscript; D.H. curated the online data portal viewer; S.P.-R., O.C.T. and K.S.-P. performed direct hepatic and renal mouse injections, K.S.-P. additionally provided primary human liver tissue and intellectual contributions; M.H. provided published datasets and intellectual contributions and critically appraised the manuscript; M.Z. supervised organoid generation and provided intellectual contributions, S.A.T. performed computational analyses planning, data interpretation, provided intellectual contributions and critically appraised the manuscript; and L.V. conceived the study, performed experimental design, interpreted data and analyses and prepared the manuscript.

Corresponding author

Correspondence to Ludovic Vallier.

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Nature Cell Biology thanks Valerie Gouon-Evans and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 Characterisation of the proliferation and differentiation of cells within the developing human liver.

a, Histology sections of key transcriptional and morphological events of primary foetal and adult liver spanning the key developmental ages analysed in this study; early foetal (6 PCW ± 4-5 days), intermediate foetal (10 PCW) and adult developmental stages; scale bars = 100 um. These immunostainings show transcriptional and morphological events including loss of AFP and increase of hepatocyte-specific antigen during development. Induction of CK19 occurs as cholangiocytes are specified from hepatoblasts, with observable increases in expression in areas of duct formation in the 10 PCW sample. Note the increase of CK8/18 expressing cells as the liver develops, and the necessary increase in vasculature indicated by CD31 expression. Importantly, VIM and CD45, staining for mesenchymal (stellate) cells and resident immune (Kupffer) cells respectively, are present throughout all stages of liver development. Scale bars = 100 um. b, Heatmap of the top 10 time-related differentially expressed genes (DEG) at each primary human hepatocyte developmental stage; Wilcoxon-Rank-Sum test, z-score>10; HB1 = hepatoblast stage 1, HB2 = hepatoblast stage 2, FH1 = foetal hepatocyte stage 1, FH2 = foetal hepatocyte stage 2, AH = adult hepatocyte. c, Immunostaining analyses showing the expression of stage-specific markers on liver tissue sections; scale bars = 50 um. HB, sample aged between 5-7 PCW; FH, sample dated at 11 PCW; AH, adult liver. d, PCA of foetal hepatoblasts/hepatocytes from 5-17 PCW and adult hepatocytes showing the progression of stages correlating to developmental time. e, UMAP representation of hepatoblast/foetal hepatocyte markers (SPINK1, G6PC, and BRI3) and WNT pathway markers (WNT5A, LGR5 and RSPO3). UMAP visualization also shows cell proliferation markers and cell cycle regulators across the hepatocyte developmental stages, showing progressive loss of proliferative capacity until the FH2 stage, which may mark commitment to the hepatocyte lineage (integrated scRNA-seq data n = 17 independent foetal livers ranging in age from 5 to 17 post-conceptional weeks and n = 16 independent adult livers). Heatmap and feature plot colour scales show “gene expression [log-normalized, scaled counts]”.

Extended Data Fig. 2 SEpro validation using an in vitro developmental model.

a, PCA analysis of hiPSC-derived hepatic stellate cells (HSLC) and hiPSC-derived endothelial cells (ELC) confirming their common bipotent stellate-endothelial progenitor (SEpro) stage following mesoderm induction and before cell lineage specification (scRNA-seq integrates n = 5 differentiation timepoints). b, UMAP visualization (30 neighbours, 500 PCs) demonstrating the highly correlative relationship of SEpros to the transcriptomically similar ELC-D3.5 and HSLC-D7 stages of in vitro differentiation. c, Diffusion pseudotime confirming the specification of foetal-like HSLCs and ELCs from their common bipotent progenitor. d, UMAP visualizations showing the co-expression of key mesenchymal and endothelial markers such as CDX1, PDGFRB and KDR during the in vitro SEpro stage, followed by upregulation of lineage-specific markers and loss of co-expression. e, QPCR analyses of hiPSC differentiated first toward endothelial cells, then transitioned into culture conditions to specify hepatic stellate cells upon reaching the bipotential SEpro stage. These qPCR analyses show the acquisition of hepatic stellate cells markers and f, the loss of endothelial markers (n = 2 independent cell lines). Data are presented as mean values + /- SEM. Feature plot colour scales show “gene expression [log-normalized, scaled counts]”.

Source data

Extended Data Fig. 3 Characterisation of progenitor hepatoblast organoids and their unique developmental identity.

a, Representative brightfield image of HBO; scale bar = 200 um. b, Total number of cells per well averaged over three HBO lines at days 1, 4 and 8 post-passage (n = 5). c, QPCR analyses confirming the expression of key hepatoblast markers in HBO. Gene expression is shown as values relative to housekeeping gene RPLP0. Each dot represents a biological replicate; HBO: ALB, SERPINA1, AFP (n = 25: derived from n = 9 primary foetal livers at progressive passages from 3 to 15); HBO: KRT18 (n = 14); primary foetal liver (PFL) (n = 3); primary adult hepatocytes (PAH) (n = 6). d, Fluorescence activated cell sorting (FACS) analysis on HBO (passage 11) using antibodies to EPCAM (647) and A1AT (488) (left), and EPCAM (647) and AFP (488) (right). e, ScRNA-seq violin plots comparing key functional markers of different stages of in vivo hepatocyte development (integrated scRNA-seq data from n = 17 independent foetal livers ranging in age from 5 to 17 post-conceptional weeks and n = 16 independent adult livers) to in vitro hepatoblast organoids (n = 2 cell lines), confirming their similarity to the HB2 stage. f, PAGA connectivity analysis confirming the resemblance of HBO and HB2. g, TdTomato-positive cell grafts (red) were identified in mouse fat (black, phase) upon explant of organoid tissues after 27 days of engraftment; scale bar = 20 um. h, Hematoxylin & Eosin staining of explanted grafts showing nodules (right, 4 images) with cells that morphologically resembled either densely packed hepatocytes (left) or biliary ductal-like structures (transverse section, right top; longitudinal section, right bottom); scale bars = 100 um. i, Immunostaining of HBO and BO for albumin (ALB), asialoglycoprotein receptor 1 (ASGR1), and cytokeratin-19 (KRT19); scale bars = 100 um. j, Concentration per litre of secreted proteins by HBO (n = 5, each line derived from an independent foetal liver) and DBO (n = 2, each line derived from an independent foetal liver) after 48 hours of freshly applied medium. Values are normalised to cell number (that is per million cells). k, ScRNA-seq violin plots highlighting the similarity in key hepatoblast functional gene expression between HB2 and HBO, and their dissimilarity to the cholangiocytic BO and DBO cultures. l, Heatmap of the top 50 absolute loadings in principal component one (32% variance) comparing DBO, FHO, HBO, PAH and PFH. Heatmap colour scales show “gene expression [log-normalized, scaled counts]”. Data are presented as mean values + /- SEM; unpaired two-tailed t-tests.

Source data

Extended Data Fig. 4 Identifying dynamic intercellular interactions during development that enable hepatocyte differentiation in the human liver.

a, CellphoneDB analysis of hepatocytes receptors-ligand interactions with cholangiocytes, endothelial cells, hepatocytes, Kupffer cells and stellate cells across all developmental timepoints. These dynamic, temporal interactions with the nonparenchymal cells establish the hepatoblast/hepatocyte developmental extracellular environment. The Y-axis shows ligand-receptor/receptor-ligand interactions, with the hepatocyte protein listed first in the pair; x-axis shows developmental time of each interacting cell type; intensity shows log2 mean of interacting molecules; size of dot shows -log10(P) values (n = 16 independent foetal livers ranging in age from 5 to 17 post-conceptional weeks and n = 16 independent adult livers). b, RNAScope images showing DES + hepatic stellate cells expressing DKK1 which interacts with the LRP5 receptor on EPCAM + hepatoblasts (left panel). RNAScope images also reveal hepatoblasts expressing WNT4 and stellate cells expressing WNT2B, which is necessary for the proliferation and expansion of the early foetal liver hepatoblasts (right panel); scale bars = 50 um. c, RNAScope images including DAPI nuclear stain to visualise the co-expression of markers demonstrated by the RNAScope images in Fig. 5; scale bars = 50 um. d, RNAScope of entire human foetal liver tissue sections, both without (top row) and with (bottom row) DAPI nuclear staining; thus demonstrating the tissue architecture and validity of selected RNAScope imagines in Fig. 5; scale bars = 1 mm.

Extended Data Fig. 5 Comparison of hPSC-derived liver cell types to their primary counterparts during human liver development.

a, PCA plot of HLC diffusion map alignment to primary hepatoblast/hepatocyte development, validating the similarity of D14 HLC to HB2 shown in Fig. 7. b, PAGA connectivity plots and connectivity values comparing HLC timepoints to primary developmental timepoints/stages, confirming the similarity of the differentiation state of HLCs at day 14 (D14) to primary HB2. c, PCA plot of cholangiocyte-like cell (CLC) differentiation timepoints (top) and pseudotime (bottom) with 0.0 being the earliest pseudo-timepoint and 1.0 being the latest for all pseudotime analyses. Alignment of CLCs differentiation time course (n = 4 sequential timepoints) to primary cholangiocyte development (right), revealing the divergence of in vitro and in vivo differentiation at an early timepoint explaining the inability of CLCs to fully resemble primary adult cells. d, PCA plot of endothelial-like cells (ELC) differentiation timepoints (top) and pseudotime (bottom). Alignment of ELC differentiation time course (n = 3 sequential timepoints) to primary endothelial cell development (right) revealing the divergence of in vitro and in vivo differentiation at an early timepoint. e, PCA plot of hepatic stellate-like cell (HSLCs) differentiation timepoints (top) and pseudotime (bottom). Alignment of HSLCs differentiation time course (n = 4 sequential timepoints) to primary hepatic stellate cell development (right) revealing the divergence of in vitro and in vivo differentiation at an early timepoint. f, PCA plot of macrophage-like cell (MLC) differentiation timepoints (top) and pseudotime (bottom). Alignment of MLC differentiation time course (n = 3 sequential timepoints) to primary Kupffer cell development (right) revealing the divergence of in vitro and in vivo differentiation at an early timepoint. Dpt pseudotime colour scale shows “geodesic distance [distance between nodes]”; cell alignment plot red colour shows regions of misalignment/dissimilarity, blue colour shows regions of close alignment/similarity. Sequential hPSC differentiation timepoints integrate scRNA-seq data from one replicate per timepoint; plots of primary liver development integrate scRNA-seq data from n = 16 independent foetal livers ranging in age from 5 to 17 post-conceptional weeks.

Source data

Extended Data Fig. 6 Improving in vitro functionality of hPSC-derived hepatocytes using the human liver developmental map.

a, QPCR analyses showing the expression of transcription factors known to affect hepatocyte maturation in adult (n = 3) and foetal (n = 3, 6-10 PCW) human liver as well as b, factors identified from the scRNA-seq human liver development map (full lists included in Source Data). c, UMAP visualization of transcription factors identified in Fig. 7 during all stages of hepatocyte development annotated in Fig. 1 (integrated scRNA-seq data from n = 17 independent foetal livers ranging in age from 5 to 17 post-conceptional weeks and n = 16 independent adult livers). Note that NFIX and NFIA are expressed at low levels in foetal stages and reach a maximum in adult hepatocytes. Colour scale shows “gene expression [log-normalized, scaled counts]”. d, t-SNE visualization of hPSC-derived hepatocytes transduced with selected transcription factors (TFs) or day 23 (D23) GFP control (top). Unique, separate Louvain clusters signify differences in expression profile among transduced cells compared to the GFP control (bottom) (n = 1 sample sequenced per transduction). e, Pathway enrichment of top 150 DEGs comparing transduced HLCs to D23 GFP control for NFIA (top) and NFIX (bottom) using the NCATS BioPlanet pathway database. Feature plot colour scales show “gene expression [log-normalized, scaled counts]”. Data are presented as mean values + /- SEM; unpaired two-tailed t-tests.

Source data

Extended Data Fig. 7 Validating the maturation of in vitro derived hepatocytes using an inducible expression culture system.

a, Schematic of the experimental design for doxycycline (DOX) induction of key transcription factors during hepatocyte-like cell differentiation from hPSCs. HiPSC cells were edited to include the transcription factor in an inducible cassette, which was upregulated at day 15 of differentiation using DOX and continued to be expressed until day 23 when the HLCs were assays for maturation via qPCR; created with BioRender.com. b, QPCR analysis showing expression of NFIX, NFIA or CAR by day 23 hepatocyte-like cells (HLCs) that were induced to express their respective transcription factors since day 15 of differentiation using a doxycycline-inducible expression system (n≥3). The induced expression levels are compared to primary foetal and adult human liver, showing that expression is within physiological bounds. c, In addition to the increase in mature albumin expression and the decrease of foetal AFP expression, qPCR shows the increase in a wide array of functional and metabolic hepatocyte markers, thus, indicating maturation due to temporally-relevant expression of key transcription factors (n = 6). Data are presented as mean values + /- SEM; Mann-Whitney unpaired two-tailed t-tests.

Source data

Supplementary information

Supplementary Information

Supplementary Tables 1–8 and Supplementary Methods.

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Source data

Source Data Fig. 1

DEGs determined from analyses of the transcriptomic profiles of each hepatocyte developmental stage in a global and pairwise manner during human liver development. GO analyses of the DEGs specific to each stage of hepatocyte development in vivo.

Source Data Fig. 2

DEGs corresponding to the transcriptomic profiles of each developmental stage of cholangiocytes, hepatic stellate cells, endothelial cells and Kupffer cells in a global and pairwise manner during human liver development. GO analyses of the DEGs specific to each stage of development in vivo.

Source Data Fig. 3

Clustering, differential gene expression analyses and pseudotemporal ordering of the specification of stellate–endothelial progenitor cells (SEpro) towards endothelial and hepatic stellate lineages during human liver development.

Source Data Fig. 4

DEGs between HBOs generated in vitro and the primary hepatoblast/hepatocyte developmental stages in vivo. These intrinsic cellular characteristics confirmed the similarity of HBOs to the bipotential progenitor stem cells in the developing liver.

Source Data Fig. 5

All intercellular interactions calculated using the CellPhoneDB software package, with an emphasis on the interactions of each liver cell type with the developing hepatocyte throughout liver organogenesis. ORA pathway enrichment using the Reactome database (www.reactome.org) for all intercellular interactions calculated using CellPhoneDB.

Source Data Fig. 6

Differential gene expression analyses using scRNA-seq comparing the transcriptomic profile of HBOs differentiated towards hepatocytes in vitro to the developmental stages of primary hepatocyte differentiation. This list of DEGs annotates the cellular progression of HBOs towards hepatocytes by decreasing their similarities to the primary hepatoblasts and increasing their transcriptomic similarities to the mature hepatocyte lineage. To confirm the bipotentiality of HBOs, HBOs were differentiated in vitro towards cholangiocytes. Differential gene expression analyses were used to compare these differentiated cells to their primary counterparts. This list of DEGs annotates the cellular progression of HBOs towards cholangiocytes by decreasing their similarities to the primary hepatoblasts and increasing their transcriptomic similarities to the mature lineage.

Source Data Fig. 7

In-depth pairwise differential gene expression characterization of hiPSC-derived hepatocytes at each stage of differentiation. In-depth differential gene expression comparison of the hepatocyte differentiation timecourse from hiPSC to the developmental stages of primary hepatocyte differentiation in vivo. HiPSC-derived hepatocytes were transduced with key transcription factors for hepatocyte maturation at day 15 and sequenced (scRNA-seq) at day 23 of differentiation. DEG analyses compared these transcriptomes with a GFP-transduced control, uncovering major transcriptomic changes to increase similarity to adult hepatocytes resulting from the time-sensitive overexpression.

Source Data Extended Data Fig. 2

qPCR analyses corresponding to the hiPSC differentiation timecourse into stellate–endothelial progenitor cells (SEpro) and subsequently endothelial or stellate cells in vitro.

Source Data Extended Data Fig. 3

In-depth characterization of HBOs using flow cytometry, qPCR, scRNA-seq and immunostaining.

Source Data Extended Data Fig. 5

In-depth pairwise DEG characterization of hiPSC-derived CLCs, HSLCs, ELCs and macrophage-like cells (MLCs) at each stage of differentiation.

Source Data Extended Data Fig. 6

qPCR analyses comparing the transcription factor expression levels in adult human liver, foetal human liver and hiPSC-derived HLCs.

Source Data Extended Data Fig. 7

qPCR analyses showing the expression of NFIX, NFIA and CAR transcription factors in differentiating HLCs, as well as the improvement in their maturation at day 23 of differentiation compared with control HLCs.

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Wesley, B.T., Ross, A.D.B., Muraro, D. et al. Single-cell atlas of human liver development reveals pathways directing hepatic cell fates. Nat Cell Biol 24, 1487–1498 (2022). https://doi.org/10.1038/s41556-022-00989-7

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