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A spatiotemporal atlas of cholestatic injury and repair in mice

Nature Genetics (2024)Cite this article

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Abstract

Cholestatic liver injuries, characterized by regional damage around the bile ductular region, lack curative therapies and cause considerable mortality. Here we generated a high-definition spatiotemporal atlas of gene expression during cholestatic injury and repair in mice by integrating spatial enhanced resolution omics sequencing and single-cell transcriptomics. Spatiotemporal analyses revealed a key role of cholangiocyte-driven signaling correlating with the periportal damage-repair response. Cholangiocytes express genes related to recruitment and differentiation of lipid-associated macrophages, which generate feedback signals enhancing ductular reaction. Moreover, cholangiocytes express high TGFβ in association with the conversion of liver progenitor-like cells into cholangiocytes during injury and the dampened proliferation of periportal hepatocytes during recovery. Notably, Atoh8 restricts hepatocyte proliferation during 3,5-diethoxycarbonyl-1,4-dihydro-collidin damage and is quickly downregulated after injury withdrawal, allowing hepatocytes to respond to growth signals. Our findings lay a keystone for in-depth studies of cellular dynamics and molecular mechanisms of cholestatic injuries, which may further develop into therapies for cholangiopathies.

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Fig. 1: Spatial and single-cell transcriptomic atlas of cholestatic liver injury and repair.
Fig. 2: Stereo-seq dissects the regional dynamics of tissue damage.
Fig. 3: Integrated transcriptomics identifies cholangiocyte-driven signaling possesses a hub-like function to coordinate the periportal damage responses.
Fig. 4: Identification of the spatial heterogeneity of LPLCs.
Fig. 5: Spatiotemporal dynamic of damage regression and cholangiocyte diminishment during liver repair.
Fig. 6: Spatiotemporal dynamic of regeneration regulating factors and regulons during liver regeneration.
Fig. 7: Atlas of cholestatic liver injury and repair.

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Data availability

All raw data generated by Stereo-seq and scRNA-seq have been deposited to CNGB Nucleotide Sequence Archive with accession number: CNP0003447 (ref. 71). Additional data, including processed spatially visually genes in Stereo-seq and gene expression of annotated cell types in scRNA-seq, can be accessed from ref. 19. All sequencing reads from Stereo-seq and scRNA-seq were mapped to the mouse reference genome (mm10). Raw and processed bulk RNA-seq data from this study are also available through the Gene Expression Omnibus under accession number GSE219169. Source data are provided with this paper.

Code availability

The upstream analyses are carried out using published and freely available software and code, which is indexed at https://github.com/MGI-tech-bioinformatics/DNBelab_C_Series_HT_scRNA-analysis-software for scRNA-seq analysis workflow and ref. 68 for Stereo-seq analysis workflow. The code used for reproducing downstream analyses and visualization in the main figure is indexed at https://github.com/XinyiST/CIRSTA_analysis (https://doi.org/10.5281/zenodo.10577701).

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Acknowledgements

We thank Qi Zhou at Chinese Academy of Sciences for the long-term support in liver regeneration and cell therapy, Zhaoyuan Liu and Florent Ginhoux from the Shanghai Institute of Immunology, Shanghai Jiao Tong University School of Medicine, for sharing transgenic mice, Shan Zhen from the Children’s Hospital of Fudan University for sharing human BA data, Hong Li, Luonan Chen and Gangqi Wang for constructive suggestions. L.H. group was supported by the National Natural Science Foundation of China (NSFC) (92368301 and 92168202), Shanghai Municipal Science and Technology Major Project, the National Key Research and Development Project (2019YFA0801503), Shanghai Science and Technology Committee (22JC1403001), the National Key Research and Development Project (2019YFA0802001) and the National Natural Science Foundation of China (NSFC) (32221002 and 31930030). X.X. was supported by the National Key Research and Development Program of China (2022YFC3400400). M.A.E. was supported by the National Natural Science Foundation of China (U20A2015). Yiwei L. was supported by National Natural Science Foundation of China (32200688). L.L. was supported by the Shenzhen Basic Research Project for Excellent Young Scholars (RCYX20200714114644191) and National Key Research and Development Program of China (2022YFC3400405). The project also received funding from the Guangdong Genomics Data Center (2021B1212100001). We thank all members of our laboratories for their support, and the China National GeneBank for providing technical support.

Author information

Author notes

  1. Shuncheng Shangguan

    Present address: BGI Research, Shenzhen, China

  2. These authors contributed equally: Baihua Wu, Xinyi Shentu, Haitao Nan, Pengcheng Guo, Shijie Hao.

Authors and Affiliations

  1. State Key Laboratory of Cell Biology, CAS Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China

    Baihua Wu, Haitao Nan, Lei Cui, Jin Cen & Lijian Hui

  2. School of Life Science and Technology, ShanghaiTech University, Shanghai, China

    Xinyi Shentu & Lijian Hui

  3. BGI Research, Hangzhou, China

    Pengcheng Guo, Shijie Hao, Jiangshan Xu, Qiuting Deng, Yan Wu, Chang Liu, Yumo Song, Xiumei Lin, Yue Yuan, Wen Ma, Ronghai Li, Chuanyu Liu, Yiwei Lai, Longqi Liu & Miguel A. Esteban

  4. BGI Research, Shenzhen, China

    Shijie Hao, Jiangshan Xu, Qiuting Deng, Yan Wu, Chang Liu, Yumo Song, Xiumei Lin, Zhifeng Wang, Yue Yuan, Wen Ma, Ronghai Li, Chuanyu Liu, Ao Chen, Xun Xu, Yiwei Lai, Longqi Liu & Miguel A. Esteban

  5. College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China

    Shijie Hao & Longqi Liu

  6. Joint School of Life Sciences, Guangzhou Institutes of Biomedicine and Health and Guangzhou Medical University, Guangzhou, China

    Shuncheng Shangguan

  7. Laboratory of Integrative Biology, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China

    Shuncheng Shangguan & Miguel A. Esteban

  8. Division of Gastroenterology and Hepatology, Key Laboratory of Gastroenterology and Hepatology, Ministry of Health, State Key Laboratory for Oncogenes and Related Genes, NHC Key Laboratory of Digestive Diseases, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai Institute of Digestive Disease, Shanghai, China

    Yikang Li, Qiwei Qian & Xiong Ma

  9. China National GeneBank, BGI Research, Shenzhen, China

    Wensi Du, Tingting Lai, Tao Yang & Longqi Liu

  10. Shanxi Medical University-BGI Collaborative Center for Future Medicine, Shanxi Medical University, Taiyuan, China

    Chuanyu Liu, Xun Xu, Yiwei Lai & Longqi Liu

  11. The Fifth Affiliated Hospital of Guangzhou Medical University-BGI Research Center for Integrative Biology, The Fifth Affiliated Hospital of Guangzhou Medical University, Guangzhou, China

    Miguel A. Esteban

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  1. Baihua Wu
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Contributions

B.W., X.S., H.N., P.G., Yiwei L., L.L., M.A.E. and L.H. conceived the idea; Yiwei L., L.L., M.A.E. and L.H. supervised the study; H.N., J.X., P.G., Yiwei L., M.A.E. and L.H. designed the experiment; H.N., J.X. and P.G. performed the majority of the experiments with the help of B.W., S.S., L.C. and J.C.; B.W., X.S., S.H. and Yiwei L. analyzed the data; Q.D., Y.W., Chang L., Y.S., X.L., Z.W., Y.Y., W.M., R.L., Chuanyu L., A.C. and X.X. provided technical support; Yikang L., Q.Q. and X.M. provided mouse disease tissues; W.D., T.L. and T.Y. constructed the CIRSTA website; B.W., X.S., H.N. and L.H. wrote the paper.

Corresponding authors

Correspondence to Yiwei Lai, Longqi Liu, Miguel A. Esteban or Lijian Hui.

Ethics declarations

Competing interests

The chip, procedure and applications of Stereo-seq are described in a pending patent, with A.C., X.X. and L.L. listed as the applicants. All other authors declare no competing interests.

Peer review

Peer review information

Meritxell Huch, Shalev Itzkovitz, Noémi Van Hu and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Characterization of DDC treatment experiment and liver zonation in Stereo-seq.

a. H&E-staining on liver sections at all time points after DDC injury and repair. The staining was repeated with at least five independent biological replicates with similar results. Scale bars, 100 μm. b. Immunostaining images to determine the abundance of cholangiocyte (Ck19+, red, top) at D0, D17 and R21, LPLC (Opn+Hnf4a+, green and red, middle, indicated by arrows) at D0, D17 and R21, and proliferating hep (Ki67+Hnf4a+, green and red, bottom, indicated by arrows) at D0, D17 and R2. The staining was repeated with at least five independent biological replicates with similar results. Scale bars, 100 μm. c. Quantification of Ck19+ area, Opn+Hnf4a+ and Ki67+ hepatocytes at all time points (D0, n = 5; D8, n = 5; D17, n = 5; R2, n = 6; R7, n = 5; R21, n = 5). Data were presented as mean ± SD. d. Principal-component analysis of global gene expression profiles in biological replicates of Stereo-seq. e. Pearson’s correlation coefficient pairwise matrix for each biological replicate of Stereo-seq. f. The workflow of zonation landmark genes identification. g. Spatial visualization of Cyp2e1 and Cyp2f2 in representative Stereo-seq sections across all timepoints. Scale bars, 500 μm. h. Line chart showing the z-score of average expression per zonation layer of landmark genes used for defining the zonation layers in all Stereo-seq sections during injury and repair.

Source data

Extended Data Fig. 2 Additional characterization of liver zonation and spatial domain.

a. Spatial visualization of the nine zonation layers from central vein to portal vein in representative Stereo-seq sections at D8, R2, R7, and R21. Scale bars, 500 μm. b. Spatial visualization of the pericentral (CV), midzonal (MZ), and periportal (PV) area in representative Stereo-seq sections at D17. Scale bars, 500 μm. Black line indicated the position of median zonation layer, which matched with the midzonal area. c. Line chart showing zonation score of 9 zonation layers among each replicate in Stereo-seq sections for each timepoint. d. UMAP of unsupervised 17 clusters in Stereo-seq. e. Hierarchical clustering of expression profiles of 17 spatial clusters (Clusters 5 and 16, chol-domain; Cluster 2, LPLC domain; Cluster 9, portal vein-area; Other clusters, hepa-domain). f. Distinct markers for each domain from Fig. 1e (log2foldchange ≥ 0.25, q < 0.05). g. UMAP of annotated domains in Stereo-seq. h. Spatial visualization of canonical signaling pathways on a representative D17 slide. Scale bar, 500 μm for whole view and 100 μm for magnified view. i. Average intensity of canonical signaling pathways in each domain on Stereo-seq sections during injury.

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Extended Data Fig. 3 Characterization of scRNA-seq data.

a. Expression of top DEGs (log2foldchange ≥ 0.25, q < 0.05) for each annotated cell type in scRNA-seq. b. GSEA analysis of reported RRG (reprogramming-related genes) in LPLC and hepatocyte pseudo-bulk scRNA-seq data during DDC injury. c. Adgre1, Clec4f, and LAM marker (Gpnmb and Fabp5) on non-KC macrophage UMAP of scRNA-seq. d. GSEA analysis of reported LAM-enriched genes in non-KC macrophages and KC pseudo-bulk scRNA-seq data during DDC injury. e. UMAP of dynamics of indicated cell types in scRNA-seq data at D0 and D17. Circle stood for the LAM populations. f. Magnified view of the cholangiocyte in the area selected from Fig. 1h and immunofluorescence staining for Ck19 (yellow) in the matched area from an adjacent section. Scale bar, 100 μm. g. Spatial correspondence of spatial domain and RCTD deconvolution result on a representative D17 slide. Left panel indicated the comparison of chol-domain and cholangiocyte in RCTD. Right panel indicated the comparison of LPLC-domain and LPLC in RCTD. Green points stood for the overlap bins of two methods. Scale bar, 500 μm.

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Extended Data Fig. 4 The spatiotemporal and cellular dynamics of tissue damage.

a. TUNEL (green) staining at D0 and D17 (n = 5 each). Scale bar, 100 μm. b. Spatial visualization of damage response signaling activities in D0, D8, and D17 sections. Scale bar, 100 μm. Black line indicated position of median zonation layer. CV indicated pericentral region. Zonal module distribution at D17 (Kruskal-Wallis test, *q < 0.05). c. DAMP gene expression for each zonation layer in Stereo-seq sections upon injury. d. Bgn (green) immunostaining at D0 and D17 (n = 2 each; a single representative field of view is shown). Scale bar, 100 μm. e. Slc4a2 expression in cholangiocytes from scRNA-seq during injury. f. Expression of genes related to activation and fibrogenesis in fibroblasts and HSCs from scRNA-seq between D0 and injury (D8 and D17). As shown in Fig. 5b, most fibroblasts were distributed in PV, indicated they were portal fibroblasts. g. GO and KEGG biological pathway analysis of DEGs of each innate immune cell cluster (log2foldchange ≥ 0.25, q < 0.05). h. Top: experiment design of DAPM-induced and BDL model. Bottom left: Pdpn (green) and Ck19 (red) staining on liver sections of WT (n = 5), DAPM (n = 4) and BDL (n = 5) mice. Arrows indicated Pdpn+Ck19− LyECs. Scale bar, 100 μm. Bottom right: quantification of Pdpn+Ck19- LyECs of WT, DAPM and BDL mice. Mean ± SD; *p = 0.0113, ***p = 0.0001, two-sided t-test. i. Top: experiment design of Mdr2-/- model. Bottom left: Pdpn (green) and Ck19 (red) staining on liver sections of control and Mdr2-/- (n = 5 each). Arrows indicated Pdpn+Ck19- LyECs. Scale bar, 100 μm. Bottom right: quantification Pdpn+Ck19- LyECs of WT and Mdr2-/- mouse. Mean ± SD; ***p = 0.0002, two-sided t-test. j. LyEC specific gene expression in cell types identified from BA scRNA-seq. k. Left: UMAP of cell types in BA scRNA-seq data. Box indicates LyEC and myeloid cell populations. Right: UMAP of LyEC dynamics in scRNA-seq data of normal and BA liver. Circle indicates the LyEC population. l. Periportal region (cluster 1) from human BA 10x Visium is spatially corresponding to LyEC enriched bins. m. Interaction strength of Vegfc/d ligand-receptor pairs for each zonation layer in Stereo-seq sections upon injury. n. Vegfc/d expression of in scRNA-seq upon injury. o. GO and KEGG terms for LSEC and LyEC DEG enriched pathway (log2foldchange ≥ 0.25, q < 0.05). p. Sema3a expression in LyEC scRNA-seq upon injury. q. Spatial visualization of Sema3a on D0, D8, and D17 sections. Zonal module distribution at D17 (Kruskal-Wallis test, *q < 0.05).

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Extended Data Fig. 5 Decoding cellular components and cell-cell interactions in chol-domain.

a. Cell score distribution of the major cell types among hepa-domain, LPLC-domain, and chol-domain at D17. Foldchange indicated the fold change of average scores between chol-domain and other domains. Min-max normalization was applied before comparison. Cell scores lower than 0.01 were presented in grey. b. Spatial visualization of ligand and receptor modules in 226 periportal ligand-receptor interactions on D0, D8, and D17 sections for a representative area. Asterisks indicated module was zonal distribution, based on module score across nine layers at D17 (Kruskal-Wallis test, p < 0.05). Black line indicated the position of median zonation layer. CV indicated the pericentral region of lobule. c. The outgoing interactions from cholangiocyte. Collagen and laminin related receptors were not shown. d. The interaction mode of injury upregulated periportal ligand-receptor pairs in each cell type from scRNA-seq. e. The functional types of injury upregulated periportal ligand-receptor pairs in each cell type from scRNA-seq. f. Incoming and outgoing interaction weights within all cell types identified by CellChat based on D17 scRNA-seq data. The total weights of incoming or outgoing interactions within each cell type were summarized and displayed in the bar plot, respectively. Cholangiocyte is high on both incoming and outgoing interactions. g. Communication possibility of ligand-receptor pairs in incoming signalings of cholangiocyte enriched in D17 than in D0 after filtration (Statistical significance of each ligand-receptor pair is calculated by CellChat, p < 0.01). h. Expression of cholangiocyte specific receptors in scRNA-seq across D0, D8 and D17. i. Expression of cholangiocyte specific ligands in scRNA-seq across D0, D8 and D17.

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Extended Data Fig. 6 LAM characterization.

a. Expression of cholangiocyte-driven signaling genes in the domain cluster of human BA 10x Visium. Cluster 9 indicated the chol-domain, which expressed high levels of KRT7 and KRT19. b. Chol-domain (unsupervised cluster 9) presented in human BA 10x Visium, which were spatially corresponding to cholangiocyte-driven signaling signature enriched bins. c. Spatial visualization of indicated interactions on D0 and D17 section for a representative area. Scale bar, 100 μm. Asterisk indicated zonal distribution of interactions across nine layers at D17 (Kruskal-Wallis test, q < 0.05). Black line indicated the position of median zonation layer. CV indicated the pericentral region of lobule. d. Left: co-staining of F4/80 (green) and Clec4f (red) on liver sections of WT (n = 5), DAPM (n = 4) and BDL (n = 5) mice. Scale bars, 100 μm. Right: the ratio of Clec4f− F4/80+ macrophage area per field of view. Data were presented as mean ± SD. ****p < 0.0001, ****p < 0.0001, two-sided unpaired Student’s t-test. e. Left: co-staining of F4/80 (green) and Clec4f (red) on liver sections of control and Mdr2-/- mouse (n = 5 for each group). Scale bars, 100 μm. Right: the ratio of Clec4f- F4/80+ macrophage area per field of view. Data were presented as mean ± SD. ****p < 0.0001, two-sided unpaired Student’s t-test. f. Expression of indicated LAM specific genes in the cell types identified from the BA scRNA-seq. g. UMAP of dynamics of LAM in scRNA-seq data of human normal and BA liver. Circle indicates the LAM population. h. Chol-domain (unsupervised cluster 9) presented in human BA 10x Visium, which are spatially corresponding to LAM signature enriched bins. i. Top GO and KEGG terms for LAMs in scRNA-seq during DDC injury (log2foldchange ≥ 0.25, q < 0.05). Metascape calculated the statistical significance of each term enrichment (p-value) based on the accumulative hypergeometric test. j. Spatial visualization of fatty acid biosynthesis pathway on D0, D8, and D17 sections for a representative area. Scale bar, 100 μm. k. Left: Oil red O staining of liver sections at D0 and D17 (n = 5 for each time point). Scale bar, 100 μm. Right: the ratio of Oil Red O area per field of view. Data were presented as mean ± SD. *p = 0.0105, two-sided unpaired Student’s t-test. l. The ratio of tdTomato+ monocyte to total Ly6c+ monocyte based on the flow cytometer data at DDC 7 days (n = 3).

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Extended Data Fig. 7 The transcriptomic heterogeneity of LPLCs.

a. Pearson’s correlation coefficient pairwise matrix between each epithelial cell cluster. b. Expression of indicated genes for each epithelial cell cluster in scRNA-seq. c. Left: co-staining of Hnf1b (green), Hnf4a (red) and Opn (white) on liver sections at all time points after DDC injury and recovery (D0, n = 5; D8, n = 5; D17, n = 5; R2, n = 6; R7, n = 5; R21, n = 5). Scale bars, 100 μm. Arrows indicated Hnf4a+Opn+Hnf1b+ LPLC2. Arrowheads indicated Hnf4a+Opn+Hnf1b− LPLC1. The periportal field of view was shown. Right: quantification of two subtypes of LPLCs at all time points after DDC injury and recovery. Data were presented as mean ± SD. d. Left: co-staining of Hnf1b (green), Hnf4a (red) and Opn (white) on liver sections of WT (n = 5), DAPM (n = 4) and BDL (n = 5) mice. Scale bars, 100 μm. Right: the ratio of two subtypes of LPLCs per field of view. Data were presented as mean ± SD. e. Left: co-staining of Hnf1b (green), Hnf4a (red) and Opn (white) on liver sections of control and Mdr2-/- mouse (n = 5 for each group). Scale bars, 100 μm. Right: the ratio of two subtypes of LPLCs per field of view. Data were presented as mean ± SD. f. GO and KEGG biological pathway analysis of the top DEGs of each epithelial cell scRNA-seq cluster at D17 (log2foldchange ≥ 0.25, q < 0.05). Heatmap color stood for -log10(p-value). g. Average module score of YAP, NOTCH, and TGFβ signaling pathways in scRNA-seq of each epithelial cell cluster. h. The ratio of cell types in Fig. 4d along the hepatocyte proliferation branch in pseudotime. i. Heatmap displaying DEG expression for each cluster along inferred differentiation to cholangiocyte branch of pseudotime. Cells are colored by pseudotime.

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Extended Data Fig. 8 The spatial heterogeneity of LPLC-domains.

a. Left: spatial visualization of subcluster of LPLC-domain across all timepoints based on Fig. 1e. Scale bars, 500 μm. Black line indicated the position of median zonation layer. Right: time point compositions of each domain labeled in the left panel. Data were presented as mean ± SEM (Section number: D0, n = 2; D8, n = 4; D17, n = 3; R2, n = 4; R7, n = 4; R21, n = 5. Rep1 and Rep2 represent the biological replicates). b. Pearson’s correlation coefficient pairwise matrix between each domain. c. Pseudotime trajectory analysis corresponding to the designated domains in Extended Data Fig. 8a from Stereo-seq data via Monocle2. d. Violin plots showing the differential enrichment of LPLC-domain1 (top) or LPLC-domain2 (bottom) genes in LPLC1 and LPLC2 from scRNA-seq. e. Cell score distribution of the major cell types along hepa-domain, LPLC-domain subtypes, and chol-domain at D17. Foldchange indicates the fold change of average scores between two LPLC-domain subtypes. Min-max normalization was applied before comparison. Cell scores lower than 0.01 were presented in grey. f. Expression of representative common ligands in domain of LPLC subtypes. g. Spatial feature plots showing the expression pattern of indicated genes (Top layer, black spots) in LPLC-domain1 and LPLC-domain2 (Bottom layer, green and yellow). h. Expression of specific ligands of LPLC-domain subtypes in the cell types identified from scRNA-seq at D17. i. Expression of specific receptors in both LPLC subtypes or LPLC2 from scRNA-seq at D17.

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Extended Data Fig. 9 The spatiotemporal cellular dynamics and extrinsic cues during liver regeneration.

a. Average intensity of indicated cell scores for each zonation layer in all Stereo-seq sections (Two-sided wilcoxon rank sum test, Monocyte: p < 2.22e-16; LAM: p < 2.22e-16; Neutrophil: p < 2.22e-16; Cholangiocyte: p = 0.22; Fibroblast: p = 0.97). b. Expression of indicated genes in all Stereo-seq sections at each time point. c. Spatial visualization of the Mki67+ bins in representative Stereo-seq sections. Bins were colored by zonation layer. Scale bar, 500 μm. d. Temporal dynamics of normalized expression of Mki67 in all Stereo-seq sections. e. Expression of Mki67 in hepatocyte from scRNA-seq at each time point during injury and repair (Two-sided wilcoxon rank sum test; At R2, Layer7-9 versus Layer1-2: p = 5.07e-11, Layer7-9 versus Layer3-6: p < 2.22e-16; Layer3-6 versus Layer1-2: p < 2.22e-16; At R7, Layer7-9 versus Layer1-2: p < 2.22e-16, Layer7-9 versus Layer3-6: p = 1.30e-05; Layer3-6 versus Layer1-2: p = 1.08e-13). The range of data in each layer at each time point is presented by grey boxes. f. Average overall, periportal and pericentral ligand-receptor interaction strength for each zonation layer in all Stereo-seq sections at each time point. g. Left: pericentral enriched ligand-receptor interaction strength for each zonation layers in all Stereo-seq sections. Right: spatial visualization of interactions in the left panel for a representative area. Asterisks indicated that interaction distribution was zonal, based on ligand-receptor interactions across nine layers at R2 (Kruskal-Wallis test, q < 0.05). Black line indicated the position of median zonation layer. CV indicated the pericentral region of lobule. h. Average normalized expression of WNT promoting ligands for each zonation layer in all Stereo-seq sections at each time point. i. Expression of WNT promoting ligands in each cell type from scRNA-seq at R2. j. Ppib (red, positive control) and DapB (red, negative control) mRNA ISH co-staining with DAPI on liver sections. The staining was repeated independently two times with similar results. The periportal field of view was shown. Scale bar, 100 μm. k. Expression of indicated ligands in each cell type from scRNA-seq at R2. l. Average module score of TGFb signaling pathways in pericentral (CV Hep), midzonal (MZ Hep), and periportal (PV Hep) hepatocyte from scRNA-seq at D17 and R2. m. Average expression of cell cycle inhibitors in each zone of hepatocyte from scRNA-seq at D17.

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Extended Data Fig. 10 Atoh8 suppressed the hepatocyte regeneration.

a. WNT suppressors’ zonation layer expression from Stereo-seq. b. Atoh8 expression in hepatocyte from scRNA-seq. c. Atoh8 expression in liver at D0, D17, and R2 via qPCR (n = 5 each). Mean ± SD; **p = 0.0025, **p = 0.0047, two-sided t-test. d. Gene expression in microarray data (GSE159676) of human healthy (LTH), primary sclerosing cholangitis (PSC), primary biliary cirrhosis (PBC) and autoimmune hepatitis (AIH) (Two-sided t-test, PSC-LTH, p = 0.024; PBC-LTH, p = 0.51; AIH-LTH, p = 0.064). e. Atoh8 and Hamp expression in WT, shNC and shAtoh8 hepatocytes at D17 via qPCR (n = 5 each). Mean ± SD; *p = 0.0128, ****p < 0.0001, two-sided t-test. f. Top: Atoh8 knockdown experiment design under resting condition. Bottom: H&E staining on liver sections from shNC (n = 5) and shAtoh8 (n = 4) mice. Scale bar, 100 μm. g. Serum ALT and AST levels in WT (n = 5), shNC (n = 5) and shAtoh8 (n = 4) mice under resting condition. Mean ± SD; ns, not significant, two-sided t-test. h. Left: Ck19 (red) staining on liver sections from shNC (n = 5) and shAtoh8 (n = 4) mice under resting condition. Right: ratio of Ck19 positive area. Scale bars, 100 μm. Mean ± SD; ns, not significant, two-sided t-test. i. Left: co-staining of Ki67 (green) and Hnf4a (red) on liver sections from shNC (n = 5) and shAtoh8 (n = 4) mice under resting condition. Right: ratio of Ki67+ hepatocytes. Scale bars, 100 μm. Mean ± SD; ns, not significant, two-sided t-test. j. Serum ALT, AST and ALP levels in shNC (n = 5) and shAtoh8 (n = 5) mice at D17. Mean ± SD; ns, not significant, ****p < 0.0001, two-sided t-test. k. Left: Ck19 (red) staining on liver sections from shNC (n = 5) and shAtoh8 (n = 5) mice at D17. Right: ratio of Ck19 positive area. Scale bars, 100 μm. Mean ± SD; ns, not significant, two-sided t-test. l. Ccnd1 and Mki67 expression in WT, shNC and shAtoh8 hepatocytes at D17 via qPCR (n = 5 each). Mean ± SD; ***p = 0.0002, two-sided t-test. m. Ki67 hepatocyte quantification in periportal, middle, and pericentral region from shNC and shAtoh8 mice (n = 5 each). Mean ± SD; ns, not significant, *p = 0.0247, **p = 0.0031, ***p = 0.0002, ***p = 0.0002, two-way ANOVA with Sidak’s test. n. ATOH8 ChIP-seq signal tracks near cell cycle gene loci in human (GSE170527). Shaded regions are ATOH8 bounding sites. o. Left: ratio of Ki67+ hepatocytes to total hepatocytes on liver under DAPM treatment (0d,n = 5; 2d,n = 5; 4d,n = 5; 7d,n = 5; 10d,n = 3). Right: Atoh8 expression in liver at 0d, 2d and 4d via qPCR (n = 5 each). Mean ± SD; ns, not significant; **p = 0.0023, two-sided t-test.

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Supplementary information

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Wu, B., Shentu, X., Nan, H. et al. A spatiotemporal atlas of cholestatic injury and repair in mice. Nat Genet (2024). https://doi.org/10.1038/s41588-024-01687-w

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