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A spatiotemporal atlas of mouse liver homeostasis and regeneration

Nature Genetics (2024)Cite this article

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Abstract

The mechanism by which mammalian liver cell responses are coordinated during tissue homeostasis and perturbation is poorly understood, representing a major obstacle in our understanding of many diseases. This knowledge gap is caused by the difficulty involved with studying multiple cell types in different states and locations, particularly when these are transient. We have combined Stereo-seq (spatiotemporal enhanced resolution omics-sequencing) with single-cell transcriptomic profiling of 473,290 cells to generate a high-definition spatiotemporal atlas of mouse liver homeostasis and regeneration at the whole-lobe scale. Our integrative study dissects in detail the molecular gradients controlling liver cell function, systematically defining how gene networks are dynamically modulated through intercellular communication to promote regeneration. Among other important regulators, we identified the transcriptional cofactor TBL1XR1 as a rheostat linking inflammation to Wnt/β-catenin signaling for facilitating hepatocyte proliferation. Our data and analytical pipelines lay the foundation for future high-definition tissue-scale atlases of organ physiology and malfunction.

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Fig. 1: Stereo-seq profiles the homeostatic liver at whole-lobe scale.
Fig. 2: Stereo-seq resolves NPC distribution and zonation pattern.
Fig. 3: Stereo-seq dissects the cell–cell communication networks of the homeostatic liver.
Fig. 4: Stereo-seq identifies the spatiotemporal kinetics of hepatocyte proliferation in the regenerating liver.
Fig. 5: Stereo-seq dissects the spatiotemporal responses of the regenerating liver.
Fig. 6: Stereo-seq dissects the dynamics of cell–cell communication networks in the regenerating liver.
Fig. 7: Spatially resolved GRNs identify TBL1XR1 as a key regulator of liver regeneration.
Fig. 8: Spatiotemporal zonated events in the homeostatic and regenerating mammalian liver.

Data availability

All raw data for Stereo-seq and scRNA-seq generated in this study have been deposited to the CNGB Nucleotide Sequence Archive under accession code CNP0002310; raw data for bulk RNA-seq generated in this study is available from the Gene Expression Omnibus database under accession number GSE254481. All processed data can be visualized and downloaded on the LISTA website (db.cngb.org/stomics/lista). Source data are provided with this paper.

Code availability

Custom code supporting the current study is available from GitHub via https://github.com/BGIResearch/SAW (ref. 85), https://github.com/MGI-tech-bioinformatics/DNBelab_C_Series_HT_scRNA-analysis-software (ref. 86) and https://github.com/haoshijie13/LISTA (https://doi.org/10.5281/zenodo.10720177)87.

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Acknowledgements

We thank all our teams’ members, Jian Zhang (Experimental Animal Center, Guangzhou Institutes of Biomedicine and Health) and the CNGB for their support. This work was supported by the National Key Research and Development Program of China (2022YFC3400400 to X.X.), National Natural Science Foundation of China (32370848 to M.A.E., 92368301, 92168202 to L.H., 32200688 to Y. Lai and 32070861 to B.Q.), National Science and Technology Innovation 2030 Major Program (2021ZD0200100 to L. Liu), Shenzhen Basic Research Project for Excellent Young Scholars (RCYX20200714114644191 to L. Liu), Guangdong Basic and Applied Basic Research Foundation (2021B1515120075 to M.A.E. and 2021A1515110180 to Y. Lai), Guangzhou Basic and Applied Basic Research Foundation (202201010408 to Y. Lai, 202201011037 to Li Li, 202201010455 to T.A.). The CNGB was supported by Guangdong Genomics Data Center (2021B1212100001).

Author information

Author notes

  1. These authors contributed equally: Jiangshan Xu, Pengcheng Guo, Shijie Hao, Shuncheng Shangguan, Quan Shi, Giacomo Volpe.

Authors and Affiliations

  1. BGI Research, Hangzhou, China

    Jiangshan Xu, Shijie Hao, Jing Zuo, Yue Yuan, Mengnan Cheng, Qiuting Deng, Xiaoyu Wei, Yujia Jiang, Fengyu Pan, Yumo Song, Ronghai Li, Zhifeng Wang, Chuanyu Liu, Shiping Liu, Xun Xu, Longqi Liu & Yiwei Lai

  2. BGI Research, Shenzhen, China

    Jiangshan Xu, Shijie Hao, Shuncheng Shangguan, Jing Zuo, Yue Yuan, Mengnan Cheng, Qiuting Deng, Xiaoyu Wei, Yujia Jiang, Xin Huang, Fengyu Pan, Yumo Song, Ronghai Li, Zhifeng Wang, Chuanyu Liu, Shiping Liu, Yuxiang Li, Xun Xu, Ao Chen & Yiwei Lai

  3. State Key Laboratory for Diagnosis and Treatment of Severe Zoonotic Infectious Diseases, Key Laboratory for Zoonosis Research of the Ministry of Education, Institute of Zoonosis, and College of Veterinary Medicine, Jilin University, Changchun, China

    Pengcheng Guo, Xiao Zhang & Miguel A. Esteban

  4. 3DC STAR, Spatiotemporal Campus at BGI Shenzhen, Shenzhen, China

    Pengcheng Guo, Yiwei Lai & Miguel A. Esteban

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

    Shijie Hao, Qiuting Deng, Xin Huang & Longqi Liu

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

    Shuncheng Shangguan & Guangyao Lai

  7. Department of Biology, University of Copenhagen, Copenhagen, Denmark

    Quan Shi, Karsten Kristiansen & Ao Chen

  8. Hematology and Cell Therapy Unit, IRCCS-Istituto Tumori ‘Giovanni Paolo II’, Bari, Italy

    Giacomo Volpe

  9. Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand

    Keke Huang & Miguel A. Esteban

  10. School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China

    Juan An

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

    Juan An, Guangyao Lai, Liang Wu, Li Li & Miguel A. Esteban

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

    Haitao Nan, Baihua Wu, Xinyi Shentu & Lijian Hui

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

    Chuanyu Liu, Xun Xu, Longqi Liu & Yiwei Lai

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

    Tao Yang, Zhicheng Xu, Wensi Du & Ling Li

  15. Guangdong Provincial Genomics Data Center, BGI Research, Shenzhen, China

    Tao Yang, Zhicheng Xu, Wensi Du & Ling Li

  16. Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, CAS Key Laboratory of Regenerative Biology, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China

    Tanveer Ahmed, Kai You, Zhen Dai, Li Li, Baoming Qin, Yinxiong Li & Liangxue Lai

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

    Dajiang Qin & Miguel A. Esteban

  18. Department of Infectious Diseases, Nanfang Hospital, Southern Medical University, Guangdong Provincial Key Laboratory of Viral Hepatitis Research, Guangzhou, China

    Junling Chen, Rong Fan, Yongyin Li & Jinlin Hou

  19. Department of Gastroenterology, Hepatology and Endocrinology, Hannover Medical School, Hannover, Germany

    Michael Ott, Amar Deep Sharma & Tobias Cantz

  20. Division of Hematology/Oncology, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA

    Axel Schambach

  21. Institute of Experimental Hematology, Hannover Medical School, Hannover, Germany

    Axel Schambach & Miguel A. Esteban

  22. Shenzhen Key Laboratory of Gene Regulation and Systems Biology, Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen, China

    Andrew P. Hutchins

  23. Department of Haematology and Wellcome MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK

    Berthold Göttgens

  24. Cambridge Institute for Medical Research, Department of Medicine, University of Cambridge, Cambridge, UK

    Patrick H. Maxwell

  25. Guangdong Provincial Key Laboratory of Genome Read and Write, Shenzhen, China

    Xun Xu

  26. BGI Research, Chongqing, China

    Ao Chen

  27. JFL-BGI STOmics Center, BGI-Shenzhen, Chongqing, China

    Ao Chen

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  1. Jiangshan Xu
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  2. Pengcheng Guo
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  6. Giacomo Volpe
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Contributions

P.G., X.X., L. Liu, A.C., Y. Lai and M.A.E. conceived the idea. X.X., L. Liu, A.C., Y. Lai and M.A.E. supervised the work. P.G., Y. Lai and M.A.E. designed the experiments. J.X., P.G. performed the majority of the experiments with the help of S.S. and G.V. S.H., P.G., Q.S. and Y. Lai analyzed the data. K.H., J.Z., J.A., Y.Y., M.C., Q.D., X.Z., G.L, H.N., B.W., X.S., L.W., X.W., Y.J., X.H., F.P., Y.S., R.L., Z.W., C.L., S.L. and Yuxiang Li provided technical support. S.H., T.Y., Z.X, W.D. and Ling Li constructed the LISTA website. T.A., K.Y., Z.D., Li Li, B.Q., Yinxiong Li, L. Lai, D.Q., J.C., R.F., Yongyin Li, J.H., M.O., A.D.S., T.C., A.S., K.K., A.P.H, B.G., P.H.M. and L.H. gave relevant advice. P.G., Y. Lai and M.A.E. wrote the manuscript.

Corresponding authors

Correspondence to Pengcheng Guo, Xun Xu, Longqi Liu, Ao Chen, Yiwei Lai or Miguel A. Esteban.

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. Liu listed as the applicants. J.X., S.H. S.S., Y.Y., M.C., Q.D., X.W., Y.J., X.H., F.P., Y.S., R.L., Z.W., C.L., S.L., Yuxiang Li, T.Y., Z.X, W.D., Ling Li, X.X., L. Liu, A.C., Y. Lai and M.A.E are employees of BGI Group. The remaining authors declare no competing interests.

Peer review

Peer review information

Nature Genetics thanks Meritxell Huch and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

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

Extended data

Extended Data Fig. 1 ScRNA-seq homeostatic liver cell matrix, Stereo-seq performance, and identification of cell type distribution in the homeostatic liver.

a Left: UMAP projection showing 51,275 single cells from the homeostatic liver cell matrix. Right: UMAP projection showing the annotated cell types and the corresponding cell numbers. b Immunofluorescence staining (phalloidin and E-cadherin) of a whole lobe section adjacent to the one shown in Fig. 1a. c Spatial visualization of the imputed expression levels for the indicated pericentral (Akr1c6, Aldh1a1, Car3, Ces1c, Cyp1a2, Cyp2c37, Cyp2c50, Cyp2e1, Gsta3, Mgst1, Mup11, Mup17, Mup18, Oat, Pon1, Rgn) and periportal (Alb, Aldob, Arg1, Asl, Cyp2f2, Fbp1, Hal, Hpx, Hsd17b13, Mup20, Pck1, Slc25a47, Trf, Ass1) hepatocyte landmark genes (section D0-DY1).

Extended Data Fig. 2 Stereo-seq profiles liver zonation in different homeostatic liver replicates and comparison of Stereo-seq with other spatial transcriptomic technologies.

a Average expression levels for the indicated landmark genes in all homeostatic sections. b Left: spatial visualization of the zonation layers (sections D0-DY2, D0-DY3, D0-DT2 and D0-DT3) from two different biological replicates of the homeostatic liver. c Average zonation score for each zonation layer in all homeostatic sections. Colors indicate different homeostatic sections. d Upper left: UMAP projection of the integrated bins of all homeostatic sections. Others: UMAP projections of integrated bins showing the zonation layers in each homeostatic section. e Pearson correlation coefficient for landmark genes in each zonation layer among all homeostatic sections. f Left: spatial visualization of the zonation layers (section D0-DY1). Right: average zonation score for each zonation layer of the indicated areas. Colors indicate different areas. g Average expression levels for the indicated landmark genes of the areas indicated by the red box in the panel f. Colors indicate different areas. h Violin plots comparing the capture efficiency (UMIs and genes) of Stereo-seq (section D0-DY1) at similar resolution with Visium (Stereo-seq bin 140, ~100 μm resolution) and Seq-Scope (Stereo-seq bin 14, ~10 μm resolution). Visium data was taken from NCBI Sequence Read Archive under BioProject: PRJNA70508515 and Seq-Scope data from the MIP Lee lab website (lee.lab.medicine.umich.edu/seq-scope)16. i Size of the average capture area in sections profiled by Stereo-seq, Visium and Seq-Scope. n = 5 sections for Stereo-seq, n = 2 sections for Visium, n = 6 sections for Seq-Scope. Data are presented as mean values +/- s.e.m. j Left: spatial visualization of the zonation score in a representative homeostatic section profiled by Visium. Top right: magnification view of the indicated area. Bottom right, spatial visualization of the expression levels of Glul, Cyp2e1, Cyp2f2 and Alb in the indicated area profiled by Visium. Visium data was visualized with the actual resolution (100 μm). k Left: spatial visualization of the zonation score in homeostatic liver sections profiled by Seq-Scope (sections 2103 and 2104). Right: spatial visualization of the expression levels of Glul, Cyp2e1, Cyp2f2 and Alb for the corresponding sections.

Extended Data Fig. 3 Stereo-seq identifies hepatocyte zonated genes, pathways, and GRNs in the homeostatic liver.

a Venn diagram comparing the hepatocyte zonated genes identified by Stereo-seq to those identified by spatial reconstruction using scRNA-seq1. b Left: representative images from immunofluorescence staining of GS and NTCP in the homeostatic liver. Scale bar, 50 μm. Right: average signal intensity. Data are presented as mean values +/- s.e.m. n = 4 biological replicates. c Spatial visualization of the module score for the selected zonated pathways (section D0-DY1). d Average expression levels of zonated fatty acid degradation pathway genes in all homeostatic sections. Newly identified zonated genes are indicated in red. e Spatial visualization of the indicated zonated genes from the fatty acid degradation pathway (section D0-DY1). f Average expression of zonated glucagon signaling pathway genes in all homeostatic sections. Newly identified zonated genes are indicated in red. g Spatial visualization of the indicated zonated genes from the glucagon signaling pathway (section D0-DY1). h Left: representative images from immunofluorescence staining of GS (pericentral marker) and LAMP2 in homeostatic liver. Scale bar, 50 μm. Right: average signal intensity. Data are presented as mean values +/- s.e.m. n = 4 biological replicates. i Spatial visualization of the indicated pericentral (TCF7, PPARG) or periportal regulon (HNF4A, ESRRA) activity (section D0-DY1).

Source data

Extended Data Fig. 4 Stereo-seq identifies NPC zonated genes.

a Schematic representation of a Stereo-seq bin 50 showing the potential co-localization of hepatocytes and NPCs. b Average cell type distribution for each zonation layer in all homeostatic sections. Cell type distribution was calculated using RCTD by transferring annotation labels from the homeostatic liver cell matrix. c Top: spatial distribution of the indicated cell types (section D0-DY1). Bottom: spatial visualization of the distribution of the indicated cell types in the indicated area. d,e. Left: average expression levels of zonated genes in (d) sphingolipid de novo biosynthesis and (e) regulation of Notch signaling pathway in LSECs for each zonation layer in all the homeostatic sections. Upper-right: spatial visualization of the imputed expression levels for selected zonated genes in the corresponding pathways (section D0-DY1) for a representative area. Bottom-right: average expression levels for the corresponding genes in each zonation layer in all the homeostatic sections. f. Top: representative smFISH images of Cyp1a2, Gls2 and Egfl7 expression in the homeostatic liver. Scale bar, 50 μm. Bottom: average signal intensity for the corresponding genes. Data are presented as mean values +/- s.e.m. n = 4 biological replicates. g,h. Left: average expression levels of zonated genes in (g) BMP signaling pathway and (h) regulation of chemotaxis in HSCs for each zonation layer of all the homeostatic sections. Upper-right: spatial visualization of the imputed expression levels for zonated genes in corresponding pathways (section D0-DY1) for a representative area. Bottom-right: average expression levels for the corresponding genes for each zonation layer in all the homeostatic sections.

Source data

Extended Data Fig. 5 Stereo-seq unveils immune spots in the homeostatic liver.

a Average expression levels for genes in the indicated spatial auto-correlated modules (section D0-DY1). Module 2, central vein area; module 3, portal vein area; module 4, immune spot; module 14, bile duct; module 19, midzonal area. Selected genes in the corresponding modules are color matched. b Spatial visualization of the module score (section D0-DY1) for the central vein area (CV, module 2), portal vein area (PV, module 3), midzonal area (MZ, module 19) and bile duct (module 14). c Average expression levels for genes in the indicated spatial auto-correlated modules (section D0-DT2). Module 1, central vein area; module 3, portal vein area; module 7, immune spot; module 2, bile duct; module 14, midzonal area. Selected genes in the corresponding modules are color matched. d Spatial visualization of the module score for the immune spot (module 7, section D0-DT2). White arrows indicate the immune spots. e Spatial visualization of immune spot genes (Ifit1 and Rsad2, section D0-DT2). Blue arrows indicate the location of the immune spots. f UMAP projection showing the annotated cell types and the corresponding cell numbers among the 20,299 cells in the liver immune cell matrix. g Bubble plot shows the expression levels for the indicated genes from the immune spot module in the cell types identified from the liver immune cell matrix. Abbreviations: Treg/Th17, regulatory T cell or T helper type 17 cell; Th0, naïve T cell; Th1, T helper type 1 cell.

Extended Data Fig. 6 Cell-cell interactions in the homeostatic liver.

a Heatmap showing the cell-cell interaction weight calculated by CellChat34 in the cell types identified from the homeostatic liver cell matrix. b Bubble plots show the expression levels for ligands (left) and receptors (right) for the indicated pathways in the cell types identified from the homeostatic liver cell matrix. Black lines connect ligands with their corresponding receptors. c Top: representative smFISH images of Cyp1a2, Gls2 and Wnt4 expression in the homeostatic liver. Scale bar, 50 μm. Bottom: average signal intensity. Data are presented as mean values +/- s.e.m. n = 4 biological replicates. d Bubble plot shows the expression of Cxcl10 and its receptors (Tlr4, Cxcr3/4) in the cell types identified from the liver immune cell matrix. e Spatial visualization of the imputed interaction score for the indicated immune spot-related interactions (section D0-DY1). f Box plots comparing the enrichment of indicated interaction score inside and outside of the immune spot. P values were calculated by the two-sided Wilcoxon rank sum test. The central line is the median, the boxes indicate the upper and lower quartiles, the whiskers indicate the 1.5 interquartile range. Intra-spot: n = 92 Stereo-seq bins; outer-spot: n = 37,577 Stereo-seq bins.

Source data

Extended Data Fig. 7 Stereo-seq dissect the zonation dynamics and cell cycle kinetics during liver regeneration.

a UMAP projection of single cells from the liver regeneration cell matrix generated in this study. Cells are colored by time points. b Average gene expression from landmark genes for each zonation layer in all regeneration sections. c Spatial visualization of the zonation layers at the indicated time points from another biological replicate (sections 8h-FH2, D1-FB2, D2-FO1, D3-FP3 and D7-FO4). d Spatial visualization for the binarized expression of Cdh1 at the indicated time points of regenerating liver (sections D0-DY1, D1-FN4 and D2-DX6). The percentage of Cdh1 highly expressing bins for each section is labeled on the bottom. Cdh1 highly expressing bins were defined by the expression of Cdh1 higher than the mean expression of Cdh1 in all the regeneration sections. e Violin plots showing the module score for pericentral, midzonal and periportal zonated genes defined in homeostasis liver for all the regeneration sections. f Left: spatial visualization of Mki67+ bins at each time point of liver regeneration from different biological replicates (upper sections: D0-DY1, 8h-DX1, D1-FN4, D2-DX6, D3-FH4 and D7-DX7; bottom sections: D0-DT2, 8h-FH2, D1-FB2, D2-FO1, D3-FP3 and D7-FO4). Bins are colored by zonation layer. Right: average expression levels of Mki67 for each zonation layer in regeneration sections of different biological replicates. g Left: average expression levels of S-phase gene Mcm6 (top) and the G2/M-phase gene Cdc20 (bottom) for each zonation layer in all the regeneration sections. Right: spatial visualization of the imputed expression for the corresponding genes at the indicated time points (upper sections: D0-DY1, D1-FN4 and D2-DX6). h Left: spatial visualization of S-phase score (top, D1-FN4) and G2/M-phase score (bottom, D2-DX6). Right: average S-phase (top) and G2/M-phase (bottom) score for each zonation layer of the indicated areas in the left panel. Colors indicate different areas. i Average module score for S-phase genes (top) or G2/M-phase genes (bottom) for each fine-grained zonation layer in all the regeneration sections.

Extended Data Fig. 8 Stereo-seq identifies zone-specific changes of gene expression in the regenerating liver.

a Average expression of genes related to indicated biological processes for each zonation layer in all the regeneration sections. b Left: average expression of the indicated genes for each zonation layer in all the regeneration sections. Right: spatial visualization of the imputed expression levels of the corresponding genes at the indicated time points. c Average module score of pathways for each zonation layer in all the regeneration sections. d Upper: spatial visualization of the module score of bile secretion pathway (sections D0-DY1, D1-FN4, and D7-DX7) for the indicated time point of the regenerating liver. Bottom: spatial visualization of the module score of bile secretion pathway in the indicated area. e Left: fuzzy clustering analysis of pseudobulk data of the regenerating liver scRNA-seq matrix showing 1,378 hypervariable genes across time course expressed in HSC that were clustered into three groups. The line charts show the standardized expression levels with each individual line representing a gene within the group. Right: bar plots show the percentage of zonated genes in each gene group. f Functional enrichment for each hypervariable gene group in HSCs. P values were calculated using the hypergeometric test and corrected with the Benjamini-Hochberg method. g Average expression of HSC hypervariable genes related to the indicated biological processes for each zonation layer in all the regeneration sections. h Spatial visualization of Ifit1/Rsad2 co-expressing bins, indicating the distribution of the immune spots (sections D0-DY1, 8h-DX1, D1-FN4, D2-DX6, D3-FH4 and D7-DX7) of the regenerating liver. i Dot plot showing the percentage of Ifit1/Rsad2 co-expressing bins in each Stereo-seq section of the regenerating liver. Colors indicate the time point, and each dot represents one replicate. Data are presented as mean values +/- s.e.m. The dots represent each section. P values were calculated by the two-sided Student’s t test. n = 5 sections for D0, n = 6 sections for 8 h, n = 4 sections for D1, n = 5 sections for D2, n = 5 sections for D3, and n = 5 sections for D7.

Source data

Extended Data Fig. 9 Stereo-seq identifies zone-specific microenvironmental changes in the regenerating liver.

a Average expression levels for ligands (top) or receptors (bottom) in the cell types identified from the liver regeneration cell matrix. b Left: average interaction score for each zonation layer in all regeneration sections. Right: spatial visualization of the imputed interaction score for the corresponding interactions at the indicated time points (sections D0-DY1, 8h-DX1, D1-FN4, D2-DX6 and D7-DX7). c Representative smFISH images of Cyp1a2, Gls2 and Fgfr1 expression in a homeostatic liver section (left) or 8 h post-PHx (right). Scale bar, 50 μm. n = 1 biological replicate. d Left: average expression of Plat, Tac1 and Serpine1/2 for each zonation layer in all the regeneration sections. Right: spatial visualization of imputed expression levels for Tac1 and Serpine1 at the indicated time points (sections D0-DY1, 8h-DX1 and D1-FN4). e Upper left: average module score for neutrophils (S100a8, S100a9, Cxcr2, F13a1, Fgr) for each zonation layer in all the regeneration sections. Other: spatial visualization of the module score for neutrophils at the indicated time points are shown on the right (sections D0-DY1, 8h-DX1 and D7-DX7).

Extended Data Fig. 10 Identification of TBL1XR1 as a rheostat for liver regeneration.

a Left: average activity score of the indicated regulons for each zonation layer of the regenerating liver (sections D0-DY1, 8h-DX1, D1-FN4, D2-DX6, D3-FH6 and D7-DX7). Right: spatial visualization of the corresponding regulon activity at the indicated time points. b Spatial visualization of the imputed expression for Tbl1xr1 (sections D0-DY1, 8h-DX1, and D1-FN4). c Expression of Tbl1xr1 in hepatocytes from the liver regeneration cell matrix. d UMAP projection of scRNA-seq data for TNFα untreated/treated (TNFα-/TNFα + ) mouse primary hepatocytes. e Expression levels for Tbl1xr1 and Mki67 for TNFα untreated/treated mouse primary hepatocytes. f Regulons controlling Tbl1xr1. g Tbl1xr1 locus in ATAC-seq for homeostatic liver. Predicted transcription factor binding motifs. h Strategy for AAV8-TBG-shRNA delivery, i Cell-area size (left, n = 7 areas, from two biological replicates); liver/body weight ratio (right, n = 4 biological replicates) at D2 post-PHx in control and Tbl1xr1 knockdown livers. j RT-qPCR for Tbl1xr1 and cell cycle-related genes comparing control and Tbl1xr1 knockdown livers at D7 post-PHx. n = 3 biological replicates. k Left: representative images of immunofluorescence staining for Ki67 at D7 post-PHx in control (top) and Tbl1xr1 knockdown (bottom). Right: magnification of the indicated area on the left. l Ratio of Ki67+ nuclei to all nuclei (left, control, n = 3 biological replicates; Tbl1xr1 knockdown, n = 5 biological replicates), cell-area size (middle, control, n = 8 areas; Tbl1xr1 knockdown, n = 6 areas; from three biological replicates) and liver/body weight ratio (right, control, n = 3 biological replicates; Tbl1xr1 knockdown, n = 5 biological replicates) at D7 post-PHx. m RT-qPCR for Wnt target genes and fatty acid β-oxidation related genes comparing control and Tbl1xr1 knockdown livers at D2 post-PHx. n = 3 biological replicates. n H&E (left) or oil red O staining (right) of representative sections from control (n = 2 biological replicates) and Tbl1xr1 knockdown (n = 3 biological replicates) livers at D2 post-PHx. Scale bar, 100 μm. o TBL1XR1 protein interaction network based on STRING database (https://cn.string-db.org/). For panel i, j, l and m, data are presented as mean values +/- s.e.m. P values were calculated by the two-sided Student’s t test.

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Supplementary information

Supplementary Information

Supplementary Notes 1–3 and Table legends.

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Supplementary Tables

Supplementary Table 1. Data quality control. Supplementary Table 2. Cell type-specific markers used for annotation in scRNA-seq. Supplementary Table 3. Zonated genes for hepatocytes, LSECs and HSCs. Supplementary Table 4. Zonated regulons in liver homeostasis and regeneration. Supplementary Table 5. Gene modules identified by Hotspot. Supplementary Table 6. Zonated interactions in liver homeostasis and regeneration. Supplementary Table 7. Hypervariable genes for hepatocytes, LSECs and HSCs in liver regeneration. Supplementary Table 8. Module score for KEGG pathways during liver regeneration and the corresponding gene sets. Supplementary Table 9. Oligonucleotide sequence of the RT-qPCR primers and RNAscope probes.

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Xu, J., Guo, P., Hao, S. et al. A spatiotemporal atlas of mouse liver homeostasis and regeneration. Nat Genet (2024). https://doi.org/10.1038/s41588-024-01709-7

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