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Spatial sorting enables comprehensive characterization of liver zonation

Abstract

The mammalian liver is composed of repeating hexagonal units termed lobules. Spatially resolved single-cell transcriptomics has revealed that about half of hepatocyte genes are differentially expressed across the lobule, yet technical limitations have impeded reconstructing similar global spatial maps of other hepatocyte features. Here, we show how zonated surface markers can be used to sort hepatocytes from defined lobule zones with high spatial resolution. We apply transcriptomics, microRNA (miRNA) array measurements and mass spectrometry proteomics to reconstruct spatial atlases of multiple zonated features. We demonstrate that protein zonation largely overlaps with messenger RNA zonation, with the periportal HNF4α as an exception. We identify zonation of miRNAs, such as miR-122, and inverse zonation of miRNAs and their hepatocyte target genes, highlighting potential regulation of gene expression levels through zonated mRNA degradation. Among the targets, we find the pericentral Wingless-related integration site (Wnt) receptors Fzd7 and Fzd8 and the periportal Wnt inhibitors Tcf7l1 and Ctnnbip1. Our approach facilitates reconstructing spatial atlases of multiple cellular features in the liver and other structured tissues.

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Fig. 1: Spatial sorting approach for isolating large amounts of hepatocytes from distinct layers with high resolution.
Fig. 2: CD73 and E-cadherin are inversely zonated surface markers.
Fig. 3: Spatial sorting reliably captures the different lobule layers.
Fig. 4: Correlations between mRNA and protein levels.
Fig. 5: A spatial atlas of the hepatocyte proteome.
Fig. 6: Zonated expression of hepatocyte miRNAs.
Fig. 7: Network analysis of miRNA-target interactions.

Code availability

The code used to generate the processed data and figures is available upon request.

Data availability

The RNA-seq data that support the findings of this study have been deposited with the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) under bioproject accession code PRJNA556572, and the SRA identifiers SAMN12360372SAMN12360382. Supplementary Table 2 summarizes the unique molecular identifier counts per million for each sample. Supplementary Table 11 summarizes the zUMI barcodes used for each sample and the corresponding zUMI settings.

The liquid chromatography–tandem mass spectrometry proteomic data were uploaded to the ProteomeXchange via the PRIDE archive, with project identifier PXD014512. Processed data can be found in Supplementary Table 3.

The miRNA microarray data have been deposited with the NCBI Gene Expression Omnibus under accession code GSE134827. Supplementary Table 6 summarizes the miRNA data.

References

  1. 1.

    Hoehme, S. et al. Prediction and validation of cell alignment along microvessels as order principle to restore tissue architecture in liver regeneration. Proc. Natl Acad. Sci. USA 107, 10371–10376 (2010).

    CAS  Article  Google Scholar 

  2. 2.

    Wang, B., Zhao, L., Fish, M., Logan, C. Y. & Nusse, R. Self-renewing diploid Axin2 + cells fuel homeostatic renewal of the liver. Nature 524, 180–185 (2015).

    CAS  Article  Google Scholar 

  3. 3.

    Colnot, S. & Perret, C. in Molecular Pathology of Liver Diseases (ed. Monga, P. S.) 7–16 (Springer, 2011).

  4. 4.

    Ben-Moshe, S. & Itzkovitz, S. Spatial heterogeneity in the mammalian liver. Nat. Rev. Gastroenterol. Hepatol. 16, 395–410 (2019).

    Article  Google Scholar 

  5. 5.

    Gebhardt, R. Metabolic zonation of the liver: regulation and implications for liver function. Pharmacol. Ther. 53, 275–354 (1992).

    CAS  Article  Google Scholar 

  6. 6.

    Jungermann, K. & Keitzmann, T. Zonation of parenchymal and nonparenchymal metabolism in liver. Annu. Rev. Nutr. 16, 179–203 (1996).

    CAS  Article  Google Scholar 

  7. 7.

    Halpern, K. B. et al. Single-cell spatial reconstruction reveals global division of labour in the mammalian liver. Nature 542, 352–356 (2017).

    CAS  Article  Google Scholar 

  8. 8.

    Lindros, K. O. & Penttilä, K. E. Digitonin-collagenase perfusion for efficient separation of periportal or perivenous hepatocytes. Biochem. J. 228, 757–760 (1985).

    CAS  Article  Google Scholar 

  9. 9.

    Quistorff, B., Grunnet, N. & Cornell, N. W. Digitonin perfusion of rat liver. A new approach in the study of intra-acinar and intracellular compartmentation in the liver. Biochem. J. 226, 289–297 (1985).

    CAS  Article  Google Scholar 

  10. 10.

    Budnik, B., Levy, E., Harmange, G. & Slavov, N. SCoPE-MS: mass spectrometry of single mammalian cells quantifies proteome heterogeneity during cell differentiation. Genome Biol. 19, 161 (2018).

    Article  Google Scholar 

  11. 11.

    Buenrostro, J. D. et al. Single-cell chromatin accessibility reveals principles of regulatory variation. Nature 523, 486–490 (2015).

    CAS  Article  Google Scholar 

  12. 12.

    Doi, Y. et al. Development of complementary expression patterns of E- and N-cadherin in the mouse liver. Hepatol. Res. 37, 230–237 (2007).

    CAS  Article  Google Scholar 

  13. 13.

    Halpern, K. B. et al. Paired-cell sequencing enables spatial gene expression mapping of liver endothelial cells. Nat. Biotechnol. 36, 962–970 (2018).

    CAS  Article  Google Scholar 

  14. 14.

    Azimifar, S. B., Nagaraj, N., Cox, J. & Mann, M. Cell-type-resolved quantitative proteomics of murine liver. Cell Metab. 20, 1076–1087 (2014).

    CAS  Article  Google Scholar 

  15. 15.

    Schwanhäusser, B. et al. Global quantification of mammalian gene expression control. Nature 473, 337–342 (2011).

    Article  Google Scholar 

  16. 16.

    Odom, D. T. et al. Control of pancreas and liver gene expression by HNF transcription factors. Science 303, 1378–1381 (2004).

    CAS  Article  Google Scholar 

  17. 17.

    Torre, C., Perret, C. & Colnot, S. Molecular determinants of liver zonation. Prog. Mol. Biol. Transl. Sci. 97, 127–150 (2010).

    CAS  Article  Google Scholar 

  18. 18.

    Stanulović, V. S. et al. Hepatic HNF4α deficiency induces periportal expression of glutamine synthetase and other pericentral enzymes. Hepatology 45, 433–444 (2007).

    Article  Google Scholar 

  19. 19.

    Colletti, M. et al. Convergence of Wnt signaling on the HNF4α-driven transcription in controlling liver zonation. Gastroenterology 137, 660–672 (2009).

    CAS  Article  Google Scholar 

  20. 20.

    Brosch, M. et al. Epigenomic map of human liver reveals principles of zonated morphogenic and metabolic control. Nat. Commun. 9, 4150 (2018).

    Article  Google Scholar 

  21. 21.

    Holloway, M. G., Miles, G. D., Dombkowski, A. A. & Waxman, D. J. Liver-specific hepatocyte nuclear factor-4α deficiency: greater impact on gene expression in male than in female mouse liver. Mol. Endocrinol. 22, 1274–1286 (2008).

    CAS  Article  Google Scholar 

  22. 22.

    Guo, H., Ingolia, N. T., Weissman, J. S. & Bartel, D. P. Mammalian microRNAs predominantly act to decrease target mRNA levels. Nature 466, 835–840 (2010).

    CAS  Article  Google Scholar 

  23. 23.

    Lagos-Quintana, M. et al. Identification of tissue-specific microRNAs from mouse. Curr. Biol. 12, 735–739 (2002).

    CAS  Article  Google Scholar 

  24. 24.

    Kota, J. et al. Therapeutic microRNA delivery suppresses tumorigenesis in a murine liver cancer model. Cell 137, 1005–1017 (2009).

    CAS  Article  Google Scholar 

  25. 25.

    Sekine, S., Ogawa, R., Mcmanus, M. T., Kanai, Y. & Hebrok, M. Dicer is required for proper liver zonation. J. Pathol. 219, 365–372 (2009).

    CAS  Article  Google Scholar 

  26. 26.

    Arvey, A., Larsson, E., Sander, C., Leslie, C. S. & Marks, D. S. Target mRNA abundance dilutes microRNA and siRNA activity. Mol. Syst. Biol. 6, 363 (2010).

    Article  Google Scholar 

  27. 27.

    Kozomara, A. & Griffiths-Jones, S. miRBase: annotating high confidence microRNAs using deep sequencing data. Nucleic Acids Res. 42, D68–D73 (2014).

    CAS  Article  Google Scholar 

  28. 28.

    Bissels, U. et al. Absolute quantification of microRNAs by using a universal reference. RNA 15, 2375–2384 (2009).

    CAS  Article  Google Scholar 

  29. 29.

    Tsai, W.-C. et al. MicroRNA-122 plays a critical role in liver homeostasis and hepatocarcinogenesis. J. Clin. Invest. 122, 2884–2897 (2012).

    CAS  Article  Google Scholar 

  30. 30.

    Li, W. F., Dai, H., Ou, Q., Zuo, G.-Q. & Liu, C. A. Overexpression of microRNA-30a-5p inhibits liver cancer cell proliferation and induces apoptosis by targeting MTDH/PTEN/AKT pathway. Tumour Biol. 37, 5885–5895 (2016).

    CAS  Article  Google Scholar 

  31. 31.

    Kornfeld, J.-W. et al. Obesity-induced overexpression of miR-802 impairs glucose metabolism through silencing of Hnf1b. Nature 494, 111–115 (2013).

    CAS  Article  Google Scholar 

  32. 32.

    Trajkovski, M. et al. MicroRNAs 103 and 107 regulate insulin sensitivity. Nature 474, 649–653 (2011).

    CAS  Article  Google Scholar 

  33. 33.

    Agarwal, V., Bell, G. W., Nam, J.-W. & Bartel, D. P. Predicting effective microRNA target sites in mammalian mRNAs. eLife 4, e05005 (2015).

    Article  Google Scholar 

  34. 34.

    Benhamouche, S. et al. Apc tumor suppressor gene is the “zonation-keeper” of mouse liver. Dev. Cell 10, 759–770 (2006).

    CAS  Article  Google Scholar 

  35. 35.

    Birchmeier, W. Orchestrating Wnt signalling for metabolic liver zonation. Nat. Cell Biol. 18, 463–465 (2016).

    CAS  Article  Google Scholar 

  36. 36.

    Burke, Z. D. & Tosh, D. The Wnt/β‐catenin pathway: master regulator of liver zonation? Bioessays 28, 1072–1077 (2006).

    CAS  Article  Google Scholar 

  37. 37.

    Gebhardt, R. & Hovhannisyan, A. Organ patterning in the adult stage: the role of Wnt/β‐catenin signaling in liver zonation and beyond. Dev. Dyn. 239, 45–55 (2010).

    CAS  PubMed  Google Scholar 

  38. 38.

    Thompson, M. D. & Monga, S. P. S. WNT/β-catenin signaling in liver health and disease. Hepatology 45, 1298–1305 (2007).

    CAS  Article  Google Scholar 

  39. 39.

    Yang, J. et al. β-catenin signaling in murine liver zonation and regeneration: a Wnt-Wnt situation! Hepatology 60, 964–976 (2014).

    CAS  Article  Google Scholar 

  40. 40.

    Planas-Paz, L. et al. The RSPO–LGR4/5–ZNRF3/RNF43 module controls liver zonation and size. Nat. Cell Biol. 18, 467–479 (2016).

    CAS  Article  Google Scholar 

  41. 41.

    Preziosi, M., Okabe, H., Poddar, M., Singh, S. & Monga, S. P. Endothelial Wnts regulate β‐catenin signaling in murine liver zonation and regeneration: a sequel to the Wnt–Wnt situation. Hepatol. Commun. 2, 845–860 (2018).

    CAS  Article  Google Scholar 

  42. 42.

    Rocha, A. S. et al. The angiocrine factor Rspondin3 is a key determinant of liver zonation. Cell Rep. 13, 1757–1764 (2015).

    CAS  Article  Google Scholar 

  43. 43.

    Shy, B. R. et al. Regulation of Tcf7l1 DNA binding and protein stability as principal mechanisms of Wnt/β-catenin signaling. Cell Rep. 4, 1–9 (2013).

    CAS  Article  Google Scholar 

  44. 44.

    Tago, K. et al. Inhibition of Wnt signaling by ICAT, a novel β-catenin-interacting protein. Genes Dev. 14, 1741–1749 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. 45.

    Wang, K. et al. Circulating microRNAs, potential biomarkers for drug-induced liver injury. Proc. Natl Acad. Sci. USA 106, 4402–4407 (2009).

    CAS  Article  Google Scholar 

  46. 46.

    Zucman-Rossi, J., Villanueva, A., Nault, J.-C. & Llovet, J. M. Genetic landscape and biomarkers of hepatocellular carcinoma. Gastroenterology 149, 1226–1239.e4 (2015).

    CAS  Article  Google Scholar 

  47. 47.

    Chu, A. et al. Large-scale profiling of microRNAs for The Cancer Genome Atlas. Nucleic Acids Res. 44, e3 (2016).

    Article  Google Scholar 

  48. 48.

    Moor, A. E. et al. Spatial reconstruction of single enterocytes uncovers broad zonation along the intestinal villus axis. Cell 175, 1156–1167.e15 (2018).

    CAS  Article  Google Scholar 

  49. 49.

    Park, J. Single-cell transcriptomics of the mouse kidney reveals potential cellular targets of kidney disease. Science 360, 758–763 (2018).

    CAS  Article  Google Scholar 

  50. 50.

    Guder, W. G. & Ross, B. D. Enzyme distribution along the nephron. Kidney Int. 26, 101–111 (1984).

    CAS  Article  Google Scholar 

  51. 51.

    Xu, H. et al. Liver‐enriched transcription factors regulate microRNA‐122 that targets CUTL1 during liver development. Hepatology 52, 1431–1442 (2010).

    CAS  Article  Google Scholar 

  52. 52.

    Berndt, N., Horger, M. S., Bulik, S. & Holzhütter, H.-G. A multiscale modelling approach to assess the impact of metabolic zonation and microperfusion on the hepatic carbohydrate metabolism. PLoS Comput. Biol. 14, e1006005 (2018).

    Article  Google Scholar 

  53. 53.

    Godoy, P. et al. Recent advances in 2D and 3D in vitro systems using primary hepatocytes, alternative hepatocyte sources and non-parenchymal liver cells and their use in investigating mechanisms of hepatotoxicity, cell signaling and ADME. Arch. Toxicol. 87, 1315–1530 (2013).

    CAS  Article  Google Scholar 

  54. 54.

    Holzhütter, H.-G., Drasdo, D., Preusser, T., Lippert, J. & Henney, A. M. The virtual liver: a multidisciplinary, multilevel challenge for systems biology. Wiley Interdiscip. Rev. Syst. Biol. Med. 4, 221–235 (2012).

    Article  Google Scholar 

  55. 55.

    Jopling, C. L., Yi, M., Lancaster, A. M., Lemon, S. M. & Sarnow, P. Modulation of hepatitis C virus RNA abundance by a liver-specific microRNA. Science 309, 1577–1581 (2005).

    CAS  Article  Google Scholar 

  56. 56.

    Roderburg, C. et al. Micro-RNA profiling reveals a role for miR-29 in human and murine liver fibrosis. Hepatology 53, 209–218 (2011).

    CAS  Article  Google Scholar 

  57. 57.

    Mitchell, P. S. et al. Circulating microRNAs as stable blood-based markers for cancer detection. Proc. Natl Acad. Sci. USA 105, 10513–10518 (2008).

    CAS  Article  Google Scholar 

  58. 58.

    Farid, W. R. et al. Hepatocyte‐derived microRNAs as serum biomarkers of hepatic injury and rejection after liver transplantation. Liver Transpl. 18, 290–297 (2012).

    Article  Google Scholar 

  59. 59.

    Dominissini, D. et al. Topology of the human and mouse m6A RNA methylomes revealed by m6A-seq. Nature 485, 201–206 (2012).

    CAS  Article  Google Scholar 

  60. 60.

    Hirayama, A. et al. Quantitative metabolome profiling of colon and stomach cancer microenvironment by capillary electrophoresis time-of-flight mass spectrometry. Cancer Res. 69, 4918–4925 (2009).

    CAS  Article  Google Scholar 

  61. 61.

    Llufrio, E. M., Wang, L., Naser, F. J. & Patti, G. J. Sorting cells alters their redox state and cellular metabolome. Redox Biol. 16, 381–387 (2018).

    CAS  Article  Google Scholar 

  62. 62.

    Manco, R. et al. Reactive cholangiocytes differentiate into proliferative hepatocytes with efficient DNA repair in mice with chronic liver injury. J. Hepatol. 70, 1180–1191 (2019).

    CAS  Article  Google Scholar 

  63. 63.

    Mederacke, I., Dapito, D. H., Affò, S., Uchinami, H. & Schwabe, R. F. High-yield and high-purity isolation of hepatic stellate cells from normal and fibrotic mouse livers. Nat. Protoc. 10, 305–315 (2015).

    CAS  Article  Google Scholar 

  64. 64.

    Tanami, S. et al. Dynamic zonation of liver polyploidy. Cell Tissue Res. 368, 405–410 (2017).

    CAS  Article  Google Scholar 

  65. 65.

    Bagnoli, J. W. et al. Sensitive and powerful single-cell RNA sequencing using mcSCRB-seq. Nat. Commun. 9, 2937 (2018).

    Article  Google Scholar 

  66. 66.

    Parekh, S., Ziegenhain, C., Vieth, B., Enard, W. & Hellmann, I. zUMIs: a fast and flexible pipeline to process RNA sequencing data with UMIs. Gigascience 7, giy059 (2018).

    Article  Google Scholar 

  67. 67.

    Robinson, M. D. & Oshlack, A. A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol. 11, R25 (2010).

    Article  Google Scholar 

  68. 68.

    Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).

    CAS  Article  Google Scholar 

  69. 69.

    Cox, J. & Mann, M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat. Biotechnol. 26, 1367–1372 (2008).

    CAS  Article  Google Scholar 

  70. 70.

    Li, B. et al. Adult mouse liver contains two distinct populations of cholangiocytes. Stem Cell Rep. 9, 478–489 (2017).

    CAS  Article  Google Scholar 

  71. 71.

    Newman, A. M. et al. Robust enumeration of cell subsets from tissue expression profiles. Nat. Methods 12, 453–457 (2015).

    CAS  Article  Google Scholar 

  72. 72.

    Kanehisa, M. & Goto, S. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 28, 27–30 (2000).

    CAS  Article  Google Scholar 

  73. 73.

    Milo, R. et al. Network motifs: simple building blocks of complex networks. Science 298, 824–827 (2002).

    CAS  Article  Google Scholar 

  74. 74.

    Nusse, R. & Clevers, H. Wnt/β-catenin signaling, disease, and emerging therapeutic modalities. Cell 169, 985–999 (2017).

    CAS  Article  Google Scholar 

  75. 75.

    Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003).

    CAS  Article  Google Scholar 

  76. 76.

    Bernhardt, J., Funke, S., Hecker, M. & Siebourg, J. Visualizing Gene Expression Data Via Voronoi Treemaps (IEEE, accessed 23 August 2019); https://ieeexplore.ieee.org/document/5362329

  77. 77.

    Liebermeister, W. et al. Visual account of protein investment in cellular functions. Proc. Natl Acad. Sci. USA 111, 8488–8493 (2014).

    CAS  Article  Google Scholar 

  78. 78.

    Otto, A. et al. Systems-wide temporal proteomic profiling in glucose-starved Bacillus subtilis. Nat. Commun. 1, 137 (2010).

    Article  Google Scholar 

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Acknowledgements

We thank E. Hagai and the Flow Cytometry Unit (Weizmann Institute of Science) for FACS technical support; T. Ziv and the Smoler Proteomics Center (Technion) for performing the liquid chromatography–tandem mass spectrometry and analysing the results; and D. Pilzer and the Genomic Technologies Unit (Weizmann Institute of Science) for performing the miRNA microarray measurements. We thank all members of the laboratory for valuable comments. S.I. is supported by the Henry Chanoch Krenter Institute for Biomedical Imaging and Genomics, The Leir Charitable Foundations, the Richard Jakubskind Laboratory of Systems Biology, the Cymerman-Jakubskind Prize, The Lord Sieff of Brimpton Memorial Fund, the I-CORE programme of the Planning and Budgeting Committee (grant no. 1902/12), the Israel Science Foundation (grant no. 1486/16), the Broad Institute-Israel Science Foundation (grant no. 2615/18), the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant no. 768956), the Bert L. and N. Kuggie Vallee Foundation, the Howard Hughes Medical Institute (HHMI) International Research Scholar Award (grant no. 55008734), The Wolfson Family Charitable Trust (grant no. 21376) and the Edmond de Rothschild Foundations.

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K.B.H. and S.I. conceived the study. S.B.M. and S.I. designed the experiments. S.B.M. prepared all the samples. S.B.M. and Y.S. analysed the data. A.E.M. contributed to the data analysis. R.M. and T.V. assisted with the immunofluorescence experiments. K.B.H. contributed to establishing the methodology. S.I. supervised the study. S.B.M., Y.S. and S.I. wrote the manuscript. All authors reviewed the manuscript and provided input.

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Correspondence to Shalev Itzkovitz.

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Ben-Moshe, S., Shapira, Y., Moor, A.E. et al. Spatial sorting enables comprehensive characterization of liver zonation. Nat Metab 1, 899–911 (2019). https://doi.org/10.1038/s42255-019-0109-9

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