Letter | Published:

Single-cell spatial reconstruction reveals global division of labour in the mammalian liver

Nature volume 542, pages 352356 (16 February 2017) | Download Citation

  • An Erratum to this article was published on 15 March 2017

This article has been updated


The mammalian liver consists of hexagon-shaped lobules that are radially polarized by blood flow and morphogens1,2,3,4. Key liver genes have been shown to be differentially expressed along the lobule axis, a phenomenon termed zonation5,6, but a detailed genome-wide reconstruction of this spatial division of labour has not been achieved. Here we measure the entire transcriptome of thousands of mouse liver cells and infer their lobule coordinates on the basis of a panel of zonated landmark genes, characterized with single-molecule fluorescence in situ hybridization7. Using this approach, we obtain the zonation profiles of all liver genes with high spatial resolution. We find that around 50% of liver genes are significantly zonated and uncover abundant non-monotonic profiles that peak at the mid-lobule layers. These include a spatial order of bile acid biosynthesis enzymes that matches their position in the enzymatic cascade. Our approach can facilitate the reconstruction of similar spatial genomic blueprints for other mammalian organs.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Change history

  • 15 February 2017

    A minor change was made to the Abstract.


Primary accessions

Gene Expression Omnibus


  1. 1.

    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)

  2. 2.

    , , , & Self-renewing diploid Axin2+ cells fuel homeostatic renewal of the liver. Nature 524, 180–185 (2015)

  3. 3.

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

  4. 4.

    & in Molecular Pathology of Liver Diseases (ed. ) 7–16 (Springer US, 2011)

  5. 5.

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

  6. 6.

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

  7. 7.

    et al. Bursty gene expression in the intact mammalian liver. Mol. Cell 58, 147–156 (2015)

  8. 8.

    & Liver zonation: novel aspects of its regulation and its impact on homeostasis. World J. Gastroenterol . 20, 8491–8504 (2014)

  9. 9.

    et al. Differential gene expression in periportal and perivenous mouse hepatocytes. FEBS J . 273, 5051–5061 (2006)

  10. 10.

    , & Sexual dimorphisms in zonal gene expression in mouse liver. Biochem. Biophys. Res. Commun . 436, 730–735 (2013)

  11. 11.

    , , & CEL-seq: single-cell RNA-seq by multiplexed linear amplification. Cell Reports 2, 666–673 (2012)

  12. 12.

    et al. Quantitative assessment of single-cell RNA-sequencing methods. Nat. Methods 11, 41–46 (2014)

  13. 13.

    et al. Brain structure. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq. Science 347, 1138–1142 (2015)

  14. 14.

    et al. Single-cell messenger RNA sequencing reveals rare intestinal cell types. Nature 525, 251–255 (2015)

  15. 15.

    , , , & Spatial reconstruction of single-cell gene expression data. Nat. Biotechnol . 33, 495–502 (2015)

  16. 16.

    et al. High-throughput spatial mapping of single-cell RNA-seq data to tissue of origin. Nat. Biotechnol . 33, 503–509 (2015)

  17. 17.

    et al. Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types. Science 343, 776–779 (2014)

  18. 18.

    et al. Hybrid periportal hepatocytes regenerate the injured liver without giving rise to cancer. Cell 162, 766–779 (2015)

  19. 19.

    et al. T-cell factor 4 and β-catenin chromatin occupancies pattern zonal liver metabolism in mice. Hepatology 59, 2344–2357 (2014)

  20. 20.

    , & Gene expression of the liver in response to chronic hypoxia. Physiol. Genomics 41, 275–288 (2010)

  21. 21.

    , , , & Zonal gene expression in murine liver: lessons from tumors. Hepatology 43, 407–414 (2006)

  22. 22.

    et al. Ha-ras and β-catenin oncoproteins orchestrate metabolic programs in mouse liver tumors. Int. J. Cancer 135, 1574–1585 (2014)

  23. 23.

    , & Identification of longevity-associated genes in long-lived Snell and Ames dwarf mice. 28, 125–144 (2016)

  24. 24.

    & Cellular energy utilization and molecular origin of standard metabolic rate in mammals. Physiol. Rev . 77, 731–758 (1997)

  25. 25.

    et al. Dynamic zonation of liver polyploidy. Cell Tissue Res . (2016)

  26. 26.

    IGF binding proteins in cancer: mechanistic and clinical insights. Nat. Rev. Cancer 14, 329–341 (2014)

  27. 27.

    , & Pleiotropic roles of bile acids in metabolism. Cell Metab. 17, 657–669 (2013)

  28. 28.

    et al. Integrated energy and flux balance based multiobjective framework for large-scale metabolic networks. Ann. Biomed. Eng . 35, 863–885 (2007)

  29. 29.

    et al. HepatoNet1: a comprehensive metabolic reconstruction of the human hepatocyte for the analysis of liver physiology. Mol. Syst. Biol . 6, 411 (2010)

  30. 30.

    et al. Circadian and feeding rhythms differentially affect rhythmic mRNA transcription and translation in mouse liver. Proc. Natl Acad. Sci. USA 112, E6579–E6588 (2015)

  31. 31.

    et al. Single-molecule transcript counting of stem-cell markers in the mouse intestine. Nat. Cell Biol . 14, 106–114 (2011)

  32. 32.

    et al. Single-molecule mRNA detection and counting in mammalian tissue. Nat. Protocols 8, 1743–1758 (2013)

  33. 33.

    et al. Nuclear retention of mRNA in mammalian tissues. Cell Reports 13, 2653–2662 (2015)

  34. 34.

    Preparation of rat liver cells. 3. Enzymatic requirements for tissue dispersion. Exp. Cell Res . 82, 391–398 (1973)

  35. 35.

    , & HISAT: a fast spliced aligner with low memory requirements. Nat. Methods 12, 357–360 (2015)

  36. 36.

    , , , & GOrilla: a tool for discovery and visualization of enriched GO terms in ranked gene lists. BMC Bioinformatics 10, 48 (2009)

  37. 37.

    et al. Strategy for studying the liver secretome on the organ level. J. Proteome Res . 9, 1894–1901 (2010)

  38. 38.

    & KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res . 28, 27–30 (2000)

Download references


We thank R. Milo and all members of our laboratory for valuable comments. We thank M. Schwarz, A. Braeuning and S. Colnot for sharing their data and A. Sharp, E. Ariel, E. Hagai and Z. Gavish for help with experimental procedures. I.A. is supported by the European Research Council (309788), and the Israel Science Foundation, the Ernest and Bonnie Beutler Research Program of Excellence in Genomic Medicine and the Helen and Martin Kimmel award for innovative investigation. I.A. is the incumbent of the Alan and Laraine Fischer Career Development Chair. S.I. is supported by the Henry Chanoch Krenter Institute for Biomedical Imaging and Genomics, The Leir Charitable Foundations, Richard Jakubskind Laboratory of Systems Biology, Cymerman-Jakubskind Prize, The Lord Sieff of Brimpton Memorial Fund, the I-CORE program of the Planning and Budgeting Committee and the Israel Science Foundation (grants 1902/ 12 and 1796/12), the Israel Science Foundation grant No. 1486/16, the EMBO Young Investigator Program and the European Research Council under the European Union’s Seventh Framework Programme (FP7/2007-2013)/ERC grant agreement number 335122. S.I. is the incumbent of the Philip Harris and Gerald Ronson Career Development Chair.

Author information

Author notes

    • Keren Bahar Halpern
    •  & Rom Shenhav

    These authors contributed equally to this work.


  1. Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel

    • Keren Bahar Halpern
    • , Rom Shenhav
    • , Beáta Tóth
    • , Doron Lemze
    • , Matan Golan
    • , Efi E. Massasa
    • , Shaked Baydatch
    • , Shanie Landen
    • , Andreas E. Moor
    • , Avigail Stokar-Avihail
    •  & Shalev Itzkovitz
  2. Department of Immunology, Weizmann Institute of Science, Rehovot, Israel

    • Orit Matcovitch-Natan
    • , Amir Giladi
    • , Eyal David
    •  & Ido Amit
  3. Biological Services, Weizmann Institute of Science, Rehovot, Israel

    • Alexander Brandis


  1. Search for Keren Bahar Halpern in:

  2. Search for Rom Shenhav in:

  3. Search for Orit Matcovitch-Natan in:

  4. Search for Beáta Tóth in:

  5. Search for Doron Lemze in:

  6. Search for Matan Golan in:

  7. Search for Efi E. Massasa in:

  8. Search for Shaked Baydatch in:

  9. Search for Shanie Landen in:

  10. Search for Andreas E. Moor in:

  11. Search for Alexander Brandis in:

  12. Search for Amir Giladi in:

  13. Search for Avigail Stokar-Avihail in:

  14. Search for Eyal David in:

  15. Search for Ido Amit in:

  16. Search for Shalev Itzkovitz in:


K.B.H., B.T., M.G., S.L., E.E.M., S.B. and A.S.A. performed smFISH experiments. K.B.H., B.T. and O.M.-N. performed the single-cell isolation and FACS sorting, O.M.-N. performed the experimental scRNA-seq procedures, A.G. and E.D. implemented the scRNA-seq computational protocols, R.S. and S.I. developed and implemented the spatial inference algorithms, R.S., D.L. and S.I. performed data analysis, A.E.M. and A.B. performed the Cyp8b1 validation experiments, I.A. and S.I. supervised the study. S.I. wrote the paper. All authors discussed the results and commented on the manuscript.

Competing interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to Ido Amit or Shalev Itzkovitz.

Reviewer Information Nature thanks K. Kaestner, A. Raj and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Extended data

Supplementary information

PDF files

  1. 1.

    Supplementary Information

    This file contains Supplementary Text and Data and additional references.

Zip files

  1. 1.

    Supplementary Table 1

    This file contains the background subtracted UMI table.

Excel files

  1. 1.

    Supplementary Table S2

    This file contains the posterior matrix, which shows the probability for each lobule layer.

  2. 2.

    Supplementary Table S3

    The file contains the zonation matrix, which shows the average cellular expression levels at each layer in fraction of total cellular mRNA.

  3. 3.

    Supplementary Table S4

    This file shows the signaling pathways affecting liver zonation.

  4. 4.

    Supplementary Table S5

    This file shows the zonation of KEGG pathways.

  5. 5.

    Supplementary Table S6

    This file shows the analysis of spatial expression of enzyme pairs from KEGG pathways.

  6. 6.

    Supplementary Table S7

    This file shows the Parameters of Gamma distributions fitted to expression profiles of the six landmark genes in each of the 9 lobule layers.

  7. 7.

    Supplementary Table S8

    This file contains the DNA sequences of the probes used for smFISH.

About this article

Publication history






Further reading Further reading


By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.