Abstract

Spatially resolved single-cell RNA sequencing (scRNAseq) is a powerful approach for inferring connections between a cell's identity and its position in a tissue. We recently combined scRNAseq with spatially mapped landmark genes to infer the expression zonation of hepatocytes. However, determining zonation of small cells with low mRNA content, or without highly expressed landmark genes, remains challenging. Here we used paired-cell sequencing, in which mRNA from pairs of attached mouse cells were sequenced and gene expression from one cell type was used to infer the pairs' tissue coordinates. We applied this method to pairs of hepatocytes and liver endothelial cells (LECs). Using the spatial information from hepatocytes, we reconstructed LEC zonation and extracted a landmark gene panel that we used to spatially map LEC scRNAseq data. Our approach revealed the expression of both Wnt ligands and the Dkk3 Wnt antagonist in distinct pericentral LEC sub-populations. This approach can be used to reconstruct spatial expression maps of non-parenchymal cells in other tissues.

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Acknowledgements

We thank M. Kolesnikov and S. Jung (Weizmann Institute) for the C57BL/6-Actb-DsRed.T3 mice and D. Jaitin (Weizmann Institute) for the C57BL/6-Tg(CAG-EGFP) mice. 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, the Bert L. and N. Kuggie Vallee Foundation and the Howard Hughes Medical Institute (HHMI) international research scholar award. 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.

Affiliations

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

    • Keren Bahar Halpern
    • , Rom Shenhav
    • , Hassan Massalha
    • , Beata Toth
    • , Adi Egozi
    • , Efi E Massasa
    • , Andreas E Moor
    •  & Shalev Itzkovitz
  2. Department of Immunology, Weizmann Institute of Science, Rehovot, Israel.

    • Chiara Medgalia
    • , Eyal David
    • , Amir Giladi
    •  & Ido Amit
  3. The Flow Cytometry Unit, Life Sciences Faculty, Weizmann Institute of Science, Rehovot, Israel.

    • Ziv Porat

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Contributions

K.B.H., C.M., B.T., A.E., A.E.M., E.E.M. and Z.P. performed the experiments. R.S., S.I., H.M., A.G. and E.D. performed the data analysis. I.A. contributed to project design. S.I. supervised the study. S.I., K.B.H. and R.S. wrote the paper. All of the authors discussed the results and commented on the manuscript.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Shalev Itzkovitz.

Integrated supplementary information

Supplementary information

PDF files

  1. 1.

    Supplementary Text and Figures

    Supplementary Figures 1–7

  2. 2.

    Life Sciences Reporting Summary

Zip files

  1. 1.

    Supplementary Data 1

    Background subtracted table of UMIs for the scRNAseq data of NPCs.

  2. 2.

    Supplementary Data 4

    Background subtracted table of UMIs for the pcRNAseq data.

  3. 3.

    Supplementary Code

    ZONATION_pcRNAseq.m - MATLAB code that loads expression data for pairs of hepatocytes and endothelial cells, localizes each cell along the lobule axis according to hepatocyte landmark genes and outputs a table of zonated expression and a list of zonated endothelial genes. The code loads three mat files contained in the zipped directory.

Excel files

  1. 1.

    Supplementary Data 2

    Average expression per cell for the different liver cell types, in units of fraction of total UMIs. Hepatocyte expression data is from Bahar Halpern et al

  2. 2.

    Supplementary Data 3

    Ligand-receptor interactions among liver cell types. MeanExp is computed over all cells from the respective cell type (expression in units of Seurat's log-transformed normalized data). Cell_Ratio (Cell_Num) are the fraction (numbers) of cells in each cell type that have expression>0 for the ligand or receptor.

  3. 3.

    Supplementary Data 5

    Zonation table based on the pcRNAseq data. Zonation_qvalue is obtained using Benjamini-Hochberg multiple hypothesis correction applied to allgenes with maximal zonation level above 5.10-6 (8,821 genes), Zonation_qvalue_endo considers only endothelial expressed genes (1,303 genes).

  4. 4.

    Supplementary Data 6

    Zonation table based on the scRNAseq data. Zonation_qvalue is obtained using Benjamini-Hochberg multiple hypothesis correction applied to allgenes with mean scRNAseq expression level above 10-4 (2,145 genes).

  5. 5.

    Supplementary Data 7

    Examples of pairs of cell types for which pcRNAseq could be readily applied to obtain spatial patterns.

  6. 6.

    Supplementary Data 8

    Sequences of the smFISH probes libraries used in this study.

  7. 7.

    Supplementary Data 9

    Sequences of the qPCR primers used in this study.

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DOI

https://doi.org/10.1038/nbt.4231