Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Paired-cell sequencing enables spatial gene expression mapping of liver endothelial cells


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.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Strategy for paired-cell reconstruction of liver LEC zonation.
Figure 2: Single-cell RNAseq reveals the expression signatures of liver non-parenchymal cells.
Figure 3: Sorting strategy to isolate pairs of attached hepatocytes and LECs.
Figure 4: Global zonation of LEC genes.
Figure 5: Expression signature of pericentral LECs.
Figure 6: Spatial reconstruction of single LECs using landmark genes obtained from pcRNAseq.

Similar content being viewed by others

Accession codes


Gene Expression Omnibus


  1. Regev, A. et al. The Human Cell Atlas. eLife 6, e27041 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  2. Tabula Muris Consortium, Quake, S.R., Wys-Coray, T. & Darmanis, S. Transcriptomic characterization of 20 organs and tissues from mouse at single cell resolution creates a Tabula Muris. Preprint at (2017).

  3. Han, X. et al. Mapping the Mouse Cell Atlas by Microwell-Seq. Cell 172, 1091–1107.e17 (2018).

    Article  CAS  PubMed  Google Scholar 

  4. Crosetto, N., Bienko, M. & van Oudenaarden, A. Spatially resolved transcriptomics and beyond. Nat. Rev. Genet. 16, 57–66 (2015).

    Article  CAS  PubMed  Google Scholar 

  5. Kolodziejczyk, A.A., Kim, J.K., Svensson, V., Marioni, J.C. & Teichmann, S.A. The technology and biology of single-cell RNA sequencing. Mol. Cell 58, 610–620 (2015).

    Article  CAS  PubMed  Google Scholar 

  6. Satija, R., Farrell, J.A., Gennert, D., Schier, A.F. & Regev, A. Spatial reconstruction of single-cell gene expression data. Nat. Biotechnol. 33, 495–502 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  8. Karaiskos, N. et al. The Drosophila embryo at single-cell transcriptome resolution. Science 358, 194–199 (2017).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Ståhl, P.L. et al. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science 353, 78–82 (2016).

    Article  PubMed  CAS  Google Scholar 

  11. Lein, E., Borm, L.E. & Linnarsson, S. The promise of spatial transcriptomics for neuroscience in the era of molecular cell typing. Science 358, 64–69 (2017).

    Article  CAS  PubMed  Google Scholar 

  12. Lee, J.H. De Novo Gene Expression Reconstruction in Space. Trends Mol. Med. 23, 583–593 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Moor, A.E. & Itzkovitz, S. Spatial transcriptomics: paving the way for tissue-level systems biology. Curr. Opin. Biotechnol. 46, 126–133 (2017).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  16. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  18. Rocha, A.S. et al. The angiocrine factor rspondin3 is a key determinant of liver zonation. Cell Reports 13, 1757–1764 (2015).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

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

  22. Aird, W.C. Phenotypic heterogeneity of the endothelium. II. Representative vascular beds. Circ. Res. 100, 174–190 (2007).

    Article  CAS  PubMed  Google Scholar 

  23. Strauss, O., Phillips, A., Ruggiero, K., Bartlett, A. & Dunbar, P.R. Immunofluorescence identifies distinct subsets of endothelial cells in the human liver. Sci. Rep. 7, 44356 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Rafii, S., Butler, J.M. & Ding, B.-S. Angiocrine functions of organ-specific endothelial cells. Nature 529, 316–325 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Chistiakov, D.A., Orekhov, A.N., Sobenin, I.A. & Bobryshev, Y.V. Plasmacytoid dendritic cells: development, functions, and role in atherosclerotic inflammation. Front. Physiol. 5, 279 (2014).

    PubMed  PubMed Central  Google Scholar 

  27. Sierro, F. et al. A liver capsular network of monocyte-derived macrophages restricts hepatic dissemination of intraperitoneal bacteria by neutrophil recruitment. Immunity 47, 374–388.e6 (2017).

    Article  CAS  PubMed  Google Scholar 

  28. Graeber, T.G. & Eisenberg, D. Bioinformatic identification of potential autocrine signaling loops in cancers from gene expression profiles. Nat. Genet. 29, 295–300 (2001).

    Article  CAS  PubMed  Google Scholar 

  29. Zhou, J.X., Taramelli, R., Pedrini, E., Knijnenburg, T. & Huang, S. Extracting intercellular signaling network of cancer tissues using ligand-receptor expression patterns from whole-tumor and single-cell transcriptomes. Sci Rep. 7, 8815 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  30. Shutter, J.R. et al. Dll4, a novel Notch ligand expressed in arterial endothelium. Genes Dev. 14, 1313–1318 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. Hellström, M. et al. Dll4 signalling through Notch1 regulates formation of tip cells during angiogenesis. Nature 445, 776–780 (2007).

    Article  CAS  PubMed  Google Scholar 

  32. Adams, R.H. & Klein, R. Eph receptors and ephrin ligands. essential mediators of vascular development. Trends Cardiovasc. Med. 10, 183–188 (2000).

    Article  CAS  PubMed  Google Scholar 

  33. Rahner, C., Mitic, L.L. & Anderson, J.M. Heterogeneity in expression and subcellular localization of claudins 2, 3, 4, and 5 in the rat liver, pancreas, and gut. Gastroenterology 120, 411–422 (2001).

    Article  CAS  PubMed  Google Scholar 

  34. FANTOM Consortium and the RIKEN PMI and CLST (DGT) & Forrest, A.R. et al. A promoter-level mammalian expression atlas. Nature 507, 462–470 (2014).

  35. Moore, K.A. & Lemischka, I.R. Stem cells and their niches. Science 311, 1880–1885 (2006).

    Article  CAS  PubMed  Google Scholar 

  36. Gregorieff, A. et al. Expression pattern of Wnt signaling components in the adult intestine. Gastroenterology 129, 626–638 (2005).

    Article  CAS  PubMed  Google Scholar 

  37. Sick, S., Reinker, S., Timmer, J. & Schlake, T. WNT and DKK determine hair follicle spacing through a reaction-diffusion mechanism. Science 314, 1447–1450 (2006).

    Article  CAS  PubMed  Google Scholar 

  38. Macosko, E.Z. et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202–1214 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Klein, A.M. et al. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell 161, 1187–1201 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Bagnoli, J.W. et al. mcSCRB-seq: sensitive and powerful single-cell RNA sequencing. Preprint at (2017).

  41. Vanlandewijck, M. et al. A molecular atlas of cell types and zonation in the brain vasculature. Nature 554, 475–480 (2018).

    Article  CAS  PubMed  Google Scholar 

  42. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Hernandez-Gea, V. & Friedman, S.L. Pathogenesis of liver fibrosis. Annu. Rev. Pathol. 6, 425–456 (2011).

    Article  CAS  PubMed  Google Scholar 

  44. Moor, A.E. et al. Spatial reconstruction of single enterocytes uncovers broad zonation along the intestinal villus axis. Preprint at (2018).

  45. Olivares-Villagómez, D. & Van Kaer, L. Intestinal intraepithelial lymphocytes: sentinels of the mucosal barrier. Trends Immunol. 39, 264–275 (2018).

    Article  PubMed  CAS  Google Scholar 

  46. Boisset, J.-C. et al. Mapping the physical network of cellular interactions. Nat. Methods 15, 547–553 (2018).

    Article  CAS  PubMed  Google Scholar 

  47. Shah, S., Lubeck, E., Zhou, W. & Cai, L. seqFISH accurately detects transcripts in single cells and reveals robust spatial organization in the hippocampus. Neuron 94, 752–758.e1 (2017).

    Article  CAS  PubMed  Google Scholar 

  48. Codeluppi, S. et al. Spatial organization of the somatosensory cortex revealed by cyclic smFISH. Preprint at (2018).

  49. Zeng, W. et al. Sympathetic neuro-adipose connections mediate leptin-driven lipolysis. Cell 163, 84–94 (2015).

    Article  CAS  PubMed  Google Scholar 

  50. Bhowmick, N.A. & Moses, H.L. Tumor-stroma interactions. Curr. Opin. Genet. Dev. 15, 97–101 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Okabe, M., Ikawa, M., Kominami, K., Nakanishi, T. & Nishimune, Y. 'Green mice' as a source of ubiquitous green cells. FEBS Lett. 407, 313–319 (1997).

    Article  CAS  PubMed  Google Scholar 

  52. Vintersten, K. et al. Mouse in red: red fluorescent protein expression in mouse ES cells, embryos, and adult animals. Genesis 40, 241–246 (2004).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

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

    Article  PubMed  CAS  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  56. George, T.C. et al. Quantitative measurement of nuclear translocation events using similarity analysis of multispectral cellular images obtained in flow. J. Immunol. Methods 311, 117–129 (2006).

    Article  CAS  PubMed  Google Scholar 

  57. Butler, A. & Satija, R. Integrated analysis of single cell transcriptomic data across conditions, technologies, and species. Nat. Biotechnol. 36, 411–420 (2017).

    Article  CAS  Google Scholar 

  58. Shay, T. & Kang, J. Immunological Genome Project and systems immunology. Trends Immunol. 34, 602–609 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Hughes, M.E., Hogenesch, J.B. & Kornacker, K. JTK_CYCLE: an efficient nonparametric algorithm for detecting rhythmic components in genome-scale data sets. J. Biol. Rhythms 25, 372–380 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

Download references


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

Authors and Affiliations



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.

Corresponding author

Correspondence to Shalev Itzkovitz.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 CD31 and CD45 enrich for distinct mouse liver single cell populations.

tSNE maps of FACS-sorted single NPCs, colored by the index-sorting values of CD31 (a) and CD45 (b). Only cells for which index sorting was obtained are shown (2,722 out of the 3,151 cells). Blue – high values, gray – low values. c) Scatter plot of CD31 and CD45 indices. Blue and red patches mark the CD31+CD45- and CD45+CD31- population analyzed in (d). d) CD31+CD45- cells have minimal contamination from other cell types. Shown are the summed UMI fraction of the cell-type specific markers for the seven cell types in Figure 2. Markers for non-activated hepatic stellate cells (Reln, Des, Gfap, Lrat) and for portal fibroblasts (Entpd2, Col15a1, Eln) were taken from61 (Thy1 was excluded as it is also a T cell marker). Boxes represent the mean of UMI fraction, n=3151 cells, Box plot elements: center line, median; box limits, first to third quartile (Q1 to Q3); whiskers, extend to the most extreme data point within 1.5× the interquartile range (IQR) from the box; circles, data points.

Supplementary Figure 2 Ligand-receptor interactions among mouse liver cell types.

a) Interaction network summarizing the cluster-specific interactions among mouse liver cell types. b-c) tSNE plots showing ligand–receptor interactions between LECs and other liver cell types, n=3151 cells. b) and between hepatocytes and Kupffer cells (c). Cells expressing the ligand are in purple, cells expressing the receptor are in yellow. Dot transparency of each cell correlates with expression level, solid colors for high expression and transparent color for low expression. Shown are four representative examples for different cluster pairs.

Supplementary Figure 3 Sorting strategy predominantly yields cell pairs composed of a single hepatocyte and an attached single LEC.

a-c) Collagenase D liver dissociation enriches for CD31-hepatocyte pairs. a) Unstained control. b) Liberase dissociated tissue9. c) Collagenase D dissociated tissue (the same data used in figure 3c). Sorting gate is identical to the one shown in Fig. 3c. In (a-c) data represents 3 independent repeats with similar results. d) Imagestream quantification of the cell configurations sorted with our sorting strategy and collagenase D-treated liver tissue. Quantification based on more than 13,000 cells from three different mice. Circles are the values for the individual mice, errorbars are s.e.m. e) Imagestream examples of pairs that include a single hepatocyte and a single LEC. f) Imagestream example of a single hepatocyte. g) Imagestream example of a cluster of more than two cells (here a single LEC and two hepatocytes). In (e-g) data represents 3 independent repeats with similar results. h) Graphic illustration showing the strategy used to validate that pairs are not formed during cell incubation. Liver from a mouse with whole body constitutive expression of EGFP or DsRed were perfused by collagenase D. Single CD31+ endothelial cells were sorted from the mice constitutively expressing EGFP and hepatocytes from the mice constitutively expressing DsRed, incubated together and then re-analyzed by FACS. i) Left panel shows the two sorted population by size (demonstrated by FSC) and CD31 expression. Right panel demonstrates that upon sorting of the mixed population no CD31+CD45- pairs appear in our paired-cell optimized sorting gate (0 pairs out of 731 cells, <0.15%, compared to the 1,626 pairs out of 87,872 cells, 1.85% obtained from the tissue, Figure 3c. two-sided Fisher test p=3.72*10−6). Results are based on a pair of EGFP and DsRed mice.

Supplementary Figure 4 Algorithm for zonation reconstruction based on the expression of hepatocytes landmark genes.

a-b) Zonation profiles set of 21 pericentral landmark genes (a) and 30 periportal genes (b). Profiles are taken from Bahar Halpern et al.9, each profile normalized by its maximum. c) The summed expression levels of the two landmark gene sets provide a convenient coordinate system to infer localization of cells. Shown are representative hepatocyte zonated genes from Bahar Halpern et al.9, each dot is a paired-cell, colors denote the genes’ expression levels, in units of log10(fraction of total UMIs). d) Gamma distribution fits for the frequencies of the scaled coordinate η in each layer (Online Methods). Distributions are based on the η values and the most likely layer from Bahar Halpern et al.9. Numbers above the curves denote the lobule layers.

Supplementary Figure 5 Overlap of hepatocyte zonation profiles obtained with the current reconstruction algorithm and the profiles of Bahar Halpern et al.9

a) Representative hepatocyte zonated genes reconstructed from the data in Bahar Halpern et al.9 with the current algorithm (blue) overlaid on the profiles reconstructed in Bahar Halpern et al.9 (red). Patches are s.e.m. n=1415 cells. (b) Centers of mass of zonation profiles for the zonated hepatocyte genes in9 highly correlate between the current reconstruction algorithm and the one used in9 (n=3496 zonated genes, Spearman R=0.93, p<1e-15).

Supplementary Figure 6 Spatial sorting of LECs

a) Zonation of transcription factors34 and b) surface markers (GO:0009986) among LEC genes. Shown are zonated endothelial genes with a ratio of maximal to minimal zonation values larger than 1.5-fold, profiles are normalized by their maximum and sorted by centers of mass. Red box marks Kit, encoding CD117, which is used for the spatial sorting in c-d. c) FACS gating of CD31+CD45- LECs population. d) FACS sorting strategy of LEC spatial sorting by Kit, encoding the CD117 surface marker. Left panel shows the distribution of the CD117+ population out of CD31+ LECs. Four populations (P1-P4) were sorted according to CD117 levels. e) qPCR validation of the four sorted populations for selected genes. Blue - zonation profiles based on pcRNAseq, Red - qPCR results. All zonation profiles normalized to their mean across zones. Patches are s.e.m. n=4 mice. KruskalWallis p-values confirming the significance between the pericentral and periportal endothelial cells are shown at each plot title. Aqp1 is presened as a control of non-zonated LEC gene. FACS sorting and validations were performed on four mice.

Supplementary Figure 7 Examples of tissues in which pcRNAseq could be readily applied to obtain spatial patterns.

a-d) Examples of cell populations that are interleaved with enterocytes within the small intestinal epithelium. a) Intra-epithelial lymphocytes express Cd3e (green dots, cells marked by white arrows). Red dots are mRNA for Slc2a2, an enterocyte marker. b) An enteroendocrine cell marked by a white arrow expresses Chga mRNA (green dots). c) Tuft cells express Dclk1 (green dots). White arrow marks a crypt cell, arrowhead marks a villus cell. Red dots are mRNA of Olfm4, expressed at the bottom of the intestinal crypts. d) Goblet cells, marked by white arrows, express Gob5 mRNA (green dots). Scale bars are 8μM. Gray in b-c is immunofluorescence for the cell surface marker E-cadherin. e) Hepatic Stellate cell expressing Acta2 mRNA (green dots, dashed yellow outline), adjacent to hepatocytes that express Acly (red dots). Blue in a-e is DAPI nuclear staining, scale bar 10 μM. f) Lymphocytes in a colonic tumor express Cd3e mRNA (red dots, cell marked with a white outline in the inset) and are adjacent to tumor cells expressing Nt5e (blue dots, yellow outline in the inset). Lgr5 mRNA (green dots) expressed in a distinct tumor cell subset. Scale bar 10 μM, inset scale bar 2 μM. In (a-f) micrographs are representative of 10 images with similar observations.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–7 (PDF 1919 kb)

Life Sciences Reporting Summary (PDF 190 kb)

Supplementary Data 1

Background subtracted table of UMIs for the scRNAseq data of NPCs. (ZIP 422 kb)

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 (XLS 4752 kb)

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. (XLSX 1504 kb)

Supplementary Data 4

Background subtracted table of UMIs for the pcRNAseq data. (ZIP 11032 kb)

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). (XLS 9986 kb)

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). (XLSX 2427 kb)

Supplementary Data 7

Examples of pairs of cell types for which pcRNAseq could be readily applied to obtain spatial patterns. (XLSX 14 kb)

Supplementary Data 8

Sequences of the smFISH probes libraries used in this study. (XLSX 28 kb)

Supplementary Data 9

Sequences of the qPCR primers used in this study. (XLSX 16 kb)

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. (ZIP 36002 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:

This article is cited by


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing