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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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.
The authors declare no competing financial interests.
Integrated supplementary information
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.
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).
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 Figures 1–7 (PDF 1919 kb)
Background subtracted table of UMIs for the scRNAseq data of NPCs. (ZIP 422 kb)
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)
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)
Background subtracted table of UMIs for the pcRNAseq data. (ZIP 11032 kb)
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)
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)
Examples of pairs of cell types for which pcRNAseq could be readily applied to obtain spatial patterns. (XLSX 14 kb)
Sequences of the smFISH probes libraries used in this study. (XLSX 28 kb)
Sequences of the qPCR primers used in this study. (XLSX 16 kb)
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)
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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). https://doi.org/10.1038/nbt.4231
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