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

An Erratum to this article was published on 15 March 2017

This article has been updated

Abstract

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.

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Figure 1: Strategy for spatially resolved single-cell reconstruction of liver zonation.
Figure 2: A spatial barcode of zonated landmark genes.
Figure 3: Genome-wide zonation patterns revealed by spatially resolved single-cell RNA-seq.
Figure 4: Non-monotonic zonation and sequential order.

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  • 15 February 2017

    A minor change was made to the Abstract.

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Acknowledgements

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

Authors and Affiliations

Authors

Contributions

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.

Corresponding authors

Correspondence to Ido Amit or Shalev Itzkovitz.

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The authors declare no competing financial interests.

Additional information

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 figures and tables

Extended Data Figure 1 Low magnification images of the six landmark genes.

smFISH examples of our 6 landmark genes—the pericentral genes Glul and Cyp2e1 and the periportal genes Ass1, Asl, Alb and Cyp2f2. Bright cells have high mRNA content. Scale bars, 100 μm. CV, central vein; PN, portal node. Micrographs are representative of at least ten lobules and at least two mice per gene.

Extended Data Figure 2 Single-cell RNA-seq reveals three distinct liver cell populations.

ac, t-SNE plots. Each dot is a cell coloured according to the aggregated expression of the hepatocyte marker genes Apoa1, Glul, Acly, Asl, Cyp2e1, Cyp2f2, Ass1, Alb, Mup3, Pck1, G6pc (a), the Kupffer cell marker genes Irf7, Spic, Clec4f (b) and the endothelial cell markers Ushbp1, Myf6, Oit3, Il1a, F8, Bmp2, C1qtnf1, Mmrn2, Pcdh12, Dpp4 (c). d, smFISH of a liver lobule demonstrating the antagonistic expression of Glul (red dots), and Ass1 (green dots). Scale bar, 20 μm. Inset of highlighted area demonstrates the minimal co-expression of these two genes. Scale bars, 5 μm. Blue, DAPI-stained nuclei. CV, central vein; PN, portal node. Micrographs are representative of at least 10 lobules and at least two mice per gene. e, Ass1 and Glul are mutually exclusive in the single-cell RNA-seq data. Each dot is a cell, x axis is the expression of Glul in fraction of total cellular UMI, y axis is the expression of Ass1.

Extended Data Figure 3 Prior and posterior probability computation of the scRNA-seq data.

a, b, Zone-dependent probabilities of observing different expression levels for each of the six landmark genes. Horizontal lines denote the UMI observed for each gene for cell 609 (a) and cell 629 (b). The posterior probabilities for each layer suggest that cell 609 most probably originated from the periportal layer 8, whereas cell 629 originated from the pericentral layer 1. Note that the posterior probabilities incorporate the lower sampling of cell 609, which had a total of 3,358 UMI, compared to cell 629, which had 9,913 UMI. c, d, Prior probability of observing a hepatocyte from each of the 9 layers. c, Hexagonal lobule geometry and 9 concentric circles at constant radii increments spanning the central vein and portal node. d, Area of each layer is the intersection with the hexagon.

Extended Data Figure 4 Sensitivity of reconstruction to number and features of landmark genes.

a, Zonation properties of our landmark genes. –log10 of the Wilcoxon rank-sum P values for the comparison of smFISH expression measurements in cells in sequential lobule layers for each of the 6 landmark genes. Stars denote layer pairs with significant changes in expression (P value <0.05). b, Mean reconstruction accuracy for different subsets of landmark genes (defined as 1 minus the mean of the sum-squared differences between the profiles predicted using each subset and the profiles obtained when using the entire panel of landmark genes). Dot colour represents the number of landmark genes in each subset. c, Reconstruction accuracy approaches saturation when using 6 landmark genes. G, Glul; Cf, Cyp2f2; Ce, Cyp2e1; As, Ass1; Al, Asl; Ab, Alb. Dashed line connects the most accurate partial subset of landmark genes for each panel size. d, The contribution of each landmark gene to zonation reconstruction is strongly dependent on its spatial non-uniformity among different layers and less on its intra-layer variability. x axis shows the entropy of the summed-normalized average profile of each landmark gene. y axis shows the average among all layers of the coefficient of variation of the intra-layer expression levels. The score for each gene is the average of the ratio in reconstruction error with and without the gene among all combinations that include the gene. High scores indicate that the landmark gene strongly improves reconstruction when added to combinations of other landmark genes.

Extended Data Figure 5 Validation of reconstructed zonation profiles using smFISH.

a, Reconstructed zonation profiles based on the scRNA-seq data (blue) and smFISH measurements (red) for our landmark genes. b, Validation of reconstructed profiles for 20 genes not used for the inference algorithm. Profiles in a, b, are normalized by the mean, patches show s.e.m. Lobule layers are numbered from the central vein (layer 1) to the portal node (layer 9). Note that Pck1 has a broader profile compared to other studies3, since our measurements were done on fasted mice, a metabolic state in which gluconeogenesis becomes substantial in pericentral layers.

Extended Data Figure 6 Reconstructed zonation profiles capture a wide dynamic range.

Reconstructed zonation profiles of the pericentral gene Oat (purple) and the pericentral progenitor marker Axin2 (red), and the periportal urea cycle enzyme gene Arg1 (green) and the periportal progenitor marker Sox9 (blue). Dashed box highlights a blow-up of Axin2 and Sox9, genes with 100-fold lower expression than Oat and Arg1. Expression values are the estimated fraction of the total cellular mRNA molecules. Lobule layers are numbered from the central vein (CV, layer 1) to the portal node (PN, layer 9). The slightly broader zonation profile of Sox9 mRNA compared to the Sox9 signal observed in18 using Sox9–GFP or Sox9–CreERT-R26RtdTomato may stem from differences in the dynamic range of the detection methods or from differences in the mouse metabolic states (fasted versus ad libitum).

Extended Data Figure 7 Porto–central ratio of reconstructed zonation profiles correlates with previous studies.

a, Correlation between the pericentral bias computed from our data and the one from ref. 9. y axis is our pericentral bias (PC/PP), computed as the ratio between the median zonation profiles in layers 1–4 and the median in layers 6–9. x axis is the ratio between expression in pericentrally and periportally enriched genes from ref. 9. 79 of the 88 genes considered pericentral according to Braeuning et al. (red circles) are also pericentrally biased in our dataset (hypergeometric test P < 4 × 10−9). 70 of the 81 genes considered periportal according to Braeuning et al. (green circles) are also periportally biased in our dataset (hypergeomtric test P < 1 × 10−16). Black squares mark genes that we found to be significantly zonated. b, Pericentral bias computed by our method correlates with previous bulk RNA-seq studies that compared pericentral and periportal populations isolated via laser capture microdissection10. Spearman R = 0.74; P < 1 × 10−80. Black dots represent genes considered significantly zonated in ref. 10. a, b, Include genes with average expression across cells which is higher than 5 × 10−6 of the total UMI counts. c, Reconstructed zonation profiles for the genes found in ref. 2 to have higher expression in Axin2+ pericentral hepatocytes (q value < 0.2). d, Reconstructed zonation profiles for the genes found in ref. 2 to have lower expression in pericentral Axin2+ hepatocytes (q value < 0.2).

Extended Data Figure 8 Zonation profiles of genes that significantly increased or decreased when perturbing signalling pathways.

a, b, Zonation profiles of liver genes previously shown to increase (a) or decrease (b) in liver of mice exposed to chronic hypoxia (pO2 = 11.5 kPa, compared to pO2 = 18.0 kPa20). c, d, Zonation profiles of genes shown to significantly increase (c, log-fold >2) or decrease (d, log-fold <−2) in expression in liver tumours harbouring an activating Ha-ras mutation22 (FDR-corrected P value <0.05). e, f, Zonation profiles of genes that have higher (e, fold >1) or lower (f, fold <1) expression in hypopituitary mice23 (FDR-corrected P value < 0.05).

Extended Data Figure 9 KEGG pathways enriched for zonated genes.

a, Average max-normalized zonation profiles of KEGG pathways enriched for zonated genes. Panel displays all pathways with more than 10 genes and hypergeometric test q value <0.1 (Supplementary Table 5 exhibits full results). Each profile was normalized by its maximum along all layers. b, Periportal bias of liver secreted proteins. Genes are based on an annotated liver secretome37. c, Pericentral bias for liver detoxification genes. Shown are genes for cytochrome P450, Uridine 5′-diphospho-glucuronosyltransferase and glutathione S-transferase. Images in b, c include significantly zonated genes from each pathway (Kruskal–Wallis q value <0.01 and more than 5 × 10−5 of the total cellular UMIs on average in at least one of the 9 layers), each profile normalized to maximum of 1.

Extended Data Figure 10 Non-monotonic zonation profiles of liver genes.

a, Max-normalized zonation profiles of the concise set of 46 non-monotonic genes (Methods). b, Mup3 is highly variable but exhibits a peak at layer 7 and a decreased expression in both the pericentral layer 1 and periportal layer 9. c, Igfbp2 exhibits a non-monotonic zonation profile, peaking at layers 3–6. CV, central vein; PN, portal node. Enlarged rectangles in b, c mark the periportal layers (blue), mid-lobule layers (green) and pericentral layers (magenta). Scale bars, 20 μm (b, c) and 8 μm (insets of b, c). Micrographs are representative of at least 10 lobules and at least two mice per gene. d, Igfbp2, Igfbp1 and Igfbp4 are downstream to Igf1, encoding the secreted protein that they bind and stabilize. e, Non-monotonic zonation profiles observed do not stem from ploidy-specific regulation. Each dot represents a pair of adjacent cells (within 30 μm of each other), x axis is the higher ploidy cell, y axis is the lower ploidy cell. P values are Wilcoxon sign-ranked tests. Gene expression for Igfbp2, Hamp and Cyp8b1 was quantified as the number of smFISH dots divided by the segmented cell volumes; Mup3 expression was quantified as the average intensity, due to its higher abundance. Quantification for each gene based on at least 60 pairs. f, Immunohistochemistry of Cyp8b1 demonstrates higher protein concentration in layers 2–3 (dashed curve) compared to layer 1 (dotted curve). Scale bar, 50 μm. Micrographs are representative of seven lobules in two mice.

Supplementary information

Supplementary Information

This file contains Supplementary Text and Data and additional references. (PDF 277 kb)

Supplementary Table 1

This file contains the background subtracted UMI table. (ZIP 8436 kb)

Supplementary Table S2

This file contains the posterior matrix, which shows the probability for each lobule layer. (XLSX 127 kb)

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. (XLSX 4958 kb)

Supplementary Table S4

This file shows the signaling pathways affecting liver zonation. (XLSX 66 kb)

Supplementary Table S5

This file shows the zonation of KEGG pathways. (XLSX 49 kb)

Supplementary Table S6

This file shows the analysis of spatial expression of enzyme pairs from KEGG pathways. (XLSX 2475 kb)

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. (XLSX 10 kb)

Supplementary Table S8

This file contains the DNA sequences of the probes used for smFISH. (XLSX 50 kb)

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Halpern, K., Shenhav, R., Matcovitch-Natan, O. et al. Single-cell spatial reconstruction reveals global division of labour in the mammalian liver. Nature 542, 352–356 (2017). https://doi.org/10.1038/nature21065

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