Spatial sorting enables comprehensive characterization of liver zonation

Abstract

The mammalian liver is composed of repeating hexagonal units termed lobules. Spatially resolved single-cell transcriptomics has revealed that about half of hepatocyte genes are differentially expressed across the lobule, yet technical limitations have impeded reconstructing similar global spatial maps of other hepatocyte features. Here, we show how zonated surface markers can be used to sort hepatocytes from defined lobule zones with high spatial resolution. We apply transcriptomics, microRNA (miRNA) array measurements and mass spectrometry proteomics to reconstruct spatial atlases of multiple zonated features. We demonstrate that protein zonation largely overlaps with messenger RNA zonation, with the periportal HNF4α as an exception. We identify zonation of miRNAs, such as miR-122, and inverse zonation of miRNAs and their hepatocyte target genes, highlighting potential regulation of gene expression levels through zonated mRNA degradation. Among the targets, we find the pericentral Wingless-related integration site (Wnt) receptors Fzd7 and Fzd8 and the periportal Wnt inhibitors Tcf7l1 and Ctnnbip1. Our approach facilitates reconstructing spatial atlases of multiple cellular features in the liver and other structured tissues.

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Fig. 1: Spatial sorting approach for isolating large amounts of hepatocytes from distinct layers with high resolution.
Fig. 2: CD73 and E-cadherin are inversely zonated surface markers.
Fig. 3: Spatial sorting reliably captures the different lobule layers.
Fig. 4: Correlations between mRNA and protein levels.
Fig. 5: A spatial atlas of the hepatocyte proteome.
Fig. 6: Zonated expression of hepatocyte miRNAs.
Fig. 7: Network analysis of miRNA-target interactions.

Code availability

The code used to generate the processed data and figures is available upon request.

Data availability

The RNA-seq data that support the findings of this study have been deposited with the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) under bioproject accession code PRJNA556572, and the SRA identifiers SAMN12360372SAMN12360382. Supplementary Table 2 summarizes the unique molecular identifier counts per million for each sample. Supplementary Table 11 summarizes the zUMI barcodes used for each sample and the corresponding zUMI settings.

The liquid chromatography–tandem mass spectrometry proteomic data were uploaded to the ProteomeXchange via the PRIDE archive, with project identifier PXD014512. Processed data can be found in Supplementary Table 3.

The miRNA microarray data have been deposited with the NCBI Gene Expression Omnibus under accession code GSE134827. Supplementary Table 6 summarizes the miRNA data.

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Acknowledgements

We thank E. Hagai and the Flow Cytometry Unit (Weizmann Institute of Science) for FACS technical support; T. Ziv and the Smoler Proteomics Center (Technion) for performing the liquid chromatography–tandem mass spectrometry and analysing the results; and D. Pilzer and the Genomic Technologies Unit (Weizmann Institute of Science) for performing the miRNA microarray measurements. We thank all members of the laboratory for valuable comments. S.I. is supported by the Henry Chanoch Krenter Institute for Biomedical Imaging and Genomics, The Leir Charitable Foundations, the Richard Jakubskind Laboratory of Systems Biology, the Cymerman-Jakubskind Prize, The Lord Sieff of Brimpton Memorial Fund, the I-CORE programme of the Planning and Budgeting Committee (grant no. 1902/12), the Israel Science Foundation (grant no. 1486/16), the Broad Institute-Israel Science Foundation (grant no. 2615/18), the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant no. 768956), the Bert L. and N. Kuggie Vallee Foundation, the Howard Hughes Medical Institute (HHMI) International Research Scholar Award (grant no. 55008734), The Wolfson Family Charitable Trust (grant no. 21376) and the Edmond de Rothschild Foundations.

Author information

K.B.H. and S.I. conceived the study. S.B.M. and S.I. designed the experiments. S.B.M. prepared all the samples. S.B.M. and Y.S. analysed the data. A.E.M. contributed to the data analysis. R.M. and T.V. assisted with the immunofluorescence experiments. K.B.H. contributed to establishing the methodology. S.I. supervised the study. S.B.M., Y.S. and S.I. wrote the manuscript. All authors reviewed the manuscript and provided input.

Correspondence to Shalev Itzkovitz.

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Ben-Moshe, S., Shapira, Y., Moor, A.E. et al. Spatial sorting enables comprehensive characterization of liver zonation. Nat Metab 1, 899–911 (2019). https://doi.org/10.1038/s42255-019-0109-9

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