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      A Single-Cell Transcriptomic Map of the Human and Mouse Pancreas Reveals Inter- and Intra-cell Population Structure

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          Summary

          Although the function of the mammalian pancreas hinges on complex interactions of distinct cell types, gene expression profiles have primarily been described with bulk mixtures. Here we implemented a droplet-based, single-cell RNA-seq method to determine the transcriptomes of over 12,000 individual pancreatic cells from four human donors and two mouse strains. Cells could be divided into 15 clusters that matched previously characterized cell types: all endocrine cell types, including rare epsilon-cells; exocrine cell types; vascular cells; Schwann cells; quiescent and activated stellate cells; and four types of immune cells. We detected subpopulations of ductal cells with distinct expression profiles and validated their existence with immuno-histochemistry stains. Moreover, among human beta- cells, we detected heterogeneity in the regulation of genes relating to functional maturation and levels of ER stress. Finally, we deconvolved bulk gene expression samples using the single-cell data to detect disease-associated differential expression. Our dataset provides a resource for the discovery of novel cell type-specific transcription factors, signaling receptors, and medically relevant genes.

          Graphical abstract

          Single-cell transcriptomics of over 12,000 cells from four human donors and two mouse strains was determined using inDrop. Cells were divided into 15 clusters that matched previously characterized cell types. Detailed analysis of each population separately revealed subpopulations within the ductal population, modes of activation of stellate cells, and heterogeneity in the stress among beta cells.

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          Author and article information

          Journal
          101656080
          43733
          Cell Syst
          Cell Syst
          Cell systems
          2405-4712
          2405-4720
          20 December 2016
          22 September 2016
          26 October 2016
          12 January 2017
          : 3
          : 4
          : 346-360.e4
          Affiliations
          [1 ]Faculty of Biology, Technion – Israel Institute of Technology, Haifa 3200003, Israel
          [2 ]Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA 02138, USA
          [3 ]Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA
          [4 ]Department of Immunology, Faculty of Medicine, Technion – Israel Institute of Technology, Haifa 3200003, Israel
          [5 ]Center for the Science of Therapeutics, Broad Institute, Cambridge, MA 02142, USA
          Author notes
          [6]

          Co-first author

          [7]

          Present address: Institute for Computational Medicine, New York University School of Medicine, New York, NY 10016, USA

          [8]

          Lead Contact

          Article
          PMC5228327 PMC5228327 5228327 nihpa832023
          10.1016/j.cels.2016.08.011
          5228327
          27667365
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