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      High Coexpression of the Ghrelin and LEAP2 Receptor GHSR With Pancreatic Polypeptide in Mouse and Human Islets

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          Abstract

          Islets represent an important site of direct action of the hormone ghrelin, with expression of the ghrelin receptor (growth hormone secretagogue receptor; GHSR) having been localized variably to alpha cells, beta cells, and/or somatostatin (SST)-secreting delta cells. To our knowledge, GHSR expression by pancreatic polypeptide (PP)-expressing gamma cells has not been specifically investigated. Here, histochemical analyses of Ghsr-IRES-Cre × Cre-dependent ROSA26-yellow fluorescent protein (YFP) reporter mice showed 85% of GHSR-expressing islet cells coexpress PP, 50% coexpress SST, and 47% coexpress PP + SST. Analysis of single-cell transcriptomic data from mouse pancreas revealed 95% of Ghsr-expressing cells coexpress Ppy, 100% coexpress Sst, and 95% coexpress Ppy + Sst. This expression was restricted to gamma-cell and delta-cell clusters. Analysis of several single-cell human pancreatic transcriptome data sets revealed 59% of GHSR-expressing cells coexpress PPY, 95% coexpress SST, and 57% coexpress PPY + SST. This expression was prominent in delta-cell and beta-cell clusters, also occurring in other clusters including gamma cells and alpha cells. GHSR expression levels were upregulated by type 2 diabetes mellitus in beta cells. In mice, plasma PP positively correlated with fat mass and with plasma levels of the endogenous GHSR antagonist/inverse agonist LEAP2. Plasma PP also elevated on LEAP2 and synthetic GHSR antagonist administration. These data suggest that in addition to delta cells, beta cells, and alpha cells, PP-expressing pancreatic cells likely represent important direct targets for LEAP2 and/or ghrelin both in mice and humans.

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          Comprehensive Integration of Single-Cell Data

          Single-cell transcriptomics has transformed our ability to characterize cell states, but deep biological understanding requires more than a taxonomic listing of clusters. As new methods arise to measure distinct cellular modalities, a key analytical challenge is to integrate these datasets to better understand cellular identity and function. Here, we develop a strategy to "anchor" diverse datasets together, enabling us to integrate single-cell measurements not only across scRNA-seq technologies, but also across different modalities. After demonstrating improvement over existing methods for integrating scRNA-seq data, we anchor scRNA-seq experiments with scATAC-seq to explore chromatin differences in closely related interneuron subsets and project protein expression measurements onto a bone marrow atlas to characterize lymphocyte populations. Lastly, we harmonize in situ gene expression and scRNA-seq datasets, allowing transcriptome-wide imputation of spatial gene expression patterns. Our work presents a strategy for the assembly of harmonized references and transfer of information across datasets.
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            Integrating single-cell transcriptomic data across different conditions, technologies, and species

            Computational single-cell RNA-seq (scRNA-seq) methods have been successfully applied to experiments representing a single condition, technology, or species to discover and define cellular phenotypes. However, identifying subpopulations of cells that are present across multiple data sets remains challenging. Here, we introduce an analytical strategy for integrating scRNA-seq data sets based on common sources of variation, enabling the identification of shared populations across data sets and downstream comparative analysis. We apply this approach, implemented in our R toolkit Seurat (http://satijalab.org/seurat/), to align scRNA-seq data sets of peripheral blood mononuclear cells under resting and stimulated conditions, hematopoietic progenitors sequenced using two profiling technologies, and pancreatic cell 'atlases' generated from human and mouse islets. In each case, we learn distinct or transitional cell states jointly across data sets, while boosting statistical power through integrated analysis. Our approach facilitates general comparisons of scRNA-seq data sets, potentially deepening our understanding of how distinct cell states respond to perturbation, disease, and evolution.
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              Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells.

              It has long been the dream of biologists to map gene expression at the single-cell level. With such data one might track heterogeneous cell sub-populations, and infer regulatory relationships between genes and pathways. Recently, RNA sequencing has achieved single-cell resolution. What is limiting is an effective way to routinely isolate and process large numbers of individual cells for quantitative in-depth sequencing. We have developed a high-throughput droplet-microfluidic approach for barcoding the RNA from thousands of individual cells for subsequent analysis by next-generation sequencing. The method shows a surprisingly low noise profile and is readily adaptable to other sequencing-based assays. We analyzed mouse embryonic stem cells, revealing in detail the population structure and the heterogeneous onset of differentiation after leukemia inhibitory factor (LIF) withdrawal. The reproducibility of these high-throughput single-cell data allowed us to deconstruct cell populations and infer gene expression relationships. VIDEO ABSTRACT.
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                Author and article information

                Contributors
                (View ORCID Profile)
                (View ORCID Profile)
                Journal
                Endocrinology
                The Endocrine Society
                0013-7227
                1945-7170
                October 01 2021
                October 01 2021
                October 01 2021
                October 01 2021
                July 21 2021
                : 162
                : 10
                Affiliations
                [1 ]Center for Hypothalamic Research, Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, Texas 75390-9077, USA
                [2 ]Medical Research Council (MRC) Metabolic Diseases Unit, University of Cambridge Metabolic Research Laboratories, Wellcome Trust–MRC Institute of Metabolic Science, Addenbrooke’s Hospital, Cambridge CB2 0QQ, UK
                [3 ]Division of Endocrinology, Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, Texas 75390-9077, USA
                [4 ]Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, Texas 75390-9077, USA
                Article
                10.1210/endocr/bqab148
                34289060
                32101151-c626-41cd-8e11-9ee4bd862111
                © 2021

                https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model

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