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      Cell Type- and Sex-Dependent Transcriptome Profiles of Rat Anterior Pituitary Cells

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          Abstract

          Understanding the physiology and pathology of an organ composed of a variety of cell populations depends critically on genome-wide information on each cell type. Here, we report single-cell transcriptome profiling of over 6,800 freshly dispersed anterior pituitary cells from postpubertal male and female rats. Six pituitary-specific cell types were identified based on known marker genes and characterized: folliculostellate cells and hormone-producing corticotrophs, gonadotrophs, thyrotrophs, somatotrophs, and lactotrophs. Also identified were endothelial and blood cells from the pituitary capillary network. The expression of numerous developmental and neuroendocrine marker genes in both folliculostellate and hormone-producing cells supports that they have a common origin. For several genes, the validity of transcriptome analysis was confirmed by qRT-PCR and single cell immunocytochemistry. Folliculostellate cells exhibit impressive transcriptome diversity, indicating their major roles in production of endogenous ligands and detoxification enzymes, and organization of extracellular matrix. Transcriptome profiles of hormone-producing cells also indicate contributions toward those functions, while also clearly demonstrating their endocrine function. This survey highlights many novel genetic markers contributing to pituitary cell type identity, sexual dimorphism, and function, and points to relationships between hormone-producing and folliculostellate cells.

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          Most cited references73

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          DoubletFinder: Doublet Detection in Single-Cell RNA Sequencing Data Using Artificial Nearest Neighbors

          Single-cell RNA sequencing (scRNA-seq) data are commonly affected by technical artifacts known as “doublets,” which limit cell throughput and lead to spurious biological conclusions. Here, we present a computational doublet detection tool—Doublet-Finder—that identifies doublets using only gene expression data. DoubletFinder predicts doublets according to each real cell’s proximity in gene expression space to artificial doublets created by averaging the transcriptional profile of randomly chosen cell pairs. We first use scRNA-seq datasets where the identity of doublets is known to show that DoubletFinder identifies doublets formed from transcriptionally distinct cells. When these doublets are removed, the identification of differentially expressed genes is enhanced. Second, we provide a method for estimating DoubletFinder input parameters, allowing its application across scRNA-seq datasets with diverse distributions of cell types. Lastly, we present “best practices” for DoubletFinder applications and illustrate that DoubletFinder is insensitive to an experimentally validated kidney cell type with “hybrid” expression features. scRNA-seq data interpretation is confounded by technical artifacts known as doublets—single-cell transcriptome data representing more than one cell. Moreover, scRNA-seq cellular throughput is purposefully limited to minimize doublet formation rates. By identifying cells sharing expression features with simulated doublets, DoubletFinder detects many real doublets and mitigates these two limitations.
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            A Single-Cell Transcriptome Atlas of the Human Pancreas

            Summary To understand organ function, it is important to have an inventory of its cell types and of their corresponding marker genes. This is a particularly challenging task for human tissues like the pancreas, because reliable markers are limited. Hence, transcriptome-wide studies are typically done on pooled islets of Langerhans, obscuring contributions from rare cell types and of potential subpopulations. To overcome this challenge, we developed an automated platform that uses FACS, robotics, and the CEL-Seq2 protocol to obtain the transcriptomes of thousands of single pancreatic cells from deceased organ donors, allowing in silico purification of all main pancreatic cell types. We identify cell type-specific transcription factors and a subpopulation of REG3A-positive acinar cells. We also show that CD24 and TM4SF4 expression can be used to sort live alpha and beta cells with high purity. This resource will be useful for developing a deeper understanding of pancreatic biology and pathophysiology of diabetes mellitus.
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              The neuropeptide NMU amplifies ILC2-driven allergic lung inflammation

              Neuromedin receptor NMUR1 is specifically expressed by a subpopulation of type 2 innate lymphoid cells and promotes the inflammatory response of these cells in response to allergens, indicating the importance of neuro-immune crosstalk in allergic responses.
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                Author and article information

                Contributors
                Journal
                Front Endocrinol (Lausanne)
                Front Endocrinol (Lausanne)
                Front. Endocrinol.
                Frontiers in Endocrinology
                Frontiers Media S.A.
                1664-2392
                18 September 2019
                2019
                : 10
                : 623
                Affiliations
                [1] 1Laboratory of Biological Modeling, National Institute of Diabetes, Digestive and Kidney Diseases, National Institutes of Health (NIH) , Bethesda, MD, United States
                [2] 2Section on Cellular Signaling, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health (NIH) , Bethesda, MD, United States
                [3] 3Molecular Genomics Core, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health (NIH) , Bethesda, MD, United States
                Author notes

                Edited by: Jeff Schwartz, Griffith University, Australia

                Reviewed by: Patrice Mollard, Centre National de la Recherche Scientifique (CNRS), France; Sylvia L. Asa, University of Toronto, Canada; Vera Chesnokova, Cedars-Sinai Medical Center, United States

                *Correspondence: Patrick A. Fletcher patrick.fletcher@ 123456nih.gov
                Stanko S. Stojilkovic stojilks@ 123456mail.nih.gov

                This article was submitted to Systems Endocrinology, a section of the journal Frontiers in Endocrinology

                Article
                10.3389/fendo.2019.00623
                6760010
                31620083
                5274101c-9a83-4ffa-87cb-399a48742535
                Copyright © 2019 Fletcher, Smiljanic, Maso Prévide, Iben, Li, Rokic, Sherman, Coon and Stojilkovic.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 12 June 2019
                : 28 August 2019
                Page count
                Figures: 12, Tables: 3, Equations: 0, References: 90, Pages: 25, Words: 18125
                Funding
                Funded by: National Institute of Diabetes and Digestive and Kidney Diseases 10.13039/100000062
                Categories
                Endocrinology
                Original Research

                Endocrinology & Diabetes
                pituitary gland,transcriptome,rat,single-cell rna sequencing,folliculostellate cells,hormone-producing cells,sexual dimorphism

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