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      Single cell transcriptome analysis of developing arcuate nucleus neurons uncovers their key developmental regulators

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          Abstract

          Despite the crucial physiological processes governed by neurons in the hypothalamic arcuate nucleus (ARC), such as growth, reproduction and energy homeostasis, the developmental pathways and regulators for ARC neurons remain understudied. Our single cell RNA-seq analyses of mouse embryonic ARC revealed many cell type-specific markers for developing ARC neurons. These markers include transcription factors whose expression is enriched in specific neuronal types and often depleted in other closely-related neuronal types, raising the possibility that these transcription factors play important roles in the fate commitment or differentiation of specific ARC neuronal types. We validated this idea with the two transcription factors, Foxp2 enriched for Ghrh-neurons and Sox14 enriched for Kisspeptin-neurons, using Foxp2- and Sox14-deficient mouse models. Taken together, our single cell transcriptome analyses for the developing ARC uncovered a panel of transcription factors that are likely to form a gene regulatory network to orchestrate fate specification and differentiation of ARC neurons.

          Abstract

          Despite the crucial physiological processes governed by neurons in the hypothalamic arcuate nucleus (ARC), the developmental pathways and regulators for ARC neurons remain understudied. In this study the authors use single cell RNA-seq analyses of mouse embryonic ARC to identify cell type-specific markers for developing ARC neurons and give key insight into the underlying developmental pathways and regulators.

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          Classification of low quality cells from single-cell RNA-seq data

          Single-cell RNA sequencing (scRNA-seq) has broad applications across biomedical research. One of the key challenges is to ensure that only single, live cells are included in downstream analysis, as the inclusion of compromised cells inevitably affects data interpretation. Here, we present a generic approach for processing scRNA-seq data and detecting low quality cells, using a curated set of over 20 biological and technical features. Our approach improves classification accuracy by over 30 % compared to traditional methods when tested on over 5,000 cells, including CD4+ T cells, bone marrow dendritic cells, and mouse embryonic stem cells. Electronic supplementary material The online version of this article (doi:10.1186/s13059-016-0888-1) contains supplementary material, which is available to authorized users.
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            Visualizing data using ti-SNE

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              Single-Cell RNA-Seq Reveals Hypothalamic Cell Diversity

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                Author and article information

                Contributors
                alessio.delogu@kcl.ac.uk
                jlee269@buffalo.edu
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                16 August 2019
                16 August 2019
                2019
                : 10
                : 3696
                Affiliations
                [1 ]ISNI 0000 0000 9758 5690, GRID grid.5288.7, Neuroscience Section, Papé Family Pediatric Research Institute, , Oregon Health and Science University, ; Portland, OR 97239 USA
                [2 ]ISNI 0000 0001 2322 6764, GRID grid.13097.3c, Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, , King’s College London, ; London, SE5 9RS UK
                [3 ]ISNI 0000 0004 0533 3568, GRID grid.263765.3, Department of Bioinformatics and Life Science, , Soongsil University, ; Seoul, Korea
                [4 ]ISNI 0000 0000 9758 5690, GRID grid.5288.7, Department of Pediatrics, Vollum Institute, , Oregon Health and Science University, ; Portland, OR 97239 USA
                [5 ]ISNI 0000 0004 1936 9887, GRID grid.273335.3, Present Address: Department of Biological Sciences, University at Buffalo, Buffalo, ; NY, 14260 USA
                Author information
                http://orcid.org/0000-0002-5245-2282
                http://orcid.org/0000-0001-6383-8546
                http://orcid.org/0000-0002-1745-256X
                http://orcid.org/0000-0001-7439-643X
                Article
                11667
                10.1038/s41467-019-11667-y
                6697706
                31420539
                ccf9443a-4d61-49f0-8f29-d94aaa03f4b3
                © The Author(s) 2019

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 18 February 2019
                : 26 July 2019
                Funding
                Funded by: FundRef https://doi.org/10.13039/100000062, U.S. Department of Health & Human Services | NIH | National Institute of Diabetes and Digestive and Kidney Diseases (National Institute of Diabetes & Digestive & Kidney Diseases);
                Award ID: DK103661
                Award Recipient :
                Categories
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                © The Author(s) 2019

                Uncategorized
                developmental biology,neuroscience
                Uncategorized
                developmental biology, neuroscience

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