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      Cellular taxonomy and spatial organization of the murine ventral posterior hypothalamus

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          Abstract

          The ventral posterior hypothalamus (VPH) is an anatomically complex brain region implicated in arousal, reproduction, energy balance, and memory processing. However, neuronal cell type diversity within the VPH is poorly understood, an impediment to deconstructing the roles of distinct VPH circuits in physiology and behavior. To address this question, we employed a droplet-based single-cell RNA sequencing (scRNA-seq) approach to systematically classify molecularly distinct cell populations in the mouse VPH. Analysis of >16,000 single cells revealed 20 neuronal and 18 non-neuronal cell populations, defined by suites of discriminatory markers. We validated differentially expressed genes in selected neuronal populations through fluorescence in situ hybridization (FISH). Focusing on the mammillary bodies (MB), we discovered transcriptionally-distinct clusters that exhibit neuroanatomical parcellation within MB subdivisions and topographic projections to the thalamus. This single-cell transcriptomic atlas of VPH cell types provides a resource for interrogating the circuit-level mechanisms underlying the diverse functions of VPH circuits.

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          Most cited references 118

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          Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets.

          Cells, the basic units of biological structure and function, vary broadly in type and state. Single-cell genomics can characterize cell identity and function, but limitations of ease and scale have prevented its broad application. Here we describe Drop-seq, a strategy for quickly profiling thousands of individual cells by separating them into nanoliter-sized aqueous droplets, associating a different barcode with each cell's RNAs, and sequencing them all together. Drop-seq analyzes mRNA transcripts from thousands of individual cells simultaneously while remembering transcripts' cell of origin. We analyzed transcriptomes from 44,808 mouse retinal cells and identified 39 transcriptionally distinct cell populations, creating a molecular atlas of gene expression for known retinal cell classes and novel candidate cell subtypes. Drop-seq will accelerate biological discovery by enabling routine transcriptional profiling at single-cell resolution. VIDEO ABSTRACT.
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            Massively parallel digital transcriptional profiling of single cells

            Characterizing the transcriptome of individual cells is fundamental to understanding complex biological systems. We describe a droplet-based system that enables 3′ mRNA counting of tens of thousands of single cells per sample. Cell encapsulation, of up to 8 samples at a time, takes place in ∼6 min, with ∼50% cell capture efficiency. To demonstrate the system's technical performance, we collected transcriptome data from ∼250k single cells across 29 samples. We validated the sensitivity of the system and its ability to detect rare populations using cell lines and synthetic RNAs. We profiled 68k peripheral blood mononuclear cells to demonstrate the system's ability to characterize large immune populations. Finally, we used sequence variation in the transcriptome data to determine host and donor chimerism at single-cell resolution from bone marrow mononuclear cells isolated from transplant patients.
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              SCANPY : large-scale single-cell gene expression data analysis

              Scanpy is a scalable toolkit for analyzing single-cell gene expression data. It includes methods for preprocessing, visualization, clustering, pseudotime and trajectory inference, differential expression testing, and simulation of gene regulatory networks. Its Python-based implementation efficiently deals with data sets of more than one million cells (https://github.com/theislab/Scanpy). Along with Scanpy, we present AnnData, a generic class for handling annotated data matrices (https://github.com/theislab/anndata).
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                Author and article information

                Contributors
                Role: Reviewing Editor
                Role: Senior Editor
                Journal
                eLife
                Elife
                eLife
                eLife
                eLife Sciences Publications, Ltd
                2050-084X
                29 October 2020
                2020
                : 9
                Affiliations
                [1 ]Department of Physiology and Neurobiology, University of Connecticut StorrsUnited States
                [2 ]Connecticut Institute for the Brain and Cognitive Sciences StorrsUnited States
                [3 ]The Jackson Laboratory for Genomic Medicine FarmingtonUnited States
                [4 ]Department of Genetics and Genome Sciences, University of Connecticut Health Center FarmingtonUnited States
                [5 ]Institute for Systems Genomics, University of Connecticut FarmingtonUnited States
                University of Texas Southwestern Medical Center United States
                Oregon Health and Science University United States
                University of Texas Southwestern Medical Center United States
                Author notes
                [‡]

                National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), Bethesda, United States.

                [§]

                Bristol-Myers Squibb, Pennington, United States.

                [†]

                These authors contributed equally to this work.

                Article
                58901
                10.7554/eLife.58901
                7595735
                33119507
                © 2020, Mickelsen et al

                This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

                Product
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000025, National Institute of Mental Health;
                Award ID: R01MH112739
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100004829, Connecticut Innovations;
                Award ID: 15-RMD-UCHC-01
                Award Recipient :
                The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
                Categories
                Research Article
                Neuroscience
                Custom metadata
                Single cell RNA–sequencing and neuroanatomical methods reveal unexpected molecular diversity and highly segregated spatial organization of neuronal cell types within the mouse ventral posterior hypothalamus, including the mammillary nuclei.

                Life sciences

                hypothalamus, mammillary bodies, single cells, neuropeptides, neurotransmitters, cell types, mouse

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