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      Molecular, spatial and functional single-cell profiling of the hypothalamic preoptic region

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          Abstract

          The hypothalamus controls essential social behaviors and homeostatic functions. However, the cellular architecture of hypothalamic nuclei, including the molecular identity, spatial organization, and function of distinct cell types, is poorly understood. Here, we developed an imaging-based in situ cell type identification and mapping method and combined it with single-cell RNA-sequencing to create a molecularly annotated and spatially resolved cell atlas of the mouse hypothalamic preoptic region. We profiled ~1 million cells, identified ~70 neuronal populations characterized by distinct neuromodulatory signatures and spatial organizations, and defined specific neuronal populations activated during social behaviors in male and female mice, providing a high-resolution framework for mechanistic investigation of behavior circuits. The approach described opens a new avenue for the construction of cell atlases in diverse tissues and organisms.

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          Most cited references56

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          Visualization of single RNA transcripts in situ.

          Fluorescence in situ hybridization (FISH) and digital imaging microscopy were modified to allow detection of single RNA molecules. Oligodeoxynucleotide probes were synthesized with five fluorochromes per molecule, and the light emitted by a single probe was calibrated. Points of light in exhaustively deconvolved images of hybridized cells gave fluorescent intensities and distances between probes consistent with single messenger RNA molecules. Analysis of beta-actin transcription sites after serum induction revealed synchronous and cyclical transcription from single genes. The rates of transcription initiation and termination and messenger RNA processing could be determined by positioning probes along the transcription unit. This approach extends the power of FISH to yield quantitative molecular information on a single cell.
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            Single-cell sequencing reveals dissociation-induced gene expression in tissue subpopulations

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              Is Open Access

              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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                Author and article information

                Journal
                Science
                Science
                American Association for the Advancement of Science (AAAS)
                0036-8075
                1095-9203
                November 01 2018
                : eaau5324
                Article
                10.1126/science.aau5324
                6482113
                30385464
                cf281a7a-744a-47ec-9b78-728561c0bee7
                © 2018
                History

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