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      Single cell transcriptome profiling of the human alcohol-dependent brain

      1 , 2 , 3 , 4 , 1 , 2 , 5
      Human Molecular Genetics
      Oxford University Press (OUP)

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          Abstract

          Alcoholism remains a prevalent health concern throughout the world. Previous studies have identified transcriptomic patterns in the brain associated with alcohol dependence in both humans and animal models. But none of these studies have systematically investigated expression within the unique cell types present in the brain. We utilized single nucleus RNA sequencing (snRNA-seq) to examine the transcriptomes of over 16 000 nuclei isolated from the prefrontal cortex of alcoholic and control individuals. Each nucleus was assigned to one of seven major cell types by unsupervised clustering. Cell type enrichment patterns varied greatly among neuroinflammatory-related genes, which are known to play roles in alcohol dependence and neurodegeneration. Differential expression analysis identified cell type-specific genes with altered expression in alcoholics. The largest number of differentially expressed genes (DEGs), including both protein-coding and non-coding, were detected in astrocytes, oligodendrocytes and microglia. To our knowledge, this is the first single cell transcriptome analysis of alcohol-associated gene expression in any species and the first such analysis in humans for any addictive substance. These findings greatly advance the understanding of transcriptomic changes in the brain of alcohol-dependent individuals.

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          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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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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              Dimensionality reduction for visualizing single-cell data using UMAP

              Advances in single-cell technologies have enabled high-resolution dissection of tissue composition. Several tools for dimensionality reduction are available to analyze the large number of parameters generated in single-cell studies. Recently, a nonlinear dimensionality-reduction technique, uniform manifold approximation and projection (UMAP), was developed for the analysis of any type of high-dimensional data. Here we apply it to biological data, using three well-characterized mass cytometry and single-cell RNA sequencing datasets. Comparing the performance of UMAP with five other tools, we find that UMAP provides the fastest run times, highest reproducibility and the most meaningful organization of cell clusters. The work highlights the use of UMAP for improved visualization and interpretation of single-cell data.
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                Author and article information

                Journal
                Human Molecular Genetics
                Oxford University Press (OUP)
                0964-6906
                1460-2083
                April 01 2020
                May 08 2020
                March 06 2020
                April 01 2020
                May 08 2020
                March 06 2020
                : 29
                : 7
                : 1144-1153
                Affiliations
                [1 ]Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA
                [2 ]Waggoner Center for Alcohol and Addiction Research, The University of Texas at Austin, Austin, TX 78712, USA
                [3 ]Department of Neuroscience, Icahn School of Medicine at Mt. Sinai, New York, NY 10029, USA
                [4 ]Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN 46202, USA
                [5 ]Institute for Neuroscience, The University of Texas at Austin, Austin, TX 78712, USA
                Article
                10.1093/hmg/ddaa038
                32142123
                8e2459d5-f068-4d95-8402-ea98047c67e3
                © 2020

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

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