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      Astrocyte ethanol exposure reveals persistent and defined calcium response subtypes and associated gene signatures

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          Abstract

          Astrocytes play a critical role in brain function, but their contribution during ethanol (EtOH) consumption remains largely understudied. In light of recent findings on the heterogeneity of astrocyte physiology and gene expression, an approach with the ability to identify subtypes and capture this heterogeneity is necessary. Here, we combined measurements of calcium signaling and gene expression to define EtOH-induced astrocyte subtypes. In the absence of a demonstrated EtOH receptor, EtOH is believed to have effects on the function of many receptors and downstream biological cascades that underlie calcium responsiveness. This mechanism of EtOH-induced calcium signaling is unknown and this study provides the first step in understanding the characteristics of cells displaying these observed responses. To characterize underlying astrocyte subtypes, we assessed the correlation between calcium signaling and astrocyte gene expression signature in response to EtOH. We found that various EtOH doses increased intracellular calcium levels in a subset of astrocytes, distinguishing three cellular response types and one nonresponsive subtype as categorized by response waveform properties. Furthermore, single-cell RNA-seq analysis of astrocytes from the different response types identified type-enriched discriminatory gene expression signatures. Combining single-cell calcium responses and gene expression analysis identified specific astrocyte subgroups among astrocyte populations defined by their response to EtOH. This result provides a basis for identifying the relationship between astrocyte susceptibility to EtOH and corresponding measurable markers of calcium signaling and gene expression, which will be useful to investigate potential subgroup-specific influences of astrocytes on the physiology and pathology of EtOH exposure in the brain.

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          Most cited references80

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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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            Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

            Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
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              clusterProfiler: an R package for comparing biological themes among gene clusters.

              Increasing quantitative data generated from transcriptomics and proteomics require integrative strategies for analysis. Here, we present an R package, clusterProfiler that automates the process of biological-term classification and the enrichment analysis of gene clusters. The analysis module and visualization module were combined into a reusable workflow. Currently, clusterProfiler supports three species, including humans, mice, and yeast. Methods provided in this package can be easily extended to other species and ontologies. The clusterProfiler package is released under Artistic-2.0 License within Bioconductor project. The source code and vignette are freely available at http://bioconductor.org/packages/release/bioc/html/clusterProfiler.html.
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                Author and article information

                Contributors
                Journal
                J Biol Chem
                J Biol Chem
                The Journal of Biological Chemistry
                American Society for Biochemistry and Molecular Biology
                0021-9258
                1083-351X
                16 June 2022
                August 2022
                16 June 2022
                : 298
                : 8
                : 102147
                Affiliations
                [1 ]Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
                [2 ]Department of Biology, School of Arts and Sciences, University of Pennsylvania, Philadelphia, Pennsylvania, USA
                [3 ]PENN Program in Single Cell Biology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
                Author notes
                []For correspondence: Jai-Yoon Sul jysul@ 123456pennmedicine.upenn.edu
                [‡]

                These authors contributed equally to this work.

                Article
                S0021-9258(22)00589-0 102147
                10.1016/j.jbc.2022.102147
                9293641
                35716779
                c37d5aa0-24ee-4920-9252-f2c7d8dda7a9
                © 2022 The Authors

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

                History
                : 18 March 2022
                : 10 June 2022
                Categories
                Research Article

                Biochemistry
                astrocyte,ethanol,calcium signal,single-cell transcriptomics,gene signature,phenotype,arna, antisense rna,bbb, blood-brain barrier,deg, differentially expressed gene,etoh, ethanol,go, gene ontology,gsea, gene set enrichment analysis,nr, nonresponsive,pca, principal component analysis

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