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      Single-Cell Transcriptomics Reveals Core Regulatory Programs That Determine the Heterogeneity of Circulating and Tissue-Resident Memory CD8+ T Cells

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      Cells
      MDPI AG

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          Abstract

          During acute infections, CD8+ T cells form various memory subpopulations to provide long-lasting protection against reinfection. T central memory (TCM), T effector memory (TEM), and long-lived effector (LLE) cells are circulating memory populations with distinct plasticity, migration patterns, and effector functions. Tissue-resident memory (TRM) cells permanently reside in the frontline sites of pathogen entry and provide tissue-specific protection upon reinfection. Here, using single-cell RNA-sequencing (scRNA-seq) and bulk RNA-seq, we examined the different and shared transcriptomes and regulators of TRM cells with other circulating memory populations. Furthermore, we identified heterogeneity within the TRM pool from small intestine and novel transcriptional regulators that may control the phenotypic and functional heterogeneity of TRM cells during acute infection. Our findings provide a resource for future studies to identify novel pathways for enhancing vaccination and immunotherapeutic approaches.

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          Most cited references53

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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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            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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              Salmon: fast and bias-aware quantification of transcript expression using dual-phase inference

              We introduce Salmon, a method for quantifying transcript abundance from RNA-seq reads that is accurate and fast. Salmon is the first transcriptome-wide quantifier to correct for fragment GC content bias, which we demonstrate substantially improves the accuracy of abundance estimates and the reliability of subsequent differential expression analysis. Salmon combines a new dual-phase parallel inference algorithm and feature-rich bias models with an ultra-fast read mapping procedure.
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                Author and article information

                Contributors
                Journal
                CELLC6
                Cells
                Cells
                MDPI AG
                2073-4409
                August 2021
                August 20 2021
                : 10
                : 8
                : 2143
                Article
                10.3390/cells10082143
                34440912
                083f6e4a-14c7-4726-a549-69503b64334c
                © 2021

                https://creativecommons.org/licenses/by/4.0/

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