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      Single-cell sequencing deconvolutes cellular responses to exercise in human skeletal muscle

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          Abstract

          Skeletal muscle adaptations to exercise have been associated with a range of health-related benefits, but cell type-specific adaptations within the muscle are incompletely understood. Here we use single-cell sequencing to determine the effects of exercise on cellular composition and cell type-specific processes in human skeletal muscle before and after intense exercise. Fifteen clusters originating from six different cell populations were identified. Most cell populations remained quantitatively stable after exercise, but a large transcriptional response was observed in mesenchymal, endothelial, and myogenic cells, suggesting that these cells are specifically involved in skeletal muscle remodeling. We found three subpopulations of myogenic cells characterized by different maturation stages based on the expression of markers such as PAX7, MYOD1, TNNI1, and TNNI2. Exercise accelerated the trajectory of myogenic progenitor cells towards maturation by increasing the transcriptional features of fast- and slow-twitch muscle fibers. The transcriptional regulation of these contractile elements upon differentiation was validated in vitro on primary myoblast cells. The cell type-specific adaptive mechanisms induced by exercise presented here contribute to the understanding of the skeletal muscle adaptations triggered by physical activity and may ultimately have implications for physiological and pathological processes affecting skeletal muscle, such as sarcopenia, cachexia, and glucose homeostasis.

          Abstract

          Single-cell RNA-sequencing of human skeletal muscle before and after exercise highlights how physical activity changes the composition and transcriptomic profile of muscle tissue.

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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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            limma powers differential expression analyses for RNA-sequencing and microarray studies

            limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
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              Comprehensive Integration of Single-Cell Data

              Single-cell transcriptomics has transformed our ability to characterize cell states, but deep biological understanding requires more than a taxonomic listing of clusters. As new methods arise to measure distinct cellular modalities, a key analytical challenge is to integrate these datasets to better understand cellular identity and function. Here, we develop a strategy to "anchor" diverse datasets together, enabling us to integrate single-cell measurements not only across scRNA-seq technologies, but also across different modalities. After demonstrating improvement over existing methods for integrating scRNA-seq data, we anchor scRNA-seq experiments with scATAC-seq to explore chromatin differences in closely related interneuron subsets and project protein expression measurements onto a bone marrow atlas to characterize lymphocyte populations. Lastly, we harmonize in situ gene expression and scRNA-seq datasets, allowing transcriptome-wide imputation of spatial gene expression patterns. Our work presents a strategy for the assembly of harmonized references and transfer of information across datasets.
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                Author and article information

                Contributors
                eric.rullman@ki.se
                Journal
                Commun Biol
                Commun Biol
                Communications Biology
                Nature Publishing Group UK (London )
                2399-3642
                22 October 2022
                22 October 2022
                2022
                : 5
                : 1121
                Affiliations
                [1 ]GRID grid.4714.6, ISNI 0000 0004 1937 0626, Department of Laboratory Medicine, , Section of Clinical Physiology, Karolinska Institutet Huddinge, ; Huddinge, Sweden
                [2 ]GRID grid.24381.3c, ISNI 0000 0000 9241 5705, Department of Clinical Physiology, , Karolinska University Hospital, ; Huddinge, Sweden
                Author information
                http://orcid.org/0000-0002-6937-6124
                http://orcid.org/0000-0003-2854-7262
                Article
                4088
                10.1038/s42003-022-04088-z
                9588010
                36273106
                9424d50d-77ce-421a-9f6e-edea918c2eca
                © The Author(s) 2022

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 26 November 2021
                : 11 October 2022
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                Custom metadata
                © The Author(s) 2022

                physiology,molecular medicine,muscle stem cells
                physiology, molecular medicine, muscle stem cells

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