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      Myonuclear transcriptional dynamics in response to exercise following satellite cell depletion

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          Summary

          Skeletal muscle is composed of post-mitotic myofibers that form a syncytium containing hundreds of myonuclei. Using a progressive exercise training model in the mouse and single nucleus RNA-sequencing (snRNA-seq) for high-resolution characterization of myonuclear transcription, we show myonuclear functional specialization in muscle. After 4 weeks of exercise training, snRNA-seq reveals that resident muscle stem cells, or satellite cells, are activated with acute exercise but demonstrate limited lineage progression while contributing to muscle adaptation. In the absence of satellite cells, a portion of nuclei demonstrates divergent transcriptional dynamics associated with mixed-fate identities compared with satellite cell replete muscles. These data provide a compendium of information about how satellite cells influence myonuclear transcription in response to exercise.

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          Highlights

          • •

            Single nucleus RNA-sequencing, trajectory inference, and entropy analyses

          • •

            Genetic depletion of satellite cells before exercise training in adult mice

          • •

            Dysregulated nuclei arise 24 h after last exercise bout when without satellite cells

          • •

            Satellite cell depletion alters myonuclear transcription during growth and adaptation

          Abstract

          Biological sciences; Stem cells research; Transcriptomics

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          Most cited references61

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

          Matthew E. Ritchie, Belinda Phipson, Di Wu … (2015)
          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.
          Article 0 comments Cited 11410 times – based on 0 reviews     Review now
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            Integrating single-cell transcriptomic data across different conditions, technologies, and species

            Rahul Satija, Efthymia Papalexi, Peter Smibert … (2018)
            Computational single-cell RNA-seq (scRNA-seq) methods have been successfully applied to experiments representing a single condition, technology, or species to discover and define cellular phenotypes. However, identifying subpopulations of cells that are present across multiple data sets remains challenging. Here, we introduce an analytical strategy for integrating scRNA-seq data sets based on common sources of variation, enabling the identification of shared populations across data sets and downstream comparative analysis. We apply this approach, implemented in our R toolkit Seurat (http://satijalab.org/seurat/), to align scRNA-seq data sets of peripheral blood mononuclear cells under resting and stimulated conditions, hematopoietic progenitors sequenced using two profiling technologies, and pancreatic cell 'atlases' generated from human and mouse islets. In each case, we learn distinct or transitional cell states jointly across data sets, while boosting statistical power through integrated analysis. Our approach facilitates general comparisons of scRNA-seq data sets, potentially deepening our understanding of how distinct cell states respond to perturbation, disease, and evolution.
            Article 0 comments Cited 3936 times – based on 0 reviews     Review now
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              Fast, sensitive, and accurate integration of single cell data with Harmony

              Ilya Korsunsky, Nghia Millard, Jean Fan … (2019)
              The emerging diversity of single cell RNAseq datasets allows for the full transcriptional characterization of cell types across a wide variety of biological and clinical conditions. However, it is challenging to analyze them together, particularly when datasets are assayed with different technologies. Here, real biological differences are interspersed with technical differences. We present Harmony, an algorithm that projects cells into a shared embedding in which cells group by cell type rather than dataset-specific conditions. Harmony simultaneously accounts for multiple experimental and biological factors. In six analyses, we demonstrate the superior performance of Harmony to previously published algorithms. We show that Harmony requires dramatically fewer computational resources. It is the only currently available algorithm that makes the integration of ~106 cells feasible on a personal computer. We apply Harmony to PBMCs from datasets with large experimental differences, 5 studies of pancreatic islet cells, mouse embryogenesis datasets, and cross-modality spatial integration.
              Article 0 comments Cited 2209 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Charlotte A. Peterson
                Journal
                Journal ID (nlm-ta): iScience
                Journal ID (iso-abbrev): iScience
                Title: iScience
                Publisher: Elsevier
                ISSN (Electronic): 2589-0042
                Publication date PMC-release: 10 July 2021
                Publication date Collection: 20 August 2021
                Publication date (Electronic): 10 July 2021
                Volume: 24
                Issue: 8
                Electronic Location Identifier: 102838
                Affiliations
                [1 ]Department of Physical Therapy, College of Health Sciences, University of Kentucky, 900 S. Limestone, Lexington, KY 40536-0200, USA
                [2 ]Center for Muscle Biology, University of Kentucky, Lexington, KY, USA
                [3 ]Department of Physiology, College of Medicine, University of Kentucky, Lexington, KY, USA
                Author notes
                [∗ ]Corresponding author cpete4@ 123456uky.edu
                [4]

                These authors contributed equally

                [5]

                Lead contact

                Article
                Publisher Item ID: S2589-0042(21)00806-3 Publisher ID: 102838
                DOI: 10.1016/j.isci.2021.102838
                PMC ID: 8326190
                PubMed ID: 34368654
                SO-VID: 073c7eb8-2132-41f2-bb8a-dd8e53969e0f
                Copyright © © 2021 The Authors
                License:

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

                History
                Date received : 21 January 2021
                Date revision received : 15 April 2021
                Date accepted : 8 July 2021
                Categories
                Subject: Article

                Keywords: biological sciences,stem cells research,transcriptomics
                Data availability:
                Keywords: biological sciences, stem cells research, transcriptomics

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