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      Spatiotemporal mapping of immune and stem cell dysregulation after volumetric muscle loss

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          Abstract

          Volumetric muscle loss (VML) is an acute trauma that results in persistent inflammation, supplantation of muscle tissue with fibrotic scarring, and decreased muscle function. The cell types, nature of cellular communication, and tissue locations that drive the aberrant VML response have remained elusive. Herein, we used spatial transcriptomics on a mouse model of VML and observed that VML engenders a unique spatial profibrotic pattern driven by crosstalk between fibrotic and inflammatory macrophages and mesenchymal-derived cells. The dysregulated response impinged on muscle stem cell–mediated repair, and targeting this circuit resulted in increased regeneration and reductions in inflammation and fibrosis. Collectively, these results enhance our understanding of the cellular crosstalk that drives aberrant regeneration and provides further insight into possible avenues for fibrotic therapy exploration.

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          Most cited references49

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          Integrating single-cell transcriptomic data across different conditions, technologies, and species

          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.
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            Inference and analysis of cell-cell communication using CellChat

            Understanding global communications among cells requires accurate representation of cell-cell signaling links and effective systems-level analyses of those links. We construct a database of interactions among ligands, receptors and their cofactors that accurately represent known heteromeric molecular complexes. We then develop CellChat, a tool that is able to quantitatively infer and analyze intercellular communication networks from single-cell RNA-sequencing (scRNA-seq) data. CellChat predicts major signaling inputs and outputs for cells and how those cells and signals coordinate for functions using network analysis and pattern recognition approaches. Through manifold learning and quantitative contrasts, CellChat classifies signaling pathways and delineates conserved and context-specific pathways across different datasets. Applying CellChat to mouse and human skin datasets shows its ability to extract complex signaling patterns. Our versatile and easy-to-use toolkit CellChat and a web-based Explorer (http://www.cellchat.org/) will help discover novel intercellular communications and build cell-cell communication atlases in diverse tissues.
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              A Unique Microglia Type Associated with Restricting Development of Alzheimer's Disease.

              Alzheimer's disease (AD) is a detrimental neurodegenerative disease with no effective treatments. Due to cellular heterogeneity, defining the roles of immune cell subsets in AD onset and progression has been challenging. Using transcriptional single-cell sorting, we comprehensively map all immune populations in wild-type and AD-transgenic (Tg-AD) mouse brains. We describe a novel microglia type associated with neurodegenerative diseases (DAM) and identify markers, spatial localization, and pathways associated with these cells. Immunohistochemical staining of mice and human brain slices shows DAM with intracellular/phagocytic Aβ particles. Single-cell analysis of DAM in Tg-AD and triggering receptor expressed on myeloid cells 2 (Trem2)(-/-) Tg-AD reveals that the DAM program is activated in a two-step process. Activation is initiated in a Trem2-independent manner that involves downregulation of microglia checkpoints, followed by activation of a Trem2-dependent program. This unique microglia-type has the potential to restrict neurodegeneration, which may have important implications for future treatment of AD and other neurodegenerative diseases. VIDEO ABSTRACT.
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                Author and article information

                Contributors
                Journal
                JCI Insight
                JCI Insight
                JCI Insight
                JCI Insight
                American Society for Clinical Investigation
                2379-3708
                10 April 2023
                10 April 2023
                10 April 2023
                : 8
                : 7
                : e162835
                Affiliations
                [1 ]Department of Biomedical Engineering,
                [2 ]Biointerfaces Institute,
                [3 ]Department of Molecular, Cellular and Developmental Biology,
                [4 ]Division of Metabolism, Endocrinology and Diabetes, Department of Internal Medicine, and
                [5 ]Program in Cellular and Molecular Biology, University of Michigan (UM), Ann Arbor, Michigan, USA.
                Author notes
                Address correspondence to: Carlos A. Aguilar, NCRC - University of Michigan, 2800 Plymouth Rd., 10-A183, Ann Arbor, Michigan 48109, USA. Phone: 734.764.8557; Email: caguilar@ 123456umich.edu .
                Author information
                http://orcid.org/0000-0001-9380-3547
                http://orcid.org/0000-0001-5220-2075
                http://orcid.org/0000-0003-3830-0634
                Article
                162835
                10.1172/jci.insight.162835
                10132146
                36821376
                57bd8a3d-9cad-49d3-86fa-452cc67b7d8f
                © 2023 Larouche et al.

                This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 17 June 2022
                : 21 February 2023
                Funding
                Funded by: Defense Sciences Office, DARPA, https://doi.org/10.13039/100006502;
                Award ID: D20AC0002
                Funded by: Congressionally Directed Medical Research Programs, https://doi.org/10.13039/100000090;
                Award ID: W81XWH2010336
                Funded by: Congressionally Directed Medical Research Programs, https://doi.org/10.13039/100000090;
                Award ID: W81XWH2110491
                Funded by: National Institute of Arthritis and Musculoskeletal and Skin Diseases, https://doi.org/10.13039/100000069;
                Award ID: P30 AR069620
                Funded by: National Science Foundation, https://doi.org/10.13039/100000001;
                Award ID: 2045977
                Categories
                Technical Advance

                muscle biology,stem cells,adult stem cells,fibrosis,skeletal muscle

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