Neutrophil and natural killer cell imbalances prevent muscle stem cell–mediated regeneration following murine volumetric muscle loss

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Significance

Skeletal muscle is one of the largest tissues in the body and can regenerate when damaged through a population of resident muscle stem cells. A type of muscle trauma called volumetric muscle loss overwhelms the regenerative capacity of muscle stem cells and engenders fibrotic supplantation. A comparison of muscle injuries resulting in regeneration or fibrosis revealed that intercellular communication between neutrophils and natural killer cells impacts muscle stem cell-mediated repair. Perturbation of neutrophil–natural killer cell interactions resulted in a variation of healing outcomes and suggested that immunomodulatory interventions can be effective to prevent aberrant healing outcomes.

Abstract

Volumetric muscle loss (VML) overwhelms the innate regenerative capacity of mammalian skeletal muscle (SkM), leading to numerous disabilities and reduced quality of life. Immune cells are critical responders to muscle injury and guide tissue resident stem cell– and progenitor-mediated myogenic repair. However, how immune cell infiltration and intercellular communication networks with muscle stem cells are altered following VML and drive pathological outcomes remains underexplored. Herein, we contrast the cellular and molecular mechanisms of VML injuries that result in the fibrotic degeneration or regeneration of SkM. Following degenerative VML injuries, we observed the heightened infiltration of natural killer (NK) cells as well as the persistence of neutrophils beyond 2 wk postinjury. Functional validation of NK cells revealed an antagonistic role in neutrophil accumulation in part via inducing apoptosis and CCR1-mediated chemotaxis. The persistent infiltration of neutrophils in degenerative VML injuries was found to contribute to impairments in muscle stem cell regenerative function, which was also attenuated by transforming growth factor beta 1 ( TGFβ1). Blocking TGFβ signaling reduced neutrophil accumulation and fibrosis and improved muscle-specific force. Collectively, these results enhance our understanding of immune cell–stem cell cross talk that drives regenerative dysfunction and provide further insight into possible avenues for fibrotic therapy exploration.

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Comprehensive Integration of Single-Cell Data

Tim Stuart, Andrew Butler, Paul Hoffman … (2019)

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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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.

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RNA velocity of single cells

Gioele La Manno, Ruslan Soldatov, Amit Zeisel … (2018)

RNA abundance is a powerful indicator of the state of individual cells. Single-cell RNA sequencing can reveal RNA abundance with high quantitative accuracy, sensitivity and throughput1. However, this approach captures only a static snapshot at a point in time, posing a challenge for the analysis of time-resolved phenomena, such as embryogenesis or tissue regeneration. Here we show that RNA velocity—the time derivative of the gene expression state—can be directly estimated by distinguishing unspliced and spliced mRNAs in common single-cell RNA sequencing protocols. RNA velocity is a high-dimensional vector that predicts the future state of individual cells on a timescale of hours. We validate its accuracy in the neural crest lineage, demonstrate its use on multiple published datasets and technical platforms, reveal the branching lineage tree of the developing mouse hippocampus, and examine the kinetics of transcription in human embryonic brain. We expect RNA velocity to greatly aid the analysis of developmental lineages and cellular dynamics, particularly in humans.

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Author and article information

Journal

Journal ID (nlm-ta): Proc Natl Acad Sci U S A

Journal ID (iso-abbrev): Proc Natl Acad Sci U S A

Journal ID (hwp): pnas

Journal ID (publisher-id): PNAS

Title: Proceedings of the National Academy of Sciences of the United States of America

Publisher: National Academy of Sciences

ISSN (Print): 0027-8424

ISSN (Electronic): 1091-6490

Publication date (Electronic): 4 April 2022

Publication date (Print): 12 April 2022

Publication date PMC-release: 4 October 2022

Volume: 119

Issue: 15

Electronic Location Identifier: e2111445119

Affiliations

[1] ^aDepartment of Biomedical Engineering, University of Michigan , Ann Arbor, MI 48109;

[2] ^bBiointerfaces Institute, University of Michigan , Ann Arbor, MI 48109;

[3] ^cDepartment of Molecular & Integrative Physiology, University of Michigan , Ann Arbor, MI 48109;

[4] ^dDepartment of Biomedical Engineering, Georgia Institute of Technology and Emory University , Atlanta, GA 30332;

[5] ^eSchool of Biological Sciences, Georgia Institute of Technology , Atlanta, GA 30332;

[6] ^fPhil and Penny Knight Campus for Accelerating Scientific Impact, University of Oregon , Eugene, OR 97403;

[7] ^gProgram in Cellular and Molecular Biology, University of Michigan , Ann Arbor, MI 48109

Author notes

¹To whom correspondence may be addressed. Email: caguilar@ 123456umich.edu .

Edited by Helen Blau, Stanford University, Stanford, CA; received July 3, 2021; accepted February 9, 2022

Author contributions: J.A.L. and C.A.A. designed research; J.A.L., P.M.F., S.J.K., B.A.Y., C.D., J.A.C.-M., K.S., S.A., J.H., M.H., and Y.C.J. performed research; S.V.B., N.W., and L.D.S. contributed new reagents/analytic tools; J.A.L., S.J.K., B.A.Y., and C.D. analyzed data; and J.A.L. and C.A.A. wrote the paper.