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Single-cell and spatial transcriptomics identify a macrophage population associated with skeletal muscle fibrosis.

ABSTRACT: Macrophages are essential for skeletal muscle homeostasis, but how their dysregulation contributes to the development of fibrosis in muscle disease remains unclear. Here, we used single-cell transcriptomics to determine the molecular attributes of dystrophic and healthy muscle macrophages. We identified six clusters and unexpectedly found that none corresponded to traditional definitions of M1 or M2 macrophages. Rather, the predominant macrophage signature in dystrophic muscle was characterized by high expression of fibrotic factors, galectin-3 (gal-3) and osteopontin (Spp1). Spatial transcriptomics, computational inferences of intercellular communication, and in vitro assays indicated that macrophage-derived Spp1 regulates stromal progenitor differentiation. Gal-3⁺ macrophages were chronically activated in dystrophic muscle, and adoptive transfer assays showed that the gal-3⁺ phenotype was the dominant molecular program induced within the dystrophic milieu. Gal-3⁺ macrophages were also elevated in multiple human myopathies. These studies advance our understanding of macrophages in muscular dystrophy by defining their transcriptional programs and reveal Spp1 as a major regulator of macrophage and stromal progenitor interactions.

SUBMITTER: Coulis G

PROVIDER: S-EPMC10328414 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature

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Single-cell and spatial transcriptomics identify a macrophage population associated with skeletal muscle fibrosis.

Coulis Gerald G Jaime Diego D Guerrero-Juarez Christian C Kastenschmidt Jenna M JM Farahat Philip K PK Nguyen Quy Q Pervolarakis Nicholas N McLinden Katherine K Thurlow Lauren L Movahedi Saba S Hughes Brandon S BS Duarte Jorge J Sorn Andrew A Montoya Elizabeth E Mozaffar Izza I Dragan Morgan M Othy Shivashankar S Joshi Trupti T Hans Chetan P CP Kimonis Virginia V MacLean Adam L AL Nie Qing Q Wallace Lindsay M LM Harper Scott Q SQ Mozaffar Tahseen T Hogarth Marshall W MW Bhattacharya Surajit S Jaiswal Jyoti K JK Golann David R DR Su Qi Q Kessenbrock Kai K Stec Michael M Spencer Melissa J MJ Zamudio Jesse R JR Villalta S Armando SA

Science advances 20230707 27

Macrophages are essential for skeletal muscle homeostasis, but how their dysregulation contributes to the development of fibrosis in muscle disease remains unclear. Here, we used single-cell transcriptomics to determine the molecular attributes of dystrophic and healthy muscle macrophages. We identified six clusters and unexpectedly found that none corresponded to traditional definitions of M1 or M2 macrophages. Rather, the predominant macrophage signature in dystrophic muscle was characterized ...[more]

PMID: 37418531

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Project description:The development of single-cell sequencing technologies has allowed researchers to gain important new knowledge about the expression profile of genes in thousands of individual cells of a model organism or tissue. A common disadvantage of this technology is the loss of the three-dimensional (3-D) structure of the cells. Consequently, the Dialogue on Reverse Engineering Assessment and Methods (DREAM) organized the Single-Cell Transcriptomics Challenge, in which we participated, with the aim to address the following two problems: (a) to identify the top 60, 40, and 20 genes of the Drosophila melanogaster embryo that contain the most spatial information and (b) to reconstruct the 3-D arrangement of the embryo using information from those genes. We developed two independent techniques, leveraging machine learning models from least absolute shrinkage and selection operator (Lasso) and deep neural networks (NNs), which are applied to high-dimensional single-cell sequencing data in order to accurately identify genes that contain spatial information. Our first technique, Lasso.TopX, utilizes the Lasso and ranking statistics and allows a user to define a specific number of features they are interested in. The NN approach utilizes weak supervision for linear regression to accommodate for uncertain or probabilistic training labels. We show, individually for both techniques, that we are able to identify important, stable, and a user-defined number of genes containing the most spatial information. The results from both techniques achieve high performance when reconstructing spatial information in D. melanogaster and also generalize to zebrafish (Danio rerio). Furthermore, we identified novel D. melanogaster genes that carry important positional information and were not previously suspected. We also show how the indirect use of the full datasets' information can lead to data leakage and generate bias in overestimating the model's performance. Lastly, we discuss the applicability of our approaches to other feature selection problems outside the realm of single-cell sequencing and the importance of being able to handle probabilistic training labels. Our source code and detailed documentation are available at https://github.com/TJU-CMC-Org/SingleCell-DREAM/.

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Single-cell and spatial transcriptomics identify a macrophage population associated with skeletal muscle fibrosis.

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Single-cell and spatial transcriptomics identify a macrophage population associated with skeletal muscle fibrosis.

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