Spatiotemporal transcriptomic mapping of regenerative inflammation in skeletal muscle reveals a dynamic multilayered tissue architecture
Andreas Patsalos, … , H. Lee Sweeney, Laszlo Nagy
Andreas Patsalos, … , H. Lee Sweeney, Laszlo Nagy
Published August 27, 2024
Citation Information: J Clin Invest. 2024;134(20):e173858. https://doi.org/10.1172/JCI173858.
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Research Article Inflammation

Spatiotemporal transcriptomic mapping of regenerative inflammation in skeletal muscle reveals a dynamic multilayered tissue architecture

Abstract

Tissue regeneration is orchestrated by macrophages that clear damaged cells and promote regenerative inflammation. How macrophages spatially adapt and diversify their functions to support the architectural requirements of actively regenerating tissue remains unknown. In this study, we reconstructed the dynamic trajectories of myeloid cells isolated from acutely injured and early stage dystrophic muscles. We identified divergent subsets of monocytes/macrophages and DCs and validated markers (e.g., glycoprotein NMB [GPNMB]) and transcriptional regulators associated with defined functional states. In dystrophic muscle, specialized repair-associated subsets exhibited distinct macrophage diversity and reduced DC heterogeneity. Integrating spatial transcriptomics analyses with immunofluorescence uncovered the ordered distribution of subpopulations and multilayered regenerative inflammation zones (RIZs) where distinct macrophage subsets are organized in functional zones around damaged myofibers supporting all phases of regeneration. Importantly, intermittent glucocorticoid treatment disrupted the RIZs. Our findings suggest that macrophage subtypes mediated the development of the highly ordered architecture of regenerative tissues, unveiling the principles of the structured yet dynamic nature of regenerative inflammation supporting effective tissue repair.

Authors

Andreas Patsalos, Laszlo Halasz, Darby Oleksak, Xiaoyan Wei, Gergely Nagy, Petros Tzerpos, Thomas Conrad, David W. Hammers, H. Lee Sweeney, Laszlo Nagy

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Figure 3

Infiltrating myeloid cell transcriptomic profile comparison from healthy, acutely injured, and early stage dystrophic muscle.

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Infiltrating myeloid cell transcriptomic profile comparison from healthy...
(A) Module score of gene sets representing functional markers of each myeloid subset visualized with a t-SNE in the CTX injury time-course scRNA-Seq dataset. Genes for each module are indicated. (B) Module score of gene sets representing functional markers of each myeloid subset visualized with a t-SNE in the 2-mo D2.mdx scRNA-Seq dataset. Genes for each module are indicated. (C) Composition bar plot of the major immune cell types in the Harmony integrated dataset (2-mo D2.mdx + CTX injury). (D) Heatmap of top genes in 2-mo D2.mdx vs. CTX-injury monocytes, MF, and DC subsets. (E) Pairwise Spearman’s correlation plot of monocytes, MF, and DC subsets identified in 2-mo D2.mdx, CTX-injury, and resident muscle MFs from healthy quadriceps (36) (1,300 total; presto Wilcox AUC; logFC > 0.5, P-adj < 0.1, AUC > 0.5). The resident MFs are represented according to the following nomenclature chosen by the authors (36): cluster 0 (cluster 0), Cd209 cluster (cluster 1), Ccr2 cluster (cluster 2), and proliferating cluster (cluster 3). Color intensity and circle size are proportional to the correlation coefficients. Note (a) the relative uniqueness of resident MFs, (b) the high correlation of proinflammatory monocytes (cluster 3) in both CTX and D2.mdx datasets, and (c) the high correlation of cycling MFs in D2.mdx (cluster 6) with the resident MFs (proliferating cluster). (F) Heatmap of known regulators of regeneration in 2-mo D2.mdx versus CTX-injury monocyte, MF, and DC subsets.