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.
View: Text | PDF
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

×

Figure 5

scRNA-Seq and ST integration with enhanced-resolution clustering resolve the complex dystrophic muscle architecture and cellular distribution and profiles of myeloid subtypes.

Options: View larger image (or click on image) Download as PowerPoint
scRNA-Seq and ST integration with enhanced-resolution clustering resolve...
(A) Left: H&E images of mouse GAST from 2-mo D2.mdx used for ST. Histopathological annotation areas are noted: regenerative muscle (yellow), necrotic/inflammatory lesions (green), healthy muscle (blue). Right: Percentage of spots in the annotated areas. (B) Enhanced subspot resolution clustering (BayesSpace) identified 7 spatial clusters (color coded), which were not resolved by pathologist annotations. The white rectangle highlights a lesion with structured inflammation and regeneration zones. (C) Top marker gene expression after z score transformation for each spatial cluster. Dot size represents the percentage of subspots expressing the gene. (D) Spatial expression of representative genes coding for markers of each spatial cluster: Ccl2 and Ccl7 (LGCs: cluster 2), Itgax, Mmp12, Trem2, and Gpnmb (resolution-related MFs and GFEMs: cluster 6), Myog and Myh3 (newly regenerating fibers: cluster 1), Pvalb and Tpm3 (healthy muscle: cluster 7), Col1a1 (ECM: cluster 4), and Esam (endothelial cell/vasculature-enriched areas: cluster 3). Note the differential spatial expression patterns in the highlighted region of B. (E) Single-cell transcriptomes derived from CD45+ cells from 2-mo D2.mdx GAST. A total number of 4,811 myeloid cells (MFs, monocytes, DCs; SingleR automated annotation using the ImmGen database) were analyzed. Data are presented as a t-SNE projection to visualize variation in single-cell transcriptomes. The subsampling-based clustering approach (chooseR) resolved 7 myeloid subsets (color coded). (F) Top marker genes for the 7 identified clusters. Dot size represents the percentage of expressing cells within a group, and color scale represents the average expression level (row z score) across all cells within the cluster. (G) Left: 2D embeddings visualizing cell cycle phases of the 2-mo D2.mdx scRNA-Seq dataset using t-SNE. NA indicates the number of unassigned cells. Right: 2D embeddings visualizing 3 subclusters of cycling cells from parent cluster 6 using VeloViz (98) embeddings. Cell numbers for each subcluster are indicated. Arrows show velocity projections (velocyto.R). (H) Identification of tissue compartments using NMF-based decomposition and 2-mo D2.mdx reference immune subtype expression signatures (7). Heatmap of the estimated NMF weights of subtypes (rows) across 6 predicted NMF components (columns), corresponding to the identified cellular compartments. Relative weights normalized across domains for every MF subtype are shown. (I) Spatial plots show cell abundance for each immune cell subtype calculated in H. Scale bars: 500 μm.