Mechanotherapy Reprograms Aged Muscle Stromal Cells to Remodel the Extracellular Matrix during Recovery from Disuse

Abstract

Aging is accompanied by reduced remodeling of skeletal muscle extracellular matrix (ECM), which is exacerbated during recovery following periods of disuse atrophy. Mechanotherapy has been shown to promote ECM remodeling through immunomodulation in adult muscle recovery, but not during the aged recovery from disuse. In order to determine if mechanotherapy promotes ECM remodeling in aged muscle, we performed single cell RNA sequencing (scRNA-seq) of all mononucleated cells in adult and aged rat gastrocnemius muscle recovering from disuse, with (REM) and without mechanotherapy (RE). We show that fibroadipogenic progenitor cells (FAPs) in aged RE muscle are highly enriched in chemotaxis genes (Csf1), but absent in ECM remodeling genes compared to adult RE muscle (Col1a1). Receptor-ligand (RL) network analysis of all mononucleated cell populations in aged RE muscle identified chemotaxis-enriched gene expression in numerous stromal cell populations (FAPs, endothelial cells, pericytes), despite reduced enrichment of genes related to phagocytic activity in myeloid cell populations (macrophages, monocytes, antigen presenting cells). Following mechanotherapy, aged REM mononuclear cell gene expression resembled adult RE muscle as evidenced by RL network analyses and KEGG pathway activity scoring. To validate our transcriptional findings, ECM turnover was measured in an independent cohort of animals using in vivo isotope tracing of intramuscular collagen and histological scoring of the ECM, which confirmed mechanotherapy-mediated ECM remodeling in aged RE muscle. Our results highlight age-related cellular mechanisms underpinning the impairment to complete recovery from disuse, and also promote mechanotherapy as an intervention to enhance ECM turnover in aged muscle recovering from disuse.

Extracellular vesicle distribution and localization in skeletal muscle at rest and following disuse atrophy

“…Furthermore, the smaller size of the soleus limits issues of oxygen diffusion during ex vivo muscle assessments [27]. The gastrocnemius muscles were used for single-cell RNA sequencing (scRNA-seq) as described in [28]. The serum was isolated by allowing blood to clot at room temperature for 30 min before centrifugation for 10 min at 2000g at 4 °C.…”

“…Cell isolations were performed as previously described in mice [37] and modified slightly for rats [28]. Briefly, the gastrocnemius muscles from WB and HS male rats were excised and placed in muscle dissociation media (MDM) (Hams F-10 (Gibco, USA), 10% Horse Serum (Thermo Fisher), 1% penicillin/streptomycin (Gibco), 800 U/ ml Collagenase II (Gibco)), and minced using sterilized surgical equipment.…”

“…scRNA-seq data were processed using the Partek Genomics Suite (Partek, USA) as previously described [28]. Briefly, following data quality control, samples were aligned to the rn6 genome and low-quality cells and/ or reads were excluded based on the following criteria: mitochondrial reads exceeding 20%, an indication of doublets via read counts/cell, lowly expressed genes in only 0.01% of total cells, and high expression of myofiberrelated RNA resulting from muscle mincing.…”

“…Briefly, following data quality control, samples were aligned to the rn6 genome and low-quality cells and/ or reads were excluded based on the following criteria: mitochondrial reads exceeding 20%, an indication of doublets via read counts/cell, lowly expressed genes in only 0.01% of total cells, and high expression of myofiberrelated RNA resulting from muscle mincing. Following dimensionality reduction, graph-based clustering was used in combination with known muscle mononuclear cell-related gene markers for population annotation [11,28,38].…”

Dynamical modeling reveals RNA decay mediates the effect of matrix stiffness on aged muscle stem cell fate

“…The classification of cells into the one of the four groups, cycling, activated, early differentiation, and late differentiation was obtained using a combination of unsupervised graph-based clustering as well as manual characterization using previously reported state-specific markers (2, 56, 103). Following dimensionality reduction using PCA and visualization using UMAP, the Louvain clustering algorithm was performed to generate clusters based on discrete cluster-specific gene expression.…”