Identification of underexplored mesenchymal and vascular-related cell populations in human skeletal muscle
Bibliographic record
Abstract
Skeletal muscle repair and maintenance are directly and indirectly supported by interstitial cell populations such as vascular cells and fibro-adipogenic progenitors (FAPs), a subset of which express Twist2 and possess direct myogenic potential. Furthermore, work in rodents has highlighted the potential of pericytes to act as progenitor cells, giving rise to muscle cells and transdifferentiating into endothelial cells. However, less is understood about these populations in human skeletal muscle. Here, we performed single-cell RNA sequencing (scRNAseq) on ∼2,000 cells isolated from the human semitendinosus muscle of young individuals. This demonstrated the presence of a vascular-related cell type that expressed pericyte and pan-endothelial genes that we localized to large blood vessels within skeletal muscle cross sections and termed endothelial-like pericytes (ELPCs). RNA velocity analysis indicated that ELPCs may represent a “transition state” between endothelial cells and pericytes. Analysis of published scRNAseq data sets revealed evidence for ELPCs in trunk and heart musculature, which showed transcriptional similarity. In addition, we identified a subset of FAPs expressing TWIST2 mRNA and protein. Human TWIST2-expressing cells were anatomically and transcriptionally comparable to mouse Twist2 cells as they were restricted to the myofiber interstitium, expressed fibrogenic genes but lacked satellite cell markers, and colocalized with the FAPs marker PDGFRα in human muscle cross sections. Taken together, these results highlight the complexity of stromal cells residing in human skeletal muscle and support the utility of scRNAseq for discovery and characterization of poorly described cell populations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".