Advancing Single-Cell Transcriptomic Analysis to Reveal Age-Related Skeletal Muscle Changes: A Systematic Review
Bibliographic record
Abstract
Population aging has become a widespread health problem that leads to huge socioeconomic burden. Skeletal muscle as an important component of motor system, gradually degenerates with age. Age-related muscle disorders, such as sarcopenia is associated with higher risks of falls, fracture, disability, and mortality in old people. As there is still no Food and Drug Administration (FDA) approved drug to treat sarcopenia, conducting research of in-depth mechanisms is warranted to develop novel treatments. The cutting-edge techniques single-cell and single-nuclei RNA sequencing can help to address this issue by discovering age-related changes of muscle at the single-cell level. This review aims to systematically explore current evidence of age-related muscle changes during normal aging, regeneration, and after treatments at the single-cell level. 29 studies were eligible and included in the current review according to the PRISMA guideline. The muscle cell composition was altered with age, such as diminished muscle stem cells (MuSCs), vascular cells, Schwann cells, and increased myocytes as well as some types of immune cells. Inflammation levels, collagen and extracellular matrix (ECM) signaling, protein catabolism, TGFβ signaling, apoptosis, and autophagy of MuSCs, myocytes, fibro-adipogenic progenitor cells, vascular cells, or immune cells were regulated with age. Delayed muscle regeneration of aged muscle was relied on disorders of cell-specific immune response, myogenesis, angiogenesis, and ECM remodeling. Three treatments involved in this review could reverse age-related dysfunction of muscle cells to some extent. Further research targeting age-related changes of muscle at the single-cell level is an important tool in assisting development of more effective treatments for sarcopenia.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".