Transcriptional reprogramming of skeletal muscle stem cells by the niche environment
Bibliographic record
Abstract
Abstract Adult stem cells are indispensable for tissue regeneration, but the number and regenerative capacity of stem cells declines with age. Whether the decrease in stem cell function is the cause or consequence of the aging of a tissue is unclear. Evidence suggests that the niche environment plays a critical role in the regulation of adult stem cell function6-10. However, quantification of the niche effect on stem cell function is an unmet challenge. Using muscle stem cells (MuSCs) as a model, we show that aging leads to a significant transcriptomic shift in MuSC subpopulations. By combining in vivo MuSC transplantation, multi-omics and computational methods, we show that the expression of approximately half of all age-altered genes in MuSCs can be restored by exposure to a young niche environment. Age-related genes whose expression is not restored exhibit altered chromatin accessibility and are associated with differentially methylated regions between young and aged cells. Our findings establish that the expression of the majority of age-related altered genes that are not epigenetically encoded is readily restorable by exposure to a young niche environment. The stem cell niche may therefore be an important therapeutic target to mitigate the negative consequences of aging on tissue regeneration.
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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 | 0.000 | 0.000 |
| 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) | 0.001 | 0.000 |
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".