Fibro-adipogenic progenitors in skeletal muscle homeostasis, regeneration and diseases
Bibliographic record
Abstract
Skeletal muscle possesses a remarkable regenerative capacity that relies on the activity of muscle stem cells, also known as satellite cells. The presence of non-myogenic cells also plays a key role in the coordination of skeletal muscle regeneration. Particularly, fibro-adipogenic progenitors (FAPs) emerged as master regulators of muscle stem cell function and skeletal muscle regeneration. This population of muscle resident mesenchymal stromal cells has been initially characterized based on its bi-potent ability to differentiate into fibroblasts or adipocytes. New technologies such as single-cell RNAseq revealed the cellular heterogeneity of FAPs and their complex regulatory network during muscle regeneration. In acute injury, FAPs rapidly enter the cell cycle and secrete trophic factors that support the myogenic activity of muscle stem cells. Conversely, deregulation of FAP cell activity is associated with the accumulation of fibrofatty tissue in pathological conditions such as muscular dystrophies and ageing. Considering their central role in skeletal muscle pathophysiology, the regulatory mechanisms of FAPs and their cellular and molecular crosstalk with muscle stem cells are highly investigated in the field. In this review, we summarize the current knowledge on FAP cell characteristics, heterogeneity and the cellular crosstalk during skeletal muscle homeostasis and regeneration. We further describe their role in muscular disorders, as well as different therapeutic strategies targeting these cells to restore muscle 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".