Direct Reprogramming of Mouse Fibroblasts into Functional Skeletal Muscle Progenitors
Bibliographic record
Abstract
Skeletal muscle harbors quiescent stem cells termed satellite cells and proliferative progenitors termed myoblasts, which play pivotal roles during muscle regeneration. However, current technology does not allow permanent capture of these cell populations in vitro . Here, we show that ectopic expression of the myogenic transcription factor MyoD, combined with exposure to small molecules, reprograms mouse fibroblasts into expandable induced myogenic progenitor cells (iMPCs). iMPCs express key skeletal muscle stem and progenitor cell markers including Pax7 and Myf5 and give rise to dystrophin-expressing myofibers upon transplantation in vivo . Notably, a subset of transplanted iMPCs maintain Pax7 expression and sustain serial regenerative responses. Similar to satellite cells, iMPCs originate from Pax7 + cells and require Pax7 itself for maintenance. Finally, we show that myogenic progenitor cell lines can be established from muscle tissue following small-molecule exposure alone. This study thus reports on a robust approach to derive expandable myogenic stem/progenitor-like cells from multiple cell types.
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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 | 0.001 |
| 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".