«Ex vivo» expansion of skeletal muscle stem cells with a novel inhibitor of eIF1α dephosphorylation
Bibliographic record
Abstract
Regeneration of adult skeletal muscle depends on rare skeletal muscle stem cells (MuSCs) that reside in a quiescent state underneath the basal lamina of the myofibre. The study, manipulation and use of MuSCs for cell-based therapies is hindered by their scarcity and the inability to expand them ex vivo under current culture conditions. We have shown that a general repression of translation, mediated by the phosphorylation of translation initiation factor eIF2α at serine 51 (P-eIF2α), is essential for maintenance of MuSC quiescence and self-renewal. MuSCs unable to phosphorylate eIF2α exit quiescence, activate the myogenic program and contribute to muscle differentiation, but do not self-renew or return to their quiescent state underneath the basal lamina of the myofibre. Here we show that pharmacological inhibition of eIF2α dephosphorylation by the novel small compound C10 permitted the expansion of MuSCs retaining capacity to regenerate muscle and self-renew after engraftment into a preclinical mouse model of Duchenne muscular dystrophy. We optimized culture conditions with C10 to facilitate a) passaging of MuSCs retaining regenerative capacity and b) genome editing of MuSCs with CRISPR/Cas9.
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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".