Robust generation and expansion of skeletal muscle progenitors and myocytes from human pluripotent stem cells
Bibliographic record
Abstract
Human pluripotent stem cells provide a developmental model to study early embryonic and tissue development, tease apart human disease processes, perform drug screens to identify potential molecular effectors of in situ regeneration, and provide a source for cell and tissue based transplantation. Highly efficient differentiation protocols have been established for many cell types and tissues; however, until very recently robust differentiation into skeletal muscle cells had not been possible unless driven by transgenic expression of master regulators of myogenesis. Nevertheless, several breakthrough protocols have been published in the past two years that efficiently generate cells of the skeletal muscle lineage from pluripotent stem cells. Here, we present an updated version of our recently described 50-day protocol in detail, whereby chemically defined media are used to drive and support muscle lineage development from initial CHIR99021-induced mesoderm through to PAX7-expressing skeletal muscle progenitors and mature skeletal myocytes. Furthermore, we report an optional method to passage and expand differentiating skeletal muscle progenitors approximately 3-fold every 2weeks using Collagenase IV and continued FGF2 supplementation. Both protocols have been optimized using a variety of human pluripotent stem cell lines including patient-derived induced pluripotent stem cells. Taken together, our differentiation and expansion protocols provide sufficient quantities of skeletal muscle progenitors and myocytes that could be used for a variety of studies.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".