Derivation and Characterization of Immortalized Human Muscle Satellite Cell Clones from Muscular Dystrophy Patients and Healthy Individuals
Bibliographic record
Abstract
In Duchenne muscular dystrophy (DMD) patients, absence of dystrophin causes muscle wasting by impacting both the myofiber integrity and the properties of muscle stem cells (MuSCs). Investigation of DMD encompasses the use of MuSCs issued from human skeletal muscle. However, DMD-derived MuSC usage is restricted by the limited number of divisions that human MuSCs can undertake in vitro before losing their myogenic characteristics and by the scarcity of human material available from DMD muscle. To overcome these limitations, immortalization of MuSCs appears as a strategy. Here, we used CDK4/hTERT expression in primary MuSCs and we derived MuSC clones from a series of clinically and genetically characterized patients, including eight DMD patients with various mutations, four congenital muscular dystrophies and three age-matched control muscles. Immortalized cultures were sorted into single cells and expanded as clones into homogeneous populations. Myogenic characteristics and differentiation potential were tested for each clone. Finally, we screened various promoters to identify the preferred gene regulatory unit that should be used to ensure stable expression in the human MuSC clones. The 38 clonal immortalized myogenic cell clones provide a large collection of controls and DMD clones with various genetic defects and are available to the academic community.
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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) | 0.003 | 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".