Differentially expressed fusogens specify myocyte states to drive myogenesis
Bibliographic record
Abstract
During myogenesis, myocyte fusion leads to the formation of multinucleated muscle fibers, but how exactly this process is initiated remains poorly understood. Here, we performed single-cell RNA-sequencing on mouse somites from E9.5-E11.5 embryos, revealing multiple differentiation states during primary myogenesis. Among these, we identified two unexpected myocyte populations: one expressing both myomaker (Mymk) and myomixer (Mymx) (termed Mc1) and another expressing only Mymk (termed Mc2). Fluorescence in situ hybridization demonstrated that both populations are mononucleated and co-exist within the same somites, with only Mc1 persisting during secondary myogenesis. Lineage tracing using Mymx:Cre; RosaTdT mice demonstrated that the Mc2 cells arise from the Mc1. Mechanistically, we show that Mef2 and Rxr factors positively and negatively regulate Mymx expression, respectively. Additionally, RXRG interacts with MYOD1 and MYOG, modulating their transcriptional activity in luciferase assays. Collectively, our findings uncover two populations among the myocytes that drive primary and secondary fiber formation, challenging the traditional view of vertebrate muscle precursor homogeneity.
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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) | 0.001 | 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".