Spatio-temporal control of myoblast identity drives muscle diversity in the Drosophila leg
Bibliographic record
Abstract
SUMMARY Skeletal muscles display remarkable morphological diversity, but the developmental mechanisms specifying distinct muscle morphology remain poorly understood. Using Drosophila leg muscles as a model, we uncover how naïve mesodermal precursors progressively acquire lineage-restricted identities through a stepwise specification program guided by epithelial morphogens. Initially, multipotent mesodermal precursors become spatially and transcriptionally restricted into two broad lineages - proximal and distal - under the combined influence of Wg/Wnt1 and Dpp/BMP signals from the overlying epithelium. By tracing mesodermal precursors that eventually give rise to distal leg muscles, we reveal a second sequence of fate bifurcations that generate distinct muscle subtypes prior to myoblast fusion, as well as a separate lineage producing neuronal lamella cells. Focusing on a single muscle lineage, we show that Wg and Dpp act again during a second phase to control the spatial and temporal deployment of specific transcription factors, ultimately specifying a unique muscle identity. These findings demonstrate that epithelial morphogens not only pattern the epithelium but also orchestrate muscle diversity by promoting stepwise mesodermal specification. Mesodermal precursors translate morphogen signals over time and space to activate distinct transcriptional programs that operate in parallel with the general program of myogenesis, enabling the emergence of distinct muscle and non- muscle lineages whose unique identities underpin their specialized functions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".