Spatial compartmentalization of signalling imparts source-specific functions on secreted factors
Bibliographic record
Abstract
Summary Efficient regeneration requires multiple cell types acting in a coordination. To better understand the intercellular networks involved and how they change when regeneration fails, we profiled the transcriptome of hematopoietic, stromal, myogenic, and endothelial cells over 14 days following acute muscle damage. A time-resolved computational model of interactions was generated, and VEGFA-driven endothelial engagement was identified as a key differentiating feature in models of successful and failed regeneration. In addition, it revealed that the majority of secreted signals, including VEGFA, are simultaneously produced by multiple cell types. To test whether the cellular source of a factor determines its function, we deleted VEGFA from two cell types residing in close proximity, stromal and myogenic progenitors. By comparing responses to different types of damage, we found that myogenic and stromal VEGFA have distinct functions in regeneration. This suggests that spatial compartmentalization of signaling plays a key role in intercellular communication networks. Highlights Ligand-receptor signaling redundancy during skeletal muscle regeneration Inflammatory cells, and muscle and fibro/adipogenic progenitors produce VEGFA VEGFA from muscle progenitors control their proliferation after muscle damage VEGFA from FAP controls angiogenesis only after ischemic damage eTOC blurb Groppa et al . performed a novel time-resolved bioinformatics analysis that revealed extensive ligand-receptor redundancy among the cell types contributing to skeletal muscle regeneration. They focused on one of these pathways, and showed that VEGFA from different cell types has distinct roles in regeneration.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".