Morphogenesis and differentiation of embryonic vascular smooth muscle cells in zebrafish
Bibliographic record
Abstract
Abstract Despite the critical role of vascular mural cells (smooth muscle cells and pericytes) in supporting the endothelium of blood vessels, we know little of their early morphogenesis and differentiation. foxc1b:EGFP expressing cells in zebrafish associate with the vascular endothelium ( kdrl ) and co-express a smooth muscle marker ( acta2 ), but not a pericyte marker ( pdgfrβ ). The expression of foxc1b in early peri-endothelial mesenchymal cells allows us to follow the morphogenesis of mesenchyme into acta2 expressing vascular smooth muscle cells. We show that mural cells expressing different markers associate with vessels of different diameters, depending on their embryonic location and developmental timing, suggesting marker expression is predictive of functional differences. We identify gene expression signatures for an enriched vascular smooth muscle cell population ( foxc1b + acta2) and all smooth muscle (acta2) using fluorescence-activated cell sorting and RNA-Seq. Finally, we demonstrate that progressive loss of foxc1a / foxc1b results in decreased smooth muscle cell coverage. Together, our data highlight the early cellular dynamics and transcriptome profiles of smooth muscle cells in vivo, using foxc1b as a unique tool to probe vascular smooth muscle cell differentiation. Summary Statement Tracing the morphogenesis and transcriptome of early vascular smooth muscle cells using foxc1b
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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 | 0.001 | 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".