eLife Assessment: Hemodynamics regulate spatiotemporal artery muscularization in the developing circle of Willis
Bibliographic record
Abstract
Brain arteries are wrapped by vascular smooth muscle cells (VSMCs). Fully differentiated VSMCs are important for brain artery homeostasis, and they are lost in several cerebrovascular diseases. How healthy VSMCs differentiate on different brain arteries during development is unclear. Such knowledge will help regenerate lost VSMCs in brain arteriopathy. To answer this question, we studied the developmental muscularization of the zebrafish circle of Willis (CW) arteries, the major arterial loop that supplies blood to the brain in all vertebrates. We found that artery specification of CW endothelial cells (ECs) happens after they migrate from primitive veins to form CW arteries. VSMCs differentiate from pdgfrb+ common vascular mural cell progenitors at the time when embryo circulation starts and progress temporally and spatially from anterior to posterior CW arteries. Computational fluid dynamic simulation confirms that earlier VSMC differentiation coincide with higher pulsatile flow hemodynamics in anterior CW arteries. Pulsatile blood flow induces the differentiation of human brain pdgfrb+ progenitors into VSMCs and reducing pulsatile blood flow by blocking the zebrafish embryo heartbeat after pdgfrb+ recruitment but before VSMC differentiation limits the number of mature VSMCs. Congruently, the flow responsive transcription factor klf2a is activated in ECs before VSMC differentiation and knockdown delays VSMC differentiation on CW arteries. Overall, our data place hemodynamic activation of endothelial klf2a signaling as key determinant of spatiotemporal VSMC differentiation on CW arteries.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.094 | 0.033 |
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".