Generation of Valvular Interstitial Cells from Human Pluripotent Stem Cells
Bibliographic record
Abstract
Heart valves are living structures whose sophisticated functions are mediated by a specialized population of mesenchymal cells known as valvular interstitial cells (VICs). Given their central role in valve homeostasis, VICs represent a promising cell population for studying heart valve diseases and developing novel therapies to treat them. Here, we describe a strategy for generating VICs from human pluripotent stem cells (hPSCs) by stage-specific manipulation of developmental signalling pathways. Our results demonstrate that hPSC-derived VICs show a high transcriptional similarity to primary human fetal VICs and can secrete key proteins of the valve extracellular matrix. We further investigate the heterogeneity of hPSC-derived VICs and identify two major subpopulations with distinct molecular and functional properties, mirroring the cellular diversity observed in vivo. Finally, we utilize an in vitro model of Noonan syndrome to demonstrate that hPSC-derived VICs can accurately recapitulate key aspects of valve disease. Collectively, these findings provide a reproducible method for the scaled generation of bona fide hPSC-derived VICs and establish their utility in disease modelling and tissue engineering applications.
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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 | 0.000 |
| 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".