Serum-free differentiation of functional human coronary-like vascular smooth muscle cells from embryonic stem cells
Bibliographic record
Abstract
AIMS: Despite the diverse developmental origins of vascular smooth muscle cells (VSMCs), recent attempts to generate VSMCs from human embryonic stem cells (hESCs) differentiated along various lineages did not yield distinct cell phenotypes. The aim of this study was to derive and characterize functional coronary-like VSMCs from hESCs using serum-free cardiac-directed differentiation. METHODS AND RESULTS: Embryoid bodies (EBs) from three pluripotent stem cell lines subjected to cardiac-directed differentiation in defined media were characterized over 30 days for VSMC-specific gene expression by qRT-PCR, immunofluorescence microscopy and fluorescence-activated cell sorting (FACS). EBs composed of cardiomyocytes, endothelial cells (ECs), fibroblasts, and VSMCs underwent FACS on d28 to reveal that the VSMCs form a distinct subpopulation, which migrate with ECs in an in vitro angiogenesis assay. To enrich for VSMCs, d28 EBs were dissociated and cultured as monolayers. Over several passages, mRNA and protein levels of cardiomyocyte, endothelial, and fibroblast markers were abolished, whereas those of mature VSMCs were unchanged. Vascular endothelial growth factor and basic fibroblast growth factor were critical for the separation of the cardiac and VSMC lineages in EBs, and for the enrichment of functional VSMCs in monolayer cultures. Calcium cycling and cell shortening responses to vasoconstrictors in hESC-derived VSMCs in vitro were indistinguishable from primary human coronary artery SMCs, and distinct from bladder and aorta SMCs. VSMCs identically derived from green fluorescent protein -expressing hESCs integrated in and contributed to new vessel formation in vivo. CONCLUSION: The ability to generate hESC-derived functional human coronary-like VSMCs in serum-free conditions has implications for disease modelling, drug screening, and regenerative therapies.
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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 | 0.001 |
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".