Single-cell transcriptomics reveals diversity during heart valve epithelial-to-mesenchymal transitions
Bibliographic record
Abstract
Abstract The epithelial-to-mesenchymal transition (EMT) is a process critical for wound healing, fibrosis, and cancer metastasis, but is also essential for atrioventricular valve formation, where distinct EMTs of endocardium (EndMT) and epicardium (EpiMT) generate mesenchyme. To track these processes, we have analyzed over 50,000 murine single-cell transcriptomes from embryonic day (E)7.75 cardiac crescent to E12.5 atrioventricular canals. We detail mesenchymal and endocardial bifurcation during EndMT, identify a unique, Hic1-expressing epicardial population during EpiMT, and reveal epithelial-mesenchymal plasticity (EMP) during both processes. Single-cell and histological analysis of Sox9-deficient valves show the accumulation of cells exhibiting EMP. Lastly, we deconvolve the signaling pathways active during the initiation and progression of EndMT and EpiMT. Overall, these data are the first to reveal mechanisms of emergence of mesenchyme from endocardium or epicardium at single-cell resolution and will serve as an atlas of EMT initiation and progression with broad implications in regenerative medicine and cancer biology.
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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".