Single-cell transcriptomics reveals cellular heterogeneity and macrophage-to-mesenchymal transition in bicuspid calcific aortic valve disease
Bibliographic record
Abstract
Abstract Background Bicuspid aortic valve (BAV) is the most prevalent congenital valvular heart defect, and around 50% of severe isolated calcific aortic valve disease (CAVD) cases are associated with BAV. Although previous studies have demonstrated the cellular heterogeneity of aortic valves, the cellular composition of specific BAV at the single-cell level remains unclear. Methods Four BAV specimens from aortic valve stenosis patients were collected to conduct single-cell RNA sequencing (scRNA-seq). In vitro experiments were performed to further validate some phenotypes. Results The heterogeneity of stromal cells and immune cells were revealed based on comprehensive analysis. We identified twelve subclusters of VICs, four subclusters of ECs, six subclusters of lymphocytes, six subclusters of monocytic cells and one cluster of mast cells. Based on the detailed cell atlas, we constructed a cellular interaction network. Several novel cell types were identified, and we provided evidence for established mechanisms on valvular calcification. Furthermore, when exploring the monocytic lineage, a special population, macrophage derived stromal cells (MDSC), was revealed to be originated from MRC1 + (CD206) macrophages (Macrophage-to-Mesenchymal transition, MMT). FOXC1 and PI3K-AKT pathway were identified as potential regulators of MMT through scRNA analysis and in vitro experiments. Conclusions With an unbiased scRNA-seq approach, we identified a full spectrum of cell populations and a cellular interaction network in stenotic BAVs, which may provide insights for further research on CAVD. Notably, the exploration on mechanism of MMT might provide potential therapeutic targets for bicuspid CAVD.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".