Abstract 12937: Single-cell Transcriptomic Analysis Reveals Developmentally Impaired Endocardial Population in Hypoplastic Left Heart Syndrome
Bibliographic record
Abstract
Hypoplastic left heart syndrome (HLHS) is one of the most challenging forms of congenital heart diseases. Previous studies were mainly focused on intrinsic defects in myocardium. However, this does not sufficiently explain the abnormal development of the cardiac valve, septum, and vasculature, known to originate from the endocardium. Here, using single-cell transcriptomic profiling, induced pluripotent stem cells (iPSC) derived endocardial cells (iEECs), human fetal heart tissue with underdeveloped left ventricle, as well as a Xenopus model, we identified a developmentally impaired endocardial population in HLHS. The intrinsic endocardial deficits contributed to abnormal endothelial to mesenchymal transition, NOTCH signaling, and extracellular matrix organization, all of which are key factors in valve formation. Consequently, in an endocardium-myocardium co-culture system, we found that endocardial abnormalities conferred reduced proliferation and maturation of iPSC derived cardiomyocyte (iPSC-CMs) judged by Ki67 staining, contractility, sarcomere organization, and related gene expressions through a disrupted fibronectin (FN1)-integrin interaction. Several recently described HLHS de novo mutations such as ETS1 and CHD7 showed reduced binding to FN1 promoter and enhancer in HLHS vs. control iEECs based on ChIP-qPCR analysis. Additionally, we found that suppression of the ETS1 in Xenopus caused reduced endocardial FN1 expression and impaired heart development. Supplementation of FN1 or ETS1 over-expression in HLHS iEECs could rescue dysfunctions in both endocardium and myocardium in HLHS. Our studies reveal a critical role of endocardial abnormality in causing HLHS, and provide a rationale for improving endocardial function in future regenerative strategies. Schematic illustration of the endocardial and myocardial defects in HLHS.
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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) | 0.002 | 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".