Epicardially secreted fibronectin drives cardiomyocyte maturation in 3D-engineered heart tissues
Bibliographic record
Abstract
Ischemic heart failure is due to irreversible loss of cardiomyocytes. Preclinical studies showed that human pluripotent stem cell (hPSC)-derived cardiomyocytes could remuscularize infarcted hearts and improve cardiac function. However, these cardiomyocytes remained immature. Incorporating hPSC-derived epicardial cells has been shown to improve cardiomyocyte maturation, but the exact mechanisms are unknown. We posited epicardial fibronectin (FN1) as a mediator of epicardial-cardiomyocyte crosstalk and assessed its role in driving hPSC-derived cardiomyocyte maturation in 3D-engineered heart tissues (3D-EHTs). We found that the loss of FN1 with peptide inhibition F(pUR4), CRISPR-Cas9-mediated FN1 knockout, or tetracycline-inducible FN1 knockdown in 3D-EHTs resulted in immature cardiomyocytes with decreased contractile function, and inefficient Ca 2+ handling. Conversely, when we supplemented 3D-EHTs with recombinant human FN1, we could recover hPSC-derived cardiomyocyte maturation. Finally, our RNA-sequencing analyses found FN1 within a wider paracrine network of epicardial-cardiomyocyte crosstalk, thus solidifying FN1 as a key driver of hPSC-derived cardiomyocyte maturation in 3D-EHTs.
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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".