Single-cell transcriptome and epigenomic reprogramming of cardiomyocyte-derived cardiac progenitor cells
Bibliographic record
Abstract
The molecular basis underlying the dedifferentiation of mammalian adult cardiomyocytes (ACMs) into myocyte-derived cardiac progenitor cells (mCPCs) during cardiac tissue regeneration is poorly understood. We present data integrating single-cell transcriptome and whole-genome DNA methylome analyses of mouse mCPCs to understand the epigenomic reprogramming governing their intrinsic cellular plasticity. Compared to parental cardiomyocytes, mCPCs display epigenomic reprogramming with many differentially-methylated regions, both hypermethylated and hypomethylated, across the entire genome. Correlating well with the methylome, our single-cell transcriptomic data show that the genes encoding cardiac structure and function proteins are remarkably down-regulated in mCPCs, while those for cell cycle, proliferation, and stemness are significantly up-regulated. In addition, implanting mCPCs into infarcted mouse myocardium improves cardiac function with augmented left ventricular ejection fraction. This dataset suggests that the cellular plasticity of mammalian cardiomyocytes is the result of a well-orchestrated epigenomic reprogramming and a subsequent global transcriptomic alteration. Understanding cardiomyocyte epigenomic reprogramming may enable the design of future clinical therapies that induce cardiac regeneration, and prevent heart failure.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.003 | 0.002 |
| 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".