Single-cell imaging and transcriptomic analyses of endogenous cardiomyocyte dedifferentiation and cycling
Bibliographic record
Abstract
While it is recognized that there are low levels of new cardiomyocyte (CM) formation throughout life, the source of these new CM generates much debate. One hypothesis is that these new CMs arise from the proliferation of existing CMs potentially after dedifferentiation although direct evidence for this is lacking. Here we explore the mechanisms responsible for CM renewal in vivo using multi-reporter transgenic mouse models featuring efficient adult CM (ACM) genetic cell fate mapping and real-time cardiomyocyte lineage and dedifferentiation reporting. Our results demonstrate that non-myocytes (e.g., cardiac progenitor cells) contribute negligibly to new ACM formation at baseline or after cardiac injury. In contrast, we found a significant increase in dedifferentiated, cycling CMs in post-infarct hearts. ACM cell cycling was enhanced within the dedifferentiated CM population. Single-nucleus transcriptomic analysis demonstrated that CMs identified with dedifferentiation reporters had significant down-regulation in gene networks for cardiac hypertrophy, contractile, and electrical function, with shifts in metabolic pathways, but up-regulation in signaling pathways and gene sets for active cell cycle, proliferation, and cell survival. The results demonstrate that dedifferentiation may be an important prerequisite for CM proliferation and explain the limited but measurable cardiac myogenesis seen after myocardial infarction (MI).
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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".