Wnt pathway activation unlocks disease-neutral proliferative potential in human iPSC-derived cardiomyocytes: A comparative study across healthy and inherited cardiac disease models
Bibliographic record
Abstract
The therapeutic potential of Wnt/β-catenin signaling to enhance proliferation in differentiated cardiomyocytes remains underexplored, particularly in genetically diverse disease models. Here, we systematically evaluated whether pharmacological Wnt activation overrides genetic constraints to drive expansion of induced pluripotent stem cell-derived cardiomyocytes (iCMs) from healthy donors and inherited cardiomyopathy models ( GAA -Pompe disease, RYR2 -catecholaminergic polymorphic ventricular tachycardia, and KCNQ1 -long QT syndrome type 1). Using a component-defined GiWi protocol, functionally mature iCMs were generated from a high-quality iPSC line with validated trilineage differentiation capacity. Longitudinal analysis of CHIR-induced Wnt/β-catenin activation demonstrated dose-dependent proliferative amplification, with CHIR-treated iCMs achieving >400-fold monolayer expansion by passage 4 versus ~8-fold in controls. Immunofluorescence quantification revealed significantly elevated Ki67 + /cTnT + double-positive cardiomyocytes under CHIR treatment (~20% vs. ~9% in controls at passage 3). Strikingly, proliferative responses showed genetic neutrality: healthy iCMs exhibited ~432-fold expansion compared to ~406-fold in disease models (p = 0.72), with comparable Ki67 + /cTnT + ratios by passage 4 (healthy: ~8.9%; disease: ~8.3%). These findings demonstrate that timed Wnt activation overrides genetic lesions to enable disease-agnostic proliferation in differentiated iCMs. This genetic neutrality supports standardized regenerative strategies for genetically heterogeneous cardiomyopathies and arrhythmias, addressing a critical challenge in developing personalized cardiac therapies.
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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".