Abstract P445: A Combinatorial Approach Comprehensively Improves The Maturation Of Human Induced Pluripotent Stem Cell Derived Atrial Cardiomyocytes
Bibliographic record
Abstract
Introduction: The limited success of pharmacological approaches to atrial fibrillation ( AF ) is due to limitations of in vitro and in vivo models and inaccessibility of human atrial tissue. Patient-specific induced pluripotent stem cell-derived atrial cardiomyocytes (iPSC-aCMs) are a robust platform to model the heterogeneous myocardial substrate of AF, but their immaturity limits their fidelity. Objective: We hypothesized that a combinatorial approach of biochemical (triiodothyronine [ T3 ], insulin-like growth factor-1 [ IGF-1 ], and dexamethasone; collectively TID ), bioenergetic (fatty acids [ FA ]), and electrical stimulation ( ES ) will enhance electrophysiological ( EP ), structural, and metabolic maturity of iPSC- a CMs. Methods: We assessed maturation with whole cell patch clamping, calcium transients, immunofluorescence (IF), Seahorse Analyzer, contractility assay, RT-PCR, Western Blotting, and RNA sequencing (RNAseq). Using a time series with RNAseq we identified signaling pathways and transcriptional regulation that drive EP, structural, and metabolic atrial development and compared iPSC-aCM maturity with human aCMs (haCMs) obtained from the same patient. Results: TID+FA+ES significantly improved structural organization and cell morphology ( Fig. 1a ), enhanced membrane potential stability and improved depolarization ( Fig. 1b ), improved Ca 2+ kinetics with faster and increased Ca 2+ release from sarcoplasmic reticulum ( Fig. 1c ), and increased expression of Na + , Ca 2+ , and K + channels, markers of structural maturity, FA metabolism, and oxidative phosphorylation ( Fig. 1d ). There was no difference in each parameter between TID+FA+ES iPSC-aCMs and haCMs from the same patient. Conclusion: Our optimized, combinatorial TID+FA+ES approach markedly enhanced EP, structural, and metabolic maturity of human iPSC-aCMs, which will be useful for elucidating the genetic basis of AF developing precision drug 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) | 0.001 | 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 | 0.000 | 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".