Abstract 278: Dual Modulation of Microscale Tissue Engineering and Energy Metabolism for Maturation of Human Pluripotent Stem Cell-Derived Cardiomyocytes
Bibliographic record
Abstract
Background: Human pluripotent stem cell-derived cardiomyocytes (hPSC-CMs) represent a viable cell source for clinical application and other purposes, such as disease modeling and drug discovery for cardiovascular diseases. However, their immature phenotype limits their utility for these applications. It is widely known that the transition from fetal to adult heart during development is accompanied by an alteration in energy metabolism from glycolysis to fatty acid oxidation (FAO), respectively. Using this metabolic hallmark of cardiomyocyte maturation, we investigated a platform that combines three-dimensional (3D) cell cultivation and molecules that target key pathways involved in the energy metabolism of cardiomyocytes. Methods: Cardiac spheres of highly-enriched hPSC-CMs were generated from cardiac-differentiated cultures and treated with a combination of five molecules or control (DMSO) for one week. We used functional, morphological, transcriptome and metabolome analyses to assess the maturation status of hPSC-CMs. RESULTS: Treatment of 3D hPSC-CMs with a combination of five molecules elicited increased FAO along with mitochondrial respiratory capacity. These changes in oxidative capacity were associated with increased mitochondrial content and DNA. In addition, cardiac spheres treated with combined molecules displayed enhanced calcium transient kinetics when compared to control cells. RNA sequencing revealed upregulation of genes involved in many cellular metabolic processes, including FAO. Lastly, metabolic profiling of these cardiac spheres identified more than one hundred metabolites that were altered upon treatment. Conclusions: Our study showed that microscale tissue engineering along with treatment of hPSC-CMs with a combination of five molecules increase mitochondrial function, alter molecular and metabolic profiles, and potentially improve cardiomyocyte maturation.
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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) | 0.002 | 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".