Evaluating the Reparative Potential of Secretome from Patient-Derived Induced Pluripotent Stem Cells during Ischemia–Reperfusion Injury in Human Cardiomyocytes
Bibliographic record
Abstract
During a heart attack, ischemia causes losses of billions of cells; this is especially concerning given the minimal regenerative capability of cardiomyocytes (CMs). Heart remuscularization utilizing stem cells has improved cardiac outcomes despite little cell engraftment, thereby shifting focus to cell-free therapies. Consequently, we chose induced pluripotent stem cells (iPSCs) given their pluripotent nature, efficacy in previous studies, and easy obtainability from minimally invasive techniques. Nonetheless, using iPSC secretome-based therapies for treating injured CMs in a clinical setting is ill-understood. We hypothesized that the iPSC secretome, regardless of donor health, would improve cardiovascular outcomes in the CM model of ischemia–reperfusion (IR) injury. Episomal-generated iPSCs from healthy and dilated cardiomyopathy (DCM) donors, passaged 6–10 times, underwent 24 h incubation in serum-free media. Protein content of the secretome was analyzed by mass spectroscopy and used to treat AC16 immortalized CMs during 5 h reperfusion following 24 h of hypoxia. IPSC-derived secretome content, independent of donor health status, had elevated expression of proteins involved in cell survival pathways. In IR conditions, iPSC-derived secretome increased cell survival as measured by metabolic activity (p < 0.05), cell viability (p < 0.001), and maladaptive cellular remodelling (p = 0.052). Healthy donor-derived secretome contained increased expression of proteins related to calcium contractility compared to DCM donors. Congruently, only healthy donor-derived secretomes improved CM intracellular calcium concentrations (p < 0.01). Heretofore, secretome studies mainly investigated differences relating to cell type rather than donor health. Our work suggests that healthy donors provide more efficacious iPSC-derived secretome compared to DCM donors in the context of IR injury in human CMs. These findings illustrate that the regenerative potential of the iPSC secretome varies due to donor-specific differences.
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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".