Abstract P1098: Molecular Maturation Of Engrafted Human Pluripotent Stem Cell-derived Cardiomyocytes
Bibliographic record
Abstract
The transplantation of human pluripotent stem cell derived cardiomyocytes (hPSC-CMs) represents an attractive strategy to regenerate myocardium lost following injury and mitigate the pathological remodeling that ultimately leads to heart failure. However, in vitro hPSC-CMs have an immature phenotype that likely contributes to the transient arrhythmias observed during the initial weeks following hPSC-CM transplantation in large-animal models, hindering safe translation into the clinic. We hypothesized that maturational changes in hPSC-CMs in vivo facilitate the improved electrophysiological behavior of graft tissue observed at later time-points. Because the changing phenotype of hPSC-CM graft tissue remains largely unexplored, we used single nucleus RNA sequencing to evaluate the transcriptional dynamics and cell population heterogeneity of hPSC-CMs prior to transplantation and at 2- and 8-weeks following transplantation in a guinea pig cardiac cryoinjury model (n=3). We used methods based on the sorting of nuclei labeled by a genetically-encoded fluorescent reporter to enrich the proportion of human nuclei sequenced from the graft site and created a comprehensive atlas (> 20,000 cells) of host and graft cells present at the implantation site at both time-points. We identified an upregulation of genes involved in intracellular calcium handling, AMPK, and cAMP signaling pathways, as well as a metabolic shift in engrafted hPSC-CMs that we speculate supports their growth and enhanced electrophysiological function. Our study includes a comprehensive description of the temporal changes in hPSC-CM graft tissue at the single-cell resolution and provides unique insights into how these cells adapt to the injured heart environment and mature in vivo.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".