Substrate and mechanotransduction influence SERCA2a localization in human pluripotent stem cell-derived cardiomyocytes affecting functional performance
Bibliographic record
Abstract
Physical cues are major determinants of cellular phenotype and evoke physiological and pathological responses on cell structure and function. Cellular models aim to recapitulate basic functional features of their in vivo counterparts or tissues in order to be of use in in vitro disease modeling or drug screening and testing. Understanding how culture systems affect in vitro development of human pluripotent stem cell (hPSC)-derivatives allows optimization of cellular human models and gives insight in the processes involved in their structural organization and function. In this work, we show involvement of the mechanotransduction pathway RhoA/ROCK in the structural reorganization of hPSC-derived cardiomyocytes after adhesion plating. These structural changes have a major impact on the intracellular localization of SERCA2 pumps and concurrent improvement in calcium cycling. The process is triggered by cell interaction with the culture substrate, which mechanical cues drive sarcomeric alignment and SERCA2a spreading and relocalization from a perinuclear to a whole-cell distribution. This structural reorganization is mediated by the mechanical properties of the substrate, as shown by the process failure in hPSC-CMs cultured on soft 4kPa hydrogels as opposed to physiologically stiff 16kPa hydrogels and glass. Finally, pharmacological inhibition of Rho-associated protein kinase (ROCK) by different compounds identifies this specific signaling pathway as a major player in SERCA2 localization and the associated improvement in hPSC-CMs calcium handling ability in vitro.
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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".