Geometric Control of Cardiomyogenic Induction in Human Pluripotent Stem Cells
Bibliographic record
Abstract
Although it has been observed that aggregate size affects cardiac development, an incomplete understanding of the cellular mechanisms underlying human pluripotent stem cell-derived cardiomyogenesis has limited the development of robust defined-condition cardiac cell generation protocols. Our objective was thus to elucidate cellular and molecular mechanisms underlying the endogenous control of human embryonic stem cell (hESC) cardiac tissue development, and to test the hypothesis that hESC aggregate size influences extraembryonic endoderm (ExE) commitment and cardiac inductive properties. hESC aggregates were generated with 100, 1000, or 4000 cells per aggregate using microwells. The frequency of endoderm marker (FoxA2 and GATA6)-expressing cells decreased with increasing aggregate size during early differentiation. Cardiogenesis was maximized in aggregates initiated from 1000 cells, with frequencies of 0.49±0.06 cells exhibiting a cardiac progenitor phenotype (KDR(low)/C-KIT(neg)) on day 5 and 0.24±0.06 expressing cardiac Troponin T on day 16. A direct relationship between ExE and cardiac differentiation efficiency was established by forming aggregates with varying ratios of SOX7 (a transcription factor required for ExE development) overexpressing or knockdown hESCs to unmanipulated hESCs. We demonstrate, in a defined, serum-free cardiac induction system, that robust and efficient cardiac differentiation is a function of endogenous ExE cell concentration, a parameter that can be directly modulated by controlling hESC aggregate size.
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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 | 0.001 |
| 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 | 0.001 |
| 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".