Aggregate Size Optimization in Microwells for Suspension-based Cardiac Differentiation of Human Pluripotent Stem Cells
Bibliographic record
Abstract
Cardiac differentiation of human pluripotent stems cells (hPSCs) is typically carried out in suspension cell aggregates. Conventional aggregate formation of hPSCs involves dissociating cell colonies into smaller clumps, with size control of the clumps crudely controlled by pipetting the cell suspension until the desired clump size is achieved. One of the main challenges of conventional aggregate-based cardiac differentiation of hPSCs is that culture heterogeneity and spatial disorganization lead to variable and inefficient cardiomyocyte yield. We and others have previously reported that human embryonic stem cell (hESC) aggregate size can be modulated to optimize cardiac induction efficiency. We have addressed this challenge by employing a scalable, microwell-based approach to control physical parameters of aggregate formation, specifically aggregate size and shape. The method we describe here consists of forced aggregation of defined hPSC numbers in microwells, and the subsequent culture of these aggregates in conditions that direct cardiac induction. This protocol can be readily scaled depending on the size and number of wells used. Using this method, we can consistently achieve culture outputs with cardiomyocyte frequencies greater than 70%.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".