Clonal analysis of individual human embryonic stem cell differentiation patterns in microfluidic cultures
Bibliographic record
Abstract
Heterogeneity in the clonal outputs of individual human embryonic stem cells (hESCs) confounds analysis of their properties in studies of bulk populations and how to manipulate them for clinical applications. To circumvent this problem we developed a microfluidic device that supports the robust generation of colonies derived from single ESCs. This microfluidic system contains 160 individually addressable chambers equipped for perfusion culture of individual hESCs that could be shown to match the growth rates, marker expression and colony morphologies obtained in conventional cultures. Use of this microfluidic device to analyze the clonal growth kinetics of multiple individual hESCs induced to differentiation revealed variable shifts in the growth rate, area per cell and expression of OCT4 in the progeny of individual hESCs. Interestingly, low OCT4 expression, a slower growth rate and low nuclear to cytoplasmic ratios were found to be correlated responses. This study demonstrates how microfluidic systems can be used to enable large scale live-cell imaging of isolated hESCs exposed to changing culture conditions, to examine how different aspects of their variable responses are correlated.
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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) | 0.000 | 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".