Quantitative screening of embryonic stem cell differentiation: Endoderm formation as a model
Bibliographic record
Abstract
Embryonic stem (ES) cells have attracted much attention as a possible source of functional cells for regenerative medicine. Therapeutic use of ES cells requires control over the types and frequencies of cells generated during their in vitro differentiation. Due to the complexity of factors that impact upon ES cell differentiation, novel approaches for the optimization of tissue-specific development are required. This motivates our use of factorial and composite design methods to make empirical investigations more efficient, and to reveal unexpected interactions missed by conventional dose-response analysis. Factorial experiments would benefit from the high content evaluation of a large number of test conditions, necessitating the development of a quantitative screening technology (QST) capable of reporting the absolute number and frequency of target cells. We have developed and validated such a technology for ES cell differentiation analysis using automated fluorescence microscopy, employing endoderm differentiation as a model system. To test this platform, a two-level factorial experiment was carried out to identify major and interactive effects of glucose, insulin, retinoic acid (RA), basic fibroblast growth factor (bFGF), and epidermal growth factor (EGF) on endoderm formation. RA was found to have inhibitory effects on endoderm formation, while low glucose proved beneficial. QST was demonstrated to be a powerful tool to study factors impacting endoderm-specific ES cell differentiation, and should be applicable to the analysis of a range of ES cell-derived tissues.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".