A high-content small molecule screen identifies novel inducers of definitive endoderm
Bibliographic record
Abstract
OBJECTIVES: Human embryonic stem cells (hESCs) and human induced pluripotent stem cells (hiPSCs) can generate any given cell type in the human body. One challenge for cell-replacement therapy is the efficient differentiation and expansion of large quantities of progenitor cells from pluripotent stem cells produced under good manufacturing practice (GMP). FOXA2 and SOX17 double positive definitive endoderm (DE) progenitor cells can give rise to all endoderm-derived cell types in the thymus, thyroid, lung, pancreas, liver, and gastrointestinal tract. FOXA2 is a pioneer transcription factor in DE differentiation that is also expressed and functionally required during pancreas development and islet cell homeostasis. Current differentiation protocols can successfully generate endoderm; however, generation of mature glucose-sensitive and insulin-secreting β-cells is still a challenge. As a result, it is of utmost importance to screen for small molecules that can improve DE and islet cell differentiation for cell-replacement therapy for diabetic patients. METHODS: The aim of this study was to identify and validate small molecules that can induce DE differentiation and further enhance pancreatic progenitor differentiation. Therefore, we developed a large scale, high-content screen for testing a chemical library of 23,406 small molecules to identify compounds that induce FoxA2 in mouse embryonic stem cells (mESCs). RESULTS: pancreatic progenitors from hESCs. CONCLUSION: Taken together, DE induction by ROCKi can simplify and improve current endoderm and pancreatic differentiation protocols towards a GMP-grade cell product for β-cell replacement.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".