E2A regulates neural ectoderm fate specification in human embryonic stem cells
Bibliographic record
Abstract
E proteins transcription factors are critical for many cell fate decisions. However, the roles of E proteins in the germ-layer specification of human embryonic stem cells (hESC) are poorly understood. We disrupted the TCF3 gene locus to delete the E protein E2A in hESCs. E2A KO hESCs retained key features of pluripotency, but displayed decreased neural ectoderm coupled with enhanced mesoendoderm outcomes. Genome-wide analyses showed that E2A directly regulates neural ectoderm and Nodal pathway genes. Accordingly, inhibition of Nodal or E2A overexpression partially rescued the neural ectoderm defect in E2A KO hESCs. Loss of E2A had little impact on the epigenetic landscape of hESCs, whereas E2A KO neural precursors displayed increased accessibility of the gene locus encoding the Nodal agonist CRIPTO. Double-deletion of both E2A and HEB (TCF12) resulted in a more severe neural ectoderm defect. Therefore, this study reveals critical context-dependent functions for E2A in human neural ectoderm fate-specification.
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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) | 0.001 | 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".