Epigenetic analysis of human embryonic carcinoma cells during retinoic acid-induced neural differentiation
Bibliographic record
Abstract
Differentiation of stem cells from a pluripotent to a committed state involves global changes in genome expression patterns, critically determined by chromatin structure and interactions of chromatin-binding proteins. The dynamics of chromatin structure are tightly regulated by multiple epigenetic mechanisms such as histone modifications and the incorporation of histone variants. In the current work, we induced neural differentiation of a human embryonal carcinoma stem cell line, NTERA2/NT2, by retinoic acid (RA) treatment, primarily according to two different methods of adherent cell culture (rosette formation) and suspension cell culture (EB formation) conditions, and histone modifications and variations were compared through these processes. Western blot analysis of histone extracts showed significant changes in the acetylation and methylation patterns of histone H3, and expression level of the histone variant H2A.Z, after RA treatment in both protocols. Using chromatin immunoprecipitation (ChIP) coupled with real-time PCR, it was shown that these epigenetic changes occurred on the regulatory regions of 4 marker genes (Oct4, Nanog, Nestin, and Pax6) in a culture condition dependent manner. This report demonstrates the dynamic interplay of histone modification and variation in regulating the gene expression profile, during stem cell differentiation and under different culture conditions.
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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".