Cell specific patterns of methylation in the human placenta
Bibliographic record
Abstract
Epigenetic processes, such as DNA methylation, are known to regulate tissue specific gene expression. We explored this concept in the placenta to define whether DNA methylation is cell-type specific. Cytotrophoblasts and fibroblasts were isolated from normal midtrimester placentas. Using immunocytochemistry, we demonstrated 95% purity for cytotrophoblasts and 60-70% for fibroblasts. We compared DNA methylation profiles from cytotrophoblasts, fibroblasts and whole placental villi using bisulfite modified genomic DNA hybridized to the Illumina Methylation27 array. Euclidean cluster analysis of the DNA methylation profiles showed 2 main clusters, one containing cytotrophoblasts and placenta, the other fibroblasts. Differential methylation analysis identified 442 autosomal CpG sites that differed between cytotrophoblasts and fibroblasts, 315 between placenta and fibroblasts and 61 between placenta and cytotrophoblasts. Three candidate methylation differences were validated by targeted pyrosequencing assays. Pyrosequencing assays were developed for CpG sites less methylated in cytotrophoblasts than fibroblasts mapping to the promoter region of the beta subunit of human chorionic gonadotropin 5 (CGB5), as well as 2 CpG sites mapping to each of 2 tumor suppressor genes. Our data suggest that epigenetic regulation of gene expression is likely to be a key factor in the functional specificity of cytotrophoblasts. These data are proof of principle for cell-type specific epigenetic regulation in placenta and demonstrate that the methylation profile of placenta is mainly driven by cytotrophoblasts.
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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".