Cell-specific characterization of the placental methylome
Bibliographic record
Abstract
BACKGROUND: DNA methylation (DNAm) profiling has emerged as a powerful tool for characterizing the placental methylome. However, previous studies have focused primarily on whole placental tissue, which is a mixture of epigenetically distinct cell populations. Here, we present the first methylome-wide analysis of first trimester (n = 9) and term (n = 19) human placental samples of four cell populations: trophoblasts, Hofbauer cells, endothelial cells, and stromal cells, using the Illumina EPIC methylation array, which quantifies DNAm at > 850,000 CpGs. RESULTS: The most distinct DNAm profiles were those of placental trophoblasts, which are central to many pregnancy-essential functions, and Hofbauer cells, which are a rare fetal-derived macrophage population. Cell-specific DNAm occurs at functionally-relevant genes, including genes associated with placental development and preeclampsia. Known placental-specific methylation marks, such as those associated with genomic imprinting, repetitive element hypomethylation, and placental partially methylated domains, were found to be more pronounced in trophoblasts and often absent in Hofbauer cells. Lastly, we characterize the cell composition and cell-specific DNAm dynamics across gestation. CONCLUSIONS: Our results provide a comprehensive analysis of DNAm in human placental cell types from first trimester and term pregnancies. This data will serve as a useful DNAm reference for future placental studies, and we provide access to this data via download from GEO (GSE159526), through interactive exploration from the web browser ( https://robinsonlab.shinyapps.io/Placental_Methylome_Browser/ ), and through the R package planet, which allows estimation of cell composition directly from placental DNAm data.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.002 | 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".