Cell type-specific DNA methylation in neonatal cord tissue and cord blood: a 850K-reference panel and comparison of cell types
Bibliographic record
Abstract
Accounting for cellular heterogeneity is essential in neonatal epigenome-wide association studies (EWAS) performed on heterogeneous tissues, such as umbilical cord tissue (CT) or cord blood (CB). Using a reference-panel-based statistical approach, the cell type composition of heterogeneous tissues can be estimated by comparison of whole tissue DNA methylation profiles with cell type-specific DNA methylation signatures. Currently, there is no adequate DNA methylation reference panel for CT, and existing CB panels have been generated on lower coverage Infinium HumanMethylation450 arrays. In this study, we generate a reference panel for CT and improve available CB panels by using the higher coverage Infinium MethylationEPIC arrays. We performed DNA methylation profiling of 9 cell types isolated from CT and CB samples from 14 neonates. In addition to these cell types, we profiled DNA methylation of unfractionated CT and CB. Cell type composition of these unfractionated tissue samples, as estimated by our reference panels, was in agreement with that obtained by flow cytometry. Expectedly, DNA methylation profiles from CT and CB were distinct, reflecting their mesenchymal and hematopoietic stem cell origins. Variable CpGs from both unfractionated CT and its isolated cell types were more likely to be located in open seas and intronic regions than those in CB. Cell type specific CpGs in CT were enriched in intercellular matrix pathways, while those from CB were enriched in immune-related pathways. This study provides an open source reference panel for estimation and adjustment of cellular heterogeneity in CT and CB, and broadens the scope of tissue utilization assessed in future neonatal EWAS studies.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".