Nucleated red blood cells impact DNA methylation and expression analyses of cord blood hematopoietic cells
Bibliographic record
Abstract
BACKGROUND: Genome-wide DNA methylation (DNAm) studies have proven extremely useful to understand human hematopoiesis. Due to their active DNA content, nucleated red blood cells (nRBCs) contribute to epigenetic and transcriptomic studies derived from whole cord blood. Genomic studies of cord blood hematopoietic cells isolated by fluorescence-activated cell sorting (FACS) may be significantly altered by heterotopic interactions with nRBCs during conventional cell sorting. RESULTS: We report that cord blood T cells, and to a lesser extent monocytes and B cells, physically engage with nRBCs during FACS. These heterotopic interactions resulted in significant cross-contamination of genome-wide epigenetic and transcriptomic data. Formal exclusion of erythroid lineage-specific markers yielded DNAm profiles (measured by the Illumina 450K array) of cord blood CD4 and CD8 T lymphocytes, B lymphocytes, natural killer (NK) cells, granulocytes, monocytes, and nRBCs that were more consistent with expected hematopoietic lineage relationships. Additionally, we identified eight highly differentially methylated CpG sites in nRBCs (false detection rate <5 %, |Δβ| >0.50) that can be used to detect nRBC contamination of purified hematopoietic cells or to assess the impact of nRBCs on whole cord blood DNAm profiles. Several of these erythroid markers are located in or near genes involved in erythropoiesis (ZFPM1, HDAC4) or immune function (MAP3K14, IFIT1B), reinforcing a possible immune regulatory role for nRBCs in early life. CONCLUSIONS: Heterotopic interactions between erythroid cells and white blood cells can result in contaminated cell populations if not properly excluded during cell sorting. Cord blood nRBCs have a distinct DNAm profile that can significantly skew epigenetic studies. Our findings have major implications for the design and interpretation of genome-wide epigenetic and transcriptomic studies using human cord blood.
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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 | 0.000 | 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) | 0.002 | 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".