Epigenetic subtyping of white blood cells using a thermoplastic elastomer-based microfluidic emulsification device for multiplexed, methylation-specific digital droplet PCR
Bibliographic record
Abstract
Epigenetic markers attract increasing attention for the study of phenotypic variations, which has led to the investigation of cell-lineage DNA methylation patterns that correlate with human leukocyte populations for obtaining counts of white blood cell (WBC) subsets. Current methods of DNA methylation analysis involve genome sequencing or loci-specific quantitative PCR (qPCR). Herein, a multiplexed digital droplet PCR (ddPCR) workflow for determining epigenetic-based WBC differential count is described for the first time. A microfluidic emulsification device fabricated from a commercially available thermoplastic elastomer (e.g., Mediprene) promotes customizability and cost-effectiveness of the methodology, which are prerequisites for translation into clinical and point-of-care diagnostics. Bisulfite-treated DNA from peripheral blood mononuclear cells and whole blood is encapsulated in droplets with ddPCR reagents containing primers and fluorescent hydrolysis probes specific for CpG loci correlated with WBC sub-population types. The method enables multiplexed detection of various methylation sites within a single droplet. Both qPCR and immunofluorescence staining (IF) were conducted to validate the capacity of the ddPCR methodology to accurately determine WBC sub-populations using epigenetic analysis of methylation sites. ddPCR results correlated closely to cell proportions obtained using IF, whereas qPCR significantly underestimated these values for both high and low copy number gene targets.
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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) | 0.001 | 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".