Genome-Wide Profiling of RNA from Dried Blood Spots: Convergence with Bioinformatic Results Derived from Whole Venous Blood and Peripheral Blood Mononuclear Cells
Bibliographic record
Abstract
Genome-wide transcriptional profiling has emerged as a powerful tool for analyzing biological mechanisms underlying social gradients in health, but utilization in population-based studies has been hampered by logistical constraints and costs associated with venipuncture blood sampling. Dried blood spots (DBS) provide a minimally invasive, low-cost alternative to venipuncture, and in this article we evaluate how closely the substantive results from DBS transcriptional profiling correspond to those derived from parallel analyses of gold-standard venous blood samples (PAXgene whole blood and peripheral blood mononuclear cells [PBMC]). Analyses focused on differences in gene expression between African-Americans and Caucasians in a community sample of 82 healthy adults (age 18-70 years; mean 35). Across 19,679 named gene transcripts, DBS-derived values correlated r = .85 with both PAXgene and PBMC values. Results from bioinformatics analyses of gene expression derived from DBS samples were concordant with PAXgene and PBMC samples in identifying increased Type I interferon signaling and up-regulated activity of monocytes and natural killer (NK) cells in African-Americans compared to Caucasian participants. These findings demonstrate the feasibility of DBS in field-based studies of gene expression and encourage future studies of human transcriptome dynamics in larger, more representative samples than are possible with clinic- or lab-based research designs.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 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".