Cell type-specific eQTL detection from single-cell RNA-seq reveals post-transcriptional regulatory mechanisms in human islets
Bibliographic record
Abstract
Summary Gene regulatory networks (GRNs) must be robust to maintain cellular identity across individuals yet flexible to accommodate population genetic variants. Comparing expression quantitative trait loci (eQTLs) in health and disease can therefore reveal hidden players in gene regulation. Here, we developed a computational pipeline to infer post-transcriptional regulatory events and their functional consequences from eQTL signals located in the 3′ untranslated region of genes using single-cell RNA sequencing. As a case study, we repurposed datasets from human islets of donors with and without type 2 diabetes (T2D). We identified eQTL landscapes differing by cell type and diabetes status, with cell-type specificity associated with gene differential expression and RNA-binding proteins. Integrating eQTLs with GWAS and miRNA hits linked variants in G6PC2 , QDPR , and RELL1 to insulin secretion, endoplasmic reticulum stress, and oxidative phosphorylation. Our pipeline provides a framework to unravel post-transcriptional regulatory mechanisms in health and disease at cell type resolution.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".