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This study performed expression quantitative trait loci (eQTL) mapping in individual pancreatic cell types using single-cell RNA sequencing data from 71 donors and deconvoluted bulk RNA-seq data from 305 donors. The researchers identified 112 unique eGenes from single-cell analysis and 3,134 from deconvoluted data, including 13 and 116 novel eGenes respectively that were not found in previous bulk pancreatic tissue studies. Fine-mapping revealed specific genetic variants associated with gene expression in distinct pancreatic cell types, such as rs2168101 linked to LMO1 expression in alpha cells, providing insights into cell-type-specific mechanisms underlying metabolic disease risk.
Why it matters
This research advances understanding of how genetic variation affects different pancreatic cell types, which could help identify specific cellular mechanisms contributing to metabolic diseases like diabetes. By pinpointing which cell types are affected by disease-associated genetic variants, this work may enable more targeted therapeutic approaches for metabolic disorders.
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⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
Aims/hypothesis The human pancreas is a central organ for metabolic regulation that is comprised of diverse cell types that uniquely contribute to its function. Previous studies have performed expression quantitative trail loci (eQTL) discovery in either whole pancreas or in pancreatic islets, but due to differences between pancreatic cell types, this approach does not reveal cell type-specific effects. In this study, we sought to either implicate the cell type of action for known eQTLs or identify new eQTLs that may have been masked in bulk studies by performing eQTL discovery in individual pancreatic cell types. Methods We clustered 153,018 single-cell RNA sequencing (scRNA-seq) data from 71 pancreatic islet donors from the Human Pancreas Analysis Program (HPAP). We performed eQTL discovery in six pancreatic cell types using this resource directly. We further utilized this single cell resource as a reference to deconvolute bulk pancreatic RNA sequencing data from 305 Genotype Tissue Expression (GTEx) project donors and performed eQTL discovery in four pancreatic cell types. Finally, we performed fine-mapping and co-localization of pancreatic cell type eQTLs with metabolic GWAS to connect our findings to metabolic disease risk. Results From analyzing 71 individuals with single cell profiles, we identified 112 unique eGenes across six pancreatic cell types, 99 of which had been identified previously and 13 unique to this study. From the deconvoluted eQTLs, we identified 3,134 unique eGenes across four pancreatic cell types, 116 of which were unique to our study. Fine-mapping and co-localization of eQTLs with metabolic GWAS yielded key leads that warrant further investigation, such as the association of rs2168101 with LMO1 expression in alpha cells. Conclusions/interpretation We identified new signals that were previously not found in bulk pancreatic eQTL studies and potential cell type of action for several signals that were identified previously. Although there are limitations to the power, and therefore, discoverability of this study, it provides insights into how individual pancreatic cells differently contribute to metabolic disease.
Source: Utilizing single-cell data for per-cell type eQTL mapping in the human pancreas