Discovering single-cell eQTLs from scRNA-seq data only.

Area of Science:

• Genomics
• Computational Biology
• Molecular Biology

Background:

• Genomic regulation is complex and varies across cell types and contexts.
• Understanding cell-type-specific gene regulation is crucial for disease research.
• Current single-cell genomic and expression data pairing technologies are limited.

Purpose of the Study:

• To develop a novel method for identifying expression quantitative trait loci (eQTLs) using only single-cell RNA sequencing (scRNA-seq) data.
• To overcome the limitations of current paired single-cell mutation and expression data.
• To enable the discovery of cell-type-specific eQTLs from existing scRNA-seq datasets.

Main Methods:

• Developed eQTLsingle, a computational method that detects mutations directly from scRNA-seq data.
• Employed a zero-inflated negative binomial (ZINB) model to associate genotypes with gene expression phenotypes at the single-cell level.
• Applied the method to a glioblastoma and gliomasphere scRNA-seq dataset.

Main Results:

• Discovered hundreds of cell-type-specific, tumor-related eQTLs in the analyzed dataset.
• Identified numerous eQTLs not detectable by traditional bulk eQTL studies.
• Detailed analysis of specific eQTL examples revealed significant underlying regulatory mechanisms.

Conclusions:

• eQTLsingle is a powerful tool for leveraging scRNA-seq data to study single-cell eQTLs.
• The method facilitates the discovery of novel gene regulatory insights from vast scRNA-seq resources.
• eQTLsingle provides a valuable approach for understanding genomic regulation in specific cellular contexts.