Single-cell allele-specific expression analysis reveals dynamic and cell-type-specific regulatory effects.

Area of Science:

• Genomics
• Molecular Biology
• Computational Biology

Background:

• Differential allele-specific expression (ASE) is crucial for understanding cis-regulation.
• Single-cell RNA sequencing (scRNA-seq) enables ASE measurement at single-cell resolution.
• Existing statistical methods are insufficient for scRNA-seq based ASE analysis.

Purpose of the Study:

• To develop a statistical method for differential ASE analysis using scRNA-seq data from multiple individuals.
• To address challenges like non-independence of cells within an individual and implicit haplotype phasing.

Main Methods:

• Introduction of Differential Allelic Expression using Single-Cell data (DAESC).
• Statistical validation through simulations.
• Incorporation of non-independence and implicit haplotype phasing.

Main Results:

• DAESC successfully analyzes scRNA-seq data for differential ASE.
• Identified 657 genes with dynamic regulation during endoderm differentiation in 105 iPSC lines.
• Found differentially regulated genes in pancreatic endocrine cells between type-2 diabetes patients and controls.

Conclusions:

• DAESC is a powerful new method for single-cell ASE analysis.
• The method provides novel insights into gene regulation in biological contexts.
• DAESC facilitates the study of dynamic gene regulation and disease mechanisms.