Single-Cell Heterogeneity Analysis and CRISPR Screen Identify Key β-Cell-Specific Disease Genes

Area of Science:

• Genomics
• Metabolic Diseases
• Cell Biology

Background:

• Identifying disease signature genes usually requires large sample sizes for statistical significance.
• Single-cell heterogeneity analysis offers a promising approach to enhance test sensitivity in disease gene discovery.

Purpose of the Study:

• To develop a sensitive single-cell analysis algorithm (RePACT) for identifying common and specific signature genes in obesity and type 2 diabetes (T2D).
• To investigate beta cell heterogeneity trajectories associated with obesity and T2D.
• To explore the roles of identified genes within the insulin regulatory network.

Main Methods:

• Analysis of transcriptomes from 39,905 single islet cells across 9 donors.
• Development and application of the RePACT algorithm for sensitive single-cell gene identification.
• Integration of gene mapping with a genome-wide CRISPR screen of the insulin regulatory network.

Main Results:

• Distinct beta cell heterogeneity trajectories were observed in obesity and T2D.
• RePACT successfully identified common and specific signature genes for obesity and T2D.
• The study uncovered previously unrecognized roles for the cohesin loading complex and the NuA4/Tip60 histone acetyltransferase complex in insulin regulation.

Conclusions:

• Single-cell heterogeneity analysis significantly improves the sensitivity for identifying disease signature genes.
• The RePACT algorithm provides a powerful tool for dissecting complex diseases at the single-cell level.
• Novel molecular mechanisms involving cohesin and NuA4/Tip60 complexes in insulin transcription and release were elucidated.