Single-Cell Gene Module Inference Reveals Alternative Polyadenylation Dynamics Associated with Autism

Area of Science:

• Neuroscience
• Genetics
• Computational Biology

Background:

• Autism spectrum disorder (ASD) exhibits significant genetic heterogeneity.
• Post-transcriptional regulation, specifically alternative polyadenylation (APA), is crucial in ASD pathogenesis.
• Current ASD research often overlooks the collective impact of APA on gene modules within specific cell types.

Purpose of the Study:

• To develop an integrative computational framework to identify ASD-associated gene modules driven by APA.
• To predict cell-type-specific ASD-related cells using APA profiles.
• To explore the role of APA in ASD at a high resolution across cell types, brain regions, and sex.

Main Methods:

• Combined matrix factorization and machine learning techniques.
• Applied the framework to human brain single-nucleus RNA sequencing (snRNA-seq) data.
• Integrated APA data with gene expression for enhanced predictive modeling.

Main Results:

• Systematically uncovered cell type, brain region, and sex-specific APA regulatory patterns in ASD.
• Identified APA modules enriched in synaptic function, neurodevelopment, and immune response pathways, particularly in prefrontal cortex excitatory neurons.
• Developed a classification model using APA genes that effectively distinguishes ASD cells from normal cells.
• Demonstrated that integrating APA with gene expression significantly improves prediction accuracy.

Conclusions:

• APA represents an independent and biologically informative regulatory layer in ASD.
• The study delineates a high-resolution APA regulatory landscape in ASD.
• Findings offer novel insights and potential therapeutic avenues beyond transcriptional abundance.