Single-cell genomics and regulatory networks for 388 human brains

Area of Science:

• Neuroscience
• Genomics
• Computational Biology

Background:

• Understanding genetic variant effects on cell-specific gene expression in complex tissues like the brain is crucial.
• Existing research has limited insight into how genetic variations impact gene regulation at the single-cell level.

Approach:

• Processed a large-scale single-nuclei multi-omics dataset (>2.8M nuclei) from the human prefrontal cortex across 388 individuals.
• Assessed population-level variation in gene expression and chromatin across 28 cell types.
• Identified cell-type-specific regulatory elements and single-cell expression quantitative trait loci (eQTLs).
• Constructed cell-type regulatory and cell-to-cell communication networks.
• Developed an integrative model for imputing single-cell expression and simulating perturbations.

Key Points:

• Discovered over 550,000 cell-type-specific regulatory elements and 1.4 million single-cell eQTLs.
• Developed functional networks revealing cellular changes in aging and neuropsychiatric disorders.
• Built a predictive model prioritizing ~250 disease-risk genes and drug targets with cell-type specificity.

Conclusions:

• This work provides a foundational resource for understanding genetic regulation in diverse brain cell types.
• The identified networks and prioritized genes offer new avenues for investigating brain function, aging, and disease.
• The integrative model enables further exploration of gene function and therapeutic target identification in specific cell populations.