Single-cell genomics and regulatory networks for 388 human brains

Area of Science:

• Neuroscience
• Genomics
• Computational Biology

Background:

• Single-cell genomics offers insights into complex tissues like the brain.
• Understanding genetic variant effects on cell-level gene expression remains limited.

Purpose of the Study:

• To create a comprehensive resource of single-nuclei multiomics data from the human prefrontal cortex.
• To investigate how genetic variations influence gene expression and regulatory elements across diverse cell types.
• To build predictive models of cell-type specific gene regulation and communication networks.

Main Methods:

• Uniform processing of single-nuclei multiomics data from 2.8 million nuclei across 388 individuals.
• Assessment of population-level variation in expression and chromatin for 28 cell types.
• Identification of cell type-specific regulatory elements and single-cell expression quantitative trait loci (eQTLs).
• Construction of cell-type regulatory and cell-to-cell communication networks.
• Development of an integrative model for imputing and simulating single-cell gene expression.

Main Results:

• Identification of over 550,000 cell type-specific regulatory elements.
• Discovery of more than 1.4 million single-cell eQTLs.
• Construction of regulatory and communication networks revealing cellular changes in aging and neuropsychiatric disorders.
• Prioritization of approximately 250 disease-risk genes and drug targets with associated cell types through an integrative model.

Conclusions:

• The study provides a valuable resource for understanding cell-type specific gene regulation in the human brain.
• The identified networks and models offer insights into the mechanisms underlying aging and neuropsychiatric conditions.
• The findings facilitate the prioritization of potential therapeutic targets and disease-risk genes at a single-cell resolution.