Single-cell genomics and regulatory networks for 388 human brains

Science. 2024 May 24;384(6698):eadi5199. doi: 10.1126/science.adi5199. Epub 2024 May 24.

Abstract

Single-cell genomics is a powerful tool for studying heterogeneous tissues such as the brain. Yet little is understood about how genetic variants influence cell-level gene expression. Addressing this, we uniformly processed single-nuclei, multiomics datasets into a resource comprising >2.8 million nuclei from the prefrontal cortex across 388 individuals. For 28 cell types, we assessed population-level variation in expression and chromatin across gene families and drug targets. We identified >550,000 cell type-specific regulatory elements and >1.4 million single-cell expression quantitative trait loci, which we used to build cell-type regulatory and cell-to-cell communication networks. These networks manifest cellular changes in aging and neuropsychiatric disorders. We further constructed an integrative model accurately imputing single-cell expression and simulating perturbations; the model prioritized ~250 disease-risk genes and drug targets with associated cell types.

MeSH terms

  • Aging / genetics
  • Brain* / metabolism
  • Cell Communication / genetics
  • Chromatin / genetics
  • Chromatin / metabolism
  • Gene Regulatory Networks*
  • Genomics
  • Humans
  • Mental Disorders* / genetics
  • Prefrontal Cortex / metabolism
  • Prefrontal Cortex / physiology
  • Quantitative Trait Loci
  • Single-Cell Analysis*

Substances

  • Chromatin

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