Single-cell genomics and regulatory networks for 388 human brains

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.

Organizational principles of the primate cerebral cortex at the single-cell level

“…Single-cell sequencing provides a profound opportunity to explore the state and gene expression profiles of individual cells in the brain, thereby facilitating the inference of their classification, properties, and potential physiological functions [1][2][3][4][5][6] . By targeting specific brain regions for sequencing, we can uncover the diversity of cell types within those areas 7,8 Moreover, applying single-cell sequencing across different brain regions and species helps us understand variations in cell type composition among various brain areas 4,5,9 .…”

Single Cell Transcriptome Defines Cell Type Repertoire of AdultDaphnia magna

“…Tracing the gene origins and gene age based on the coding sequence has met with various difficulties (Capra et al 2013), since genes have complex phylogenies, evolve at different rates and processes like subfunctionalization can only be identified in light of a function. Observing a gene in constellations with others in cell type atlases is one scalable way to gain insight into gene functions; observing concordant changes in cell-specificity of gene expression yields insights in mechanisms of complex processes, such as aging (Lu et al 2023) or brain functioning (Emani et al 2024).…”