Yale researchers who conducted the largest human brain analysis from a single-cell perspective hope their findings lead to better prediction of medicine that will target certain cells.
The study was led by co-corresponding authors Matthew Girgenti, PhD, assistant professor of psychiatry, and Mark Gerstein, PhD, Albert L. Williams Professor of Biomedical Informatics and professor of molecular biophysics & biochemistry, of computer science, and of statistics & data science at Yale School of Medicine. The findings were published in Science.
Single-cell genomics offers a powerful method to understand how genetic variants influence gene expression, especially across the numerous cell types in the human brain.
Moreover, it can potentially refine our understanding of the regulatory mechanisms underlying brain-related traits such as psychiatric disorders.
Girgenti and Gerstein participate in the PsychENCODE Consortium, which performed single-cell experiments (single-nucleus RNA-Seq, ATAC-Seq, and Multiome plus DNA sequencing) and computational analyses on samples from almost 400 human prefrontal-cortex samples of adults with a range of brain-related disorders such as schizophrenia, autism spectrum disorder, bipolar disorder, and Alzheimer’s disease, as well as controls.
These population-scale cohorts, with a wide range of brain phenotypes, are needed to infer significant associations among genetic variants and to develop models of regulation in specific cell types of the brain.
Integration of RNA expression and genotype data revealed >1.4M single-cell eQTLs (DNA positions that regulate gene expression), many of which were not seen in prior gene-expression datasets and a subset of which are involved in brain disorders. The researchers also found that expression patterns across cell types recapitulated the spatial architecture of neurons and enabled the identification of "dynamic eQTLs," with changes in regulatory effects across cortical layers.
The chromatin datasets in the resource allowed for identification of >550K single-cell cis regulatory elements, which were enriched at loci linked to brain-related traits. Combining gene expression, chromatin, and eQTL datasets, the researchers built cell-type-specific gene-regulatory networks and developed cell-to-cell communication networks, which highlighted differences in signaling pathways in the schizophrenic and bipolar disorder brain, including altered WNT and FGF signaling.
This integration allowed for accurate imputation of cell-type-specific expression and phenotype from genotype and allowed them to prioritize >250 risk genes and drug targets for brain-related disorders within specific cell types. Computationally simulated perturbation of individual genes led to predicted expression changes mirroring those for disease cases, increasing confidence in the predicted drug targets.
“This is the largest single cell multi-omic dataset of the human brain to date,” Girgenti said. “This population-scale resource for the human brain will help facilitate precision-medicine approaches for neuropsychiatric disorders, especially by prioritizing follow-up genes and drug targets linked to specific cell types.”
Funding was provided by the Simons Foundation and National Institute of Mental Health.