Single-cell genomics and regulatory networks for 388 human brains
Author:
Emani Prashant S., Liu Jason J., Clarke Declan, Jensen Matthew, Warrell JonathanORCID, Gupta Chirag, Meng Ran, Lee Che Yu, Xu SiweiORCID, Dursun Cagatay, Lou Shaoke, Chen Yuhang, Chu Zhiyuan, Galeev Timur, Hwang Ahyeon, Li Yunyang, Ni Pengyu, Zhou Xiao, , Bakken Trygve E.ORCID, Bendl Jaroslav, Bicks Lucy, Chatterjee Tanima, Cheng Lijun, Cheng Yuyan, Dai Yi, Duan Ziheng, Flaherty Mary, Fullard John F., Gancz Michael, Garrido-Martín Diego, Gaynor-Gillett Sophia, Grundman Jennifer, Hawken Natalie, Henry Ella, Hoffman Gabriel E.ORCID, Huang Ao, Jiang Yunzhe, Jin Ting, Jorstad Nikolas L., Kawaguchi Riki, Khullar Saniya, Liu Jianyin, Liu Junhao, Liu Shuang, Ma Shaojie, Margolis Michael, Mazariegos Samantha, Moore Jill, Moran Jennifer R., Nguyen Eric, Phalke Nishigandha, Pjanic Milos, Pratt Henry, Quintero Diana, Rajagopalan Ananya S., Riesenmy Tiernon R., Shedd Nicole, Shi Manman, Spector Megan, Terwilliger Rosemarie, Travaglini Kyle J.ORCID, Wamsley Brie, Wang Gaoyuan, Xia Yan, Xiao Shaohua, Yang Andrew C., Zheng Suchen, Gandal Michael J., Lee DonghoonORCID, Lein Ed S., Roussos Panos, Sestan Nenad, Weng Zhiping, White Kevin P., Won HyejungORCID, Girgenti Matthew J., Zhang Jing, Wang DaifengORCID, Geschwind Daniel, Gerstein Mark
Abstract
AbstractSingle-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, multi-omics datasets into a resource comprising >2.8M 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 >550K cell-type-specific regulatory elements and >1.4M 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.Summary Figure
Publisher
Cold Spring Harbor Laboratory
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