TY - JOUR
T1 - Single-cell genomics and regulatory networks for 388 human brains
AU - PsychENCODE Consortium
AU - Emani, Prashant S.
AU - Liu, Jason J.
AU - Clarke, Declan
AU - Jensen, Matthew
AU - Warrell, Jonathan
AU - Gupta, Chirag
AU - Meng, Ran
AU - Lee, Che Yu
AU - Xu, Siwei
AU - Dursun, Cagatay
AU - Lou, Shaoke
AU - Chen, Yuhang
AU - Chu, Zhiyuan
AU - Galeev, Timur
AU - Hwang, Ahyeon
AU - Li, Yunyang
AU - Ni, Pengyu
AU - Zhou, Xiao
AU - Bakken, Trygve E.
AU - Bendl, Jaroslav
AU - Bicks, Lucy
AU - Chatterjee, Tanima
AU - Cheng, Lijun
AU - Cheng, Yuyan
AU - Dai, Yi
AU - Duan, Ziheng
AU - Flaherty, Mary
AU - Fullard, John F.
AU - Gancz, Michael
AU - Garrido-Martín, Diego
AU - Gaynor-Gillett, Sophia
AU - Grundman, Jennifer
AU - Hawken, Natalie
AU - Henry, Ella
AU - Hoffman, Gabriel E.
AU - Huang, Ao
AU - Jiang, Yunzhe
AU - Jin, Ting
AU - Jorstad, Nikolas L.
AU - Kawaguchi, Riki
AU - Khullar, Saniya
AU - Liu, Jianyin
AU - Liu, Junhao
AU - Liu, Shuang
AU - Ma, Shaojie
AU - Margolis, Michael
AU - Mazariegos, Samantha
AU - Moore, Jill
AU - Lee, Donghoon
AU - Roussos, Panos
N1 - Publisher Copyright:
© 2024 the authors, some rights reserved.
PY - 2024/5/24
Y1 - 2024/5/24
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/85194360455
U2 - 10.1126/science.adi5199
DO - 10.1126/science.adi5199
M3 - Article
AN - SCOPUS:85194360455
SN - 0036-8075
VL - 384
JO - Science
JF - Science
IS - 6698
M1 - eadi5199
ER -