Single-cell genomics and regulatory networks for 388 human brains [preprint]
Emani, Prashant S ; Liu, Jason J ; Clarke, Declan ; Jensen, Matthew ; Warrell, Jonathan ; Gupta, Chirag ; Meng, Ran ; Lee, Che Yu ; Xu, Siwei ; Dursun, Cagatay ... show 10 more
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Authors
Liu, Jason J
Clarke, Declan
Jensen, Matthew
Warrell, Jonathan
Gupta, Chirag
Meng, Ran
Lee, Che Yu
Xu, Siwei
Dursun, Cagatay
Lou, Shaoke
Chen, Yuhang
Chu, Zhiyuan
Galeev, Timur
Hwang, Ahyeon
Li, Yunyang
Ni, Pengyu
Zhou, Xiao
Bakken, Trygve E
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
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 E
Moran, Jennifer R
Nguyen, Eric
Phalke, Nishigandha
Pjanic, Milos
Pratt, Henry E
Quintero, Diana
Rajagopalan, Ananya S
Riesenmy, Tiernon R
Shedd, Nicole
Shi, Manman
Spector, Megan
Terwilliger, Rosemarie
Travaglini, Kyle J
Wamsley, Brie
Wang, Gaoyuan
Xia, Yan
Xiao, Shaohua
Yang, Andrew C
Zheng, Suchen
Gandal, Michael J
Lee, Donghoon
Lein, Ed S
Roussos, Panos
Sestan, Nenad
Weng, Zhiping
White, Kevin P
Won, Hyejung
Girgenti, Matthew J
Zhang, Jing
Wang, Daifeng
Geschwind, Daniel
Gerstein, Mark
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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, 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.
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Emani PS, Liu JJ, Clarke D, Jensen M, Warrell J, Gupta C, Meng R, Lee CY, Xu S, Dursun C, Lou S, Chen Y, Chu Z, Galeev T, Hwang A, Li Y, Ni P, Zhou X; PsychENCODE Consortium; Bakken TE, Bendl J, Bicks L, Chatterjee T, Cheng L, Cheng Y, Dai Y, Duan Z, Flaherty M, Fullard JF, Gancz M, Garrido-Martín D, Gaynor-Gillett S, Grundman J, Hawken N, Henry E, Hoffman GE, Huang A, Jiang Y, Jin T, Jorstad NL, Kawaguchi R, Khullar S, Liu J, Liu J, Liu S, Ma S, Margolis M, Mazariegos S, Moore J, Moran JR, Nguyen E, Phalke N, Pjanic M, Pratt H, Quintero D, Rajagopalan AS, Riesenmy TR, Shedd N, Shi M, Spector M, Terwilliger R, Travaglini KJ, Wamsley B, Wang G, Xia Y, Xiao S, Yang AC, Zheng S, Gandal MJ, Lee D, Lein ES, Roussos P, Sestan N, Weng Z, White KP, Won H, Girgenti MJ, Zhang J, Wang D, Geschwind D, Gerstein M. Single-cell genomics and regulatory networks for 388 human brains. bioRxiv [Preprint]. 2024 Mar 30:2024.03.18.585576. doi: 10.1101/2024.03.18.585576. PMID: 38562822; PMCID: PMC10983939.
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This article is a preprint. Preprints are preliminary reports of work that have not been certified by peer review.
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Now published in Science doi: 10.1126/science.adi5199