1. Single-cell genomics and regulatory networks for 388 human brains.
- Author
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Emani, Prashant, Liu, Jason, 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, Bendl, Jaroslav, Bicks, Lucy, Chatterjee, Tanima, Cheng, Lijun, Cheng, Yuyan, Dai, Yi, Duan, Ziheng, Flaherty, Mary, Fullard, John, Gancz, Michael, Garrido-Martín, Diego, Gaynor-Gillett, Sophia, Grundman, Jennifer, Hawken, Natalie, Henry, Ella, Hoffman, Gabriel, Huang, Ao, Jiang, Yunzhe, Jin, Ting, Jorstad, Nikolas, Kawaguchi, Riki, Khullar, Saniya, Liu, Jianyin, Liu, Junhao, Liu, Shuang, Ma, Shaojie, Margolis, Michael, Mazariegos, Samantha, Moore, Jill, Moran, Jennifer, Nguyen, Eric, Phalke, Nishigandha, Pjanic, Milos, Pratt, Henry, Quintero, Diana, Rajagopalan, Ananya, Riesenmy, Tiernon, Shedd, Nicole, Shi, Manman, Spector, Megan, Terwilliger, Rosemarie, Travaglini, Kyle, Wamsley, Brie, Wang, Gaoyuan, Xia, Yan, Xiao, Shaohua, Yang, Andrew, Zheng, Suchen, Gandal, Michael, Lee, Donghoon, Lein, Ed, Roussos, Panos, Sestan, Nenad, Weng, Zhiping, White, Kevin, Won, Hyejung, Girgenti, Matthew, Zhang, Jing, Wang, Daifeng, Geschwind, Daniel, and Gerstein, Mark
- Subjects
Humans ,Aging ,Brain ,Cell Communication ,Chromatin ,Gene Regulatory Networks ,Genomics ,Mental Disorders ,Prefrontal Cortex ,Quantitative Trait Loci ,Single-Cell Analysis - 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.
- Published
- 2024