Single-cell genomics and regulatory networks for 388 human brains
Authors: Emani Prashant S. , 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 ,   PsychENCODE Consortium‡, 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 , 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. , 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 , Akbarian Schahram , Abyzov Alexej , Ahituv Nadav , Arasappan Dhivya , Almagro Armenteros Jose Juan , Beliveau Brian J. , Berretta Sabina , Bharadwaj Rahul A. , Bhattacharya Arjun , Brennand Kristen , Capauto Davide , Champagne Frances A. , Chatzinakos Chris , Chen H. Isaac , Cheng Lijun , Chess Andrew , Chien Jo-fan , Clement Ashley , Collado-Torres Leonardo , Cooper Gregory M. , Crawford Gregory E. , Dai Rujia , Daskalakis Nikolaos P. , Davila-Velderrain Jose , Deep-Soboslay Amy , Deng Chengyu , DiPietro Christopher P. , Dracheva Stella , Drusinsky Shiron , Duong Duc , Eagles Nicholas J. , Edelstein Jonathan , Galani Kiki , Girdhar Kiran , Goes Fernando S. , Greenleaf William , Guo Hanmin , Guo Qiuyu , Hadas Yoav , Hallmayer Joachim , Han Xikun , Haroutunian Vahram , He Chuan , Hicks Stephanie C. , Ho Marcus , Ho Li-Lun , Huang Yiling , Huuki-Myers Louise A. , Hyde Thomas M. , Iatrou Artemis , Inoue Fumitaka , Jajoo Aarti , Jiang Lihua , Jin Peng , Jops Connor , Jourdon Alexandre , Kellis Manolis , Kleinman Joel E. , Kleopoulos Steven P. , Kozlenkov Alex , Kriegstein Arnold , Kundaje Anshul , Kundu Soumya , Li Junhao , Li Mingfeng , Lin Xiao , Liu Shuang , Liu Chunyu , Loupe Jacob M. , Lu Dan , Ma Liang , Mariani Jessica , Martinowich Keri , Maynard Kristen R. , Myers Richard M. , Micallef Courtney , Mikhailova Tatiana , Ming Guo-li , Mohammadi Shahin , Monte Emma , Montgomery Kelsey S. , Mukamel Eran A. , Nairn Angus C. , Nemeroff Charles B. , Norton Scott , Nowakowski Tomasz , Omberg Larsson , Page Stephanie C. , Park Saejeong , Patowary Ashok , Pattni Reenal , Pertea Geo , Peters Mette A. , Pinto Dalila , Pochareddy Sirisha , Pollard Katherine S. , Pollen Alex , Przytycki Pawel F. , Purmann Carolin , Qin Zhaohui S. , Qu Ping-Ping , Raj Towfique , Reach Sarah , Reimonn Thomas , Ressler Kerry J. , Ross Deanna , Rozowsky Joel , Ruth Misir , Ruzicka W. Brad , Sanders Stephan J. , Schneider Juliane M. , Scuderi Soraya , Sebra Robert , Seyfried Nicholas , Shao Zhiping , Shieh Annie W. , Shin Joo Heon , Skarica Mario , Snijders Clara , Song Hongjun , State Matthew W. , Stein Jason , Steyert Marilyn , Subburaju Sivan , Sudhof Thomas , Snyder Michael , Tao Ran , Therrien Karen , Tsai Li-Huei , Urban Alexander E. , Vaccarino Flora M. , van Bakel Harm , Vo Daniel , Voloudakis Georgios , Wang Tao , Wang Sidney H. , Wang Yifan , Wei Yu , Weimer Annika K. , Weinberger Daniel R. , Wen Cindy , Whalen Sean , Willsey A. Jeremy , Wong Wing , Wu Hao , Wu Feinan , Wuchty Stefan , Wylie Dennis , Yap Chloe X. , Zeng Biao , Zhang Pan , Zhang Chunling , Zhang Bin , Zhang Yanqiong , Ziffra Ryan , Zeier Zane R. , Zintel Trisha M. 
Published: May 2024

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.