Dataset Information

Single-cell genomics and regulatory networks for 388 human brains.

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.

SUBMITTER: Emani PS

PROVIDER: S-EPMC10983939 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature

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Single-cell genomics and regulatory networks for 388 human brains.

Emani Prashant S PS Liu Jason J JJ Clarke Declan D Jensen Matthew M Warrell Jonathan J Gupta Chirag C Meng Ran R Lee Che Yu CY Xu Siwei S Dursun Cagatay C Lou Shaoke S Chen Yuhang Y Chu Zhiyuan Z Galeev Timur T Hwang Ahyeon A Li Yunyang Y Ni Pengyu P Zhou Xiao X Bakken Trygve E TE Bendl Jaroslav J Bicks Lucy L Chatterjee Tanima T Cheng Lijun L Cheng Yuyan Y Dai Yi Y Duan Ziheng Z Flaherty Mary M Fullard John F JF Gancz Michael M Garrido-Martín Diego D Gaynor-Gillett Sophia S Grundman Jennifer J Hawken Natalie N Henry Ella E Hoffman Gabriel E GE Huang Ao A Jiang Yunzhe Y Jin Ting T Jorstad Nikolas L NL Kawaguchi Riki R Khullar Saniya S Liu Jianyin J Liu Junhao J Liu Shuang S Ma Shaojie S Margolis Michael M Mazariegos Samantha S Moore Jill J Moran Jennifer R JR Nguyen Eric E Phalke Nishigandha N Pjanic Milos M Pratt Henry H Quintero Diana D Rajagopalan Ananya S AS Riesenmy Tiernon R TR Shedd Nicole N Shi Manman M Spector Megan M Terwilliger Rosemarie R Travaglini Kyle J KJ Wamsley Brie B Wang Gaoyuan G Xia Yan Y Xiao Shaohua S Yang Andrew C AC Zheng Suchen S Gandal Michael J MJ Lee Donghoon D Lein Ed S ES Roussos Panos P Sestan Nenad N Weng Zhiping Z White Kevin P KP Won Hyejung H Girgenti Matthew J MJ Zhang Jing J Wang Daifeng D Geschwind Daniel D Gerstein Mark M

bioRxiv : the preprint server for biology 20240330

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 cel ...[more]

PMID: 38562822

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Project description:BackgroundComparative genomics methods enable the reconstruction of bacterial regulatory networks using available experimental data. In spite of their potential for accelerating research into the composition and evolution of bacterial regulons, few comparative genomics suites have been developed for the automated analysis of these regulatory systems. Available solutions typically rely on precomputed databases for operon and ortholog predictions, limiting the scope of analyses to processed complete genomes, and several key issues such as the transfer of experimental information or the integration of regulatory information in a probabilistic setting remain largely unaddressed.ResultsHere we introduce CGB, a flexible platform for comparative genomics of prokaryotic regulons. CGB has few external dependencies and enables fully customized analyses of newly available genome data. The platform automates the merging of experimental information and uses a gene-centered, Bayesian framework to generate and integrate easily interpretable results. We demonstrate its flexibility and power by analyzing the evolution of type III secretion system regulation in pathogenic Proteobacteria and by characterizing the SOS regulon of a new bacterial phylum, the Balneolaeota.ConclusionsOur results demonstrate the applicability of the CGB pipeline in multiple settings. CGB's ability to automatically integrate experimental information from multiple sources and use complete and draft genomic data, coupled with its non-reliance on precomputed databases and its easily interpretable display of gene-centered posterior probabilities of regulation provide users with an unprecedented level of flexibility in launching comparative genomics analyses of prokaryotic transcriptional regulatory networks. The analyses of type III secretion and SOS response regulatory networks illustrate instances of convergent and divergent evolution of these regulatory systems, showcasing the power of formal ancestral state reconstruction at inferring the evolutionary history of regulatory networks.

Dataset Information

Single-cell genomics and regulatory networks for 388 human brains.

Publications

Single-cell genomics and regulatory networks for 388 human brains.

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