据悉，单细胞基因组学是研究大脑等异质组织的有力工具。然而，人们对遗传变异如何影响细胞级基因表达知之甚少。
为了解决这个问题，研究人员将单个细胞核、多组学数据集统一处理成一个资源，其中包括来自388名个体前额叶皮层的超过280万个细胞核。对于28种细胞类型，研究人员评估了跨基因家族和药物靶点的表达和染色质的群体级变异。研究人员确定了超过55万个细胞类型特异性调控元件和超过140万个单细胞表达定量性状位点，并利用它们构建了细胞类型调控和细胞间通讯网络。
这些网络显示了衰老和神经精神疾病中的细胞变化。研究人员进一步构建了一个综合模型，精确推算单细胞表达并模拟扰动；该模型优先考虑了约250个疾病风险基因和与相关细胞类型的药物靶点。
附：英文原文
Title: Single-cell genomics and regulatory networks for 388 human brains
Author: Prashant S. Emani, Jason J. Liu, Declan Clarke, Matthew Jensen, Jonathan Warrell, Chirag Gupta, Ran Meng, Che Yu Lee, Siwei Xu, Cagatay Dursun, Shaoke Lou, Yuhang Chen, Zhiyuan Chu, Timur Galeev, Ahyeon Hwang, Yunyang Li, Pengyu Ni, Xiao Zhou, PsychENCODE Consortium, Trygve E. Bakken, Jaroslav Bendl, Lucy Bicks, Tanima Chatterjee, Lijun Cheng, Yuyan Cheng, Yi Dai, Ziheng Duan, Mary Flaherty, John F. Fullard, Michael Gancz, Diego Garrido-Martín, Sophia Gaynor-Gillett, Jennifer Grundman, Natalie Hawken, Ella Henry, Gabriel E. Hoffman, Ao Huang, Yunzhe Jiang, Ting Jin, Nikolas L. Jorstad, Riki Kawaguchi, Saniya Khullar, Jianyin Liu, Junhao Liu, Shuang Liu, Shaojie Ma, Michael Margolis, Samantha Mazariegos, Jill Moore, Jennifer R. Moran, Eric Nguyen, Nishigandha Phalke, Milos Pjanic, Henry Pratt, Diana Quintero, Ananya S. Rajagopalan, Tiernon R. Riesenmy, Nicole Shedd, Manman Shi, Megan Spector, Rosemarie Terwilliger, Kyle J. Travaglini, Brie Wamsley, Gaoyuan Wang, Yan Xia, Shaohua Xiao, Andrew C. Yang, Suchen Zheng, Michael J. Gandal, Donghoon Lee, Ed S. Lein, Panos Roussos, Nenad Sestan, Zhiping Weng, Kevin P. White, Hyejung Won, Matthew J. Girgenti, Jing Zhang, Daifeng Wang, Daniel Geschwind, Mark Gerstein
Issue&Volume: 2024-05-24
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.
DOI: adi5199
Source: https://www.science.org/doi/10.1126/science.adi5199