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科学家解码果蝇大脑中的基因调控
作者:小柯机器人 发布时间:2022/1/9 12:31:51

比利时鲁汶大学Stein Aerts研究小组揭示果蝇大脑中的基因调控。相关论文于2022年1月5日在线发表于国际学术期刊《自然》。

为了在果蝇大脑的细胞类型水平上描述基因调控网络(GRN),研究人员对跨越9个发育时间点的240,919个单细胞的染色质可及性进行了分析,并将这些数据与单细胞转录组结合起来。研究人员确定了95,000多个用于不同神经细胞类型的调控区域,其中70,000个与涉及神经发生、重编程和成熟的发育轨迹相关。对于40种细胞类型,通过模体发现、网络推理和深度学习的结合,将唯一可访问的区域与其表达的转录因子和下游靶基因联系起来,形成增强子GRN。
 
DeepFlyBrain揭示的增强子架构使人们更好地了解神经元调控的多样性,并可用于在特定的时间点为细胞类型设计遗传驱动线路,从而促进其表征和操控。
 
据介绍,果蝇的大脑是神经科学中经常使用的一个模型。单细胞转录组分析、三维形态学分类和电子显微镜下的连接组图揭示了神经元和胶质细胞类型的巨大多样性,它们是果蝇一系列功能和行为特征的基础。这些细胞类型的特性由GRN控制,涉及转录因子的组合,它们与基因组增强子结合来调节其靶标基因。
 
附:英文原文
 
Title: Decoding gene regulation in the fly brain

Author: Janssens, Jasper, Aibar, Sara, Taskiran, Ibrahim Ihsan, Ismail, Joy N., Gomez, Alicia Estacio, Aughey, Gabriel, Spanier, Katina I., De Rop, Florian V., Gonzlez-Blas, Carmen Bravo, Dionne, Marc, Grimes, Krista, Quan, Xiao Jiang, Papasokrati, Dafni, Hulselmans, Gert, Makhzami, Samira, De Waegeneer, Maxime, Christiaens, Valerie, Southall, Tony, Aerts, Stein

Issue&Volume: 2022-01-05

Abstract: The Drosophila brain is a frequently used model in neuroscience. Single-cell transcriptome analysis1,2,3,4,5,6, three-dimensional morphological classification7 and electron microscopy mapping of the connectome8,9 have revealed an immense diversity of neuronal and glial cell types that underlie an array of functional and behavioural traits in the fly. The identities of these cell types are controlled by gene regulatory networks (GRNs), involving combinations of transcription factors that bind to genomic enhancers to regulate their target genes. Here, to characterize GRNs at the cell-type level in the fly brain, we profiled the chromatin accessibility of 240,919 single cells spanning 9 developmental timepoints and integrated these data with single-cell transcriptomes. We identify more than 95,000 regulatory regions that are used in different neuronal cell types, of which 70,000 are linked to developmental trajectories involving neurogenesis, reprogramming and maturation. For 40 cell types, uniquely accessible regions were associated with their expressed transcription factors and downstream target genes through a combination of motif discovery, network inference and deep learning, creating enhancer GRNs. The enhancer architectures revealed by DeepFlyBrain lead to a better understanding of neuronal regulatory diversity and can be used to design genetic driver lines for cell types at specific timepoints, facilitating their characterization and manipulation.

DOI: 10.1038/s41586-021-04262-z

Source: https://www.nature.com/articles/s41586-021-04262-z

期刊信息

Nature:《自然》,创刊于1869年。隶属于施普林格·自然出版集团,最新IF:43.07
官方网址:http://www.nature.com/
投稿链接:http://www.nature.com/authors/submit_manuscript.html