Meta-analysis to define neuronal diversity: from genes to functions across species

定义神经元多样性的荟萃分析：从基因到跨物种功能

• 批准号: 9395464
• 负责人: MEGAN CROW
• 金额: $ 5.92万
• 依托单位: COLD SPRING HARBOR LABORATORY
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-06-16 至 2020-06-15
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9395464
• 关键词: Address; Animals; Area; Behavior; Brain; Brain Diseases; Cells; Collaborations; Communities; Complex; Data; Detection; Electrophysiology (science); Gene Expression; Gene Expression Profile; Gene Targeting; Generations; Genes; Genetic; Genetic Transcription; Heterogeneity; Human; In Situ Hybridization; Individual; Laboratories; Machine Learning; Mental disorders; Meta-Analysis; Methods; Molecular Genetics; Mus; National Institute of Mental Health; Nervous system structure; Neurons; Ontology; Pathway interactions; Pattern; Performance; Reporting; Research; Strategic Planning; Taxonomy; Technology; Testing; Tissues; Training; Translations; Validation; Work; base; career; cell type; comparative; computerized data processing; design; experimental study; follow-up; high dimensionality; improved; molecular pathology; neural circuit; novel; predictive modeling; skills; transcriptome sequencing; transcriptomics

Project Summary To begin unraveling the mysteries of the brain we must first understand its fundamental unit: the neuron. Recent advances in single cell RNA-sequencing (scRNA-seq) technology have resulted in the rapid generation of high dimensional data for individual cells, revealing vast heterogeneity and enabling the prediction of novel cell subtypes based on expression patterns. In spite of this wealth of data, a theoretical framework to define subtype identity is lacking. Cross-laboratory expression analysis has emerged as a powerful approach to obtain robust and replicable results and we have previously shown that scRNA-seq data is readily amenable to gene co-expression meta-analysis. Here, we propose that these methods, in combination with cross-species analysis, will provide important validation of novel subtypes, and allow for the identification of new gene functional modules that drive neuronal diversity. Our overall hypothesis is that coordinated gene expression patterns underlie neuronal identity and function. The specific aims to address this hypothesis combine both computational and experimental approaches. First, we will characterize cell-type specific co-expression using RNA-seq data from bulk tissue and from single cells, defining best practices for network construction and identifying and characterizing gene modules that are unique to single cell data. Second, we will define a data- driven cell type taxonomy for the mouse and human nervous systems, and use this to identify neuronal subtype marker genes for validation with co-in situ hybridization and follow up with gene targeting and further scRNA-seq experiments. Finally, we will elucidate functional pathways that are unique to or conserved across species by comparing mouse and human neuronal subtype co-expression networks, with a particular focus on characterizing the cell type specific connectivity of psychiatric disease genes. These aims are consistent with the National Institute of Mental Health's strategic plan to describe the molecules, cells and neural circuits associated with complex behaviors. As a result, these studies will have a significant impact on our understanding of neuronal diversity and identity, leading to improved understanding of the molecular pathology of brain disorders.

项目摘要 要开始揭开大脑的奥秘，我们必须首先了解其基本单位：神经元。 单细胞RNA测序（SCRNA-SEQ）技术的最新进展已导致快速生成 单个细胞的高维数据，揭示了巨大的异质性并实现了新颖的预测 基于表达模式的细胞亚型。尽管有很多数据，但一个理论框架来定义 缺乏亚型身份。跨实验室表达分析已成为一种强大的方法 获得可靠且可复制的结果，我们先前已经表明，SCRNA-SEQ数据很容易适应 基因共表达荟萃分析。在这里，我们建议这些方法与跨物种结合 分析将提供新型亚型的重要验证，并允许鉴定新基因 驱动神经元多样性的功能模块。我们的总体假设是协调的基因表达 模式是神经元身份和功能的基础。解决这一假设的具体目的结合了这两个假设 计算和实验方法。首先，我们将使用使用细胞类型的特定共表达来表征 来自散装组织和单个细胞的RNA-seq数据，定义了网络构建的最佳实践和 识别和表征单细胞数据独有的基因模块。其次，我们将定义一个数据 - 驱动小鼠和人类神经系统的驱动细胞类型分类学，并使用它来识别神经元 亚型标记基因，用于通过共同杂交进行验证，并跟进基因靶向并进一步 SCRNA-SEQ实验。最后，我们将阐明在整个环境中唯一或保守的功能途径 通过比较小鼠和人类神经元亚型共表达网络的物种，特别关注 表征精神病基因的细胞类型特异性连通性。这些目标与 国家心理健康研究所的战略计划，描述分子，细胞和神经回路 与复杂的行为相关。结果，这些研究将对我们的 了解神经元的多样性和身份，从而提​​高对分子病理的理解 脑疾病。