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

项目摘要 要开始解开大脑的奥秘，我们必须首先了解它的基本单位：神经元。 单细胞rna测序(scrna-seq)技术的最新进展导致了快速生成 单个细胞的高维数据，揭示了巨大的异质性，并使新的 基于表达模式的细胞子类型。尽管有如此丰富的数据，一个理论框架来定义 缺乏亚型同一性。跨实验室的表达分析已经成为一种强有力的方法 获得稳健和可复制的结果，我们以前已经表明scRNA-seq数据很容易服从于 基因共表达荟萃分析。在这里，我们提出这些方法，结合跨物种 分析，将提供新亚型的重要验证，并允许识别新基因 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是将两者结合起来 计算方法和实验方法。首先，我们将使用以下方法表征细胞类型特定的共表达 来自大量组织和单个细胞的RNA-seq数据，定义了网络建设和 识别和表征单细胞数据所特有的基因模块。第二，我们将定义一个数据- 小鼠和人类神经系统的驱动细胞类型分类，并用此来识别神经细胞 亚型标记基因用于共原位杂交验证和后续基因打靶及进一步 ScRNA-seq实验。最后，我们将阐明独特或保守的功能通路 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 表征精神疾病基因的细胞类型特定的连接性。这些目标与 国家心理健康研究所的战略计划描述了分子、细胞和神经回路 与复杂的行为相关联。因此，这些研究将对我们的 了解神经元的多样性和同一性，有助于更好地理解分子病理学 脑部疾病。

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.