Revealing the transcriptomic basis of neuronal identity through functional meta-analysis

项目总结 我们的首要目标是了解基因之间的关系是如何影响功能特性的 以及这些功能如何结合在一起来定义神经元的类型。这是基本的核心问题 神经科学，以及最近可以使用单细胞RNA测序(scRNA-seq)进行评估的一种。这些数据 提供跨越数千个细胞的基因活动的高通量快照，从而为 细胞内和细胞间基因之间的关系。我们建议将这些数据与 了解以前已知的基因功能和神经元特性的细节，以了解两者的新特征。我们的 研究方法是元分析，使用来自许多不同实验室的数据来获得更稳健的 聚合信号。除了开发元分析方法来追求我们的直接研究兴趣之外， 方法对于神经科学实验室研究许多不同的问题具有广泛的实用意义， 包括神经系统疾病。以方便使用的形式传播我们的软件交付成果 是我们每个研究目标的核心组成部分。 该项目的三个相辅相成的目标是： 1.了解表征已知细胞特性的基因表达模式。建立在我们的 之前的研究显示了不同细胞类型之间的保守表达模式，我们将定义共享基因 表达模式，称为共表达，特定于神经元亚群。这些共享的表达 模式将被用来作为对细胞身份的分析。 2.通过基因间表达关系的变化确定新的细胞亚型。 共表达的变化是转录重新连接的一种形式，这通常意味着功能的变化。至 寻找新的神经元亚型，我们将评估数据中共同表达的变化，以反映 与神经元身份相关的功能。我们将识别新的转录签名，这些签名可以在 实验室。 3.确定定制细胞类型学习的共识方法。定义完全未知 表达模式可能会从各种方法中受益。为了在这些人之间达成一致 方法，我们将开发一种算法来高效地搜索可能找到那些具有 互补价值。这些基因组随后将通过许多先前存在的方法进行评估， 通过公共网络服务器报告和提供定制的组合和综合产出。

PROJECT SUMMARY Our overarching goal is to understand how the relationships between genes contribute to functional properties in neurons and how those functions combine to define types of neurons. This is a central question of basic neuroscience, and one which is newly assessable using single cell RNA sequencing (scRNA-seq). These data provide high-throughput snapshots of gene activities across thousands of cells and thus shed new light on the relationships between genes within and across cells. We propose to exploit this data in conjunction with previously known details about gene function and neuronal identity to learn new features of both. Our research approach is meta-analytic, using data from many different laboratories to obtain a more robust aggregate signal. In addition to developing meta-analytic methods to pursue our direct research interests, the methods are of broad practical relevance to neuroscience laboratories studying many different questions, including diseases of the nervous system. Disseminating our software deliverables in a convenient-to-use form is a central component of each of our research objectives. The three complementary objectives in this project are to: 1. Learn patterns of gene expression which characterize known cell identity. Building on our previous research showing conserved expression patterns across cell-types, we will define shared gene expression patterns, called co-expression, specific to neuronal sub-populations. These shared expression patterns will be used as an assay into cellular identity. 2.Identify novel cell subtypes through changes in the expression relationships between genes. Variation in co-expression is a form of transcriptional rewiring which often indicates a change in function. To find novel neuronal sub-types we will assess the data for changes in co-expression reflecting a change in functions linked to neuronal identity. We will identify novel transcriptional signatures which replicate across laboratories. 3. Determine consensus methods for customized cell-type learning. Defining wholly unknown expression profiles is likely to benefit from a variety of approaches. In order to find agreement between those approaches, we will develop an algorithm to efficiently search through gene sets likely to find those with complementary value. These gene sets will then be assessed across many pre-existing methods, with customized combinations and aggregate output reported and made available through a public web-server.