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

项目摘要 我们的首要目标是了解基因之间的关系如何有助于功能特性 以及这些功能如何联合收割机来定义神经元的类型。这是一个基本的核心问题， 神经科学，和一个是新的评估使用单细胞RNA测序（scRNA-seq）。这些数据 提供了数千个细胞中基因活动的高通量快照，从而揭示了 细胞内和细胞间的基因关系。我们建议利用这些数据， 以前已知的基因功能和神经元身份的细节，以了解两者的新特征。我们 研究方法是元分析，使用来自许多不同实验室的数据，以获得一个更强大的 集合信号除了发展元分析方法来追求我们的直接研究兴趣， 方法对于研究许多不同问题的神经科学实验室具有广泛的实际相关性， 包括神经系统疾病。以易于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.了解表征已知细胞身份的基因表达模式。充分发挥两国 先前的研究显示了跨细胞类型的保守表达模式，我们将定义共享基因 表达模式，称为共表达，特异于神经元亚群。这些共同的表达 模式将被用作对细胞身份的测定。 2.通过基因间表达关系的变化识别新的细胞亚型。 共表达的变异是一种转录重连的形式，通常表明功能的变化。到 发现新的神经元亚型，我们将评估共表达变化的数据， 与神经元身份相关的功能。我们将确定新的转录签名， laboratories. 3.确定定制细胞类型学习的共识方法。完全未知的定义 表达谱可能受益于多种方法。为了在这些人之间达成协议， 方法，我们将开发一种算法，有效地搜索基因集可能找到那些与 互补价值然后，这些基因集将通过许多现有的方法进行评估， 定制的组合和总产出报告，并通过公共网络服务器提供。

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.