权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

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

通过功能荟萃分析揭示神经元身份的转录组学基础

基本信息

批准号：
10224662
负责人：
Jesse Gillis
金额：
$ 48万
依托单位：
COLD SPRING HARBOR LABORATORY
依托单位国家：
美国
项目类别：
财政年份：
2017
资助国家：
美国
起止时间：
2017-07-13 至 2024-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10224662
关键词：
Ablation Affect Agreement Algorithms Biological Biological Assay Cells Characteristics Communities Computer software Consensus Crowding Custom Data Data Analyses Data Discovery Disease Gene Expression Gene Expression Profile Genes Genetic Genetic Transcription Goals Grouping Human Individual Joints Knowledge Label Laboratories Laboratory Study Learning Libraries Light Link Machine Learning Meta-Analysis Methods Nervous system structure Neurons Neurosciences Noise Output Pathway interactions Pattern Phenotype Positioning Attribute Property Publishing Reporting Reproducibility Research Resources Role Signal Transduction Structure System Transcript Validation Variant Work analytical method base candidate marker cell type data resource exhaustion functional group gene function improved in situ sequencing interest learning algorithm machine learning algorithm nervous system disorder novel programs screening single-cell RNA sequencing transcriptomics web server

项目摘要

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.

项目概要我们的首要目标是了解基因之间的关系如何影响功能特性神经元的功能以及这些功能如何结合起来定义神经元的类型。这是一个基本的核心问题神经科学，以及最近可使用单细胞 RNA 测序 (scRNA-seq) 进行评估的一门学科。这些数据提供数千个细胞基因活动的高通量快照，从而为了解细胞内和细胞间基因之间的关系。我们建议结合使用这些数据先前已知的有关基因功能和神经元身份的详细信息，以了解两者的新特征。我们的研究方法是荟萃分析，使用来自许多不同实验室的数据来获得更可靠的结果聚合信号。除了开发元分析方法来追求我们的直接研究兴趣之外，方法对于研究许多不同问题的神经科学实验室具有广泛的实际意义，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。该项目的三个互补目标是： 1. 了解表征已知细胞身份的基因表达模式。建立在我们的先前的研究显示跨细胞类型的保守表达模式，我们将定义共享基因特定于神经元亚群的表达模式，称为共表达。这些共同的表达模式将被用作细胞身份的测定。 2.通过基因之间表达关系的变化识别新的细胞亚型。共表达的变异是转录重连的一种形式，通常表明功能的变化。到找到新的神经元亚型，我们将评估共表达变化的数据，反映了与神经元身份相关的功能。我们将识别新的转录特征，这些特征可以在不同的区域复制实验室。 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会受益于多种方法。为了在这些人之间达成一致方法，我们将开发一种算法来有效地搜索基因集，可能会找到那些具有互补价值。然后，这些基因集将通过许多现有的方法进行评估，其中定制的组合和聚合输出通过公共网络服务器报告和提供。