High-throughput technology for automated single cell expression analysis for C. e

用于大肠杆菌自动单细胞表达分析的高通量技术

基本信息

批准号：
7830334
负责人：
STUART K KIM
金额：
$ 50万
依托单位：
STANFORD UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2009
资助国家：
美国
起止时间：
2009-09-30 至 2011-08-31
项目状态：
已结题

项目摘要

DESCRIPTION (provided by applicant): Research Area: This proposal is for 06: Enabling Technologies and specific Challenge Topic HG-102 Technologies for obtaining genomic, proteomic, and metabolomic data from individual viable cells in complex tissues. Abstract Most existing technologies can only measure the properties of a population of cells and not the properties of individual cells. C. elegans is unique among model organisms in that the complete cell lineage is known, enabling one to identify each nucleus in an individual. We have developed an automated cell lineage analyzer capable of extracting digital, single-cell expression information from confocal images of worms expressing GFP reporters. This cell analyzer can be used to extract expression data in a semi-high throughput manner. As proof-of-principle, we generated expression profiles of 93 genes in 363 specific cells from L1 stage larvae, and were able to quantitatively analyze expression of each gene as well as the molecular expression signature for each cell. This proposal is to first develop a generalized method to automatically annotate nuclei from confocal images, by assigning them a specific name from the cell lineage. We will then use the automated cell lineage analyzer in a data pipeline to analyze images from 1000 genes at six different developmental stages in 8000 confocal images. By generating a single cell expression database over the next two years, we will create a rich, new source of data for many years to come. Currently, images of worms in confocal data stacks can only be browsed manually, one gene at a time. Our database will convert images to quantitative expression values in an expression table that is suitable for computational analysis. Single cell expression analysis is a conceptually new way to study development and aging in C. elegans, using quantitative molecular signatures rather than cellular morphology. For instance, we can use the molecular signatures to determine how many different cell types are formed out of the total 959 cells in the lineage, to determine when and where during development cells begin to express different sets of genes, and to look for the way in which molecular fates are repeated in the cell lineage in order to extract the underlying regulatory modules that guide developmental pattern. This digital, single-cell expression database will be unique because C. elegans is the only model organism in which we can map the identities of individual nuclei and to our knowledge, we are the only group with a cell lineage annotator capable of extracting single cell expression information from larvae and adults.

描述（由申请人提供）：研究领域：本提案适用于 06：使能技术和特定挑战主题 HG-102 从复杂组织中的单个活细胞获取基因组、蛋白质组和代谢组数据的技术。摘要大多数现有技术只能测量细胞群体的特性，而不能测量单个细胞的特性。秀丽隐杆线虫在模式生物中是独一无二的，因为它的完整细胞谱系是已知的，使得人们能够识别个体中的每个细胞核。我们开发了一种自动化细胞谱系分析仪，能够从表达 GFP 报告基因的蠕虫共聚焦图像中提取数字单细胞表达信息。该细胞分析仪可用于以半高通量方式提取表达数据。作为原理验证，我们生成了 L1 期幼虫 363 个特定细胞中 93 个基因的表达谱，并且能够定量分析每个基因的表达以及每个细胞的分子表达特征。该提案首先开发一种通用方法，通过为细胞谱系分配特定名称，自动注释共焦图像中的细胞核。然后，我们将在数据管道中使用自动细胞谱系分析仪来分析 8000 个共聚焦图像中六个不同发育阶段的 1000 个基因的图像。通过在未来两年内生成单细胞表达数据库，我们将为未来许多年创建丰富的新数据源。目前，共焦数据堆栈中的蠕虫图像只能手动浏览，一次浏览一个基因。我们的数据库将图像转换为适合计算分析的表达表中的定量表达值。单细胞表达分析是一种研究秀丽隐杆线虫发育和衰老的概念新方法，它使用定量分子特征而不是细胞形态。例如，我们可以使用分子特征来确定谱系中总共 959 个细胞中形成了多少种不同的细胞类型，确定细胞在发育过程中何时何地开始表达不同的基因组，并寻找途径其中分子命运在细胞谱系中重复，以提取指导发育模式的潜在调控模块。这个数字化的单细胞表达数据库将是独一无二的，因为线虫是唯一的模型生物，我们可以在其中绘制单个细胞核的身份，并且据我们所知，我们是唯一拥有能够提取单细胞的细胞谱系注释器的群体来自幼虫和成虫的表达信息。