Optimized Microarray Analysis of Neural Differentiation

神经分化的优化微阵列分析

• 批准号: 6423596
• 负责人: David C Spray
• 金额: $ 20.92万
• 依托单位: ALBERT EINSTEIN COLLEGE OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2001
• 资助国家: 美国
• 起止时间: 2001-09-27 至 2003-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6423596
• 关键词: cell line; computational neuroscience; computer data analysis; computer system design /evaluation; developmental neurobiology; gap junctions; gene expression; genetic techniques; genetic transcription; mathematical model; method development; microarray technology; neuroblastoma; technology /technique development

DESCRIPTION (Provided By Applicant): Neuronal differentiation is characterized by changes in both the extent of intercellular coupling and the expression patterns of connexin genes that encode gap junction proteins. Using a neuroblastoma culture system, we have found that stable exogenous expression of different gap junction genes results in either retarded or accelerated response to differentiating agents. Comparing gene expression using locally produced cDNA microarrays indicates candidate genes whose expression is enhanced or depressed in the transfected neuroblastoma cells. However, the preliminary studies raise important questions regarding the technique, including threshold of detection, reliability for each of the spots in the array and detection of clusters representing gene expression patterns. The Specific Aims of this application are to apply mathematical algorithms and modify experimental techniques so as to: 1) minimize the spot ratio errors; 2) determine expected values and controllable fluctuations of gene expression; 3) build the pre-Hilbert space of standard gene expression (SSGE); 4) compute the so-called "pathologs" of gene expression for parental and transfected cells; and 5) define gene clusters within the SSGE. We expect that these studies will not only improve our use of the microarray analysis, but will develop new concepts by which such expression patterns are generally used.

描述(由申请人提供)： 神经元分化的特征是两者的程度都发生了变化 细胞间偶联和连接蛋白基因的表达模式 编码缝隙连接蛋白。使用神经母细胞瘤培养系统，我们有 发现不同缝隙连接基因的稳定外源表达导致 对分化剂反应迟缓或加速。比较 利用本地生产的cDNA微阵列进行基因表达表明候选基因 在转染体中表达增强或抑制的基因 神经母细胞瘤细胞。然而，初步研究提出了一些重要问题。 关于该技术，包括检测阈值、每种技术的可靠性 阵列中的斑点和代表基因的簇的检测 表情模式。此应用程序的具体目标是应用 数学算法和修改实验技术，以便：1) 最大限度地减少光斑比误差；2)确定期望值和可控性 基因表达的涨落；3)构建标准的前希尔伯特空间 基因表达(SSGE)；4)计算基因表达的所谓“病理” 用于亲本细胞和转基因细胞；以及5)定义 SSGE。我们期望这些研究不仅会改善我们对 微阵列分析，但将开发新的概念，通过这种表达 通常使用模式。