Optimized Microarray Analysis of Neural Differentiation

描述（申请人提供）： 神经元分化的特征是神经元分化程度的变化， 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 发现不同缝隙连接基因的稳定外源表达导致 对分化剂的反应迟缓或加速。比较 使用本地生产的cDNA微阵列的基因表达表明候选 在转染细胞中表达增强或抑制的基因 神经母细胞瘤细胞然而，初步研究提出了重要的问题， 关于该技术，包括检测阈值，每个的可靠性， 和代表基因的簇的检测 表达模式本申请的具体目的是适用于 数学算法和修改实验技术，以便：1） 最大限度地减少光点比误差; 2）确定期望值和可控 3）建立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“病理学” 对于亲本和转染的细胞;和5）定义基因簇内的 SSGE。我们希望这些研究不仅能改善我们对 微阵列分析，但将开发新的概念， 通常使用模式。

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.