EXPRESSION MICROARRAYS FOR SCORING OF NEUROPHENOTYPES

神经系统由于其极端的细胞异质性和单个细胞内信使rna的复杂分布，对基因表达分析提出了独特的挑战。事实上，神经表型的数量在很大程度上是未知的。我们建议开发新的方法来分析神经表型，利用神经元的cDNA在体内荧光标记作为杂交到微阵列的目标。这些方法需要协调开发新的硬件和软件工具。为此，分子生物学家和计算生物学家之间提出了一个合作计划，该计划将：1)通过比较凝胶垫微阵列的性能，为微阵列开发新的硬件，作为与摩托罗拉公司合作研究的一部分，与其他方法进行比较。凝胶垫微阵列代表了一种新技术，相对于玻璃微阵列或寡核苷酸阵列有几个理论上的优势2)开发了从单个神经元或至少单个神经元类型制备cDNA的方法。cDNA将从在转基因小鼠体内标记的神经元中制备，其中荧光蛋白的表达已使用修饰的bac。不同的“颜色”荧光蛋白首先用于表达NPY（青色荧光蛋白）或POMC（黄玉荧光蛋白）的神经元；3)开发新的用于跟踪、管理、查询和检索生成的表达数据以及识别所收集图像中的表达数据点。此外，通过分析来自10个小鼠器官的CY3标记RNA池中神经元RNA的表达水平来聚类细胞的新算法。这些算法还将纳入一组100个被识别和独立量化的不变rna的数据（具体目标3）。人们普遍认为cDNA微阵列技术的应用将对生物学研究产生巨大的影响。在神经系统的研究中，甚至连细胞类型的总数都不完全清楚，这种影响可能特别大。本文提出的方法旨在推进该技术建立神经细胞表型。

The nervous system poses unique challenges to gene expression analysis because of its extreme cellular heterogeneity and complex distributions of messenger RNAs within individual cells. Indeed the number of neural phenotypes is largely unknown. We propose to develop new methods for analyzing neural phenotypes by using cDNA from neurons that are fluorescently labeled in vivo as targets for hybridization to microarrays. These methods will require the coordinate development of new hardware and software tools. Toward that end, a collaborative program among molecular biologists and computational biologists is proposed that will: 1) develop new hardware for microarrays by comparing the performance of Gel pad microarrays, provided as part of collaborative studies with the Motorola Corporation, to alternative methodologies. Gel pad microarrays represent a new technology that has several theoretical advantages relative to glass microarrays or arrays of oligonucleotides 2) develop methods for the preparation of cDNA from single neurons or at least single neuronal types. cDNA will be prepared from neurons that have been marked in vivo in transgenic mice in which the expression of fluorescent proteins has been using modified BACs. Different "color" fluorescent proteins are first being to neurons expressing NPY (Cyan Fluorescent protein) or POMC (Topaz Fluorescent protein) 3) develop new for tracking, management, querying and retrieval of generated expression data and identification of expression data points within the collected images. In addition, new algorithms for clustering cell by analyzing the level of expression of a neuronal RNAs to those in a pool of CY3 labeled RNA from ten mouse organs. These algorithms will also incorporate data from a set of 100 unchanging RNAs identified and independently quantitated (Specific aim 3). It is widely believed that the use of cDNA microarrays will have an enormous impact on biological research. This impact is likely to be especially great in efforts to study the nervous system where even the total number of cell types is not entirely clear. The methods proposed in this are designed to advance this technology to establish the phenotype of neural cells.