EXPRESSION MICROARRAYS FOR SCORING OF NEUROPHENOTYPES

神经系统对基因表达分析提出了独特的挑战，因为它具有极端的细胞异质性和单个细胞内信使RNA的复杂分布。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，通过使用荧光标记的神经元在体内作为杂交到微阵列的目标的cDNA。 这些方法将需要协调发展新的硬件和软件工具。 为此，提出了分子生物学家和计算生物学家之间的合作计划，该计划将：1）通过比较与摩托罗拉公司合作研究的一部分提供的凝胶垫微阵列与替代方法的性能，开发用于微阵列的新硬件。 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具有几个理论优势2）开发用于从单个神经元或至少单个神经元类型制备cDNA的方法。 将从转基因小鼠体内标记的神经元制备cDNA，其中荧光蛋白的表达已使用修饰的BAC。 不同的“颜色”荧光蛋白首先被用于表达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、查询和检索所生成的表达数据以及识别所收集的图像内的表达数据点。 此外，通过分析神经元RNA与来自十个小鼠器官的CY3标记RNA池中的那些RNA的表达水平，用于聚类细胞的新算法。 这些算法还将纳入来自一组100个经鉴定和独立定量的不变RNA的数据（具体目标3）。 人们普遍认为，cDNA微阵列的使用将对生物学研究产生巨大影响。 这种影响在研究神经系统的努力中可能特别大，因为即使是细胞类型的总数也不完全清楚。 本文提出的方法旨在推进这项技术，以建立神经细胞的表型。

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.