The identification of genes with brain subregion- and/or neuron subtype-restricted patterns of expression is a central goal of Molecular Neuroscience in the post-genome era, for several reasons. First, such information is critical in order to attach meaning to the quantitative data that will be obtained by massively parallel analysis of gene expression in the brain under various normal and pathological conditions. Second, it is important to identify genes, or sets of genes, that can be used to mark, functionally manipulate, and map the connections of, specific brain regions or neuron subtypes, in order to experimentally define the neuroanatomical substrates of behavior and brain disease. Third, the identification of such genes should yield not only valuable markers, but also subjects for functional genetic studies of brain and behavior. The goal of this project is to determine the feasibility of using microarrays to identify brain region- and/or neuron subtype-restricted patterns of gene expression in the murine central nervous system. Using Affymetrix chips and flexible content cDNA and 50-mer oligomer microarrays, we will compare patterns of gene expression across five structurally and functionally distinct adult brain regions (amygdala, hippocampus, olfactory bulb, cerebellum and peraqueductal gray). Genes exhibiting differential expression between these regions will be validated by in situ hybridization. The strategy is to carry out an initial analysis using the amygdala as the reference region against the other four test regions, validate selected clones by in situ hybridization, adjust the stringency of the search algorithms based on the results of these initial data and iterate the process until all of the candidates have been tested. Once the algorithms have been optimized, the procedure can be repeated for each of the other four brain regions. To facilitate this analysis, we will develop and apply a series of algorithms that take into account structural information (sequence motifs) in adjusting stringency criteria for analysis of microarray data. Finally, we will extend a prototypic object-oriented database to store, manage and access both quantitative and spatial information about gene expression patterns in the brain. At the end of this exercise, we should have accumulated both a comprehensive data set that will indicate which microarray technology and search algorithms are most effective for this type of endeavor, an initial collection of clones and expression patterns that should be of interest to the community, and a system for the management, storage and accessing of brain in situ hybridization data that may achieve widespread use.
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