We have recently embarked on a multidisciplinary project to acquire, analyze, and distribute a rich and extensible multidimensional phenome data set of the developing mouse cerebellum. Successful cerebellar development requires the careful coordination of numerous cell and time-specific interactions of gene expression and function. Thus, we have begun an analysis of gene expression patterns throughout cerebellar development. An ideal way to test causality in these regulatory networks is to analyze not only the normal developmental system but also the effects of a perturbation of that system. As an initial step in this project we have used mouse models, with genetic lesions that affect aspects of cerebellar development (Math1-KO and Pax6-Sey mutants), i.e., specific perturbations to compare with normal development. Subsequent additions to this database will examine a wider set of mouse mutants as well as the influence of genetic variation on gene expression patterns arising in recombinant inbred mouse lines.

As one part of the analysis of this growing dataset of developmental expression profiles, we are developing a relational database that allows the comparison of various types of data derived from microarray experiments and in situ hybridization studies. The CGDB (http://cerebellar.dglab.org) is our initial foray into the development of a publicly accessible web-portal for gene expression analyses in the wildtype and mutant cerebellum. Exploration of this web-base allows the investigator to assess differential gene expression profiles from developing mutant cerebella, to gain an appreciation for the developmental expression profile of these genes in the wildtype cerebellum, and to verify the cellular expression of these genes in images from our in situ hybridization library. Further exploration of the in situ hybridization library allows for the navigation through high-resolution images and the opportunity to add individual annotations to these images through the online VirtualSlide™ community environment, accessible through links to http://neurophenotyping.utmem.edu/geneexpression.htm.

This poster will guide the investigator through an exploration of differential expression profiles (or developmental expression patterns) of a particular gene of interest, the creation of lists of similarly expressed genes by building simple search algorithms, the exportation of these data to other analysis packages for further study, and an exploration of the VirtualSlide environment. In addition, we will discuss our plans for further development of this database.