Data Mining in situ Gene Expression Patterns at Cellular Resolution

Abstract

Non-radioactive in situ hybridization (ISH) is a powerful technique for revealing gene expression in individual cells, the level of detail necessary for investigating how genes control cell type identity, cell differentiation, and cell-cell signaling. Although the availability of robotic ISH enables the expeditious determination of expression patterns for thousands of genes in serially sectioned tissues, a large collection of ISH images is, per se, of limited benefit. However, via accurate detection of expression strength and spatial normalization of expression location across different specimens, ISH images become a minable resource of annotated gene expression capable of advancing functional genomics in a mode similar to DNA sequence databases. We have developed computational methods to automate robotic ISH image annotation and applied these to over 200 genes throughout the postnatal mouse brain. Gene expression strengths were quantified for each cell tissue section images, and these images were subjected to atlas-based segmentation using a series of subdivision mesh maps that comprise our atlas of the postnatal mouse brain. With this common geometric representation of gene expression, patterns are automatically annotated and spatial searches success fully find the genes expressed in a similar fashion to custom query patterns. Cluster analysis of spatially normalized expression patterns identifies potential relationships in gene networks. Annotated gene expression patterns and query interfaces are publicly accessible at wv/spl bsol/w. geneattas. org.