Ascertaining when and where genes are expressed is of crucial importance in order to understand the physiological role of a given gene/protein and the interactions between them. In addition, the normal expression patterns can then be compared to those observed in a variety of pathological conditions to identify pathological hallmarks of gene expression. The EURExpress, an integrated project funded by the EU under the VI Framework proposes a transcriptome-wide acquisition of expression patterns chiefly by means of in situ hybridization (ISH) with non-radioactive probes and will use this data to establish a web-linked, interactive digital transcriptome atlas of embryonic mouse. The final goal of the project is to create the expression data of > 20,000 genes by RNA in situ hybridization on sagittal sections from E14.5 wild type murine embryos. This data will result in a detailed description (at a cellular level) of gene expression patterns in the developing mouse. The “transcriptome atlas” will be generated using a newly developed automated RNA in situ hybridization system. Automated scanning microscopes will collect image data, which will be electronically sent out in a digital format for annotation. The latter will be performed using a web-based “virtual” microscope and be entered in a hierarchical database specifically designed to hold large amounts of image data and display them in a user-friendly format. For a subset of genes, mainly those directly involved in human diseases, expression data will also be generated by using human and murine tissue arrays. This will offer the opportunity to compare human and mouse expression patterns in adult tissues. This project builds on a strong European concentration of skills in gene expression analysis and mouse genomics and integrates European skills, efforts, resources and information in the field of systematic gene expression analysis. All expression data generated by EURExpress will be made readily available to the scientific community via the EURExpress web-linked database, considerably advancing our knowledge of gene function and having a significant impact on the identification of gene expression markers of disease processes.
An overall data flow in the project is organised as follows: