DNA Microarrays: Perspectives for Hypothesis-Driven Transcriptome Research and for Clinical Applications

During the last few years, high throughput RNA profiling technologies have become nearly indispensable tools in biomedical research. Optimization strategies for the different technologies and bioinformatics tools to mine the wealth of data have developed rapidly to meet the researcher's need calling for more sophisticated clinical and pharmaceutical applications and a more integrated view of the cell's primary molecules DNA, RNAs and proteins. Integrative network analyses based on quantitative expression data, and the integration of data gained from genome analysis defining the relationships between the cell's transcriptome and proteome, are becoming the focus of current research. Key issues to resolve include method developments to optimize analysis of small amounts of tissue or cells and low-abundance messages, and minimize biological noise. DNA microarray technology, oligonucleotide-based microarrays in particular, holds much promise, but may still be largely unexploited. In the wake of experience gathered during expression profiling of human prostate tumors, tissues from human infectious disease models and experimental rodent systems, we discuss here a number of novel DNA microarray-based approaches that may be used in the near future to conduct hypothesis-driven transcriptome research aiming at quantitative models of genetic networks and at narrowing the gap between the transcriptome and the proteome. Such research may also establish microarray technology as indispensable for clinical research near the patient's bedside.