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Jan 1, 2010 (Vol. 30, No. 1)

Exploiting Multi-Methodological Synergies

Microarrays Are Being Partnered with Cutting-Edge Technologies to Fuel Rapid Growth in Sector

  • The rapidly expanding field of microarrays has begun harnessing the power of other cutting-edge technologies such as next-generation sequencing, quantitative polymerase chain reaction (qPCR), and systems biology. Merging the best of each area is fueling new growth especially in the areas of molecular diagnostics and personalized medicine.

    Microarray advances were featured at the recent Australasian Microarray & Associated Technologies Association  meeting and more will be highlighted at CHI’s upcoming “Molecular Medicine Tri-Conference”.

    “The last three years have seen a virtual revolution in next-generation sequencing,” notes Vishy Iyer, Ph.D., professor, Institute for Cellular and Molecular Biology, University of Texas at Austin. “We are now able to look at the whole genome in much more detail than ever before. This is clearly important for understanding the functional behavior of the genome. Additionally, the phenomenal popularity of microarrays has been fueled by their ability to assess global gene-expression profiles of RNA. Coupling the capabilities of both provides a powerful tool to examine whole genomes in incredible detail.”

    Dr. Iyer’s studies utilize yeast as a model system to address various aspects of global gene expression and use next-generation sequencing coupled with microarrays. “One of our projects involves delineating the role of chromatin in gene regulation. Chromosomes consist of building blocks called nucleosomes that carry epigenetically inherited information mediated by their core histone proteins. We wanted to learn where nucleosomes sit on DNA and how they respond to cellular perturbations.

    “We compared yeast that were heat shocked or not, extracted DNA that was wrapped around the nucleosomes, sequenced it to identify the nucleosome locations, and assessed the corresponding RNA via microarrays. We were able to identify specific chromatin-remodeling patterns associated with different sets of genes that were activated and repressed by heat shock.”

    Ultimately, such studies may shed more light on understanding how somatic mutations and genomic rearrangements contribute to cancer.

    “A very important basic question now is to determine the spectrum of variation that occurs in different populations and to correlate that to disease. The technology will likely continue to advance in sophistication, but also become more affordable as more people employ such methods.”

  • Patient Stratification

    Traditional biological approaches focus on identifying and separately studying individual genes, proteins, and cells. Systems biology, however, views organisms more holistically, i.e., as interacting and integrated networks of genes, proteins, and life-sustaining biochemical reactions.

    Systems biology and microarrays are proving indispensable for determining how to get the right drug to the right patient, according to Peter J. van der Spek, Ph.D., department of bioinformatics, Erasmus University Medical Center. “Systems biology coupled with microarray approaches opens new perspectives for expression-based patient stratification. Microarray and next-generation sequencing techniques provide vast volumes of data and detailed information about natural variants versus mutations that underlie the molecular etiology of disease.”

    How does one distinguish between the two? “The key is carefully analyzing public and private reference data,” Professor van der Spek explains. “It’s a little like finding a needle in a haystack sometimes. But, we consult our huge reference archives, baseline archives, and subscriptions to knowledge archives in order to make that determination.”

    Professor van der Spek and colleagues are examining tissues from patients who have specific cancers. “Systems biology helps us group patients sharing a common genetic mechanism underlying the disease subgroup. Sometimes this can be easily detected at the single nucleotide polymorphism (SNP) level using copy-number variation. Most often we use gene-expression data to classify the distinct subgroups based on differences in differentiation status in combination with cytogenetic data.”

    Personalized medicine is making gains, Professor van der Spek says. “I expect more and more molecular diagnostics to come in the form of companion diagnostics allowing for targeted therapy. Moreover, evidence-based medicine eventually will make medicine more cost effective. Introduction to the clinic takes time, but small biotech companies are pushing the borders in a healthy climate of public private partnerships.”


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