Beyond gene expression: Promising new uses for microarrays

The commercial microarray industry is a little over a decade old. While gene expression studies are still a mainstay in the search for blockbuster drugs and novel biomarkers, this is a good time to look at some of the emerging trends that are changing the face of microarray research. When combined with gene expression analysis, these new applications can play a crucial role in unraveling complex molecular pathways.

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The commercial microarray industry is a little over a decade old. While gene expression studies are still a mainstay in the search for blockbuster drugs and novel biomarkers, this is a good time to look at some of the emerging trends that are changing the face of microarray research. When combined with gene expression analysis, these new applications can play a crucial role in unraveling complex molecular pathways.
The state of commercial microarray analysis can be compared to the transition from word processing programs like WordPerfect to multi-application suites like Microsoft Office. WordPerfect offered significant productivity gains compared to a typewriter.  But PC users ultimately needed Office, which offered not only complementary applications but also seamless integration. A similar trend is occurring in microarray technology. To answer complex biological questions, researchers need a range of stand-alone microarray applications they can integrate.
The software analogy works particularly well for the ink-jet printing platform, which affords unprecedented levels of probe-content (and therefore experimental-design) flexibility within applications. We believe that custom microarray design flexibility will become increasingly important as researchers move to more focused, hypothesis-driven research aimed at querying their relevant biology from multiple molecular perspectives.
Emerging Microarray Applications
Beyond gene expression, the study of chromosome alterations is being investigated in a growing number of labs. Gene introns and exons are also gaining interest, as are gene regulatory sequences, SNPs, and translation modifiers such as microRNAs. This molecular complexity builds upon itself, creating enormous challenges to researchers trying to characterize pathways or networks.
Emerging applications target all of these points of molecular complexity.  They include array-based Comparative Genomic Hybridization (aCGH), SNP analysis, Location Analysis (ChIP-on-chip), methylation analysis, splice variant analysis, and microRNA studies. PubMed shows citations for these applications growing steeply.
The value of these highly-focused new techniques can be impressive. Traditionally, CGH has been done through the optical imaging of whole chromosomes; a technique with limited sensitivity, resolution, quantification, and throughput. Efforts to overcome these limitations by using cloned DNA microarrays have been hampered by inadequate sensitivity, specificity and flexibility, as well as inconvenience of maintaining clone libraries.
The right combination of probe length and design flexibility produced an aCGH tool sensitive enough to identify the most challenging single-copy chromosome abnormalities, even in complex tumor samples where normal and cancerous cells are intermixed. In fact, the platform developed at Agilent can use native genomic DNA without complexity reduction, making experiments easier to run while facilitating experimental design.
Furthermore, researchers can now do high-resolution aCGH analysis of specific chromosomes, genome regions, or families of genes using "high-definition" aCGH microarrays. For Agilent, these are custom microarrays that users design via an online database of ~4 million predesigned and computationally validated probes. These arrays provide 10-100 times higher resolution than genome-wide aCGH microarrays. With this type of database, researchers can concentrate more on their own specific scientific investigations without being restricted by catalogue microarray content.
Another new application is Location Analysis (ChIP-on-chip), which is used to detect regulatory proteins binding to genomic DNA to control DNA replication and gene expression. In other words, it looks at switches in regulatory circuitry of cells. Combined with gene expression data, ChIP-on-chip data provides powerful insights for biomarker discovery. Dr. Richard Young at the Whitehead Institute recently published a paper in Cell describing how his lab used ChIP-on-chip to discover the mechanism behind the most intriguing aspect of human embryonic stem cells, pluripotency—the potential for cells to differentiate.
Other applications of Location Analysis include identifying and characterizing processes of methylation and histone modification as well as DNA replication, modification and repair.  There is also evidence suggesting that it can be used to elucidate mechanism-of-action and the potential therapeutic value of compounds and target genes by mapping regulatory networks relevant to disease.
Delivering New Applications
There are three keys to developing the next generation of microarray-based applications, as we see it at Agilent:
Sensitivity—based on the longer (60-mer) and higher quality probes that result from direct ink-jet-based oligonucleotide synthesis. Applications like aCGH require reliable resolution of small-fold (2x) changes.  In many new applications, sample quantities are extremely limited, placing a premium on a low limit of detection.
Flexibility—the ability to quickly and easily print digital design files to a variety of slide formats. In emerging applications, for example, microRNA and splice variant analysis, new information must be incorporated into new microarray designs frequently. Flexibility also enables the technology development required to perfect new microarray applications
Higher densities—important because, unlike gene expression profiling that queries 30,000 or so genes, scanning experiments in genomic applications like aCGH and Location Analysis benefit from high resolution, as does splice variant analysis.
Informatics—represents not only the key to delivering powerful stand-alone applications, but also the hub through which data from multiple applications can be integrated to triangulate biological questions.
Agilent's microarray program is built on the principle that higher sensitivity and probe content flexibility are becoming as important as density in the emerging world of microarray-based science. Researchers are increasingly recognizing the value of being able to design their own experiments on their own computers, including colleagues online, and then sending the electronic files to be printed on slides.  Because of this, Agilent is making a significant investment in developing the next-generation ink-jet manufacturing technology and providing ready access to large number of probe sequences for a variety of application.
In addition, the new technology will enable us to at least double feature density on our microarrays every year for the next several. We can now synthesize in situ up to 185,000 features on a standard 1-inch by 3-inch glass slide.  At this density level, an eight array slide configuration offers roughly 13,000 features per array, which is more than sufficient for most focused, theme array experiments across applications.
But all of this flexibility means little if researchers don't have access to validated probe sets based on current research and the ability to conveniently design microarrays using those probes. Thus, Agilent completes this picture through its eArray online design tool that gives scientists Web access to our DNA microarray content, annotation information, and novel probe sequences, enabling online custom microarray design, collaboration, and sharing tools.
Toward Hypothesis-Driven Research
These next-generation microarray applications aren't disruptive to existing genomic experimentation. Rather, they're often used to validate or complement results of gene expression studies, to extract higher value information from existing gene expression databases, and to yield more complete answers to high-value biological questions. For example, an investigator might run a gene expression study and then use Location Analysis to characterize and validate complete regulatory networks. CGH can be used in combination with gene expression to clarify cause and effect relationships. The key to triangulating data sets from multiple microarray applications is developing truly integrated informatics tools, and researchers can expect suppliers to expend much effort in this area in the coming years.
Finally, with the emergence of new applications that can be integrated, labs want to extend microarray analysis into a more hypothesis-driven, model-based research.  One can think of an experimental workflow beginning with scanning, where the goal is to identify a list of molecular "suspects"—in the case of gene expression, a list of highly up- and down-regulated genes associated with a given biological state or process.  Once a list of relevant molecules is compiled, pathway or network models can be created, and then validated and iteratively refined. This typically involves perturbing the system and predicting changes. For microarray suppliers, delivering tools to perform both scanning experiments and hypothesis-driven experiments is crucial.
Delivering hypothesis-driven research places a premium on flexibility, sensitivity and informatics. If a researcher can identify a subset of genes or chromosomal regions to study, it should then be possible to create focused custom arrays that address specific biology of interest. This underscores the value of flexibility.  Paired with the ability to put multiple arrays on a single glass slide, it has the potential to both drive array cost down and avoid generating vastly more data than is needed to refine a biological model.
When refining a model, our belief is that detecting subtle molecular changes will be even more important than it is in scanning experiments. Hence, the growing value of higher sensitivity probes. Finally, integrated informatics solutions will ultimately be the key to delivering hypothesis-driven research—creating a model and then using microarray data iteratively to refine that model.
DNA microarray technology is at a crossroads. Gene expression analysis has become an essential drug discovery tool for target identification. As the technology has matured, creative researchers are seeking to answer highly-specific, high-value biological questions. The challenge for the tool providers is to provide the flexibility, resolution, sensitivity and the informatics hub to facilitate these creative scientific investigations affordably.
Scott Cole is Marketing Director, Genomics at Agilent Technologies. Previously, he was founder and CEO of Genetic Health and Product Line Manager, Genetic Analysis at Applied Biosystems.

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