Towards the end of the previous millennium the field of biological science witnessed an immense explosion of genomic data which was mainly due to a breakthrough in the field of informatics. One aspect of this phenomenon was the invention of the gene microarrays in the late 1980s .
Array based high throughput gene expression analysis is widely used in many research fields; gene expression microarrays have been used in numerous applications, including the identification of novel genes associated with certain diseases, most notably cancer, tumour classification, and prediction of patient outcome. The main idea behind microarray technology is based on the fact that the abundance of a particular species of mRNA is indirectly proportional to the amount of protein in the cell.
Medical research studies using microarrays encompass a wide variety of disease-related objectives that generally fall into 1 of 3 broad categories : class comparison, class prediction, and class discovery. Each category of study concludes with a list or groups of lists of differentially expressed genes (DEGs). Following the identification of differentially expressed genes comes the real challenge of assigning biological significance to the results and attempting to reconstruct pathways of interactions among DEGs. Several software tools are being used to further analyze the list of DEGs, thus providing a way to filter or mine the data. Tools for pathway analysis, semantic gene ontology analysis and gene prioritization are routinely used for data-mining, providing information on common features in the expression of the genes contained in the list of DEGs.
The microarray — the dense, two-dimensional grid of biosensors — is the critical component of a biochip platform. Typically, the probes are deposited on a flat substrate, which may either be passive (e.g. silicon or glass) or active, the latter consisting of integrated electronics or micromechanical devices that perform or assist signal transduction. Surface chemistry is used to covalently bind the sensor molecules to the substrate medium. The fabrication of microarrays is non-trivial and is a major economic and technological hurdle that may ultimately decide the success of future biochip platforms. The primary manufacturing challenge is the process of placing each sensor at a specific position (typically on a Cartesian grid) on the substrate. Various means exist to achieve the placement, but typically robotic micro-pipetting  or micro-printing  systems are used to place tiny spots of sensor material on the chip surface. Because each sensor is unique, only a few spots can be placed at a time. The low-throughput nature of this process results in high manufacturing costs.
Fodor and colleagues developed a unique fabrication process -later used by Affymetrix Inc., (Santa Clara, CA 95051, USA) - in which a series of microlithography steps is used to combinatorially synthesize hundreds of thousands of unique, single-stranded DNA sensors on a substrate one nucleotide at a time [1, 3]. One lithography step is needed per base type; thus, a total of four steps is required per nucleotide level. Although this technique is very powerful in that many sensors can be created simultaneously, it is currently only feasible for creating short DNA strands (15–25 nucleotides). Reliability and cost factors limit the number of photolithography steps that can be done. Furthermore, light-directed combinatorial synthesis techniques are not currently possible for proteins or other sensing molecules.
As noted above, most microarrays consist of a Cartesian grid of sensors. This approach is used chiefly to map or "encode" the coordinate of each sensor to its function. Sensors in these arrays typically use a universal signaling technique (e.g. fluorescence), thus making coordinates their only identifying feature. These arrays must be made using a serial process (i.e. requiring multiple, sequential steps) to ensure that each sensor is placed at the correct position.
"Random" fabrication, in which the sensors are placed at arbitrary positions on the chip, is an alternative to the serial method. The tedious and expensive positioning process is not required, enabling the use of parallelized self-assembly techniques. In this approach, large batches of identical sensors can be produced; sensors from each batch are then
combined and assembled into an array. A non-coordinate based encoding scheme must be used to identify each sensor. Such a design was first demonstrated - and later commercialized by Illumina Inc. (San Diego, CA 92121-1975 USA) - using functionalized beads placed randomly in the wells of an etched fiber optic cable [4, 5]. Each bead was uniquely encoded with a fluorescent signature. However,
this encoding scheme is limited in the number of unique dye combinations that can be used and successfully differentiated.
1. Fodor, S.P., et al., Light-directed, spatially addressable parallel chemical synthesis. Science, 1991. 251(4995): p. 767-73.
2. Ballman, K.V., Genetics and genomics: gene expression microarrays. Circulation, 2008. 118(15): p. 1593-7.
3. Pease, A.C., et al., Light-generated oligonucleotide arrays for rapid DNA sequence analysis. Proc Natl Acad Sci U S A, 1994. 91(11): p. 5022-6.
4. Steemers, F.J., J.A. Ferguson, and D.R. Walt, Screening unlabeled DNA targets with randomly ordered fiber-optic gene arrays. Nat Biotechnol, 2000. 18(1): p. 91-4.
5. Michael, K.L., et al., Randomly ordered addressable high-density optical sensor arrays. Anal Chem, 1998. 70(7): p. 1242-8.