Hybridization Array

Abstract: DNA hybridization arrays [also known as macroarrays, microarrays and/or high-density oligonucleotide arrays (Gene Chips)] bring gene expression analysis to a genomic scale by permitting investigators to simultaneously examine changes in the expression of literally thousands of genes. For hybridization arrays, the general approach is to immobilize gene-specific sequences (probes) on a solid state matrix (nylon membranes, glass microscope slides, silicon/ceramic chips). These sequences are then queried with labeled copies of nucleic acids from biological samples (targets). The underlying theory is that the greater the expression of a gene, the greater the amount of labeled target, and hence, the greater output signal. In spite of the simplicity of the experimental design, there are at least four different platforms and several different approaches to processing and labeling the biological samples. Moreover, investigators must also determine whether they will utilize commercially available arrays or generate their own. This review will cover the status of the hybridization array field with an eye toward underlying principles and available technologies. Future developments and technological trends will also be evaluated.

Abstract: Biosensors and more specifically biochips exploit the interactions between a target analyte and an immobilized biological recognition element to produce a measurable signal. Systems based on surface nucleic acid hybridization, such as microarrays, are particularly attractive due to the high degree of selectivity in the binding interactions. One of the drawbacks of this reaction is the relatively long time required for complete hybridization to occur, which is often the result of diffusion-limited reaction kinetics. In this work, an electrokinetically controlled DNA hybridization microfluidic chip will be introduced. The electrokinetic delivery technique provides the ability to dispense controlled samples of nanoliter volumes directly to the hybridization array (thereby increasing the reaction rate) and rapidly remove nonspecific adsorption, enabling the hybridization, washing, and scanning procedures to be conducted simultaneously. The result is that all processes from sample dispensing to hybridization detection can be completed in as little as 5 min. The chip also demonstrates an efficient hybridization scheme in which the probe saturation level is reached very rapidly as the targets are transported over the immobilized probe site enabling quantitative analysis of the sample concentration. Detection levels as low as 50 pM have been recorded using an epifluorescence microscope.

Abstract: As biology enters an era where the genomes of several organisms have been completely sequenced, the next great challenge is determining gene regulatory networks. Every gene has one or more activators, biochemical signals which are necessary to start transcription of the gene. Without the presence of the activator, only low level expression of the given gene can occur. Genes also have inhibitors, biochemical signals which prevent the expression of a particular gene even in the presence of an appropriate activator. Only a small number of genes function as activators or inhibitors, but identifying them is an important and diflicult problem. A gene regufatory network defines the complicated structure of gene products which activate/inhibit other gene products. Identifying gene regulatory networks from experimental data is now an area of extremely active research. New experimental technologies in molecular biology (particularly oligonucleotide arrays [ll] and micro arrays) now make it possible to quickly obtain vast amounts of data on gene expression in a particular organism under particular conditions. For example, Cho, etal [3] recently published a 17-point time series data set mea suring the expression level of each of 6601 d&rent genes for the yeast Saccharomycea cemhiae, obtained using an Affymetrix hybridization array. Wen, et.al [16] has generated Qpoint times series for the expression levels using RT-PCR of each of 112 genes involved in the rat nervous system development. Associating functions to genes based on this huge amount of data is en important and challenging problem.