DNA Microarray Basic Information
A 2D array, typically on a glass, filter, or silicon wafer, upon which genes or gene fragments are deposited or synthesized in a predetermined spatial order allowing them to be made available as probes in a high-throughput, parallel manner.
Microarrays that consist of ordered sets of DNA fixed to solid surfaces provide pharmaceutical firms with a means to identify drug targets.
In the future, the emerging technology promises to help physicians decide the most effective drug treatments for individual patients.
Microarrays are simply ordered sets of DNA molecules of known sequence. Usually rectangular, they can consist of a few hundred to hundreds of thousands of sets. Each individual feature goes on the array at precisely defined location on the substrate. The identity of the DNA molecule fixed to each feature never changes. Scientists use that fact in calculating their experimental results. Microarray analysis permits scientists to detect thousands of genes in a small sample simultaneously and to analyze the expression of those genes. As a result, it promises to enable biotechnology and pharmaceutical companies to identify drug targets - the proteins with which drugs actually interact. Since it can also help identify individuals with similar biological patterns, microarray analysis can assist drug companies in choosing the most appropriate candidates for participating in clinical trials of new drugs. In the future, this emerging technology has the potential to help medical professionals select the most effective drugs, or those with the fewest side effects, for individual patients.
Potential of Microarray analysis:
The academic research community stands to benefit from microarray technology just as much as the pharmaceutical industry. The ability to use it in place of existing technology will allow researchers to perform experiments faster and more cheaply, and will enable them to concentrate on analyzing the results of microarray experiments rather than simply performing the experiments. This research could then lead to a better understanding of the disease process. That will require many different levels of research. While the field of expression has received most attention so far, looking at the gene copy level and protein level is just as important. Microarray technology has potential applications in each of these three levels.
Identifying drug targets provided the initial market for the microarrays. A good drug target has extraordinary value for developing pharmaceuticals. By comparing the ways in which genes are expressed in a normal and diseased heart, for example, scientists might be able to identify the genes and hence the associated proteins -- that are part of the disease process. Researchers could then use that information to synthesize drugs that interact with these proteins, thus reducing the disease's effect on the body.
Gene sequences can be measured simultaneously and calculated instantly when an ordered set of DNA molecules of known sequence a microarray is used. Consequently, scientists can evaluate an entire set of genes at once, rather than looking at physiological changes one gene at a time. For example, Genetics Institute, a biotechnology company in Cambridge, Massachusetts, built an array consisting of genes for cytokines, which are proteins that affect cell physiology during the inflammatory response, among other effects. The full set of DNA molecules contained more than 250 genes. While that number was not large by current standards of microarrays, it vastly outnumbered the one or two genes examined in typical pre-microarray experiments. The Genetics Institute scientists used the array to study how changes experienced by cells in the immune system during the inflammatory response are reflected in the behavior of all 250 genes at the same time. This experiment established the potential for using the patterns of response to help locate points in the body at which drugs could prove most effective.
Within that basic technological foundation, microarray companies have created a variety of products and services. They range in price, and involve several different technical approaches. A kit containing a simple array with limited density can cost as little as $1,100, while a versatile system favored by R&D laboratories in pharmaceutical and biotechnology companies costs more than $200,000. The differences among products lies in the basic components and the precise nature of the DNA on the arrays.
The type of molecule placed on the array units also varies according to circumstances. The most commonly used molecule is cDNA, or complementary DNA, which is derived from messenger RNA and cloned. Since they are derived from a distinct messenger RNA, each feature represents an expressed gene.
To detect interactions at microarray features, scientists must label the test sample in such a way that an appropriate instrument can recognize it. Since the minute size of microarray features limits the amount of material that can be located at any feature, detection methods must be extremely sensitive.
Other than a few low-end systems that use radioactive or chemiluminescent tagging, most microarrays use fluorescent tags as their means of identification. These labels can be delivered to the DNA units in several different ways. One simple and flexible approach involves attaching a fluorophore such as fluorescein or Cy3 to the oligonucleotide layer. While relatively simple, this approach has low sensitivity because it delivers only one unit of label per interaction. Technologists can achieve more sensitivity by multiplexing the labeled entity -- that is, delivering more than one unit of label per interaction.
Microarrays and bioinformatics
Experimental Design Due to the biological complexity of gene expression, the considerations of experimental design that are discussed in the expression profiling article are of critical importance if statistically and biologically valid conclusions are to be drawn from the data.
The lack of standardization in arrays presents an interoperability problem in bioinformatics, which hinders the exchange of array data. Various grass-roots open-source projects are attempting to facilitate the exchange and analysis of data produced with non-proprietary chips. The "Minimum Information About a Microarray Experiment" (MIAME) checklist helps define the level of detail that should exist and is being adopted by many journals as a requirement for the submission of papers incorporating microarray results. MIAME describes possible content but is not a format, many formats can in turn support the MIAME requirements yet there is no way to computationally determine semantic compliance.
There is currently an ongoing project being conducted by the FDA to develop standards and quality control metrics which will eventually allow the use of MicroArray data in drug discovery, clinical practice and regulatory decision-making.
The analysis of DNA microarrays poses a large number of statistical problems, including the normalization of the data. There are dozens of proposed normalization methods in the published literature; as in many other cases where authorities disagree, a sound conservative approach is to try a number of popular normalization methods and compare the conclusions reached: how sensitive are the main conclusions to the method chosen? From a hypothesis-testing perspective, the large number of genes present on a single array means that the experimenter must take into account a multiple testing problem: even if each gene is extremely unlikely to randomly yield a result of interest, the combination of all the genes is likely to show at least one or a few occurrences of this result which are false positives.
A basic difference between microarray data analysis and much traditional biomedical research is the dimensionality of the data. A large clinical study might collect, say, 100 data items per patient for thousands of patients. A medium-size microarray study will obtain many thousands of numbers per sample for perhaps a hundred samples. Many analysis techniques treat each sample as a single point in a space with thousands of dimensions, then attempt by various techniques to reduce the dimensionality of the data to something humans can visualize.
Relation between probe and gene
The relation between a probe and the mRNA that it is expected to detect is problematic. On the one hand, some mRNAs may cross-hybridize probes in the array that are supposed to detect another mRNA. On the other hand, probes that are designed to detect the mRNA of a particular gene may be relying on genomic EST information that is incorrectly associated with that gene.