DNA Mircoarrays is a collection of genes that allows the study of gene expression in the biological response to further studies of the genome. Examples of the usage in mircoarrays are pathogen response to genetic materials, mutant development in the genes, and the drug discovery. Mircoarrays can be used to study the gene expression of a certain disease and that analysis and data acquired from this method can be used to develop a complementary drug that can cure or suppress the disease. Although the development of DNA microarrays has facilitated many studies in the biology and chemistry, the analysis of microarrays can be difficult. Because there are so many genes in a cell the analysis of such large gene expression presented in the microarrays can be rather time consuming and difficult to analyze.

History

The history of microarrays is rather recent. The first array was created in the mid 1980s. It was first called Macro arrays. It was only used to spot DNA probes, but more of it was used for studies of DNA clones, PCR products and oligonucleotides. They were all used with radioactively labeled targets. After further studies, Micro arrays were developed; it was created by pin spotters. Pin spotters were pin based robotic system that can accurately dispense a certain amount of DNA solution into the spots on a glass slide. By the mid 1990s, the technology of the microarrays was used to mainly investigate gene expressions. Some of the studies involved the gene expression profiles for tissues to study the life cycles of a bacteria. Also the study of cell division and which gene was responsible the different stages of cell division. Lastly, another application of microarrays is drug dosing, which can be effectively used to find the correct amount of drugs to use for a certain disease by examining the disease's gene expressions.

Theory

DNA microarrays can be used to explore thousand of sequence of gene in a single run. The fundamental basis of DNA microarray is based on the process of hybridization. The level of hybridization can be detected by the level of detectable chemical level, which is used to mark the target or the probe sequence in the experiment. DAN micrarrays or gene chip, which is high-density array of oligonucleotides, can be built either with light directed chemical synthesis conducted by the photolitographic microfabrication technique used by the semiconductor industry or by putting very small dots of oligonucleotides or cDNA on a solid support such as microscope slide. The expression of the gene level is revealed by the fluorescence level of the cDNA which is hybridized to the chip, which can be identified by the known location on the chip. The extent of the transcription of a particular gene can be seen by the intensity of the fluorescent spot on the chip. This method can be used to detect the variation in expression level shown by specific genes under different growth conditions

A somewhat simpler picture of gene chip theory begins with the goal. The purpose of this technique is to be able to qualitatively determine the amount of a certain mRNA fragment expressed by a gene. First, the mRNA is extracted from cells. The mRNA can then be turned into cDNA via reverse transcriptase, which is tagged with a fluorescent marker. That fluorescent cDNA is then added to a silicon (or glass) chip studded with lots of DNA fragments (oligonucleotides). The gene chip, with all of its DNA fragments, contains one particular fragment which base-pairs perfectly to your desired cDNA (or hybridizes). The RNA/cDNA that don't match the gene chip are washed off (aww), and the gene chip is seen under a special light that allows the fluorescent cDNA to glow. If your mRNA was present in the cell, it can now be seen on the chip in pretty colors. If not, then that part of the chip is blank. It is easy to see now how gene chips can be used to study gene expression by exploring its mRNA.

An example of how one can use microarray testing in order to analyze RNA is in the case of cancerous skin cells. A microarray test can be done to see what genes in the cancerous skin cells are similar to healthy skin cells and which ones are different. This is done by taking a sample of mRNA from healthy skin cells and a sample of mRNA from cancerous skin cells. Both of the mRNA of these skin cells are converted into cDNA. The healthy cDNA can then be labeled with a green fluorescent marker. The cancerous cDNA can then be labeled with a red fluorescent marker. Then a microarray containing all of the DNA in a skin cell is made. The fluorescent cDNAs are then both put onto the microarray. The specific cDNA sequences then hybridize to the corresponding DNA on the microarray. The excess cDNA is then washed off. Then a laser is used to analyze where the fluorescent cDNA hybridized. A computer takes this information and analyzes it giving several dots of color on a sheet indicating which genes are expressed by the healthy skin cell (which would show up as a green dot) and which are expressed by the cancerous skin cell (which would show up as a red dot). The yellow dots would indicate that the healthy and cancerous skin cell both express that certain gene. A black dot indicates that neither the healthy cell nor the cancerous skin cell expressed that gene. Thus by analyzing this data, one can find out what genes are expressed in cancerous skin cells that are not expressed in healthy ones.

Applications

Microarrays can be used for a variety of applications including analysis of genomic DNA, however, mRNA gene expression profiling dominates because of the amount of information we can get about the function of genes in cells and tissues using these two applications.

Gene Expression Profiling

Expression profiling using microarrays identifies genes whose expression depends on a specific biological state. For example, the amount of gene mRNAs can be more abundant during a disease state biological condition than in a normal state. Genes expressed in the disease state can then be identified using microarrays. Drugs can then be made to specifically inhibit these genes. Microarrays can also be tack gene expression during cell development, during which genes are grouped according to their pattern of expression over different phases of development. This then helps to identify a gene of unknown function by associating it to a functional role of a known gene whose expression pattern is similar to the unknown gene.

Pathway analysis can also be useful in identifying coordinated changes in expression affecting many genes at a time, which then helps us know the characterizing behavior of these genes that makes them act together to carry out a specific function. The detection of consistent changes in expression of a group of genes that have related function allows us to know the biological aspects that would otherwise not be known from gene expression analysis.

Reconstruction of functional, regulatory networks from gene expression by reverse engineering techniques can also be used to characterize the relationship among genes starting from their expression values in different conditions.

Another way we can use expression profiles is to use them as a fingerprint of a certain biological state by using the analysis of other hybridization experiments to identify common patterns of gene expression among samples with similar biological characteristics. This is useful in finding correlations between gene expression behavior and a phenotype.

Analysis of Genomic DNA

Analysis of genomic DNA is the other dominant microarray application. DNA microarrays can be used to directly measure the concentration of genomic DNA fragments from particular genomic regions. For example, microarrays can be applied in this way to scan changes in the gene copy number associated to cancer. Another way microarrays can be used for genomic DNA analysis is to identify the complement of regulatory DNA sequences that are bound by transcriptional regulators. DNA microarrays can be used for a genome-wide identification of in vivo transcription factor binding sites by chromatin immunopercipitation (ChIP) coupled with array hybridization. The overlapping probes on the array yields complete genomic coverage and allows for the identification of all DNA binding regions for a specific transcription factor. Analysis of genomic DNA using Microarrays can also be applied to characterize the presence of specific genetic sequences that can be used for parallel interrogation of pathogen genomes. This can then help us detect the presence of pathogens that cause infective diseases and we can monitor if they are in biological samples, for example, or food/water. Finally, analysis of genomic DNA using microarrays can be applied to genotyping. Microarrays can be designed for the genome-wide identification of single nucleotide polymorphisms (SNPs). Microarray usage for this has contributed greatly to the human genome project regarding human SNPs data. If one is interested in a particular list of known polymorphisms scattered throughout the genome, then probe sets can be designed just for them and chips can be used as a tool for rapid screening of biological samples. Chips were designed for this reason of detecting mutations in genes of interest to human health.

Data Analysis

It can be difficult to interpret the data that comes out on the microarray analyzer because there are many factors that can effect the accuracy of the data. The difficulty about getting clear results on the microarray is due to the fact that there are many different methods for normalizing the data of a microarray chip. Another factor to take into consideration is that the genes can produce false positives or false negatives because there are thousands of tests being run at the same time, which cause these errors. A false positive in this case would be concluding that a certain gene is expressed on the microarray when it really is not. A false negative would be concluding that the gene is not expressed on the microarray when it really is.

The statistical p-value is used in microarrays since there are these factors that effect the accuracy of the data. The p-value is the probability that the microarray results for a specific gene being hybridized to the DNA or not hybridized happened by chance alone. It is not accurate to treat the p-value as a statistic for an individual test when you are running a microarray since thousands of tests are being performed simultaneously. That is why one must adjust the p-value in order to account for this error. The Bonferroni correction is a statistical device that corrects the p-value making it more accurate.

Two additional statistical analysis that are useful for microarrays are

1. SAM (statistical analysis of microarrays) This test helps determine which data acquired from the microarray is useful and which is not. It does this by running a bunch of t-tests and then calculates a value for each gene. This value represents the correlation between the strength of the gene expression and its response variable.

2. ANOVA (analysis of variance) This is a statistical test used for analyzing experiments when two or more variables are present.

Reference

DNA Microarrays Current Applications. Emanuele de Rinaldis and Armin Lahm. Horizon bioscience. 2007.