Sunday 31 January 2016

How Microarray works ? Here is the methodology



Methodology:

A typical microarray experiment involves the hybridization of an mRNA molecule to the DNA template from which it is originated. Many DNA samples are used to construct an array. The amount of mRNA bound to each site on the array indicates the expression level of the various genes. This number may run in thousands. All the data is collected and a profile is generated for gene expression in the cell. 
There are actually several different ways in which nucleic acid probes can be arrayed at high density for interrogation of labelled mRNA samples. Usually, microarray preparation is initiated by obtaining end-sequences for several thousand of the clones, and a unique set of these expressed sequence tags (ESTs), is selected for amplification. These products are robotically deposited at a density of around 30 clones per square millimetre on the end of a special glass microscope slide or filter, in batches of perhaps 100 slides. The cDNA microarray probe is then hybridized to radioactively or fluorescently labelled cDNA prepared by reverse transcription of mRNA isolated from the cells or tissues of interest. Competitive hybridization of two samples labelled with different dyes, commonly Cy3 and Cy5, allows an estimate of the ratio of transcript abundance in the two RNA samples being compared, for each spot (clone) on the microarray independently. The levels of fluorescence or radioactivity are not regarded as a reliable indicator of the absolute level of transcript, but as described below, it is possible to infer changes in gene expression from changes in the signal intensity of each clone relative to the sample mean.
The alternate oligonucleotide technology pioneered by Affymetrix GeneChips® differs in two important respects (Lockhart et al. 1996). First, the probes are a set of up to 20 short, 25 mer oligonucleotides that are specific for each gene or exon, along with the related set with single base mismatches incorporated at the middle position of each oligonucleotide. These are synthesized in situ on each silicon chip using genome sequence information to guide photolithographic deposition. Second, the arrays are hybridized to a single biotinylated amplified RNA sample, and the intensity measure for each gene is currently computed by an algorithm that shows the difference between the match and mismatch measurements and averages over each oligonucleotide. Rather than comparing ratios, inferences are drawn by contrasting differences in magnitude of these intensity scores. This technology is expensive, but has greater genome coverage than microarrays and may be more replicable and comparable across research groups, so is seeing wide application for model organisms such as yeast, Drosophila, Arabidopsis, and mice. 
To date, most microarray studies have focused on fold-change in transcript abundance as the measure of interest, often employing a common reference sample as the standard against which experimental treatments are compared. 
The experimental sample is competitively hybridized with a reference sample that consists of pooled RNAs from multiple treatments, and the fold-difference between two experimental samples is inferred by comparing the two ratio measurements. In many aspects microarrays resemble miniature agricultural plots (Kerr & Churchill 2001), and the data can be parsed with linear regression and mixed model analysis of variance. Replicate sample sizes of just five or six will generally be adequate to demonstrate that just a 1.5 fold-change in transcript level of a particular gene is statistically significant, while twice that number may be adequate to study differences as small as 1.2-fold. By contrasting expression relative to the sample mean, reference samples that provide no biological information can be avoided, so these quantitative microarray approaches work well with as few as twice as many replicates as the simplest duplicate experiments.

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