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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