 |
Microarray Databases
 Microarrays and gene expression databases
Microarray technology makes use of the sequence resources created by the genome projects and other sequencing efforts to answer the question, what genes are expressed in a particular cell type of an organism, at a particular time and under particular conditions. For instance, they allow comparison of gene expression between normal and diseased (e.g., cancerous) cells. There are several names for this technology - DNA microarrays, DNA arrays, DNA chips, gene chips, others. Sometimes a distinction is made between these names but in fact they are all synonyms as there are no standard definitions for which type of microarray technology should be called by which name.
Microarray technology and applications
Microarrays exploit the preferential binding of complementary single-stranded nucleic acid sequences. A microarray is typically a glass slide, on to which DNA molecules are attached at fixed locations (spots). There may be tens of thousands of spots on an array, each containing a huge number of identical DNA molecules (or fragments of identical molecules), of lengths from twenty to hundreds of nucleotides. (According to quick napkin calculations by Wilhelm Ansorge and John Quackenbush in Schnookeloch in Heidelberg on 4  October, 2001, the number of DNA molecules in a microarry spot is 107-108). For gene expression studies, each of these molecules ideally should identify one gene or one exon in the genome, however, in practice this is not always so simple and may not even be generally possible due to families of similar genes in a genome. Microarrays that contain all of the approximate 6000 genes of the yeast genome have been available since 1997. The spots are either printed on the microarrays by a robot, or synthesised by photo-lithography (similarly as in computer chip productions) or by ink-jet printing.
The picture on the right shows an illuminated microarray (enlarged). A typical dimension of such an array is about 1 inch or less, the spot diameter is of the order of 0.1 mm, for some microarray types can be even smaller.
There are different ways how microarrays can be used to measure the gene expression levels. One of the most popular micorarray applications allows the comparison of gene expression levels in two different samples, e.g., the same cell type in a healthy and diseased state (see picture below).
The total mRNA from the cells in two different conditions is extracted and labelled with two different fluorescent labels: for example a green dye for cells at condition 1 and a red dye for cells at condition 2 (to be more accurate, the labelling is typically done by synthesising single stranded DNAs that are complementary to the extracted mRNA by a enzyme called reverse transcriptase). Both extracts are washed over the microarray. Labelled gene products from the extracts hybridise to their complementary sequences in the spots due to the preferential binding - complementary single stranded nucleic acid sequences tend to attract to each other and the longer the comlementary parts, the stronger the attraction.
The dyes enable the amount of sample bound to a spot to be measured by the level of fluorescence emitted when it is excited by a laser. If the RNA from the sample in condition 1 is in abundance, the spot will be green, if the RNA from the sample in condition 2 is in abundance, it will be red. If both are equal, the spot will be yellow, while if neither are present it will not fluoresce and appear black. Thus, from the fluorescence intensities and colours for each spot, the relative expression levels of the genes in both samples can be estimated.
The raw data that are produced from microarray experiments are the hybridised microarray images. To obtain information about gene expression levels, these images should be analysed, each spot on the array identified, its intensity measured and compared to the background. This is called image quantitation.
Image quantiation is done by image analysis software. To obtain the final gene expression matrix from spot quantitations, all the quantities related to some gene (either on the same array or on arrays measuring the same conditions in repeated experiments) have to be combined and the entire matrix has to be scaled to make different arrays comparable.
Gene expression monitoring is not the only microarray application, another one is SNP detection.
|
|
|
Microarray Databases <<< 1/2 >>> |
Reference:
This article has been contributed by
Alvis Brazma, Helen Parkinson, Thomas Schlitt and Mohammadreza Shojatalab. |
|