ABSTRACT
For those of us who study the expression of genes-the expressed genome or transcriptome — an array of state-of-the-art technologies exists that allows us to assess the expression of tens of thousands of genes simultaneously. These technologies, collectively called differential gene expression (DGE) technologies, allow scientists for the first time to investigate how global changes in gene expression contribute to a host of biological phenomena including cellular differentiation, development, aging, and progression of disease and injury. This chapter is intended to describe current technologies and assist in the selection of the appropriate currently available tools. The pivotal factors to be considered when choosing a method of gene expression analysis include, but are not limited to: the amount of biological sample required for a particular method, the sensitivity and coverage of detecting and quantifying differentially expressed transcripts, the number of samples to be processed, and the propensity for scale-up to industrial high-throughput. There is a wide range (from tens to hundreds of nanograms) in the amount of material (mRNA, cDNA, cRNA, etc.) required for each of the various methods. This may be an important concern if one wishes to quantify transcript abundance from a source that is limiting (e.g., tissue biopsy). It is essential to have a technology sensitive enough to detect mRNAs present as rare as one copy per 100,000 mRNAs. Achieving this level of sensitivity is necessary given the fact that 90 to 95% of all mRNA species are present at five or fewer copies per cell.1,2,3 Equally significant is the coverage or percentage of all possible transcripts assayed by a technology. The strengths and weaknesses of a gene expression technology can be further evaluated via the following elements: resolution, falsepositive rates, and false-negative rates. Resolution is a measure of the ability of a method to distinguish any gene from its nearest neighbor-both in terms of sequence similarity (e.g., splice variants, gene families) and gene expression profile (e.g., co-regulated genes). The false
positive rate is the amount of genes incorrectly identified as differentially expressed. The false negative rate is the number of genes that are truly differentially expressed but not detected by the technology.4 All of the differential gene expression technologies are grouped into two major categories, closed and open architecture systems, and are summarized below. The aforementioned variables need to be considered when selecting the most effective and appropriate application of one of these two main classes of DGE technologies.
