Integrated Work Flow
Pre-amplification is, of course, not limited to single cells, but can be used any time the amount of starting material is limited. This makes it possible to study the expression of a reasonably large number of transcripts per sample, and we already see more usage of qPCR in exploratory phases, where the objective is to identify candidate markers for subsequent confirmatory studies.
Of course, qPCR is not close to whole transcriptome profiling, which in the future will be done using microarrays or, even more likely, next-generation sequencing.
Major efforts by leading qPCR companies are focused on developing carefully selected, optimized, and validated assays in sets of 96, 384, and even 2×384 for affordable screening. As opposed to whole transcriptome analysis, this type of selected screening is performed on a larger number of samples than the number of genes analyzed. This makes the analysis more robust.
There are a number of dedicated tools available to identify markers for validation. For example, MultiD Analyses, a company that I co-founded, develops GenEx software for qPCR data mining with multivariate strategies.
With methods such as principal component analysis (PCA), hierarchical clustering, self organizing maps (SOM), and support vector machines (SVM), the optimum set of markers that distinguishes between classes of samples is selected based on the genes’ combined expression profiles. This is a much more powerful approach than selecting markers individually based on differential expression only.
Starting with a smaller number of preselected markers (than essentially the whole transcriptome) has provided the important advantage that false positive rates are greatly reduced and confounding noise is substantially smaller. Important markers not present in the original set can be identified by correlation based on function, property, or disease mining databases. Two of the most powerful products for studying qPCR data are IPA and the iReport, both from Ingenuity Systems.