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May 1, 2012 (Vol. 32, No. 9)

Drivers and Hurdles for qPCR

  • Click Image To Enlarge +
    Individual astrocytes from injured mice brains collected by FACS and classified based on the expression of 47 genes: Undamaged brain (blue), three days after injury (yellow), seven days after injury (green), and 14 days after injury (red). The profiling reveals expression pathways activated by the injury that induce the healing process. [Graph made with GenEx. Courtesy of Vendula Rusnakova and Miroslava Anderova.]

    According to a recent report on the gene-amplification technologies market from Global Industry Analysts, there are close to 70,000 bioresearchers using real-time quantitative PCR (qPCR) in North America alone. They spend $740 million annually on instruments and reagents. Annual growth, notes the market report, is about 16% and may even increase as the major hurdle for small companies trying to enter the field—basic PCR technology patent protection—expired last year. The global market is forecast to reach $1.9 billion by 2015.

    Several technological improvements and innovations, as well as new applications and procedures, are driving the field, but there are also roadblocks to overcome for continued growth.

    Tissue heterogeneity, where many types of cells respond differently to stimuli, is major complication in biomolecular research. When studying the effect of a certain environmental change or the response to a drug only some of the cells typically respond and they may do so differently. Nonresponsive cells confound the measurement response and obscure analysis.

    In other cases the response of a particular rare cell, such as a pluripotent cell or a mutated cell, may be of prime interest. Cells from body fluids can be labeled and sorted on the basis of surface markers using fluorescence activated cell sorting (FACS). Many tissues can be carefully disintegrated into individual cells for FACS analysis.

    A sorter can then be used to deposit individual cells of the appropriate kind for further analysis by qPCR. Other means to extract individual cells are aspiration and laser capture.

    Once individual cells have been collected, reagents compatible with downstream reverse transcription and qPCR are available for gentle lysis. Also, robust methods for pre-amplification are available. Pre-amplification is a step in single-cell expression profiling that is performed after reverse transcription but before qPCR to amplify the cDNA moderately.

    Typically multiplex PCR runs for a limited number of cycles. This produces a sufficient number of copies of each cDNA so that the sample can be aliquoted for parallel singleplex PCR analysis of all the targeted genes. With this workflow up to 96 transcripts per cells have been measured in several studies related to early development and differentiation.

  • 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.


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