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Feb 15, 2012 (Vol. 32, No. 4)

Aiming to Optimize qPCR Steps

  • Click Image To Enlarge +
    Medians provide a more robust measure for qPCR analysis when confronted with outliers. [Biogazelle]

    Progress in real-time quantitative PCR (qPCR) technology has been steady since its invention approximately 15 years ago. Recent innovations and where the technology is headed in the future will be discussed at a Select Biosciences’ upcoming conference on “Advances in qPCR”.

    Jan Hellemans, co-founder and CEO of Biogazelle, emphasizes the critical importance of optimizing both the front end (experimental design) and the back end (data analysis) of the qPCR workflow. Biogazelle’s flagship product, qbasePLUS, is a software solution for the analysis of qPCR data.

    Hellemans notes that “there are still misconceptions as to how to best address the problem of inter-run variation with the experimental design.” The key principles, he says, are to “avoid the problem if possible, minimize the problem if it is not avoidable, and to correct for any variation that should actually occur.”

    It would be ideal to screen all samples for a given gene on the same plate, he says, adding that it is not necessary to screen the reference gene(s) on the same plate. “This is a common misconception,” he says, acknowledging that using the same plate for a gene is not always possible, particularly given the large number of samples in the increasingly large studies being carried out today.

    Measures should be taken to ensure that the potential variation is as small as possible. These measures would include using the same qPCR instrument and Cq value determination software settings, using the same batch of reagents, and minimizing the plate-to-plate variation by standardization. When variation does occur, Hellemans says that at least one sample should be re-analyzed in two different runs to enable correction for the variation.

    According to Hellemans, the use of imputation statistical methods (commonly used by statisticians, but not yet widely adopted in qPCR data analysis) is a useful approach to recovering crucial missing data from qPCR experiments. The gold standard for normalization of qPCR expression data is normalization against multiple validated reference genes, he says. With the increasing size of experiments, there is an increased risk of missing data from one or more of these reference genes due to technical failure. Imputation is an effective approach to recovering this missing data, he adds.

  • Maximizing Optimization

    Marina Guillet is the co-founder and vp lab director of TcLand Expression, which is developing companion diagnostics in immune-mediated disorders and biomarkers in solid organ transplantation. TcLand’s lead programs target rheumatoid arthritis and liver transplantation.

    To ensure that a robust technological platform was used for biomarker development and validation, the entire qPCR process at TcLand was standardized and each step of the process optimized, Guillet explains. This included the development of stringent and robust standard operating procedures as well as a comprehensive knowledge of all sources of variabilities within the process, from the choice of the sampling procedure to data generation.

    According to Guillet, there are a multitude of blood collection techniques to obtain RNA. However, only a few of these are compatible with the logistical and technical constraints associated with the usage of retrospective samples and large multicenter application.

    PAXgene blood RNA tubes (PreAnalytiX) were used to extract RNA. The PAXgene systems are the only blood collection tubes and associated RNA extraction kits to be CE-marked and to have received 510(k) clearance from the FDA, Guillet explains.

    In a recent 30 gene study, Guillet and colleagues demonstrated that it is possible to obtain trustworthy results in qPCR assays by paying particular attention to every single step of the workflow. The TcLand investigation was a nested study similar to those that have been used to identify the most important sources of error in qPCR experiments.

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