ABSTRACT
Real-time quantitative PCR represents a highly sensitive and powerful technique for the quantitation of nucleic acids. It has a tremendous potential for the high-throughput analysis of gene expression in research and routine diagnostics. However, the major hurdle is not the practical performance of the experiments themselves but rather the efficient evaluation and the mathematical and statistical analysis of the enormous amount of data gained by this technology, as these functions are not included in the software provided by the manufacturers of the detection systems. In this work, we focus on the mathematical evaluation and analysis of the data generated by real-time quantitative
PCR, the calculation of the final results, the propagation of experimental variation of the measured values to the final results, and the statistical analysis. We developed a Microsoft®
Excel®-based software application coded in Visual Basic for Applications, called Q-Gene, which addresses these points. Q-Gene manages and expedites the planning, performance, and evaluation of real-time quantitative PCR experiments, as well as the mathematical and statistical analysis, storage, and graphical presentation of the data. The Q-Gene software application is a tool to cope with complex realtime quantitative PCR experiments at a high-throughput scale and considerably expedites and rationalizes the experimental setup, data analysis, and data management while ensuring highest reproducibility.
INTRODUCTION
One of the best characteristics for the functional status of a certain cell is its gene expression pattern. Cells belonging to different tissues, cells in different developmental or metabolic stages, cells under the influence of specific compounds, or cells within a carcinogenic process differ by their gene expression patterns and thus by their mRNA pools. Currently, the most important technique for the accurate quantitation of gene expression is the fluorescent quantitative real-time RT-PCR
(8). Various variants of this technique are currently applied. The fluorescence signal is either generated by dyes intercalating into dsDNA [e.g., SYBR®
Green (13)] or by hybridization probes
[TaqMan® (9) or molecular beacons
(6)] relying on fluorescence resonance energy transfer (FRET). Within the last few years, quantitative real-time RTPCR has become an important method for the analysis of gene expression in a vast variety of samples (3,5,8).
The quantitative real-time RT-PCR technique represents a sensitive and powerful method for the gel-free detection of mRNA with a tremendous potential for quantitative applications.
Typically, the expression of the target gene is analyzed (in different reaction tubes or in the same reaction tube; i.e., multiplex) together with a reference gene to normalize the amount of the
PCR template and, thus, to enable the calculation of the relative expression level of the target gene (i.e., normalized gene expression) (12). Instead of using a standard curve, the target gene expression levels are calculated relative to the reference. Therefore, it is critical that the reference is a housekeeping gene [such as glyceraldehyde-triphosphate dehydrogenase (GAPDH), β- actin, or an rRNA gene] that is not influenced by the experimental situation.
However, it is still a matter of debate whether the expressions of such housekeeping genes are in fact unaffected by a particular experimental setup or not
(4,7,16). In parallel, the normalized copy number of any genomic DNA sequence can be determined as well by quantitative real-time RT-PCR. However, in this article, for practical reasons, we focus on quantitative RT-PCR to quantify gene expression.
Due to the rapid development of reagents and hardware components, the practical realization of quantitative realtime
RT-PCR has become user-friendly and feasible at a high-throughput scale in basic and applied research. However, the major hurdle is neither the isolation of the mRNA nor the RT-PCR itself but rather the coordination of the experiments and the efficient management of the collected data. The main challenge remains the evaluation and the mathematical and statistical analysis of the enormous amount of data gained by this technology, as these functions are not included in the software provided by the manufacturers of the detection systems.