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Twenty-five years of quantitative PCR for gene expression analysis
Heather D. VanGuilder1, Kent E. Vrana1, and Willard M. Freeman1,2
1Department of Pharmacology
2Functional Genomics Core Facility, Penn State College of Medicine, Hershey, PA, USA
BioTechniques 25th Anniversary, Vol. 44, No. 5, April 2008, pp. 619–626
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Following its invention 25 years ago, PCR has been adapted for numerous molecular biology applications. Gene expression analysis by reverse-transcription quantitative PCR (RT-qPCR) has been a key enabling technology of the post-genome era. Since the founding of BioTechniques, this journal has been a resource for the improvements in qPCR technology, experimental design, and data analysis. qPCR and, more specifically, real-time qPCR has become a routine and robust approach for measuring the expression of genes of interest, validating microarray experiments, and monitoring biomarkers. The use of real-time qPCR has nearly supplanted other approaches (e.g., Northern blotting, RNase protection assays). This review examines the current state of qPCR for gene expression analysis now that the method has reached a mature stage of development and implementation. Specifically, the different fluorescent reporter technologies of real-time qPCR are discussed as well as the selection of endogenous controls. The conceptual framework for data analysis methods is also presented to demystify these analysis techniques. The future of qPCR remains bright as the technology becomes more rapid, cost-effective, easier to use, and capable of higher throughput.


BioTechniques began publication in 1983, the same year as the discovery of PCR by Kary Mullis (1). Both PCR and BioTechniques have enjoyed an exponential rise in popularity in the ensuing 25 years. Although quantitative PCR (qPCR) was originally developed for, and is still used for, DNA quantitation (e.g. viral load), this review focuses on the use of qPCR for quantitation of RNA levels (2,3). A number of early qPCR methods relying on end point analysis of PCR products were proposed soon after its development (4,5,6). qPCR came of age, however, with the introduction of real-time qPCR methods. It is now more than a decade since the initial publications describing real-time quantitative PCR (7,8,9,10). As can be seen in Figure 1, citations of qPCR increased dramatically (following a brief lag during adoption of the technology) with the introduction of real-time qPCR. In fact, the level of citations from qPCR is reaching the end of the exponential growth phase (not dissimilar from a real-time amplification plot).

Throughout the past 25 years, BioTechniques has been an important source of information on qPCR. The initial report of qPCR methods in BioTechniques in 1992 (11) has been followed by more than 80 additional reports in the journal. Additionally, two major reviews of the qPCR method have been published in BioTechniques, including one from Zimmermann and Mannhalter in 1996 (12) and a contribution by us in 1999 (13). In this review, we will assess the changes in the qPCR field for gene expression analysis in the past nine years and describe the maturation of this technology from optimization to standard use. We will focus on the use of qPCR to measure levels of RNA species and specifically try to demystify a recurrent confusion in the field: mathematical analysis of qPCR data. There are numerous applications for qPCR in the genetics field (e.g., gene deletion, gene duplication (14)), but we will not address these in the present context. Instead, our focus is on the ability of qPCR—more specifically reverse-transcription quantitative PCR (RT-qPCR)—to measure the levels of mRNAs, miRNAs, and other RNA species. This is obviously of great significance to modern biology and biomedical sciences. In fact, qPCR has progressed in tandem with the microarray field, which we have reviewed previously in BioTechniques (15).

qPCR now represents the method of choice for analyzing gene expression of a moderate number of genes in anywhere from a small number to thousands of samples. For investigators studying gene expression, there is a multitiered technological approach depending on the number of genes and samples being examined. Gene expression microarrays are still the preferred method for large-scale (e.g., whole-genome) discovery experiments (Figure 2). Due to the logistics, sensitivity, and costs of whole-genome micorarrays, there is also a niche for focused microarrays that allow for analysis of a smaller number of genes in a larger number of samples. Nonetheless, for validation of microarray discovery, RT-qPCR remains the gold standard (16). The current maturation of real-time qPCR with fluorescent probes allows for rapid and easy confirmation of microarray results in a large number of samples. Often, a whole-genome discovery experiment is not required, as the gene or pathway of interest is already known. In that case, the data collection can begin with qPCR. Finally, qPCR has also shown great utility in biomarker monitoring. In this scenario, previously developed identified targets can be assayed in very large numbers of samples (1000s).

While we have focused on gene expression, it is important to define that term more specifically. Generally, this refers to mRNA levels. However, since the original discovery of microRNAs (miRNA) (17), thousands of these regulating RNAs have been described (18). In the same manner as mRNAs, miRNA assays have been developed for real-time qPCR (19). The expanding understanding of other RNA species, including small nuclear, small nucleolar, and piwi-interacting RNAs, means that more types of RNAs will be assayed in the future (19).


In 1977, the Northern blot was introduced for RNA analysis and named after the DNA Southern blot invented by Sir Edwin Southern (20). Northern blotting uses denaturing gel electrophoresis and blotting with hybridization probe-dependent detection of target RNAs. RNA levels can be quantified and directly compared between multiple samples on a single membrane, although this method lacks the accuracy of fluorescence-based qPCR. The Northern blot is, however, still quite useful in the study of RNA degradation and transcript size, although nonspecific hybridization can confound data interpretation and the use of radio-activity is frequently necessary (21,22). The RNase protection assay is also used to detect, quantify, and characterize RNA species due to its sensitivity and specificity (23,24). Hybridization of antisense RNA corresponding to a known complementary target sequence prevents target digestion by single strand–specific RNase activity. This process results in the degradation of all remaining single-stranded RNAs (i.e., those not hybridized to the probe sequence), enabling the accurate quantitation of specific target sequences. These techniques, while revolutionary at the time of their inception, are long and involved protocols that are not amenable to the examination of many different transcripts in a high-throughput manner. Furthermore, the required amount of RNA can be quite large.

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