qPCR has become a ubiquitous technology for nucleic acid detection and quantification. It remains the gold standard for validation of microarray and next generation sequencing data and the method of choice for both clinical and basic research labs for a wide range of applications that include: 1) monitoring viral and bacterial infection; 2) tracking environmental microbial populations; and 3) the quantification of differential gene expression levels between experimental groups. However, there remains general concern about the production of data that truly reflects the tested experimental conditions (Bustin and Nolan 2017). This stems from the general perception that the generation of qPCR amplification curves and Cq values implies the production of interpretable data. Unfortunately, qPCR itself is highly robust and can yield Cq values regardless of the level of sample quality and purity. Only data generated from samples and primers that have undergone rigorous validation will ensure accurate and reproducible interpretations.
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Without following a rigorous, stepwise approach and checkpoints throughout a given qPCR experiment, the results and conclusions can be far from valid or reproducible. This has led to a growing number of questionable articles employing qPCR, estimated to total well above 50% of the published literature (Bustin and Nolan 2017). To assist the scientific community in publishing high-quality, reproducible data that reflect true experimental conditions, we have developed a comprehensive guide to performing the ultimate qPCR experiment. The following is a snapshot of the critical steps needed to achieve excellent results.
A guided, rigorous approach to qPCR
Step 1 — Experimental Design
Take the time to design the experiment bearing in mind that qPCR is one of the most sensitive molecular biology assays (Figure 1A). The advantage of sensitivity is the detection and quantification of low-abundance targets, which are often the most interesting. However, the disadvantage is the high potential for producing artefactual data from a poorly designed experiment. The assessment of time points (Figure 1B) and tissue sub sections (Figure 1C) coupled with the selection of an appropriate number of biological replicates per treatment group are good examples of key design parameters that are essential considerations to assure publication of precise, accurate, and reproducible data.
Step 2 — Sample Extraction and Nucleic Acid Isolation
Harvest the samples with appropriate kits, reagents, and instrumentation to minimize time and maximize yield in isolating RNA or DNA. A good nucleic acid purification methodology will reduce protein and chemical contaminants that can partially inhibit the reverse transcription and qPCR reactions or perturb primer annealing (Gibson et al. 2012, Tan and Yiap 2009). This can dramatically alter the Cq values producing data and interpretations that are unrelated to the tested experimental conditions. A good kit, such as the Aurum Total RNA Isolation Kits from Bio‑Rad, assures high-quality samples with excellent purity. RNA and DNA samples should be assessed for purity using a spectrophotometric method and for quality by either running a gel or using a Bioanalyzer System.
Step 3 — Reverse Transcription
For RNA isolation, use a good reverse transcription (RT) kit to produce a complete and representative cDNA copy of the mRNA. The hallmarks of a good RT kit include: 1) a mixture of random hexamers and oligo(dT)s to assure complete coverage of the transcriptome; 2) RNase H to digest the RNA while the cDNA is synthesized, which minimizes bias in cDNA production preventing the Cq values from being skewed to lower and variable values that would be unrepresentative of the true target amount in each sample; 3) an RNase inhibitor to prevent degradation of the RNA prior to RT; 4) a highly robust RT enzyme that permits RT of the widely ranging concentration of transcripts populating each sample. iScript Reverse Transcription Reagents from Bio‑Rad contain a blended mixture of each of these components to assure cDNA that closely reflects the transcriptome.
Step 4 — Primer Validation
Validate primers initially with a thermal gradient to assess annealing temperature followed by a standard curve to assess reaction efficiency (Figure 2A). Use an equalized pool of cDNA from all of the experimental groups including control for both primer validation experiments. Thermal validation assures that primer annealing is optimized based on the unique salt concentration, pH, and contaminants in the experimental samples (Figure 2B). The standard curve permits a guided approach to correctly diluting individual samples to ensure adequate contaminant and transcript dilution such that the reaction efficiency for each primer pair is close to 100% (Figure 2C). Only under these conditions will the Cq values reflect the true target concentrations for accurate interpretations. A good qPCR supermix can help assure solid results; the SsoAdvanced Universal Inhibitor-Tolerant SYBR® Green Supermix from Bio‑Rad contains a chimerized Taq polymerase that is more inhibitor tolerant to give better reproducibility between individual samples with variable levels of contaminants.
Step 5 — Reference Gene Selection
Test a panel of at least seven to ten reference genes to uncover two or three that are stably expressed between the treatment groups. Poor reference gene selection can dramatically alter the data and produce misleading conclusions (Figure 2D) (Robledo et al. 2014, Vandesompele et al. 2002). PrimePCR Assays are wet-lab validated and sequence-verified primers from Bio‑Rad for several genomes, including human, mouse, and rat. These assays can be purchased individually or in panel assays with suggested reference gene targets that can be assessed for stability between the experimental groups.
Step 6 — Working Up the Data
Apply care in performing the calculations for relative gene expression (Figure 3). There are several steps and multiple formulas required for qPCR data analysis and many labs employ Excel spreadsheets. Since qPCR experiments often require combining data from multiple plates, copy/paste and formula propagation errors can yield inaccurate data and interpretations that are very challenging to recognize and troubleshoot. CFX Maestro Software is a solution from Bio‑Rad that is paired with data generated from our qPCR instruments, permitting the data produced from a given project to be calculated seamlessly, reproducibly, and correctly for multiple plates. Since the raw data never leaves the software and the calculations are based on both the Pfaffl (Pfaffl 2001) and Vandesompele (Vandesompele et al. 2002) methods, the risk of data-manipulation errors is eliminated and ensures that all lab members are producing consistently and correctly calculated final results.
Although it is easy to produce data from qPCR reactions, only through the application of a rigorous, stepwise approach will the data and interpretations be reflective of the tested experimental conditions. Bio‑Rad not only offers excellent reagent and instrument solutions but also a superior technical team to guide and support the scientific community in producing excellent results.
To learn the fundamentals and best practices of qPCR and to schedule a departmental qPCR workshop with a member of our Field Application Scientist team, contact your local Bio‑Rad representative today.Open access article: Trends in Biotechnology, July 2019, Vol. 37, Issue 7, P761-774.
YouTube Tutorial Video Series Playlist “Achieving the Ultimate qPCR Experiment Tutorial Videos”.
Bustin S and Nolan T (2017). Talking the talk, but not walking the walk: RT-qPCR as a paradigm for the lack of reproducibility in molecular research. Eur J Clin Invest 47, 756–774.
Gibson KE et al. (2012). Measuring and mitigating inhibition during quantitative real time PCR analysis of viral nucleic acid extracts from large-volume environmental water samples. Water Res 46, 4,281–4,291.
Pfaffl MW (2001). A new mathematical model for relative quantification in real-time RT-PCR. Nucleic Acids Res 29, e45.
Robledo D et al. (2014). Analysis of qPCR reference gene stability determination methods and a practical approach for efficiency calculation on a turbot (Scophthalmus maximus) gonad dataset. BMC genomics 15, 648.
Tan SC and Yiap BC (2009). DNA, RNA, and protein extraction: the past and the present. J Biomed Biotechnol 2009, 574398.
Vandesompele J et al. (2002). Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biol 3, RESEARCH0034.
Taylor SC et al. (2019).The ultimate qPCR experiment: Producing publication quality, reproducible data the first time. Trends Biotechnol [published online ahead of print January 14, 2019]. Accessed May 29, 2019.
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