Why Are We Still Using qPCR For The Quantification Of Nucleic Acids?
Anglia Ruskin University, United Kingdom
Real-time fluorescence-dependent quantitative PCR, with or without a preceding reverse transcription (RT) step, has long been embraced as a valuable and valid means for quantifying nucleic acids. Its results are widely used to report molecular biomarkers of disease, quantify changes to RNA levels in cells or biopsies and identify the tissue of origin from deposits on forensic samples. Clearly, qPCR has an important and effective use for detecting pathogens, SNPs or other DNA-associated applications. However, this acceptance for RNA is remarkable, given the numerous reports that have emerged over the last twenty years or so that should cast significant doubts on how reliable RT-qPCR data really are. Whilst RT-qPCR can undoubtedly be used to distinguish fairly large differences or changes in nucleic acid levels, the majority of conclusions are based on results that report small changes that are neither robust nor consistent. This raises the far-reaching question of whether it is time to abandon RT-qPCR as a method for quantifying RNAs and replace it with more accurate and less error-prone digital PCR technology.
Target Concentration and Replicate qPCR Reactions
1Anatomy, Embryology and Physiology, Academic Medical Center, Amsterdam, the Netherlands; 2Vascular Medicine & Clinical Epidemiology, Biostatistics and Bioinformatics, Academic Medical Center, Amsterdam, the Netherlands; 3Heart Failure Unit, Germans Trias i Pujol Hospital, Universitat Autònoma, Barcelona, Spain; 4Experimental Cardiology, Academic Medical Center, Amsterdam, the Netherlands
In the analysis of qPCR data, the criterion for reproducibility between replicate reactions is set to 0.5 cycles; when the Cq values of replicates differ more than 0.5, the results are often discarded. The idea is that this rule-of-thumb should be enforced to protect against the inclusion of technical variation in the analysis of the qPCR results. However, a technical pipetting error of 15% is required to reach a 0.5 cycles difference. Smaller technical errors are already unacceptably large but would go unnoticed and be tolerated. On the other hand, at high Cq values the sampling error that occurs when pipetting a low number of target molecules into the PCR plate is governed by the Poisson distribution. To calculate the magnitude of this sampling error for different Cq values, we started with the assumption that for a PCR efficiency of about 1.8, an input of 10 copies of target leads to a Cq value of about 35 when the primer and amplicon concentration become similar. The observed Cq value will depend on the actual PCR efficiency, the monitoring chemistry, the (pre-) processing of the fluorescence data and the way the Cq value is determined. When the PCR reaction reaches the quantification threshold the general kinetic equation can be written as Nq = N.ECq (Eq. 1) which with the above assumption is equal to Nq = 10.E35 (Eq. 2). This equality provides us with an equation to estimate the target input from the observed Cq value and PCR efficiency: N = 10.E35-Cq (Eq. 3). Using the relationship between Poisson and Chi2 distributions the 95% confidence interval of N is then given by: 0.5χ20.025;2N ≤ N ≤ 0.5χ20.975;2N+2 (Eq. 4). For each Cq and PCR efficiency value we can thus calculate a number of copies with Eq. 3, the sampling error range with Eq. 4 and then, with the inverse of Eq. 1, the expected range of Cq values due to unavoidable sampling variation. This range increases with higher Cq values (less target) and easily leads to a replicate variation above 0.5 cycles. Discarding such discordant replicates will lead to bias and loss of power in the analysis. For a dataset that resulted from the qPCR measurement of 12 miRNA targets in 834 patients with a total of 20,016 reactions, the strict application of the rule that the Cq values of replicates should be within 0.5 cycles led to rejection of 7752 (39%) measurements. When the unavoidable Cq range, calculated as described above, is used to determine which Cq difference between replicates should lead to the decision that the replicate discordance is still acceptable, the number of reactions that should be discarded decreases to 1414 (7%). Not only less reactions are discarded, also the distribution of Cq values changes showing that strict application of the Cq<0.5 cycle rule has led to bias in the results of the qPCR experiment. A decision to exclude replicate reactions based on the expected sampling error avoids this bias, prevents unwanted loss of data and therefore increases the statistical power.
Laboratory Automation And Data Management In Diagnostics – qPCR ZIKV Detection And Quantification
1SCINOTE, LLC, United States of America; 2Institute of Microbiology and Immunology, Faculty of Medicine, University of Ljubljana, Slovenia; 3BioSistemika, d.o.o., Slovenia; 4Gilson, Inc., United States of America
Automation has significantly improved human diagnostics in the developed world by increasing patient throughput and decreasing diagnostic variability caused by human interaction. The outbreak and expansion of Zika virus (ZIKV) has increased the need for rapid and reliable diagnostics in the early stages of infection and for virus quantification in clinical studies. Real time PCR (qPCR) has been found to be the most sensitive, specific and rapid detection system for ZIKV detection.
Our study addressed automation of qPCR plate setup for ZIKV detection and quantification which includes management of samples, preparation of sample dilutions, preparation of master mix and their application to the qPCR plate. This was compared to manual qPCR setup which was performed by an experienced lab diagnostician.
In this study samples were tested from five human patients with suspicion on ZIKV infection that were sent for routine diagnostics of ZIKV to the Institute of Microbiology and Immunology, Faculty of Medicine, University of Ljubljana, Slovenia (IMI). RNA was isolated from different tissues or fluids including fetus brain, blood, semen, plasma, or urine. Additionally, for an absolute quantification study, six different ZIKV strains maintained in cell culture lines were analysed. Necessary controls were included in every step of the process. The same samples were used for manual and automated qPCR experiments on the same day.
The automated setup included management of samples in sciNote Open Source Electronic Lab Notebook while sample dilutions, master mix preparation and qPCR plate setup was done by using Gilson qPCR Assistant with PIPETMAX®268 automated pipetting workstation (PIPETMAX®).
Analysis of the qPCR data revealed that the accuracy of performance of automated qPCR plate setup was comparable to manual setup done by a very experienced lab diagnostician while the speed of the setup was improved by automating the sample import, sample dilutions and qPCR plate setup.
Incorporating an automation platform into the diagnostic protocol allows for accurate standard dilution and assay setup with a significant increase in throughput compared to manual processing. Moreover, integration of data management software with an automation system further increases the throughput and reduces the possibility of user error. More importantly, data management software, such as sciNote, enables full traceability of samples and results which is critical for accuracy in human diagnostics.
Validation of a qPCR method - Determining Limit of Detection, Limit of Quantification and Dynamic Range.
1TATAA Biocenter AB, Sweden; 2MultiD Analyses AB, Sweden; 3LGC Douglas Scientific USA
Quantitative Real-Time Polymerase Chain Reaction (qPCR) is the most sensitive and specific technique we have for the detection of nucleic acids. Even though it has been around for more than 30 years and is preferred in research applications, it has yet to win broad acceptance in routine practice. This requires a means to unambiguously assess the performance of specific qPCR analyses. Here we present methods to determine the limit of detection (LoD), the limit of quantification (LoQ) and the Dynamic Range as applicable to qPCR. These are based on standard statistical methods as recommended by regulatory bodies and adapted to the logarithmic response characteristic of qPCR.
The MIQE guidelines: minimum information for publication of quantitative real-time PCR experiments
SA Bustin, V Benes, JA Garson, J Hellemans, J Huggett, M Kubista, ...
Clinical chemistry 55 (4), 611-622
Prime time for qPCR–raising the quality bar
Eur. Pharm. Rev. 19, 60-67
Influence of PCR consumables on the accuracy of real-time PCR experiments and NGS sample preparation
4titude Ltd, Germany
Classical PCR and qPCR plates are one-component plates made out of polypropylene (PP). PP is the best plastic material for PCR tubes as it is chemically inert and allows for the production of ultra-thin tube walls which is important for fast temperature transfer. While PP has become the standard material for PCR consumables some of its properties question its suitability for applications like qPCR and NGS:
The material characteristics of PP exhibit a Vicat Softening Temperature (VST) of 90°C and a coefficient of thermal expansion of 180x10-6 K-1 which are potential weaknesses for its usage at typical (q)PCR temperatures.
When used for (q)PCR, not only do the plates soften during the denaturation step, but measurements also show that the plates expand by up to 2mm in the diagonal plane (from room temperature to 95°C) and they shrink again as the temperature decreases. Therefore, the plate will undergo expansion and contraction in every cycle, placing significant tension on the plate seals. As a result, contact between the seal and plate will be particularly weakened in the corner positions and outer rows leading to evaporation from the plate in these areas, while centre wells will only be affected minimally. This differential evaporation effect is especially eminent when adhesive seals are used (as opposed to heat seals).
Evaporation has a significant effect on the reaction conditions resulting in noticeable effects, especially for qPCR. Identical samples can exhibit significant differences in their Ct values, depending on their position on the plate. This often remains unnoticed as triplicates are typically placed in neighbouring wells which are affected by similar levels of evaporation.
A solution to the problem of evaporation related qPCR inaccuracies is the usage of two-component plates. These plates consist of tubes made out of PP but a frame made out of polycarbonate (PC). PC does not show significant temperature-dependant expansion and contraction as the VST of this material is 147°C and the coefficient of thermal expansion is 70x10-6 K-1.
The ongoing trend to continually reduce DNA concentrations can make additional modifications or choices necessary. DNA has been shown to bind to PP tubes in trace amounts, especially in highly ionic conditions. Different PP polymers are used for the production of PCR consumables as they differ in their characteristics, such as surface charge. As a result, different PCR plates bind DNA in varying amounts. Furthermore, the commercially used term “low-binding” is poorly defined.
DNA binding to PP surfaces has not been reported as an issue for PCR/qPCR as DNA stuck to the walls will be released during denaturation steps. However, NGS protocols contain a number of transfer steps from one tube to another and ultra-low DNA binding characteristics may be required if only trace amounts of nucleic acids are used.
How to monitor analytical and technical factors influencing qPCR
TATAA Biocenter, Sweden
qPCR is today a mature and most established method. Still it is easy to neglect the underlying factors that may compromise the results. With an easy-to-use highly automated qPCR instrument in every lab, scientist can (if they choose) generate data with minimum focus on the pre-qPCR parameters that may have significant impact on the data. It is not difficult to produce Cq-values, but how do we know that they truly reflect the amount of target that was actually present in the original sample or even in vivo? As described in the MIQE guidelines proper controls should be included in order to identify and in some cases correct for the bias caused by pre-analytical parameters and technical variation. I will describe quality control measures to test for degradation of RNA, RT and PCR inhibition, genomic DNA background, and provide means to compensate for interplate variation to perform high quality quantitative PCR measurements.