Whether you’re a genomics beginner or a seasoned diagnostician, sooner or later, you’ll need to know something about quantitative PCR (qPCR). Also called real-time PCR, you cannot swing a stick around genomics publications without hitting qPCR data.
So, you might be designing the experiments or reviewing the data; either way, you should be familiar with qPCR papers the moment someone says Ct value!
Why? Because to properly perform qPCR and interpret the data, you need to understand if it’s actually showing differential gene expression, if the experiment conforms to the Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE) guidelines, or if you have a valid standard curve.
What’s a Ct value, you say? Don’t despair! We’ve highlighted the greatest qPCR literature hits that are your gateway to becoming a qPCR pro. And if you are already a pro, a review never hurts!
Literature on Best Practices in qPCR
Designing a successful qPCR does require some careful planning and optimization. The qPCR literature in this section lists publications that contain best practices for creating robust assays.
This paper, written by Stephen Bustin and colleagues in 2009,  has become the go-to guide for standardizing qPCR experiments. It, therefore, fits as #1 on the list of qPCR literature!
MIQE was developed to compare qPCR results from different labs and allow others to reproduce published experiments. All too often, researchers may gloss over details like primers and probe sequence, reaction conditions, or any modifications made to commercially available kits. Read our article on MIQE guidelines for a more detailed summary of this paper.
More than just providing rules of what to publish about your qPCR assay, the MIQE guidelines can also serve as a checklist for developing your assay. For example, inhibition testing is listed as essential information. This forces the researcher (you) to perform that test and might even prompt you to include an internal control if you hadn’t considered including one.
Before you pipette a single drop, read this paper!
In this Trends in Biotechnology review, Taylor et al. discuss techniques to make qPCR robust and the data comparable between different experiments and research groups. 
This review is a great place to start for assay developers and is also a go-to reference to keep handy. The authors also touch upon the MIQE guidelines, assay design, and potential sources of error.
The authors provide a comprehensive description of assay design, development, and quality control, meaning you could use this review as a checklist when designing your qPCR assays. Some essential considerations highlighted in this paper include:
- Subsampling error due to pipetting aliquots of your nucleic acid extract as input into the qPCR.
- Quality control of experiments, including internal controls to monitor PCR inhibition.
- Importance of evaluating the end-to-end procedure, including the consistent and proper storage and handling of biological samples.
- Multiplexing considerations, such as dye compatibility in a single tube and off-target amplification of primers.
And there’s much more! This review is well worth a read.
Nolan et al.  provide essential qPCR reading with their concise summary of planning your qPCR experiment. The flow charts within the paper make for excellent quick reference guides.
The authors highlight various steps and options when planning a qPCR experiment.
- Sample type considerations. Is the sample clinical or a cell line? Do you need to perform subsequent enrichments like laser capture microdissection (LCM) on formalin-fixed paraffin embedded (FFPE) sections?
- Choice of extracting mRNA vs. total RNA or even using crude lysates.
- Measuring the quality of RNA using methods such as the Agilent bioanalyzer and selecting your RT priming strategy and the enzyme.
- Reaction format is important too, where you need to decide on a 2-step (i.e., separate tubes) for RT and the qPCR and whether to use probes or intercalation dyes.
The optimum conditions for your qPCR need to be determined experimentally, but the effort applied at this step pays off long term.
Choosing and Using Housekeeping Genes in qPCR
We’ve covered how to choose the right housekeeping gene before, but it’s such a critical step for the normalization of qPCR data that we’ve highlighted not one but two key papers below.
4. The implications of using an inappropriate reference gene for real-time reverse transcription PCR data normalization
One way to measure gene expression is to compare the amount of mRNA corresponding to your gene of interest to that of a “housekeeping” gene. The term housekeeping gene is a bit of a misnomer since so-called housekeeping genes do vary in their expression levels and can be affected by different experimental conditions or treatments. 
This could, of course, impact the accuracy of your quantification. Therefore, validation of housekeeping genes is critical.
Dheda et al.  provide a case study comparing gene expression of the TLR2 gene when normalized against different housekeeping genes. This paper is an excellent guide for your own validation experiments.
5. Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes
When selecting a housekeeping gene for normalization, you should consider the source tissue or cell line. Fortunately, Vandesompele et al.  provide a comprehensive approach to characterizing different housekeeping genes from various tissue types.
And who says you only have to pick one? The authors recommend using at least three to normalize your RT-qPCR results.
You can use this publication to help you select housekeeping genes for normalization or to validate your own selection of housekeeping genes
qPCR Papers on Quantification
This section covers how to ensure your qPCR is quantitative, as this is just as important as how you design and optimize your assay.
6. Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method
The measurement of gene expression is among the top applications of qPCR. In this case, you are adding a reverse transcription step to create cDNA (RT-qPCR).
Livak and Schmittgen  describe a relative quantification strategy that compares the Ct value obtained for your assay’s target and the Ct value obtained for an internal control gene (also known as a housekeeping gene) such as GAPDH.
There are two important caveats:
- You can only express your data in relative terms, like fold-change, and not absolute terms like copies/reaction.
- You should characterize how your experimental treatment affects your control gene before employing it as your calibrator. It is no good if it does not remain at a constant expression level.
The paper describes how the 2 –ΔΔCt equation was derived and provides example data comparing Ct values for the target gene and internal control gene. If you plan to use the 2 –ΔΔCt approach for quantification, this is a great how-to guide for crunching the numbers and even setting up your replicate reactions.
You can get more information about double delta Ct analysis here
This publication by Michael Pfaffl goes a step further in calculating gene expression. 
As discussed above, housekeeping genes can be affected by different experimental conditions, so they must be validated upfront. And PCR primers against different targets may amplify them with varying efficiencies.
So, it is essential to determine the PCR efficiency of both your target gene and housekeeping gene primers. That is easily done with the equation [Equation][-1/slope].
Once you have the amplification efficiencies, you can calculate relative gene expression by taking the ratio of changes to crossing point (Cp) values (Cp values are just another name for Ct values) of your target and housekeeping genes.
The paper also investigated the effect of the amount of cDNA input has on PCR efficiency and hence the accuracy of quantification.
Using these calculations on your target and validated housekeeping genes will help to provide you with reliable and reproducible data.
Quantification using a standard curve is molecular biology 101, and if you are not familiar with constructing one for qPCR experiments, read our article on The Obligate qPCR Standard Curve.
Many people use plasmids or cloned amplicons as their standards, but one thing that may surprise you is that supercoiled plasmids vs. linearized or nicked plasmids can yield different Ct values!
Plasmid preparations consisting entirely of supercoiled species become relaxed due to freeze/thaw cycles and shearing from pipetting. This creates inconsistencies in your results.
How you measure the concentration of your plasmid standards is also essential. Whether by UV absorbance, or a fluorescent DNA-binding dye, consistency is vital when measuring the concentration of your standards.
There’s nothing right or wrong about using supercoiled or relaxed plasmids as qPCR standards. You just want to stick with the same approach for your work.
You may decide to fully linearize your plasmid standards to eliminate multiple supercoiled and relaxed species, but whatever your approach, be sure to include details, including how you isolated and quantified the plasmids in your protocol or publications. This ensures conformity to MIQE standards and enables others to reproduce your results.
Lin et al.  did an extensive job comparing different means of measuring plasmid concentrations and how supercoiled vs. relaxed plasmids impact the Ct values obtained with various qPCR formats (e.g., SYBR, MGB probes, etc.).
Read this paper before developing your qPCR protocol if you plan on running plasmid standards.
This paper by Karlen et al.  goes beyond providing methods for quantifying qPCR targets and compares the robustness, accuracy, and precision of different methods. As you’ll see from this article, there are a few different approaches to analyzing your qPCR data. Just as you would tailor experimental approaches to different experimental applications, you can also tailor your analysis approach to different applications.
The study does an excellent job of analyzing data with a few different models. The authors cite three methods as the standouts and provide guidance on the best picks for screening-type experiments instead of longitudinal experiments on varying sample types.
Data analysis or assay design—there is software to help! This section describes qPCR literature on software used to analyze data and help design assays.
10. qBase relative quantification framework and software for management and automated analysis of real-time quantitative PCR data
Hellemans et al.  describe algorithms and calculations used to power analysis software known as qBase. The software enables the end-user to analyze large qPCR datasets and compare results across experiments.
qBase can evaluate normalization and calculate relative gene expression. It can also compare results between runs.
When this paper was published, qBase was free, but now it seems available only as a paid version here. A free trial is provided, though, so there is no harm in trying it!
If you are considering the relative quantification approaches described above, then qBase would be a good, automated solution given that the algorithms are based on them.
This publication by SantaLucia  is essential qPCR reading, even if you do not use the software.
The basis of what is called visual-OMP (oligonucleotide modeling platform), is reaction kinetics. In other words, good old-fashioned biochemistry!
For every qPCR, you have primers, probes, enzymes, and your template DNA, among other factors.
Consider also that your primers may form hairpins or bind to each other as primer-dimers. It gets even more complicated for a multiplex reaction with primer/probe sets against more than one target.
Finally, the hybridization of your primers and probe can be affected by mismatches, and the exact position of the mismatch really matters.
A 5′ mismatch between your primer and template is not likely a big deal, but adding a mismatch at the 3′ end may prevent amplification. And what about mismatches right in the middle of an oligo?
You can deal with these by creating your own alignments and empirically testing oligos, but that can be painstaking and gets expensive quickly.
Visual-OMP can help you model these interactions and is a valuable tool when designing complex qPCRs and targeting sequences with significant genetic variability.
Good data comes from robust assays!
Additional Resources to qPCR papers
We’ve covered the top 11 qPCR papers every researcher should know, but there are also other great qPCR resources out there. The table below lists some handy reference guides and other resources that you may find helpful in all of your qPCR adventures!
e-Book from Bio-Rad that provides guidance on choosing the qPCR format, probes, intercalation dyes, and data analysis. Also (obviously!) suggests reagents offered by Bio-Rad.
e-Book from Thermo Fisher providing guidance on the basics, consumables, assay design, troubleshooting, and data analysis.
Recent and brief YouTube video. A perfect introduction if you are completely new to qPCR.
e-Book brought to you by the Gene Quantification platform, which is the go-to source for gene expression analysis used in qPCR, dPCR, and even Next Generation Sequencing
e-Book on everything you need to know about qPCR from Bitesize Bio!
I hope this listing of qPCR literature and resources helps you. Please feel free to provide your tips and tricks for successful qPCR in the comments below!
- Bustin SA, et al. (2009) The MIQE guidelines: minimum information for publication of quantitative real-time PCR experiments. Clin Chem. 55:611-22.
- Taylor SC, et al. (2019) The Ultimate qPCR Experiment: Producing Publication Quality, Reproducible Data the First Time. Trends Biotechnol. 37:761-774.
- Nolan, T et al. (2006) Quantification of mRNA using real-time RT-PCR. Nat Protoc 1:1559–1582
- Tricarico C, et al. (2002) Quantitative real-time reverse transcription polymerase chain reaction: normalization to rRNA or single housekeeping genes is inappropriate for human tissue biopsies. Anal Biochem. 309:293-300.
- Dheda K et al. (2005) The implications of using an inappropriate reference gene for real-time reverse transcription PCR data normalization. Anal Biochem. 344:141-3.
- 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:research0034.1
- Livak KJ and Schmittgen TD. (2001) Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method. Methods. 25:402-8.
- Pfaffl MW. (2001) A new mathematical model for relative quantification in real-time RT-PCR. Nucleic Acids Res. 29:e45.
- Lin CH et al. (2011) Quantification bias caused by plasmid DNA conformation in quantitative real-time PCR assay. PLoS One. 6:e29101
- Karlen, Y et al. (2007) Statistical significance of quantitative PCR. BMC Bioinformatics 8:131.
- Hellemans, J. et al. (2007) qBase relative quantification framework and software for management and automated analysis of real-time quantitative PCR data. Genome Biol 8; R19.
- SantaLucia J. (2007) Physical principles and visual-OMP software for optimal PCR design. Methods Mol Biol. 402:3-34.