How to bring light to PCR: TaqMan probe or SYBR Green dye?

Bram De Craene - Apr 24, 2018

As a qPCR trainer, I often get the question why to worry about careful design and validation of dye-based assays when an easier alternative is available: TaqMan probes. This question results from the widespread believe that probe-based assays are superior in specificity to assays that rely on SYBR Green I chemistry for detection.

In this blog post, I want to nuance this point of view: dye-based assays are perfect for many real-time qPCR applications. When you understand the true advantages and disadvantages of probe-based assays, you can reduce the cost while keeping the quality of your qPCR experiments high. And, in the end, isn’t that what we all want?

Topics: Insider- sensitivity- qPCR- RT-qPCR- gene expression- primer

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Functional validation of a qPCR instrument

Jan Hellemans - Nov 21, 2017

There is the saying “quantitative PCR is easy to perform, but hard to do it right”. With high quality instruments, robust reagents and pre-designed (pre-validated) assays, even novice users can easily generate qPCR data. The challenge is in ensuring that the qPCR data accurately reflect the measure you are interested in. Many variables may negatively impact results: use of non-validated reference genes, non-specific assays, trace amounts of genomic DNA that may be co-amplified, non-calibrated pipets or instruments with too large well-to-well variation. In this blog, I will describe a method to assess instrument related measurement error.

Topics: quality control- testing- homgeneity

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Is it better to pipet duplicates or triplicate reactions in real-time PCR?

Bram De Craene - Mar 19, 2017

To understand the need for PCR replicates (duplicates or triplicates) in the experimental set-up of a real-time qPCR reaction, it is good to first think about the different sources of variation contributing to the measurement values. Far too often, the experimenter is too focused on the very last step of the workflow, i.e. the final pipetting step to obtain reproducible quantification cycle (Cq) values. Sources of variation in upstream processing steps are often underestimated, and are not always adequately assessed. A good understanding of a robust workflow is key to trust the actual Cq values obtained.

Topics: replicates- variability- reproducibility

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Why is the PCR amplification efficiency still ignored?

J.M. Ruijter - Sep 12, 2016

Most quantitative PCR (qPCR) results in journals are nowadays presented as Ct, delta Ct (dCt) or delta delta Ct (ddCt) values. Often the correct interpretation of the Y-axis in graphs has to be derived from the numbers given, which are not always in line with the axis title. This title most probably indicates that the axis represents the fold difference of the normalized expression of the target gene between the experimental and control conditions. It is of note that by now the 2 and the minus sign that should be there to turn a delta delta Ct into such a fold-difference have disappeared almost completely from publications. This tendency to avoid formulas has turned quantitative PCR into a field of meaningless numbers. For references to illustrate this, just google delta delta Ct.

Topics: qPCR- PCR efficiency- delta delta Ct

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Benchmarking RNA sequencing sensitivity using transcriptome-wide RT-qPCR data

Jan Hellemans - Aug 26, 2014

In the US FDA-led SEQC (aka MAQC-III) study, different sequencing platforms were tested across more than ten sites using well established reference RNA samples with built-in truths in order to assess the discovery and expression profiling performances of platforms and analysis pipelines (Su, Łabaj, Li et al., Nature Biotechnology, 2014). The entire SEQC data set comprises over 100 billion reads (10 Tb) thus providing a unique resource for thorough assessment of RNA-seq performance. Biogazelle co-authored and complemented this study by defining the first human transcriptome using well-established RT-qPCR technology. Using almost 21,000 PrimePCR qPCR assays (jointly developed by Biogazelle and Bio-Rad), the mRNA expression repertoire of the four MAQC samples was established.

Topics: RNA sequencing- sensitivity- RT-qPCR- read depth

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The impact of pipetting errors on PCR amplification efficiencies

Jan Hellemans - Aug 14, 2014

Bioazelle has always validated quantitative PCR assays by combining specificity analysis (targeted sequencing or microfluidic electrophoresis) with an assessment of the PCR amplification efficiency from the slope of a standard curve or dilution series, in accordance with the MIQE guidelines (Bustin et al., Clinical Chemistry, 2009). Having wet-lab validated more than 100,000 assays [see tech note 6262 for materials and methods; assays commercialized as PrimePCR assays by Bio-Rad] with an average efficiency of 99% and more than 98% of the assays with an efficiency of at least 90% [Figure 1] we were quite satisfied with the observed performance. However, recently we started to see more assays failing to meet our quality criterion of acceptable PCR efficiency within the 90-110% range. Nothing that would raise an eyebrow for a handful of assays – some assays are simply not good enough – but worrisome when seeing large numbers deviate. Not only did we observe a drop in efficiency but also a concurrent increase in the y-intercept of the standard curve. Once again this could indicate inferior assay performance, but it was suspicious when observed as a trend across thousands of assays (we have a peak wet lab validation capacity of over 2000 assays per week).

Topics: pipetting error- PCR efficiency

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Novel splice junctions identified in RNA sequencing studies: noise or interesting biology?

Jan Hellemans - Apr 23, 2014

What is a gene? Perhaps a simple question, but no clear answer is given by geneticists. Although the concept of a gene has been shown to be much more complex than ‘a DNA sequence transcribed into RNA and translated into a single protein product’, scientists often ignore this complexity. For example, while the majority of human genes show differential splicing, expression analysis is typically performed in function of genes, not transcripts (PubMed for instance has more than 800,000 ‘gene expression’ papers but only around 2,000 ‘transcript expression’ papers). I do believe that most of the authors of these papers are not ignorant of the concept of alternative splicing, but rather that transcripts are harder to study because less is known about specific transcripts functions than about gene functions, and because transcript analysis used to be more challenging from a technological perspective.

Topics: splice variants- RNA sequencing- RT-qPCR

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Seven tips for bio-statistical analysis of gene expression data

Jo Vandesompele - Dec 11, 2013

Many scientists have a hate-love relationship with statistics. Personally, I didn’t like statistics (at all) during my masters degree education [1]. Too theoretical, didn’t see the utility of it. Only when I generated my first data during my PhD research, I started realizing the necessity and power of bio-statistics. Later, I almost really fell in love with statistics after reading Intuitive biostatistics by Harvey Motulsky. This excellent book is written by an author who graduated from medical school; this probably explains why it contains only the most pertinent formulas. I particularly appreciate the book as it really is intuitive; it almost reads like a novel, and you could read it in bed, next to the fireplace with a glass of your favorite wine, or even when you’re on holidays. If you always felt the need to sharpen your basic bio-statistics skills, then this book may be really something for you.

Topics: statistics- gene expression

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Four tips for RT-qPCR data normalization using reference genes

Barbara D'haene - Oct 23, 2013

A measured difference in RNA expression level between two samples is the result of both true biological as well as experimentally induced (technical) variation. Different variables, inherent to the RT-qPCR workflow need to be controlled for in order to minimize the technical variation. Influencing parameters include the amount and quality of starting material, enzymatic efficiencies, and overall transcriptional activity.

It is highly recommended to minimize the technical variation by using standard operating procedures throughout the entire qPCR workflow. The remaining technical variation should then be further reduced or removed by using a proper normalization approach, enabling a better appreciation of the true biological variation.

Topics: normalization- reference genes

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How to improve qPCR assay design by understanding the impact of primer mismatches?

Jo Vandesompele - Sep 8, 2013

Designing a well working and reliable qPCR assay is a lot of work. Apart from prediction of specificity, the assay should also be screened for possible secondary DNA structures that interfere with efficient amplification (see Figure 1 in D'haene et al., Methods, 2010) and for the presence of single nucleotide polymorphisms (SNPs) that impair amplification of the variant allele.

Topics: quality control- SNP- primer

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Embracing digital PCR in peaceful coexistence with qPCR

Jo Vandesompele - May 10, 2013

Digital PCR is a nucleic acid molecule counting method with unprecedented resolution, relative sensitivity and accuracy. The template is diluted to such an extent that on average 1 template molecule is present in a PCR reaction, and many different small reactions are run in parallel. By counting the number of positive and negative reactions (hence the term ‘digital’) at the end of the PCR, it is possible to determine the starting number of template molecules in the sample under investigation. In other words, the question of how much nucleic acid target molecules are present in a particular sample is answered by measuring how many miniaturized reactions are positive if template is extremely diluted. Importantly, a Poisson correction needs to be applied on the counts, to correct for the fact that some reactions may contain more than 1 template molecule.

Topics: digital PCR- dMIQE

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How to find stably expressed microRNAs

Jo Vandesompele - Apr 15, 2013

Back in 2002 (Vandesompele et al, Genome Biology, 2002), we pioneered a new normalization strategy for more accurate normalization of mRNA expression data from RT-qPCR studies. Since then, the use of multiple stably expressed reference genes has become the gold standard method (see also MIQE guidelines in Bustin et al., Clinical Chemistry, 2009). Today, more than 11000 papers have cited our seminal paper (according to Google Scholar)!

Topics: normalization- microRNA- global mean- geNorm

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