Market research shows that 66.6% of researchers use qRT-PCR for gene-expression profiling. Clearly, this is a very effective and popular technique for detecting RNA expression levels, but it’s still prone to pitfalls that can sometimes lead to disappointing results. To help improve your experiments, we’ve made a new infographic with some interesting facts about qRT-PCR and 5 tips to help with your gene expression studies. This will cover everything from experimental design to data analysis.

1. In molecular biology, qRT-PCR (Quantitative Reverse Transcription Polymerase Chain Reaction)
is a very popular technique for detecting levels of RNA expression. Here are some key facts
and workflow ideas to help you get better results.
of researchers used kits for RNA
isolation in 2014 and this is
forecast to rise to 73% by 2017[2]
of researchers use qRT-PCR
for gene expression profiling,
making it the primary
downstream application[1]
of researchers select
specificity and fidelity
to be the most important
characteristics in
choosing reagents
for nucleic acid
amplification (followed
by yield at >45%)[1]
was the year that
RDML v 1.0 (an
instrument-independent
Real-Time PCR Data
Markup Language)
was launched to store,
exchange, publish
and standardize
qPCR data[3]
5 facts
about
qRT-PCR
66.6%
57.8%
5 things to think about
Experimental
Design
Sample
Management
RNA Isolation
and gDNA
Removal
Reference
Gene
Selection
Data
Analysis
Good planning is key to experimental success, avoiding
the need to rationalize results. First define the problem and
formulate hypotheses. Then think about constructing experiments
that represent all elements, conditions and relations.
Be sure to consider:
• The control group
• The type and number of replicates
• Sample handling and sources of variation
mRNA expression profiles can change over several orders of
magnitude, even in a short time, after tissue/blood collection.
Stabilization is crucial to avoiding inaccurate results.[4]
Stabilize RNA degradation using:
• Liquid nitrogen
• RNase denaturing reagents
• RNA stabilization reagents
gDNA contamination in RNA samples leads to inaccurate
quantification.
Avoid unwanted DNA detection in your experiments by:
• Removing gDNA with eliminator columns
• Digesting gDNA with DNase I
• Removing gDNA during cDNA synthesis
• Using specially designed primers
Using a single non-validated reference gene results in a
significant bias. This can range from a 3-fold bias in 25% of
the results, up to a 6-fold bias in 10% of results.[5]
Think about using between two and five validated, stably-
expressed reference genes for normalization.
The journals Science[6]
and Nature[7]
remind the scientific
community about standardization and just how important open
computer programs/standards like RDML are.
By using real time cyclers – which implement the RDML
export capabilities into the instrument software – you enable
transparent exchange of qPCR data with fellow researchers,
authors, journals and readers.
110040602/2016
Sample to Insight
References:
1. The Life Science Dashboard™ Nucleic Acid Amplification (North America & Europe). Percepta Associates, Inc. (2014), Series Four
2. DNA & RNA Isolation Techniques. Biopharm Reports 2014
3. RDML: structured language and reporting guidelines for real-time quantitative PCR data. S. Lefever et al (2009); Nucleic Acids Res 37, 2065-2069
4. Reliability of reverse transcription-polymerase chain reaction (RT-PCR)-based assays for the detection of circulating tumour cells: a quality-assurance initiative of the EORTC melanoma
cooperative group. U Keilholz et al (1998); European Journal of Cancer 34, 750–753
5. Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. J. Vandesompele et al.(2002); Genome Biol. 3
6. Shining light into black boxes. A. Morin et al. (2012) Science 336, 159–160
7. The case for open computer programs. D.C. Ince et al. (2012) Nature 482, 485–488
63%
of researchers currently
use SYBR®
Green in
qRT-PCR experiments[1]
65%2009
Successful qRT-PCR