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End the Cycle of Repeated, Failed Experimentswith a Robust Field Guide to qPCR
By Sean C. Taylor

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 ofdifferential gene expression levels between experimental groups.
However, there remains general concern about the productionof data that truly reflects the tested experimental conditions(Bustin and Nolan 2017). This stems from the generalperception 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 haveundergone rigorous validation will ensure accurate andreproducible interpretations.
Without following a rigorous, stepwise approach andcheckpoints 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 publishedliterature (Bustin and Nolan 2017). To assist the scientificcommunity in publishing high-quality, reproducible data thatreflect 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 andquantification of low-abundance targets, which are often
the most interesting. However, the disadvantage is the highpotential for producing artefactual data from a poorly designed
experiment. The assessment of time points (Figure 1B) andtissue subsections (Figure 1C) coupled with the selection ofan appropriate number of biological replicates per treatmentgroup 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, andinstrumentation 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, Tanand Yiap 2009). This can dramatically alter the Cq values,
producing data and interpretations that are unrelated to thetested 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 themRNA. The hallmarks of a good RT kit include: 1) a mixtureof 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 tolower and variable values that would be unrepresentative ofthe true target amount in each sample; 3) an RNase inhibitor

AAC
BG1 vs BG2 T 0 vs T1
(2)
(2)
(10)

20

+

20

Σ(Bio Reps)
=

40

×

Tech Reps
3

Wells perTarget Targets

Wells

=

480

120

×

4

=

C
Control

Frontallobe

Whole brain
RNA extract

(4)

8

+

8

=

16

×

3

=

48

×

4

=

192

(50)

100

+

100

=

200

×

3

=

600

×

4

=

2400

Treatment

Parietal
lobe

Temporallobe

Occipitallobe

Δ Cq = 0 (no change: whole brain)

25

25

B

Δ Cq = 2 (fourfold increase: occipital lobe)

A

B

C

23

25

Time
course

21

23

24

24

24

26

26

X

23

25

Dissected
brain RNA
extract

Transcription

B

As featured on bioradiations.com

26

Δ Cq = -2 (fourfold decrease: temporal lobe)

Time

Transcription
window

Fig. 1. Planning and scoping a quantitative PCR (qPCR) experiment. A, biological group (BG): tested experimental conditions (i.e., control (BG1) vs treatment(BG2). Time point (T) (i.e., 0 hour (T0) and 1 hour (T1) post-treatment). Biological replicate (Bio reps): number of independent biological specimens from which
cDNA/gDNA is produced (i.e., individual samples). Technical replicate (Tech reps): number of repeats per Bio rep (i.e., same cDNA/gDNA sample pipetted into
multiple wells). Targets: genes interrogated for differential expression or abundance, including chosen reference genes. Wells: total number of microtiter plate wells
required for qPCR exclusive of optimization experiments for primer validation (see Figure 2). B, time dependence on transcription of targets A, B, and C posttreatment. Choosing a random time point (X) post-treatment may result in no data and/or artefactual data that do not reflect the true response of the tested targets.
Performing a preliminary time course study (dotted line) ensures the selection of optimal time points for each target. C, influence of tissue sectioning on data
quality. Whole tissue RNA extract may show no change, whereas dissected tissue may produce expression differences between biological groups (i.e., fourfold
change when comparing expression of occipital lobe or temporal lobe) consequent to target dilution in whole tissue and enrichment in sections. Adapted andupdated under a Creative Commons license from Taylor et al. 2019.

to prevent degradation of the RNA prior to RT; 4) a highlyrobust RT enzyme that permits RT of the widely ranging
concentration of transcripts populating each sample. iScript
Reverse Transcription Reagents from Bio‑Rad contain ablended mixture of each of these components to assure cDNA
that closely reflects the transcriptome.

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 levelsof contaminants.

Step 4 — Primer Validation

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 andsequence-verified primers from Bio‑Rad for several genomes,
including human, mouse, and rat. These assays can bepurchased individually or in panel assays with suggested
reference gene targets that can be assessed for stability
between the experimental groups.

Validate primers initially with a thermal gradient to assessannealing temperature followed by a standard curve to assess
reaction efficiency (Figure 2A). Use an equalized pool ofcDNA from all of the experimental groups, including control
for both primer validation experiments. Thermal validation
assures that primer annealing is optimized based on theunique salt concentration, pH, and contaminants in theexperimental samples (Figure 2B). The standard curve permits
a guided approach to correctly diluting individual samples toensure adequate contaminant and transcript dilution suchthat the reaction efficiency for each primer pair is close to100% (Figure 2C). Only under these conditions will the Cq
values reflect the true target concentrations for accurateinterpretations. A good qPCR supermix can help assure

2

Bioradiations June 11, 2019

Step 5 — Reference Gene Selection© 2019 Bio-Rad Laboratories, Inc.

Lowest Cq corresponding
optimal annealing tempera
range (see Step #1A)

As featured on bioradiations.com
A
The problem:
The problem:

Breflectprimers
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C
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The solution:
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Standard
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to determine
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D

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step#1A: melt curve,
Temperature
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using melta pooledsampleTemperature
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35
product using a pooled
sampleDilute samples to midpoint of linear

Cq

A

A. The problem
Nonvalidated primers could give Cq values that do not

The problem:

Δ Cq = 5
ΔRelative
Cq = 5 Quantity (RQ)
Cq = 25 = 32
Relative URG
Quantity= 2Δ(RQ)
URG = 2Δ Cq = 25 = 32

Δ Cq = 0
Δ CqSRG= 0 = 2Δ Cq = 20 = 1
RQ
RQ SRG = 2Δ Cq = 20 = 1

Gene of Interest (GOI)
Gene of Interest (GOI)

ΔCq = 2ΔCqGOI= 2= 2Δ Cq = 22 = 4
RQCqΔ= 22 = 4
RQ GOI = 2
Normalized expression to SRG
Normalized expression to URG
RQ
GOI/RQ SRG= 4/1 = four
RQ expression
GOI/RQ URG= 4/32 = eight
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expression
to SRG
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to URG
fold
increase
folddecrease
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Use the geometric mean stability of at least two or three reference genes
forleast
normalization
Use the geometric mean stability of at
two or three reference genes
for normalization

Nonvalidated, unstable reference genes can give

Fig. 2. Validating
a quantitative PCR (qPCR) experiment to minimize error and maximize data quality. A, nonvalidated primers could give variable and/artefactual dataor artefactual data that do not reflect the true target abundance in each sample. B, primer validation using thermal gradient and gel electrophoresis. Step #1A:
Thermal gradient and agarose gel validation. To validate primers, an equalized pool of samples from each biological group is diluted 1:20 and initially tested using athermal gradient to determine the optimal annealing temperature, average level of expression, and unique product for each target from melt curve and gel analysis.
Step #1B: The quantitative cycle (Cq) value from the optimal annealing temperature range can be used as a guide to establish the standard curve dilution factor
for each target (i.e., if the Cq value for optimized temperature range is between 10 and 16, use a 1:8 serial dilution series of the pooled cDNA sample in water).
C, standard curve
validation.
standard
curve is tested for each primer pair using the same pooled sample and the appropriate dilution factor as
Biologicalgroup An eight-point
B
A
determined from the thermal gradient data (Step #1B) to cover the widest dynamic range possible. Amplification efficiency, as determined from the slope, should
Unstable
Gene(URG) theStable
Reference
Gene(SRG) concentration points from each primer validation standard curve may be necessary to achieve
range between
90% Reference
and 110%.
Deleting
highestand/orlowestthe best efficiency. The dilution factor from the midpoint is then used to dilute the individual experimental samples per target, assuming that the pooled DNA
sample represents the average abundance of each target for the experiment (i.e., equalized pool of the same number of DNA samples from each biological group).
This ensures minimal presence of contaminants affecting primer efficiency and accurate quantitative data. D, consequence of using poorly validated reference
genes. qPCR data can change dramatically when normalization is performed using a stable versus unstable reference gene. Adapted and updated under aCreative Commons license
Taylor et al. 2019. Δ Cq = 0
Δ Cq = from
5
Relative Quantity (RQ)
URG = 2Δ Cq = 25 = 32

RQ SRG = 2Δ Cq = 20 = 1

Visit us on the Web at bioradiations.com
Gene of Interest (GOI)3

As featured on bioradiations.com

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, andcorrectly for multiple plates. Since the raw data never leave
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 andensures that all lab members are producing consistently andcorrectly calculated final results.

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/pasteand formula propagation errors can yield inaccurate dataand interpretations that are very challenging to recognize
andA troubleshoot. CFX Maestro Software is a solution from
A
1

2

3

4

5

6

7

8

9

10

11

TargetBio Group_
SampleMean
CqControlGroup
Avg Cqper
TargetDCq
per Sample
per Target

Relative
Quantity(RQ)

Normalized
Factor
GEOMEAN

Normalized
Expression
per Sample

Log 2
Normalized
Expression

Bio Group
Expression

Log 2(C)(Col. 4–Col. 3)

2^DCq [RQ(HPRT+Actin)]

(A)

(B)

(C)

(D)

Actin

Untreated_S1

28.7

Actin

Unreated_S2

28.9

Actin

Untreated_S3

28.6

0.11

1.08

Actin

Treated_S4

28.8

-0.10

0.94

Log 2(A)GEOMEAN(A)

28.74

0.00

1.00

-0.12

0.92

Actin

Treated_S5

29.1

-0.34

0.79

Actin

Treated_S6

28.8

-0.02

0.99

-0.50

0.71

HPRT

Untreated_S1

29.0

HPRT

Unreated_S2

28.0

HPRT

Untreated_S3

HPRT

28.50

0.50

1.41

28.5

0.00

1.00

RQ (DER5)

Treated_S4

27.8

0.70

1.62
0.62

Normalized
factorHPRT

Treated_S5

29.2

-0.70

HPRT

Treated_S6

28.5

0.00

1.00

DER5

Untreated_S1

31.3

-0.18

0.88

0.84

1.05

0.07

DER5

Unreated_S2

31.0

0.07

1.05

1.14

0.92

-0.12

DER5

Untreated_S3

31.0

0.11

1.08

1.04

1.04

0.06

DER5

Treated_S4

32.7

-1.57

0.34

1.23

0.27

-1.87

DER5

Treated_S5

32.0

-0.95

0.52

0.70

0.74

-0.43

DER5

Treated_S6

32.4

-1.32

0.40

0.99

0.40

-1.31

B

31.08

1.20

Bars derived from the data in
Figure 3A, column 10

Normalized fold expression

1.00

Error bars calculated as follows:2^(column 11+/- column 13)

0.80

Unreated
0.60

Treated

0.40

0.20

0

4

Biological Group

Bioradiations June 11, 2019

12

13

14

Standard Standard Confidence
Deviation
Error ofInterval(SD) of (B) the Mean
Lower
(SEM)
Limit
(E)

SD(B)

15
ConfidenceInterval
Upper
Limit

(F)

SD(B)
÷n

(1+E) D-t*F (1+E) D+t*F

1.00

0.00

0.11

0.06

0.83

1.20

0.43

-1.21

0.73

0.42

0.12

1.51

Fig. 3. Data analysis for a typical relative quantification experiment.
A, rigorous data workup. In this example, DER5 expression is measured
between treated and untreated biological groups, each containing threebiological replicates and two technical replicates (data for technical replicatesare not shown). Actin and HPRT are used as reference genes. First, themean quantitative cycle (mean Cq; column 3) of the technical replicatesfor each sample/target combination is calculated [i.e., for actin from theuntreated sample 1 (Untreated_S1) the mean Cq is 28.7]. The average Cq
of all samples in the control group (i.e., untreated) for each target is thendetermined (column 4). The relative difference (ΔCq) between the average Cq
for the control group (column 4) and the mean Cq (column 3) per individual
sample within each target is assessed (column 5). The relative quantities arecalculated from the ΔCq (i.e., 2ΔCq) where in this case, it is assumed thatreaction efficiency is 100%, and hence a base of two is applied (column 6).
Otherwise the base should be one + PCR efficiency (E) [as determined fromthe standard curve (Figure 2C)]. In effect, the relative quantity represents thefold change between the biological groups for each sample/target combination
prior to reference gene normalization. For each biological group/sample
combination, a normalization factor is determined from the geometric meanof the associated reference gene relative quantities (column 7). The relative
normalized expression for each target gene (DER5 in this example) is thencalculated per sample by dividing the relative quantity by the normalization
factor (column 8) followed by log transformation (column 9). The averagerelative normalized and log transformed expression for each biological group isthen calculated using the geometric mean (columns 10 and 11). The standard
deviation (SD), standard error of the mean (SEM), and 95% confidence interval[in this case a t statistic of 4.3 was used based on three samples per biologicalgroup (i.e., 2 degrees of freedom)] of each group are then calculated from thelog transformed normalized expression (columns 12–15). B, average relative
normalized expression graphical representation with appropriate error bars (inthis case SEM). Adapted and updated under a Creative Commons license from
Taylor et al. 2019.

© 2019 Bio-Rad Laboratories, Inc.

As featured on bioradiations.com

Conclusions
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 technicalteam to guide and support the scientific community inproducing excellent results.
To learn the fundamentals and best practices of qPCR andto schedule a departmental qPCR workshop with a memberof our Field Application Scientist team, contact your local
Bio‑Rad representative today.

References
Bustin S and Nolan T (2017). Talking the talk, but not walking the walk: RTqPCR 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 realtime 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 andthe present. J Biomed Biotechnol 2009, 574398.
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.
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.
Bio-Rad is a trademark of Bio-Rad Laboratories, Inc. in certain jurisdictions.
SYBR is a trademark of Thermo Fisher Scientific Inc. Bio‑Rad Laboratories, Inc.
is licensed by Thermo Fisher Scientific Inc to sell reagents containing SYBRGreen I for use in real-time PCR, for research purposes only.
All trademarks used herein are the property of their respective owner.

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