A new mathematical model for relative quantification in real-time RT–PCR
- PubMed: 11328886
Use of the real-time polymerase chain reaction (PCR) to amplify cDNA products reverse transcribed from mRNA is on the way to becoming a routine tool in molecular biology to study low abundance gene expression. Real-time PCR is easy to perform, provides the necessary accuracy and produces reliable as well as rapid quantification results. But accurate quantification of nucleic acids requires a reproducible methodology and an adequate mathematical model for data analysis. This study enters into the particular topics of the relative quantification in real-time RT-PCR of a target gene transcript in comparison to a reference gene transcript. Therefore, a new mathematical model is presented. The relative expression ratio is calculated only from the real-time PCR efficiencies and the crossing point deviation of an unknown sample versus a control. This model needs no calibration curve. Control levels were included in the model to standardise each reaction run with respect to RNA integrity, sample loading and inter-PCR variations. High accuracy and reproducibility (<2.5% variation) were reached in LightCycler PCR using the established mathematical model.
A new mathematical model for rela...
A new mathematical model for relative quantification in
Michael W. Pfaffl*
Institute of Physiology, FML-Weihenstephan, Center of Life and Food Sciences, Technical University of Munich,
Received December 18, 2000; Revised February 21, 2001; Accepted March 14, 2001
Use of the real-time polymerase chain reaction (PCR)
to amplify cDNA products reverse transcribed from
mRNA is on the way to becoming a routine tool in
molecular biology to study low abundance gene
expression. Real-time PCR is easy to perform,
provides the necessary accuracy and produces reli-
able as well as rapid quantification results. But accu-
rate quantification of nucleic acids requires a
reproducible methodology and an adequate mathe-
matical model for data analysis. This study enters
into the particular topics of the relative quantification
in real-time RT–PCR of a target gene transcript in
comparison to a reference gene transcript. There-
fore, a new mathematical model is presented. The
relative expression ratio is calculated only from the
real-time PCR efficiencies and the crossing point
deviation of an unknown sample versus a control.
This model needs no calibration curve. Control levels
were included in the model to standardise each reac-
tion run with respect to RNA integrity, sample
loading and inter-PCR variations. High accuracy and
reproducibility (<2.5% variation) were reached in
LightCycler PCR using the established mathematical
Reverse transcription (RT) followed by the polymerase chain
reaction (PCR) is the technique of choice to analyse mRNA
expression derived from various sources. Real-time RT–PCR
is highly sensitive and allows quantification of rare transcripts
and small changes in gene expression. As well as this, it is easy
to perform, provides the necessary accuracy and produces
reliable as well as rapid quantification results. The simplest
detection technique for newly synthesised PCR products in
real-time PCR uses SYBR Green I fluorescence dye that binds
specifically to the minor groove double-stranded DNA (1). The
quantification method of choice depends on the target
sequence, the expected range of mRNA amount present in the
tissue, the degree of accuracy required and whether quantifica-
tion needs to be relative or absolute (2). Generally two quanti-
fication types in real-time RT-PCR are possible. (i) A relative
quantification based on the relative expression of a target gene
versus a reference gene. To investigate the physiological
changes in gene expression, the relative expression ratio is
adequate for the most purposes. (ii) An absolute quantification,
based either on an internal or an external calibration curve
(1,3). Using such a calibration curve, the methodology has to
be highly validated and the identical LightCycler PCR amplifi-
cation efficiencies for standard material and target cDNA must
be confirmed (4–6). Nevertheless, the generation of stable and
reliable standard material, either recombinant DNA or recom-
binant RNA, is very time consuming and it must be precisely
quantified (2,7,8). Furthermore, a normalisation of the target
gene with an endogenous standard is recommended. Therefore,
mainly non-regulated reference genes or housekeeping genes like
glyceraldehyde-3-phosphate dehydrogenase (G3PDH or
GAPDH), albumin, actins, tubulins, cyclophilin, 18S rRNA or
28S rRNA (9) were applicable. Housekeeping genes are
present in all nucleated cell types since they are necessary for
basis cell survival. The mRNA synthesis of these genes is
considered to be stable and secure in various tissues, even
under experimental treatments (9–11). But numerous studies
have already shown that the housekeeping genes are regulated
and vary under experimental conditions (12–15). To circum-
vent the high expenditure of design and production of standard
material, as well as optimisation and validation of a calibration
curve based quantification model, and finally the need for
normalisation of the target transcripts to an endogenous house-
keeping transcript, a reliable and accurate relative quantifica-
tion model in real-time RT–PCR is needed.
This study enters into the particular topics of the relative
quantification of a target gene in comparison to a reference
gene. A new and simple mathematical model for data analysis
was established, the application of the new model was tested
and compared with available mathematical calculation models.
Derived reproducibility, based on intra- and inter-test variation
of this relative quantification and accuracy of the model will be
MATERIALS AND METHODS
RNA source, total RNA extraction and RT
RNA extraction was performed as described previously (16) in
bacterial Escherichia coli culture grown either in M9 minimal
*To whom correspondence should be addressed at present address: Institut für Physiologie, Weihenstephaner Berg 3, 85354 Freising, Weihenstephan, Germany.
Tel: +49 8161 71 3511; Fax: +49 8161 71 4204; Email: firstname.lastname@example.org
media (sample preparation) or LB rich media (control prepara-
tion), both with 0.4% glucose concentration (17). RNA integrity
was electrophoretically verified by ethidium bromide staining
and by OD260/OD280 nm absorption ratio >1.95. Escherichia
coli total RNA (1 µg) was reverse transcribed with 100 U of
Superscript II Plus RNase H– Reverse Trancriptase (Gibco
BRL Life Technologies, Gaithersburg, MD) using 100 µM
random hexamer primers (Pharmacia Biotech, Uppsala,
Sweden) according to the manufacturer’s instructions.
Optimisation of RT–PCR
Highly purified salt-free primer for target gene1 (TyrA, tryptophan
operon: forward primer, AAG CGT CTG GAA CTG GTT GC;
reverse primer, AAA CGC TGT GCG TAA TCG CC), target
gene 2 (PyrB, aspartate transcarbamylase: forward primer,
GCT CCA ACC AAC ATC CGA; reverse primer, TTC ACG
TTG GCG TAC TCG G) and reference gene (Gst, glutathione
transferase: forward primer, CTT TGC CGT TAA CCC TAA
GGG; reverse primer, GCT GCA ATG TGC TCT AAC CC)
were generated (MWG Biotech, Ebersberg, Germany) and
optimised to an equal annealing temperature of 60°C. Condi-
tions for all PCRs were optimised in a gradient cycler (Master-
cycler Gradient, Eppendorf, Germany) with regard to Taq DNA
polymerase (Roche Diagnostics, Basel, Switzerland), forward
and reverse primers, MgCl2 concentrations (Roche Diagnostics),
dNTP concentrations (Roche Diagnostics) and various
annealing temperatures (55–65°C). RT–PCR amplification
products were separated on a 4% high resolution NuSieve
agarose (FMC Bio Products, Rockland, ME) gel electro-
phoresis and analysed with the Image Master system (Pharmacia
Biotech). Optimised results were transferred on the following
LightCycler PCR protocol.
LightCycler real-time PCR
For LightCycler reaction a mastermix of the following reaction
components was prepared to the indicated end-concentration:
13 µl water, 2.4 µl MgCl2 (4 mM), 0.8 µl forward primer
(0.4 µM), 0.8 µl reverse primer (0.4 µM) and 2.0 µl LightCyler
(Fast Start DNA Master SYBR Green I; Roche Diagnostics).
LightCycler mastermix (19 µl) was filled in the LightCycler
glass capillaries and 1 µl cDNA (3.2, 4.0, 4.8, 16, 20 or 24 ng
reverse transcribed total RNA) was added as PCR template.
Capillaries were closed, centrifuged and placed into the Light-
Cycler rotor. The following LightCycler experimental run
protocol was used: denaturation program (95°C for 10 min),
amplification and quantification program repeated 40 times
(95°C for 15 s, 60°C for 10 s, 72°C for 60 s with a single
fluorescence measurement), melting curve program (60–95°C
with a heating rate of 0.1°C per second and a continuous fluo-
rescence measurement) and finally a cooling step to 40°C. For
the mathematical model it is necessary to determine the
crossing points (CP) for each transcript. CP is defined as the
point at which the fluorescence rises appreciably above the
background fluorescence. ‘Fit Point Method’ must be
performed in the LightCycler software 3.3 (Roche Diagnostics),
at which CP will be measured at constant fluorescence level
Confirmation of primer specificity
Specificity of RT–PCR products was documented with high
resolution gel electrophoresis and resulted in a single product
with the desired length (TyrA, 978 bp; PyrB, 530 bp; and Gst,
Table 1. Intra-assay (test precision) and inter-assay variation (test variability) of LightCycler real-time RT–PCR
Determination of variation was done in 20 ng reverse transcribed total RNA. Test variation is based on CP variation and expressed
as mean CP with CV.
Intra-assay variation (n = 3) Inter-assay variation (n = 3)
Mean CP CV Mean CP CV
TyrA (sample) 15.020 0.06% 15.311 3.58%
TyrA (control) 20.303 0.09% 20.426 2.98%
PyrB (sample) 16.031 2.16% 16.289 3.91%
PyrB (control) 11.720 1.32% 12.011 3.18%
Gst (sample) 14.533 0.65% 14.371 2.26%
Gst (control) 14.277 0.11% 13.997 2.62%
Figure 1. Determination of real-time PCR efficiencies of reference gene (Gst),
target gene 1 (TyrA) and target gene 2 (PyrB). CP cycles versus cDNA (reverse
transcribed total RNA) concentration input were plotted to calculate the slope
(mean ± SD; n = 3). The corresponding real-time PCR efficiencies were calcu-
lated according to the equation: E = 10[–1/slope] (18).
402 bp). In addition a LightCycler melting curve analysis was
performed which resulted in single product specific melting
temperatures as follows: TyrA, 89.6°C; PyrB, 88.5°C; and Gst,
88.3°C. No primer-dimers were generated during the applied 40
real-time PCR amplification cycles.
Real-time PCR amplification efficiencies and linearity
Real-time PCR efficiencies were calculated from the given
slopes in LightCycler software. The corresponding real-time
PCR efficiency (E) of one cycle in the exponential phase was
calculated according to the equation: E = 10[–1/slope] (Fig. 1) (18).
Investigated transcripts showed high real-time PCR efficiency
rates; for TyrA, 2.09; PyrB, 2.16; and Gst, 1.99 in the investi-
gated range from 0.40 to 50 ng cDNA input (n = 3) with high
linearity (Pearson correlation coefficient r > 0.95).
Intra- and inter-assay variation
To confirm accuracy and reproducibility of real-time PCR the
intra-assay precision was determined in three repeats within
one LightCycler run. Inter-assay variation was investigated in
three different experimental runs performed on 3 days using
three different premix cups of LightCycler, Fast Start DNA
Master SYBR Green I kit (Roche Diagnostics). Determination
of variation was done in 20 ng transcribed total RNA (Table 1).
Test reproducibility for all investigated transcripts was low in
inter-test experiments (<3.91%) and even lower in intra-test
experiments (<2.16%). The calculation of test precision and
test variability is based on the CP variation from the CP mean
Mathematical model for relative quantification in real-
A new mathematical model was presented to determine the
relative quantification of a target gene in comparison to a refer-
ence gene. The relative expression ratio (R) of a target gene is
calculated based on E and the CP deviation of an unknown
sample versus a control, and expressed in comparison to a
Equation 1 shows a mathematical model of relative expression
ratio in real-time PCR. The ratio of a target gene is expressed
in a sample versus a control in comparison to a reference gene.
Etarget is the real-time PCR efficiency of target gene transcript;
Eref is the real-time PCR efficiency of a reference gene tran-
script; ∆CPtarget is the CP deviation of control – sample of the
target gene transcript; ∆CPref = CP deviation of control –
sample of reference gene transcript. The reference gene could
be a stable and secure unregulated transcript, e.g. a house-
keeping gene transcript. For the calculation of R, the individual
real-time PCR efficiencies and the CD deviation (∆CP) of the
investigated transcripts must be known. Real-time PCR
efficiencies were calculated, according to E = 10[–1/slope] (18), as
shown in Figure 1. CP deviations of control cDNA minus
sample of the target gene and reference genes were calculated
according to the derived CP values. Mean CP, variation of CP
and ∆CP values between control and sample of investigated
transcripts are listed in Table 2. The influence of differing
cDNA input concentrations on ∆CP are also shown. Intended
cDNA input concentration variation of control and sample
were compared at different levels (low level, 3.2, 4.0, 4.8 ng
cDNA; high level, 16, 20 and 24 ng cDNA). They resulted in
stable and constant ∆CP cycle numbers. In Table 3 the corre-
sponding ratios of target genes in comparison to the reference
gene were calculated, through to the established mathematical
model (equation 1). The expression ratios of target genes
remain stable, even under intended ±20% cDNA variation and
low and high cDNA input levels, performed in two runs. A
minimal coefficient of variation (CV) of 2.50 and 1.74% was
Regulation of investigated gene transcripts
All investigated transcript expressions were regulated diver-
gently (Table 3). The expression of Gst was constant, inde-
pendent of media conditions, and therefore was chosen as
endogenous standard or reference gene transcript Fig. 2. TyrA
mRNA expression, measured in 20 ng cDNA, was up-regulated
49.1-fold (2.095.283) in M9 minimal compared to LB rich
medium under high cDNA input conditions. Under the considera-
tion of the reference gene expression the real up-regulation
ratio was, on average, 58.5-fold. PyrB mRNA expression was
down-regulated under M9 minimal medium conditions by a
factor of 27.6 (2.164.311). With the normalisation of the endog-
enous standard transcript, the exact relative expression ratio
can be calculated to 23.2.
RT followed by PCR is the most powerful tool to amplify
small amounts of mRNA (19). Because of its high ramping
rates, limited annealing and elongation time, the rapid cycle
PCR in the LightCycler system offers stringent reaction condi-
tions to all PCR components and leads to a primer sensitive
and template specific PCR (20). The application of fluo-
rescence techniques to real-time PCR combines the PCR
amplification, product detection and quantification of newly
synthesised DNA, as well as verification in the melting curve
analysis. This led to the development of new kinetic RT–PCR
∆CPtarget control sample–( )
∆CPref control sample–( )
Figure 2. Real-time RT–PCR SYBR Green I fluorescence history versus cycle
number of target gene 1 (TyrA), target gene 2 (PyrB) and reference gene (Gst)
in sample cDNA and control cDNA. CP determination was done at fluores-
cence level 1.
methodologies that are revolutionising the possibilities of mRNA
In this paper, we focused on the relative quantification of
target gene transcripts in comparison to a reference gene tran-
script. A new mathematical model for data analysis was
presented to calculate the relative expression ratio on the basis
of the PCR efficiency and crossing point deviation of the
investigated transcripts (equation 1). The concept of threshold
fluorescence is the basis of an accurate and reproducible quan-
tification using fluorescence-based RT–PCR methods (22).
Threshold fluorescence is defined as the point at which the
fluorescence rises appreciably above the background fluores-
cence. In the Fit Point Method, the threshold fluorescence and
therefore the DNA amount in the capillaries is identical for all
samples. CP determination with the ‘Second Derivative
Maximum Method’ is not adequate for our mathematical
model, because quantification is done at the point of most effi-
cient real-time PCR where the second derivative is at its
A linear relationship between the CP, crossing the threshold
fluorescence, and the log of the start molecules input in the
reaction is given (18,23). Therefore, quantification will always
occur during the exponential phase, and it will not be affected
by any reaction components becoming limited in the plateau
phase (7). In the established model the relative expression ratio
of a target gene is normalised with the expression of an endo-
genous desirable unregulated reference gene transcript to
compensate inter-PCR variations between the runs. The CP of
the chosen reference gene is the same in the control and the
sample (∆CP = 0). Stable and constant reference gene mRNA
levels are given. Under these considerations of an unregulated
reference gene transcript, no normalisation is needed and equa-
tion 1 can be shortened to equation 2.
Equation 2 shows a mathematical model of relative expression
ratio in real-time PCR under constant reference gene expression.
CP values in the sample and control are equal and represent
ideal housekeeping conditions (∆CPref = 0, Eref = 1).
Two other mathematical models are available for the relative
quantification during real-time PCR. The ‘efficiency calibrated
mathematical method for the relative expression ratio in real-
time PCR’ is presented by Roche Diagnostics in a truncated
form in an internal publication (24). The complete equation is,
Table 2. Mean CP and CV of target gene 1 (TyrA) and target gene 2 (PyrB) in comparison to intended
concentration variation of reference gene (Gst) low level (3.2, 4.0 and 4.8 ng per capillary) and high level cDNA
input (16, 20 and 24 ng)
cDNA input (ng) Mean CP (n = 3) CV (%) ∆CP (cycles)
High cDNA input level
TyrA (sample) 20 15.020 0.06 +5.283
TyrA (control) 20 20.303 0.09
PyrB (sample) 20 16.031 2.16 –4.311
PyrB (control) 20 11.720 1.32
Gst (sample) 16 14.290 0.73 –0.277
Gst (control) 16 14.013 0.39
Gst (sample) 20 14.533 0.65 –0.256
Gst (control) 20 14.277 0.11
Gst (sample) 24 14.957 1.29 –0.227
Gst (control) 24 14.730 1.14
Low cDNA input level
TyrA (sample) 4.0 17.353 2.16 +5.487
TyrA (control) 4.0 22.840 1.26
PyrB (sample) 4.0 17.667 0.75 –4.277
PyrB (control) 4.0 13.390 0.12
Gst (sample) 3.2 16.927 0.643 –0.050
Gst (control) 3.2 16.877 1.81
Gst (sample) 4.0 16.750 2.31 –0.127
Gst (control) 4.0 16.623 1.04
Gst (sample) 4.8 16.297 1.68 –0.077
Gst (control) 4.8 16.220 0.69
∆CPtarget control-sample )(
ratio = Etarget( )
∆CPtarget control-sample( )
in principle, the same and the results are in identical relative
expression ratio like our model (equation 3).
Efficiency calibrated mathematical method for the relative
expression ratio in real-time PCR presented by Soong et al.
(24). But the method of calculation in the described mathematical
model is hard to understand. The second model available, the
‘Delta–delta method’ for comparing relative expression results
between treatments in real-time PCR (equation 4) is presented
by PE Applied Biosystems (Perkin Elmer, Forster City, CA).
Equation 4 shows a mathematical delta–delta method for
comparing relative expression results between treatments in
real-time PCR developed by PE Applied Biosystems
(Perkin Elmer). Optimal and identical real-time amplification
efficiencies of target and reference gene of E = 2 are presumed.
The delta–delta method is only applicable for a quick estima-
tion of the relative expression ratio. For such a quick estima-
tion, equation 1 can be shortened and transferred into equation
4, under the condition that Etarget = Eref = 2. Our presented
formula combines both models in order to better understand
the mode of CP data analysis and for a more reliable and exact
relative gene expression.
Relative quantification is always based on a reference tran-
script. Normalisation of the target gene with an endogenous
standard was done via the reference gene expression, to
compensate inter-PCR variations. Beside this further control
levels were included in the mathematical model to standardise
each reaction run with respect to RNA integrity, RT efficiency
or cDNA sample loading variations. The reproducibility of the
RT step varies greatly between tissues, the applied RT isola-
tion methodology (25) and the RT enzymes used (26).
Different cDNA input concentrations were tested on low and
high cDNA input ranges, to mimic different RT efficiencies
(±20%) at different quantification levels. In the applied two-
step RT–PCR, using random hexamer primers, all possible
interferences during RT will influence all target transcripts as
well as the internal reference transcript in parallel. Occurring
background interferences retrieved from extracted tissue
components, like enzyme inhibitors, and cDNA synthesis
efficiency were related to target and reference similarly. All
products underwent identical reaction conditions during RT
and variations only disappear during real-time PCR. Any
source of error during RT will be compensated through the
model itself. Widely distributed single-step RT–PCR models
are not applicable, because in each reaction set-up and for each
investigated factor individual and slightly different RT condi-
tions will occur. Therefore, the variation in a two-step RT–PCR
will always be lower, and the reproducibility of the assay will
be higher, that in a single-step RT–PCR (8). Reproducibility of
the developed mathematical model was dependent on the exact
determination of real-time amplification efficiencies and on
the given low LightCycler CP variability. In our mathematical
model the necessary reliability and reproducibility was given,
which was confirmed by high accuracy and a relative error of
<2.5% using low and high template concentration input.
LightCycler real-time PCR using SYBR Green I fluorescence
dye is a rapid and sensitive method to detect low amounts of
mRNA molecules and therefore offers important physiological
insights on mRNA expression level. The established mathe-
matical model is presented in order to better understand the
mode of analysis in relative quantification in real-time RT–
PCR. It is only dependent on ∆CP and amplification efficiency
of the transcripts. No additional artificial nucleic acids, like
recombinant nucleic acid constructs in external calibration
curve models, are needed. Reproducibility of LightCycler RT–
PCR in general and the minimal error rate of the model allows
for an accurate determination of the relative expression ratio.
Even different cDNA input resulted in minor variations. Rela-
tive expression is adequate for the most relevant physiological
expression changes. In future it is not necessary to establish
more complex and time consuming quantification models
based on calibration curves. For the differential display of
mRNA the relative expression ratio is an ideal and simple tool
for the verification of RNA or DNA array chip technology
The author thanks D.Schmidt for technical assistance. Primers,
primer sequences and samples were kindly donated by Drs
Eref( ) Sample
Etarget( ) Sample
Eref( ) Calibrator
Etarget( ) Calibrator
ratio = 2 ∆CP sample - ∆CP control[ ]–
ratio = 2 ∆∆CP–
Table 3. Influence of variation in cDNA input (±20%) of control and sample
(high and low level) on the variation in relative expression ratio based on
equation 1 and the error of mathematical model, expressed as CV of R
Gst TyrA PyrB
E = 1.99 E = 2.09 E = 2.16
High cDNA input level ∆CP = +5.283 ∆CP = –4.311
80% cDNA input ∆CP = –0.277 R = 59.476 R = 0.04375
100% cDNA input ∆CP = –0.256 R = 58.625 R = 0.04312
120% cDNA input ∆CP = –0.227 R = 57.459 R = 0.04226
Mean of R 58.45900 0.04304
Error/CV of R 1.73% 1.74%
Low cDNA input level ∆CP = +5.487 ∆CP = –4.277
16% cDNA input ∆CP = –0.050 R = 59.104 R = 0.03844
20% cDNA input ∆CP = –0.127 R = 62.102 R = 0.04031
24% cDNA input DCP = –0.077 R = 60.208 R = 0.03914
Mean of R 60.471 0.03930
Error/CV of R 2.50% 2.49%
S.Wegener and W.Mann in collaboration with the BioChip
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