Quantification strategies in real-time RT-PCR The quantification strategy
is the principal marker in gene quantification. Generally two
strategies can be performed in real-time RT-PCR. The levels of
expressed genes may be measured by absolute or relative quantitative
real-time RT-PCR. Absolute quantification relates the PCR signal to
input copy number
using a calibration curve, while relative quantification measures the
relative change in mRNA expression levels. The reliability of an
absolute
real-time RT-PCR assay depends on the condition of ‘identical’
amplification
efficiencies for both the native target and the calibration curve in
RT reaction and in following kinetic PCR. Relative quantification is
easier to perform than absolute quantification because a calibration
curve is not necessary. It is based on the expression levels of a
target
gene versus a housekeeping gene (reference or control gene) and in
theory
is adequate for most purposes to investigate physiological changes in
gene expression levels. The units used to express relative quantities
are
irrelevant, and the relative quantities can be compared across multiple
real-time RT-PCR experiments.
 Calibration curves are highly
reproducible and allow the generation of highly specific, sensitive and
reproducible data. However, the external calibration curve model has to
be thoroughly validated as the accuracy of absolute quantification in
real-time RT-PCR depends entirely on the accuracy
of the standards. Standard design, production, determination of the
exact standard concentration and stability over long storage time is
not straightforward and can be problematic. The dynamic range of the
performed calibration curve can be up to nine orders of magnitude from
<101 to >1010
start molecules, depending on the applied standard material. The
calibration curves used in absolute quantification can be based on
known concentrations of DNA standard molecules, e.g. recombinant
plasmid DNA (recDNA), genomic DNA, RT-PCR product, commercially
synthesized big oligonucleotide. Sability and reproducibility in
kinetic RT-PCR depends on the type of standard used and depends
strongly on ‘good laboratory practice’. Cloned recDNA and genomic DNA
are very stable and generate highly reproducible standard curves even
after a long storage time, in comparison to freshly synthesized RNA.
Furthermore, the longer templates derived from recDNA and genomic DNA
mimic the average native mRNA length of about 2 kb better than shorter
templates derived
from RT-PCR product or oligonucleotides. They are more resistant
against
unspecific cleavage and proofreading activity of polymerase during
reaction
setup and in kinetic PCR (own unpublished results). One advantage of
the shorter templates and commercially available templates is an
accurate knowledge of its concentration and length. A second advantage
is that
their use avoids the very time consuming process of having to produce
standard material: standard synthesis, purification, cloning,
transformation,
plasmid preparation, linearization, verification and exact
determination
of standard concentration.
A problem with DNA based
calibration curves is that they are subject to the PCR step only,
unlike the unknown mRNA samples that must first be reverse transcribed.
This increases the potential for variability of the RT-PCR results and
the amplification results may not be strictly comparable with the
results from the unknown samples. However, the problem of the
sensitivity of the RT-PCR to small variations in the reaction setup is
always lurking in the background as a potential drawback to this simple
procedure. Therefore, quantification with external standards requires
careful optimization of its precision (replicates in the same
kinetic PCR run – intra-assay variation) and reproducibility
(replicates in separate kinetic PCR runs – inter-assay variation) in
order to understand the limitations within the given application.
A recombinant RNA (recRNA)
standard that was synthesized in vitro from a cloned RT-PCR fragment in
plasmid DNA is one option. However, identical RT efficiency, as well as
real-time PCR amplification efficiencies for calibration curve and
target cDNA must be tested and confirmed if the recDNA is to provide a
valid standard for mRNA quantification. This is because only the
specific recRNA molecules are present during RT and the kinetics of
cDNA synthesis are not like those in native RNA (the unknown sample)
that also contain a high percentage of natural occurring sub-fractions,
e.g. ribosomal RNA (rRNA, ~80%) and transfer RNA (tRNA, 10-15%). These
missing RNA sub-fractions can influence the cDNA synthesis rate and in
consequence RT efficiency rises and calibration curves are then
overestimated in
gene quantification. To compensate for background effects and mimic a
natural RNA distribution like in native total RNA, total RNA isolated
from bacterial or insect cell lines, can be used. Alternatively
commercially available RNA sources can be used as RNA background, e.g.
poly-A RNA or tRNA, but they do not represent a native RNA distribution
over all RNA sub-species.
Earlier results suggest, that a minimum of RNA background is generally
needed and that it enhances RT synthesis efficiency rate. Low
concentrations
of recRNA used in calibration curves should always be buffered with
background
or carrier RNA, otherwise the low amounts can be degraded easily by
RNAses. Very high background concentrations had a more significant
suppression
effect in RT synthesis rate and in later real-time PCR efficiency.
No matter how accurately the
concentration of the standard material is known, the final result is
always reported relatively compared to a defined unit of interest: e.g.
copies per defined ng of total RNA, copies per genome (6.4 pg DNA),
copies per cell, copies per gram of tissue, copies per ml blood, etc.
If absolute changes in copy number are important then the denominator
still must be shown to be absolute stable across the comparison. This
accuracy may only be needed in screening experiments (amount of
microorganism in food), to measure the percentage of GMO (genetic
modified organism) in food, to measure the viral load or bacterial load
in immunology and microbiology. The quality of your gene quantification
data cannot be better than the quality of your denominator. Any
variation in your denominator will obscure real changes, produce
artificial changes and wrong quantification results. Careful use of
controls is critical to demonstrate that your choice of denominator was
a wise one. Under certain circumstances, absolute quantification models
can also be normalized using suitable and unregulated references or
housekeeping genes (see Normalization).
Relative quantification
determines the changes in steady-state mRNA levels of a gene across
multiple samples and expresses it relative to the
levels of an internal control RNA. This reference gene is often a
housekeeping gene and can be co-amplified in the same tube in a
multiplex assay
or can be amplified in a separate tube. Therefore, relative
quantification does not require standards with known concentrations and
the reference can be any transcript, as long as its sequence is known.
Relative quantification is based on the expression levels of a target
gene versus a reference gene and in many experiments is adequate for
investigating physiological changes in gene expression levels. To
calculate the expression of a
target gene in relation to an adequate reference gene various
mathematical
models are established. Calculations are based on the comparison of
the distinct cycle determined by various methods, e.g. crossing points
(CP) and threshold values (Ct) at a constant level of fluorescence; or
CP acquisition according to established mathematic algorithm. To date
several calculation mathematical models that calculate the relative
expression ratio have been developed. Relative quantification models
without efficiency correction are available and published (equations
1-2)
equation
1 and with kinetic PCR efficiency
correction (equations 3-6). Further, the available models allow for the
determination of single transcription difference between one control
and one sample, assayed in triplicates (n =1/3), e.g. LightCycler
Relative Quantification Software, or Q-Gene or for a
groupwise comparison for more samples (up to 100), e.g. REST and
REST-XL. The relative expression ratio of a target gene is computed,
based on its real-time PCR efficiencies (E) or a static efficiency of
2, and the crossing point (CP) difference (D) of one unknown sample
(treatment) versus one control (DCP control - treatment). Using REST
and REST-XL the relative calculation procedure is based on the MEAN CP
of the experimental groups (equation
4).
equation
3 In these models the target gene expression is normalized by a non regulated reference gene expression, e.g. derived from classical and frequently described housekeeping genes. The crucial problem in this relative approach is that the most common reference gene transcripts from so-called housekeeping genes, whose mRNA expression can be regulated and whose levels vary significantly with treatment or between individuals. However, relative quantification can generate useful and biologically relevant information when used appropriately. equation
5 Advantages and disadvantages of external standards External standard
quantification is the method of choice for the nucleic acid
quantification, independent of any hardware platform used. The
specificity, sensitivity, linearity and reproducibility allows for the
absolute and accurate quantification of molecules even in tissues with
low mRNA abundance (<100 molecules) and a detection down to a few
molecules (<10 molecules). The dynamic range of an optimal validated
and optimized external standardized real-time RT-PCR assay can
accurately detect target mRNA up to nine orders of magnitude or a
billion-fold range with high assay linearity (Pearson correlation
coefficient; r>0.99). In general a mean intra-assay variation of
10-20% and a mean inter-assay variation of 15-30% on molecule basis
(maximal 2-4% variability on CP basis, respectively) is realistic over
the wide dynamic range. At high (> 107) and low (< 103)
template copy input levels the assay variability is higher than in the
range between the two. At very low copy numbers, under 20 copies per
tube, the random variation due to sampling error (Poisson´s error
law) becomes significant.
A recDNA calibration curve
model can quantify precisely only cDNA molecules derived from the RT
step; it says nothing about the conversion to
cDNA of the mRNA molecules present in the native total RNA sample.
Variability in cDNA synthesis efficiency during reverse transcription
must be always be kept in mind. Therefore, a recRNA calibration curve
model has the advantage that both RNA templates undergo parallel RT and
real-time PCR steps. However, a direct comparison suggests that the
recDNA
quantification model shows higher sensitivity, exhibits a larger
quantification
range, has a higher reproducibility, and is more stable than the recRNA
model. Furthermore, recDNA external calibration curves exhibit lower
variation
(intra-assay variation < 0.7%; inter-assay variation < 2.6% on CP
basis) than the recRNA model (< 2.7% and < 4.5%, respectively).
Clearly,
the RT step has a profound affect on the overall result obtained from
an
RT-PCR assay and more thorough consideration of RT efficiency is
needed.
The main disadvantage of external standards is the lack of internal control for RT and PCR inhibitors. All quantitative PCR methods assume that the target and the sample amplify with similar efficiency. The risk with external standards is that some of the unknown samples may contain substances that significantly reduce the efficiency of the PCR reaction in the unknown samples. As discussed, sporadic RT and PCR inhibitors or different RNA/cDNA distributions can occur. A dilution series can be run on the unknown samples and the inhibitory factors can often be diluted out, causing a non-linear standard curve. Real-time assays using SYBR
Green I can easily reveal the presence of primer dimers, which are the
product of nonspecific annealing and primer elongation events. These
events take place as soon as PCR reagents are combined. During PCR,
formation of primer dimers competes with formation of specific PCR
product, leading to reduced amplification efficiency and a less
successful specific RT-PCR product. To distinguish primer dimers from
the specific amplicon a melting curve analysis can be performed in
all available quantification software. The pure and homogeneous RT-PCR
product produce a single, sharply defined melting curve with a narrow
peak. In contrast, the primer dimers melt at relatively low
temperatures
and have broader peaks. To avoid primer dimer formation an intensive
primer
optimization is needed, by testing multiple primer pair by cross-wise
combinations.
Multiple optimization strategies have been developed and are published.
The easiest and most affective way to get rid of any dimer structures,
at
least during the quantification procedure, is to add an additional 4th
segment to the classical three segmented PCR procedure: 1st
segment
with denaturation at 95°C; 2nd segment with primer
annealing
at 55-65°C; 3rd segment with elongation at 72°C; 4th
segment with fluorescence acquisition at elevated temperatures.
The fluorescence acquisition in 4th segment is performed mainly in the
range of 80-87°C, eliminates the non-specific fluorescence signals
derived by primer dimers or unspecific minor products and ensures
accurate
quantification of the desired product. High temperature quantification
keeps the background fluorescence and the ‘no template control’
fluorescence
under 2-3% of maximal fluorescence at plateau.
“Do we need to run a calibration curve in each run ?” and “ Do we need a calibration curve at all ?” are frequently posed questions, together with “What about the reproducibility between the runs?” (http://www.idahotec.com/lightcycler_u/lectures/quantification_on_lc.htm).
Repeated runs of the same standard curve give minor variations of a
2-3% in the slope (real-time PCR efficiency) and about 10% in the
intercept of calibration curve. Since the variation in the standard
curve correlates with variation in the unknowns, a detection of a
2-fold
difference over a wide range of target concentrations is possible. The
slope of the calibration curve is more reproducible than the intercept,
hence only a single standard point will be required to “re-register” a
previously
performed calibration curve level for the new unknowns. The curve can
be
imported into any run, as done in the LightCycler software. Never
changing
variations and 100% reproducibility are the big advantages of such a
calibration
curve import, but there are also disadvantages as variations of
reagents,
primers and probe (sequence alterations and fluorescence intensity),
day-to-day
or sample-to-sample variations will not be covered in this ‘copy and
paste’ approach. Since these affect PCR efficiency, such an approach
can introduce significant errors into the quantification.
Real-time
RT-PCR: Neue Ansätze zur exakten mRNA Quantifizierung
BioSpektrum 1/2004  Die molekularen Technologien
Genomics, Transcriptomics und Proteomics erobern immer mehr die
klassischen Forschungsgebiete der Biowissenschaften. Die enorme Flut an
gewonnenen Daten und Ergebnissen ist von überproportionalem Nutzen
in der molekularen Diagnostik und Physiologie sowie die „Functional
Genomics“. Immer neue ausgeklügelte Methoden und Anwendungen sind
daher nötig um komplexe physiologische Vorgänge zu
beschreiben. Da wir uns erst an Anfang dieser molekularen Ära
befinden, ist es notwendig diese Techniken zu optimieren und komplett
zu verstehen. Eine dieser technisch ausgefeilten Methoden zur
zuverlässigen und exakten Quantifizierung spezifischer mRNA,
stellt die real-time RT-PCR dar. Dieser Artikel beschreibt im
Wesentlichen die effizienzkorrigierte
relative Quantifizierung, die Normalisierung der Expressionsergebnisse anhand eines nicht regulierten „Housekeeping Gens“, die Berechnung der real-time PCR Effizienz sowie die Verrechnung und statistische Auswertung der Expressionsergebnisse. Alle beschriebenen Themenkomplexe können im Detail auf der korrespondierenden Internetseite (http://www.gene-quantification.info) in internationalen publizierten Originalarbeiten nachgeschlagen werden.
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