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Supplementary Material
Estimation of reaction efficiency and data analysis
A range of newly available, free software tools (e.g. DART-PCR, Peirson et al, 2003;
LinRegPCR, Ruijter et al, 2009; PCR-Miner, Zhao and Fernald, 2005) are available and their
performance has been compared in two excellent reviews (Cikos et al, 2007; Karlen et al,
2007). The use of these tools improves the estimation of reaction efficiencies and allows the
adjustment of cycle threshold levels to minimise variation among sample PCR efficiencies.
This also allows the identification and exclusion of replicates that fall outside a predefined
level of average PCR efficiency: outliers in efficiency may indicate irredeemable problems
with sample amplification. Incorporating this step into the analysis of RTL will considerably
improve the precision of the estimates (Ehrlenbach et al, 2009). In brief, RTL can be
calculated according to the following formula:
RTLS = (ECT^CqCT / EST^CqST) / (ECN^CqCN / ESN^CqSN)
Where: RTLS = relative telomere length for a given sample; ECT = average efficiency
of the control telomere reactions; CqCT = average cycle threshold for the control telomere
reactions; EST = average efficiency of all sample telomere reactions; CqST = average cycle
threshold of within-sample telomere replicates; ECN = average efficiency of the control
non-VCN reactions; CqCN = average cycle threshold for the control non-VCN reactions;
ESN = average efficiency of all sample N-VCN reactions; and CqSN = average cycle threshold
of within-sample non-VCN replicates.
This type of analysis requires that at least one control DNA sample be included on
every plate (i.e. PCR ‘run’). The added benefit of this approach is that the control sample can
be used to account for any inter-assay variability. The use of a multiplex approach
(MMQPCR) may further improve accuracy by reducing within-sample variation in RTL
caused by small differences in the concentration of DNA between paired telomere and
reference gene replicates (see below).
Identifying and optimising appropriate non-variable copy number (VCN) genes
Genes or gene fragments that do have copy number differences among samples can
lead to gross errors in estimating RTL. There is no easy way to find a non-VCN gene for the
species of interest. A non-VCN gene that has been identified for another species, even a
closely related one, is not guaranteed to amplify in your species of interest and even then
cannot be assumed to also be non-variable in copy number. A database exists for reference
genes in expression studies (http://www.rtprimerdb.org) and although these cannot be
assumed to be non-variable in copy number simply because expression levels are constant
across tissue types, they are a good starting point for selecting candidate reference genes.
Candidate genes need to be tested on a range of samples representing all the populations/races
in the sample set. Primers can also be selected or designed for candidate genes based on
conserved regions in alignments of related species but still need to be tested on the species of
interest. The use of in silico tools for primer testing can be helpful for filtering the list of
candidate genes but cannot be used as confirmation of the non-variable copy number status of
a gene region. Once a panel of 3-5 candidate non-VCN genes has been identified, the actual
process of PCR optimisation can begin. We recommend conducting a pilot study comprising a
subset of 10-20 samples to allow the pairwise correlation of RTLs estimated from the
different non-VCN genes (hence, using this method a minimum combination of two non-VCN
genes is required). Significant deviations from a slope of one and/or poor R2 values would
indicate inappropriate genes for RTL estimation as would multiple peaks in the 2nd derivative
meltcurve. All suitable non-VCN genes should be deposited in an online database for the
benefit of others.
Using external size standards as controls to gauge absolute TL
Horn et al. cite a study by O’Callaghan et al. (2008) as an example of their perceived
problems with external size standards. They claim that O’Callaghan et al. did not validate
their results and consequently failed to recognise the disparity between their estimates of TL
and that of all other studies of human telomeres. This criticism is mistaken because
O’Callaghan et al. clearly describe how they compared their technique against the traditional
TRF method and present these results in graphical format (see O'Callaghan et al, 2008: Figure
2B). They also discuss how their estimates vary by “an approximate 7kb difference” with the
traditional TRF approach and suggest potential reasons for the discrepancy. Regardless of the
cause of this difference in accuracy however, their R value of 0.88 when comparing estimates
of RTL for the same group of samples is comparable in terms of precision with the TRF
method (i.e., it is reproducible). Thus, within study comparisons using the data of
O’Callaghan et al. would have similar power to that using the TRF method. O’Callaghan et
al. used an uncorrected standard curve approach to qPCR and failed to incorporate efficiency
in their analysis of telomere and reference gene quantities. This step alone may account for
the shortfall in telomere length reported. Obviously, further work is warranted to understand
the vast discrepancy in absolute estimates between the two methods, but it is too early to
completely abandon the approach based on a single unusual result. The difference in sizes
reported by the two methods may also become clearer with a deeper knowledge of telomere
biology.
Improved precision with multiplex PCR
A recent improvement to the original RTL method has been to combine the telomere and the
N-VCN assays together in the same tube, which is a technique known as monochrome
multiplex quantitative PCR (MMQPCR, Cawthon, 2009). This method uses differences in
target abundance and melting temperature to allow the two assays to be conducted and
detected sequentially during a normal cycling set-up using just the standard Sybr green
detection chemistry. It offers advantages over the single-plex method in terms of time, reagent
costs, and most importantly, in the reduction of intra-sample variability. It should be noted
that this assay set-up differs from a standard multiplex assay in that the amplicon extension
segment of the cycle does not occur simultaneously for the two targets. Rather, the telomere
target is extended first at a standard temperature of 72°C, fluorescence is recorded, and then
the temperature is raised until that fragment is completely disassociated. This allows the DNA
polymerase to be released and available to extend the non-VCN product. The non-VCN
primers possess 5’ GC-clamp portions which raise the primer annealing temperature such that
amplicon synthesis can proceed at temperatures in excess of 80°C, after the telomere product
has completely melted. This clever cycle modification circumvents traditional multiplex
problems of inhibition by early amplifying products and removes one of the major
optimisation steps of balancing primer concentrations to favour the least abundant template.
Clearly, optimisation of this assay is still required – the introduction of GC-clamps can
complicate the reaction – but such optimisation is not beyond what is expected of the standard
molecular ecology practitioner. Some effect on PCR efficiency by the addition of GC-clamps
is unavoidable; however, as stated previously, efficiency differences can be handled via the
use of an appropriate analytical approach that normalises individual PCR efficiencies against
those of a control sample (Pfaffl, 2001).
Acknowledgements
We would like to thank Richard Cawthon from the Department of Human Genetics,
University of Utah, Salt Lake City for comments on a previous draft.
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