Telomeres play a key role in maintaining chromosome integrity and are crucial for normal cell function . In vertebrates, telomeres consist of tandem repeated TTAGGG sequences at the end of chromosomes . Telomere repeats are lost during cell replication and by oxidative damage [3–5], and telomeres thus tend to shorten with age, ultimately reaching a threshold which likely contributes to cellular and organismal senescence [6–10].
The predicted loss of telomere repeats with age, and observations that the rate at which this happens correlate with fitness, lifespan, and susceptibility to a range of diseases, have sparked a general interest in understanding the role of telomere dynamics in life histories [11–20], as well as using telomeres as a molecular tool for determination of chronological and biological age in non-model species [21–23].
One approach to telomere length estimation is the qPCR method developed by Cawthon [24, 25]. Targeting telomere repeat sequences in a qPCR assay is possible by the use of specially designed primers with build-in mismatches that allow for binding and amplification of the telomere target, but not amplification of primer dimers [24, 25]. In qPCR, a DNA-binding fluorescence-dye such as SYBR green is used to monitor the amplification in individual PCR reactions and determine the point in time during cycling, the Cq value, when amplification of target crosses a fixed threshold. The resulting sample Cq values can be translated into estimates of the amount of telomere repeats (T) in a sample by means of a standard curve under the assumption that the amount of fluorescence directly correlate with the amount of double-stranded DNA that is amplified. This T can be scaled against the amount of a single copy reference gene (S) to obtain the T/S ratio, which is an estimate of the relative amount of telomere repeats in the sample.
The general speed, sensitivity, and conceptual and practical simplicity of the PCR technique have resulted in qPCR becoming the touchstone for nucleic acid quantification and comparison in several disciplines [26, 27], and likewise, the telomere qPCR approach to telomere length estimation have become one of the most common methods for studying telomere dynamics.
However, despite its conceptual transparency and practical simplicity, obtaining, analyzing, and interpreting qPCR data is not a trivial issue. In particular, the high sensitivity of the technique implies that results may be of low precision and/or misleading if the qPCR assay is not adequately optimized. These problems have been exacerbated by the wide applicability and popularity of the technique along with a lack of general guidelines for how to perform and report qPCR experiments. As a consequence “qPCR has become an inadequately standardized, complex, and frequently, inconsistent technique that invites the publication of flawed conclusions” . A similar critique has been directed towards the qPCR approach to telomere measurement and there is a growing debate about its research applications [29–33].
Still, there are several reasons why telomere estimation by the qPCR method may be attractive. First, many of the above issues are not inherent characteristics of the qPCR technique but mainly result from unfamiliarity with its technical requirements and limitations. This issue has been addressed by the recent formulation of the MIQE guidelines (Minimum Information for publication of Quantitative real-time PCR Experiments) with the purpose to “ensure the integrity of the scientific literature, promote consistency between laboratories, and increase experimental transparency” [26–28]. Second, scientific progress is made by developing, testing and optimizing, rather than just criticizing, as also noted by Monaghan  and Smith and co-authors . That is, estimation of measurement precision and accuracy may provide the information required to identify and reduce the factors causing variability, and hence optimize the qPCR method. Finally, until recently, the qPCR method provided the only avenue for measuring telomere length in skin biopsies from free-ranging and generally inaccessible species for which even little information on telomere dynamics, however preliminary, is of value.
Here, we evaluate the performance of four assays based on the qPCR methods described by Cawthon [24
]. The four assays differed with respect to primers, reagents, qPCR platform, and experimental setup, and were all modified specifically for estimation of telomere lengths in humpback whale skin samples. Our goals were to;
Arrive at a reliable qPCR assay specifically tailored for measuring telomeres in humpback whale skin samples
on the MIQE guidelines and work of Karlen and co-authors  to propose a set of quality controls which may serve for further standardization of the qPCR method
Highlight a subset of the factors affecting the precision and accuracy of telomere length estimates by qPCR; and, if nothing else
Prevent others from making the same mistakes as we did by reporting all relevant aspects of our workflow and results.
Our approach is unique in its use of skin samples from a large, free-ranging marine mammal and its comparison of several different qPCR assays. However, although the focus is that of telomere estimation in humpback whale skin samples, the factors causing variability and the principles of quality control are universal and should apply to many qPCR applications.