Abstract
Technical challenges associated with telomere length (TL) measurements have prompted concerns regarding their utility as a biomarker of aging. Several factors influence TL assessment via qPCR, the most common measurement method in epidemiological studies, including storage conditions and DNA extraction method. Here, we tested the impact of power supply during the qPCR assay. Momentary fluctuations in power can affect the functioning of high-performance electronics, including real-time thermocyclers. We investigated if mitigating these fluctuations by using an uninterruptible power supply (UPS) influenced TL assessment via qPCR. Samples run with a UPS had significantly lower standard deviation (p < 0.001) and coefficient of variation (p < 0.001) across technical replicates than those run without a UPS. UPS usage also improved exponential amplification efficiency at the replicate, sample, and plate levels. Together these improvements translated to increased performance across metrics of external validity including correlation with age, within-person correlation across tissues, and correlation between parents and offspring.
Highlights
Telomeres, the repetitive nucleoprotein regions at chromosome ends, are hallmarks of biological aging (Lopez-Otin et al, 2013)
The standard deviation and coefficient of variation were significantly lower across replicate CtT values and natural log transformed T estimates for samples assessed on runs utilizing a uninterruptible power supply (UPS) relative to those run without a UPS (Figure 2A;Table 1:)
telomere length (TL) assessment via qPCR is subject to bias from a host of analytical and pre-analytical factors, leading some to challenge the utility of telomeres as a biomarker of aging (Boonekamp et al, 2013)
Summary
The repetitive nucleoprotein regions at chromosome ends, are hallmarks of biological aging (Lopez-Otin et al, 2013). Technical challenges with TL measurement have led to questions regarding their utility as a biomarker of aging The most common approach to quantify TL in epidemiological studies is quantitative-PCR (qPCR), which expresses telomeric content relative to a single copy gene (Cawthon, 2002). In addition to concerns of being less precise than measures generated by Southern Blot (Aubert et al, 2012), TL measurement via qPCR is subject to influence by several pre-analytical factors including DNA extraction method (Cunningham et al, 2013), sample storage conditions (Dagnall et al, 2017), and analytical factors such as PCR mastermix (Jiménez & Forero, 2018) and well position on plate-based thermocyclers (Eisenberg et al, 2015). Whether power supply during the qPCR assay influences TL measurement has remained unconsidered
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