Telomere Core Services & Resources
The Telomere Research Core
The Blackburn Telomere Research Core was created in 2009 to meet the needs of researchers to examine the role of telomere maintenance in human aging, diseases, and risk factors. To date, the Core has provided telomere assays through recharge to over 100 research teams from 50 institutions (US and 7 other countries) that resulted in over 150 publications.
Selected publications featuring our assays, available upon request
- Consultation for study design, sample collection, processing and transportation
- PBMC sample preparation
- Protocols for sample collection, processing and transportation upon request
- Telomere length measurement in human samples: venous blood, dry blood spots, saliva
- Telomere length measurement in non-human primate (mouse, macaque, lemur, rat, cat)
- Telomerase activity measurement from human PBMCs
- Telomerase activity measurement in mouse (various tissues)
- Mitochondrial DNA copy number assay (cellular and cell-free)
- Genomic SNP analyses
- Development and validation of new telomere function assays and other molecular assays
Telomere length assay precision is critical for determining whether a study is statistically powered to detect associations with TL (Nettle et al, 2019; Lindrose et al. 2020).
In the recent years, intra-class correlation coefficient (ICC), as opposed to coefficient of variation (CV), is considered a valid metric for TL assay precision estimation (Verhulst et al., 2015; Eisenberg, 2016; Verhulst et al., 2016). According to the NIH funded Telomere Research Network’s guideline (https://trn.tulane.edu), ICC of repeat DNA extractions from the same source specimen captures the whole process of TL assay and should be use assess the TL assay precision for each lab. We provide examples of ICCs for whole blood, peripheral blood mononuclear cells (PBMCs), dried blood spots and saliva samples here to illustrate high precision of our lab’s TL assay.
Detailed instructions of how to calculate the ICC using R and an example data set can be found at the Telomere Research Network’s website (https://trn.tulane.edu/wp-content/uploads/sites/445/2020/10/How-to-calculate-repeatability.pdf)
Telomere Core ICC measurements
Study
Specimen
DNA Extration Kit
No. Samples
ICC
CI
1
Human PBMC
QIAamp DNA Blood Mini
36
0.955
[0.914, 0.977]
2
Human Dried Blood Spot
QIAamp DNA Investigator
74
0.786
[0.683, 0.860]
3
Human Whole Blood
QIAamp DNA Blood Mini
48
0.944
[0.903, 0.968]
4
Human Whole Blood
QIAamp DNA Blood Mini
101
0.851
[0.787, 0.896]
5
Human Whole Blood
QIAamp DNA Blood Mini
30
0.726
[0.428, 0.872]
6
Human Saliva
Agencourt DNAdvance
45
0.95
[0.911, 0.972]
7
Human Saliva
PrepIT.L2P
47
0.809
[0.678, 0.889]
8
Human Saliva
QIAamp DNA Blood Mini
47
0.937
[0.891, 0.965]
Eisenberg, D. T. (2016). Telomere length measurement validity: the coefficient of variation is invalid and cannot be used to compare quantitative polymerase chain reaction and Southern blot telomere length measurement techniques. International Journal of Epidemiology, 45(4), 1295-1298. doi:10.1093/ije/dyw191
Nettle, D., Seeker, L., Nussey, D., Froy, H., & Bateson, M. (2019). Consequences of measurement error in qPCR telomere data: A simulation study. PLoS ONE, 14(5), e0216118. doi:10.1371/journal.pone.0216118
Lindrose, A.R., McLester-Davis. L.W.Y., Tristano, R.I., Kataria, L., Gadalla, S.M., Eisenberg, D.T.A., Verhulst, S., & Drury, S. Method comparison studies of telomere length measurement using qPCR approaches: a critical appraisal of the literature. bioRxiv preprint doi: https://doi.org/10.1101/2020.09.04.282632.
Verhulst, S., Susser, E., Factor-Litvak, P. R., Simons, M., Benetos, A., Steenstrup, T., . . . Aviv, A. (2016). Response to: Reliability and validity of telomere length measurements. International Journal of Epidemiology, 45(4), 1298-1301. doi:10.1093/ije/dyw194 Verhulst, S., Susser, E., Factor-Litvak, P. R., Simons, M. J., Benetos, A., Steenstrup, T., Aviv, A. (2015). Commentary: The reliability of telomere length measurements. International Journal of Epidemiology, 44(5), 1683-1686. doi:10.1093/ije/dyv166
TL, observed TL: The observed telomere length (often simply referred to as TL ); is the measured phenotypically observed telomere length as measured by various methods. Of note, the observed TL is the cumulative result of both genetic and non-genetic contributions.
Telomere length genetic inheritance: This includes non-telomere regions (genes and their control regions) determined by genetic variance as well as direct transmission of the telomere ends from the parental gametes to the zygote. Therefore telomere length inheritance is therefore only partially determined by gene variants 1, 2.
gTL: Genome-wide complex trait analysis (GCTA) estimated that additive genetic variance, that is, the totality of single nucleotide polymorphisms (SNPs), contributed 28% of total phenotypic variance of TL3. Several dozen individual single nucleotide polymorphisms have been found to be associated with measured TL in large genome-wide association studies 4, 5. It is believed that many SNPs with smaller effects are yet to be discovered. In this sense, genetically determined telomere length (gTL) of an individual cannot be accurately determined currently. Nevertheless, the sum score of top TL SNPs has been proved to be a powerful tool in Mendelian Randomization studies that illustrated the causal effect of telomere length in several diseases including both degenerative diseases and cancers 4, 6.
DNAmTL: Recently, a DNA methylation estimator of TL (DNAmTL) based on 140 CpGs was reported to be more strongly associated with age than measured TL and a better predictor of certain diseases and conditions 7. The modest correlations between DNAmTL and TL measured by qPCR and Flow-FISH8 suggest that DNAmTL and TL may reflect different aspects of telomere biology and requires further investigation.
- Delgado DA, Zhang C, Gleason K, Demanelis K, Chen LS, Gao J, et al. The contribution of parent-tooffspringtransmission of telomeres to the heritability of telomere length in humans. Hum Genet. 2018. Epub 2018/12/12. https://doi.org/10.1007/s00439-018-1964-2 PMID: 30536049.
- De Meyer T, Vandepitte K, Denil S, De Buyzere ML, Rietzschel ER, Bekaert S. A non-genetic, epigenetic-like mechanism of telomere length inheritance? Eur J Hum Genet. 2014; 22(1):10–1. Epub 2013/10/24. https://doi.org/10.1038/ejhg.2013.255 PMID: 24149546; PubMed Central PMCID:PMC3865398.
- Faul JD, Mitchell CM, Smith JA, Zhao W. Estimating Telomere Length Heritability in an Unrelated Sample of Adults: Is Heritability of Telomere Length Modified by Life Course Socioeconomic Status? BiodemographySoc Biol. 2016; 62(1):73–86. https://doi.org/10.1080/19485565.2015.1120645 PMID: 27050034; PubMed Central PMCID: PMC5117361.
- Li C, Stoma S, Lotta LA, Warner S, Albrecht E, Allione A, et al. Genome-wide Association Analysis in Humans. Links Nucleotide Metabolism to Leukocyte Telomere Length. Am J Hum Genet. 2020; 106 (3):389–404. Epub 2020/02/29. https://doi.org/10.1016/j.ajhg.2020.02.006 PMID: 32109421; PubMed Central PMCID: PMC7058826.
- Protsenko E, Rehkopf D, Prather AA, Epel E, Lin J. Are long telomeres better than short? Relative contributions of genetically predicted telomere length to neoplastic and non-neoplastic disease risk and population health burden. PLoS One. 2020 Oct 8;15(10):e0240185. doi: 10.1371/journal.pone.0240185. eCollection 2020.PMID: 33031470.
- Telomeres Mendelian Randomization C, Haycock PC, Burgess S, Nounu A, Zheng J, Okoli GN, et al. Association Between Telomere Length and Risk of Cancer and Non-Neoplastic Diseases: A Mendelian Randomization Study. JAMA Oncol. 2017; 3(5):636–51. Epub 2017/02/28. https://doi.org/10.1001/jamaoncol.2016.5945 PMID: 28241208; PubMed Central PMCID: PMC5638008.
- Lu AT, Seeboth A, Tsai PC, Sun D, Quach A, Reiner AP, Kooperberg C, Ferrucci L, Hou L, Baccarelli AA, Li Y, Harris SE, Corley J, Taylor A, Deary IJ, Stewart JD, Whitsel EA, Assimes TL, Chen W, Li S, Mangino M, Bell JT, Wilson JG, Aviv A, Marioni RE, Raj K, Horvath S. DNA methylation-based estimator of telomere length. Aging (Albany NY). 2019 Aug 18;11(16):5895-5923. doi: 10.18632/aging.102173. Epub 2019 Aug 18.PMID: 31422385
- Pearce EE, Horvath S, Katta S, Dagnall C, Aubert G, Hicks BD, Spellman SR, Katki H, Savage SA, Alsaggaf R, Gadalla SM.DNA-methylation-based telomere length estimator: comparisons with measurements from flow FISH and qPCR. Aging (Albany NY). 2021 Jun 3;13(11):14675-14686. doi: 10.18632/aging.203126. Epub 2021 Jun 3.PMID: 34083495.