Analytical Validation of Telomere Analysis Technology® for the High-Throughput Analysis of Multiple Telomere-Associated Variables

端粒分析技术®对多个端粒相关变量进行高通量分析的分析验证

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作者:Nuria de Pedro, María Díez, Irene García, Jorge García, Lissette Otero, Luis Fernández, Beatriz García, Rut González, Sara Rincón, Diego Pérez, Estefanía Rodríguez, Enrique Segovia, Pilar Najarro

Background

A large number of studies have suggested a correlation between the status of telomeres and disease risk. High-throughput quantitative fluorescence in situ hybridization (HT Q-FISH) is a highly accurate telomere measurement technique that can be applied to the study of large cell populations. Here we describe the analytical performance testing and validation of Telomere Analysis Technology (TAT®), a laboratory-developed HT Q-FISH-based methodology that includes HT imaging and software workflows that provide a highly detailed view of telomere populations.

Conclusions

Analytical validation of TAT shows that is a robust and reliable technique for the characterization of a detailed telomere profile in large cell populations. The combination of high-throughput imaging and software workflows allows for the collection of a large number of telomere-associated variables from each sample, which can then be used in epidemiological and clinical studies.

Methods

TAT was developed for the analysis of telomeres in peripheral blood mononuclear cells (PBMCs). TAT was compared with Terminal Restriction Fragment (TRF) length analysis, and tested for accuracy, precision, limits of detection (LOD) and specificity, reportable range and reference range.

Results

Using 6 different lymphocyte cell lines, we found a high correlation between TAT and TRF for telomere length (R2 ≥ 0.99). The standard variation (assay error) of TAT was 454 base pairs, and the limit of detection of 800 base pairs. A standard curve was constructed to cover human median reportable range values and defined its lower limit at 4700 bp and upper limits at 14,400 bp. Using TAT, up to 223 telomere associated variables (TAVs) can be obtained from a single sample. A pilot, population study, of telomere analysis using TAT revealed high accuracy and reliability of the methodology. Conclusions: Analytical validation of TAT shows that is a robust and reliable technique for the characterization of a detailed telomere profile in large cell populations. The combination of high-throughput imaging and software workflows allows for the collection of a large number of telomere-associated variables from each sample, which can then be used in epidemiological and clinical studies.

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