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bioRxiv · 10.1101/352468

Validation and automation of a high-throughput multi-targeted method for semi-quantification of endogenous metabolites from different biological matrices using tandem mass spectrometry

Abstract

The use of metabolomics profiling to understand metabolism under different physiological states has increased in recent years, which created the need for robust analytical platforms. Here, we present a validated method for targeted and semi-quantitative analysis of 102 polar metabolites that covers major metabolic pathways from 24 classes in a single 17.5-min assay. The method has been optimized for a wide range of biological matrices from various organisms, and involves automated sample preparation, and data processing using in-house developed R package. To ensure reliability, the method was validated for accuracy, precision, selectivity, specificity, linearity, recovery, and stability according to European Medicines Agency guidelines. We demonstrated excellent repeatability of the retention times (CV<4%), calibration curves (R2[&ge;]0.980) in their respective wide dynamic concentration ranges (CV<3%), and concentrations (CV<25%) of quality control samples interspersed within 25 batches analyzed over a period of one-year. The robustness was demonstrated through high correlation between metabolite concentrations measured using our method and NIST reference values (R2=0.967), including cross-platform comparability against the BIOCRATES AbsoluteIDQp180 kit (R2=0.975) and NMR analyses (R2=0.884). We have shown that our method can be successfully applied in many biomedical research fields and clinical trials, including epidemiological studies for biomarker discovery. In summary, a thorough validation demonstrated that our method is reproducible, robust, reliable, and suitable for metabolomics studies.

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BibTeXRIS

Nandania, J., Peddinti, G., Pessia, A., Kokkonen, M., Velagapudi, V.. 2018-06-20. Validation and automation of a high-throughput multi-targeted method for semi-quantification of endogenous metabolites from different biological matrices using tandem mass spectrometry. https://doi.org/10.1101/352468

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