Abstract
The use of metabolomics profiling to understand the 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 semiquantitative analysis of 102 polar metabolites that cover 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 an inhouse 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 an excellent repeatability of retention times (CV < 4%), calibration curves (R2 ≥ 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 a high correlation between metabolite concentrations measured using our method and the 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.
Highlights
Metabolomics has a great influence on many disciplines, as metabolites are intermediates or end products of cellular functions
We validated our high-throughput targeted and semiquantitative analytical method according to the European Medicines Agency (EMA) guidelines for bioanalytical methods
(vii) Reproducibility: low %Coefficient of variation (CV) of concentrations, retention times, correlation coefficient of calibration curves for an extended period of time, very important in metabolomics studies when comparing the data produced at different points of time
Summary
Metabolomics has a great influence on many disciplines, as metabolites are intermediates or end products of cellular functions. Global metabolomics has been widely used in discovery studies for understanding cellular responses to normal and abnormal biological conditions, targeted metabolomics has more advantages for addressing biological questions in a more hypothesis-driven manner than global untargeted metabolomics [2]. Targeted metabolomics can quantify metabolites that are low in abundance, which are difficult to assess using an untargeted approach. An appropriate sample pretreatment is required to obtain reproducible and high-quality quantitative data in targeted metabolomics. Metabolites are present in a wide dynamic range with great diversity in physicochemical properties in the biological matrices [3]. Recent advancements in extraction techniques and automated approaches for sample preparation have partially satisfied the demands of targeted metabolomics. Standardization of sample preparation is a fundamental requirement in metabolomics studies [4]
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