Metabolomics is only truly unbiased if the whole metabolome is captured. Current metabolomics technologies capture only a part of the metabolome and therefore produce inherently biased results. Important factors that introduce such bias into a metabolomic analysis may include but are not limited to, timing of sample collection, the sample collection procedure, sample processing, stabilization, stability and storage, extraction procedures, dilution of sample, type and number of analytical methods used, preferences of analytical assays for metabolites with certain physico-chemical properties, ion suppression (LC-MS), derivatization (GC-MS), sensitivity of the assay, range of reliable response and the ability to allow at least for semi-quantitative comparison. Consideration of the many computational, chemometric and biostatistical steps required to link changes in metabolite patterns to metabolic pathways and the additional bias and risks that these steps entail, brings up the question of whether or not screening for changes in known metabolic pathways using a set of validated, quantitative multiplexing LC-MS assays (targeted pathway screening, TAPAS) would be a more robust and reliable approach. Instead of non-selectively screening for changes in metabolite patterns, TAPAS screens for changes in metabolic pathways. Since such assays are designed for specific groups of metabolites, TAPAS can cover a larger number of metabolic pathways including metabolites of a wide variety of physicochemical properties and concentration ranges and thus, although based on a suite of targeted assays, TAPAS may ultimately be a less biased strategy than current nontargeted metabolomics technologies.
Keywords: Chemometrics, metabolic pathways, metabolomics, quantitation, targeted/ non-targeted, qualification, validation, verification, metabolomic analysis, sample processing, quantitative multiplexing LC-MS assays, nontargeted metabolomics technologies
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