Credentialing Features: A Platform to Benchmark andOptimize Untargeted Metabolomic Methods

摘要

The aim of untargeted metabolomics is to profile as many metabolites as possible, yet a major challenge is comparing experimental method performance on the basis of metabolome coverage. To date, most published approaches have compared experimental methods by counting the total number of features detected. Due to artifactual interference, however, this number is highly variable and therefore is a poor metric for comparing metabolomic methods. Here we introduce an alternative approach to benchmarking metabolome coverage which relies on mixed Escherichia coli extracts from cells cultured in regular and ¹³C-enriched media. After mass spectrometry-based metabolomic analysis of these extracts, we “credential” features arising from E. coli metabolites on the basis of isotope spacing and intensity. This credentialing platform enables us to accurately compare the number of nonartifactual features yielded by different experimental approaches. We highlight the value of our platform by reoptimizing a published untargeted metabolomic method for XCMS data processing. Compared to the published parameters,the new XCMS parameters decrease the total number of features by 15%(a reduction in noise features) while increasing the number of truemetabolites detected and grouped by 20%. Our credentialing platformrelies on easily generated E. coli samplesand a simple software algorithm that is freely available on our laboratoryWeb site (). We have validated the credentialing platform with reversed-phaseand hydrophilic interaction liquid chromatography as well as Agilent,Thermo Scientific, AB SCIEX, and LECO mass spectrometers. Thus, thecredentialing platform can readily be applied by any laboratory tooptimize their untargeted metabolomic pipeline for metabolite extraction,chromatographic separation, mass spectrometric detection, and bioinformaticprocessing.