Post-acquisition filtering of salt cluster artefacts for LC-MS based human metabolomic studies

Abstract

Liquid chromatography-high resolution mass spectrometry (LC-MS) has emerged as one of the most widely used platforms for untargeted metabolomics due to its unparalleled sensitivity and metabolite coverage. Despite its prevalence of use, the proportion of true metabolites identified in a given experiment compared to background contaminants and ionization-generated artefacts remains poorly understood. Salt clusters are well documented artefacts of electrospray ionization MS, recognized by their characteristically high mass defects (for this work simply generalized as the decimal numbers after the nominal mass). Exploiting this property, we developed a method to identify and remove salt clusters from LC-MS-based human metabolomics data using mass defect filtering. By comparing the complete set of endogenous metabolites in the human metabolome database to actual plasma, urine and stool samples, we demonstrate that up to 28.5 % of detected features are likely salt clusters. These clusters occur irrespective of ionization mode, column type, sweep gas and sample type, but can be easily removed post-acquisition using a set of R functions presented here. Our mass defect filter removes unwanted noise from LC-MS metabolomics datasets, while retaining true metabolites, and requires only a list of m/z and retention time values. Reducing the number of features prior to statistical analyses will result in more accurate multivariate modeling and differential feature selection, as well as decreased reporting of unknowns that often constitute the largest proportion of human metabolomics data.