From Metabolomics to HRMS-Based Exposomics: Adapting Peak Picking and Developing Scoring for MS1 Suspect Screening.

The technological advances of cutting-edge high-resolution mass spectrometry (HRMS) have set the stage for a new paradigm for exposure assessment. However, some adjustments of the metabolomics workflow are needed before HRMS-based methods can detect the low-abundant exogenous chemicals in human matrixes. It is also essential to provide tools to speed up marker identifications. Here, we first show that metabolomics software packages developed for automated optimization of XCMS parameters can lead to a false negative rate of up to 80% for chemicals spiked at low levels in blood. We then demonstrate that manual selection criteria in open-source (XCMS, MZmine2) and vendor software (MarkerView, Progenesis QI) allow to decrease the rate of false negative up to 4% (MZmine2). We next report an MS1 automatized suspect screening workflow that allows for a rapid preannotation of HRMS data sets. The novelty of this suspect screening workflow is to combine several predictors based on /, retention time () prediction models, and isotope ratio to generate intermediate and global scorings. Several prediction models were tested and hierarchized (PredRet, Retip, retention time indices, and a log model), and a nonlinear scoring was developed to account for variations observed within individual runs. We then tested the efficiency of this suspect screening tool to detect spiked and nonspiked chemicals in human blood. Compared to other existing annotation tools, its main advantages include the use of predictors using different models, its speed, and the use of efficient scoring algorithms to prioritize preannotated markers and reduce false positives.