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

Area of Science:

• Environmental chemistry
• Analytical chemistry
• Metabolomics

Background:

• High-resolution mass spectrometry (HRMS) offers new possibilities for exposure assessment.
• Current metabolomics workflows require optimization for detecting low-abundance exogenous chemicals in human samples.
• Efficient tools are needed to accelerate the identification of chemical markers.

Purpose of the Study:

• To evaluate automated metabolomics software for detecting low-level spiked chemicals in blood.
• To develop and validate a novel automated suspect screening workflow for HRMS data.
• To improve the accuracy and speed of identifying exogenous chemicals in human matrices.

Main Methods:

• Assessed false negative rates of automated XCMS parameter optimization for spiked chemicals.
• Compared manual selection criteria in XCMS, MZmine2, MarkerView, and Progenesis QI.
• Developed and tested an MS1 automated suspect screening workflow combining m/z, retention time (Rt) prediction models, and isotope ratios.
• Evaluated Rt prediction models (PredRet, Retip, Rt indices, log P) and a nonlinear scoring system.

Main Results:

• Automated XCMS optimization yielded up to 80% false negatives for low-level spiked chemicals.
• Manual selection criteria reduced false negatives to 4% with MZmine2.
• The novel suspect screening workflow demonstrated rapid preannotation of HRMS data.
• The workflow effectively detected spiked and non-spiked chemicals in human blood, outperforming existing tools in speed and accuracy.

Conclusions:

• Automated metabolomics workflows require careful parameter selection to avoid high false negative rates.
• Manual criteria in specific software packages significantly enhance the detection of low-abundance chemicals.
• The developed automated suspect screening workflow offers a fast and accurate method for HRMS data preannotation.
• This tool has the potential to advance exposure assessment by improving the identification of exogenous chemicals.