Use of ion mobility-high resolution mass spectrometry in metabolomics studies to provide near MS/MS quality data in a single injection

J Mass Spectrom. 2021 Mar 10;56(5):e4718. doi: 10.1002/jms.4718. Online ahead of print.

Abstract

The use of ion mobility separations (IMSs) in metabolomics approaches has started to be deeply explored in the last years. In this work, the use of liquid chromatography (LC) coupled to IMS-quadrupole time-of-flight mass spectrometry (QTOF MS) has been evaluated in a metabolomics experiments using single injection of the samples. IMS has allowed obtaining cleaner fragmentation spectra, of nearly tandem MS quality, in data-independent acquisition mode. This is much useful in this research area as a second injection, generally applied in LC-QTOF MS workflows to obtain tandem mass spectra, is not necessary, saving time and evading possible compound degradation. As a case study, the smoke produced after combustion of herbal blends used to spray synthetic cannabinoids has been selected as study matrix. The smoke components were trapped in carbon cartridges, desorbed and analyzed by LC-IMS-QTOF MS using different separation mechanisms (reversed phase and HILIC) and acquiring in both positive and negative mode to widen the chemical domain. Partial Least Squares-Discriminant Analysis highlighted several compounds, and ratio between N-Isopropyl-3-(isoquinolinyl)-2-propen-1-amine and quinoline allowed differentiating between tobacco and herbal products. These two compounds were tentatively identified using the cleaner fragmentation spectra from a single injection in the IMS-QTOF MS, with additional confidence obtained by retention time (Rt) and collisional cross section (CCS) prediction using artificial neural networks. Data from this work show that LC-IMS-QTOF is an efficient technique in untargeted metabolomics, avoiding re-injection of the samples for elucidation purposes. In addition, the prediction models for Rt and CCS resulted of help in the elucidation process of potential biomarkers.

Keywords: herbal blends smoke; high resolution mass spectrometry; in-silico prediction; ion mobility; omics approaches.