Novel acquisition strategies for metabolomics using drift tube ion mobility-quadrupole resolved all ions time-of-flight mass spectrometry (IM-QRAI-TOFMS)

Abstract

The focus of this work was the implementation of ion mobility (IM) and a prototype quadrupole driver within data independent acquisition (DIA) using a drift tube IM-QTOFMS aiming to improve the level of confidence in identity confirmation workflows for non-targeted metabolomics. In addition to conventional all ions (IM-AI) acquisition, quadrupole resolved all ions (IM-QRAI) acquisition allows a drift time-directed precursor ion isolation in DIA using sequential isolation of precursor ions using mass windows of up to 100 Da which can be rapidly ramped across single ion mobility transients (i.e., <100 ms) according to the arrival times of precursor ions. Both IM-AI and IM-QRAI approaches were used for identity confirmation and relative quantification of metabolites in cellular extracts of the cell factory host Pichia pastoris. Samples were spiked with a uniformly ¹³C-labeled (U¹³C) internal standard and LC with low-field drift tube IM separation was used in combination with IM-AI and IM-QRAI. Combining excellent hardware performance and correlation of IM arrival times of natural (^natC) and U¹³C metabolites enabled alignment of signals in the arrival time domain (^DTCCS_N2 differences ≤0.3%), and, in the case of IM-QRAI operation, maintenance of quantitative signals in comparison to IM-AI. The combination of tailored IM-QRAI methods for precursor ion isolation and IM separation also minimized the occurrence of spectral interferences in complex DIA datasets. Combined use of the software tools MS-DIAL, MS-Finder and Skyline for peak picking, feature alignment, reconciliation of ^natC and U¹³C isotopologue pairs, deconvolution of fragment spectra from DIA data, identity confirmation (including ^DTCCS_N2) and targeted re-extraction of datafiles were employed for the data processing workflow. Overall, the combined new acquisition and data processing approaches enabled 87 metabolites to be identified between Level 1 (identified by standard compound) and Level 3.2 (accurate mass spectrum and number of carbons confirmed). The developed methods constitute promising metabolomics discovery tools and can be used to elucidate the number of carbon atoms present in unknown metabolites in stable isotope-supported metabolomics.

Keywords: CCS; HPLC; Ion mobility-mass spectrometry; Metabolomics; Stable isotope labeling; Yeast.