Ion Mobility-Mass Spectrometry Strategies for Broadscale Structural Mapping in Life Science Research

Abstract

Mass spectrometry (MS) approaches have long been utilized in scientific research (e.g., proteomics, lipidomics, clinical analysis) for their sensitivity and selectivity, though added separation dimensions are typically required to manage sample complexity and to reduce ion suppression. By coupling ion mobility (IM) with mass spectrometry (IM-MS), these obstacles can potentially be reduced or mitigated altogether. Specifically, the IM dimension provides separation of molecules based on shape, size, and charge, allowing for the discrimination of isomeric and isobaric species otherwise unresolvable by MS alone. Additionally, IM-derived collision cross section (CCS) values serve as an added molecular descriptor which can support annotation confidence for molecular classes of interest. Advancements in high resolution ion mobility (HRIM) platforms can provide even greater structural insights by separating previously unresolved isomers, further aiding in structural analysis of analytically challenging molecular classes. This work demonstrates the multifaceted utility of IM-MS through the support of lipidomic research and small molecule analysis. Specifically, HRIM-MS was leveraged to analyze human plasma samples consisting of multiple isomeric species, resulting in the analysis and characterization of 250 unique lipid species. Additionally, conventional and high resolution IM techniques were used for the characterization of polysorbates: a complex, heterogenous mixture utilized as a surfactant in a variety of pharmaceutical, food, and cosmetic applications. Altogether, a multitude of IM-based analytical tools were developed to support small molecule analysis across disciplines, including the curation of several CCS databases from both conventional and HRIM platforms. Subsequent work to harmonize CCS values across IM techniques provided novel workflows which can help unify IM measurements and findings. Ultimately, the aim of this work is to leverage the strengths of IM-MS to provide analytical strategies and tools for the advancement of life science research.