Advancing Integration of Multidimensional Separations and High-Resolution Ion Mobility to Benefit Untargeted Small Molecule Analyses for Clinical and Biological Applications

Abstract

Multidimensional separations combined with ion mobility can enhance untargeted small molecule analyses by increasing peak capacity, providing unique molecular identifiers, and, in some cases, separating isomers and isobars. However, the integration of ion mobility (IM) into liquid chromatography-mass spectrometry (LC-MS) untargeted metabolomic, lipidomic, and exposomic studies remains an active area of research due to the high analytical demand of complex sample types. The analysis time of these three separation techniques allows them to be nested together with liquid chromatography, ion mobility, and mass spectrometry operating typically on the order of seconds to minutes, milliseconds, and microseconds, respectively. A combination of these bioanalytical techniques (LC-IM-MS) can be used to separate isomers that otherwise could not be resolved by liquid chromatography or ion mobility alone. They can also be used in data filtering workflows to identify unknown candidate metabolites of compounds. Furthermore, high-resolution demultiplexing ion mobility combined with LC-MS provides increased feature detection in untargeted studies to better understand the isomer heterogeneity of complex biological systems, such as discovery lipidomics. Moreover, integrating multiple separation techniques with large dataset outputs can be challenging due to the lack of strategies to make the data meaningful. The large amount of data that untargeted LC-IM-MS analyses produce requires novel data acquisition and analysis solutions that have been described herein. Collectively, the untargeted separation strategies and software pipelines developed in this work can be applied to several biological and clinical settings to better understand the molecular complexity of primary samples, and ultimately, with further development, provide a global overview of systems biology to benefit clinical applications.