Ion mobility based informatics and visualization strategies in support of metabolomics

Vanderbilt University, Nashville TN

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Abstract

Project Summary Metabolites are building blocks of cellular function, thus understanding the mechanisms that underlie various physiological conditions and processes will provide insight into disease or aberrant states. Innovative developments in high-throughput analytical technologies and data analysis have allowed for systems-level metabolomics analyses to be performed, many of these technologies have centered around mass spectrometry. The diverse chemical structures in the human metabolome exhibit a wide range of concentration, solubility, polarity and volatility with highly diverse structural forms and physiochemical properties as well as a high number of isomers, therefore the need for as many orthogonal separations as possible is necessary for metabolomics experiments (i.e., multidimensional data sets). Mass spectrometry approaches incorporating liquid chromatography ion mobility mass spectrometry (LC-IM-MS/MS) analyses have shown utility for global untargeted metabolic profiling experiments. Since ion mobility coupled to mass spectrometry (IM-MS) is a relatively new commercially available technology, the incorporation of the ion mobility measurement (via collision cross section, CCS) into current metabolomics data analysis identification strategies is minimal. The typical analytical use of ion mobility is as a quick chemical separation (which allows for noise reduction, increase in peak capacity, etc.), however IM can also be used to increase the confidence in identification and characterization because CCS provides structure specific information about individual metabolites. The long term objectives of this proposal are all centered around incorporating IM measurements into metabolomics based chemoinformatic and bioinformatics pipelines. These include: (1) determining the extent at which the ion mobility dimension can address molecular specificity of isomeric metabolites, (2) developing an IM-based library using CCS values as a descriptor to screen and assign identities to unknown metabolites and lastly, (3) incorporating visualization tools for navigating multidimensional datasets which will allow scientists to better uncover relationships between metabolites and human health. Molecular specificity of the IM dimension will be addressed by analyzing previously generated IM-MS data from a commercially available metabolite library (>600 primary metabolites, of which >20% are isomeric). Individual metabolites in the metabolite library will also be interrogated for curation of the CCS library and these values will be used in the molecular identification pipeline for global untargeted metabolite studies generated previously. Lastly, multidimensional self-organizing maps will be utilized to visualize and navigate various dimensions of data (LC, CCS, m/z, etc.) and ultimately allow the user to prioritize and identify with high confidence metabolites indicative of disease state. Accomplishing the aims outlined in this proposal will be seen as overcoming several critical barriers which have so far hindered the routine and widespread use of ion mobility in MS-based metabolomics workflows.