A Novel Approach for Processing LC – Ion Mobility – MS Metabolomics Data

MS interfaced with LC and ion mobility (IM) is routinely used to measure the level and variation of metabolites within biofluids as data generated through metabolomics studies may yield insight into disease onset and progression. LC-IM-MS based metabolomics generates large and complex data sets with analysis and interpretation of the results being the rate determining steps. This has led to a demand for improved data analysis, including processing and advanced multivariate approaches, which are described for the large scale analysis of metabolomics datasets. Urine from a healthy individual was centrifuged and the supernatant diluted. The urine was divided into control, low dosed (LD) and high dosed (HD) groups. To create a sample set, 11 different drugs were differentially spiked into LD and HD urine, contrasted with blank urine. A reversed phase gradient was applied and MS data acquired in positive ion data independent (LC-DIA-MS) and mobility assisted data independent (LC-IM-DIA-MS) modes. Distinguishing biological variation and metabolic change from analytical interference is key to data processing and analysis. Samples were randomized and measured six times, including QC runs, to ensure statistically valid analysis. LC-MS data were retention time aligned and deconvoluted to produce a feature list. Identified features were compound searched and interrogated with multivariate statistics to provide marker ions of interest. Relative high abundance levels of the standards were reported for LD and HD compared to controls, confirmed by trend plots analysis showing an increase in LD and HD groups compared to control. The standards were identified with an average score of 91 and mass error of 1.2 ppm. Three sample clusters were produced with the standards being the most differentiating features (top 20 based on q value) between groups. Functionality of the software will be demonstrated using biological samples.