Automating Multidimensional Liquid Chromatography, Ion Mobility Spectrometry and Mass Spectrometry Data Analyses Using Skyline

Recent advances in ion mobility spectrometry (IMS) have illustrated its power in the structural characteristics of a molecule, especially when coupled with other separations dimensions such as liquid chromatography (LC) and mass spectrometry (MS). However, these three separation techniques together greatly complicate data analyses, so making better informatics tools are essential for assessing the resulting data. In this manuscript, Skyline was adapted to analyze LC-IMS-(CID)-MS data and determine the effect of adding the IMS dimension into the normal LC-MS molecular pipeline. For the evaluation, a tryptic digest of bovine serum albumin (BSA) was spiked into a yeast digest at 7 different concentrations, and calibration curves for both the precursor and all-ions fragments were analysesassessed with and without utilizing the IMS dimension. Skyline was able to rapidly analyze the MS and MS/MS data from 38 of the BSA peptides and in all cases the addition of the IMS dimension removed noise from interfering peptides resulting is in better calibration curves with higher correlation and lower limits of detection. This study presents an important informatics development since currently most LC-IMS-(CID)-MS data is studied manually and cannot be analyzed quickly. Since these evaluations require days for the analysis of only a few target molecules in a limited number of samples, it is unfeasible to evaluate hundreds of targets in numerous samples. Thus, this study showcases Skyline’s ability to work with multidimensional LC-IMS-(CID)-MS data and provide biological and environmental insights rapidly.