Due the various biological functions involving lipids, disease and exposure studies are often aided by non-targeted lipidomic analyses, which prioritize identifying as many lipids as possible. Using an instrumental platform that couples liquid chromatography, ion mobility spectrometry, and mass spectrometry (LC-IMS-MS) allows for the separation and identification of hundreds of lipids from a single sample injection. Despite the power of this multidimensional analytical tool, there are still many challenges associated with non-targeted workflows, primarily stemming from the complex sample matrices associated with biological samples. For example, LC retention time values are typically inconsistent across different sample types, methods, or instrument runs, which can result in less confident identifications or even mislabeled annotations. This work aims to 1) optimize and standardize an LC gradient method for non-targeted lipidomic analyses, 2) characterize retention time shifting patterns, 3) improve our lab’s existing lipidomic methods by using heavy-labeled internal standard mixes to predict LC retention times, and 4) develop a quantitative method with said standard mixes.
Quentin DuVal-Smith (Fri,) studied this question.