High-defined quantitative snapshots of the ganglioside lipidome using high resolution ion mobility SLIM assisted shotgun lipidomics.

Defects in sphingolipid metabolism have emerged as a common link across neurodegenerative disorders, and a deeper understanding of the lipid content in preclinical models and patient specimens offers opportunities for development of new therapeutic targets and biomarkers. Sphingolipid metabolic pathways include the formation of glycosphingolipid species that branch into staggeringly complex structural heterogeneity within the globoside and ganglioside sub-lipidomes. Characterization of these sub-lipidomes has typically relied on liquid chromatography-mass spectrometry-based (LC-MS) approaches, but such assays are challenging and resource intensive due to the close structural heterogeneity, the presence of isobaric and isomeric species, and broad dynamic range of endogenous glycosphingolipids. Here, we apply Structures for Lossless Ion Manipulations (SLIM)-based High Resolution Ion Mobility (HRIM)-MS to enable rapid, repeatable, quantitative assays with deep structural information sufficient to resolve endogenous brain gangliosides at the level of individual molecular species. Analyses were performed using a prototype SLIM-MS instrument equipped with a 13-m serpentine path which enabled resolution of closely related isomeric analytes such as GD1a d36:1 and GD1b d36:1 based on recorded mass-to-charge (m/z) and arrival times. To demonstrate the power of our methodology, brain extracts derived from wild-type mice hemi-brains were analyzed by HRIM-MS using flow injection analyses (FIA) without the need for additional separation by liquid chromatography. Endogenous ganglioside species were readily resolved, identified, and quantified by FIA-SLIM-MS analyses within 2 min per sample. Thus, the FIA-SLIM-MS platform enables robust quantification across a broad range of lipid species in biological specimens in a standardized assay format that is readily scalable to support studies with large sample numbers.