Trapped ion mobility spectrometry and PASEF enable in-depth lipidomics from minimal sample amounts

Abstract

A comprehensive characterization of the lipidome from limited starting material remains very challenging. Here we report a high-sensitivity lipidomics workflow based on nanoflow liquid chromatography and trapped ion mobility spectrometry (TIMS). Taking advantage of parallel accumulation-serial fragmentation (PASEF), we fragment on average 15 precursors in each of 100 ms TIMS scans, while maintaining the full mobility resolution of co-eluting isomers. The acquisition speed of over 100 Hz allows us to obtain MS/MS spectra of the vast majority of isotope patterns. Analyzing 1 µL of human plasma, PASEF increases the number of identified lipids more than three times over standard TIMS-MS/MS, achieving attomole sensitivity. Building on high intra-and inter-laboratory precision and accuracy of TIMS collisional cross sections (CCS), we compile 1856 lipid CCS values from plasma, liver and cancer cells. Our study establishes PASEF in lipid analysis and paves the way for sensitive, ion mobilityenhanced lipidomics in four dimensions.

In situ isobaric lipid mapping by MALDI–ion mobility separation–mass spectrometry imaging

“…The 20 min LC‐gradient started with 40% solvent B and was increased in the following way: 2 min, 43%; 2.1 min, 50%; 12 min, 54%; 12.1 min, 70%; 18 min, 99%, 18.1 min, 40%, 20 min, 40% solvent B. The LC system was coupled to a timsTOF Pro instrument with ESI source (Bruker Daltonics, Bremen, Germany) essentially as described . Briefly, ions were detected in positive ion mode in the mass range of m/z 100–1300 using PASEF technology .…”

Ion mobility collision cross-section atlas for known and unknown metabolite annotation in untargeted metabolomics

“…Our group and others developed machine-learning-based prediction (e.g., MetCCS 27,28 , LipidCCS 29 , DeepCCS 30 ) and quantum chemistry-based theoretical calculation (e.g., ISiCLE 31 ) approaches to generate large-scale CCS values for metabolites, lipids and other compounds. Coupling IM-MS with LC separation and data-independent or data-dependent MS/MS techniques (e.g., MS E , AIF, and PASEF) enables simultaneous acquisition of four-dimensional metabolomics data within one analysis, including MS1, RT, CCS, and MS/MS 32,33 . However, limited studies have integrated multi-dimensional properties in IM-MS towards the large-scale annotation of both known and unknown metabolite in untargeted metabolomics 11 .…”

Deep learning the collisional cross sections of the peptide universe from a million training samples

“…In MS/MS mode, this opens up the possibility to step the precursor selection window as a function of ion mobility, allowing the fragmentation of multiple precursors during a single TIMS scan 13 . We termed this novel scan mode parallel accumulation -serial fragmentation (PASEF) and demonstrated that it increases MS/MS rates more than ten-fold without any loss in sensitivity as is otherwise inherent to faster scanning rates 10,15 .…”