Lipidomics

Lipidomics is the study of pathways and networks of cellular lipids in biological systems. Lipidomics research studies the structure and function of the complete set of lipids (the lipidome) in a given cell or organism as well as their interactions with other cellular components.

Profiling and identification of lipids

Profiling and identification of lipids

Lipidomics

The lipidome is defined as complete lipid profile within a cell, tissue or organism, i.e. the subset of all lipids. Many metabolic diseases involve lipids as regulators for complex cellular processes substantiating the increasing interest in lipidomics research. The major aim is to discover biomarkers characteristic for a disease, or as marker for an internal or external perturbation.

In lipidomics research, the confident identification of lipids is crucial. This annotation of lipids can be demanding due to the large number of structural variations. Using Bruker’s timsTOF fleX technology, lipids can be extensively characterised giving molecular formula and chain composition using LC-PASEF and linked to the spatial distribution on tissue using the SpatialOMx® workflow.

The 4D-Lipidomics™ workflow addresses these challenges in a complete solution by providing unique benefits:

  • Acquisition of LC-PASEF data on timsTOF Pro or timsTOF fleX
  • High acquisition speed of PASEF resulting in > 10x more MS/MS in a single run
  • Cleaner MS/MS spectra using MOMA (mobility offset mass aligned) to remove isobaric interferences
  • Data processing and annotation in MetaboScape® and SCiLS™ or both ESI & MALDI imaging data
  • Rule based lipid annotation routines enable the identification of lipid species in accordance to the Lipidomics Standards Initiative guidelines
  • Verification of annotations by CCSPredict, which further increases the confidence in annotations by predicting CCS values based on structure
  • Intuitive 4D visualization investigation of lipid assignments tools using CCS and retention time aware Kendrick Mass Defect (KMD) plot
  • LipidBlast1 support, the largest Lipidomics library including > 550,000 lipid structures containing CCS values
[1] Tsugawa H., et al. 2020, Nature Biotech, https://doi.org/10.1038/s41587-020-0531-2
The Lipid Class (LC) annotation tool avoids the risk of over annotation and simplifies the automatic identification of lipid features by using selected fragmentation rules.