Prediction, Targeting, and Annotation of Oxidized Lipids Using LPPtiger2 Software

The epilipidome, a subset of the natural lipidome arising from enzymatic and non-enzymatic lipid modifications, remains largely unexplored. Within this emerging class, oxidized complex lipids have raised considerable interest due to their diverse biological functions, including the modulation of inflammation, cell fate decisions, and the execution of programmed cell death. However, the discovery and annotation of these typically low-abundant yet structurally diverse lipid species present significant analytical challenges, often necessitating advanced bioinformatics tools. Here, we present a computational pipeline powered by LPPtiger2 software, designed for the comprehensive discovery, detection, and annotation of complex oxidized lipids within the context of a defined lipidome. The LPPtiger2 hybrid workflow offers a robust solution for high-quality epilipid profiling by integrating a predictive algorithm with a semi-targeted experimental protocol. Using a knowledge-based in silico epilipidome prediction algorithm, it generates a highly customized, sample-specific search space prior to data acquisition. This approach transforms the conventional untargeted lipidomics pipeline into a semi-targeted workflow that selectively focuses on predicted epilipid precursors. Such specificity enhances LPPtiger2-supported annotation of modified epilipids through improved sensitivity, superior MS/MS spectral quality, and a tailored lipid search space.