Fig. 4: Schematic representation of analytical and bioinformatics workflow developed for identification and relative quantification of oxidized complex lipids.

Blood plasma lipids are extracted and experimental group-specific pooled samples are prepared by mixing equal amounts of each individual sample within the group. Step 1: Group-specific pooled samples are used for untargeted LC-MS/MS analysis (e.g. using data-dependent acquisition, DDA) to identify native, non-oxidized lipidome of biological system in question (here, human blood plasma). Data processing for identification of native lipidome can be done by any suitable software of user choice (e.g. Lipostar 2). Step 2: Most abundant and/or most regulated lipids containing polyunsaturated acyl chains (and thus susceptible to oxidation) are selected for in silico epilipidome prediction. In silico prediction step is fully automated within LPPtiger 2.0 software (for details see methods section as well as User Manual available at https://github.com/LMAI-TUD/lpptiger2). Step 3: LPPtiger 2.0-generated inclusion list containing m/z values of predicted epilipidome can be directly imported in a semi-targeted DDA (stDDA) method template and used to analyze group-specific pooled samples. stDDA raw files are then analyzed by LPPtiger 2.0 to identify oxidized complex lipids. Manual inspection of MS2 spectra and RT mapping (e.g. by using KMD plots) are advised to support accurate annotation of modification type- and position specific-isomers. Step 4: List of identified species can be exported by LPPtiger 2.0 in a form of PRM/MRM lists for targeted analysis of oxidized lipids in individual samples. Targeted dataset can be processed by any dedicated software (e.g. here Skyline). Obtained results can be worked up by any available statistical/visualization tools.