LipidHunter Identifies Phospholipids by High-Throughput Processing of LC-MS and Shotgun Lipidomics Datasets

Abstract

Lipids are dynamic constituents of biological systems, rapidly responding to any changes in physiological conditions. Thus, there is a large interest in lipid-derived markers for diagnostic and prognostic applications, especially in translational and systems medicine research. As lipid identification remains a bottleneck of modern untargeted lipidomics, we developed LipidHunter, a new open source software for the high-throughput identification of phospholipids in data acquired by LC-MS and shotgun experiments. LipidHunter resembles a workflow of manual spectra annotation. Lipid identification is based on MS/MS data analysis in accordance with defined fragmentation rules for each phospholipid (PL) class. The software tool matches product and neutral loss signals obtained by collision-induced dissociation to a user-defined white list of fatty acid residues and PL class-specific fragments. The identified signals are tested against elemental composition and bulk identification provided via LIPID MAPS search. Furthermore, LipidHunter provides information-rich tabular and graphical reports allowing to trace back key identification steps and perform data quality control. Thereby, 202 discrete lipid species were identified in lipid extracts from rat primary cardiomyocytes treated with a peroxynitrite donor. Their relative quantification allowed the monitoring of dynamic reconfiguration of the cellular lipidome in response to mild nitroxidative stress. LipidHunter is available free for download at https://bitbucket.org/SysMedOs/lipidhunter .

A novel role for NUPR1 in the keratinocyte stress response to UV oxidized phospholipids

“…Precursor ion survey scans (scan time 0.5 s) were acquired for m/z 200–1200. Tandem mass spectra (ramp collision energy: LM CE start/end 10–40 and HM CE start/end 20–60) were recorded (scan time 0.25 s) for the 12 most intense signals in each survey scan using a dynamic exclusion for 30 s. The signal of Leu-encephalin (554.26151 - ) was acquired as lock mass [54] . Tandem mass spectra were manually analyzed.…”

LPPtiger software for lipidome-specific prediction and identification of oxidized phospholipids from LC-MS datasets

“…4 ): The Spectra Similarity Score uses the spectra matching approach, which compares measured spectra with in silico -generated LPPs spectra libraries using a reverse dot-product algorithm 17 . The Rank Score provides an intensity-dependent score based on a bottom-up strategy by implementing an intensity-ranked comparison of observed fragment ions with a predicted white list of oxidized fatty acyl residues and PL head group-specific signals 18 . The Rank Score helps to assign structure-related signals of low intensity with no bias for sn -1 or sn -2 FA residues assignment by default settings.…”

“…The previously identified native CM lipidome, characterized by 202 discrete lipid species from six PL classes 18 , was used by the LPPtiger for oxLipidome prediction. The application of an in silico oxidation algorithm resulted in 22,817 predicted LPPs (530 oxPA, 10,458 oxPC, 5,085 oxPE, 3,875 oxPG, 2,869 oxPS) summarized as .sdf libraries (available at https://bitbucket.org/SysMedOs/filesrepository ).…”

“…The Rank Score is a bottom-up strategy scoring system upgraded from LipidHunter software 18 in which discrete LPP species are identified by comparing each signal of an MS/MS peak list with a predefined list of possible FA and PL head group-specific fragment and neutral loss signals, generated from the in silico oxidation step, and can be customized based on the .xlsx template. A list of all possible LPP sn-1 and sn-2 residues generated by an in silico oxidation algorithm, together with a list of all PL head group-specific signals, are used as a white list of FA and PL class specific signals.…”

New tools and resources in metabolomics: 2016–2017

“…LipidHunter is a Python‐based package for high throughput identification of phospholipids acquired via LC‐MS and shotgun experimental workflows, where identification is based on MS/MS data analysis that follows defined fragmentation roles for each phospholipid class . Furthermore, the identified signals are tested for elemental composition and bulk identification via the LIPID MAPS search with outputs such as information‐rich tabular and graphical reports.…”