LipidMiner: a software for automated identification and quantification of lipids from multiple liquid chromatography/mass spectrometry data files.

Comprehensive understanding of the roles and functions of lipids in cellular physiology and pathology requires unambiguous identification and accurate quantification of individual lipid molecular species. However, the enormous structural diversity of lipids (e.g., >37,000 lipid molecules cataloged in the LIPID MAPS database) presents a significant challenge in the high-throughput analysis of lipidomics data. Traditionally, triple quadrupole or quadrupole time-of-flight (QTOF) mass spectrometers are utilized for lipids analysis using either precursor ion scanning (PIS) or neutral loss scanning (NLS) methods, which exploit lipid class-characteristic ions or neutral loss fragments generated in collision-induced dissociation of gas-phase lipid molecular ions. These methods are readily carried out in direct infusion mode (also known as shotgun lipidomics) without on-line chromatographic separation of lipids, and software tools such as LIMSA, Lipid Profiler, AMDMS-SL and MS-LAMP were written to specifically analyze these types of data. Recently, data-dependent acquisition of full scan tandem mass spectrometric (MS/MS) spectra from all detectable precursor ions has gained increasing use in lipid profiling studies. This method is mainly implemented on ion trap, high-resolution QTOF or hybrid Orbitrap instruments. The spectra obtained can be considered as the emulation of simultaneous acquisition of an unlimited number of product and neutral loss scans in a single analysis. Accordingly, tools for analyzing this type of data have also been developed, such as LipidQA, LipidInspector, and LipidXplorer, mainly for direct infusion based shotgun lipidomics workflow. (Common features of previous lipidomics software tools are summarized in Herzog et al.) More recently, an in silico tandem mass spectral database, LipidBlast, was developed to match with the experimentally acquired MS/MS spectrum, which provided a new avenue for lipids identification. We and others have found that coupling liquid chromatographic (LC) separation with data-dependent MS/MS adds additional confidence in lipid isobar/isomer identification, particularly for quantifying lipid molecular species from complex mixtures. This is due to the fact that even when signals of different species overlap in the mass dimension, they usually can be separated in the retention time domain to a degree that allows reliable assignment and quantification. Although lipID was developed to handle LC/MS-based lipidomics data analysis, it has limited capability in processing LC/MS/MS data. To more effectively analyze this type of multidimensional data (intensity, m/z from MS, m/z from MS/MS and chromatographic retention time) in LC/MS/MS-based