Lipid data analyzer: Unattended identification and quantitation of lipids in LC-MS data

Abstract

Motivation: The accurate measurement of the lipidome permits insights into physiological and pathological processes. Of the present high-throughput technologies, LC-MS especially bears potential of monitoring quantitative changes in hundreds of lipids simultaneously. In order to extract valuable information from huge amount of mass spectrometry data, the aid of automated, reliable, highly sensitive and specific analysis algorithms is indispensable. Results: We present here a novel approach for the quantitation of lipids in LC-MS data. The new algorithm obtains its analytical power by two major innovations: (i) a 3D algorithm that confines the peak borders in m/z and time direction and (ii) the use of the theoretical isotopic distribution of an analyte as selection/exclusion criterion. The algorithm is integrated in the Lipid Data Analyzer (LDA) application which additionally provides standardization, a statistics module for results analysis, a batch mode for unattended analysis of several runs and a 3D viewer for the manual verification. The statistics module offers sample grouping, tests between sample groups and export functionalities, where the results are visualized by heat maps and bar charts. The presented algorithm has been applied to data from a controlled experiment and to biological data, containing analytes distributed over an intensity range of 106. Our approach shows improved sensitivity and an extremely high positive predictive value compared with existing methods. Consequently, the novel algorithm, integrated in a user-friendly application, is a valuable improvement in the high-throughput analysis of the lipidome. © The Author 2010. Published by Oxford University Press. All rights reserved.