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The quantification of free cholesterol (FC) and cholesteryl ester (CE) in mammalian samples is of great interest for basic science and clinical lipidomics. Here, we evaluated the feasibility of direct flow injection analysis (FIA) coupled to electrospray ionization high-resolution mass spectrometry (ESI-HRMS) to quantify FC and CE in lipid extracts from human serum, cultured cells, and mouse liver. Despite poor ionization efficiency of FC, the limit of quantitation was sufficient for precise and accurate quantification of FC by multiplexed HRMS (MSX) analysis without using a derivatization step. However, it was demonstrated that, upon full scan Fourier transform MS (FTMS) quantification, CE species show substantial differences in their analytical responses depending on number of double bonds, length of the acyl chain, infused lipid concentration, and other lipid components. A major determinant for these response differences is their susceptibility to in-source fragmentation. In particular, introduction of double bonds lowers the degree of in-source fragmentation. Therefore, CE species-specific response factors need to be applied for CE quantification by FTMS to achieve accurate concentrations. Method validation demonstrated that FIA-ESI-HRMS (MSX and FTMS) is applicable for quantification of FC and CE in samples used in basic science as well as clinical studies such as cultured cells, tissue homogenates, and serum.

New software tools for quantitative analysis of mass spectrometric lipidome data have been developed. The LIMSA tool finds and integrates peaks in a mass spectrum, matches the peaks with a user-supplied list of expected lipids, corrects for overlap in their isotopic patterns, and quantifies the identified lipid species according to internal standards. Three different algorithms for isotopic correction (deconvolution) were implemented and compared. LIMSA has a convenient user interface and can be applied on any type of MS spectrum. Typically, analysis of one spectrum takes only a few seconds. The SECD tool, designed for analysis of LC-MS data sets, provides an intuitive and informative display of MS chromatograms as two-dimensional "maps" for visual inspection of the data and allows the user to extract mass spectra, to be further analyzed with LIMSA, from arbitrary regions of these maps. More reliable analysis of complex lipidome data with improved signal-to-noise ratio is obtained when compared to standard time-range averaged spectra. The functionality of these tools is demonstrated by analysis of standard mixtures as well as complex biological samples. The tools described here make accurate, high-throughput analysis of extensive sample sets feasible and are made available to the scientific community free of charge.

Recent advances in mass spectrometry have revolutionized the analysis of lipid compositions of cells and other biomaterials by simplifying the analytical protocol dramatically and by increasing the sensitivity of detection by several orders of magnitude. However, the throughput of the published mass spectrometric methods is severely limited by data analysis, which requires extensive operator involvement. Consequently, we have developed an automated method that allows unattended identification and quantification of lipid molecular species of all the major lipid classes from a two-dimensional chromatographic/mass spectrometric data set. More than 100 polar lipid species could be automatically quantified from different biological samples with good accuracy and reproducibility. The response was linear over approximately 3 orders of magnitude with the equipment used, and approximately 35 samples could be analyzed in a day. This method makes high-throughput lipidomics feasible in biology, biotechnology, and medicine.