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Review
. 2009 Sep 15;877(26):2847-54.
doi: 10.1016/j.jchromb.2008.12.043. Epub 2008 Dec 25.

Algorithms for automatic processing of data from mass spectrometric analyses of lipids

Affiliations
Review

Algorithms for automatic processing of data from mass spectrometric analyses of lipids

Haowei Song et al. J Chromatogr B Analyt Technol Biomed Life Sci. .

Erratum in

  • J Chromatogr B Analyt Technol Biomed Life Sci. 2011 Jan 1;879(1):129

Abstract

Lipidomics comprises large-scale studies of the structures, quantities, and functions of lipid molecular species. Recently developed mass spectrometric methods for lipid analyses, especially electrospray ionization (ESI) tandem mass spectrometry, permit identification and quantitation of an enormous variety of distinct lipid molecular species from small amounts of biological samples but generate a huge amount of experimental data within a brief interval. Processing such data sets so that comprehensible information is derived from them requires bioinformatics tools, and algorithms developed for proteomics and genomics have provided some strategies that can be directly adapted to lipidomics. The structural diversity and complexity of lipids, however, also requires the development and application of new algorithms and software tools that are specifically directed at processing data from lipid analyses. Several such tools are reviewed here, including LipidQA. This program employs searches of a fragment ion database constructed from acquired and theoretical spectra of a wide variety of lipid molecular species, and raw mass spectrometric data can be processed by the program to achieve identification and quantification of many distinct lipids in mixtures. Other approaches that are reviewed here include LIMSA (Lipid Mass Spectrum Analysis), SECD (Spectrum Extraction from Chromatographic Data), MPIS (Multiple Precursor Ion Scanning), FIDS (Fragment Ion Database Searching), LipidInspector, Lipid Profiler, FAAT (Fatty Acid Analysis Tool), and LIPID Arrays. Internet resources for lipid analyses are also summarized.

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Figures

Figure 1
Figure 1
General procedure for data processing
Figure 2
Figure 2
MS spectrum of 18:2/20:4 GPE and 18:0/20:4 GPE. With a symmetric window of 100 Th, the calculated S/N value for the low intensity peak at m/z 762 is 5.15 if the spectrum is divided into sub-intervals of 5 Th. In contrast, if the selected sub-interval is 20 Th, the calculated S/N value is 2.81, which would fall below a specified threshold value of 3.0.
Figure 3
Figure 3
Selection of an inappropriate smoothing width causes a reduction in peak amplitude and an increase in bandwidth.
Figure 4
Figure 4
MS/MS spectra of 16:0/18:1-GPC-Li+ (a) and 18:1/16:0-GPC-Li+ (b) obtained with a Finnigan TSQ-7000 tandem quadrupole instrument via product ion scan mode [9].
Figure 5
Figure 5
MS/MS spectrum obtained from CAD of the ion of m/z 865.33 in a lipid extract from mouse peritoneal leukocytes. (a) Complete spectrum. (b) Expanded spectrum from m/z 80 to 260. Data were acquired with a Micromass Q-TOF Micro mass spectrometer in negative ion mode [9].
Figure 6
Figure 6
Calibration method used in the LipidQA program involves a combination of corrections from both internal and external standards.

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