Summary
Mass spectrometry (MS), particularly electrospray-MS, is the key tool in modern lipidomics. However, as even a modest scale experiment produces a great amount of data, data processing often becomes limiting. Notably, the software provided with MS instruments are not well suited for quantitative analysis of lipidomes because of the great variety of species present and complexities in response calibration. Here we describe the use of two recently introduced software tools: lipid mass spectrum analysis (LIMSA) and spectrum extraction from chromatographic data (SECD), which significantly increase the speed and reliability of mass spectrometric analysis of complex lipidomes. LIMSA is a Microsoft Excel add-on that (1) finds and integrates the peaks in an imported spectrum, (2) identifies the peaks, (3) corrects the peak areas for overlap by isotopic peaks of other species and (4) quantifies the identified species using included internal standards. LIMSA is instrument-independent because it processes text-format MS spectra. Typically, the analysis of one spectrum takes only a few seconds.
The SECD software allows one to display MS chromatograms as two-dimensional maps, which is useful for visual inspection of the data. More importantly, however, SECD allows one to extract mass spectra from user-defined regions of the map for further analysis with, e.g., LIMSA. The use of select regions rather than simple time-range averaging significantly improves the signal-to-noise ratio as signals outside the region of interest are more efficiently excluded. LIMSA and SECD have proven to be robust and convenient tools and are available free of charge from the authors.
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Acknowledgments
Authors thank Dr. Andreas Uphoff for his invaluable contribution in developing SECD and other MS tools. This research was supported by Finnish Academy and University of Helsinki Funds to P.S.
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Haimi, P., Chaithanya, K., Kainu, V., Hermansson, M., Somerharju, P. (2009). Instrument-Independent Software Tools for the Analysis of MS–MS and LC–MS Lipidomics Data. In: Armstrong, D. (eds) Lipidomics. Methods in Molecular Biology™, vol 580. Humana Press. https://doi.org/10.1007/978-1-60761-325-1_16
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DOI: https://doi.org/10.1007/978-1-60761-325-1_16
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