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. 2007 Oct;18(10):1848-58.
doi: 10.1016/j.jasms.2007.07.023. Epub 2007 Jul 29.

Algorithm for processing raw mass spectrometric data to identify and quantitate complex lipid molecular species in mixtures by data-dependent scanning and fragment ion database searching

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Algorithm for processing raw mass spectrometric data to identify and quantitate complex lipid molecular species in mixtures by data-dependent scanning and fragment ion database searching

Haowei Song et al. J Am Soc Mass Spectrom. 2007 Oct.

Abstract

We developed the Lipid Qualitative/Quantitative Analysis (LipidQA) software platform to identify and quantitate complex lipid molecular species in biological mixtures. LipidQA can process raw electronic data files from the TSQ-7000 triple stage quadrupole and LTQ linear ion trap mass spectrometers from Thermo-Finnigan and the Q-TOF hybrid quadrupole/time-of-flight instrument from Waters-Micromass and could readily be modified to accommodate data from others. The program processes multiple spectra in a few seconds and includes a deisotoping algorithm that increases the accuracy of structural identification and quantitation. Identification is achieved by comparing MS(2) spectra obtained in a data-dependent manner to a library of reference spectra of complex lipids that we have acquired or constructed from established fragmentation rules. The current form of the algorithm can process data acquired in negative or positive ion mode for glycerophospholipid species of all major head-group classes.

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Figures

Figure 1
Figure 1
Working flow chart of LipidQA program
Figure 2
Figure 2. MS/MS spectra of 16:0/18:1-GPC-Li+ (A) and 18:1/16:0-GPC-Li+ (B)
Data acquired with a Finnigan TSQ-7000 via product ion scan mode.
Figure 3
Figure 3. 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.
Figure 4
Figure 4. Schematic illustration of deisotoping algorithms
A, peaks are grouped into clusters based on their charge state z; B, theoretical isotope profile is calculated; and C, subtracted from the clustered spectrum to correct isotopic overlap
Figure 5
Figure 5. Distinguishing different lipid molecular species with overlapping isotopic envelopes in a bacterial extract
A, MS spectrum of a bacterial lipid extract in negative ion mode; B, MS/MS spectrum of peak at m/z 861.42. Data acquired with a Finnigan LTQ mass spectrometer.

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