Journal of the American Society for Mass Spectrometry

, Volume 21, Issue 12, pp 1969–1978

Imaging mass spectrometry data reduction: Automated feature identification and extraction

  • Liam A. McDonnell
  • Alexandra van Remoortere
  • Nico de Velde
  • René J. M. van Zeijl
  • André M. Deeldera
Article

DOI: 10.1016/j.jasms.2010.08.008

Cite this article as:
McDonnell, L.A., van Remoortere, A., de Velde, N. et al. J Am Soc Mass Spectrom (2010) 21: 1969. doi:10.1016/j.jasms.2010.08.008
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Abstract

Imaging MS now enables the parallel analysis of hundreds of biomolecules, spanning multiple molecular classes, which allows tissues to be described by their molecular content and distribution. When combined with advanced data analysis routines, tissues can be analyzed and classified based solely on their molecular content. Such molecular histology techniques have been used to distinguish regions with differential molecular signatures that could not be distinguished using established histologic tools. However, its potential to provide an independent, complementary analysis of clinical tissues has been limited by the very large file sizes and large number of discrete variables associated with imaging MS experiments. Here we demonstrate data reduction tools, based on automated feature identification and extraction, for peptide, protein, and lipid imaging MS, using multiple imaging MS technologies, that reduce data loads and the number of variables by >100×, and that highlight highly-localized features that can be missed using standard data analysis strategies. It is then demonstrated how these capabilities enable multivariate analysis on large imaging MS datasets spanning multiple tissues.

Supplementary material

13361_2011_211201969_MOESM1_ESM.pdf (3.4 mb)
Supplementary material, approximately 3550 KB.

Copyright information

© American Society for Mass Spectrometry. Published by Elsevier Inc 2010

Authors and Affiliations

  • Liam A. McDonnell
    • 1
  • Alexandra van Remoortere
    • 1
  • Nico de Velde
    • 2
  • René J. M. van Zeijl
    • 1
  • André M. Deeldera
    • 1
  1. 1.Biomolecular Mass Spectrometry Unit, Department of ParasitologyLeiden University Medical CenterLeidenThe Netherlands
  2. 2.Hogeschool LeidenLeidenThe Netherlands

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