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Statistical Procedure for IMS Data Analysis

  • Yuki Sugiura
  • Mitsutoshi Setou

Abstract

In MALDI-IMS of tissue samples, since the tissue contains an enormous variety of biomolecules, a complex mass spectrum with hundreds of to a thousand peaks can be obtained from a single data point. Furthermore, several thousands of spectra with spatial data are obtained at one IMS experiment. Because of the complexity and enormousness of the IMS dataset, manual processing of the dataset to obtain significant information (e.g., identification of disease-specific mass signature) is not a realistic procedure. In this regard, today, multivariate analysis becomes a powerful tool in IMS data analysis. In this chapter, we describe an unsupervised multivariate data analysis technique that enables us to sort the data sets without any reference information. Particularly, two major methods that are related to IMS, namely, hierarchical clustering and principal component analysis (PCA), are described in detail with examples. Finally a basic procedure for PCA with familiar software (such as Microsoft Excel) is introduced.

Keywords

Principal Component Analysis Component Score Independent Component Analysis Mass Peak Imaging Mass Spectrometry 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Altelaar AF, Luxembourg SL, McDonnell LA, et al. (2007) Imaging mass spectrometry at cellular length scales. Nat Protocols 2:1185–1196CrossRefGoogle Scholar
  2. 2.
    McCombie G, Staab D, Stoeckli M, et al. (2005) Spatial and spectral correlations in MALDI mass spectrometry images by clustering and multivariate analysis. Anal Chem 77:6118–6124PubMedCrossRefGoogle Scholar
  3. 3.
    Plas RV, Moor BD, Waelkens E (2007) Imaging mass spectrometry-based exploration of biochemical tissue composition using peak intensity weighted PCA. In: 2007 IEEE/NIH Life Science Systems and Applications Workshop, pp 209–212Google Scholar
  4. 4.
    Yanagisawa K, Shyr, Y, Xu, B J, et al. (2003) Proteomic patterns of tumour subsets in non-small-cell lung cancer. Lancet 362:433–439PubMedCrossRefGoogle Scholar
  5. 5.
    Mantini D, Petrucci F, Del Boccio P, et al. (2008) Independent component analysis for the extraction of reliable protein signal profiles from MALDI-TOF mass spectra. Bioinformatics 24:63–70PubMedCrossRefGoogle Scholar
  6. 6.
    Prideaux B, Atkinson SJ, Carolan VA, et al. (2007) Sample preparation and data interpretation procedures for the examination of xenobiotic compounds in skin by indirect imaging MALDI-MS. Int J Mass Spectrom 260:243–251CrossRefGoogle Scholar
  7. 7.
    Yao I, Sugiura Y, Matsumoto M, et al. (2008) In situ proteomics with imaging mass spectrometry and principal component analysis in the Scrapper-knockout mouse brain. Proteomics 8:3692–3701PubMedCrossRefGoogle Scholar
  8. 8.
    Hanselmann M, Kirchner M, Renard B Y, et al. (2008) Concise representation of mass spec-trometry images by probabilistic latent semantic analysis. Anal Chem 80(24):9649–9658PubMedCrossRefGoogle Scholar
  9. 9.
    Fowlkes EB, Mallows CL (1983) A method for comparing two hierarchical clusterings. J Am Stat Assoc 78:553–584CrossRefGoogle Scholar
  10. 10.
    Deininger SO, Schürenberg M, Suckau D, et al. (2007) Class imaging: classification of breast cancer sections by MALDI tissue imaging. Poster presentation in HUPOGoogle Scholar
  11. 11.
    Denkert C, Budczies J, Kind T, et al. (2006) Mass spectrometry-based metabolic profiling reveals different metabolite patterns in invasive ovarian carcinomas and ovarian borderline tumors. Cancer Res 66:10795–10804PubMedCrossRefGoogle Scholar
  12. 12.
    Lapolla A, Ragazzi E, Andretta B, et al. (2007) Multivariate analysis of matrix-assisted laser desorption/ionization mass spectrometric data related to glycoxidation products of human globins in nephropathic patients. J Am Soc Mass Spectrom 18:1018–1023PubMedCrossRefGoogle Scholar
  13. 13.
    Wong WHJ, Cagney G, Cartwright HM (2005) SpecAlign: processing and alignment of mass spectra datasets. Bioinformatics 21:2088–2090PubMedCrossRefGoogle Scholar
  14. 14.
    Yao I, Takagi H, Ageta H, et al. (2007) SCRAPPER-dependent ubiquitination of active zone protein RIM1 regulates synaptic vesicle release. Cell 130:943–957PubMedCrossRefGoogle Scholar
  15. 15.
    He L, Lu X Y, Jolly AF, et al. (2003) Spongiform degeneration in mahoganoid mutant mice. Science 299:710–712PubMedCrossRefGoogle Scholar
  16. 16.
    Sugiura Y, Shimma S, Setou M (2006) Thin sectioning improves the peak intensity and signal-to-noise ratio in direct tissue mass spectrometry. J Mass Spectrom Soc Jpn 54:4CrossRefGoogle Scholar
  17. 17.
    Schwartz SA, Reyzer ML, Caprioli RM (2003) Direct tissue analysis using matrix-assisted laser desorption/ionization mass spectrometry: practical aspects of sample preparation. J Mass Spectrom 38:699–708PubMedCrossRefGoogle Scholar
  18. 18.
    Norris JL, Cornett DS, Mobley JA, et al. (2006) Processing MALDI mass spectra to improve mass spectral direct tissue analysis. Int J Mass Spectrom 260:212–221Google Scholar
  19. 19.
    Sugiura Y, Konishi Y, Zaima N, et al. (2009) Visualization of the cell-selective distribution of PUFA-containing phosphatidylcholines in mouse brain by imaging mass spectrometry. J Lipid Res (in press)Google Scholar
  20. 20.
    McDonnell LA, Piersma SR, Maarten Altelaar AF, et al. (2005) Subcellular imaging mass spectrometry of brain tissue. J Mass Spectrom 40:160–168PubMedCrossRefGoogle Scholar
  21. 21.
    Andersson M, Groseclose MR, Deutch AY, et al. (2008) Imaging mass spectrometry of proteins and peptides: 3D volume reconstruction. Nat Methods 5:101–108PubMedCrossRefGoogle Scholar
  22. 22.
    McLean JA, Ridenour WB, Caprioli RM (2007) Profiling and imaging of tissues by imaging ion mobility-mass spectrometry. J Mass Spectrom 42:1099–1105PubMedCrossRefGoogle Scholar

Copyright information

© Springer 2010

Authors and Affiliations

  1. 1.Department of Bioscience and BiotechnologyTokyo Institute of TechnologyMidori-ku, YokohamaJapan
  2. 2.Department of Molecular AnatomyHamamatsu University School of MedicineHigashi-ku, HamamatsuJapan

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