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Multivariate analyses for biomarkers hunting and validation through on-tissue bottom-up or in-source decay in MALDI-MSI: application to prostate cancer

Analytical and Bioanalytical Chemistry volume 401, pages 149–165 (2011)Cite this article

Abstract

The large amount of data generated using matrix-assisted laser desorption/ionization mass spectrometric imaging (MALDI-MSI) poses a challenge for data analysis. In fact, generally about 1.108–1.109 values (m/z, I) are stored after a single MALDI-MSI experiment. This imposes processing techniques using dedicated informatics tools to be used since manual data interpretation is excluded. This work proposes and summarizes an approach that utilizes a multivariable analysis of MSI data. The multivariate analysis, such as principal component analysis–symbolic discriminant analysis, can remove and highlight specific m/z from the spectra in a specific region of interest. This approach facilitates data processing and provides better reproducibility, and thus, broadband acquisition for MALDI-MSI should be considered an effective tool to highlight biomarkers of interest. Additionally, we demonstrate the importance of the hierarchical classification of biomarkers by analyzing studies of clusters obtained either from digested or undigested tissues and using bottom-up and in-source decay strategies for in-tissue protein identification. This provides the possibility for the rapid identification of specific markers from different histological samples and their direct localization in tissues. We present an example from a prostate cancer study using formalin-fixed paraffin-embedded tissue.

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Acknowledgments

This research was supported by grants from Centre National de la Recherche Scientifique (CNRS), Ministère de L’Education Nationale, de L’Enseignement Supérieur et de la Recherche, Agence Nationale de la Recherche (ANR PCV to IF), Institut du Cancer (INCA to IF), Région Nord-Pas de Calais (PhD financing to DB and RL), the Canadian Institutes of Health Research (CIHR to RD) and the Ministère du Développement Économique, de l’Innovation et de l’Exportation (MDEIE to RD) du Québec and the Fonds de recherche en santé du Québec (FRSQ to RD). RD is a member of the Centre de Recherche Clinique Étienne-Le Bel (Sherbrooke, QC, Canada).

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Authors and Affiliations

  1. MALDI Imaging team, Laboratoire de Spectrométrie de Masse Biologique Fondamentale et Appliquée (FABMS), EA 4550, Université Nord de France, Université Lille 1, 59650, Villeneuve d’Ascq, France

    David Bonnel, Rémi Longuespee, Julien Franck, Michel Salzet & Isabelle Fournier

  2. Institut de pharmacologie de Sherbrooke, Faculté de médecine et des Sciences de la santé, Université de Sherbrooke, Sherbrooke, QC, J1H 5N4, Canada

    David Bonnel, Rémi Longuespee & Robert Day

  3. INSERM, U-1003, Equipe labellisée par la Ligue Nationale contre le cancer, Université Nord de France, Université Lille 1, 59650, Villeneuve d’Ascq, France

    Morad Roudbaraki

  4. Faculté Libre de Médecine, Laboratoire d’Anatomie et de Cytologie Pathologique du groupement hospitalier de l’Institut Catholique de Lille, 59000, Lille, France

    Pierre Gosset

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  1. David Bonnel
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  2. Rémi Longuespee
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  3. Julien Franck
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  4. Morad Roudbaraki
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  5. Pierre Gosset
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  6. Robert Day
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  7. Michel Salzet
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  8. Isabelle Fournier
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Correspondence to Michel Salzet.

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Published in the special issue MALDI Imaging with Guest Editor Olivier Laprévote.

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Bonnel, D., Longuespee, R., Franck, J. et al. Multivariate analyses for biomarkers hunting and validation through on-tissue bottom-up or in-source decay in MALDI-MSI: application to prostate cancer. Anal Bioanal Chem 401, 149–165 (2011). https://doi.org/10.1007/s00216-011-5020-5

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  • DOI: https://doi.org/10.1007/s00216-011-5020-5

Keywords

  • MALDI mass spectrometry imaging
  • Principal component analysis
  • Symbolic discriminant analysis
  • Hierarchical clustering
  • Bottom-up
  • In-source decay
  • Biomarkers
  • Prostate cancer