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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


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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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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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).

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