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Standardized processing of MALDI imaging raw data for enhancement of weak analyte signals in mouse models of gastric cancer and Alzheimer’s disease

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Abstract

Conventional mass spectrometry image preprocessing methods used for denoising, such as the Savitzky-Golay smoothing or discrete wavelet transformation, typically do not only remove noise but also weak signals. Recently, memory-efficient principal component analysis (PCA) in conjunction with random projections (RP) has been proposed for reversible compression and analysis of large mass spectrometry imaging datasets. It considers single-pixel spectra in their local context and consequently offers the prospect of using information from the spectra of adjacent pixels for denoising or signal enhancement. However, little systematic analysis of key RP-PCA parameters has been reported so far, and the utility and validity of this method for context-dependent enhancement of known medically or pharmacologically relevant weak analyte signals in linear-mode matrix-assisted laser desorption/ionization (MALDI) mass spectra has not been explored yet. Here, we investigate MALDI imaging datasets from mouse models of Alzheimer’s disease and gastric cancer to systematically assess the importance of selecting the right number of random projections k and of principal components (PCs) L for reconstructing reproducibly denoised images after compression. We provide detailed quantitative data for comparison of RP-PCA-denoising with the Savitzky-Golay and wavelet-based denoising in these mouse models as a resource for the mass spectrometry imaging community. Most importantly, we demonstrate that RP-PCA preprocessing can enhance signals of low-intensity amyloid-β peptide isoforms such as Aβ1-26 even in sparsely distributed Alzheimer’s β-amyloid plaques and that it enables enhanced imaging of multiply acetylated histone H4 isoforms in response to pharmacological histone deacetylase inhibition in vivo. We conclude that RP-PCA denoising may be a useful preprocessing step in biomarker discovery workflows.

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Acknowledgments

The authors thank Viola Nordström and Hermann-Josef Gröne for brains from mouse models of Alzheimer’s disease and Bogdan Munteanu for helpful discussions. This work was supported by the following grants: Baden-Württemberg Ministry of Science and Culture INST 874/2-1 LAGG (to C.H.), “ZAFH ABIMAS” by ZO IV/Landesstiftung Baden-Württemberg and the European fund for regional development (EFRE; to C.H. and B.W.), and by EU CMST COST Action TD0905: Epigenetics: Bench to Bedside (to C.H.).

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Authors and Affiliations

  1. Center for Applied Research in Biomedical Mass Spectrometry (ABIMAS), Mannheim University of Applied Sciences, Paul-Wittsack-Str. 10, 68163, Mannheim, Germany

    Matthias Schwartz, Björn Meyer, Bernhard Wirnitzer & Carsten Hopf

  2. Instrumental Analytics and Bioanalytics, Department of Biotechnology, Mannheim University of Applied Sciences, Paul-Wittsack-Str. 10, 68163, Mannheim, Germany

    Matthias Schwartz, Björn Meyer & Carsten Hopf

  3. Institute of Digital Signal Processing, Department of Information Technology, Mannheim University of Applied Sciences, Paul-Wittsack-Str. 10, 68163, Mannheim, Germany

    Matthias Schwartz & Bernhard Wirnitzer

  4. Institute of Medical Technology of University of Heidelberg and Mannheim University of Applied Sciences, Mannheim University of Applied Sciences, Paul-Wittsack-Str. 10, 68163, Mannheim, Germany

    Matthias Schwartz, Björn Meyer & Carsten Hopf

Authors
  1. Matthias Schwartz
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  4. Carsten Hopf
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Correspondence to Bernhard Wirnitzer or Carsten Hopf.

Additional information

Published in the topical collection Mass Spectrometry Imaging with guest editors Andreas Römpp and Uwe Karst.

Matthias Schwartz and Björn Meyer contributed equally to this publication.

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Schwartz, M., Meyer, B., Wirnitzer, B. et al. Standardized processing of MALDI imaging raw data for enhancement of weak analyte signals in mouse models of gastric cancer and Alzheimer’s disease. Anal Bioanal Chem 407, 2255–2264 (2015). https://doi.org/10.1007/s00216-014-8356-9

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  • DOI: https://doi.org/10.1007/s00216-014-8356-9

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