Spatial segmentation of imaging mass spectrometry data with edge-preserving image denoising and clustering

J Proteome Res. 2010 Dec 3;9(12):6535-46. doi: 10.1021/pr100734z. Epub 2010 Nov 15.


In recent years, matrix-assisted laser desorption/ionization (MALDI)-imaging mass spectrometry has become a mature technology, allowing for reproducible high-resolution measurements to localize proteins and smaller molecules. However, despite this impressive technological advance, only a few papers have been published concerned with computational methods for MALDI-imaging data. We address this issue proposing a new procedure for spatial segmentation of MALDI-imaging data sets. This procedure clusters all spectra into different groups based on their similarity. This partition is represented by a segmentation map, which helps to understand the spatial structure of the sample. The core of our segmentation procedure is the edge-preserving denoising of images corresponding to specific masses that reduces pixel-to-pixel variability and improves the segmentation map significantly. Moreover, before applying denoising, we reduce the data set selecting peaks appearing in at least 1% of spectra. High dimensional discriminant clustering completes the procedure. We analyzed two data sets using the proposed pipeline. First, for a rat brain coronal section the calculated segmentation maps highlight the anatomical and functional structure of the brain. Second, a section of a neuroendocrine tumor invading the small intestine was interpreted where the tumor area was discriminated and functionally similar regions were indicated.

MeSH terms

  • Animals
  • Brain / anatomy & histology*
  • Computational Biology / methods*
  • Humans
  • Intestine, Small / pathology
  • Models, Anatomic*
  • Neuroendocrine Tumors / pathology
  • Rats
  • Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization / methods*