Purpose: For several decades, conventional histological staining and immunohistochemistry (IHC) have been the main tools to visualize and understand tissue morphology and structure. IHC visualizes the spatial distribution of individual protein species directly in tissue. However, a specific antibody is required for each protein, and multiplexing capabilities are extremely limited, rarely visualizing more than two proteins simultaneously. With the recent emergence of matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI-imaging), it is becoming possible to study more complex proteomic patterns directly in tissue. However, the analysis and interpretation of large and complex MALDI-imaging data requires advanced computational methods. In this paper, we show how the recently introduced method of spatial segmentation can be applied to analysis and interpretation of a larynx carcinoma section and compare the spatial segmentation with the histological annotation of the same tissue section.
Methods: Matrix-assisted laser desorption/ionization imaging is a label-free spatially resolved analytical technique, which allows detection and visualization of hundreds of proteins at once. Spatial segmentation of the MALDI-imaging data by clustering of spectra by their similarity was performed, automatically generating a spatial segmentation map of the tissue section, where regions of similar proteomic patterns were highlighted. The tissue was stained with the hematoxylin and eosin (H&E), histopathologically analyzed and annotated. The segmentation map was interpreted after its overlay with the H&E microscopy image.
Results: The automatically generated segmentation map exhibits high correspondence to the detailed histological annotation of the larynx carcinoma tissue section. By superimposing, the segmentation map based on the proteomic profiles with H&E-stained microscopic images, we demonstrate precise localization of complex and histopathologically relevant tissue features in an automated way.
Conclusions: The combination of MALDI-imaging and automatic spatial segmentation is a useful approach in analyzing carcinoma tissue and provides a deeper insight into the functional proteomic organization of the respective tissue.