MALDI-MSI Towards Multimodal Imaging: Challenges and Perspectives

Abstract

Multimodal imaging is a powerful strategy for combining information from multiple images. It involves several fields in the acquisition, processing and interpretation of images. As multimodal imaging is a vast subject area with various combinations of imaging techniques, it has been extensively reviewed. Here we focus on Matrix-assisted Laser Desorption Ionization Mass Spectrometry Imaging (MALDI-MSI) coupling other imaging modalities in multimodal approaches. While MALDI-MS images convey a substantial amount of chemical information, they are not readily informative about the morphological nature of the tissue. By providing a supplementary modality, MALDI-MS images can be more informative and better reflect the nature of the tissue. In this mini review, we emphasize the analytical and computational strategies to address multimodal MALDI-MSI.

Keywords: MALDI mass spectrometry imaging; analytical strategy; biological applications; computational strategy; multimodal imaging.

FIGURE 1

Typical processing and analysis workflow for multimodal MALDI-MSI. Top-row: the MALDI image (on the left), and the complementary images (on the right) are processed so as to have comparable shapes, through various specific processing steps, including segmentation. Middle-row: the images are spatially aligned through registration. Bottom-row: once the images are registered, they can be visualized on-top of each other to reveal similar spatial distributions (on the right). Patterns can be objectified by a joint statistical analysis to, e.g., find spatial correlations, by investigating spatial clusters (class discovery), produce an enriched dataset from both modalities (class prediction) or find region-specific ions in two different ROIs (class comparison).