An overview of image registration for aligning mass spectrometry imaging with clinically relevant imaging modalities.

Mass spectrometry imaging (MSI) is used in many aspects of clinical research, including pharmacokinetics, toxicology, personalised medicine, and surgical decision-making. Maximising its potential requires the spatial integration of MSI images with imaging data from existing clinical imaging modalities, such as histology and MRI. To ensure that the information is properly integrated, all contributing images must be accurately aligned. This process is called image registration and is the focus of this review. In light of the ever-increasing spatial resolution of MSI instrumentation and a diversification of multi-modal MSI studies (e.g., spatial omics, 3D-MSI), the accuracy, versatility, and precision of image registration must increase accordingly. We review the application of image registration to align MSI data with different clinically relevant ex vivo and in vivo imaging techniques. Based on this, we identify steps in the current image registration processes where there is potential for improvement. Finally, we propose a roadmap for community efforts to address these challenges in order to increase registration quality and help MSI to fully exploit its multi-modal potential.

High mass resolution, spatial metabolite mapping enhances the current plant gene and pathway discovery toolbox.

Understanding when and where metabolites accumulate provides important cues to the gene function. Mass spectrometry imaging (MSI) enables in situ temporal and spatial measurement of a large assortment of metabolites, providing mapping information regarding their cellular distribution. To describe the current state and technical advances using MSI in plant sciences, we employed MSI to demonstrate its significant contribution to the study of plant specialised metabolism. We show that coupling MSI with: (1) RNA interference (RNAi), (2) virus induced gene silencing (VIGS), (3) agroinfiltration or (4) samples derived from plant natural variation provides great opportunities to understand the accurate gene-metabolite relationship and discover novel gene-associated metabolites. This was exemplified in three plant species (i.e. tomato, tobacco and wheat) by mapping the distribution of metabolites possessing a range of polarities. In particular, we demonstrated that MSI is able to spatially map an entire metabolic pathway, including intermediates and final products, in the intricate biosynthetic route to tomato fruit steroidal glycoalkaloids. We therefore envisage MSI as a key component of the metabolome analysis arsenal employed in plant gene discovery strategies.