Untargeted Pixel-by-Pixel Imaging of Metabolite Ratio Pairs as a Novel Tool for Biomedical Discovery in Mass Spectrometry Imaging

Abstract

Mass spectrometry imaging (MSI) is a powerful technology that can be employed to define the spatial distribution and relative abundance of structurally identified and yet-undefined metabolites across a tissue cryosection. While numerous software packages enable pixel-by-pixel imaging of individual metabolite distributions, the research community lacks a discovery tool that provides spatial imaging of all metabolite abundance ratio pairs. Importantly, the recognition of correlating metabolite pairs offers a strategy to discover unanticipated molecules that contribute to or regulate a shared metabolic pathway, uncover hidden metabolic heterogeneity across cells and tissue subregions, and offers a single timepoint indicator of flux through a particular metabolic pathway of interest. Here, we describe the development and implementation of an untargeted R package workflow for pixel-by-pixel imaging of ratios for all metabolites detected in an MSI experiment. Considering untargeted MSI studies of murine brain and embryogenesis, we demonstrate that ratio imaging offers the opportunity to minimize systematic data variation introduced during sample handling or due to instrument drift, markedly enhances spatial image resolution, and can serve to reveal previously unrecognized tissue regions that are metabotype-distinct. Furthermore, ratio imaging facilitates the discovery of novel regional biomarkers, and can provide anatomical information regarding the spatial distribution of metabolite-linked biochemical pathways. The algorithm described herein is generic and can be applied to any MSI dataset containing spatial information for metabolites, peptides or proteins. Importantly, this software package offers a powerful add-on tool that can significantly enhance knowledge obtained from currently employed spatial metabolite profiling technologies.