Spatial probabilistic mapping of metabolite ensembles in mass spectrometry imaging

Mass spectrometry imaging vows to enable simultaneous spatially resolved investigation of hundreds of metabolites in tissues, but it primarily relies on traditional ion images for non-data-driven metabolite visualization and analysis. The rendering and interpretation of ion images neither considers nonlinearities in the resolving power of mass spectrometers nor does it yet evaluate the statistical significance of differential spatial metabolite abundance. Here, we outline the computational framework moleculaR (https://github.com/CeMOS-Mannheim/moleculaR) that is expected to improve signal reliability by data-dependent Gaussian-weighting of ion intensities and that introduces probabilistic molecular mapping of statistically significant nonrandom patterns of relative spatial abundance of metabolites-of-interest in tissue. moleculaR also enables cross-tissue statistical comparisons and collective molecular projections of entire biomolecular ensembles followed by their spatial statistical significance evaluation on a single tissue plane. It thereby fosters the spatially resolved investigation of ion milieus, lipid remodeling pathways, or complex scores like the adenylate energy charge within the same image.

Automated summary

Mass spectrometry imaging utilizes traditional ion images for metabolite visualization and analysis, but lacks consideration for nonlinearities and statistical significance. The computational framework moleculaR aims to improve signal reliability by Gaussian-weighting ion intensities and introduces probabilistic molecular mapping for statistically significant spatial abundance analysis. It allows cross-tissue comparisons and spatial statistical significance evaluation, facilitating the investigation of ion milieus, lipid remodeling pathways, and complex scores within the same image.