Spatial Probabilistic Mapping of Metabolite Ensembles in Mass Spectrometry Imaging

Denis Abu Sammour; James Lucas Cairns; Tobias Boskamp; Tobias Keßler; Carina Ramallo Guevara; Verena Panitz; Ahmed Sadik; Jonas Cordes; Christian Marsching; Mirco Friedrich; Michael Platten; Ivo Wolf A; Andreas von Deimling; Christiane A. Opitz; Wolfgang Wick; Carsten Hopf

doi:10.1101/2021.10.27.466114

BioinformaticsbioRxivOct 27, 2021

Preprint

Spatial Probabilistic Mapping of Metabolite Ensembles in Mass Spectrometry Imaging

Denis Abu Sammour iD, James Lucas Cairns iD, Tobias Boskamp iD, Tobias Keßler iD, Carina Ramallo Guevara iD, Verena Panitz iD, Ahmed Sadik iD, Jonas Cordes iD, Christian Marsching iD, Mirco Friedrich iD, Michael Platten iD, Ivo Wolf A iD, Andreas von Deimling iD, Christiane A. Opitz iD, Wolfgang Wick iD, Carsten Hopf iD

Abstract

Mass spectrometry imaging (MSI) vows to enable simultaneous spatially-resolved investigation of hundreds of metabolites in tissue sections, but it still relies on poorly defined ion images for data interpretation. Here, we outline moleculaR, a computational framework (https://github.com/CeMOS-Mannheim/moleculaR) that introduces probabilistic mapping and point-for-point statistical testing of metabolites in tissue. It enables collective molecular projections and consequently spatially-resolved investigation of ion milieus, lipid pathways or user-defined biomolecular ensembles within the same image.

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