Spatial probabilistic mapping of metabolite ensembles in mass spectrometry imaging

Denis Abu Sammour, James L. Cairns, Tobias Boskamp, Christian Marsching, Tobias Kessler, Carina Ramallo Guevara, Verena Panitz, Ahmed Sadik, Jonas Cordes, Stefan Schmidt, Shad A. Mohammed, Miriam F. Rittel, Mirco Friedrich, Michael Platten, Ivo Wolf, Andreas Deimling, Christiane A. Opitz, Wolfgang Wick and Carsten Hopf ()
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Denis Abu Sammour: Mannheim University of Applied Sciences
James L. Cairns: Mannheim University of Applied Sciences
Tobias Boskamp: Bruker Daltonics GmbH & Co. KG
Christian Marsching: Mannheim University of Applied Sciences
Tobias Kessler: German Cancer Consortium, German Cancer Research Center
Carina Ramallo Guevara: Mannheim University of Applied Sciences
Verena Panitz: German Cancer Research Center (DKFZ)
Ahmed Sadik: German Cancer Research Center (DKFZ)
Jonas Cordes: Mannheim University of Applied Sciences
Stefan Schmidt: Mannheim University of Applied Sciences
Shad A. Mohammed: Mannheim University of Applied Sciences
Miriam F. Rittel: Mannheim University of Applied Sciences
Mirco Friedrich: Heidelberg University
Michael Platten: Heidelberg University
Ivo Wolf: Mannheim University of Applied Sciences
Andreas Deimling: University Hospital Heidelberg
Christiane A. Opitz: German Cancer Research Center (DKFZ)
Wolfgang Wick: German Cancer Consortium, German Cancer Research Center
Carsten Hopf: Mannheim University of Applied Sciences

Nature Communications, 2023, vol. 14, issue 1, 1-15

Abstract: Abstract 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.

Date: 2023
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DOI: 10.1038/s41467-023-37394-z

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