Spatial Probabilistic Mapping of Metabolite Ensembles in Mass Spectrometry Imaging

Abu Sammour, D.; Cairns, J. L.; Boskamp, T.; Guevara, C. R.; Panitz, V.; Sadik, A.; Cordes, J.; Marsching, C.; Friedrich, M.; Platten, M.; Wolf, I.; von Deimling, A.; Opitz, C. A.; Wick, W.; Hopf, C.

doi:10.1101/2021.10.27.466114

bioRxiv · 10.1101/2021.10.27.466114

Spatial Probabilistic Mapping of Metabolite Ensembles in Mass Spectrometry Imaging

Abu Sammour, D.·Cairns, J. L.·Boskamp, T.·Guevara, C. R.·Panitz, V.·Sadik, A.·Cordes, J.·Marsching, C.·Friedrich, M.·Platten, M.·Wolf, I.·von Deimling, A.·Opitz, C. A.·Wick, W.·Hopf, C.

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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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Abu Sammour, D., Cairns, J. L., Boskamp, T., Guevara, C. R., Panitz, V., Sadik, A., Cordes, J., Marsching, C., Friedrich, M., Platten, M., Wolf, I., von Deimling, A., Opitz, C. A., Wick, W., Hopf, C.. 2021-10-28. Spatial Probabilistic Mapping of Metabolite Ensembles in Mass Spectrometry Imaging. https://doi.org/10.1101/2021.10.27.466114

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