TY  - JOUR
AU  - Abu Sammour, Denis
AU  - Cairns, James L
AU  - Boskamp, Tobias
AU  - Marsching, Christian
AU  - Kessler, Tobias
AU  - Ramallo Guevara, Carina
AU  - Panitz, Verena
AU  - Sadik, Ahmed
AU  - Cordes, Jonas
AU  - Schmidt, Stefan
AU  - Mohammed, Shad A
AU  - Rittel, Miriam F
AU  - Friedrich, Mirco
AU  - Platten, Michael
AU  - Wolf, Ivo
AU  - von Deimling, Andreas
AU  - Opitz, Christiane
AU  - Wick, Wolfgang
AU  - Hopf, Carsten
TI  - Spatial probabilistic mapping of metabolite ensembles in mass spectrometry imaging.
JO  - Nature Communications
VL  - 14
IS  - 1
SN  - 2041-1723
CY  - [London]
PB  - Nature Publishing Group UK
M1  - DKFZ-2023-00677
SP  - 1823
PY  - 2023
AB  - 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.
LB  - PUB:(DE-HGF)16
C6  - pmid:37005414
DO  - DOI:10.1038/s41467-023-37394-z
UR  - https://inrepo02.dkfz.de/record/275219
ER  -