![]() Among various flavors of imaging MS, matrix-assisted laser-desorption ionization MALDI-imaging is the most widespread. The capacities and potential of imaging MS were boosted with the introduction of high- and ultrahigh- resolving power mass spectrometry analyzers such as FTICR (Fourier-transform ion cyclotron resonance) and Orbitrap that rapidly shifted the current focus towards applications in spatial metabolomics and lipidomics. ![]() Imaging mass spectrometry (imaging MS) emerged as a powerful and versatile technology for spatial molecular analysis with a particular interest in clinical and pharma applications. Overall, our work illustrates how artificial intelligence approaches enabled by open-access data, web technologies, and machine and deep learning open novel avenues to address long-standing challenges in imaging MS. In a test-case study, we investigated off-sample images corresponding to the most common MALDI matrix (2,5-dihydroxybenzoic acid, DHB) and characterized properties of matrix clusters. The following methods were able to reproduce expert judgements with a high agreement: residual deep learning ( F1-score 0.97), semi-automated spatio-molecular biclustering ( F1-score 0.96), and molecular co-localization ( F1-score 0.90). Next, we developed several machine and deep learning methods for recognizing off-sample ion images. First, we created a high-quality gold standard of 23,238 expert-tagged ion images from 87 public datasets from the METASPACE knowledge base. We developed an artificial intelligence approach to recognize off-sample ion images. Off-sample ion images confound and hinder statistical analysis, metabolite identification and downstream analysis with no automated solutions available. However, imaging MS data is polluted with off-sample ions caused by sample preparation, particularly by the MALDI (matrix-assisted laser desorption/ionization) matrix application. Imaging mass spectrometry (imaging MS) is an enabling technology for spatial metabolomics of tissue sections with rapidly growing areas of applications in biology and medicine.
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