Current metabolomics approaches, especially in multicellular and metabolically complex species e.g. plants, suffer from issues of metabolite identification and lack of spatial information. In my presentation, I will describe our efforts to tackle these concerns by establishing metabolite libraries, developing new methodologies and assimilating cutting-edge technologies for increasing the spatial resolution in metabolomics experiments. We developed ‘WEIZMASS’, a comprehensive spectral library representing more than 10,000 plant metabolites based on highly pure and NMR elucidated authentic standards. This resource combined with an analysis pipeline (termed ‘MATCHWEIZ’) contribute significantly to the degree of confidence in metabolite identification. Furthermore, the WEIZMASS spectral database also allowed us to develop an annotation scheme based on deep learning algorithms for predicting metabolite chemical classes. Research at the cell type and subcellular level resolution is key to the study of metabolism. For cell-type specific profiling, we employ a variety of technologies based on MALDI sources and the integration of stable isotope labelling in mass spectrometry imaging (MSI). The new HybridSIMS technology allowing nanometer, subcellular resolution and efficient production of 3-D metabolite maps has recently been introduced to our MSI toolbox. We also developed ‘mINTACT’ as a new and efficient system for isolating single organelle types from specific cell layers and further metabolite profiling. These new capabilities enhance the resolution of metabolomics analysis in terms of quantity and quality of metabolite identification as well as facilitate a more ‘realistic’ analysis of the plant metabolic network.