High‐resolution dual‐polarity molecular imaging platform unraveling spatial metabolic heterogeneity in multiple plant tissues

Exploring the metabolic characteristics of different plant organs and tissues at a spatial level can help us to better understand the functional mechanisms of biological tissues and cells. Mass spectrometry imaging (MSI) provides a reliable tool for this purpose. However, its application for high-resolution metabolic mapping across various plant organs remains a significant challenge due to the intrinsic biological properties of plant samples and unfavorable analysis conditions. This study aimed to develop a novel MSI platform that can expand more diverse plant samples in spatial metabolomics research and enhance the detection efficiency of plant metabolites. The platform (AMG-LDI-MSI) based on an Au nanoparticles-loaded MoS₂ and doped graphene oxide (Au@MoS₂/GO) flexible film substrate combined with laser desorption/ionization (LDI)-MSI was established to enhance the detection and visualization of metabolites in various plant tissues. It has a non-sectioning, matrix-free, dual-ion mode imaging strategy, enabling high-throughput detection of metabolites and high-resolution molecular imaging within a micrometer scale. The Au@MoS₂/GO as a new substrate can offer high sensitivity and molecular coverage for diverse plant metabolites (10 classes) under the positive and negative ion modes. Moreover, the AMG-LDI-MSI platform overcomes the limitations of plant tissues (e.g., fragile leaf, water-rich fruit, or lignified roots) for in situ imaging. We successfully applied the platform to map the metabolite spatial dynamics in different types of fresh tissues (rhizome, main root, branch root, fruit, leaf, and root nodule) from medicinal plants, obtained the high-quality mass spectral imaging data, and demonstrated the universality and applicability of the platform to multiple plant tissues. These results demonstrate the significant advantages of enhancing the detection of multiple tissue metabolites in plants and their high-resolution imaging. It has overcome the limitations of previously reported MSI methods, suggesting that it could become a widely used tool for deciphering metabolic networks in plant biology.