Enhanced Spatial Metabolomics Enabled by On-Tissue Chemistry, Computational Metabolome Mining and Mass Spectrometry Imaging

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DreisbachDomenic-2024-12-04.pdf (9.81 MB)

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2024

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Dreisbach, Domenic

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Spengler, Bernhard
Petschenka, Georg
Schindler, Siegfried
Hamscher, Gerd

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DOI:
https://doi.org/10.22029/jlupub-19357

Abstract

Spatial metabolomics is an emerging field of research and provides deep insight into biochemical pathways and transport processes for low-molecular-weight chemical compounds in tissues and cells. Yet, current approaches predominantly focused on analyzing a limited number of previously known molecules of interest in mammalian tissues. In the first part of this thesis, we set out to apply mass spectrometry imaging (MSI)-based spatial metabolomics for investigating complex metabolic networks in the delicate sample system of insects. Specifically, we acquired spatially-resolved metabolomes related to plant toxin sequestration in milkweed butterflies. Despite being famous for sequestering milkweed steroidal glycosides (i.e. cardenolides), the underlying mechanism of uptake and toxin distribution in caterpillars of the monarch butterfly (Danaus plexippus) is still unknown. We revealed cardenolide distributions at unprecedent detail across the whole body of monarch caterpillars and the closely related nonsequestering common crow butterfly (Euploea core) to determine at which physiological scale quantitative and qualitative differences between both species manifest: while monarchs retain and accumulate cardenolides above plant concentrations, the toxins are degraded in the gut lumen of E. core. We visualized cardenolide transport over the monarch midgut epithelium at the low-micrometer-scale and identified integument cells as the final site of storage to provide chemical defenses against predators. Collectively, these results provided novel insight into the selectivity and the mechanism of cardenolide sequestration. Yet, we encountered significant challenges in terms of visualizing and identifying the complex metabolic network of steroid glycosides. In the second part of this thesis, we therefore enhanced our spatial metabolomic approach with liquid chromatography mass spectrometry (LC-MS)-based computational metabolome mining and on-tissue chemistry to counteract the absence of respective databases and low intrinsic ionization efficiency for steroidal glycosides. We conducted feature-based molecular networking in combination with artificial intelligence (AI)-driven molecular characterization to characterize and annotate 32 different cardenolides. This comprehensive database guided matrix-assisted laser desorption/ionization (MALDI) MSI of cardenolide sequestration in D. plexippus. We developed a spatial-context preserving on-tissue chemical derivatization (OTCD) protocol, which improved ion yields for cardenolides by at least 1 order of magnitude compared to untreated samples. Empowered by this approach, we visualized previously inaccessible cardenolide distributions with 2 µm and 5 µm pixel size across complex insect organs, such as Malpighian tubules. In addition to unraveling intricate details regarding cardenolide sequestration, the results underscore the potential of OTCD MALDI MSI for deeper spatial metabolomic research

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