Spatial metabolomics describes the spatially resolved
analysis
of interconnected pathways, biochemical reactions, and transport processes
of small molecules in the spatial context of tissues and cells. However,
a broad range of metabolite classes (e.g., steroids) show low intrinsic
ionization efficiencies in mass spectrometry imaging (MSI) experiments,
thus restricting the spatial characterization of metabolic networks.
Additionally, decomposing complex metabolite networks into chemical
compound classes and molecular annotations remains a major bottleneck
due to the absence of repository-scaled databases. Here, we describe
a multimodal mass-spectrometry-based method combining computational
metabolome mining tools and high-resolution on-tissue chemical derivatization
(OTCD) MSI for the spatially resolved analysis of metabolic networks
at the low micrometer scale. Applied to plant toxin sequestration
in Danaus plexippus as a model system,
we first utilized liquid chromatography (LC)–MS-based molecular
networking in combination with artificial intelligence (AI)-driven
chemical characterization to facilitate the structural elucidation
and molecular identification of 32 different steroidal glycosides
for the host-plant Asclepias curassavica. These comprehensive metabolite annotations guided the subsequent
matrix-assisted laser desorption/ionization mass spectrometry imaging
(MALDI MSI) analysis of cardiac-glycoside sequestration in D. plexippus. We developed a spatial-context-preserving
OTCD protocol, which improved cardiac glycoside ion yields by at least
1 order of magnitude compared to results with untreated samples. To
illustrate the potential of this method, we visualized previously
inaccessible (sub)cellular distributions (2 and 5 μm pixel size)
of steroidal glycosides in D. plexippus, thereby providing a novel insight into the sequestration of toxic
metabolites and guiding future metabolomics research of other complex
sample systems.