Authors
Qian Shi, Yuerong Tang, Haonan Yang, Xuedong Huang, Xiaoni Fang, Baohong Liu
Published in
Analytical chemistry. Jul 16, 2026. Epub Jul 16, 2026.
Abstract
Metabolites packaged within extracellular vesicles (EVs) are increasingly recognized for their role in intercellular communication and metabolic regulation. However, their comprehensive characterization remains challenging due to the low abundance of metabolites in EVs, interference from complex biological matrixes, and poor ionization efficiency of certain metabolite classes. Here, we developed a membrane-fusion-assisted laser desorption/ionization mass spectrometry (MF-LDI-MS) strategy for in situ profiling of EV-associated metabolites. The MF-LDI-MS utilizes liposome-coated gold nanoparticles (Lipo@Au NPs) composed of a Au NP core and a cationic lipid bilayer shell. Driven by electrostatic interactions, the cationic liposomes fuse with the negatively charged EV membranes, enabling effective delivery of Au NPs into EVs. The internalized Lipo@Au NPs not only modulate EV sedimentation, facilitating enrichment under mild centrifugation, but also serve as inorganic matrixes for direct LDI-MS detection of retained metabolites within intact EVs. This integrative approach eliminates many of the aforementioned limitations by allowing direct, enrichment-free, and matrix interference-free metabolite analysis while maintaining EV integrity. Combining machine learning, the MF-LDI-MS successfully discriminated pancreatic cancer (PC) patients from healthy controls (HCs) in a cohort of 144 plasma samples, achieving a diagnostic accuracy of 92.1% and identifying nine metabolites as potential biomarkers, demonstrating its applicability to metabolic-level liquid biopsy.
PMID:
42460502
Bibliographic data and abstract were imported from PubMed on 16 Jul 2026.
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