Membrane Fusion-Assisted Laser Desorption/Ionization Mass Spectrometry for In Situ Extracellular Vesicle Metabolic Fingerprinting

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.

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Publication Details

Journal
Analytical Chemistry
Published
2026-07-16
DOI
https://doi.org/10.1021/acs.analchem.6c04341
Primary Topic
Extracellular vesicles in disease
Type
article
Field-Weighted Citation Impact
0.00

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article

Membrane Fusion-Assisted Laser Desorption/Ionization Mass Spectrometry for In Situ Extracellular Vesicle Metabolic Fingerprinting

Baohong Liu, Xiaoni Fang, Haonan Yang, Xuedong Huang et al.
Analytical Chemistry
Extracellular vesicles in disease
article

Membrane Fusion-Assisted Laser Desorption/Ionization Mass Spectrometry for In Situ Extracellular Vesicle Metabolic Fingerprinting

Baohong Liu, Xiaoni Fang, Haonan Yang, Xuedong Huang, Yuerong Tang, S J Qian
article en

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.

Analytical Chemistry
Fudan University (CN), Shanghai Stomatological Hospital (CN)
Natural Science Foundation of Shanghai, National Natural Science Foundation of China
Peace, Justice and strong institutions, Reduced inequalities
Openalex Percentile: Top 14%
Extracellular vesicles in disease
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