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

Area of Science:

• Biochemistry
• Analytical Chemistry
• Biotechnology

Background:

• Metabolites within extracellular vesicles (EVs) are crucial for intercellular communication and metabolic regulation.
• Characterizing EV metabolites is difficult due to low abundance, complex matrices, and poor ionization.
• Existing methods face challenges in sensitivity, specificity, and maintaining EV integrity.

Purpose of the Study:

• To develop a novel strategy for in situ profiling of EV-associated metabolites.
• To overcome limitations of current metabolite analysis in EVs, including low abundance and matrix interference.
• To establish a sensitive and specific method for EV metabolite detection applicable to disease diagnostics.

Main Methods:

• Developed a membrane-fusion-assisted laser desorption/ionization mass spectrometry (MF-LDI-MS) approach.
• Utilized liposome-coated gold nanoparticles (Lipo@Au NPs) for EV membrane fusion and internal nanoparticle delivery.
• Integrated machine learning for data analysis and biomarker identification.

Main Results:

• MF-LDI-MS enabled direct, enrichment-free, and matrix interference-free metabolite analysis within intact EVs.
• The method successfully discriminated pancreatic cancer (PC) patients from healthy controls (HCs) in 144 plasma samples with 92.1% accuracy.
• Identified nine potential metabolite biomarkers for pancreatic cancer detection.

Conclusions:

• The MF-LDI-MS strategy offers a robust platform for analyzing EV-associated metabolites.
• This technique demonstrates significant potential for non-invasive cancer diagnostics via metabolic liquid biopsy.
• The findings highlight the utility of MF-LDI-MS in advancing the understanding of EV-mediated intercellular communication in disease.