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Related Experiment Video

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Multimodal Analytical Platform on a Multiplexed Surface Plasmon Resonance Imaging Chip for the Analysis of Extracellular Vesicle Subsets
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Integrated Multi-Omics Enabled by Sequential Extraction for Comprehensive Molecular Profiling of Small Extracellular

Andrew J Perciaccante, Holden T Rogers, Yanlong Zhu

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    This study introduces a mass spectrometry multi-omics platform for analyzing small extracellular vesicles (sEVs). The method enables deep characterization of proteins, lipids, and metabolites from limited sEV samples for biomarker discovery.

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    Area of Science:

    • Biochemistry
    • Molecular Biology
    • Biotechnology

    Background:

    • Small extracellular vesicles (sEVs) contain molecular cargo reflecting their cell of origin.
    • sEVs are promising for biomarker discovery and therapeutics.
    • Comprehensive characterization of sEVs is hindered by limited sample material.

    Purpose of the Study:

    • To develop an integrated mass spectrometry-based multi-omics platform for simultaneous protein, lipid, and metabolite analysis from a single sEV sample.
    • To maximize sample utilization and enhance molecular coverage and analytical depth.
    • To address the challenge of low-input sEV analysis.

    Main Methods:

    • Integrated mass spectrometry-based multi-omics platform using sequential extraction.
    • Iterative tandem mass spectrometry for small-molecule fragmentation.
    • Nano-flow proteomics with data-independent acquisition.
    • Analysis of sEVs isolated by ultracentrifugation, size-exclusion chromatography, and polymer precipitation.

    Main Results:

    • Achieved deep and reproducible multi-omic characterization of proteins, lipids, and metabolites from 10 million sEVs.
    • Demonstrated platform compatibility with sEVs isolated using various methods.
    • Revealed purification-dependent differences in molecular profiles, highlighting yield-purity tradeoffs.

    Conclusions:

    • The developed platform enables integrated multi-omics analysis from limited sEV samples.
    • This approach overcomes a key challenge in low-input sEV analysis.
    • Establishes a robust foundation for synergistic biomarker discovery and therapeutic applications of sEVs.