Multiplexed Data-Independent Acquisition (mDIA) to Profile Extracellular Vesicle Proteomes

Area of Science:

• Proteomics
• Extracellular Vesicles Biology
• Biomarker Discovery

Background:

• Extracellular vesicles (EVs) are crucial in disease and contain potential biomarkers.
• Low protein abundance in EVs necessitates sensitive quantitative proteomic methods.
• Multiplexed data-independent acquisition (mDIA) offers improved sensitivity over traditional methods.

Purpose of the Study:

• To establish a robust dimethyl labeling-based mDIA pipeline for quantitative EV proteomics.
• To evaluate various mDIA strategies for EV proteome analysis.
• To apply the optimized pipeline to identify EV proteome changes in intrahepatic cholangiocarcinoma.

Main Methods:

• EVs isolated using the EVtrap technique.
• On-bead, one-pot sample preparation for digested peptides.
• Evaluation of library-free and library-based mDIA on the timsTOF HT platform.

Main Results:

• Library-based mDIA with project-specific spectral libraries outperformed other pipelines.
• Enhanced protein identification and quantification achieved with the optimized pipeline.
• Demonstrated EV proteome landscape changes linked to IDH1 mutation and inhibitor treatment.

Conclusions:

• A robust mDIA pipeline was established for quantitative EV proteomics.
• Library-based mDIA is superior for EV proteome analysis.
• This approach facilitates EV-based biomarker discovery in diseases like cholangiocarcinoma.