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.
• Accurate quantification of low-abundance EV proteins requires sensitive proteomic methods.
• Multiplexed data-independent acquisition (mDIA) offers improved sensitivity over traditional methods.

Purpose of the Study:

• To develop and validate a robust dimethyl labeling-based mDIA pipeline for quantitative EV proteomics.
• To evaluate various mDIA strategies for EV proteome analysis.
• To investigate EV proteome alterations in intrahepatic cholangiocarcinoma linked to IDH1 mutation.

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.
• Generation of project-specific spectral libraries via StageTip-based fractionation.

Main Results:

• Library-based mDIA with custom spectral libraries demonstrated superior protein identification and quantification.
• The optimized mDIA pipeline successfully identified EV proteome changes.
• EV proteome landscape alterations associated with IDH1 mutation and inhibitor treatment in cholangiocarcinoma were revealed.

Conclusions:

• A robust library-based mDIA pipeline is established for quantitative EV proteomics.
• This approach enhances sensitivity and accuracy for EV biomarker discovery.
• The study highlights the potential of mDIA in understanding EV-associated disease mechanisms, exemplified by cholangiocarcinoma.