Ultrasensitive and High-Resolution Protein Spatially Decoding Framework for Tumor Extracellular Vesicles.

Area of Science:

• Biochemistry
• Molecular Biology
• Nanotechnology

Background:

• Extracellular vesicles (EVs) carry surface and luminal proteins crucial for cancer progression.
• Current methods for quantifying EV proteins are limited by sensitivity and inefficient isolation techniques.

Purpose of the Study:

• To present the eSimoa framework, an innovative approach for sensitive and specific spatial decoding of EV protein biomarkers.
• To quantify luminal RAS or KRAS^G12D proteins in pancreatic tumor-derived EVs and measure low-abundance EV subpopulations.

Main Methods:

• Development and application of the eSimoa framework, including luminal and pulldown eSimoa pipelines.
• Detection of EVs in phosphate-buffered saline (PBS) and plasma samples.
• Quantification of absolute protein concentrations at femtomolar (fM) levels.

Main Results:

• The eSimoa framework achieved unmatched sensitivity and specificity in spatial decoding of EV protein biomarkers.
• Absolute concentrations of luminal RAS or KRAS^G12D proteins were measured in pancreatic tumor-derived EVs.
• EVs were detected at concentrations as low as 10⁵ EVs mL^-1 in plasma, and protein concentrations as low as fM were quantified.

Conclusions:

• The eSimoa framework provides a sensitive tool for detecting and quantifying EV protein biomarkers, including spatial distribution.
• It enables the identification of disease-specific EV protein biomarkers in clinical samples with minimal pre-purification.
• This technology holds significant potential for advancing the clinical translation of EV-based diagnostics.