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Agarose Microgel-Based In Situ Cleavable Immuno-Rolling Circle Amplification for Multiplexed Single-Molecule
Juhwan Park1,2, Michelle Feng1, Jingbo Yang3
1Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States.
ACS Nano
|May 5, 2025
Summary
We developed GDEVA, a novel platform for ultrasensitive, multiplexed profiling of individual extracellular vesicles (EVs) in plasma. This digital assay significantly improves detection limits for EV subpopulations, aiding cancer immunology research.
Area of Science:
- Biotechnology
- Nanotechnology
- Immunology
Background:
- Extracellular vesicles (EVs) are crucial in disease progression and therapy response.
- Current methods for EV profiling lack sensitivity and multiplexing capabilities, especially in complex samples like plasma.
Purpose of the Study:
- To develop a platform for ultrasensitive and multiplexed profiling of individual EVs directly in human plasma.
- To overcome limitations of existing EV detection technologies, including steric hindrance and lack of quantitation.
Main Methods:
- Developed the GDEVA (Agarose microGel-based Digital single-molecule-single EV Assay) platform.
- Integrated rolling circle amplification (RCA) immunoassay with agarose microgels and flow cytometry readout.
- Utilized fluorescence-activated cell sorting (FACS) for imaging and analysis.
Main Results:
- Achieved single-molecule sensitivity and moderate multiplexing (3-plex) with high throughput (∼10^4 EVs/min).
- Demonstrated a ~100x improvement in the limit of detection for EV subpopulations compared to conventional methods.
- Successfully profiled individual EVs for immune markers (PD-L1, CD155) and a melanoma marker (TYRP-1) in patient plasma.
Conclusions:
- GDEVA enables precise quantification and detection of rare EV subpopulations in complex clinical specimens.
- The platform shows significant potential for advancing cancer immunology research by analyzing EV heterogeneity.
- GDEVA overcomes key technical challenges in EV analysis, paving the way for improved diagnostics and therapeutics.
