Sessile droplet array for sensitive profiling of multiple extracellular vesicle immuno-subtypes

Area of Science:

• Biotechnology
• Nanotechnology
• Medical Diagnostics

Background:

• Cancer-specific extracellular vesicles (EVs) are promising biomarkers for multiclass cancer diagnosis.
• Current methods for EV detection face limitations in sensitivity, accessibility, and multiplexed subtype analysis.

Purpose of the Study:

• To develop a novel platform for sensitive and multiplexed detection of cancer-specific EVs.
• To overcome the limitations of existing EV detection technologies for improved cancer diagnostics.

Main Methods:

• A sessile droplet array (eSD) platform was developed, utilizing evaporation-driven radial flows for enhanced EV immuno-capture.
• The platform was tested for sensitivity, low-volume sample consumption (∼0.2 μL plasma), and detection of multiple EV immuno-subtypes against seven antibodies.
• A two-staged quadratic discriminant analysis algorithm was employed for machine learning-based cancer classification.

Main Results:

• The eSD platform demonstrated significantly enhanced EV capture and detection compared to conventional micro-wells, with a detection limit of 384.7 EVs per microliter.
• The platform successfully detected EV immuno-subtypes in plasma samples from patients with liver, colon, lung, breast, and prostate cancers.
• The diagnostic performance showed 100% sensitivity and 100% specificity for cancer diagnosis, with an overall accuracy of 96% for classifying cancer types.

Conclusions:

• The sessile droplet array (eSD) platform offers a sensitive, accessible, and multiplexed approach for cancer-specific EV detection.
• This technology holds significant potential for advancing early and accurate multiclass cancer diagnosis.
• The high diagnostic accuracy suggests the platform's clinical utility in cancer patient management.