Extracellular vesicles (EVs) have
attracted enormous attention
for their diagnostic and therapeutic potential. However, it has proven
challenging to achieve the sensitivity to detect individual nanoscale
EVs, the specificity to distinguish EV subpopulations, and a sufficient
throughput to study EVs among an enormous background. To address this
fundamental challenge, we developed a droplet-based optofluidic platform
to quantify specific individual EV subpopulations at high throughput.
The key innovation of our platform is parallelization of droplet generation,
processing, and analysis to achieve a throughput (∼20 million
droplets/min) more than 100× greater than typical microfluidics.
We demonstrate that the improvement in throughput enables EV quantification
at a limit of detection = 9EVs/μL, a >100× improvement
over gold standard methods. Additionally, we demonstrate the clinical
potential of this system by detecting human EVs in complex media.
Building on this work, we expect this technology will allow accurate
quantification of rare EV subpopulations for broad biomedical applications.
We have developed a web-based, self-improving and overfitting-resistant automated machine learning tool tailored specifically for liquid biopsy data, where machine learning models can be built without the user's input.
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