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Rapid Size-Based Isolation of Extracellular Vesicles by Three-Dimensional Carbon Nanotube Arrays

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Date 2020 Feb 20
PMID 32073255
Citations 11
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Abstract

Recent discoveries reveal that extracellular vesicles (EVs) play an important role in transmitting signals. Although this emerging transcellular pathway enables a better understanding of neural communication, the lack of techniques for effectively isolating EVs impedes their studies. Herein, we report an emergent high-throughput platform consisting of three-dimensional carbon nanotube arrays that rapidly capture different EVs based on their sizes, without any labels. More importantly, this label-free capture maintains the integrity of the EVs when they are excreted from a host cell, thus allowing comprehensive downstream analyses using conventional approaches. To study neural communication, we developed a stamping technique to construct a gradient of nanotube herringbone arrays and integrated them into a microdevice that allowed us processing of a wide range of sample volumes, microliters to milliliters, in several minutes through a syringe via manual hand pushing and without any sample preparation. This microdevice successfully captured and separated EVs excreted from glial cells into subgroups according to their sizes. During capture, this technology preserved the structural integrity and originality of the EVs that enabled us to monitor and follow internalization of EVs of different sizes by neurons and cells. As a proof of concept, our results showed that smaller EVs (∼80 nm in diameter) have a higher uptake efficiency compared to larger EVs (∼300 nm in diameter). In addition, after being internalized, small EVs could enter endoplasmic reticulum and Golgi but not the largest ones. Our platform significantly shortens sample preparation, allows the profiling of the different EVs based on their size, and facilitates the understanding of extracellular communication. Thus, it leads to early diagnostics and the development of novel therapeutics for neurological diseases.

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