外体
微泡
生物传感器
化学
纳米技术
药物输送
脂质体
生物物理学
生物分子
材料科学
生物
生物化学
基因
小RNA
作者
Changheon Kim,Bo Hoon Han,Dong-Woo Kim,Gyubok Lee,Changgi Hong,Ji Yoon Kang,Kangwon Lee
标识
DOI:10.1016/j.snb.2021.131286
摘要
Exosomes are extracellular vesicles 15–150 nm in size and serve as delivery vehicles for long-distance inter-cellular communication. Exosomes contain various biomolecules derived from the cell cytosol and have potential applications in disease diagnosis and drug delivery. However, due to their low density, small size, and scarcity, reliable methods for the selective capture or efficient isolation of exosomes are required to broaden their range of applications. Particularly, the conventional method for selective exosome capture is based on a ligand-receptor interaction using a receptor that targets tetraspanin on the exosome surface. Sensing platforms detect the signal generated by the binding ligand in the sensing region; therefore, efficient exosome capture in a receptor-based exosome sensor platform has a significant impact on the detection performance. The conducting polymer polydiacetylene (PDA) has unique optical properties resulting from the polymerization of self-assembled diacetylene molecules, and PDA based platform can be used to indicate the degree of ligand-receptor interaction. When the PDA backbone is disturbed by an external stimulus, it produces a blue to red colorimetric transition and fluoresces in the red region without a label. Herein, it was confirmed that exosomes were more efficiently captured by a multi-target platform compared to a single-target platform. The PDA array platform demonstrated significantly improved exosome binding efficiency with a 4–8-fold increase in sensitivity compared to the single-target system. Moreover, the binding efficiency was high in low exosome concentration environments. Therefore, the multi-target exosome platform increases detection sensitivity through efficient exosome capture and this strategy can be universally applied to applications that use exosome-targeting receptors.
科研通智能强力驱动
Strongly Powered by AbleSci AI