期刊
SENSORS AND ACTUATORS B-CHEMICAL
卷 358, 期 -, 页码 -出版社
ELSEVIER SCIENCE SA
DOI: 10.1016/j.snb.2022.131473
关键词
Biomarker; Quantification; Extracellular vesicle; Membranes; Microfluidics
资金
- Ministry of Science and Technology (MOST) of Taiwan [MOST 109-2221-E-007006-MY3, MOST 110-2221-E-007-010-MY3, MOST 1102628-B-001-032]
- National Health Research Institutes of Taiwan [NHRI-EX110-11020EI]
- Academia Sinica Innovative Materials and Analysis Technology Exploration (iMATE) Program [AS-iMATE-107-33]
This study developed a membrane-based EV isolation/counting platform that can efficiently isolate and quantify specific EVs from blood. The platform has a limit of detection of 105 EVs/mL, demonstrating high sensitivity for disease diagnostics.
Extracellular vesicles (EVs), existing in body fluids, have exhibited a significant potential in clinical diagnostics since many harbor biomarkers specific to certain diseases. Therefore, platforms capable of isolating EVs and quantifying these disease-associated proteins and nucleic acids are under current development. Herein, this study devised a membrane-based EV isolation/counting (mEVic) microfluidic platform that combined two membrane filters to carry out EV isolation from blood, followed by quantification of immuno-stained particles on a single chip. A 0.2-mu m polycarbonate membrane was first used for small EV (sEV) isolation via stirring-enhanced filtration, and over 99% of sEV were isolated from only 2 mu L of blood. For quantification, aluminum oxide membrane (20 nm)-based immunostaining with in situ fluorescent signal amplification was achieved to where fluorescence-labeled EVs could be individually visualized and counted under a microscope. The limit of detection of CD63 + EVs and PalmGRET EVs spiked in plasma was 105 EVs/mL. Moreover, the membrane-based EV staining assay was able to measure exosomal protein expression on single EVs. This new mEVic platform may therefore serve as a promising tool for isolating and quantifying specific EVs and could be integrated with fingertip-based disease diagnostic applications.
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