Calibration-free analysis of surface proteins on single extracellular vesicles enabled by DNA nanostructure

被引:0
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作者
Guo, Kaizhu [1 ]
Li, Zongbo [1 ]
Win, Allison [1 ]
Coreas, Roxana [2 ]
Adkins, Gary Brent [1 ]
Cui, Xinping [3 ]
Yan, Dong [4 ]
Cao, Minghui [5 ]
Wang, Shizhen Emily [5 ]
Zhong, Wenwan [1 ,2 ]
机构
[1] Department of Chemistry, University of California-Riverside, Riverside,CA,92521, United States
[2] Environmental Toxicology Graduate Program, University of California-Riverside, Riverside,CA,92521, United States
[3] Department of Statistics, University of California-Riverside, Riverside,CA,92521, United States
[4] Nanofabrication Facility, University of California-Riverside, Riverside,CA,92521, United States
[5] Department of Pathology, University of California San Diego, La Jolla,CA,92093, United States
基金
美国国家卫生研究院;
关键词
Fluorescence microscopy - Nanostructures - DNA - Biomarkers - Calibration - Diseases - Diagnosis;
D O I
暂无
中图分类号
学科分类号
摘要
Extracellular vesicles (EVs) are essential intercellular communicators that are of increasing interest as diagnostic biomarkers. Exploring their biological functions and clinical values, however, remains challenging due to their small sizes and high heterogeneity. Herein, we report an ultrasensitive method that employs target-initiated construction of DNA nanostructure to detect single EVs with an input as low as 100 vesicles/μL. Taking advantage of both DNA nanostructure labeling and EV membrane staining, the method can also permit calibration-free analysis of the protein profiles among different EV samples, leading to clear EV differentiation by their cell of origin. Moreover, this method allows co-localization of dual protein markers on the same EV, and the increased number of EVs carrying dual tumor proteins present in human serum could differentiate cancer patients at the early developmental stage from healthy controls. Our results demonstrate the great potential of this single-EV visualization method in non-invasive detection of the EV-based protein biomarkers for cancer diagnosis and treatment monitoring. © 2021 Elsevier B.V.
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