Quinn, Steven orcid.org/0000-0003-3442-4103, Conteduca, Donato and Krauss, Thomas Fraser orcid.org/0000-0003-4367-6601 (2021) Dielectric metasurface for high-precision detection of large unilamellar vesicles. Journal of Optics. 114002. ISSN 2040-8986
Abstract
Extracellular vesicles (EVs) are very promising biomarkers for the diagnosis of various diseases, including cardiovascular, infectious and neurodegenerative disorders. Of particular relevance is their importance in cancer liquid biopsy, where they play a key role in the early detection and monitoring of the tumour. A number of technologies have recently been developed to improve the performance of current EV detection methods, but a technique that can provide high resolution, high accuracy and a multiplexing capability for the detection of several biomarkers in parallel remains a challenge in this field. Here, we demonstrate the detection of large unilamellar vesicles, which are excellent models of EVs, down to a concentration <103 EV ml−1 (<10 pM) using a dielectric resonant metasurface. This result represents an improvement in performance and functionality compared to competing plasmonic and electrochemical modalities and is due to the strong resonance amplitude and high Q-factor of our metasurface. We also verify the selectivity of the approach by detecting vesicles that have been surface-functionalised with a CD9 protein. The ease of integration of our method into a point-of-care instrument offers a path towards personalised cancer medicine.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Physics (York) |
Funding Information: | Funder Grant number EPSRC EP/P030017/1 |
Depositing User: | Pure (York) |
Date Deposited: | 17 Dec 2021 15:40 |
Last Modified: | 05 Mar 2025 00:07 |
Published Version: | https://doi.org/10.1088/2040-8986/ac2dd7 |
Status: | Published |
Refereed: | Yes |
Identification Number: | 10.1088/2040-8986/ac2dd7 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:181759 |
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