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Space curvature-inspired nanoplasmonic sensor for breast cancer extracellular vesicle fingerprinting and machine learning classification
Author(s) -
Mohammadrahim Kazemzadeh,
Colin L. Hisey,
Anastasiia Artuyants,
Cherie Blenkiron,
Lawrence W. Chamley,
Kamran ZargarShoshtari,
Weiliang Xu,
Neil G. R. Broderick
Publication year - 2021
Publication title -
biomedical optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.362
H-Index - 86
ISSN - 2156-7085
DOI - 10.1364/boe.428302
Subject(s) - extracellular vesicle , breast cancer , nanotechnology , computer science , fingerprint (computing) , extracellular vesicles , cancer , materials science , artificial intelligence , biomedical engineering , chemistry , biology , medicine , microrna , microvesicles , microbiology and biotechnology , biochemistry , gene
Extracellular vesicles (EVs) are micro and nanoscale lipid-enclosed packages that have shown potential as liquid biopsy targets for cancer because their structure and contents reflect their cell of origin. However, progress towards the clinical applications of EVs has been hindered due to the low abundance of disease-specific EVs compared to EVs from healthy cells; such applications thus require highly sensitive and adaptable characterization tools. To address this obstacle, we designed and fabricated a novel space curvature-inspired surfaced-enhanced Raman spectroscopy (SERS) substrate and tested its capabilities using bioreactor-produced and size exclusion chromatography-purified breast cancer EVs of three different subtypes. Our findings demonstrate the platform's ability to effectively fingerprint and efficiently classify, for the first time, three distinct subtypes of breast cancer EVs following the application of machine learning algorithms on the acquired spectra. This platform and characterization approach will enhance the viability of EVs and nanoplasmonic sensors towards clinical utility for breast cancer and many other applications to improve human health.

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