Space curvature-inspired nanoplasmonic sensor for breast cancer extracellular vesicle fingerprinting and machine learning classification

Mohammadrahim Kazemzadeh; Colin L Hisey; Anastasiia Artuyants; Cherie Blenkiron; Lawrence W Chamley; Kamran Zargar-Shoshtari; Weiliang Xu; Neil G R Broderick

doi:10.1364/BOE.428302

Space curvature-inspired nanoplasmonic sensor for breast cancer extracellular vesicle fingerprinting and machine learning classification

Biomed Opt Express. 2021 Jun 9;12(7):3965-3981. doi: 10.1364/BOE.428302. eCollection 2021 Jul 1.

Authors

Mohammadrahim Kazemzadeh^{1

2

3}, Colin L Hisey^{4

5

3}, Anastasiia Artuyants^{4

5}, Cherie Blenkiron^{6

7}, Lawrence W Chamley^{4

5}, Kamran Zargar-Shoshtari⁸, Weiliang Xu^{1

2}, Neil G R Broderick^{2

9}

Affiliations

¹ Department of Mechanical Engineering, University of Auckland, Auckland 1010, New Zealand.
² Dodd-Walls Centre for Photonic and Quantum Technologies, New Zealand.
³ Authors contributed equally.
⁴ Department of Obstetrics and Gynaecology, University of Auckland, Auckland, 1023, New Zealand.
⁵ Hub for Extracellular Vesicle Investigations, University of Auckland, Auckland 1023, New Zealand.
⁶ Department of Molecular Medicine and Pathology, University of Auckland, Auckland 1023, New Zealand.
⁷ Auckland Cancer Society Research Centre, University of Auckland, Auckland 1023, New Zealand.
⁸ Department of Surgery, University of Auckland, Auckland 1023, New Zealand.
⁹ Department of Physics, University of Auckland, Auckland 1010, New Zealand.

Abstract

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.