O’Dwyer, Kevin and Domijan, Katarina and Dignam, Adam and Butler, Marion and Hennelly, Bryan M. (2021) Automated Raman Micro-Spectroscopy of Epithelial Cell Nuclei for High-Throughput Classification. Cancers, 13 (19). p. 4767. ISSN 2072-6694

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Abstract

Raman micro-spectroscopy is a powerful technique for the identification and classification of cancer cells and tissues. In recent years, the application of Raman spectroscopy to detect bladder, cervical, and oral cytological samples has been reported to have an accuracy greater than that of standard pathology. However, despite being entirely non-invasive and relatively inexpensive, the slow recording time, and lack of reproducibility have prevented the clinical adoption of the technology. Here, we present an automated Raman cytology system that can facilitate high-throughput screening and improve reproducibility. The proposed system is designed to be integrated directly into the standard pathology clinic, taking into account their methodologies and consumables. The system employs image processing algorithms and integrated hardware/software architectures in order to achieve automation and is tested using the ThinPrep standard, including the use of glass slides, and a number of bladder cancer cell lines. The entire automation process is implemented, using the open source Micro-Manager platform and is made freely available. We believe that this code can be readily integrated into existing commercial Raman micro-spectrometers.

Item Type:	Article
Keywords:	Raman spectroscopy; automated cytology; high-throughput classification; cellular classification; screening; ThinPrep;
Academic Unit:	Faculty of Science and Engineering > Biology Faculty of Science and Engineering > Research Institutes > Human Health Institute
Item ID:	16218
Identification Number:	https://doi.org/10.3390/cancers13194767
Depositing User:	Marion Butler
Date Deposited:	04 Jul 2022 15:54
Journal or Publication Title:	Cancers
Publisher:	MDPI
Refereed:	Yes
URI:	Publisher
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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