Label-free discrimination of hepatoma cells based on Raman spectroscopy and multivariate statistical algorithms

Siqi Gao; Mengmeng Zheng; Yamin Lin; Shuzhen Tang; Shusen Xie; Yun Yu; Juqiang Lin

doi:10.1117/12.2537282

20 November 2019 Label-free discrimination of hepatoma cells based on Raman spectroscopy and multivariate statistical algorithms

Siqi Gao, Mengmeng Zheng, Yamin Lin, Shuzhen Tang, Shusen Xie, Yun Yu, Juqiang Lin

Author Affiliations +

Proceedings Volume 11190, Optics in Health Care and Biomedical Optics IX; 1119024 (2019) https://doi.org/10.1117/12.2537282
Event: SPIE/COS Photonics Asia, 2019, Hangzhou, China

Abstract

In this article, we have studied the feasibility of using Raman spectroscopy and multivariate statistical algorithms to distinguish human hepatoma cells from normal human liver cells with the aim to explore a label-free and non-invasive method for detecting and screening hepatoma cells. High-quality Raman spectra were obtained from 50 normal liver cells (Lo2 cell line) and 50 hepatoma cells (HepG2 cell line) in the range of 500-1750 cm-1. There are significant differences in Raman spectra between normal liver cells and hepatoma cells, which indicated special changes in the content of biomolecules including nucleic acids, proteins and lipid in different cell lines. Principal component analysis (PCA) and linear discriminate analysis (LDA) were used to classify the Raman spectra obtained from hepatoma cells and normal liver cells, and the discrimination sensitivity and specificity were 98% and 100%, respectively. In addition, PCA in conjunction with support vector machine (SVM) (with a Gaussian radial basis function) was also employed to classify the same Raman spectra dataset, and the sensitivity and specificity could be improved to 100% and 100%, respectively, indicating that the classification performance of PCA-SVM is superior to that of PCA-LDA. This exploratory study demonstrated that Raman spectroscopy technique combined with multivariate statistical algorithms as a clinical cell-based biosensor has great potential for noninvasive cancer cell detection and screening.

Citation Download Citation

Siqi Gao, Mengmeng Zheng, Yamin Lin, Shuzhen Tang, Shusen Xie, Yun Yu, and Juqiang Lin "Label-free discrimination of hepatoma cells based on Raman spectroscopy and multivariate statistical algorithms", Proc. SPIE 11190, Optics in Health Care and Biomedical Optics IX, 1119024 (20 November 2019); https://doi.org/10.1117/12.2537282

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