Label-Free Differentiation of Cancer and Non-Cancer Cells Based on Machine-Learning-Algorithm-Assisted Fast Raman Imaging

He, Qing; Yang, Wen; Luo, Weiquan; Wilhelm, Stefan; Weng, Binbin

doi:10.3390/bios12040250

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Label-Free Differentiation of Cancer and Non-Cancer Cells Based on Machine-Learning-Algorithm-Assisted Fast Raman Imaging

^1,*,†,‡

^2,†

^3,§,

and

^1,*

School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK 73072, USA

Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, OK 73072, USA

Agricultural and Biosystems Engineering, Iowa State University, Ames, IA 50010, USA

Authors to whom correspondence should be addressed.

^†

These authors contributed equally to this work.

^‡

Current address: Mechanical Engineering, Tufts University, Medford, MA 02155, USA.

^§

Current address: Department of Radiology, University of Pittsburgh, Pittsburgh, PA 15260, USA.

Biosensors 2022, 12(4), 250; https://doi.org/10.3390/bios12040250

Received: 16 March 2022 / Revised: 10 April 2022 / Accepted: 12 April 2022 / Published: 15 April 2022

(This article belongs to the Special Issue Nanosensors for the Detection and Characterization of Food, Environmental and Medical Samples)

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Abstract

This paper proposes a rapid, label-free, and non-invasive approach for identifying murine cancer cells (B16F10 melanoma cancer cells) from non-cancer cells (C2C12 muscle cells) using machine-learning-assisted Raman spectroscopic imaging. Through quick Raman spectroscopic imaging, a hyperspectral data processing approach based on machine learning methods proved capable of presenting the cell structure and distinguishing cancer cells from non-cancer muscle cells without compromising full-spectrum information. This study discovered that biomolecular information–nucleic acids, proteins, and lipids—from cells could be retrieved efficiently from low-quality hyperspectral Raman datasets and then employed for cell line differentiation. View Full-Text

Keywords: Raman spectroscopy; PCA; machine learning; non-invasive imaging; fast Raman imaging; cancer cells

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He, Q.; Yang, W.; Luo, W.; Wilhelm, S.; Weng, B. Label-Free Differentiation of Cancer and Non-Cancer Cells Based on Machine-Learning-Algorithm-Assisted Fast Raman Imaging. Biosensors 2022, 12, 250. https://doi.org/10.3390/bios12040250

AMA Style

He Q, Yang W, Luo W, Wilhelm S, Weng B. Label-Free Differentiation of Cancer and Non-Cancer Cells Based on Machine-Learning-Algorithm-Assisted Fast Raman Imaging. Biosensors. 2022; 12(4):250. https://doi.org/10.3390/bios12040250

Chicago/Turabian Style

He, Qing, Wen Yang, Weiquan Luo, Stefan Wilhelm, and Binbin Weng. 2022. "Label-Free Differentiation of Cancer and Non-Cancer Cells Based on Machine-Learning-Algorithm-Assisted Fast Raman Imaging" Biosensors 12, no. 4: 250. https://doi.org/10.3390/bios12040250

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Label-Free Differentiation of Cancer and Non-Cancer Cells Based on Machine-Learning-Algorithm-Assisted Fast Raman Imaging

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