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

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

doi:10.3390/bios12040250

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

Biosensors (Basel). 2022 Apr 15;12(4):250. doi: 10.3390/bios12040250.

Authors

Qing He¹, Wen Yang², Weiquan Luo³, Stefan Wilhelm², Binbin Weng¹

Affiliations

¹ School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK 73072, USA.
² Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, OK 73072, USA.
³ Agricultural and Biosystems Engineering, Iowa State University, Ames, IA 50010, USA.

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.

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

MeSH terms

Algorithms
Animals
Cell Differentiation
Machine Learning*
Mice
Neoplasms*
Proteins
Spectrum Analysis, Raman

Substances

Proteins