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

• Published in: Biosensors (Basel) Vol. 12; no. 4; p. 250
• Main Authors: He, Qing, Yang, Wen, Luo, Weiquan, Wilhelm, Stefan, Weng, Binbin
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 15.04.2022; MDPI
• Subjects: Algorithms; Animals; Biosensors; Cancer; cancer cells; Cell Differentiation; Cosmic rays; Cytology; Data analysis; Data processing; Datasets; fast Raman imaging; Imaging; Lasers; Learning algorithms; Lipids; Machine Learning; Medical diagnosis; Melanoma; Metastasis; Mice; Microscopy; Multivariate analysis; Muscles; Neoplasms; non-invasive imaging; Nucleic acids; PCA; Proteins; Raman spectroscopy; Sensors; Skin cancer; Spectroscopy; Spectrum analysis; Spectrum Analysis, Raman; United States > US; cancer cells; Raman spectroscopy; machine learning; non-invasive imaging; fast Raman imaging; PCA
• ISSN: 2079-6374; 2079-6374
• DOI: 10.3390/bios12040250

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.