4.7 Article

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

Journal

BIOSENSORS-BASEL
Volume 12, Issue 4, Pages -

Publisher

MDPI
DOI: 10.3390/bios12040250

Keywords

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

Funding

  1. Chevron Corporation's OU-MCEE Funding Program

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This study introduces a rapid method using machine-learning-assisted Raman spectroscopic imaging to distinguish cancer cells from non-cancer cells, efficiently retrieving biomolecular information for cell line differentiation.
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.

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