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

Journal: Biosensors
Published Date: Apr 15, 2022

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.

Authors

  • Qing He
  • Wen Yang
    Department of Pharmaceutical Analysis, China Pharmaceutical University, 24 Tongjiaxiang, Nanjing, 210009, China; Key Laboratory of Drug Quality Control and Pharmacovigilance, Ministry of Education, China Pharmaceutical University, 24 Tongjiaxiang, Nanjing, 210009, China.
  • Weiquan Luo
    Agricultural and Biosystems Engineering, Iowa State University, Ames, IA 50010, USA.
  • Stefan Wilhelm
    Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, OK 73072, USA.
  • Binbin Weng
    School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK 73072, USA.

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