Rapid and accurate identification of pathogenic bacteria at the single-cell level using laser tweezers Raman spectroscopy and deep learning.

Journal: Journal of biophotonics
PMID: 35150463

Abstract

We report a new method for the rapid identification of pathogenic bacterial species at the single-cell level that combines laser tweezers Raman spectroscopy (LTRS) with deep learning (DL). LTRS can accurately measure single-cell Raman spectra (scRS) without destroying and labeling cells. Based on the scRS data, DL rapidly and accurately identifies pathogenic bacteria. We measured scRS of 15 species bacteria using homemade LTRS. For each species, approximately, 160 cells from three different patients were measured, one patient's data were used as test set, and the rest after being augmented was used as training set. A residual network (ResNet) model, trained on the augmented training set, achieved an accuracy of 94.53% on the test set. Moreover, we applied gradient-weighted class activation mapping to visualize the proposed model. Finally, we demonstrated the advantages of ResNet over traditional machine-learning algorithms.

Authors

  • Bo Zhou
    Department of Neurology, The Third People's Hospital of Yibin, Yibin, China.
  • Liying Sun
    Clinical Laboratory, Peking University First Hospital, Beijing, China.
  • Teng Fang
    Key Laboratory for the Physics and Chemistry of Nanodevices, School of Electronics, Peking University, Beijing, China.
  • Haixia Li
    State Key Laboratory of Toxicology and Medical Countermeasures, Beijing Institute of Pharmacology and Toxicology, Beijing 100850, China.
  • Ru Zhang
    School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing, China.
  • Anpei Ye
    Key Laboratory for the Physics and Chemistry of Nanodevices, School of Electronics, Peking University, Beijing, China.

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