Nondestructive microbial discrimination using single-cell Raman spectra and random forest machine learning algorithm.

Journal: STAR protocols
PMID: 36386892

Abstract

Raman microspectroscopy is a powerful tool for obtaining biomolecular information from single microbial cells in a nondestructive manner. Here, we detail steps to discriminate prokaryotic species using single-cell Raman spectra acquisitions followed by data preprocessing and random forest model tuning. In addition, we describe the steps required to evaluate the model. This protocol requires minimal preprocessing of Raman spectral data, making it accessible to non-spectroscopists, yet allows intuitive visualization of feature importance. For complete details on the use and execution of this protocol, please refer to Kanno et al. (2021).

Authors

  • Nanako Kanno
    Department of Chemistry, School of Science, Kwansei Gakuin University, Sanda, Hyogo 669-1330, Japan. Electronic address: nkanno@kwansei.ac.jp.
  • Shingo Kato
    Diagnostic Radiology, Yokohama City University Graduate School of Medicine, Yokohama-shi, Japan.
  • Moriya Ohkuma
    Japan Collection of Microorganisms, RIKEN BioResource Research Center, Tsukuba, Ibaraki 305-0074, Japan.
  • Motomu Matsui
    Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Bunkyo-ku, Tokyo 113-0032, Japan; Department of Integrated Biosciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba 277-0882, Japan.
  • Wataru Iwasaki
    Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Bunkyo-ku, Tokyo 113-0032, Japan; Department of Integrated Biosciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba 277-0882, Japan.
  • Shinsuke Shigeto
    Department of Chemistry, School of Science, Kwansei Gakuin University, Sanda, Hyogo 669-1330, Japan. Electronic address: shigeto@kwansei.ac.jp.

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