Machine learning-assisted single-cell Raman fingerprinting for in situ and nondestructive classification of prokaryotes

Nanako Kanno; Shingo Kato; Moriya Ohkuma; Motomu Matsui; Wataru Iwasaki; Shinsuke Shigeto

doi:10.1016/j.isci.2021.102975

Machine learning-assisted single-cell Raman fingerprinting for in situ and nondestructive classification of prokaryotes

iScience. 2021 Aug 11;24(9):102975. doi: 10.1016/j.isci.2021.102975. eCollection 2021 Sep 24.

Authors

Nanako Kanno¹, Shingo Kato², Moriya Ohkuma², Motomu Matsui³, Wataru Iwasaki^{3

4}, Shinsuke Shigeto¹

Affiliations

¹ Department of Chemistry, School of Science, Kwansei Gakuin University, 2-1 Gakuen, Sanda, Hyogo 669-1337, Japan.
² Japan Collection of Microorganisms, RIKEN BioResource Research Center, 3-1-1 Koyadai, Tsukuba, Ibaraki 305-0074, Japan.
³ Department of Biological Sciences, Graduate School of Science, the University of Tokyo, 2-11-16 Yayoi, Bunkyo-ku, Tokyo 113-0032, Japan.
⁴ Department of Integrated Biosciences, Graduate School of Frontier Sciences, the University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-0882, Japan.

Abstract

Accessing enormous uncultivated microorganisms (microbial dark matter) in various Earth environments requires accurate, nondestructive classification, and molecular understanding of the microorganisms in in situ and at the single-cell level. Here we demonstrate a combined approach of random forest (RF) machine learning and single-cell Raman microspectroscopy for accurate classification of phylogenetically diverse prokaryotes (three bacterial and three archaeal species from different phyla). Our RF classifier achieved a 98.8 ± 1.9% classification accuracy among the six species in pure populations and 98.4% for three species in an artificially mixed population. Feature importance scores against each wavenumber reveal that the presence of carotenoids and structure of membrane lipids play key roles in distinguishing the prokaryotic species. We also find unique Raman markers for an ammonia-oxidizing archaeon. Our approach with moderate data pretreatment and intuitive visualization of feature importance is easy to use for non-spectroscopists, and thus offers microbiologists a new single-cell tool for shedding light on microbial dark matter.

Keywords: Machine learning; Microbiology; Molecular spectroscopy techniques.