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

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 pl

SUBMITTER: Kanno N 

PROVIDER: S-EPMC8397914 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature