Machine Learning-Assisted Single-Cell Raman Fingerprinting for in Situ and Nondestructive Prokaryotic Classification in Complex Microbial Populations

Accessing enormous uncultivated microorganisms (known as microbial dark matter) in a variety of Earth environments requires accurate, nondestructive classification and molecular understanding of the microorganisms in situ and at the single-cell level, which are not feasible with the current genomics approaches. Here we demonstrate a combined approach of random forest (RF) machine learning and less-invasive, single-cell Raman microspectroscopy for accurate classification of phylogenetically diverse prokaryotes (three bacterial and three archaeal species from different phyla). Our RF classifier achieved 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 in the Raman spectrum reveal that, in addition to protein and DNA/RNA abundances, 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.