期刊
ISCIENCE
卷 24, 期 9, 页码 -出版社
CELL PRESS
DOI: 10.1016/j.isci.2021.102975
关键词
-
资金
- Japan Society for the Promotion of Science KAKENHI [JP19H05679, JP19H05681, JP19H05689]
- Japan Science and Technology Agency Exploratory Research for Advanced Technology (ERATO) [JPMJER1502]
Utilizing a combination of machine learning and single-cell Raman microspectroscopy allows for accurate classification of phylogenetically diverse prokaryotes, shedding light on microbial dark matter. The presence of carotenoids and membrane lipid structure play key roles in distinguishing prokaryotic species, with unique Raman markers identified for an ammonia-oxidizing archaeon.
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 Ramanmarkers for an ammonia-oxidizing archaeon. Our approachwithmoderate 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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据