Researchers in AIST developed a novel analytical technology that can determine the characteristics of the bacterial composition of gut microbiota with high accuracy, using polymers that emit blue fluorescence when in contact with bacteria and machine learning to screen the characteristics of the fluorescence intensity patterns.
This technology uses a bioanalytical method called a chemical nose. The chemical nose developed consists of 12 types of polymers with fluorophores that emit light when aggregating. By mixing these polymers with intestinal bacteria, various fluorescent signals can be detected, and the bacteria can be characterized based on those patterns. Using the developed chemical nose, the researchers succeeded in determining the health status of mice with a high degree of accuracy by comparative analysis of gut microbiome samples collected from healthy and insomniac mice. This technology enabled to characterize the state of gut microbiota from a different perspective than the standard gut microbiome analysis method (e.g., 16S rRNA gene amplicon sequencing analysis), and has the advantages of being faster, easier, and less expensive than amplicon sequencing analysis. In the future, it is expected to be applied as a diagnostic technique for health care, using human gut microbiome samples as specimens.
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