Development of single-cell level bacteria detection and species identification method using super-resolution microscopy

Abstract

The microbiome is a community of bacteria, viruses, and other microorganisms that exists in a particular environment. Although most of these microorganisms are harmless or beneficial to humans, recent research has shown that they play a critical role in a variety of human health problems and disease processes, including digestion, immunity, and brain health. In particular, the ratio of beneficial to harmful bacteria in the body can determine the health status of the human body. Therefore, it is important to accurately distinguish the types of these bacteria in samples taken from the human body. However, conventional diagnostic methods for the human microbiome are not sensitive enough to detect bacteria at low concentrations and suffer from poor specificity, thus limiting early diagnosis of bacterial infections. In this study, we developed novel approaches for bacterial species detection and identification method with single-cell sensitivity using super-resolution microscopy and AI-based image analysis: a protein quantification-based method and an AI-based bacterial image analysis method. We demonstrate that these methods can differentiate between common bacterial members of the skin flora, including Staphylococcus aureus and Staphylococcus epidermidis, and different ribotypes of Cutibacterium acnes, both in purified bacterial samples and in scaling skin samples. The advantages of these methods, including the lack of time-consuming amplification or purification steps and single-cell level detection sensitivity, allow early diagnosis of bacterial infections, even from bacterial samples at extremely low concentrations, thus showing promise as a next-generation platform for microbiome detection as single-cell diagnostics.