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An RGB-emitting molecular cocktail for the detection of bacterial fingerprints

Accumulating evidence indicates that colonized microbes play a crucial role in regulating health and disease in the human body. Detecting microbes should be essential for understanding the relationship between microbes and diseases, as well as increasing our ability to detect diseases. Here, a combi...

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Detalles Bibliográficos
Autores principales: Hong, Sheng, Zheng, Di-Wei, Zhang, Qiu-Ling, Deng, Wei-Wei, Song, Wen-Fang, Cheng, Si-Xue, Sun, Zhi-Jun, Zhang, Xian-Zheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Royal Society of Chemistry 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7643548/
https://www.ncbi.nlm.nih.gov/pubmed/33209242
http://dx.doi.org/10.1039/d0sc01704c
Descripción
Sumario:Accumulating evidence indicates that colonized microbes play a crucial role in regulating health and disease in the human body. Detecting microbes should be essential for understanding the relationship between microbes and diseases, as well as increasing our ability to detect diseases. Here, a combined metabolic labeling strategy was developed to identify different bacterial species and microbiota by the use of three different fluorescent metabolite derivatives emitting red, green, and blue (RGB) fluorescence. Upon co-incubation with microbes, these fluorescent metabolite derivatives are incorporated into bacteria, generating unique true-color fingerprints for different bacterial species and different microbiota. A portable spectrometer was also fabricated to automate the colorimetric analysis in combination with a smartphone to conveniently identify different bacterial species and microbiota. Herein, the effectiveness of this system was demonstrated by the identification of certain bacterial species and microbiota in mice with different diseases, such as skin infections and bacteremia. By analyzing the microbiota fingerprints of saliva samples from clinical patients and healthy people, this system was proved to precisely distinguish oral squamous cell carcinoma (OSCC, n = 29) samples from precancerous (n = 10) and healthy (n = 5) samples.