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Rapid Microbial Profiling through Multimodal Biosensors for Transversal Analysis.
Lee, Jyong-Huei; Chin, Siew Mei; Chan, Dennis C; Liao, Joseph C; Yang, Samuel; Zhang, Nanying; Wong, Pak Kin.
Afiliação
  • Lee JH; Department of Biomedical Engineering, The Pennsylvania State University, University Park, Pennsylvania 16802, United States.
  • Chin SM; Department of Biomedical Engineering, The Pennsylvania State University, University Park, Pennsylvania 16802, United States.
  • Chan DC; Department of Biomedical Engineering, The Pennsylvania State University, University Park, Pennsylvania 16802, United States.
  • Liao JC; Department of Urology, Stanford University School of Medicine, Stanford, California 94305, United States.
  • Yang S; Department of Emergency Medicine, Stanford University School of Medicine, Palo Alto, California 94304, United States.
  • Zhang N; Department of Biomedical Engineering, The Pennsylvania State University, University Park, Pennsylvania 16802, United States.
  • Wong PK; Neuroscience Program, The Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, Pennsylvania 16802, United States.
Anal Chem ; 2024 Jul 15.
Article em En | MEDLINE | ID: mdl-39007543
ABSTRACT
The intricate interactions between host and microbial communities hold significant implications for biology and medicine. However, traditional microbial profiling methods face limitations in processing time, measurement of absolute abundance, detection of low biomass, discrimination between live and dead cells, and functional analysis. This study introduces a rapid multimodal microbial characterization platform, Multimodal Biosensors for Transversal Analysis (MBioTA), for capturing the taxonomy, viability, and functional genes of the microbiota. The platform incorporates single cell biosensors, scalable microwell arrays, and automated image processing for rapid transversal analysis in as few as 2 h. The multimodal biosensors simultaneously characterize the taxon, viability, and functional gene expression of individual cells. By automating the image processing workflow, the single cell analysis techniques enable the quantification of bacteria with sensitivity down to 0.0075%, showcasing its capability in detecting low biomass samples. We illustrate the applicability of the MBioTA platform through the transversal analysis of the gut microbiota composition, viability, and functionality in a familial Alzheimer's disease mouse model. The effectiveness, rapid turnaround, and scalability of the MBioTA platform will facilitate its application from basic research to clinical diagnostics, potentially revolutionizing our understanding and management of diseases associated with microbe-host interactions.

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article