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Real-time non-invasive fluorescence imaging of gut commensal bacteria to detect dynamic changes in the microbiome of live mice.
Apostolos, Alexis J; Chordia, Mahendra D; Kolli, Sree H; Dalesandro, Brianna E; Rutkowski, Melanie R; Pires, Marcos M.
Afiliação
  • Apostolos AJ; Department of Chemistry, University of Virginia, Charlottesville, VA 22904, USA.
  • Chordia MD; Department of Chemistry, University of Virginia, Charlottesville, VA 22904, USA.
  • Kolli SH; Department of Microbiology, Immunology, and Cancer Biology, University of Virginia, Charlottesville, VA 22904, USA.
  • Dalesandro BE; Department of Chemistry, University of Virginia, Charlottesville, VA 22904, USA.
  • Rutkowski MR; Department of Microbiology, Immunology, and Cancer Biology, University of Virginia, Charlottesville, VA 22904, USA.
  • Pires MM; Department of Chemistry, University of Virginia, Charlottesville, VA 22904, USA. Electronic address: mpires@virginia.edu.
Cell Chem Biol ; 2022 Dec 05.
Article em En | MEDLINE | ID: mdl-36516833
ABSTRACT
In mammals, gut commensal microbiota interact extensively with the host, and the same interactions can be dysregulated in diseased states. Animal imaging is a powerful technique that is widely used to diagnose, measure, and track biological changes in model organisms such as laboratory mice. Several imaging techniques have been discovered and adopted by the research community that provide dynamic, non-invasive assessment of live animals, but these gains have not been universal across all fields of biology. Herein, we describe a method to non-invasively image commensal bacteria based on the specific metabolic labeling of bacterial cell walls to illuminate the gut bacteria of live mice. This tagging strategy may additionally provide unprecedented insight into cell wall turnover of gut commensals, which has implications for bacterial cellular growth and division, in a live animal.
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Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2022 Tipo de documento: Article