Uncovering the mechanisms of virus infection and assembly is crucial for preventing the spread of viruses and treating viral disease. The technique of single-virus tracking (SVT), also known as single-virus tracing, allows one to follow individual viruses at different parts of their life cycle and thereby provides dynamic insights into fundamental processes of viruses occurring in live cells. SVT is typically based on fluorescence imaging and reveals insights into previously unreported infection mechanisms. In this review article, we provide the readers a broad overview of the SVT technique. We first summarize recent advances in SVT, from the choice of fluorescent labels and labeling strategies to imaging implementation and analytical methodologies. We then describe representative applications in detail to elucidate how SVT serves as a valuable tool in virological research. Finally, we present our perspectives regarding the future possibilities and challenges of SVT.
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http://dx.doi.org/10.1021/acs.chemrev.9b00692 | DOI Listing |
Proc Natl Acad Sci U S A
January 2025
Department of Chemistry, Michigan State University, East Lansing, MI 48824.
The natural vibrational frequencies of biological particles such as viruses and bacteria encode critical information about their mechanical and biological states as they interact with their local environment and undergo structural evolution. However, detecting and tracking these vibrations within a biological context at the single particle level has remained elusive. In this study, we track the vibrational motions of single, unlabeled virus particles under ambient conditions using ultrafast spectroscopy.
View Article and Find Full Text PDFProc Natl Acad Sci U S A
December 2024
Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT 06520.
Phages, viruses of bacteria, play a pivotal role in Earth's biosphere and hold great promise as therapeutic and diagnostic tools in combating infectious diseases. Attachment of phages to bacterial cells is a crucial initial step of the interaction. The classic assay to quantify the dynamics of phage attachment involves coculturing and enumeration of bacteria and phages, which is laborious, lengthy, hence low-throughput, and only provides ensemble estimates of model-based adsorption rate constants.
View Article and Find Full Text PDFACS Nano
December 2024
State Key Laboratory of Medicinal Chemical Biology, Frontiers Science Center for New Organic Matter, Tianjin Key Laboratory of Biosensing and Molecular Recognition, Research Center for Analytical Sciences, College of Chemistry, School of Medicine and Frontiers Science Center for Cell Responses, Nankai University, Tianjin 300071, P. R. China.
The outbreak of emerging acute viral diseases urgently requires the acceleration of specialized antiviral drug development, thus widely adopting phenotypic screening as a strategy for drug repurposing in antiviral research. However, traditional phenotypic screening methods typically require several days of experimental cycles and lack visual confirmation of a drug's ability to inhibit viral infection. Here, we report a robust method that utilizes quantum-dot-based single-virus tracking and machine learning to generate unique single-virus infection fingerprint data from viral trajectories and detect the dynamic changes in viral movement following drug administration.
View Article and Find Full Text PDFMethods Mol Biol
November 2024
National Research Center for the Control and Prevention of Infectious Diseases, Nagasaki University, Nagasaki, Japan.
bioRxiv
October 2024
Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT 06520, USA.
Phages, viruses of bacteria, play a pivotal role in Earth's biosphere and hold great promise as therapeutic and diagnostic tools in combating infectious diseases. Attachment of phages to bacterial cells is a crucial initial step of the interaction. The classic assay to quantify the dynamics of phage attachment involves co-culturing and enumeration of bacteria and phages, which is laborious, lengthy, hence low-throughput, and only provides ensemble estimates of model-based adsorption rate constants.
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