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Isolation and Genome Analysis of Single Virions using 'Single Virus Genomics'
Published on: May 26, 2013
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Tools for Single-Cell Kinetic Analysis of Virus-Host Interactions
Jay W Warrick1, Andrea Timm1, Adam Swick1
1Systems Biology Theme, Wisconsin Institute for Discovery, Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, WI, United States of America.
Plos One
|January 12, 2016
Summary
This study introduces a new microwell device and open-source software for analyzing individual cell responses to viral infections. The integrated system offers dynamic, standardized, and labor-efficient single-cell gene expression analysis.
Area of Science:
- Cellular biology
- Virology
- Biotechnology
Background:
- Single-cell analysis reveals diverse cellular behaviors and outcomes in health and disease.
- Viral infections present extreme diversity in outcomes due to virus-host interactions.
- Current single-cell methods are often labor-intensive, require specialized equipment, and use end-point measurements.
Purpose of the Study:
- To develop a novel, accessible platform for dynamic single-cell analysis of viral infections.
- To overcome limitations of existing single-cell measurement techniques.
- To enable standardized and efficient protocols for studying cellular responses.
Main Methods:
- A new microwell-based device for isolating individual cells using simple fluid handling.
- Integration of a dual-color fluorescent reporter system for dynamic gene expression monitoring.
- Development of free, open-source software for data management and batch image analysis.
Main Results:
- The integrated device and software system was validated for single-cell analysis.
- Demonstrated unique dynamic measures of cellular responses to viral infection.
- Enabled streamlined data management and batch image analysis for complex single-cell experiments.
Conclusions:
- The developed platform provides a standardized, efficient, and dynamic approach to single-cell viral infection studies.
- This innovation facilitates deeper understanding of cellular responses at the single-cell level.
- The combination of user-friendly hardware and open-source software democratizes advanced single-cell analysis.
