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Results for “Quantitative high-content microscopy”

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Monitoring Influenza A Virus Entry Using Quantitative Fluorescence Microscopy.

Influenza A virus (IAV) is a major threat to global human health and is a topic of intense research. With the continuous problem of seasonal influenza and the threat of potential pandemics due to frequent emergence of new viral strains, development of new, broad-spectrum antivirals is an urgent priority. In antiviral development against influenza, the process of host cell entry of IAV is of particular interest as inhibiting the virus at the entry step should stop infection early on, blocking the downstream infection processes including viral replication and transcription. Therefore, a detailed understanding of the IAV entry processes is essential to illuminate virus-assisting host factors that can serve as potentially valuable targets for therapeutic interventions. To accelerate the identification of novel antivirals or host-directed targets that play essential role in IAV entry, quantitative assays that can be used to monitor the virus at sequential entry steps would be important for performing high-content genetic or inhibitor screens. In this chapter, we describe how IAV entry can be monitored at the sequential entry steps, spanning from the initial attachment of the virus particle to the cell surface to the transmission of the viral genome to the nucleus, by fluorescence microscopy. Further, we provide the methods to quantify the images acquired with high-content microscope for each of the major IAV entry steps. The fluorescence microscopy-based IAV entry assays and the image quantification methods described here can be used to boost our understanding of the virus-host cell interactions and can lead to the discovery of novel host-directed prophylactic or therapeutic interventions.

Humans

The Open Microscopy Environment (OME) Data Model and XML file: open tools for informatics and quantitative analysis in biological imaging.

The Open Microscopy Environment (OME) defines a data model and a software implementation to serve as an informatics framework for imaging in biological microscopy experiments, including representation of acquisition parameters, annotations and image analysis results. OME is designed to support high-content cell-based screening as well as traditional image analysis applications. The OME Data Model, expressed in Extensible Markup Language (XML) and realized in a traditional database, is both extensible and self-describing, allowing it to meet emerging imaging and analysis needs.

Computational Biology