US Pharm. 2023;48(10):4.
Researchers have developed an automated version of the viral plaque assay, the gold-standard method for detecting and quantifying viruses. The new method uses deep learning and time-lapse holographic imaging to greatly reduce detection time and eliminate staining and manual counting. This advance could help streamline the development of new vaccines and antiviral drugs.
“By cutting down the detection time compared to traditional viral plaque assays, this technique might help expedite vaccine and drug development research by significantly reducing the detection time needed and eliminating chemical staining and manual counting entirely,” explained Yuzhu Li from the Ozcan Lab at the University of California, Los Angeles. “In the event of a new virus outbreak, vaccines or antiviral treatments could be developed, tested, and made available to the public at a significantly accelerated rate, resulting in a faster response time to virus-induced health emergencies.”
Although the viral plaque assay is a cost-effective way to assess virus infectivity and quantify the amount of a virus in a sample, it is time consuming to perform. Samples are first diluted and then added to cultured cells. If the virus kills the infected cells, a region free of cells—a plaque—develops. Experts then manually count the stained plaque-forming units (PFUs), a process that is susceptible to staining irregularities and human counting errors.
The new stain-free automated viral plaque assay system replaces manual plaque counting with a lens-free holographic imaging system that images the spatiotemporal features of PFUs during incubation. A deep-learning algorithm is then used to detect, classify, and locate PFUs based on observed changes.
To show the efficacy of their system, the researchers infected cultured cells with the vesicular stomatitis virus. After just 20 hours of incubation, the automated system detected more than 90% of the viral PFUs without any false positives. This was much faster than the traditional plaque assay, which requires 48 hours of incubation for this virus. They also applied the automated approach to herpes simplex virus type 1 and encephalomyocarditis virus. They demonstrated even shorter incubation times for these viruses, saving an average of approximately 48 and 20 hours, respectively.
The researchers report that no false positives were detected across all time points. In addition, because the system can identify individual PFUs during their early growth, before the formation of PFU clusters, it can be used to analyze viral samples with about 10-times-higher virus concentrations than traditional approaches.
The research team is also exploring other potential applications of this technique in virology research for high-throughput and cost-effective screening of antiviral drugs.
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