Yuzhu Li, a Ph.D. student in Prof. Aydogan Ozcan’s group at UCLA Henry Samueli School of Engineering, has been awarded the “Emil Wolf Outstanding Student Paper Award” for the paper titled “Stain-free, rapid, and automated viral plaque assay using time-lapse holographic imaging and deep learning” at the Optica Frontiers in Optics (FIO) Conference held in Tacoma, Washington, in October.
Yuzhu, in her conference talk, presented how the combination of time-lapse holographic imaging and deep learning can greatly accelerate the detection of viral plaques, while also entirely bypassing the need for chemical staining and manual counting.
In the fight against viral infections, a variety of techniques have been established for detecting and quantifying viruses, contributing significantly to the development of critical vaccines and antiviral medications. Among these techniques, the viral plaque assay stands out as the gold standard due to its unique ability to assess virus infectivity in a cost-effective way by observing the formation of viral plaques caused by viral infections over a layer of cells. Nevertheless, the traditional viral plaque assays require an incubation period of 2-14 days, followed by sample staining using chemicals and human visual inspection to count the number of viral plaques. This procedure is time-consuming and susceptible to staining artifacts and counting errors induced by human technicians. Therefore, an accurate, automated, rapid, and cost-effective viral plaque quantification technique is urgently needed.
Yuzhu’s presentation at this Optica conference in October reported a rapid, stain-free and automated viral plaque detection system enabled by holography and deep learning. This system incorporates a cost-effective and high-throughput holographic imaging device that continuously monitors the unstained virus-infected cells during their incubation process. At each imaging cycle, these time-lapse holograms captured by the device are periodically analyzed by an AI-powered algorithm to automatically detect and count the viral plaques that appear due to virus replication.
The proof-of-concept and effectiveness of this system were demonstrated using three different types of viruses: vesicular stomatitis virus (VSV), herpes simplex virus type-1 (HSV-1), and encephalomyocarditis virus (EMCV). By utilizing this system, Yuzhu and her colleagues at UCLA achieved the detection of over 90% of VSV viral plaques within 20 hours of incubation without any chemical staining, demonstrating a time saving of more than 24 hours in comparison to the traditional plaque assay, which requires 48 hours of sample incubation. In the case of HSV-1 and EMCV, this system effectively reduced their viral plaque detection times by approximately 48 and 20 hours, respectively, compared to the detection time needed for the traditional staining-based viral plaque assay.
In addition to offering major time savings, this stain-free and cost-effective system can successfully identify individual viral plaques within clusters as opposed to the traditional viral plaque assays, which fail to separately detect and count those individual plaques within clusters due to the spatial overlap of their signatures.
All these findings highlight the transformative potential of this AI-powered viral plaque detection system to be used with various plaque assays in virology, which might help expedite vaccine and drug development research by significantly reducing the detection time needed for traditional viral plaque assays and eliminating chemical staining and manual counting entirely.
More details about this research can be found in a paper that the Ozcan Lab published in Nature Biomedical Engineering: https://doi.org/10.1038/s41551-023-01057-7. Funding for a portion of this project was provided by NSF with Prof. Aydogan Ozcan of the Electrical & Computer Engineering Department as the principal investigator.