EAGER: High-throughput early detection and analysis of COVID-19 plaque formation using time-lapse coherent imaging and deep learning

Plaque assays are widely used for measuring the infectious concentration of viral samples and form a very important tool for vaccine development, especially for the evaluation of the performance of new vaccines at the exploratory and preclinical stages. This standard method is laborious and takes days to get the results, and is subject to human errors since it depends on manual plaque counting. Molecular techniques such as polymerase chain reaction (PCR or reverse transcription PCR) and western blots can be used to quantify the viral genome. However, none of these methods provide information about the infectivity of the virus and cannot measure plaque forming units. This proposal aims to create a computational sensor platform for accelerated testing of SARSCoV-2 viability and infectivity using deep learning-based plaque assays and achieve accurate and automated plaque forming unit (PFU) measurements within hours as opposed to days with standard plaque assays.

The proposed computational imaging system will periodically capture coherent microscopic images of the cytopathogenic effects of viruses on cell cultures and analyze these time lapsed holographic images using deep neural networks (DNNs) for rapid detection of viral destruction of the cell monolayer. In addition to early and automated detection of plaque forming units, this unique platform will further make use of deep learning for high-throughput holographic image reconstruction of the assay volume to perform tile-scan imaging of the entire well plate within 5 min, corresponding to an imaging throughput of ~50 cm2/min. Powered by deep learning, this automated and cost-effective viral plaque monitoring platform can be transformative for a wide range of applications in microbiology and virology by significantly reducing the detection time without labeling or the need for an expert, or manual inspection. The project will also establish a complementary educational outreach program that will involve (1) public interviews and popular science articles in news media and internet; (2) undergraduate research opportunities in the PI?s laboratory involving minority students; and (3) graduate student training through organization of workshops, seminars and conferences. Furthermore, research projects, seminars and open house visits will serve undergrads and high school students (especially from minority groups) to interact with a cutting edge research environment, helping to increase their scientific curiosity and shaping their career goals in science and engineering.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.