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6 March 2023 Stain-free, rapid, and quantitative viral plaque assay using deep learning and time-lapse holographic imaging
Yuzhu Li, Tairan Liu, Hatice Ceylan Koydemir, Yijie Zhang, Ethan Yang, Hongda Wang, Jingxi Li, Bijie Bai, Aydogan Ozcan
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Proceedings Volume PC12369, Optics and Biophotonics in Low-Resource Settings IX; PC123690D (2023) https://doi.org/10.1117/12.2648162
Event: SPIE BiOS, 2023, San Francisco, California, United States
Abstract
We present a stain-free, rapid, and automated viral plaque assay using deep learning and holography, which needs significantly less sample incubation time than traditional plaque assays. A portable and cost-effective lens-free imaging prototype was built to record the spatio-temporal features of the plaque-forming units (PFUs) during their growth, without the need for staining. Our system detected the first cell lysing events as early as 5 hours of incubation and achieved >90% PFU detection rate with 100% specificity in <20 hours, saving >24 hours compared to the traditional viral plaque assays that take ≥48 hours.
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Yuzhu Li, Tairan Liu, Hatice Ceylan Koydemir, Yijie Zhang, Ethan Yang, Hongda Wang, Jingxi Li, Bijie Bai, and Aydogan Ozcan "Stain-free, rapid, and quantitative viral plaque assay using deep learning and time-lapse holographic imaging", Proc. SPIE PC12369, Optics and Biophotonics in Low-Resource Settings IX, PC123690D (6 March 2023); https://doi.org/10.1117/12.2648162
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KEYWORDS
Holography
3D image reconstruction
Gold
Neural networks
Prototyping
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