2023 journal article

A rapid, high-throughput, viral infectivity assay using automated brightfield microscopy with machine learning

SLAS Technology.

By: R. Dodkins*, J. Delaney*, T. Overton n, F. Scholle n, A. Frias-De-Diego n, E. Crisci n, N. Huq, I. Jordan ...

UN Sustainable Development Goal Categories
3. Good Health and Well-being (OpenAlex)
Source: ORCID
Added: July 13, 2023

Infectivity assays are essential for the development of viral vaccines, antiviral therapies, and the manufacture of biologicals. Traditionally, these assays take 2-7 days and require several manual processing steps after infection. We describe an automated viral infectivity assay (AVIATM), using convolutional neural networks (CNNs) and high-throughput brightfield microscopy on 96-well plates that can quantify infection phenotypes within hours, before they are manually visible, and without sample preparation. CNN models were trained on HIV, influenza A virus, coronavirus 229E, vaccinia viruses, poliovirus, and adenoviruses, which together span the four major categories of virus (DNA, RNA, enveloped, and non-enveloped). A sigmoidal function, fit between virus dilution curves and CNN predictions, results in sensitivity ranges comparable to or better than conventional plaque or TCID50 assays, and a precision of ∼10%, which is considerably better than conventional infectivity assays. Because this technology is based on sensitizing CNNs to specific phenotypes of infection, it has potential as a rapid, broad-spectrum tool for virus characterization, and potentially identification.