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

Abstract

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.

Keywords

• Viral infectivity assay
• Antiviral screening
• Brightfield microscopy
• Label-free assays
• Cell-based assays
• Machine learning