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 (AVIA TM ), 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 TCID 50 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.
Keyphrases
- high throughput
- convolutional neural network
- sars cov
- machine learning
- single cell
- deep learning
- single molecule
- antiretroviral therapy
- hepatitis c virus
- hiv infected
- human immunodeficiency virus
- hiv positive
- hiv aids
- artificial intelligence
- liquid chromatography tandem mass spectrometry
- risk assessment
- circulating tumor
- coronavirus disease
- climate change
- nucleic acid
- disease virus
- circulating tumor cells
- optical coherence tomography
- quantum dots