RT Journal Article SR Electronic T1 Structured illumination microscopy combined with machine learning enables the high throughput analysis and classification of virus structure JF bioRxiv FD Cold Spring Harbor Laboratory SP 266551 DO 10.1101/266551 A1 Romain F. Laine A1 Gemma Goodfellow A1 Laurence J. Young A1 Jon Travers A1 Danielle Carroll A1 Oliver Dibben A1 Helen Bright A1 Clemens F. Kaminski YR 2018 UL http://biorxiv.org/content/early/2018/07/11/266551.abstract AB Optical super-resolution microscopy techniques enable high molecular specificity with high spatial resolution and constitute a set of powerful tools in the investigation of the structure of supramolecular assemblies such as viruses. Here, we report on a new methodology which combines Structured Illumination Microscopy (SIM) with machine learning algorithms to image and classify the structure of large populations of biopharmaceutical viruses with high resolution. The method offers information on virus morphology that can ultimately be linked with functional performance. We demonstrate the approach on viruses produced for oncolytic viriotherapy (Newcastle Disease Virus) and vaccine development (Influenza). This unique tool enables the rapid assessment of the quality of viral production with high throughput obviating the need for traditional batch testing methods which are complex and time consuming. We show that our method also works on non-purified samples from pooled harvest fluids directly from the production line.