RT Journal Article
SR Electronic
T1 MiLeSIM: combining super-resolution and machine learning permits high-throughput virus structure analysis
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/2018/02/16/266551.abstract
AB The development of super-resolution microscopy techniques has enabled unprecedented structural description of supramolecular assemblies such as viruses. Here, we developed a methodology based on Structured Illumination Microscopy (SIM) combined with machine learning classification and followed by class-specific image quantification in order to perform high-resolution structural analysis of large population of viruses. This allows us to fully quantify the structural content of virus populations with important applications in the biopharmaceutical industry. 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 and the molecular specificity of fluorescence microscopy, which allows the use of direct un-purified samples from pooled harvest fluids.