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Structured illumination microscopy combined with machine learning for the high throughput analysis of virus structure

Laine, RF; Goodfellow, G; Young, LJ; Travers, J; Carroll, D; Dibben, O; Bright, H; (2018) Structured illumination microscopy combined with machine learning for the high throughput analysis of virus structure. eLife , 7 , Article e40183. 10.7554/eLife.40183. Green open access

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Abstract

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.

Type: Article
Title: Structured illumination microscopy combined with machine learning for the high throughput analysis of virus structure
Location: England
Open access status: An open access version is available from UCL Discovery
DOI: 10.7554/eLife.40183
Publisher version: https://doi.org/10.7554/eLife.40183
Language: English
Additional information: © 2018, Laine et al. This article is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) permitting unrestricted use and redistribution provided that the original author and source are credited.
Keywords: epidemiology, global health, infectious disease, microbiology, viruses
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Lab for Molecular Cell Bio MRC-UCL
URI: https://discovery.ucl.ac.uk/id/eprint/10064456
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