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.
Preview |
Text
Laine_Velife-40183-v2.pdf Download (3MB) | Preview |
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 > 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 |
Loading...
Loading...Loading...Loading...Archive Staff Only
 |
View Item |
