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Structured illumination microscopy combined with machine learning enables the high throughput analysis and classification of virus structure

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 Illu...

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Autores principales: Laine, Romain F, Goodfellow, Gemma, Young, Laurence J, Travers, Jon, Carroll, Danielle, Dibben, Oliver, Bright, Helen, Kaminski, Clemens F
Formato: Online Artículo Texto
Lenguaje:English
Publicado: eLife Sciences Publications, Ltd 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6331195/
https://www.ncbi.nlm.nih.gov/pubmed/30543181
http://dx.doi.org/10.7554/eLife.40183
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author Laine, Romain F
Goodfellow, Gemma
Young, Laurence J
Travers, Jon
Carroll, Danielle
Dibben, Oliver
Bright, Helen
Kaminski, Clemens F
author_facet Laine, Romain F
Goodfellow, Gemma
Young, Laurence J
Travers, Jon
Carroll, Danielle
Dibben, Oliver
Bright, Helen
Kaminski, Clemens F
author_sort Laine, Romain F
collection PubMed
description 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.
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spelling pubmed-63311952019-01-16 Structured illumination microscopy combined with machine learning enables the high throughput analysis and classification of virus structure Laine, Romain F Goodfellow, Gemma Young, Laurence J Travers, Jon Carroll, Danielle Dibben, Oliver Bright, Helen Kaminski, Clemens F eLife Epidemiology and Global Health 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. eLife Sciences Publications, Ltd 2018-12-13 /pmc/articles/PMC6331195/ /pubmed/30543181 http://dx.doi.org/10.7554/eLife.40183 Text en © 2018, Laine et al http://creativecommons.org/licenses/by/4.0/ http://creativecommons.org/licenses/by/4.0/This article is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use and redistribution provided that the original author and source are credited.
spellingShingle Epidemiology and Global Health
Laine, Romain F
Goodfellow, Gemma
Young, Laurence J
Travers, Jon
Carroll, Danielle
Dibben, Oliver
Bright, Helen
Kaminski, Clemens F
Structured illumination microscopy combined with machine learning enables the high throughput analysis and classification of virus structure
title Structured illumination microscopy combined with machine learning enables the high throughput analysis and classification of virus structure
title_full Structured illumination microscopy combined with machine learning enables the high throughput analysis and classification of virus structure
title_fullStr Structured illumination microscopy combined with machine learning enables the high throughput analysis and classification of virus structure
title_full_unstemmed Structured illumination microscopy combined with machine learning enables the high throughput analysis and classification of virus structure
title_short Structured illumination microscopy combined with machine learning enables the high throughput analysis and classification of virus structure
title_sort structured illumination microscopy combined with machine learning enables the high throughput analysis and classification of virus structure
topic Epidemiology and Global Health
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6331195/
https://www.ncbi.nlm.nih.gov/pubmed/30543181
http://dx.doi.org/10.7554/eLife.40183
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