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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...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
eLife Sciences Publications, Ltd
2018
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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. |
format | Online Article Text |
id | pubmed-6331195 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | eLife Sciences Publications, Ltd |
record_format | MEDLINE/PubMed |
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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