Structured illumination microscopy combined with machine learning enables the high throughput analysis and classification of virus structure.
Young, Laurence J
eLife Sciences Publications Ltd
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Laine, R. F., Goodfellow, G., Young, L. J., Travers, J., Carroll, D., Dibben, O., Bright, H., & et al. (2018). Structured illumination microscopy combined with machine learning enables the high throughput analysis and classification of virus structure.. eLife, 7 https://doi.org/10.7554/elife.40183
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.
Newcastle disease virus, Orthomyxoviridae, Vaccines, Attenuated, Influenza Vaccines, Microscopy, Fluorescence, Algorithms, Automation, Image Processing, Computer-Assisted, Machine Learning
European Commission Horizon 2020 (H2020) Marie Sk?odowska-Curie actions (722380)
Wellcome Trust (203249/Z/16/Z)
Wellcome Trust (089703/Z/09/Z)
External DOI: https://doi.org/10.7554/elife.40183
This record's URL: https://www.repository.cam.ac.uk/handle/1810/287497
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