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

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.

Data availability

Analysis code for image segmentation, machine learning training, classification and structural analysis is available via GitHub (https://github.com/Romain-Laine/MiLeSIM). Source data files have been provided for Figures 4 and 5.

Article and author information

Author details

  1. Romain F Laine

    Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, United Kingdom
    For correspondence
    r.laine@ucl.ac.uk
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-2151-4487
  2. Gemma Goodfellow

    Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  3. Laurence J Young

    Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  4. Jon Travers

    Virology, Medimmune, Cambridge, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  5. Danielle Carroll

    Virology, Medimmune, Cambridge, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  6. Oliver Dibben

    Flu-BPD, Medimmune, Liverpool, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  7. Helen Bright

    Virology, Medimmune, Liverpool, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  8. Clemens Kaminski

    Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, United Kingdom
    For correspondence
    cfk23@cam.ac.uk
    Competing interests
    The authors declare that no competing interests exist.

Funding

Engineering and Physical Sciences Research Council (EP/L015889/1)

  • Romain F Laine
  • Gemma Goodfellow
  • Laurence J Young
  • Clemens Kaminski

Medical Research Council (MR/K015850/1)

  • Romain F Laine
  • Gemma Goodfellow
  • Laurence J Young
  • Clemens Kaminski

Medical Research Council (MR/K02292X/1)

  • Romain F Laine
  • Gemma Goodfellow
  • Laurence J Young
  • Clemens Kaminski

Wellcome (3-3249/Z/16/Z)

  • Romain F Laine
  • Gemma Goodfellow
  • Laurence J Young
  • Clemens Kaminski

Engineering and Physical Sciences Research Council (EP/H018301/1)

  • Romain F Laine
  • Gemma Goodfellow
  • Laurence J Young
  • Clemens Kaminski

Biotechnology and Biological Sciences Research Council (BB/P027431/1)

  • Romain F Laine

MedImmune (NA)

  • Romain F Laine
  • Gemma Goodfellow
  • Jon Travers
  • Danielle Carroll
  • Oliver Dibben
  • Helen Bright

Infinitus (NA)

  • Clemens Kaminski

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. Antoine M van Oijen, University of Wollongong, Australia

Version history

  1. Received: July 17, 2018
  2. Accepted: December 10, 2018
  3. Accepted Manuscript published: December 13, 2018 (version 1)
  4. Version of Record published: January 14, 2019 (version 2)

Copyright

© 2018, Laine et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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  1. Romain F Laine
  2. Gemma Goodfellow
  3. Laurence J Young
  4. Jon Travers
  5. Danielle Carroll
  6. Oliver Dibben
  7. Helen Bright
  8. Clemens Kaminski
(2018)
Structured illumination microscopy combined with machine learning for the high throughput analysis of virus structure
eLife 7:e40183.
https://doi.org/10.7554/eLife.40183

Share this article

https://doi.org/10.7554/eLife.40183

Further reading

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    Results:

    Median tracing delay decreased from 7 to 3.1 days and the percentage of the known infection source increased from 34–54.8% (incident rate ratio-IRR 1.61 1.40–1.86). Implementation of prompt contact tracing was associated with a 10% decrease in the number of secondary cases (excess relative risk –0.1 95% CI –0.35–0.15). Knowing the source of infection of the index case led to a decrease in secondary transmission (IRR 0.75 95% CI 0.63–0.91) while the decrease in tracing delay was associated with decreased risk of secondary cases (1/IRR 0.97 95% CI 0.94–1.01 per one day of delay). The direct effect of the intervention accounted for the 29% decrease in the number of secondary cases (excess relative risk –0.29 95%–0.61 to 0.03).

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    Prompt contact testing in the community reduces the time of contact tracing and increases the ability to identify the source of infection in school outbreaks. Although there are strong reasons for thinking it is a causal link, observed differences can be also due to differences in the force of infection and to other control measures put in place.

    Funding:

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