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

  1. Romain F Laine  Is a corresponding author
  2. Gemma Goodfellow
  3. Laurence J Young
  4. Jon Travers
  5. Danielle Carroll
  6. Oliver Dibben
  7. Helen Bright
  8. Clemens Kaminski  Is a corresponding author
  1. University of Cambridge, United Kingdom
  2. Medimmune, United Kingdom

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

Publication 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

Further reading

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    Irene Man, Damien Georges ... Iacopo Baussano
    Research Article

    Local cervical cancer epidemiological data essential to project the context-specific impact of cervical cancer preventive measures are often missing. We developed a framework, hereafter named Footprinting, to approximate missing data on sexual behaviour, human papillomavirus (HPV) prevalence, or cervical cancer incidence, and applied it to an Indian case study. With our framework, we (1) identified clusters of Indian states with similar cervical cancer incidence patterns, (2) classified states without incidence data to the identified clusters based on similarity in sexual behaviour, (3) approximated missing cervical cancer incidence and HPV prevalence data based on available data within each cluster. Two main patterns of cervical cancer incidence, characterized by high and low incidence, were identified. Based on the patterns in the sexual behaviour data, all Indian states with missing data on cervical cancer incidence were classified to the low-incidence cluster. Finally, missing data on cervical cancer incidence and HPV prevalence were approximated based on the mean of the available data within each cluster. With the Footprinting framework, we approximated missing cervical cancer epidemiological data and made context-specific impact projections for cervical cancer preventive measures, to assist public health decisions on cervical cancer prevention in India and other countries.

    1. Epidemiology and Global Health
    2. Microbiology and Infectious Disease
    Kennedy Lushasi, Kirstyn Brunker ... Katie Hampson
    Research Article

    Background:

    Dog-mediated rabies is endemic across Africa causing thousands of human deaths annually. A One Health approach to rabies is advocated, comprising emergency post-exposure vaccination of bite victims and mass dog vaccination to break the transmission cycle. However, the impacts and cost-effectiveness of these components are difficult to disentangle.

    Methods:

    We combined contact tracing with whole-genome sequencing to track rabies transmission in the animal reservoir and spillover risk to humans from 2010-2020, investigating how the components of a One Health approach reduced the disease burden and eliminated rabies from Pemba Island, Tanzania. With the resulting high-resolution spatiotemporal and genomic data we inferred transmission chains and estimated case detection. Using a decision tree model we quantified the public health burden and evaluated the impact and cost-effectiveness of interventions over a ten-year time horizon.

    Results:

    We resolved five transmission chains co-circulating on Pemba from 2010 that were all eliminated by May 2014. During this period, rabid dogs, human rabies exposures and deaths all progressively declined following initiation and improved implementation of annual islandwide dog vaccination. We identified two introductions to Pemba in late 2016 that seeded re-emergence after dog vaccination had lapsed. The ensuing outbreak was eliminated in October 2018 through reinstated islandwide dog vaccination. While post-exposure vaccines were projected to be highly cost-effective ($256 per death averted), only dog vaccination interrupts transmission. A combined One Health approach of routine annual dog vaccination together with free post-exposure vaccines for bite victims, rapidly eliminates rabies, is highly cost-effective ($1657 per death averted) and by maintaining rabies freedom prevents over 30 families from suffering traumatic rabid dog bites annually on Pemba island.

    Conclusions:

    A One Health approach underpinned by dog vaccination is an efficient, cost-effective, equitable and feasible approach to rabies elimination, but needs scaling up across connected populations to sustain the benefits of elimination, as seen on Pemba, and for similar progress to be achieved elsewhere.

    Funding:

    Wellcome [207569/Z/17/Z, 095787/Z/11/Z, 103270/Z/13/Z], the UBS Optimus Foundation, the Department of Health and Human Services of the National Institutes of Health [R01AI141712] and the DELTAS Africa Initiative [Afrique One-ASPIRE/DEL-15-008] comprising a donor consortium of the African Academy of Sciences (AAS), Alliance for Accelerating Excellence in Science in Africa (AESA), the New Partnership for Africa's Development Planning and Coordinating (NEPAD) Agency, Wellcome [107753/A/15/Z], Royal Society of Tropical Medicine and Hygiene Small Grant 2017 [GR000892] and the UK government. The rabies elimination demonstration project from 2010-2015 was supported by the Bill & Melinda Gates Foundation [OPP49679]. Whole-genome sequencing was partially supported from APHA by funding from the UK Department for Environment, Food and Rural Affairs (Defra), Scottish government and Welsh government under projects SEV3500 & SE0421.