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.

Metrics

  • 2,549
    Page views
  • 390
    Downloads
  • 8
    Citations

Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  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

    1. Epidemiology and Global Health
    Wan Yang, Jeffrey L Shaman
    Research Article

    Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern (VOCs) have been key drivers of new coronavirus disease 2019 (COVID-19) pandemic waves. To better understand variant epidemiologic characteristics, here we apply a model-inference system to reconstruct SARS-CoV-2 transmission dynamics in South Africa, a country that has experienced three VOC pandemic waves (i.e. Beta, Delta, and Omicron BA.1) by February 2022. We estimate key epidemiologic quantities in each of the nine South African provinces during March 2020 to February 2022, while accounting for changing detection rates, infection seasonality, nonpharmaceutical interventions, and vaccination. Model validation shows that estimated underlying infection rates and key parameters (e.g. infection-detection rate and infection-fatality risk) are in line with independent epidemiological data and investigations. In addition, retrospective predictions capture pandemic trajectories beyond the model training period. These detailed, validated model-inference estimates thus enable quantification of both the immune erosion potential and transmissibility of three major SARS-CoV-2 VOCs, that is, Beta, Delta, and Omicron BA.1. These findings help elucidate changing COVID-19 dynamics and inform future public health planning.

    1. Epidemiology and Global Health
    Tom G Richardson et al.
    Short Report

    Background:

    Vitamin D supplements are widely prescribed to help reduce disease risk. However, this strategy is based on findings using conventional epidemiological methods which are prone to confounding and reverse causation.

    Methods:

    In this short report, we leveraged genetic variants which differentially influence body size during childhood and adulthood within a multivariable Mendelian randomization (MR) framework, allowing us to separate the genetically predicted effects of adiposity at these two timepoints in the lifecourse.

    Results:

    Using data from the Avon Longitudinal Study of Parents and Children (ALSPAC), there was strong evidence that higher childhood body size has a direct effect on lower vitamin D levels in early life (mean age: 9.9 years, range = 8.9–11.5 years) after accounting for the effect of the adult body size genetic score (beta = −0.32, 95% CI = −0.54 to –0.10, p=0.004). Conversely, we found evidence that the effect of childhood body size on vitamin D levels in midlife (mean age: 56.5 years, range = 40–69 years) is putatively mediated along the causal pathway involving adulthood adiposity (beta = −0.17, 95% CI = −0.21 to –0.13, p=4.6 × 10-17).

    Conclusions:

    Our findings have important implications in terms of the causal influence of vitamin D deficiency on disease risk. Furthermore, they serve as a compelling proof of concept that the timepoints across the lifecourse at which exposures and outcomes are measured can meaningfully impact overall conclusions drawn by MR studies.

    Funding:

    This work was supported by the Integrative Epidemiology Unit which receives funding from the UK Medical Research Council and the University of Bristol (MC_UU_00011/1).