Phenotyping the virulence of SARS-CoV-2 variants in hamsters by digital pathology and machine learning

Gavin R Meehan, Vanessa Herder, Jay Allan, Xinyi Huang, Karen Kerr, Diogo Correa Mendonca, Georgios Ilia, Derek W. Wright, Kyriaki Nomikou, Quan Gu, Sergi Molina Arias, Florian Hansmann, Alexandros Hardas, Charalampos Attipa, Giuditta De Lorenzo, Vanessa Cowton, Nicole Upfold, Natasha Palmalux, Jonathan C. Brown, Wendy BarclayAna da Silva Filipe, Wilhelm Furnon, Arvind H Patel, Massimo Palmarini

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has continued to evolve throughout the coronavirus disease-19 (COVID-19) pandemic, giving rise to multiple variants of concern (VOCs) with different biological properties. As the pandemic progresses, it will be essential to test in near real time the potential of any new emerging variant to cause severe disease. BA.1 (Omicron) was shown to be attenuated compared to the previous VOCs like Delta, but it is possible that newly emerging variants may regain a virulent phenotype. Hamsters have been proven to be an exceedingly good model for SARS-CoV-2 pathogenesis. Here, we aimed to develop robust quantitative pipelines to assess the virulence of SARS-CoV-2 variants in hamsters. We used various approaches including RNAseq, RNA in situ hybridization, immunohistochemistry, and digital pathology, including software assisted whole section imaging and downstream automatic analyses enhanced by machine learning, to develop methods to assess and quantify virus-induced pulmonary lesions in an unbiased manner. Initially, we used Delta and Omicron to develop our experimental pipelines. We then assessed the virulence of recent Omicron sub-lineages including BA.5, XBB, BQ.1.18, BA.2, BA.2.75 and EG.5.1. We show that in experimentally infected hamsters, accurate quantification of alveolar epithelial hyperplasia and macrophage infiltrates represent robust markers for assessing the extent of virus-induced pulmonary pathology, and hence virus virulence. In addition, using these pipelines, we could reveal how some Omicron sub-lineages (e.g., BA.2.75 and EG.5.1) have regained virulence compared to the original BA.1. Finally, to maximise the utility of the digital pathology pipelines reported in our study, we developed an online repository containing representative whole organ histopathology sections that can be visualised at variable magnifications (https://covid-atlas.cvr.gla.ac.uk). Overall, this pipeline can provide unbiased and invaluable data for rapidly assessing newly emerging variants and their potential to cause severe disease.
Original languageEnglish
Article numbere1011589
Pages (from-to)1-27
Number of pages27
JournalPLoS Pathogens
Volume19
Early online date7 Nov 2023
DOIs
Publication statusPublished - Nov 2023

Keywords / Materials (for Non-textual outputs)

  • Animals
  • COVID-19
  • Cricetinae
  • Humans
  • Machine Learning
  • SARS-CoV-2/genetics
  • Virulence

Fingerprint

Dive into the research topics of 'Phenotyping the virulence of SARS-CoV-2 variants in hamsters by digital pathology and machine learning'. Together they form a unique fingerprint.

Cite this