Imperial College London
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ProfessorWendyBarclay

Faculty of Medicine, Department of Infectious Disease

Action Medical Research Chair Virology. Head of Department
 
 
 
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Contact

 

+44 (0)20 7594 5035w.barclay

 
 
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Location

 

416Medical SchoolSt Mary's Campus

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Summary

 

Publications

Citation

BibTex format

@article{Meehan:2023:10.1371/journal.ppat.1011589,
author = {Meehan, GR and Herder, V and Allan, J and Huang, X and Kerr, K and Mendonca, DC and Ilia, G and Wright, DW and Nomikou, K and Gu, Q and Molina, Arias S and Hansmann, F and Hardas, A and Attipa, C and De, Lorenzo G and Cowton, V and Upfold, N and Palmalux, N and Brown, JC and Barclay, WS and Filipe, ADS and Furnon, W and Patel, AH and Palmarini, M},
doi = {10.1371/journal.ppat.1011589},
journal = {PLoS Pathog},
title = {Phenotyping the virulence of SARS-CoV-2 variants in hamsters by digital pathology and machine learning.},
url = {http://dx.doi.org/10.1371/journal.ppat.1011589},
volume = {19},
year = {2023}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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 an
AU  - Meehan,GR
AU  - Herder,V
AU  - Allan,J
AU  - Huang,X
AU  - Kerr,K
AU  - Mendonca,DC
AU  - Ilia,G
AU  - Wright,DW
AU  - Nomikou,K
AU  - Gu,Q
AU  - Molina,Arias S
AU  - Hansmann,F
AU  - Hardas,A
AU  - Attipa,C
AU  - De,Lorenzo G
AU  - Cowton,V
AU  - Upfold,N
AU  - Palmalux,N
AU  - Brown,JC
AU  - Barclay,WS
AU  - Filipe,ADS
AU  - Furnon,W
AU  - Patel,AH
AU  - Palmarini,M
DO  - 10.1371/journal.ppat.1011589
PY  - 2023///
TI  - Phenotyping the virulence of SARS-CoV-2 variants in hamsters by digital pathology and machine learning.
T2  - PLoS Pathog
UR  - http://dx.doi.org/10.1371/journal.ppat.1011589
UR  - https://www.ncbi.nlm.nih.gov/pubmed/37934791
VL  - 19
ER  -
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