Development of an automated counting method to evaluate SARS-CoV-2 positive nasal mucosa epithelial cells in SARS-CoV-2 infected Syrian hamsters

Abstract

The COVID-19 pandemic caused by SARS-CoV-2 requires effective strategies to prevent viral transmission. The potential of nasal vaccination as a mean of inhibiting viral entry and replication in the nasal mucosa, which serves as the primary site of SARS-CoV-2 infection, needs to be investigated. This research aims to develop a new method which would allow for an automated cell counting in order to compare the results obtained in SARS-CoV-2 infected an unvaccinated Syrian hamster to SARS-CoV-2 infected Syrian hamsters vaccinated either by the nasal or the systemic route, in order to assess the efficacy of the vaccines.

Immunostaining combined with image analysis using QuPath software was used to count the number of SARS-CoV-2 infected cells. A novel method of digesting whole nasal mucosa was developed to obtain a cell suspension for flow cytometry, the results of which will be used to validate the QuPath results. Ten Syrian hamsters were used as positive controls to aid method development. Results highlight the importance of repeatable sampling techniques, mucosal dissection and staining techniques. This research shows that the QuPath open source image analysis software using a trained cell classifier is promising for automating the counting of SARS-CoV-2 infected cells. Furthermore, the new method for whole nasal mucosa dissection worked, suggesting that our new counting method would allow the establishment of a baseline number of SARS-CoV-2 infected cells in the nasal mucosa of SARS-CoV-2 infected Syrian hamsters, which could be compared to hamsters vaccinated by the nasal and systemic routes in order to assess their efficacy.