There is a need to standardize pathologic endpoints in animal models of SARS-CoV-2 infection to help benchmark study quality, improve cross-institutional comparison of data, and assess therapeutic efficacy so that potential drugs and vaccines for SARS-CoV-2 can rapidly advance. The Syrian hamster model is a tractable small animal model for COVID-19 that models clinical disease in humans. Using the hamster model, the authors used traditional pathologic assessment with quantitative image analysis to assess disease outcomes in hamsters administered polyclonal immune sera from previously challenged rhesus macaques. The authors then used quantitative image analysis to assess pathologic endpoints across studies performed at different institutions using different tissue processing protocols. The authors detail pathological features of SARS-CoV-2 infection longitudinally and use immunohistochemistry to quantify myeloid cells and T lymphocyte infiltrates during SARS-CoV-2 infection. High-dose immune sera protected hamsters from weight loss and diminished viral replication in tissues and reduced lung lesions. Cumulative pathology scoring correlated with weight loss and was robust in distinguishing IgG efficacy. In formalin-infused lungs, quantitative measurement of percent area affected also correlated with weight loss but was less robust in non-formalin-infused lungs. Longitudinal immunohistochemical assessment of interstitial macrophage infiltrates showed that peak infiltration corresponded to weight loss, yet quantitative assessment of macrophage, neutrophil, and CD3+ T lymphocyte numbers did not distinguish IgG treatment effects. Here, the authors show that quantitative image analysis was a useful adjunct tool for assessing SARS-CoV-2 treatment outcomes in the hamster model.
Keywords: IgG; SARS-CoV-2; hamster; immunohistochemistry; pathology; quantitative image analysis; technology.