Use of Micro-Computed Tomography to Visualize and Quantify COVID-19 Vaccine Efficiency in Free-Breathing Hamsters.

ABSTRACT

The SARS-CoV-2 pandemic has impacted the health of humanity after the outbreak in Hubei, China in late December 2019. Ever since, it has taken unprecedented proportions and rapidity causing over a million fatal cases. Recently, a robust Syrian golden hamster model recapitulating COVID-19 was developed in search for effective therapeutics and vaccine candidates. However, overt clinical disease symptoms were largely absent despite high levels of virus replication and associated pathology in the respiratory tract. Therefore, we used micro-computed tomography (µCT) to longitudinally visualize lung pathology and to preclinically assess candidate vaccines. µCT proved to be crucial to quantify and noninvasively monitor disease progression, to evaluate candidate vaccine efficacy, and to improve screening efforts by allowing longitudinal data without harming live animals. Here, we give a comprehensive guide on how to use low-dose high-resolution µCT to follow-up SARS-CoV-2-induced disease and test the efficacy of COVID-19 vaccine candidates in hamsters. Our approach can likewise be applied for the preclinical assessment of antiviral and anti-inflammatory drug treatments in vivo.