Host-Viral Infection Maps Reveal Signatures of Severe COVID-19 Patients

Viruses are a constant threat to global health as highlighted by the current COVID-19 pandemic. Currently, lack of data underlying how the human host interacts with viruses, including the SARS-CoV-2 virus, limits effective therapeutic intervention. We introduce Viral-Track, a computational method that globally scans unmapped scRNA-seq data for the presence of viral RNA, enabling transcriptional cell sorting of infected versus bystander cells. We demonstrate the sensitivity and specificity of Viral-Track to systematically detect viruses from multiple models of infection, including hepatitis B virus in an unsupervised manner. Applying Viral-Track to Bronchoalveloar-Lavage samples from severe and mild COVID-19 patients reveals a dramatic impact of the virus on the immune system of severe patients compared to mild cases. Viral-Track detects an unexpected co-infection of the human MetaPneumoVirus, present mainly in monocytes perturbed in type-I IFN-signaling. Viral-Track provides a robust technology for dissecting the mechanisms of viral-infection and pathology.
Highlights Viral-track: a computational framework to analyze host-viral infection maps Viral-track sorts infected from bystander cells and reveals virus-induced expression SARS-CoV-2 infects epithelial cells and alters immune landscape in severe patients Coinfection of SARS-Cov-2 and hMPV affects monocytes and dampen interferon response
A computational framework that allows for the identification and characterizaton of virus-infected cells as well as bystander cell responses reveals how SARS-CoV-2 alters the immune responses of patients.