Host-viral infection maps at single-cell resolution reveal signatures of severe COVID-19 patients

Viruses are a constant threat to global health as shown by the current COVID-19 pandemic. Currently, lack of data underlying the biology of the interaction of the human host with SARS-CoV-2 virus is limiting 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 ability of Viral-Track to detect various viruses from multiple models of infection. Applying Viral-Track to Bronchoalveloar Lavage samples from severe and mild COVID-19 patients reveals a dramatic impact of the SARS-CoV-2 virus on the immune system of severe patients as compared to mild cases. The SARS-CoV-2 infection is mainly restricted to epithelial and macrophage subsets. In addition, Viral-Track detects in one of the severe patients an unexpected co-infection of the human MetaPneumoVirus, present mainly in monocytes and strongly dampening their type-I IFN-signaling. Overall design: single cell RNA-seq of hepatitis-B positive liver and male severe covid19 positive lung.