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 single-cell RNA sequencing (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 interferon (IFN)-signaling. Viral-Track provides a robust technology for dissecting the mechanisms of viral-infection and pathology.

正如当前的COVID-19大流行所强调的那样，病毒是对全球健康的持续威胁。当前，缺乏有关人宿主如何与包括SARS-CoV-2病毒在内的病毒相互作用的数据，限制了有效的治疗干预。我们介绍了病毒跟踪，一种计算方法，该方法可全局扫描未映射的单细胞RNA测序（scRNA-seq）数据中是否存在病毒RNA，从而能够对受感染细胞和旁观者细胞进行转录细胞分类。我们展示了病毒跟踪以无监督的方式系统地检测多种感染模型（包括乙肝病毒）中的病毒的敏感性和特异性。将病毒追踪技术应用于重度和轻度COVID-19患者的支气管肺灌洗液样本，与轻度患者相比，该病毒对重度患者的免疫系统产生了巨大影响。病毒追踪检测到人类偏肺病毒的意外合并感染，主要存在于受I型干扰素（IFN）信号干扰的单核细胞中。病毒追踪技术为剖析病毒感染和病理机制提供了强大的技术。