| Literature DB >> 33780969 |
André F Rendeiro1,2, Hiranmayi Ravichandran2,3,4, Yaron Bram5, Vasuretha Chandar5, Junbum Kim1, Cem Meydan1,4, Jiwoon Park4, Jonathan Foox1,4, Tyler Hether6, Sarah Warren6, Youngmi Kim6, Jason Reeves6, Steven Salvatore7, Christopher E Mason1,3,4,8, Eric C Swanson9, Alain C Borczuk10, Olivier Elemento11,12,13,14, Robert E Schwartz15,16.
Abstract
Recent studies have provided insights into the pathology of and immune response to COVID-191-8. However, a thorough investigation of the interplay between infected cells and the immune system at sites of infection has been lacking. Here we use high-parameter imaging mass cytometry9 that targets the expression of 36 proteins to investigate the cellular composition and spatial architecture of acute lung injury in humans (including injuries derived from SARS-CoV-2 infection) at single-cell resolution. These spatially resolved single-cell data unravel the disordered structure of the infected and injured lung, alongside the distribution of extensive immune infiltration. Neutrophil and macrophage infiltration are hallmarks of bacterial pneumonia and COVID-19, respectively. We provide evidence that SARS-CoV-2 infects predominantly alveolar epithelial cells and induces a localized hyperinflammatory cell state that is associated with lung damage. We leverage the temporal range of fatal outcomes of COVID-19 in relation to the onset of symptoms, which reveals increased macrophage extravasation and increased numbers of mesenchymal cells and fibroblasts concomitant with increased proximity between these cell types as the disease progresses-possibly as a result of attempts to repair the damaged lung tissue. Our data enable us to develop a biologically interpretable landscape of lung pathology from a structural, immunological and clinical standpoint. We use this landscape to characterize the pathophysiology of the human lung from its macroscopic presentation to the single-cell level, which provides an important basis for understanding COVID-19 and lung pathology in general.Entities:
Mesh:
Year: 2021 PMID: 33780969 PMCID: PMC8204801 DOI: 10.1038/s41586-021-03475-6
Source DB: PubMed Journal: Nature ISSN: 0028-0836 Impact factor: 49.962