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Discriminating Mild from Critical COVID-19 by Innate and Adaptive Immune Single-cell Profiling of Bronchoalveolar Lavages (preprint)
biorxiv; 2020.
Preprint
in English
| bioRxiv | ID: ppzbmed-10.1101.2020.07.09.196519
ABSTRACT
How innate and adaptive lung immune responses to SARS-CoV-2 synchronize during COVID-19 pneumonitis and regulate disease severity is poorly established. To address this, we applied single-cell profiling to bronchoalveolar lavages from 44 patients with mild or critical COVID-19 versus non-COVID-19 pneumonia as control. Viral RNA-tracking delineated the infection phenotype to epithelial cells, but positioned mainly neutrophils at the forefront of viral clearance activity during COVID-19. In mild disease, neutrophils could execute their antiviral function in an immunologically controlled fashion, regulated by fully-differentiated T-helper-17 (TH17)-cells, as well as T-helper-1 (TH1)-cells, CD8+ resident-memory (TRM) and partially-exhausted (TEX) T-cells with good effector functions. This was paralleled by orderly phagocytic disposal of dead/stressed cells by fully-differentiated macrophages, otherwise characterized by anti-inflammatory and antigen-presenting characteristics, hence facilitating lung tissue repair. In critical disease, CD4+ TH1- and CD8+ TEX-cells were characterized by inflammation-associated stress and metabolic exhaustion, while CD4+ TH17- and CD8+ TRM-cells failed to differentiate. Consequently, T-cell effector function was largely impaired thereby possibly facilitating excessive neutrophil-based inflammation. This was accompanied by impaired monocyte-to-macrophage differentiation, with monocytes exhibiting an ATP-purinergic signalling-inflammasome footprint, thereby enabling COVID-19 associated fibrosis and worsening disease severity. Our work represents a major resource for understanding the lung-localised immunity and inflammation landscape during COVID-19.
Full text:
Available
Collection:
Preprints
Database:
bioRxiv
Main subject:
COVID-19
Language:
English
Year:
2020
Document Type:
Preprint
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