Large-scale single-cell analysis reveals critical immune characteristics of COVID-19 patients
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Abstract
Dysfunctional immune response in the COVID-19 patients is a recurrent theme impacting symptoms and mortality, yet the detailed understanding of pertinent immune cells is not complete. We applied single-cell RNA sequencing to 284 samples from 205 COVID-19 patients and controls to create a comprehensive immune landscape. Lymphopenia and active T and B cell responses were found to coexist and associated with age, sex and their interactions with COVID-19. Diverse epithelial and immune cell types were observed to be virus-positive and showed dramatic transcriptomic changes. Elevation of ANXA1 and S100A9 in virus-positive squamous epithelial cells may enable the initiation of neutrophil and macrophage responses via the ANXA1-FPR1 and S100A8/9-TLR4 axes. Systemic upregulation of S100A8/A9, mainly by megakaryocytes and monocytes in the peripheral blood, may contribute to the cytokine storms frequently observed in severe patients. Our data provide a rich resource for understanding the pathogenesis and designing effective therapeutic strategies for COVID-19.
HIGHLIGHTS
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Large-scale scRNA-seq analysis depicts the immune landscape of COVID-19
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Lymphopenia and active T and B cell responses coexist and are shaped by age and sex
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SARS-CoV-2 infects diverse epithelial and immune cells, inducing distinct responses
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Cytokine storms with systemic S100A8/A9 are associated with COVID-19 severity
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SciScore for 10.1101/2020.10.29.360479: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources The remaining cells were then used for dimension reduction and unsupervised clustering using Python package scanpy (Wolf et al., 2018) In brief, the top 500 genes with the highest variance were selected and the dimensionality of the data was reduced by principal component analysis (PCA) (30 components) first and then with t-SNE, followed by Louvain clustering (Traag et al., 2019) performed on the 30 principal components (resolution = 1). Pythonsuggested: (IPython, RRID:SCR_001658)Based on these genes, enriched GO terms were then acquired for each group of cells using R package … SciScore for 10.1101/2020.10.29.360479: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources The remaining cells were then used for dimension reduction and unsupervised clustering using Python package scanpy (Wolf et al., 2018) In brief, the top 500 genes with the highest variance were selected and the dimensionality of the data was reduced by principal component analysis (PCA) (30 components) first and then with t-SNE, followed by Louvain clustering (Traag et al., 2019) performed on the 30 principal components (resolution = 1). Pythonsuggested: (IPython, RRID:SCR_001658)Based on these genes, enriched GO terms were then acquired for each group of cells using R package clusterProfiler (Yu et al., 2012). clusterProfilersuggested: (clusterProfiler, RRID:SCR_016884)Cell-cell communication analysis between PBMC and BALF by iTALK: To identify and visualize the possible cell-cell interactions in terms of cytokine storm between the highly inflammation-correlated cell types evaluated by the inflammation score within each tissue and the crosstalk between lung and circulating blood, we employed an R package iTALK introduced by Wang et al. (Wang et al., 2019, bioRxiv, bioRxivsuggested: (bioRxiv, RRID:SCR_003933)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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