Cell Reports Medicine
Volume 2, Issue 12, 21 December 2021, 100476
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Report
People critically ill with COVID-19 exhibit peripheral immune profiles predictive of mortality and reflective of SARS-CoV-2 lung viral burden

https://doi.org/10.1016/j.xcrm.2021.100476Get rights and content
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Highlights

  • Transcription-factor-driven gene modules define immune states in blood

  • Gene modules are associated with mortality in people critically ill with COVID-19

  • Inflammatory monocyte states precede upregulation of inflammatory cytokines

  • SARS-CoV-2-infected macrophages in lung express high levels of CXCL10

Summary

Despite extensive analyses, there remains an urgent need to delineate immune cell states that contribute to mortality in people critically ill with COVID-19. Here, we present high-dimensional profiling of blood and respiratory samples from people with severe COVID-19 to examine the association between cell-linked molecular features and mortality outcomes. Peripheral transcriptional profiles by single-cell RNA sequencing (RNA-seq)-based deconvolution of immune states are associated with COVID-19 mortality. Further, persistently high levels of an interferon signaling module in monocytes over time lead to subsequent concerted upregulation of inflammatory cytokines. SARS-CoV-2-infected myeloid cells in the lower respiratory tract upregulate CXCL10, leading to a higher risk of death. Our analysis suggests a pivotal role for viral-infected myeloid cells and protracted interferon signaling in severe COVID-19.

Keywords

COVID-19
severe COVID-19
COVID-19 outcome
inflammatory monocytes
type I interferon
inflammatory cytokines
machine learning
single-cell RNA-seq
gene modules

Data and code availability

  • Data: Both raw and processed transcriptomic are available through the Gene Expression Omnibus (GEO: GSE180578). Additional data are available via Mendeley Data (Mendeley Data: http://doi.org/10.17632/r83csstphc.2).

  • Code: The Seurat package was used for scRNaseq normalization, scaling, dimensionality reduction, UMAP visualization, clustering, and differential gene expression analysis. Code for these steps is available through Seurat’s website (https://satijalab.org/seurat/). Code for gene set enrichment analysis is available at https://www.github.com/arc85/singleseqgset. Code is provided at https://github.com/arc85/covid_analyses files to demonstrate gene module discovery, training of machine learning algorithms and leave one out cross validation analysis, and meta-analysis of infected myeloid cells.

  • General statement: Any additional information required to reanalyze the data reported in this paper is available from the Lead Contact upon request.

Cited by (0)

15

These authors contributed equally

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These authors contributed equally

17

Senior author

18

Twitter: @tonyrcillo

19

Twitter: @BcellBruno

20

Twitter: @Vignali_Lab

21

Lead contact