Abstract
SARS-CoV-2 triggers a complex systemic immune response in circulating blood mononuclear cells. The relationship between immune cell activation of the peripheral compartment and survival in critical COVID-19 remains to be established. Here we use single-cell RNA sequencing and Cellular Indexing of Transcriptomes and Epitomes by sequence mapping to elucidate cell type specific transcriptional signatures that associate with and predict survival in critical COVID-19. Patients who survive infection display activation of antibody processing, early activation response, and cell cycle regulation pathways most prominent within B-, T-, and NK-cell subsets. We further leverage cell specific differential gene expression and machine learning to predict mortality using single cell transcriptomes. We identify interferon signaling and antigen presentation pathways within cDC2 cells, CD14 monocytes, and CD16 monocytes as predictors of mortality with 90% accuracy. Finally, we validate our findings in an independent transcriptomics dataset and provide a framework to elucidate mechanisms that promote survival in critically ill COVID-19 patients. Identifying prognostic indicators among critical COVID-19 patients holds tremendous value in risk stratification and clinical management.
Highlights
SARS-CoV-2 triggers a complex systemic immune response in circulating blood mononuclear cells
While prior studies have utilized single-cell omics to unravel the immunological landscape of COVID-19 in peripheral blood mononuclear cells (PBMCs)[13–22], there remains an incomplete understanding of the relationship between peripheral immune cell activation and patient survival[11,23–25]
Through an integrated approach consisting of differential gene expression analysis and gene ranking by feature importance score, we further show that CEBPD, MAFB, IFITM3, and LGALS1 expression within CD14 monocytes robustly predict mortality
Summary
SARS-CoV-2 triggers a complex systemic immune response in circulating blood mononuclear cells. We use single-cell RNA sequencing and Cellular Indexing of Transcriptomes and Epitomes by sequence mapping to elucidate cell type specific transcriptional signatures that associate with and predict survival in critical COVID-19. We further leverage cell specific differential gene expression and machine learning to predict mortality using single cell transcriptomes. Current cross-sectional studies have yet to identify immune cell types and transcriptional programs that contribute to survival in critical COVID-19. This information is necessary to effectively develop strategies to treat the sickest COVID-19 patients. Our findings provide a framework to elucidate mechanisms that promote COVID-19 survival among critically ill patients and delineate key cell-specific transcriptional signatures that are associated with mortality in critical COVID-19
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
