The current spreading coronavirus SARS-CoV-2 is highly infectious and pathogenic. In this study, we screened the gene expression of three host receptors (ACE2, DC-SIGN and L-SIGN) of SARS coronaviruses and dendritic cells (DCs) status in bulk and single cell transcriptomic datasets of upper airway, lung or blood of COVID-19 patients and healthy controls. In COVID-19 patients, DC-SIGN gene expression was interestingly decreased in lung DCs but increased in blood DCs. Within DCs, conventional DCs (cDCs) were depleted while plasmacytoid DCs (pDCs) were augmented in the lungs of mild COVID-19. In severe cases, we identified augmented types of immature DCs (CD22+ or ANXA1+ DCs) with MHCII downregulation. In this study, our observation indicates that DCs in severe cases stimulate innate immune responses but fail to specifically present SARS-CoV-2. It provides insights into the profound modulation of DC function in severe COVID-19.
Gene expression in mammalian cells is inherently stochastic and mRNAs are synthesized in discrete bursts. Single-cell transcriptomics provides an unprecedented opportunity to explore the transcriptome-wide kinetics of transcriptional bursting. However, current analysis methods provide limited accuracy in bursting inference due to substantial noise inherent to single-cell transcriptomic data. In this study, we developed BISC, a Bayesian method for inferring bursting parameters from single cell transcriptomic data. Based on a beta-gamma-Poisson model, BISC modeled the mean–variance dependency to achieve accurate estimation of bursting parameters from noisy data. Evaluation based on both simulation and real intron sequential RNA fluorescence in situ hybridization data showed improved accuracy and reliability of BISC over existing methods, especially for genes with low expression values. Further application of BISC found bursting frequency but not bursting size was strongly associated with gene expression regulation. Moreover, our analysis provided new mechanistic insights into the functional role of enhancer and superenhancer by modulating both bursting frequency and size. BISC also formulated a downstream framework to identify differential bursting (in frequency and size separately) genes in samples under different conditions. Applying to multiple datasets (a mouse embryonic cell and fibroblast dataset, a human immune cell dataset and a human pancreatic cell dataset), BISC identified known cell-type signature genes that were missed by differential expression analysis, providing additional insights in understanding the cell-specific stochastic gene transcription. Applying to datasets of human lung and colon cancers, BISC successfully detected tumor signature genes based on alterations in bursting kinetics, which illustrates its value in understanding disease development regarding transcriptional bursting. Collectively, BISC provides a new tool for accurately inferring bursting kinetics and detecting differential bursting genes. This study also produced new insights in the role of transcriptional bursting in regulating gene expression, cell identity and tumor progression.
Motivation Recent advancements in single-cell RNA sequencing (scRNA-seq) have enabled time-efficient transcriptome profiling in individual cells. To optimize sequencing protocols and develop reliable analysis methods for various application scenarios, solid simulation methods for scRNA-seq data are required. However, due to the noisy nature of scRNA-seq data, currently available simulation methods cannot sufficiently capture and simulate important properties of real data, especially the biological variation. In this study, we developed SCRIP, a novel simulator for scRNA-seq that is accurate and enables simulation of bursting kinetics. Results Compared to existing simulators, SCRIP showed a significantly higher accuracy of stimulating key data features, including mean-variance dependency in all experiments. SCRIP also outperformed other methods in recovering cell-cell distances. The application of SCRIP in evaluating differential expression analysis methods showed that edgeR outperformed other examined methods in differential expression analyses, and ZINB-WaVE improved the AUC at high dropout rates. Collectively, this study provides the research community with a rigorous tool for scRNA-seq data simulation. Availability and implementation https://CRAN.R-project.org/package=SCRIP. Supplementary information Supplementary files are available at Bioinformatics online.
SummaryThe current spreading novel coronavirus SARS-CoV-2 is highly infectious and pathogenic. In this study, we screened the gene expression of three SARS-CoV-2 host receptors (ACE2, DC-SIGN and L-SIGN) and DC status in bulk and single cell transcriptomic datasets of upper airway, lung or blood of smokers, non-smokers and COVID-19 patients. We found smoking increased DC-SIGN gene expression and inhibited DC maturation and its ability of T cell stimulation. In COVID-19, DC-SIGN gene expression was interestingly decreased in lung DCs but increased in blood DCs. Strikingly, DCs shifted from cDCs to pDCs in COVID-19, but the shift was trapped in an immature stage (CD22+ or ANXA1+ DC) with MHCII downregulation in severe cases. This observation indicates that DCs in severe cases stimulate innate immune responses but fail to specifically recognize SARS-CoV-2. Our study provides insights into smoking effect on COVID-19 risk and the profound modulation of DC function in severe COVID-19.Graphical AbstractHighlightsSmoking upregulates the expression of ACE2 and CD209 and inhibits DC maturation in lungs. SARS-CoV-2 modulates the DCs proportion and CD209 expression differently in lung and blood.Severe infection is characterized by DCs less capable of maturation, antigen presentation and MHCII expression.DCs shift from cDCs to pDCs with SARS-CoV-2 infection but are trapped in an immature stage in severe cases.
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