Project description:Coronavirus disease-2019 (COVID-19) is a global pandemic and caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which has resulted in millions of deaths worldwide. Reports denote SARS-CoV-2 uses angiotensin-converting enzyme 2 (ACE2), transmembrane serine protease 2 (TMPRSS2) as its primary entry point into the host cell. However, understanding the biology behind this viral replication, disease mechanism and drug discovery efforts are limited due to the lack of a suitable experimental model. Here, we used single-cell RNA sequencing data of human organoids to analyze expressions of ACE2 and TMPRSS2, in addition to an array of RNA receptors to examine their role in SARS-CoV-2 pathogenesis. ACE2 is abundant in all organoids, except the prostate and brain, and TMPRSS2 is omnipresent. Innate immune pathways are upregulated in ACE2(+) cells of all organoids, except the lungs. Besides this, the expression of low-density lipoprotein receptor is highly enriched in ACE2(+) cells in intestinal, lung, and retinal organoids, with the highest expression in lung organoids. Collectively, this study demonstrates that the organoids can be used as an experimental platform to explore this novel virus disease mechanism and for drug development.
Project description:Detailed knowledge about the dynamics of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is important for uncovering the viral and host factors that contribute to coronavirus disease 2019 (COVID-19) pathogenesis. Old-World nonhuman primates recapitulate mild to moderate cases of COVID-19, thereby serving as important pathogenesis models. We compared African green monkeys inoculated with infectious SARS-CoV-2 or irradiated, inactivated virus to study the dynamics of virus replication throughout the respiratory tract. Genomic RNA from the animals inoculated with the irradiated virus was found to be highly stable, whereas subgenomic RNA, an indicator of viral replication, was found to degrade quickly. We combined this information with single-cell RNA sequencing of cells isolated from the lung and lung-draining mediastinal lymph nodes and developed new analysis methods for unbiased targeting of important cells in the host response to SARS-CoV-2 infection. Through detection of reads to the viral genome, we were able to determine that replication of the virus in the lungs appeared to occur mainly in pneumocytes, whereas macrophages drove the inflammatory response. Monocyte-derived macrophages recruited to the lungs, rather than tissue-resident alveolar macrophages, were most likely to be responsible for phagocytosis of infected cells and cellular debris early in infection, with their roles switching during clearance of infection. Together, our dataset provides a detailed view of the dynamics of virus replication and host responses over the course of mild COVID-19 and serves as a valuable resource to identify therapeutic targets.
Project description:Single cell RNA sequencing (scRNAseq) studies have provided critical insight into the pathogenesis of Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2), the causative agent of COronaVIrus Disease 2019 (COVID-19). scRNAseq workflows are generally designed for the detection and quantification of eukaryotic host mRNAs and not viral RNAs. The performance of different scRNAseq methods to study SARS-CoV-2 RNAs has not been thoroughly evaluated. Here, we compare different scRNAseq methods for their ability to quantify and detect SARS-CoV-2 RNAs with a focus on subgenomic mRNAs (sgmRNAs), which are produced only during active viral replication and not present in viral particles. We present a data processing strategy, single cell CoronaVirus sequencing (scCoVseq), which quantifies reads unambiguously assigned to sgmRNAs or genomic RNA (gRNA). Compared to standard 10X Genomics Chromium Next GEM Single Cell 3' (10X 3') and Chromium Next GEM Single Cell V(D)J (10X 5') sequencing, we find that 10X 5' with an extended R1 sequencing strategy maximizes the unambiguous detection of sgmRNAs by increasing the number of reads spanning leader-sgmRNA junction sites. Differential gene expression testing and KEGG enrichment analysis of infected cells compared with bystander or mock cells showed an enrichment for COVID19-associated genes, supporting the ability of our method to accurately identify infected cells. Our method allows for quantification of coronavirus sgmRNA expression at single-cell resolution, and thereby supports high resolution studies of the dynamics of coronavirus RNA synthesis.ImportanceSingle cell RNA sequencing (scRNAseq) has emerged as a valuable tool to study host-viral interactions particularly in the context of COronaVIrus Disease-2019 (COVID-19). scRNAseq has been developed and optimized for analyzing eukaryotic mRNAs, and the ability of scRNAseq to measure RNAs produced by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has not been fully characterized. Here we compare the performance of different scRNAseq methods to detect and quantify SARS-CoV-2 RNAs and develop an analysis workflow to specifically quantify unambiguous reads derived from SARS-CoV-2 genomic RNA and subgenomic mRNAs. Our work demonstrates the strengths and limitations of scRNAseq to measure SARS-CoV-2 RNA and identifies experimental and analytical approaches that allow for SARS-CoV-2 RNA detection and quantification. These developments will allow for studies of coronavirus RNA biogenesis at single-cell resolution to improve our understanding of viral pathogenesis.
Project description:Single-cell RNA sequencing (scRNA-Seq) studies have provided critical insight into the pathogenesis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of coronavirus disease 2019 (COVID-19). scRNA-Seq library preparation methods and data processing workflows are generally designed for the detection and quantification of eukaryotic host mRNAs and not viral RNAs. Here, we compare different scRNA-Seq library preparation methods for their ability to quantify and detect SARS-CoV-2 RNAs with a focus on subgenomic mRNAs (sgmRNAs). We show that compared to 10X Genomics Chromium Next GEM Single Cell 3' (10X 3') libraries or 10X Genomics Chromium Next GEM Single Cell V(D)J (10X 5') libraries sequenced with standard read configurations, 10X 5' libraries sequenced with an extended length read 1 (R1) that covers both cell barcode and transcript sequence (termed "10X 5' with extended R1") increase the number of unambiguous reads spanning leader-sgmRNA junction sites. We further present a data processing workflow, single-cell coronavirus sequencing (scCoVseq), which quantifies reads unambiguously assigned to viral sgmRNAs or viral genomic RNA (gRNA). We find that combining 10X 5' with extended R1 library preparation/sequencing and scCoVseq data processing maximizes the number of viral UMIs per cell quantified by scRNA-Seq. Corresponding sgmRNA expression levels are highly correlated with expression in matched bulk RNA-Seq data sets quantified with established tools for SARS-CoV-2 analysis. Using this scRNA-Seq approach, we find that SARS-CoV-2 gene expression is highly correlated across individual infected cells, which suggests that the proportion of viral sgmRNAs remains generally consistent throughout infection. Taken together, these results and corresponding data processing workflow enable robust quantification of coronavirus sgmRNA expression at single-cell resolution, thereby supporting high-resolution studies of viral RNA processes in individual cells. IMPORTANCE Single-cell RNA sequencing (scRNA-Seq) has emerged as a valuable tool to study host-virus interactions, especially for coronavirus disease 2019 (COVID-19). Here we compare the performance of different scRNA-Seq library preparation methods and sequencing strategies to detect SARS-CoV-2 RNAs and develop a data processing workflow to quantify unambiguous sequence reads derived from SARS-CoV-2 genomic RNA and subgenomic mRNAs. After establishing a workflow that maximizes the detection of SARS-CoV-2 subgenomic mRNAs, we explore patterns of SARS-CoV-2 gene expression across cells with variable levels of total viral RNA, assess host gene expression differences between infected and bystander cells, and identify non-canonical and lowly abundant SARS-CoV-2 RNAs. The sequencing and data processing strategies developed here can enhance studies of coronavirus RNA biology at single-cell resolution and thereby contribute to our understanding of viral pathogenesis.
Project description:Gaining a better understanding of the immune cell subsets and molecular factors associated with protective or pathological immunity against severe acute respiratory syndrome coronavirus (SARS-CoV)-2 could aid the development of vaccines and therapeutics for coronavirus disease 2019 (COVID-19). Single-cell technologies, such as flow cytometry, mass cytometry, single-cell transcriptomics and single-cell multi-omic profiling, offer considerable promise in dissecting the heterogeneity of immune responses among individual cells and uncovering the molecular mechanisms of COVID-19 pathogenesis. Single-cell immune-profiling studies reported to date have identified innate and adaptive immune cell subsets that correlate with COVID-19 disease severity, as well as immunological factors and pathways of potential relevance to the development of vaccines and treatments for COVID-19. For facilitation of integrative studies and meta-analyses into the immunology of SARS-CoV-2 infection, we provide standardized, download-ready versions of 21 published single-cell sequencing datasets (over 3.2 million cells in total) as well as an interactive visualization portal for data exploration.
Project description:SARS-CoV-2, the causative agent of COVID-19, has tragically burdened individuals and institutions around the world. There are currently no approved drugs or vaccines for the treatment or prevention of COVID-19. Enhanced understanding of SARS-CoV-2 infection and pathogenesis is critical for the development of therapeutics. To reveal insight into viral replication, cell tropism, and host-viral interactions of SARS-CoV-2 we performed single-cell RNA sequencing of experimentally infected human bronchial epithelial cells (HBECs) in air-liquid interface cultures over a time-course. This revealed novel polyadenylated viral transcripts and highlighted ciliated cells as a major target of infection, which we confirmed by electron microscopy. Over the course of infection, cell tropism of SARS-CoV-2 expands to other epithelial cell types including basal and club cells. Infection induces cell-intrinsic expression of type I and type III IFNs and IL6 but not IL1. This results in expression of interferon-stimulated genes in both infected and bystander cells. We observe similar gene expression changes from a COVID-19 patient ex vivo. In addition, we developed a new computational method termed CONditional DENSity Embedding (CONDENSE) to characterize and compare temporal gene dynamics in response to infection, which revealed genes relating to endothelin, angio-genesis, interferon, and inflammation-causing signaling pathways. In this study, we conducted an in-depth analysis of SARS-CoV-2 infection in HBECs and a COVID-19 patient and revealed genes, cell types, and cell state changes associated with infection.
Project description:COVID-19 (Coronavirus Disease 2019) has been an ongoing pandemic, resulting in an increase in people being infected globally. Understanding the potential risk of infection for people under different respiratory system conditions is important and will help prevent disease spreading. We explored and collected five published and one unpublished single-cell respiratory system tissue transcriptome datasets, including idiopathic pulmonary fibrosis (IPF), aging lungs (mouse origin data), lung cancers, and smoked branchial epithelium, for specifically reanalyzing the ACE2 and TMPRSS2 expression profiles. Compared to normal people, we found that smoking and lung cancer increase the risk for COVID-19 infection due to a higher expression of ACE2 and TMPRSS2 in lung cells. Aged lung does not show increased risk for infection. IPF patients may have a lower risk for original COVID-19 infection due to lower expression in AT2 cells but may have a higher risk for severity due to a broader expression spectrum of TMPRSS2. Further investigation and validation on these cell types are required. Nonetheless, this is the first report to predict the risk and potential severity for COVID-19 infection for people with different respiratory system conditions. Our analysis is the first systematic description and analysis to illustrate how the underlying respiratory system conditions contribute to a higher infection risk.
Project description:The in vivo phenotypic profile of T cells reactive to severe acute respiratory syndrome (SARS)-CoV-2 antigens remains poorly understood. Conventional methods to detect antigen-reactive T cells require in vitro antigenic re-stimulation or highly individualized peptide-human leukocyte antigen (pHLA) multimers. Here, we use single-cell RNA sequencing to identify and profile SARS-CoV-2-reactive T cells from Coronavirus Disease 2019 (COVID-19) patients. To do so, we induce transcriptional shifts by antigenic stimulation in vitro and take advantage of natural T cell receptor (TCR) sequences of clonally expanded T cells as barcodes for 'reverse phenotyping'. This allows identification of SARS-CoV-2-reactive TCRs and reveals phenotypic effects introduced by antigen-specific stimulation. We characterize transcriptional signatures of currently and previously activated SARS-CoV-2-reactive T cells, and show correspondence with phenotypes of T cells from the respiratory tract of patients with severe disease in the presence or absence of virus in independent cohorts. Reverse phenotyping is a powerful tool to provide an integrated insight into cellular states of SARS-CoV-2-reactive T cells across tissues and activation states.
Project description:Dysregulated immune responses contribute to the excessive and uncontrolled inflammation observed in severe COVID-19. However, how immunity to SARS-CoV-2 is induced and regulated remains unclear. Here we uncover a role of the complement system in the induction of innate and adaptive immunity to SARS-CoV-2. Complement rapidly opsonizes SARS-CoV-2 particles via the lectin pathway. Complement-opsonized SARS-CoV-2 efficiently induces type-I interferon and pro-inflammatory cytokine responses via activation of dendritic cells, which are inhibited by antibodies against the complement receptors (CR) 3 and 4. Serum from COVID-19 patients, or monoclonal antibodies against SARS-CoV-2, attenuate innate and adaptive immunity induced by complement-opsonized SARS-CoV-2. Blocking of CD32, the FcγRII antibody receptor of dendritic cells, restores complement-induced immunity. These results suggest that opsonization of SARS-CoV-2 by complement is involved in the induction of innate and adaptive immunity to SARS-CoV-2 in the acute phase of infection. Subsequent antibody responses limit inflammation and restore immune homeostasis. These findings suggest that dysregulation of the complement system and FcγRII signaling may contribute to severe COVID-19.