Dataset Information

Single-cell RNA sequencing reveals SARS-CoV-2 infection dynamics in lungs of African green monkeys.

ABSTRACT: 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.

SUBMITTER: Speranza E

PROVIDER: S-EPMC7875333 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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Single-cell RNA sequencing reveals SARS-CoV-2 infection dynamics in lungs of African green monkeys.

Speranza Emily E Williamson Brandi N BN Feldmann Friederike F Sturdevant Gail L GL Pérez-Pérez Lizzette L Meade-White Kimberly K Smith Brian J BJ Lovaglio Jamie J Martens Craig C Munster Vincent J VJ Okumura Atsushi A Shaia Carl C Feldmann Heinz H Best Sonja M SM de Wit Emmie E

Science translational medicine 20210111 578

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 r ...[more]

PMID: 33431511

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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.

Dataset Information

Single-cell RNA sequencing reveals SARS-CoV-2 infection dynamics in lungs of African green monkeys.

Publications

Single-cell RNA sequencing reveals SARS-CoV-2 infection dynamics in lungs of African green monkeys.

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