scholarly journals Unambiguous detection of SARS-CoV-2 subgenomic mRNAs with single cell RNA sequencing

2021 ◽  
Author(s):  
Phillip Cohen ◽  
Emma J DeGrace ◽  
Oded Danziger ◽  
Roosheel Patel ◽  
Brad R Rosenberg
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Enrichment Analysis ◽  
Viral Particles ◽  
Single Cell Rna Sequencing ◽  
Active Viral Replication ◽  
Sequencing Strategy ◽  
Infected Cells ◽  
Differential Gene ◽  
Unambiguous Detection

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.

scholarly journals Single-Cell Transcriptome Analysis Reveals Dynamic Cell Populations and Differential Gene Expression Patterns in Control and Aneurysmal Human Aortic Tissue

Circulation ◽  
2020 ◽  
Vol 142 (14) ◽  
pp. 1374-1388
Author(s):  
Yanming Li ◽  
Pingping Ren ◽  
Ashley Dawson ◽  
Hernan G. Vasquez ◽  
Waleed Ageedi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Aortic Wall ◽  
Genome Wide Association ◽  
Aortic Tissue ◽  
Sequencing Data ◽  
Genome Wide ◽  
Single Cell Rna Sequencing ◽  
Differential Gene

Background: Ascending thoracic aortic aneurysm (ATAA) is caused by the progressive weakening and dilatation of the aortic wall and can lead to aortic dissection, rupture, and other life-threatening complications. To improve our understanding of ATAA pathogenesis, we aimed to comprehensively characterize the cellular composition of the ascending aortic wall and to identify molecular alterations in each cell population of human ATAA tissues. Methods: We performed single-cell RNA sequencing analysis of ascending aortic tissues from 11 study participants, including 8 patients with ATAA (4 women and 4 men) and 3 control subjects (2 women and 1 man). Cells extracted from aortic tissue were analyzed and categorized with single-cell RNA sequencing data to perform cluster identification. ATAA-related changes were then examined by comparing the proportions of each cell type and the gene expression profiles between ATAA and control tissues. We also examined which genes may be critical for ATAA by performing the integrative analysis of our single-cell RNA sequencing data with publicly available data from genome-wide association studies. Results: We identified 11 major cell types in human ascending aortic tissue; the high-resolution reclustering of these cells further divided them into 40 subtypes. Multiple subtypes were observed for smooth muscle cells, macrophages, and T lymphocytes, suggesting that these cells have multiple functional populations in the aortic wall. In general, ATAA tissues had fewer nonimmune cells and more immune cells, especially T lymphocytes, than control tissues did. Differential gene expression data suggested the presence of extensive mitochondrial dysfunction in ATAA tissues. In addition, integrative analysis of our single-cell RNA sequencing data with public genome-wide association study data and promoter capture Hi-C data suggested that the erythroblast transformation-specific related gene( ERG ) exerts an important role in maintaining normal aortic wall function. Conclusions: Our study provides a comprehensive evaluation of the cellular composition of the ascending aortic wall and reveals how the gene expression landscape is altered in human ATAA tissue. The information from this study makes important contributions to our understanding of ATAA formation and progression.

Single-Cell RNA Sequencing of Mouse Left Ventricle Reveals Cellular Diversity and Intercommunication

2021 ◽  
Author(s):  
Lan Wu ◽  
Yan-Fei Li ◽  
Jun-wei Shen ◽  
Qian Zhu ◽  
Jing Jiang ◽  
...  
Keyword(s):  
Endothelial Cells ◽  
Left Ventricle ◽  
Single Cell ◽  
Rna Sequencing ◽  
Therapeutic Targets ◽  
Enrichment Analysis ◽  
Left Ventricular ◽  
Cardiovascular Research ◽  
Gene Markers ◽  
Single Cell Rna Sequencing

Previous studies have revealed the diversity of the whole cardiac cellulome but not refined the left ventricle, which was essential for finding therapeutic targets. Here, we characterized single-cell transcriptional profiles of the mouse left ventricular cellular landscape using single-cell RNA sequencing (10×Genomics). Detailed t-Distributed Stochastic Neighbor Embedding (tSNE) analysis revealed the cell types of left ventricle with gene markers. Left ventricular cellulome contained cardiomyocytes highly expressed Trdn, endothelial cells highly expressed Pcdh17, fibroblast highly expressed Lama2 and macrophages highly expressed Hpgds, also proved by in situ hybridization. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analysis (ListHits>2, p<0.05) were employed with the DAVID database to investigate subtypes of each cell type with the underlying functions of differentially expressed genes (DEGs). Endothelial cells included five subtypes, fibroblasts comprised of seven subtypes and macrophages contained eleven subtypes. The key representative DEGs (p<0.001) were Gja4 and Gja5 in cluster 3 of endothelial cells, Aqp2 and Thbs4 in cluster 2 of fibroblasts, as well as Clec4e and Trem-1 in in cluster 3 of marcophages perhaps involved in the occur of atherosclerosis, heart failure and acute myocardial infarction proved by literature review. We also revealed extensive networks of intercellular communication in left ventricle. We suggested possible therapeutic targets for cardiovascular disease and autocrine and paracrine signaling underpins left ventricular homeostasis. This study provided new insights into the structure and function of the mammalian left ventricular cellulome and offers an important resource that will stimulate studies in cardiovascular research.

scholarly journals Single-Cell RNA-Sequencing Portraying Functional Diversity and Clinical Implications of IFI6 in Ovarian Cancer

2021 ◽  
Vol 9 ◽  
Author(s):  
Hongyu Zhao ◽  
Zhefeng Li ◽  
Yan Gao ◽  
Jie Li ◽  
Xiaoting Zhao ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Single Cell ◽  
Rna Sequencing ◽  
Clustering Algorithm ◽  
Cisplatin Resistance ◽  
Enrichment Analysis ◽  
Functional Enrichment ◽  
Consensus Clustering ◽  
Single Cell Rna Sequencing

Ovarian cancer (OC) is one of the most lethal gynecologic malignancies. Most patients die of metastasis due to a lack of other treatments aimed at improving the prognosis of OC patients. In the present study, we use multiple methods to identify prognostic S1 as the dominant subtype in OC, possessing the most ligand–receptor pairs with other cell types. Based on markers of S1, the consensus clustering algorithm is used to explore the clinical treatment subtype in OC. As a result, we identify two clusters associated with distinct survival and drug response. Notably, IFI6 contributes to the cluster classification and seems to be a vital gene in OC carcinogenesis. Functional enrichment analysis demonstrates that its functions involve G2M and cisplatin resistance, and downregulation of IFI6 suppresses proliferation capabilities and significantly potentiates cisplatin-induced apoptosis of OC cells in vitro. To explore possible mechanisms of IFI6 influencing OC proliferation and cisplatin resistance, GSEA is conducted and shows that IFI6 is positively correlated with the NF-κB pathway, which is validated by RT-qPCR. Significantly, we develop a prognostic model including IFI6, RiskScore, which is an independent prognostic factor and presents encouraging prognostic values. Our findings provide novel insights into elucidating the biology of OC based on single-cell RNA-sequencing. Moreover, this approach is potentially helpful for personalized anti-cancer strategies and predicting outcomes in the setting of OC.

scholarly journals Benchmarking supervised signature-scoring methods for single-cell RNA sequencing data in cancer

2021 ◽  
Author(s):  
Siyuan Zheng ◽  
Noureen Nighat ◽  
Zhenqing Ye ◽  
Yidong Chen ◽  
Xiaojing Wang
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Enrichment Analysis ◽  
Bulk Sample ◽  
Gene Set Enrichment Analysis ◽  
Sequencing Data ◽  
Gene Set Enrichment ◽  
Single Cell Rna Sequencing ◽  
Supervised Methods ◽  
Cell Data

Quantifying the activity of gene expression signatures is common in analyses of single-cell RNA sequencing data. Methods originally developed for bulk samples are often used for this purpose without accounting for contextual differences between bulk and single-cell data. More broadly, these methods have not been benchmarked. Here we benchmark four such supervised methods, including single sample gene set enrichment analysis (ssGSEA), AUCell, Single Cell Signature Explorer (SCSE), and a new method we developed, Jointly Assessing Signature Mean and Inferring Enrichment (JASMINE). Using cancer as an example, we show cancer cells consistently express more genes than normal cells. This imbalance leads to bias in performance by bulk-sample-based ssGSEA in gold standard tests and down sampling experiments. In contrast, single-cell-based methods are less susceptible. Our results suggest caution should be exercised when using bulk-sample-based methods in single-cell data analyses, and cellular contexts should be taken into consideration when designing benchmarking strategies.

scholarly journals PseudotimeDE: inference of differential gene expression along cell pseudotime with well-calibrated p-values from single-cell RNA sequencing data

2020 ◽  
Author(s):  
Dongyuan Song ◽  
Jingyi Jessica Li
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Molecular Mechanisms ◽  
Real Data ◽  
Sequencing Data ◽  
Single Cell Rna Sequencing ◽  
Ill Posed ◽  
Differential Gene ◽  
Cell Trajectory ◽  
Downstream Analysis

AbstractIn the investigation of molecular mechanisms underlying cell state changes, a crucial analysis is to identify differentially expressed (DE) genes along a continuous cell trajectory, which can be estimated by pseudotime inference from single-cell RNA-sequencing (scRNA-seq) data. However, existing methods that identify DE genes based on inferred pseudotime do not account for the uncertainty in pseudotime inference. Also, they either have ill-posed p-values that hinder the control of false discovery rate (FDR) or have restrictive models that reduce the power of DE gene identification. To overcome these drawbacks, we propose PseudotimeDE, a robust method that accounts for the uncertainty in pseudotime inference and thus identifies DE genes along cell pseudotime with well-calibrated p-values. PseudotimeDE is flexible in allowing users to specify the pseudotime inference method and to choose the appropriate model for scRNA-seq data. Comprehensive simulations and real-data applications verify that PseudotimeDE provides well-calibrated p-values essential for controlling FDR and downstream analysis and that PseudotimeDE is more powerful than existing methods to identify DE genes.

scholarly journals Interactions of Monocytes, HIV, and ART Identified by an Innovative scRNAseq Pipeline: Pathways to Reservoirs and HIV-Associated Comorbidities

mBio ◽  
2020 ◽  
Vol 11 (4) ◽  
Author(s):  
Rosiris León-Rivera ◽  
Brenda Morsey ◽  
Meng Niu ◽  
Howard S. Fox ◽  
Joan W. Berman
Keyword(s):  
Gene Expression ◽  
Antiretroviral Therapy ◽  
Single Cell ◽  
Rna Sequencing ◽  
Differential Gene Expression ◽  
Viral Reservoirs ◽  
Hiv Reservoirs ◽  
Single Cell Rna Sequencing ◽  
Monocyte Subpopulations ◽  
Differential Gene

ABSTRACT HIV reservoirs persist despite successful antiretroviral therapy (ART) and are a major obstacle to the eradication and cure of HIV. The mature monocyte subset, CD14+CD16+, contributes to viral reservoirs and HIV-associated comorbidities. Only a subset of monocytes harbors HIV (HIV+), while the rest remain uninfected, exposed cells (HIVexp). We developed an innovative single cell RNA sequencing (scRNAseq) pipeline that detects HIV and host transcripts simultaneously, enabling us to examine differences between HIV+ and HIVexp mature monocytes. Using this, we characterized uninfected, HIV+, and HIVexp primary human mature monocytes with and without ART. We showed that HIV+ mature monocytes do not form their own cluster separately from HIVexp but can be distinguished by significant differential gene expression. We found that ART decreased levels of unspliced HIV transcripts potentially by modulating host transcriptional regulators shown to decrease viral infection and replication. We also identified and characterized mature monocyte subpopulations differentially impacted by HIV and ART. We identified genes dysregulated by ART in HIVexp monocytes compared to their uninfected counterpart and, of interest, the junctional protein ALCAM, suggesting that ART impacts monocyte functions. Our data provide a novel method for simultaneous detection of HIV and host transcripts. We identify potential targets, such as those genes whose expression is increased in HIV+ mature monocytes compared to HIVexp, to block their entry into tissues, preventing establishment/replenishment of HIV reservoirs even with ART, thereby reducing and/or eliminating viral burden and HIV-associated comorbidities. Our data also highlight the heterogeneity of mature monocyte subsets and their potential contributions to HIV pathogenesis in the ART era. IMPORTANCE HIV enters tissues early after infection, leading to establishment and persistence of HIV reservoirs despite antiretroviral therapy (ART). Viral reservoirs are a major obstacle to the eradication and cure of HIV. CD14+CD16+ (mature) monocytes may contribute to establishment and reseeding of reservoirs. A subset of monocytes, consisting mainly of CD14+CD16+ cells, harbors HIV (HIV+), while the rest remain uninfected, exposed cells (HIVexp). It is important to identify cells harboring virus to eliminate reservoirs. Using an innovative single-cell RNA sequencing (scRNAseq) pipeline to detect HIV and host transcripts simultaneously, we characterized HIV+ and HIVexp primary human mature monocytes with and without ART. HIV+ mature monocytes are not a unique subpopulation but rather can be distinguished from HIVexp by differential gene expression. We characterized mature monocyte subpopulations differently impacted by HIV and ART, highlighting their potential contributions to HIV-associated comorbidities. Our data propose therapeutic targets to block HIV+ monocyte entry into tissues, preventing establishment and replenishment of reservoirs even with ART.

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