An Interactive Single Cell Web Portal Identifies Gene and Cell Networks in COVID-19 Host Responses

Numerous studies have provided single-cell transcriptome profiles of host responses to SARS-CoV-2 infection. Critically lacking however is a reusable datamine to allow users to compare and explore these data for insight, inference, and hypothesis generation. To accomplish this, we harmonized datasets from blood, bronchoalveolar lavage and tissue samples from COVID-19 and other control conditions and derived a compendium of gene signature modules per cell type, subtype, clinical condition and compartment. We demonstrate approaches for exploring and evaluating their significance via a new interactive web portal (ToppCell). As examples, we develop three hypotheses: (1) a multicellular signaling cascade among alternatively differentiated monocyte-derived macrophages whose tasks include T cell recruitment and activation; (2) novel platelet subtypes with drastically modulated expression of genes responsible for adhesion, coagulation and thrombosis; (3) a multilineage cell activator network able to drive extrafollicular B maturation via an ensemble of genes extensively associated with risk for developing autoimmunity.

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Single-Cell Analysis Reveals Heterogeneity of Virus Infection, Pathogenicity, and Host Responses: HIV as a Pioneering Example

Annual Review of Virology ◽

10.1146/annurev-virology-021820-102458 ◽

2020 ◽

Vol 7 (1) ◽

pp. 333-350

Author(s):

Ludivine Brandt ◽

Sara Cristinelli ◽

Angela Ciuffi

Keyword(s):

Hiv Infection ◽

Single Cell ◽

Viral Infections ◽

Single Cell Analysis ◽

Host Responses ◽

Cell Analysis ◽

Host Interactions ◽

Immunodeficiency Virus ◽

Extreme Phenotypes ◽

Individual Consideration

While analyses of cell populations provide averaged information about viral infections, single-cell analyses offer individual consideration, thereby revealing a broad spectrum of diversity as well as identifying extreme phenotypes that can be exploited to further understand the complex virus-host interplay. Single-cell technologies applied in the context of human immunodeficiency virus (HIV) infection proved to be valuable tools to help uncover specific biomarkers as well as novel candidate players in virus-host interactions. This review aims at providing an updated overview of single-cell analyses in the field of HIV and acquired knowledge on HIV infection, latency, and host response. Although HIV is a pioneering example, similar single-cell approaches have proven to be valuable for elucidating the behavior and virus-host interplay in a range of other viruses.

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Unraveling the host responses to hepatitis B virus infection using single cell RNAseq

Journal of Hepatology ◽

10.1016/s0168-8278(17)30319-7 ◽

2017 ◽

Vol 66 (1) ◽

pp. S28

Author(s):

E.R. Verrier ◽

T. Croonenborghs ◽

L. Heydmann ◽

C. Bach ◽

C. Schuster ◽

...

Keyword(s):

Hepatitis B Virus ◽

Virus Infection ◽

Hepatitis B ◽

Single Cell ◽

Host Responses ◽

Hepatitis B Virus Infection ◽

B Virus

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Meta-Transcriptome Detector (MTD): a novel pipeline for metatranscriptome analysis of bulk and single-cell RNAseq data

10.1101/2021.11.16.468881 ◽

2021 ◽

Author(s):

Fei Wu ◽

Yaozhong Liu ◽

Binhua Ling

Keyword(s):

Gene Expression ◽

Single Cell ◽

Host Cells ◽

Host Responses ◽

Rna Seq ◽

Cell Level ◽

Gene Expressions ◽

Rnaseq Data ◽

Software Program ◽

User Friendly

RNA-seq data contains not only host transcriptomes but also non-host information that comprises transcripts from active microbiota in the host cells. Therefore, metatranscriptomics can reveal gene expression of the entire microbial community in a given sample. However, there is no single tool that can simultaneously analyze host-microbiota interactions and to quantify microbiome at the single-cell level, particularly for users with limited expertise of bioinformatics. Here, we developed a novel software program that can comprehensively and synergistically analyze gene expression of the host and microbiome as well as their association using bulk and single-cell RNA-seq data. Our pipeline, named Meta-Transcriptome Detector (MTD), can identify and quantify microbiome extensively, including viruses, bacteria, protozoa, fungi, plasmids, and vectors. MTD is easy to install and is user-friendly. This novel software program empowers researchers to study the interactions between microbiota and the host by analyzing gene expressions and pathways, which provides further insights into host responses to microorganisms.

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CLARA: A web portal for interactive exploration of the cardiovascular cellular landscape in health and disease

10.1101/2021.07.18.452862 ◽

2021 ◽

Author(s):

Malathi S.I. Dona ◽

Ian Hsu ◽

Thushara S Rathnayake ◽

Gabriella E. Farrugia ◽

Taylah L Gaynor ◽

...

Keyword(s):

Single Cell ◽

Web Application ◽

Rapid Development ◽

Expression Patterns ◽

Cardiac Cells ◽

Ease Of Use ◽

Molecular Networks ◽

Cardiovascular Development ◽

Web Portal ◽

Cellular Phenotypes

Mammalian cardiovascular tissues are comprised of complex and diverse collections of cells. Recent advances in single-cell profiling technologies have accelerated our understanding of tissue cellularity and the molecular networks that orchestrate cardiovascular development, maintain homeostasis, and are disrupted in pathological states. Despite the rapid development and application of these technologies, many cardiac single-cell functional genomics datasets remain inaccessible for most cardiovascular biologists. Access to custom visual representations of the data, including querying changes in cellular phenotypes and interactions in diverse contexts, remains unavailable in publicly accessible data portals. Visualizing data is also challenging for scientists without expertise in processing single-cell genomic data. Here we present CLARA—CardiovascuLAR Atlas—a web portal facilitating exploration of the cardiovascular cellular landscape. Using mouse and human single-cell transcriptomic datasets, CLARA enables scientists unfamiliar with single-cell-omic data analysis approaches to examine gene expression patterns and the cell population dynamics of cardiac cells in a range of contexts. The web-application also enables investigation of intercellular interactions that form the cardiac cellular niche. CLARA is designed for ease-of-use and we anticipate that the portal will aid deeper exploration of cardiovascular cellular landscapes in the context of development, homeostasis and disease. CLARA is freely available at https://clara.baker.edu.au.

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Personalized single-cell networks: a framework to predict the response of any gene to any drug for any patient

10.21203/rs.3.rs-57913/v2 ◽

2020 ◽

Author(s):

HARIPRIYA HARIKUMAR ◽

Thomas P Quinn ◽

Santu Rana ◽

Sunil Gupta ◽

Svetha Venkatesh

Keyword(s):

Single Cell ◽

Drug Response ◽

Single Cells ◽

Biological Data ◽

Patient Specific ◽

Expression Data ◽

Drug Responses ◽

Dual Channel ◽

Cell Gene ◽

Cell Networks

Abstract Background: The last decade has seen a major increase in the availability of genomic data. This includes expert-curated databases that describe the biological activity of genes, as well as high-throughput assays that measure gene expression in bulk tissue and single cells. Integrating these heterogeneous data sources can generate new hypotheses about biological systems. Our primary objective is to combine population-level drug-response data with patient-level single-cell expression data to predict how any gene will respond to any drug for any patient.Methods: We take 2 approaches to benchmarking a “dual-channel” random walk with restart (RWR) for data integration. First, we evaluate how well RWR can predict known gene functions from single-cell gene co-expression networks. Second, we evaluate how well RWR can predict known drug responses from individual cell networks. We then present two exploratory applications. In the first application, we combine the Gene Ontology database with glioblastoma single cells from 5 individual patients to identify genes whose functions differ between cancers. In the second application, we combine the LINCS drug-response database with the same glioblastoma data to identify genes that may exhibit patient-specific drug responses.Conclusions: Our manuscript introduces two innovations to the integration of heterogeneous biological data. First, we use a “dual-channel” method to predict up-regulation and down-regulation separately. Second, we use individualized single-cell gene co-expression networks to make personalized predictions. These innovations let us predict gene function and drug response for individual patients. Taken together, our work shows promise that single-cell co-expression data could be combined in heterogeneous information networks to facilitate precision medicine.

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Network modeling of single-cell omics data: challenges, opportunities, and progresses

Emerging Topics in Life Sciences ◽

10.1042/etls20180176 ◽

2019 ◽

Vol 3 (4) ◽

pp. 379-398 ◽

Cited By ~ 7

Author(s):

Montgomery Blencowe ◽

Douglas Arneson ◽

Jessica Ding ◽

Yen-Wei Chen ◽

Zara Saleem ◽

...

Keyword(s):

Single Cell ◽

Communication Networks ◽

Network Modeling ◽

Dynamic Networks ◽

Cell Interaction ◽

Omics Data ◽

Powerful Approach ◽

Challenges And Opportunities ◽

Cell Gene ◽

Cell Networks

Abstract Single-cell multi-omics technologies are rapidly evolving, prompting both methodological advances and biological discoveries at an unprecedented speed. Gene regulatory network modeling has been used as a powerful approach to elucidate the complex molecular interactions underlying biological processes and systems, yet its application in single-cell omics data modeling has been met with unique challenges and opportunities. In this review, we discuss these challenges and opportunities, and offer an overview of the recent development of network modeling approaches designed to capture dynamic networks, within-cell networks, and cell–cell interaction or communication networks. Finally, we outline the remaining gaps in single-cell gene network modeling and the outlooks of the field moving forward.

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On the performance of a joint TDD CDMA/PRMA and CDMA/ALOHA protocols with 2D RAKE receivers in single-cell and multi-cell networks

IEEE 54th Vehicular Technology Conference. VTC Fall 2001. Proceedings (Cat. No.01CH37211) ◽

10.1109/vtc.2001.956448 ◽

2002 ◽

Cited By ~ 1

Author(s):

R.P.F. Hoefel

Keyword(s):

Single Cell ◽

Rake Receivers ◽

Cell Networks

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Hepatitis B virus compartmentalization and single-cell differentiation in hepatocellular carcinoma

Life Science Alliance ◽

10.26508/lsa.202101036 ◽

2021 ◽

Vol 4 (9) ◽

pp. e202101036

Author(s):

Frank Jühling ◽

Antonio Saviano ◽

Clara Ponsolles ◽

Laura Heydmann ◽

Emilie Crouchet ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Cell Differentiation ◽

Hepatitis B Virus ◽

Hepatitis B ◽

Single Cell ◽

Host Cell ◽

Molecular Mechanisms ◽

Host Responses ◽

Chronic Hepatitis B Virus ◽

B Virus

Chronic hepatitis B virus (HBV) infection is a major cause of hepatocellular carcinoma (HCC) world-wide. The molecular mechanisms of viral hepatocarcinogenesis are still partially understood. Here, we applied two complementary single-cell RNA-sequencing protocols to investigate HBV–HCC host cell interactions at the single cell level of patient-derived HCC. Computational analyses revealed a marked HCC heterogeneity with a robust and significant correlation between HBV reads and cancer cell differentiation. Viral reads significantly correlated with the expression of HBV-dependency factors such as HLF in different tumor compartments. Analyses of virus-induced host responses identified previously undiscovered pathways mediating viral carcinogenesis, such as E2F- and MYC targets as well as adipogenesis. Mapping of fused HBV–host cell transcripts allowed the characterization of integration sites in individual cancer cells. Collectively, single-cell RNA-Seq unravels heterogeneity and compartmentalization of both, virus and cancer identifying new candidate pathways for viral hepatocarcinogenesis. The perturbation of pro-carcinogenic gene expression even at low HBV levels highlights the need of HBV cure to eliminate HCC risk.

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