scholarly journals FlyPhoneDB: An integrated web-based resource for cell-cell communication prediction in Drosophila

Genetics ◽  
2021 ◽  
Author(s):  
Yifang Liu ◽  
Joshua Shing Shun Li ◽  
Jonathan Rodiger ◽  
Aram Comjean ◽  
Helen Attrill ◽  
...  
Keyword(s):  
Cell Communication ◽  
Cell Types ◽  
Data Sets ◽  
High Confidence ◽  
Web Based ◽  
Communication Events ◽  
Single Cell Rna Sequencing ◽  
Biological Interpretation ◽  
Multicellular Organisms ◽  
Cell Cell

Abstract Multicellular organisms rely on cell-cell communication to exchange information necessary for developmental processes and metabolic homeostasis. Cell-cell communication pathways can be inferred from transcriptomic datasets based on ligand-receptor (L-R) expression. Recently, data generated from single cell RNA sequencing (scRNA-seq) have enabled L-R interaction predictions at an unprecedented resolution. While computational methods are available to infer cell-cell communication in vertebrates such a tool does not yet exist for Drosophila. Here, we generated a high confidence list of L-R pairs for the major fly signaling pathways and developed FlyPhoneDB, a quantification algorithm that calculates interaction scores to predict L-R interactions between cells. At the FlyPhoneDB user interface, results are presented in a variety of tabular and graphical formats to facilitate biological interpretation. To demonstrate that FlyPhoneDB can effectively identify active ligands and receptors to uncover cell-cell communication events, we applied FlyPhoneDB to Drosophila scRNA-seq data sets from adult midgut, abdomen, and blood, and demonstrate that FlyPhoneDB can readily identify previously characterized cell-cell communication pathways. Altogether, FlyPhoneDB is an easy-to-use framework that can be used to predict cell-cell communication between cell types from scRNA-seq data in Drosophila.

scholarly journals FlyPhoneDB: An integrated web-based resource for cell-cell communication prediction in Drosophila

2021 ◽  
Author(s):  
Yifang Liu ◽  
Yanhui Hu ◽  
Joshua Shing Shun Li ◽  
Jonathan Rodiger ◽  
Aram Comjean ◽  
...  
Keyword(s):  
Cell Communication ◽  
Cell Types ◽  
Data Sets ◽  
High Confidence ◽  
Web Based ◽  
Communication Events ◽  
Single Cell Rna Sequencing ◽  
Biological Interpretation ◽  
Multicellular Organisms ◽  
Cell Cell

Multicellular organisms rely on cell-cell communication to exchange information necessary for developmental processes and metabolic homeostasis. Cell-cell communication pathways can be inferred from transcriptomic datasets based on ligand-receptor (L-R) expression. Recently, data generated from single cell RNA sequencing (scRNA-seq) have enabled L-R interaction predictions at an unprecedented resolution. While computational methods are available to infer cell-cell communication in vertebrates such a tool does not yet exist for Drosophila. Here, we generated a high confidence list of L-R pairs for the major fly signaling pathways and developed FlyPhoneDB, a quantification algorithm that calculates interaction scores to predict L-R interactions between cells. At the FlyPhoneDB user interface, results are presented in a variety of tabular and graphical formats to facilitate biological interpretation. To demonstrate that FlyPhoneDB can effectively identify active ligands and receptors to uncover cell-cell communication events, we applied FlyPhoneDB to Drosophila scRNA-seq data sets from adult midgut, abdomen, and blood, and demonstrate that FlyPhoneDB can readily identify previously characterized cell-cell communication pathways. Altogether, FlyPhoneDB is an easy-to-use framework that can be used to predict cell-cell communication between cell types from scRNA-seq data in Drosophila.

scholarly journals Inference and analysis of cell-cell communication using CellChat

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Suoqin Jin ◽  
Christian F. Guerrero-Juarez ◽  
Lihua Zhang ◽  
Ivan Chang ◽  
Raul Ramos ◽  
...  
Keyword(s):  
Communication Networks ◽  
Cell Communication ◽  
Molecular Complexes ◽  
Web Based ◽  
Single Cell Rna Sequencing ◽  
Context Specific ◽  
Global Communications ◽  
Complex Signaling ◽  
Intercellular Communications ◽  
Cell Cell

AbstractUnderstanding global communications among cells requires accurate representation of cell-cell signaling links and effective systems-level analyses of those links. We construct a database of interactions among ligands, receptors and their cofactors that accurately represent known heteromeric molecular complexes. We then develop CellChat, a tool that is able to quantitatively infer and analyze intercellular communication networks from single-cell RNA-sequencing (scRNA-seq) data. CellChat predicts major signaling inputs and outputs for cells and how those cells and signals coordinate for functions using network analysis and pattern recognition approaches. Through manifold learning and quantitative contrasts, CellChat classifies signaling pathways and delineates conserved and context-specific pathways across different datasets. Applying CellChat to mouse and human skin datasets shows its ability to extract complex signaling patterns. Our versatile and easy-to-use toolkit CellChat and a web-based Explorer (http://www.cellchat.org/) will help discover novel intercellular communications and build cell-cell communication atlases in diverse tissues.

scholarly journals CellPhoneDB v2.0: Inferring cell-cell communication from combined expression of multi-subunit receptor-ligand complexes

2019 ◽  
Author(s):  
Mirjana Efremova ◽  
Miquel Vento-Tormo ◽  
Sarah A. Teichmann ◽  
Roser Vento-Tormo
Keyword(s):  
Communication Networks ◽  
Cell Communication ◽  
Cell Types ◽  
Published Data ◽  
Data Sets ◽  
Cellular Interactions ◽  
Receptor Ligand ◽  
Link Type ◽  
Transcriptomics Data ◽  
Cell Cell

AbstractCell-cell communication mediated by receptor-ligand complexes is crucial for coordinating diverse biological processes, such as development, differentiation and responses to infection. In order to understand how the context-dependent crosstalk of different cell types enables physiological processes to proceed, we developed CellPhoneDB, a novel repository of ligands, receptors and their interactions1. Our repository takes into account the subunit architecture of both ligands and receptors, representing heteromeric complexes accurately. We integrated our resource with a statistical framework that predicts enriched cellular interactions between two cell types from single-cell transcriptomics data. Here, we outline the structure and content of our repository, the procedures for inferring cell-cell communication networks from scRNA-seq data and present a practical step-by-step guide to help implement the protocol. CellPhoneDB v2.0 is a novel version of our resource that incorporates additional functionalities to allow users to introduce new interacting molecules and reduce the time and resources needed to interrogate large datasets. CellPhoneDB v2.0 is publicly available at https://github.com/Teichlab/cellphonedb and as a user-friendly web interface at http://www.cellphonedb.org/. In our protocol, we demonstrate how to reveal meaningful biological discoveries from CellPhoneDB v2.0 using published data sets.

scholarly journals New avenues for systematically inferring cell-cell communication: through single-cell transcriptomics data

2020 ◽  
Vol 11 (12) ◽  
pp. 866-880 ◽  
Author(s):  
Xin Shao ◽  
Xiaoyan Lu ◽  
Jie Liao ◽  
Huajun Chen ◽  
Xiaohui Fan
Keyword(s):  
Single Cell ◽  
Communication Networks ◽  
Cell Communication ◽  
Receptor Interaction ◽  
Communication Studies ◽  
Transcriptomics Data ◽  
Phenotype Heterogeneity ◽  
Multicellular Organisms ◽  
Communication Study ◽  
Cell Cell

AbstractFor multicellular organisms, cell-cell communication is essential to numerous biological processes. Drawing upon the latest development of single-cell RNA-sequencing (scRNA-seq), high-resolution transcriptomic data have deepened our understanding of cellular phenotype heterogeneity and composition of complex tissues, which enables systematic cell-cell communication studies at a single-cell level. We first summarize a common workflow of cell-cell communication study using scRNA-seq data, which often includes data preparation, construction of communication networks, and result validation. Two common strategies taken to uncover cell-cell communications are reviewed, e.g., physically vicinal structure-based and ligand-receptor interaction-based one. To conclude, challenges and current applications of cell-cell communication studies at a single-cell resolution are discussed in details and future perspectives are proposed.

scholarly journals Chromatin interactions reveal novel gene targets for drug repositioning in rheumatic diseases

2019 ◽  
Vol 78 (8) ◽  
pp. 1127-1134 ◽  
Author(s):  
Paul Martin ◽  
James Ding ◽  
Kate Duffus ◽  
Vasanthi Priyadarshini Gaddi ◽  
Amanda McGovern ◽  
...  
Keyword(s):  
Rheumatic Diseases ◽  
Pathway Analysis ◽  
Drug Targets ◽  
Drug Repositioning ◽  
Healthcare Providers ◽  
Cell Types ◽  
Data Sets ◽  
High Confidence ◽  
Causal Genes ◽  
Additional Cell

ObjectivesThere is a need to identify effective treatments for rheumatic diseases, and while genetic studies have been successful it is unclear which genes contribute to the disease. Using our existing Capture Hi-C data on three rheumatic diseases, we can identify potential causal genes which are targets for existing drugs and could be repositioned for use in rheumatic diseases.MethodsHigh confidence candidate causal genes were identified using Capture Hi-C data from B cells and T cells. These genes were used to interrogate drug target information from DrugBank to identify existing treatments, which could be repositioned to treat these diseases. The approach was refined using Ingenuity Pathway Analysis to identify enriched pathways and therefore further treatments relevant to the disease.ResultsOverall, 454 high confidence genes were identified. Of these, 48 were drug targets (108 drugs) and 11 were existing therapies used in the treatment of rheumatic diseases. After pathway analysis refinement, 50 genes remained, 13 of which were drug targets (33 drugs). However considering targets across all enriched pathways, a further 367 drugs were identified for potential repositioning.ConclusionCapture Hi-C has the potential to identify therapies which could be repositioned to treat rheumatic diseases. This was particularly successful for rheumatoid arthritis, where six effective, biologic treatments were identified. This approach may therefore yield new ways to treat patients, enhancing their quality of life and reducing the economic impact on healthcare providers. As additional cell types and other epigenomic data sets are generated, this prospect will improve further.

scholarly journals Evaluation of single-cell classifiers for single-cell RNA sequencing data sets

2019 ◽  
Vol 21 (5) ◽  
pp. 1581-1595 ◽  
Author(s):  
Xinlei Zhao ◽  
Shuang Wu ◽  
Nan Fang ◽  
Xiao Sun ◽  
Jue Fan
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Reference Data ◽  
Predictive Accuracy ◽  
Cell Types ◽  
Superior Performance ◽  
Marker Genes ◽  
Data Sets ◽  
Sequencing Data ◽  
Single Cell Rna Sequencing

Abstract Single-cell RNA sequencing (scRNA-seq) has been rapidly developing and widely applied in biological and medical research. Identification of cell types in scRNA-seq data sets is an essential step before in-depth investigations of their functional and pathological roles. However, the conventional workflow based on clustering and marker genes is not scalable for an increasingly large number of scRNA-seq data sets due to complicated procedures and manual annotation. Therefore, a number of tools have been developed recently to predict cell types in new data sets using reference data sets. These methods have not been generally adapted due to a lack of tool benchmarking and user guidance. In this article, we performed a comprehensive and impartial evaluation of nine classification software tools specifically designed for scRNA-seq data sets. Results showed that Seurat based on random forest, SingleR based on correlation analysis and CaSTLe based on XGBoost performed better than others. A simple ensemble voting of all tools can improve the predictive accuracy. Under nonideal situations, such as small-sized and class-imbalanced reference data sets, tools based on cluster-level similarities have superior performance. However, even with the function of assigning ‘unassigned’ labels, it is still challenging to catch novel cell types by solely using any of the single-cell classifiers. This article provides a guideline for researchers to select and apply suitable classification tools in their analysis workflows and sheds some lights on potential direction of future improvement on classification tools.

scholarly journals IL-6 loss causes ventricular dysfunction, fibrosis, reduced capillary density, and dramatically alters the cell populations of the developing and adult heart

2009 ◽  
Vol 296 (5) ◽  
pp. H1694-H1704 ◽  
Author(s):  
Indroneal Banerjee ◽  
John W. Fuseler ◽  
Arti R. Intwala ◽  
Troy A. Baudino
Keyword(s):  
Cardiac Function ◽  
Cardiac Fibroblasts ◽  
Cell Communication ◽  
Cell Types ◽  
Capillary Density ◽  
Cell Populations ◽  
Cardiac Cell ◽  
Altered Expression ◽  
Cell Cell

Interleukin-6 (IL-6) is a pleiotropic cytokine responsible for many different processes including the regulation of cell growth, apoptosis, differentiation, and survival in various cell types and organs, including the heart. Recent studies have indicated that IL-6 is a critical component in the cell-cell communication between myocytes and cardiac fibroblasts. In this study, we examined the effects of IL-6 deficiency on the cardiac cell populations, cardiac function, and interactions between the cells of the heart, specifically cardiac fibroblasts and myocytes. To examine the effects of IL-6 loss on cardiac function, we used the IL-6 −/− mouse. IL-6 deficiency caused severe cardiac dilatation, increased accumulation of interstitial collagen, and altered expression of the adhesion protein periostin. In addition, flow cytometric analyses demonstrated dramatic alterations in the cardiac cell populations of IL-6 −/− mice compared with wild-type littermates. We observed a marked increase in the cardiac fibroblast population in IL-6 −/− mice, whereas a concomitant decrease was observed in the other cardiac cell populations examined. Moreover, we observed increased cell proliferation and apoptosis in the developing IL-6 −/− heart. Additionally, we observed a significant decrease in the capillary density of IL-6 −/− hearts. To elucidate the role of IL-6 in the interactions between cardiac fibroblasts and myocytes, we performed in vitro studies and demonstrated that IL-6 deficiency attenuated the activation of the STAT3 pathway and VEGF production. Taken together, these data demonstrate that a loss of IL-6 causes cardiac dysfunction by shifting the cardiac cell populations, altering the extracellular matrix, and disrupting critical cell-cell interactions.

scholarly journals ICELLNET: a transcriptome-based framework to dissect intercellular communication

2020 ◽  
Author(s):  
Floriane Noël ◽  
Lucile Massenet-Regad ◽  
Irit Carmi-Levy ◽  
Antonio Cappuccio ◽  
Maximilien Grandclaudon ◽  
...  
Keyword(s):  
Cell Population ◽  
Cell Communication ◽  
Cell Types ◽  
Receptor Expression ◽  
Global Integration ◽  
Cancer Associated Fibroblast ◽  
Receptor Interactions ◽  
Biological Interpretation ◽  
A Cell ◽  
Multiple Cell

AbstractCell-to-cell communication can be inferred from ligand-receptor expression in cell transcriptomic datasets. However, important challenges remain: 1) global integration of cell-to-cell communication, 2) biological interpretation, and 3) application to individual cell population transcriptomic profiles. We developed ICELLNET, a transcriptomic-based framework integrating: 1) an original expert-curated database of ligand-receptor interactions accounting for multiple subunits expression, 2) quantification of communication scores, 3) the possibility to connect a cell population of interest with 31 reference human cell types (BioGPS), and 4) three visualization modes to facilitate biological interpretation. We applied ICELLNET to uncover different communication in breast cancer associated fibroblast (CAF) subsets. ICELLNET also revealed autocrine IL-10 as a switch to control human dendritic cell communication with up to 12 other cell types, four of which were experimentally validated. In summary, ICELLNET is a global, versatile, biologically validated, and easy-to-use framework to dissect cell communication from single or multiple cell-based transcriptomic profile(s).

scholarly journals A System of Cytokines Encapsulated in ExtraCellular Vesicles

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Wendy Fitzgerald ◽  
Michael L. Freeman ◽  
Michael M. Lederman ◽  
Elena Vasilieva ◽  
Roberto Romero ◽  
...  
Keyword(s):  
Extracellular Vesicles ◽  
Ex Vivo ◽  
Biological Effects ◽  
Cell Communication ◽  
Health And Disease ◽  
Mediate Cell ◽  
Multicellular Organisms ◽  
Cell Cell

Abstract Cytokines are soluble factors that mediate cell–cell communications in multicellular organisms. Recently, another system of cell–cell communication was discovered, which is mediated by extracellular vesicles (EVs). Here, we demonstrate that these two systems are not strictly separated, as many cytokines in vitro, ex vivo, and in vivo are released in EV-encapsulated forms and are capable of eliciting biological effects upon contact with sensitive cells. Association with EVs is not necessarily a property of a particular cytokine but rather of a biological system and can be changed upon system activation. EV-encapsulated cytokines were not detected by standard cytokine assays. Deciphering the regulatory mechanisms of EV-encapsulation will lead to a better understanding of cell–cell communications in health and disease.

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