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 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 FunRes: resolving tissue-specific functional cell states based on a cell–cell communication network model

2020 ◽  
Author(s):  
Sascha Jung ◽  
Kartikeya Singh ◽  
Antonio del Sol
Keyword(s):  
Communication Network ◽  
Communication Networks ◽  
Cell Communication ◽  
Cell Types ◽  
Computational Method ◽  
Tissue Specific ◽  
Great Utility ◽  
A Cell ◽  
Cell Cell

Abstract The functional specialization of cell types arises during development and is shaped by cell–cell communication networks determining a distribution of functional cell states that are collectively important for tissue functioning. However, the identification of these tissue-specific functional cell states remains challenging. Although a plethora of computational approaches have been successful in detecting cell types and subtypes, they fail in resolving tissue-specific functional cell states. To address this issue, we present FunRes, a computational method designed for the identification of functional cell states. FunRes relies on scRNA-seq data of a tissue to initially reconstruct the functional cell–cell communication network, which is leveraged for partitioning each cell type into functional cell states. We applied FunRes to 177 cell types in 10 different tissues and demonstrated that the detected states correspond to known functional cell states of various cell types, which cannot be recapitulated by existing computational tools. Finally, we characterize emerging and vanishing functional cell states in aging and disease, and demonstrate their involvement in key tissue functions. Thus, we believe that FunRes will be of great utility in the characterization of the functional landscape of cell types and the identification of dysfunctional cell states in aging and disease.

scholarly journals Patient-Specific Cell Communication Networks Associate With Disease Progression in Cancer

2021 ◽  
Vol 12 ◽  
Author(s):  
David L. Gibbs ◽  
Boris Aguilar ◽  
Vésteinn Thorsson ◽  
Alexander V. Ratushny ◽  
Ilya Shmulevich
Keyword(s):  
Communication Networks ◽  
Probabilistic Method ◽  
Cell Communication ◽  
Cell Types ◽  
Tumor Stage ◽  
The Cancer Genome Atlas ◽  
Patient Specific ◽  
Specific Cell ◽  
High Level ◽  
Cell Cell

The maintenance and function of tissues in health and disease depends on cell–cell communication. This work shows how high-level features, representing cell–cell communication, can be defined and used to associate certain signaling “axes” with clinical outcomes. We generated a scaffold of cell–cell interactions and defined a probabilistic method for creating per-patient weighted graphs based on gene expression and cell deconvolution results. With this method, we generated over 9,000 graphs for The Cancer Genome Atlas (TCGA) patient samples, each representing likely channels of intercellular communication in the tumor microenvironment (TME). It was shown that cell–cell edges were strongly associated with disease severity and progression, in terms of survival time and tumor stage. Within individual tumor types, there are predominant cell types, and the collection of associated edges were found to be predictive of clinical phenotypes. Additionally, genes associated with differentially weighted edges were enriched in Gene Ontology terms associated with tissue structure and immune response. Code, data, and notebooks are provided to enable the application of this method to any expression dataset (https://github.com/IlyaLab/Pan-Cancer-Cell-Cell-Comm-Net).

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 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 Probabilistic Harmonization and Annotation of Single-cell Transcriptomics Data with Deep Generative Models

2019 ◽  
Author(s):  
Chenling Xu ◽  
Romain Lopez ◽  
Edouard Mehlman ◽  
Jeffrey Regier ◽  
Michael I. Jordan ◽  
...  
Keyword(s):  
Single Cell ◽  
Probabilistic Approach ◽  
Cell Types ◽  
Generative Models ◽  
Marker Genes ◽  
Data Sets ◽  
Data Set ◽  
Cell State ◽  
Transcriptomics Data ◽  
Single Data

AbstractAs single-cell transcriptomics becomes a mainstream technology, the natural next step is to integrate the accumulating data in order to achieve a common ontology of cell types and states. However, owing to various nuisance factors of variation, it is not straightforward how to compare gene expression levels across data sets and how to automatically assign cell type labels in a new data set based on existing annotations. In this manuscript, we demonstrate that our previously developed method, scVI, provides an effective and fully probabilistic approach for joint representation and analysis of cohorts of single-cell RNA-seq data sets, while accounting for uncertainty caused by biological and measurement noise. We also introduce single-cell ANnotation using Variational Inference (scANVI), a semi-supervised variant of scVI designed to leverage any available cell state annotations — for instance when only one data set in a cohort is annotated, or when only a few cells in a single data set can be labeled using marker genes. We demonstrate that scVI and scANVI compare favorably to the existing methods for data integration and cell state annotation in terms of accuracy, scalability, and adaptability to challenging settings such as a hierarchical structure of cell state labels. We further show that different from existing methods, scVI and scANVI represent the integrated datasets with a single generative model that can be directly used for any probabilistic decision making task, using differential expression as our case study. scVI and scANVI are available as open source software and can be readily used to facilitate cell state annotation and help ensure consistency and reproducibility across studies.

scholarly journals Community Detection in a Weighted Directed Hypergraph Representation of Cell-to-cell Communication Networks

2020 ◽  
Author(s):  
Rui Hou ◽  
Michael Small ◽  
Alistair R. R. Forrest
Keyword(s):  
Communication Networks ◽  
Community Detection ◽  
Cell Communication ◽  
Cell Types ◽  
Community Structures ◽  
Simple Graphs ◽  
Detection Algorithms ◽  
Complex Processes ◽  
Community Identification ◽  
Directed Hypergraph

AbstractCell-to-cell communication is mainly triggered by ligand-receptor activities. Through ligandreceptor pairs, cells coordinate complex processes such as development, homeostasis, and immune response. In this work, we model the ligand-receptor-mediated cell-to-cell communication network as a weighted directed hypergraph. In this mathematical model, collaborating cell types are considered as a node community while the ligand-receptor pairs connecting them are considered a hyperedge community. We first define the community structures in a weighted directed hypergraph and develop an exact community detection method to identify these communities. We then modify approximate community detection algorithms designed for simple graphs to identify the nodes and hyperedges within each community. Application to synthetic hypergraphs with known community structure confirmed that one of the proposed approximate community identification strategies, named HyperCommunity algorithm, can effectively and precisely detect embedded communities. We then applied this strategy to two organism-wide datasets and identified putative community structures. Notably the method identifies non-overlapping edge-communities mediated by different sets of ligand-receptor pairs, however node-communities can overlap.

scholarly journals Predicting cell-to-cell communication networks using NATMI

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Rui Hou ◽  
Elena Denisenko ◽  
Huan Ting Ong ◽  
Jordan A. Ramilowski ◽  
Alistair R. R. Forrest
Keyword(s):  
Single Cell ◽  
Communication Networks ◽  
Cell Communication ◽  
Cost Effective ◽  
Cell Types ◽  
Sequencing Technologies ◽  
Multiple Cell ◽  
Multicellular Interactions ◽  
Autocrine Signalling ◽  
Different Cell Types

Abstract Development of high throughput single-cell sequencing technologies has made it cost-effective to profile thousands of cells from diverse samples containing multiple cell types. To study how these different cell types work together, here we develop NATMI (Network Analysis Toolkit for Multicellular Interactions). NATMI uses connectomeDB2020 (a database of 2293 manually curated ligand-receptor pairs with literature support) to predict and visualise cell-to-cell communication networks from single-cell (or bulk) expression data. Using multiple published single-cell datasets we demonstrate how NATMI can be used to identify (i) the cell-type pairs that are communicating the most (or most specifically) within a network, (ii) the most active (or specific) ligand-receptor pairs active within a network, (iii) putative highly-communicating cellular communities and (iv) differences in intercellular communication when profiling given cell types under different conditions. Furthermore, analysis of the Tabula Muris (organism-wide) atlas confirms our previous prediction that autocrine signalling is a major feature of cell-to-cell communication networks, while also revealing that hundreds of ligands and their cognate receptors are co-expressed in individual cells suggesting a substantial potential for self-signalling.

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