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 Inference and analysis of cell-cell communication using CellChat

2020 ◽  
Author(s):  
Suoqin Jin ◽  
Christian F. Guerrero-Juarez ◽  
Lihua Zhang ◽  
Ivan Chang ◽  
Peggy Myung ◽  
...  
Keyword(s):  
Communication Networks ◽  
Cell Communication ◽  
Mouse Skin ◽  
Molecular Complexes ◽  
Mass Action ◽  
A Cell ◽  
Context Specific ◽  
Global Communications ◽  
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 constructed a database of interactions among ligands, receptors and their cofactors that accurately represents known heteromeric molecular complexes. Based on mass action models, we then developed CellChat, a tool that is able to quantitively 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. Applications of CellChat to several mouse skin scRNA-seq datasets for embryonic development and adult wound healing shows its ability to extract complex signaling patterns, both previously known as well as novel. 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 a cell-cell communication atlas in diverse tissues.

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 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 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 Cellinker: a platform of ligand–receptor interactions for intercellular communication analysis

2021 ◽  
Author(s):  
Yang Zhang ◽  
Tianyuan Liu ◽  
Jing Wang ◽  
Bohao Zou ◽  
Le Li ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Intercellular Communication ◽  
Rapid Development ◽  
Cell Communication ◽  
Supplementary Information ◽  
Protein Protein Interactions ◽  
Communication Analysis ◽  
Mediate Cell ◽  
Intercellular Communications ◽  
Cell Cell

Abstract Motivation Ligand–receptor (L–R) interactions mediate cell adhesion, recognition and communication and play essential roles in physiological and pathological signaling. With the rapid development of single-cell RNA sequencing (scRNA-seq) technologies, systematically decoding the intercellular communication network involving L–R interactions has become a focus of research. Therefore, construction of a comprehensive, high-confidence and well-organized resource to retrieve L–R interactions in order to study the functional effects of cell–cell communications would be of great value. Results In this study, we developed Cellinker, a platform of literature-supported L–R interactions that play roles in cell–cell communication. We aimed to provide a useful platform for studies on cell–cell communication mediated by L–R interactions. The current version of Cellinker documents over 3700 human and 3200 mouse L–R protein–protein interactions (PPIs) and embeds a practical and convenient webserver with which researchers can decode intercellular communications based on scRNA-seq data. And over 400 endogenous small molecule (sMOL) related L–R interactions were collected as well. Moreover, to help with research on coronavirus (CoV) infection, Cellinker collects information on 16L–R PPIs involved in CoV–human interactions (including 12L–R PPIs involved in SARS-CoV-2 infection). In summary, Cellinker provides a user-friendly interface for querying, browsing and visualizing L–R interactions as well as a practical and convenient web tool for inferring intercellular communications based on scRNA-seq data. We believe this platform could promote intercellular communication research and accelerate the development of related algorithms for scRNA-seq studies. Availability and implementation Cellinker is available at http://www.rna-society.org/cellinker/ Supplementary information Supplementary data are available at Bioinformatics online.

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 Patient-specific cell communication networks associate with disease progression in cancer

2021 ◽  
Author(s):  
David L Gibbs ◽  
Boris Aguilar ◽  
Vésteinn Thorsson ◽  
Alexander V Ratushny ◽  
Ilya Shmulevich
Keyword(s):  
Gene Expression ◽  
Communication Networks ◽  
Cancer Cell ◽  
Probabilistic Method ◽  
Cell Communication ◽  
Tumor Stage ◽  
Patient Specific ◽  
Specific Cell ◽  
Pan Cancer ◽  
Cell Cell

AbstractThe 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. Using cell-sorted gene expression data, we generated a scaffold of cell-cell interactions and define 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 TCGA patient samples, each representing likely channels of intercellular communication in the tumor microenvironment. It was shown that particular 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 Investigating immune and non-immune cell interactions in head and neck tumors by single-cell RNA sequencing

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Cornelius H. L. Kürten ◽  
Aditi Kulkarni ◽  
Anthony R. Cillo ◽  
Patricia M. Santos ◽  
Anna K. Roble ◽  
...  
Keyword(s):  
Head And Neck ◽  
Single Cell ◽  
Rna Sequencing ◽  
Immune Cell ◽  
Cell Communication ◽  
Cell Interactions ◽  
Cell Populations ◽  
Hpv Status ◽  
Single Cell Rna Sequencing ◽  
Cell Cell

AbstractHead and neck squamous cell carcinoma (HNSCC) is characterized by complex relations between stromal, epithelial, and immune cells within the tumor microenvironment (TME). To enable the development of more efficacious therapies, we aim to study the heterogeneity, signatures of unique cell populations, and cell-cell interactions of non-immune and immune cell populations in 6 human papillomavirus (HPV)+ and 12 HPV– HNSCC patient tumor and matched peripheral blood specimens using single-cell RNA sequencing. Using this dataset of 134,606 cells, we show cell type-specific signatures associated with inflammation and HPV status, describe the negative prognostic value of fibroblasts with elastic differentiation specifically in the HPV+ TME, predict therapeutically targetable checkpoint receptor-ligand interactions, and show that tumor-associated macrophages are dominant contributors of PD-L1 and other immune checkpoint ligands in the TME. We present a comprehensive single-cell view of cell-intrinsic mechanisms and cell-cell communication shaping the HNSCC microenvironment.

Sign in / Sign up

Export Citation Format

Share Document