Cell-Cell Communication Networks in Tissue: Toward Quantitatively Linking Structure with Function

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.

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Inference and analysis of cell-cell communication using CellChat

Nature Communications ◽

10.1038/s41467-021-21246-9 ◽

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.

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

Briefings in Bioinformatics ◽

10.1093/bib/bbaa283 ◽

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.

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

Frontiers in Genetics ◽

10.3389/fgene.2021.667382 ◽

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).

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

10.1101/2021.02.08.430343 ◽

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).

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Inference and analysis of cell-cell communication using CellChat

10.1101/2020.07.21.214387 ◽

2020 ◽

Cited By ~ 7

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.

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

10.1101/680926 ◽

2019 ◽

Cited By ~ 25

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.

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Engineering cell–cell communication networks: programming multicellular behaviors

Current Opinion in Chemical Biology ◽

10.1016/j.cbpa.2019.04.020 ◽

2019 ◽

Vol 52 ◽

pp. 31-38 ◽

Cited By ~ 9

Author(s):

Satoshi Toda ◽

Nicholas W Frankel ◽

Wendell A Lim

Keyword(s):

Communication Networks ◽

Cell Communication ◽

Cell Cell

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LuxR Solos from Environmental Fluorescent Pseudomonads

mSphere ◽

10.1128/msphere.01322-20 ◽

2021 ◽

Vol 6 (2) ◽

Author(s):

Cristina Bez ◽

Sonia Covaceuzach ◽

Iris Bertani ◽

Kumari Sonal Choudhary ◽

Vittorio Venturi

Keyword(s):

Quorum Sensing ◽

Cell Signaling ◽

Communication Networks ◽

Cell Communication ◽

Binding Pocket ◽

Homoserine Lactone ◽

Fluorescent Pseudomonads ◽

Content Type ◽

Gene Expression Studies ◽

Cell Cell

ABSTRACT LuxR solos are related to quorum sensing (QS) LuxR family regulators; however, they lack a cognate LuxI family protein. LuxR solos are widespread and almost exclusively found in proteobacteria. In this study, we investigated the distribution and conservation of LuxR solos in the fluorescent pseudomonads group. Our analysis of more than 600 genomes revealed that the majority of fluorescent Pseudomonas spp. carry one or more LuxR solos, occurring considerably more frequently than complete LuxI/LuxR archetypical QS systems. Based on the adjacent gene context and conservation of the primary structure, nine subgroups of LuxR solos have been identified that are likely to be involved in the establishment of communication networks. Modeling analysis revealed that the majority of subgroups shows some substitutions at the invariant amino acids of the ligand-binding pocket of QS LuxRs, raising the possibility of binding to non-acyl-homoserine lactone (AHL) ligands. Several mutants and gene expression studies on some LuxR solos belonging to different subgroups were performed in order to shed light on their response. The commonality of LuxR solos among fluorescent pseudomonads is an indication of their important role in cell-cell signaling. IMPORTANCE Cell-cell communication in bacteria is being extensively studied in simple settings and uses chemical signals and cognate regulators/receptors. Many Gram-negative proteobacteria use acyl-homoserine lactones (AHLs) synthesized by LuxI family proteins and cognate LuxR-type receptors to regulate their quorum sensing (QS) target loci. AHL-QS circuits are the best studied QS systems; however, many proteobacterial genomes also contain one or more LuxR solos, which are QS-related LuxR proteins which are unpaired to a cognate LuxI. A few LuxR solos have been implicated in intraspecies, interspecies, and interkingdom signaling. Here, we report that LuxR solo homologs occur considerably more frequently than complete LuxI/LuxR QS systems within the Pseudomonas fluorescens group of species and that they are characterized by different genomic organizations and primary structures and can be subdivided into several subgroups. The P. fluorescens group consists of more than 50 species, many of which are found in plant-associated environments. The role of LuxR solos in cell-cell signaling in fluorescent pseudomonads is discussed.

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