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 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.

CellTalkDB: a manually curated database of ligand–receptor interactions in humans and mice

2020 ◽  
Author(s):  
Xin Shao ◽  
Jie Liao ◽  
Chengyu Li ◽  
Xiaoyan Lu ◽  
Junyun Cheng ◽  
...  
Keyword(s):  
Single Cell ◽  
Protein Interactions ◽  
Cell Communication ◽  
Systematic Investigation ◽  
Protein Protein Interactions ◽  
Biological Phenomena ◽  
Type Composition ◽  
Dependent Cell ◽  
Multicellular Organisms ◽  
Cell Cell

Abstract Cell–cell communications in multicellular organisms generally involve secreted ligand–receptor (LR) interactions, which is vital for various biological phenomena. Recent advancements in single-cell RNA sequencing (scRNA-seq) have effectively resolved cellular phenotypic heterogeneity and the cell-type composition of complex tissues, facilitating the systematic investigation of cell–cell communications at single-cell resolution. However, assessment of chemical-signal-dependent cell–cell communication through scRNA-seq relies heavily on prior knowledge of LR interaction pairs. We constructed CellTalkDB (http://tcm.zju.edu.cn/celltalkdb), a manually curated comprehensive database of LR interaction pairs in humans and mice comprising 3398 human LR pairs and 2033 mouse LR pairs, through text mining and manual verification of known protein–protein interactions using the STRING database, with literature-supported evidence for each pair. Compared with SingleCellSignalR, the largest LR-pair resource, CellTalkDB includes not only 2033 mouse LR pairs but also 377 additional human LR pairs. In conclusion, the data on human and mouse LR pairs contained in CellTalkDB could help to further the inference and understanding of the LR-interaction-based cell–cell communications, which might provide new insights into the mechanism underlying biological processes.

scholarly journals Comparison of Resources and Methods to infer Cell-Cell Communication from Single-cell RNA Data

2021 ◽  
Author(s):  
Daniel Dimitrov ◽  
Dénes Türei ◽  
Charlotte Boys ◽  
James S. Nagai ◽  
Ricardo O. Ramirez Flores ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Communication ◽  
Rna Seq ◽  
Intercellular Interactions ◽  
Communication Events ◽  
Transcriptomics Data ◽  
The Impact ◽  
Cell Data ◽  
Shed Light ◽  
Cell Cell

The growing availability of single-cell data has sparked an increased interest in the inference of cell-cell communication from this data. Many tools have been developed for this purpose. Each of them consists of a resource of intercellular interactions prior knowledge and a method to predict potential cell-cell communication events. Yet the impact of the choice of resource and method on the resulting predictions is largely unknown. To shed light on this, we created a framework, available at https://github.com/saezlab/ligrec_decoupler, to facilitate a comparative assessment of methods for inferring cell-cell communication from single cell transcriptomics data and then compared 15 resources and 6 methods. We found few unique interactions and a varying degree of overlap among the resources, and observed uneven coverage in terms of pathways and biological categories. We analysed a colorectal cancer single cell RNA-Seq dataset using all possible combinations of methods and resources. We found major differences among the highest ranked intercellular interactions inferred by each method even when using the same resources. The varying predictions lead to fundamentally different biological interpretations, highlighting the need to benchmark resources and methods.

scholarly journals Analysis of Single-Cell RNA-Seq Identifies Cell-Cell Communication Associated with Tumor Characteristics

Cell Reports ◽  
2018 ◽  
Vol 25 (6) ◽  
pp. 1458-1468.e4 ◽  
Author(s):  
Manu P. Kumar ◽  
Jinyan Du ◽  
Georgia Lagoudas ◽  
Yang Jiao ◽  
Andrew Sawyer ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Communication ◽  
Tumor Characteristics ◽  
Rna Seq ◽  
Cell Cell

Single-Cell Analysis of Cell-Cell Communication of Exosome Components Derived from Melanoma Cells

2020 ◽  
Vol MA2020-02 (44) ◽  
pp. 2825-2825
Author(s):  
Miyu Fukaya ◽  
Tomohiro Hatakenaka ◽  
Nahoko Matsuki ◽  
Seiya Minagawa ◽  
Mikako Saito
Keyword(s):  
Single Cell ◽  
Single Cell Analysis ◽  
Cell Communication ◽  
Melanoma Cells ◽  
Cell Analysis ◽  
Cell Cell

scholarly journals Transcriptional signatures of cell-cell interactions are dependent on cellular context

2021 ◽  
Author(s):  
Brendan T Innes ◽  
Gary D Bader
Keyword(s):  
Gene Expression ◽  
Gene Expression Signature ◽  
Cell Interaction ◽  
Cell Interactions ◽  
Receptor Interaction ◽  
Perturbation Data ◽  
Cellular Context ◽  
Transcriptomics Data ◽  
Downstream Gene Expression ◽  
Cell Cell

Cell-cell interactions are often predicted from single-cell transcriptomics data based on observing receptor and corresponding ligand transcripts in cells. These predictions could theoretically be improved by inspecting the transcriptome of the receptor cell for evidence of gene expression changes in response to the ligand. It is commonly expected that a given receptor, in response to ligand activation, will have a characteristic downstream gene expression signature. However, this assumption has not been well tested. We used ligand perturbation data from both the high-throughput Connectivity Map resource and published transcriptomic assays of cell lines and purified cell populations to determine whether ligand signals have unique and generalizable transcriptional signatures across biological conditions. Most of the receptors we analyzed did not have such characteristic gene expression signatures - instead these signatures were highly dependent on cell type. Cell context is thus important when considering transcriptomic evidence of ligand signaling, which makes it challenging to build generalizable ligand-receptor interaction signatures to improve cell-cell interaction predictions.

scholarly journals A Critical-like Collective State Leads to Long-range Cell Communication in Dictyostelium discoideum Aggregation

2016 ◽  
Author(s):  
Giovanna De Palo ◽  
Darvin Yi ◽  
Robert G. Endres
Keyword(s):  
Single Cell ◽  
Dictyostelium Discoideum ◽  
Long Range ◽  
Early Development ◽  
Cell Communication ◽  
Multiscale Model ◽  
Wild Type ◽  
Social Amoeba ◽  
Range Cell ◽  
Cell Cell

AbstractThe transition from single-cell to multicellular behavior is important in early development but rarely studied. The starvation-induced aggregation of the social amoeba Dictyostelium discoideum into a multicellular slug is known to result from single-cell chemotaxis towards emitted pulses of cyclic adenosine monophosphate (cAMP). However, how exactly do transient short-range chemical gradients lead to coherent collective movement at a macroscopic scale? Here, we developed a multiscale model verified by quantitative microscopy to describe wide-ranging behaviors from chemotaxis and excitability of individual cells to aggregation of thousands of cells. To better understand the mechanism of long-range cell-cell communication and hence aggregation, we analyzed cell-cell correlations, showing evidence of self-organization at the onset of aggregation (as opposed to following a leader cell). Surprisingly, cell collectives, despite their finite size, show features of criticality known from phase transitions in physical systems. By comparing wild-type and mutant cells with impaired aggregation, we found the longest cellcell communication distance in wild-type cells, suggesting that criticality provides an adaptive advantage and optimally sized aggregates for the dispersal of spores.Author SummaryCells are often coupled to each other in cell collectives, such as aggregates during early development, tissues in the developed organism, and tumors in disease. How do cells communicate over macroscopic distances much larger than the typical cell-cell distance to decide how they should behave? Here, we developed a multiscale model of social amoeba, spanning behavior from individuals to thousands of cells. We show that local cell-cell coupling via secreted chemicals may be tuned to a critical value, resulting in emergent long-range communication and heightened sensitivity. Hence, these aggregates are remarkably similar to bacterial biofilms and neuronal networks, all communicating in a pulse-like fashion. Similar organizing principles may also aid our understanding of the remarkable robustness in cancer development.

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.

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 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.

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