Deciphering cell–cell interactions and communication from gene expression

Armingol, Erick; Officer, Adam; Harismendy, Olivier; Lewis, Nathan E.

doi:10.1038/s41576-020-00292-x

Cited by 777 publications

(792 citation statements)

References 186 publications

(236 reference statements)

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“…Although we found CellChat’s predictions can recapitulate known biology to a substantial degree, systematic evaluation of predicted cell–cell communication networks is challenging due to the lack of ground truth 7 . Here we employed three strategies to compare the performance of different computational methods.…”

Section: Discussionmentioning

confidence: 95%

“…While most current scRNA-seq data analysis approaches allow detailed cataloging of cell types and prediction of cellular differentiation trajectories, they have limited capability in probing underlying intercellular communications that often drive heterogeneity and cell state transitions. Yet, scRNA-seq data inherently contains gene expression information that could be used to infer such intercellular communications 6 , 7 .…”

Section: Introductionmentioning

confidence: 99%

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Inference and analysis of cell-cell communication using CellChat

et al. 2021

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Understanding 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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Section: Discussionmentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

Inference and analysis of cell-cell communication using CellChat

et al. 2021

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“…Next, we used this list to determine the presence or absence of ligands and receptors in each cell identified in the single-cell transcriptome of C. elegans 36 , and ultimately the active LR pairs in all pairs of cells. To determine presence and absence of proteins, we used expression thresholding 9,24 , the most common strategy for analyzing CCIs and CCC due to its binary nature and easy interpretation 7 , and used the derived ligand and receptor scores as input of our CCI score to represent the overall potential of its cell types to interact.…”

Section: Resultsmentioning

confidence: 99%

“…CCIs and CCC can be inferred from transcriptomic data 7 . Computational analysis of CCIs usually consists of examining the coexpression of secreted proteins by a sender cell (e.g.…”

Section: Introductionmentioning

confidence: 99%

Inferring a spatial code of cell-cell interactions across a whole animal body

Armingol

Ghaddar

Joshi

et al. 2020

Preprint

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Cell-cell interactions are crucial for multicellular organisms as they shape cellular function and ultimately organismal phenotype. However, the spatial code embedded in the molecular interactions that drive and sustain spatial organization, and in the organization that in turns drives intercellular interactions across a living animal remains to be elucidated. Here we use the expression of ligand-receptor pairs obtained from a whole-body single-cell transcriptome of Caenorhabditis elegans larvae to compute the potential for intercellular interactions through a Bray-Curtis-like metric. Leveraging a 3D atlas of C. elegans’ cells, we implement a genetic algorithm to select the ligand-receptor pairs most informative of the spatial organization of cells. Validating the strategy, the selected ligand-receptor pairs are involved in known cell-migration and morphogenesis processes and we confirm a negative correlation between cell-cell distances and interactions. Thus, our computational framework helps identify cell-cell interactions and their relationship with intercellular distances, and decipher molecular bases encoding spatial information in a whole animal. Furthermore, it can also be used to elucidate associations with any other intercellular phenotype and applied to other multicellular organisms.Graphical abstract

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“…Single cell RNA sequencing (scRNA-seq) allows the characterization of tissue heterogeneity at an unprecedented level, however, information of cellular proximity and crosstalk is not captured. Computational methods, of which search for pairs of cell types expressing compatible ligand-receptor (LR) pairs, have become a powerful approach for dissecting cellular crosstalk from scRNA-seq data (1). However, current LR inference methods usually predicts, hundreds of potential LR pairs for a given scRNA-seq dataset making their interpretation 1 .…”

Section: Introductionmentioning

confidence: 99%

CrossTalkeR: Analysis and Visualisation of Ligand Receptor Networks

Nagai

Leimkühler

Schneider

et al. 2021

Preprint

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MotivationLigand-receptor (LR) analysis allows the characterization of cellular crosstalk from single cell RNA-seq data. However, current LR methods provide limited approaches for prioritization of cell types, ligands or receptors or characterizing changes in crosstalk between two biological conditions.ResultsCrossTalkeR is a framework for network analysis and visualisation of LR networks. CrossTalkeR identifies relevant ligands, receptors and cell types contributing to changes in cell communication when contrasting two biological states: disease vs. homeostasis. A case study on scRNA-seq of human myeloproliferative neoplasms reinforces the strengths of CrossTalkeR for characterisation of changes in cellular crosstalk in disease state.Availability and ImplementationCrosstalkeR is as a package in R and available as a package in https://github.com/CostaLab/CrossTalkeR.

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Deciphering cell–cell interactions and communication from gene expression

Cited by 777 publications

References 186 publications

Inference and analysis of cell-cell communication using CellChat

Inference and analysis of cell-cell communication using CellChat

Inferring a spatial code of cell-cell interactions across a whole animal body

CrossTalkeR: Analysis and Visualisation of Ligand Receptor Networks

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