Inference and analysis of cell-cell communication using CellChat

Jin, Suoqin; Guerrero‐Juarez, Christian F.; Zhang, Lihua; Chang, Irene; Myung, Peggy; Plikus, Maksim V.; Nie, Qing

doi:10.1101/2020.07.21.214387

Cited by 240 publications

(414 citation statements)

References 76 publications

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“…These studies further reinforce the notion that excessive inflammation correlates with negative disease outcome (17,18). Beyond cell identification (19), single-cell analysis can also be used to infer cell-cell interactions (20)(21)(22) and these approaches can help inform disease mechanisms.…”

Section: Introductionsupporting

confidence: 65%

Characterization of cell-cell communication in COVID-19 patients

Lin

Loo

Tran

et al. 2020

Preprint

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COVID-19 patients display a wide range of disease severity, ranging from asymptomatic to critical symptoms with high mortality risk. Our ability to understand the interaction of SARS-CoV-2 infected cells within the lung, and of protective or dysfunctional immune responses to the virus, is critical to effectively treat these patients. Currently, our understanding of cell-cell interactions across different disease states, and how such interactions may drive pathogenic outcomes, is incomplete. Here, we developed a generalizable workflow for identifying cells that are differentially interacting across COVID-19 patients with distinct disease outcomes and use it to examine five public single-cell RNA-seq datasets with a total of 85 individual samples. By characterizing the cell-cell interaction patterns across epithelial and immune cells in lung tissues for patients with varying disease severity, we illustrate diverse communication patterns across individuals, and discover heterogeneous communication patterns among moderate and severe patients. We further illustrate patterns derived from cell-cell interactions are potential signatures for discriminating between moderate and severe patients.

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Section: Introductionsupporting

confidence: 65%

Characterization of cell-cell communication in COVID-19 patients

Lin

Loo

Tran

et al. 2020

Preprint

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“…We summarized different methods of inferring cell–cell communication signaling networks in Table 1 . Among these methods, SoptSC [ 8 ], SingleCellSignalR [ 9 ], CellPhoneDB [ 10 ], SpaOTsc [ 11 ], CellChat [ 12 ] and iTALK [ 24 ] are mainly designed for inferring intercellular signaling networks, while NicheNet [ 13 ], CytoTalk [ 25 ], CCCExplorer [ 26 ] and our method scMLnet are designed to infer inter- and intra-cellular signaling networks. The differences between these methods in input, output, main goal, prior information and main algorithm are also summarized in Table 1 .…”

Section: Discussionmentioning

confidence: 99%

“…Furthermore, considering important spatial distance between cells, SpaOTsc [ 11 ] infers cell–cell communications by ‘optimally transporting’ signal senders to target signal receivers via structured optimal transport to recover spatial properties of scRNA-seq data in the aid of the spatial reference. More recently, CellChat [ 12 ] has been developed to quantitively infer and analyze intercellular communication networks from scRNA-seq data by means of network analysis, pattern recognition and manifold learning approaches.…”

Section: Introductionmentioning

confidence: 99%

Inferring microenvironmental regulation of gene expression from single-cell RNA sequencing data using scMLnet with an application to COVID-19

Cheng

Zhang

et al. 2020

Briefings in Bioinformatics

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Inferring how gene expression in a cell is influenced by cellular microenvironment is of great importance yet challenging. In this study, we present a single-cell RNA-sequencing data based multilayer network method (scMLnet) that models not only functional intercellular communications but also intracellular gene regulatory networks ( https://github.com/SunXQlab/scMLnet ). scMLnet was applied to a scRNA-seq dataset of COVID-19 patients to decipher the microenvironmental regulation of expression of SARS-CoV-2 receptor ACE2 that has been reported to be correlated with inflammatory cytokines and COVID-19 severity. The predicted elevation of ACE2 by extracellular cytokines EGF, IFN-γ or TNF-α were experimentally validated in human lung cells and the related signaling pathway were verified to be significantly activated during SARS-COV-2 infection. Our study provided a new approach to uncover inter-/intra-cellular signaling mechanisms of gene expression and revealed microenvironmental regulators of ACE2 expression, which may facilitate designing anti-cytokine therapies or targeted therapies for controlling COVID-19 infection. In addition, we summarized and compared different methods of scRNA-seq based inter-/intra-cellular signaling network inference for facilitating new methodology development and applications.

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“…More comprehensive lists might generate a better correlation or contain more important LR pairs for the GA selection than the ones selected here (Supplementary Table 3). Furthermore, lists of ligand-receptor pairs considering the formation of multimeric complexes can improve the reliability of the results 11,94 , so considering structural information of proteins may also improve the predictions. However, in contrast to mammals such as mice and humans, C. elegans has considerably less information for building comprehensive lists of ligand-receptor interactions containing multimeric complexes.…”

Section: Discussionmentioning

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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Inference and analysis of cell-cell communication using CellChat

Cited by 240 publications

References 76 publications

Characterization of cell-cell communication in COVID-19 patients

Characterization of cell-cell communication in COVID-19 patients

Inferring microenvironmental regulation of gene expression from single-cell RNA sequencing data using scMLnet with an application to COVID-19

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

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