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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. Single-cell methods record molecule expressions of cells in a given tissue, but understanding interactions between cells remains challenging. Here the authors show by applying systems biology and machine learning approaches that they can infer and analyze cell-cell communication networks in an easily interpretable way.

Cell–cell communication mediated by ligand–receptor complexes is critical to coordinating diverse biological processes, such as development, differentiation and inflammation. To investigate 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 interactions. In contrast to other repositories, our database 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, provide procedures for inferring cell–cell communication networks from single-cell RNA sequencing data and present a practical step-by-step guide to help implement the protocol. CellPhoneDB v.2.0 is an updated version of our resource that incorporates additional functionalities to enable users to introduce new interacting molecules and reduces the time and resources needed to interrogate large datasets. CellPhoneDB v.2.0 is publicly available, both as code and as a user-friendly web interface; it can be used by both experts and researchers with little experience in computational genomics. In our protocol, we demonstrate how to evaluate meaningful biological interactions with CellPhoneDB v.2.0 using published datasets. This protocol typically takes ~2 h to complete, from installation to statistical analysis and visualization, for a dataset of ~10 GB, 10,000 cells and 19 cell types, and using five threads. CellPhoneDB combines an interactive database and a statistical framework for the exploration of ligand–receptor interactions inferred from single-cell transcriptomics measurements.

Mammalian cells release different types of vesicles, collectively termed extracellular vesicles (EVs). EVs contain cellular microRNAs (miRNAs) with an apparent potential to deliver their miRNA cargo to recipient cells to affect the stability of individual mRNAs and the cells’ transcriptome. The extent to which miRNAs are exported via the EV route and whether they contribute to cell-cell communication are controversial. To address these issues, we defined multiple properties of EVs and analyzed their capacity to deliver packaged miRNAs into target cells to exert biological functions. We applied well-defined approaches to produce and characterize purified EVs with or without specific viral miRNAs. We found that only a small fraction of EVs carried miRNAs. EVs readily bound to different target cell types, but EVs did not fuse detectably with cellular membranes to deliver their cargo. We engineered EVs to be fusogenic and document their capacity to deliver functional messenger RNAs. Engineered fusogenic EVs, however, did not detectably alter the functionality of cells exposed to miRNA-carrying EVs. These results suggest that EV-borne miRNAs do not act as effectors of cell-to-cell communication.

The mammalian brain is complex, with multiple cell types performing a variety of diverse functions, but exactly how each cell type is affected in aging remains largely unknown. Here we performed a single-cell transcriptomic analysis of young and old mouse brains. We provide comprehensive datasets of aging-related genes, pathways and ligand–receptor interactions in nearly all brain cell types. Our analysis identified gene signatures that vary in a coordinated manner across cell types and gene sets that are regulated in a cell-type specific manner, even at times in opposite directions. These data reveal that aging, rather than inducing a universal program, drives a distinct transcriptional course in each cell population, and they highlight key molecular processes, including ribosome biogenesis, underlying brain aging. Overall, these large-scale datasets (accessible online at https://portals.broadinstitute.org/single_cell/study/aging-mouse-brain) provide a resource for the neuroscience community that will facilitate additional discoveries directed towards understanding and modifying the aging process.

The extracellular matrix (ECM) is a key component of the stem cell niche and is now emerging as more than just an inert scaffold. Indeed, new technologies have provided mechanistic insights into the effects of the ECM on stem cell fate choice. The field of stem cells and regenerative medicine offers considerable promise as a means of delivering new treatments for a wide range of diseases. In order to maximize the effectiveness of cell-based therapies — whether stimulating expansion of endogenous cells or transplanting cells into patients — it is essential to understand the environmental (niche) signals that regulate stem cell behaviour. One of those signals is from the extracellular matrix (ECM). New technologies have offered insights into how stem cells sense signals from the ECM and how they respond to these signals at the molecular level, which ultimately regulate their fate.

The growing availability of single-cell data, especially transcriptomics, has sparked an increased interest in the inference of cell-cell communication. Many computational tools were 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 systematically compare 16 cell-cell communication inference resources and 7 methods, plus the consensus between the methods’ predictions. Among the resources, we find few unique interactions, a varying degree of overlap, and an uneven coverage of specific pathways and tissue-enriched proteins. We then examine all possible combinations of methods and resources and show that both strongly influence the predicted intercellular interactions. Finally, we assess the agreement of cell-cell communication methods with spatial colocalisation, cytokine activities, and receptor protein abundance and find that predictions are generally coherent with those data modalities. To facilitate the use of the methods and resources described in this work, we provide LIANA, a LIgand-receptor ANalysis frAmework as an open-source interface to all the resources and methods. Multiple methods to infer cell-cell communication (CCC) from single cell data are currently available. Here, the authors systematically compare 16 CCC inference resources and 7 methods, and develop the LIANA framework as an interface to use and compare all these approaches.

Recent advances in single-cell sequencing technologies offer an opportunity to explore cell–cell communication in tissues systematically and with reduced bias. A key challenge is integrating known molecular interactions and measurements into a framework to identify and analyze complex cell–cell communication networks. Previously, we developed a computational tool, named CellChat, that infers and analyzes cell–cell communication networks from single-cell transcriptomic data within an easily interpretable framework. CellChat quantifies the signaling communication probability between two cell groups using a simplified mass-action-based model, which incorporates the core interaction between ligands and receptors with multisubunit structure along with modulation by cofactors. Importantly, CellChat performs a systematic and comparative analysis of cell–cell communication using a variety of quantitative metrics and machine-learning approaches. CellChat v2 is an updated version that includes additional comparison functionalities, an expanded database of ligand–receptor pairs along with rich functional annotations, and an Interactive CellChat Explorer. Here we provide a step-by-step protocol for using CellChat v2 on single-cell transcriptomic data, including inference and analysis of cell–cell communication from one dataset and identification of altered intercellular communication, signals and cell populations from different datasets across biological conditions. The R implementation of CellChat v2 toolkit and its tutorials together with the graphic outputs are available at https://github.com/jinworks/CellChat . This protocol typically takes ~5 min depending on dataset size and requires a basic understanding of R and single-cell data analysis but no specialized bioinformatics training for its implementation. Key points CellChat is a software package for systematic inference, quantitative analysis and intuitive visualization of cell–cell communication in an easily interpretable way from single-cell transcriptomic data; it also enables comparative analysis of intercellular communication across different conditions. CellChat v2 is an updated version that includes additional functionalities for comparative analysis and an expanded database of ligand–receptor pairs along with rich functional annotations. CellChat enables systematic inference, quantitative analysis and intuitive visualization of cell–cell communication from single-cell transcriptomic data, as well as comparative analysis of intercellular communication across biological conditions.

Models of intercellular communication in tissues are based on molecular profiles of dissociated cells, are limited to receptor–ligand signaling and ignore spatial proximity in situ. We present node-centric expression modeling, a method based on graph neural networks that estimates the effects of niche composition on gene expression in an unbiased manner from spatial molecular profiling data. We recover signatures of molecular processes known to underlie cell communication. How cells in a tissue communicate is modeled with a graph neural network.

Immunotherapy with checkpoint blockade induces rapid and durable immune control of cancer in some patients and has driven a monumental shift in cancer treatment. Neoantigen-specific CD8+ T cells are at the forefront of current immunotherapy strategies, and the majority of drug discovery and clinical trials revolve around further harnessing these immune effectors. Yet the immune system contains a diverse range of antitumour effector cells, and these must function in a coordinated and synergistic manner to overcome the immune-evasion mechanisms used by tumours and achieve complete control with tumour eradication. A key antitumour effector is the natural killer (NK) cells, cytotoxic innate lymphocytes present at high frequency in the circulatory system and identified by their exquisite ability to spontaneously detect and lyse transformed or stressed cells. Emerging data show a role for intratumoural NK cells in driving immunotherapy response and, accordingly, there have been renewed efforts to further elucidate and target the pathways controlling NK cell antitumour function. In this Review, we discuss recent clinical evidence that NK cells are a key immune constituent in the protective antitumour immune response and highlight the major stages of the cancer–NK cell immunity cycle. We also perform a new analysis of publicly available transcriptomic data to provide an overview of the prognostic value of NK cell gene expression in 25 tumour types. Furthermore, we discuss how the role of NK cells evolves with tumour progression, presenting new opportunities to target NK cell function to enhance cancer immunotherapy response rates across a more diverse range of cancers.This Review discusses the key role that natural killer (NK) cells play in driving an antitumour immune response throughout the progression of cancer from its initial development to its metastatic spread and eventual treatment, defined herein as the cancer–NK cell immunity cycle.

No cell lives in a vacuum, and the molecular interactions between cells define most phenotypes. Transcriptomics provides rich information to infer cell–cell interactions and communication, thus accelerating the discovery of the roles of cells within their communities. Such research relies heavily on algorithms that infer which cells are interacting and the ligands and receptors involved. Specific pressures on different research niches are driving the evolution of next-generation computational tools, enabling new conceptual opportunities and technological advances. More sophisticated algorithms now account for the heterogeneity and spatial organization of cells, multiple ligand types and intracellular signalling events, and enable the use of larger and more complex datasets, including single-cell and spatial transcriptomics. Similarly, new high-throughput experimental methods are increasing the number and resolution of interactions that can be analysed simultaneously. Here, we explore recent progress in cell–cell interaction research and highlight the diversification of the next generation of tools, which have yielded a rich ecosystem of tools for different applications and are enabling invaluable discoveries.In this Review, the authors summarize recent progress in cell–cell interaction (CCI) research. They describe the recent evolution in computational tools that underpin CCI studies, discuss improvements in experimental methods enabling more high-throughput analyses of CCIs, and highlight future directions for the field.