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Background Cell-cell interactions are important for information exchange between different cells, which are the fundamental basis of many biological processes. Recent advances in single-cell RNA sequencing (scRNA-seq) enable the characterization of cell-cell interactions using computational methods. However, it is hard to evaluate these methods since no ground truth is provided. Spatial transcriptomics (ST) data profiles the relative position of different cells. We propose that the spatial distance suggests the interaction tendency of different cell types, thus could be used for evaluating cell-cell interaction tools. Results We benchmark 16 cell-cell interaction methods by integrating scRNA-seq with ST data. We characterize cell-cell interactions into short-range and long-range interactions using spatial distance distributions between ligands and receptors. Based on this classification, we define the distance enrichment score and apply an evaluation workflow to 16 cell-cell interaction tools using 15 simulated and 5 real scRNA-seq and ST datasets. We also compare the consistency of the results from single tools with the commonly identified interactions. Our results suggest that the interactions predicted by different tools are highly dynamic, and the statistical-based methods show overall better performance than network-based methods and ST-based methods. Conclusions Our study presents a comprehensive evaluation of cell-cell interaction tools for scRNA-seq. CellChat, CellPhoneDB, NicheNet, and ICELLNET show overall better performance than other tools in terms of consistency with spatial tendency and software scalability. We recommend using results from at least two methods to ensure the accuracy of identified interactions. We have packaged the benchmark workflow with detailed documentation at GitHub ( https://github.com/wanglabtongji/CCI ).

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.

In the field of cancer research, scientific investigations are based on analysing differences in the secretome, the proteome, the transcriptome, the expression of cell surface molecules, and the deregulation of signal transduction pathways between neoplastic and normal cells. Accumulating evidence indicates a crucial role in carcinogenesis concerning not only stromal cells but also normal cells from target organs and tissue where tumours emerge. The tumour microenvironment (TME) definitively plays an important role in regulating neighbouring cell behaviour. To date, limited attention has been focused upon interactions between cancer cells and normal cells. This review concentrates on the interactions between stromal and healthy cells from the TME in cancer development. In the article, the authors also describe mutations, genes and proteins expression pattern that are involved in tumour development in target organ.

Phenotypic alterations in the lung epithelium have been widely implicated in chronic obstructive pulmonary disease (COPD) pathogenesis, but the precise mechanisms orchestrating this persistent inflammatory process remain unknown because of the complexity of lung parenchymal and mesenchymal architecture. To identify cell type–specific mechanisms and cell–cell interactions among the multiple lung resident cell types and inflammatory cells that contribute to COPD progression, we profiled 57,918 cells from lungs of patients with COPD, smokers without COPD, and never-smokers using single-cell RNA sequencing technology. We predicted pseudotime of cell differentiation and cell-to-cell interaction networks in COPD. Although epithelial components in never-smokers were relatively uniform, smoker groups represent extensive heterogeneity in epithelial cells, particularly in alveolar type 2 (AT2) clusters. Among AT2 cells, which are generally regarded as alveolar progenitors, we identified a unique subset that increased in patients with COPD and specifically expressed a series of chemokines including CXCL1 and CXCL8. A trajectory analysis revealed that the inflammatory AT2 cell subpopulation followed a unique differentiation path, and a prediction model of cell-to-cell interactions inferred significantly increased intercellular networks of inflammatory AT2 cells. Our results identify previously unidentified cell subsets and provide an insight into the biological and clinical characteristics of COPD pathogenesis.

To investigate the immune response and mechanisms associated with severe coronavirus disease 2019 (COVID-19), we performed single-cell RNA sequencing on nasopharyngeal and bronchial samples from 19 clinically well-characterized patients with moderate or critical disease and from five healthy controls. We identified airway epithelial cell types and states vulnerable to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. In patients with COVID-19, epithelial cells showed an average three-fold increase in expression of the SARS-CoV-2 entry receptor ACE2 , which correlated with interferon signals by immune cells. Compared to moderate cases, critical cases exhibited stronger interactions between epithelial and immune cells, as indicated by ligand–receptor expression profiles, and activated immune cells, including inflammatory macrophages expressing CCL2 , CCL3 , CCL20 , CXCL1 , CXCL3 , CXCL10 , IL8 , IL1B and TNF . The transcriptional differences in critical cases compared to moderate cases likely contribute to clinical observations of heightened inflammatory tissue damage, lung injury and respiratory failure. Our data suggest that pharmacologic inhibition of the CCR1 and/or CCR5 pathways might suppress immune hyperactivation in critical COVID-19. Single-cell analysis of COVID-19 patient samples identifies activated immune pathways that correlate with severe disease.

Glandular epithelia, including the mammary and prostate glands, are composed of basal cells (BCs) and luminal cells (LCs) 1 , 2 . Many glandular epithelia develop from multipotent basal stem cells (BSCs) that are replaced in adult life by distinct pools of unipotent stem cells 1 , 3 – 8 . However, adult unipotent BSCs can reactivate multipotency under regenerative conditions and upon oncogene expression 3 , 9 – 13 . This suggests that an active mechanism restricts BSC multipotency under normal physiological conditions, although the nature of this mechanism is unknown. Here we show that the ablation of LCs reactivates the multipotency of BSCs from multiple epithelia both in vivo in mice and in vitro in organoids. Bulk and single-cell RNA sequencing revealed that, after LC ablation, BSCs activate a hybrid basal and luminal cell differentiation program before giving rise to LCs—reminiscent of the genetic program that regulates multipotency during embryonic development 7 . By predicting ligand–receptor pairs from single-cell data 14 , we find that TNF—which is secreted by LCs—restricts BC multipotency under normal physiological conditions. By contrast, the Notch, Wnt and EGFR pathways were activated in BSCs and their progeny after LC ablation; blocking these pathways, or stimulating the TNF pathway, inhibited regeneration-induced BC multipotency. Our study demonstrates that heterotypic communication between LCs and BCs is essential to maintain lineage fidelity in glandular epithelial stem cells. The multipotency of basal stem cells is directly regulated by luminal cells through the secretion of TNF, and, following luminal cell ablation, the Notch, Wnt and EGFR signalling pathways reactivate basal cell multipotency.

Coordinated dynamics of individual components in active matter are an essential aspect of life on all scales. Establishing a comprehensive, causal connection between intracellular, intercellular, and macroscopic behaviors has remained a major challenge due to limitations in data acquisition and analysis techniques suitable for multiscale dynamics. Here, we combine a high-throughput adaptive microscopy approach with machine learning, to identify key biological and physical mechanisms that determine distinct microscopic and macroscopic collective behavior phases which develop as Bacillus subtilis swarms expand over five orders of magnitude in space. Our experiments, continuum modeling, and particle-based simulations reveal that macroscopic swarm expansion is primarily driven by cellular growth kinetics, whereas the microscopic swarming motility phases are dominated by physical cell–cell interactions. These results provide a unified understanding of bacterial multiscale behavioral complexity in swarms.

Cell–cell interactions orchestrate organismal development, homeostasis and single-cell functions. When cells do not properly interact or improperly decode molecular messages, disease ensues. Thus, the identification and quantification of intercellular signalling pathways has become a common analysis performed across diverse disciplines. The expansion of protein–protein interaction databases and recent advances in RNA sequencing technologies have enabled routine analyses of intercellular signalling from gene expression measurements of bulk and single-cell data sets. In particular, ligand–receptor pairs can be used to infer intercellular communication from the coordinated expression of their cognate genes. In this Review, we highlight discoveries enabled by analyses of cell–cell interactions from transcriptomic data and review the methods and tools used in this context.Cell–cell interactions and communication can be inferred from RNA sequencing data of, for example, ligand–receptor pairs. The authors review insights gained and the methods and tools used in studies of cell–cell interactions based on transcriptomic data.

Extracellular vesicles (EVs) are heterogeneous lipid containers with a complex molecular cargo comprising several populations with unique roles in biological processes. These vesicles are closely associated with specific physiological features, which makes them invaluable in the detection and monitoring of various diseases. EVs play a key role in pathophysiological processes by actively triggering genetic or metabolic responses. However, the heterogeneity of their structure and composition hinders their application in medical diagnosis and therapies. This diversity makes it difficult to establish their exact physiological roles, and the functions and composition of different EV (sub)populations. Ensemble averaging approaches currently employed for EV characterization, such as western blotting or ‘omics’ technologies, tend to obscure rather than reveal these heterogeneities. Recent developments in single-vesicle analysis have made it possible to overcome these limitations and have facilitated the development of practical clinical applications. In this review, we discuss the benefits and challenges inherent to the current methods for the analysis of single vesicles and review the contribution of these approaches to the understanding of EV biology. We describe the contributions of these recent technological advances to the characterization and phenotyping of EVs, examination of the role of EVs in cell-to-cell communication pathways and the identification and validation of EVs as disease biomarkers. Finally, we discuss the potential of innovative single-vesicle imaging and analysis methodologies using microfluidic devices, which promise to deliver rapid and effective basic and practical applications for minimally invasive prognosis systems. Understanding the heterogeneity of extracellular vesicles is crucial for unraveling their functions. This review describes the benefits, challenges and contributions of the state-of-the art methods used in single-vesicle analysis.

The migratory dynamics of cells in physiological processes, ranging from wound healing to cancer metastasis, rely on contactmediated cell–cell interactions. These interactions play a key role in shaping the stochastic trajectories of migrating cells. While data-driven physical formalisms for the stochastic migration dynamics of single cells have been developed, such a framework for the behavioral dynamics of interacting cells still remains elusive. Here, we monitor stochastic cell trajectories in a minimal experimental cell collider: a dumbbell-shaped micropattern on which pairs of cells perform repeated cellular collisions. We observe different characteristic behaviors, including cells reversing, following, and sliding past each other upon collision. Capitalizing on this large experimental dataset of coupled cell trajectories, we infer an interacting stochastic equation of motion that accurately predicts the observed interaction behaviors. Our approach reveals that interacting noncancerous MCF10A cells can be described by repulsion and friction interactions. In contrast, cancerous MDA-MB-231 cells exhibit attraction and antifriction interactions, promoting the predominant relative sliding behavior observed for these cells. Based on these experimentally inferred interactions, we show how this framework may generalize to provide a unifying theoretical description of the diverse cellular interaction behaviors of distinct cell types.