scholarly journals COMUNET: a tool to explore and visualize intercellular communication

2019 ◽  
Author(s):  
Maria Solovey ◽  
Antonio Scialdone
Keyword(s):  
Single Cell ◽  
Intercellular Communication ◽  
Myeloid Leukemia ◽  
Cell Communication ◽  
Cell Types ◽  
Mouse Embryos ◽  
Multiplex Networks ◽  
Leukemia Patient ◽  
Patterns Of Communication ◽  
Multicellular Organisms

AbstractIntercellular communication plays an essential role in multicellular organisms and several algorithms to analyse it from single-cell transcriptional data have been recently published, but the results are often hard to visualize and interpret. We developed COMUNET (Cell cOMmunication exploration with MUltiplex NETworks), a tool that streamlines the interpretation of the results from cell-cell communication analyses. COMUNET uses multiplex networks to represent and cluster all potential communication pathways between cell types. The algorithm also enables the search for specific patterns of communication and can perform comparative analysis between two biological conditions.To exemplify its use, here we apply COMUNET to investigate cell communication patterns in single-cell transcriptomic datasets from mouse embryos and from an acute myeloid leukemia patient at diagnosis and after treatment. Our algorithm is available on GitHub, along with all the code to perform the analysis reported.

scholarly journals COMUNET: a tool to explore and visualize intercellular communication

2020 ◽  
Vol 36 (15) ◽  
pp. 4296-4300
Author(s):  
Maria Solovey ◽  
Antonio Scialdone
Keyword(s):  
Single Cell ◽  
Intercellular Communication ◽  
Cell Communication ◽  
Cell Types ◽  
R Package ◽  
Communication Patterns ◽  
Supplementary Information ◽  
Multiplex Networks ◽  
Patterns Of Communication ◽  
Multicellular Organisms

Abstract Motivation Intercellular communication plays an essential role in multicellular organisms and several algorithms to analyze it from single-cell transcriptional data have been recently published, but the results are often hard to visualize and interpret. Results We developed Cell cOmmunication exploration with MUltiplex NETworks (COMUNET), a tool that streamlines the interpretation of the results from cell–cell communication analyses. COMUNET uses multiplex networks to represent and cluster all potential communication patterns between cell types. The algorithm also enables the search for specific patterns of communication and can perform comparative analysis between two biological conditions. To exemplify its use, here we apply COMUNET to investigate cell communication patterns in single-cell transcriptomic datasets from mouse embryos and from an acute myeloid leukemia patient at diagnosis and after treatment. Availability and implementation Our algorithm is implemented in an R package available from https://github.com/ScialdoneLab/COMUNET, along with all the code to perform the analyses reported here. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Single-cell map of diverse immune phenotypes in the acute myeloid leukemia microenvironment

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Rongqun Guo ◽  
Mengdie Lü ◽  
Fujiao Cao ◽  
Guanghua Wu ◽  
Fengcai Gao ◽  
...  
Keyword(s):  
Acute Myeloid Leukemia ◽  
Single Cell ◽  
Myeloid Leukemia ◽  
Immune Cell ◽  
Immune Surveillance ◽  
Cell Types ◽  
Developmental Trajectory ◽  
Significant Difference ◽  
Cell Phenotypes ◽  
Acute Myeloid

Abstract Background Knowledge of immune cell phenotypes, function, and developmental trajectory in acute myeloid leukemia (AML) microenvironment is essential for understanding mechanisms of evading immune surveillance and immunotherapy response of targeting special microenvironment components. Methods Using a single-cell RNA sequencing (scRNA-seq) dataset, we analyzed the immune cell phenotypes, function, and developmental trajectory of bone marrow (BM) samples from 16 AML patients and 4 healthy donors, but not AML blasts. Results We observed a significant difference between normal and AML BM immune cells. Here, we defined the diversity of dendritic cells (DC) and macrophages in different AML patients. We also identified several unique immune cell types including T helper cell 17 (TH17)-like intermediate population, cytotoxic CD4+ T subset, T cell: erythrocyte complexes, activated regulatory T cells (Treg), and CD8+ memory-like subset. Emerging AML cells remodels the BM immune microenvironment powerfully, leads to immunosuppression by accumulating exhausted/dysfunctional immune effectors, expending immune-activated types, and promoting the formation of suppressive subsets. Conclusion Our results provide a comprehensive AML BM immune cell census, which can help to select pinpoint targeted drug and predict efficacy of immunotherapy.

scholarly journals Single-cell analysis reveals cell communication triggered by macrophages associated with the reduction and exhaustion of CD8+ T cells in COVID-19

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Lei He ◽  
Quan Zhang ◽  
Yue Zhang ◽  
Yixian Fan ◽  
Fahu Yuan ◽  
...  
Keyword(s):  
T Cells ◽  
T Cell ◽  
Single Cell ◽  
Cell Communication ◽  
Cell Types ◽  
Trial Registration ◽  
T Cell Subsets ◽  
Disease Group ◽  
Communication Analysis ◽  
Receptor Interactions

Abstract Background The coronavirus disease 2019 (COVID-19) outbreak caused by severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2) has become an ongoing pandemic. Understanding the respiratory immune microenvironment which is composed of multiple cell types, together with cell communication based on ligand–receptor interactions is important for developing vaccines, probing COVID-19 pathogenesis, and improving pandemic control measures. Methods A total of 102 consecutive hospitalized patients with confirmed COVID-19 were enrolled in this study. Clinical information, routine laboratory tests, and flow cytometry analysis data with different conditions were collected and assessed for predictive value in COVID-19 patients. Next, we analyzed public single-cell RNA-sequencing (scRNA-seq) data from bronchoalveolar lavage fluid, which offers the closest available view of immune cell heterogeneity as encountered in patients with varying severity of COVID-19. A weighting algorithm was used to calculate ligand–receptor interactions, revealing the communication potentially associated with outcomes across cell types. Finally, serum cytokines including IL6, IL1β, IL10, CXCL10, TNFα, GALECTIN-1, and IGF1 derived from patients were measured. Results Of the 102 COVID-19 patients, 42 cases (41.2%) were categorized as severe. Multivariate logistic regression analysis demonstrated that AST, D-dimer, BUN, and WBC were considered as independent risk factors for the severity of COVID-19. T cell numbers including total T cells, CD4+ and CD8+ T cells in the severe disease group were significantly lower than those in the moderate disease group. The risk model containing the above mentioned inflammatory damage parameters, and the counts of T cells, with AUROCs ranged from 0.78 to 0.87. To investigate the molecular mechanism at the cellular level, we analyzed the published scRNA-seq data and found that macrophages displayed specific functional diversity after SARS-Cov-2 infection, and the metabolic pathway activities in the identified macrophage subtypes were influenced by hypoxia status. Importantly, we described ligand–receptor interactions that are related to COVID-19 serverity involving macrophages and T cell subsets by communication analysis. Conclusions Our study showed that macrophages driving ligand–receptor crosstalk contributed to the reduction and exhaustion of CD8+ T cells. The identified crucial cytokine panel, including IL6, IL1β, IL10, CXCL10, IGF1, and GALECTIN-1, may offer the selective targets to improve the efficacy of COVID-19 therapy. Trial registration: This is a retrospective observational study without a trial registration number.

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 Single-Cell RNA Sequencing Identifies New Inflammation-Promoting Cell Subsets in Asian Patients With Chronic Periodontitis

2021 ◽  
Vol 12 ◽  
Author(s):  
Shu-jiao Qian ◽  
Qian-ru Huang ◽  
Rui-ying Chen ◽  
Jia-ji Mo ◽  
Lin-yi Zhou ◽  
...  
Keyword(s):  
Single Cell ◽  
Chronic Periodontitis ◽  
Inflammatory Responses ◽  
Cell Communication ◽  
Cell Types ◽  
Chronic Inflammatory Disease ◽  
Periodontal Tissues ◽  
Asian Patients ◽  
Healthy Donors ◽  
Human Gingiva

Periodontitis is a highly prevalent chronic inflammatory disease leading to periodontal tissue breakdown and subsequent tooth loss, in which excessive host immune response accounts for most of the tissue damage and disease progression. Despite of the imperative need to develop host modulation therapy, the inflammatory responses and cell population dynamics which are finely tuned by the pathological microenvironment in periodontitis remained unclear. To investigate the local microenvironment of the inflammatory response in periodontitis, 10 periodontitis patients and 10 healthy volunteers were involved in this study. Single-cell transcriptomic profilings of gingival tissues from two patients and two healthy donors were performed. Histology, immunohistochemistry, and flow cytometry analysis were performed to further validate the identified cell subtypes and their involvement in periodontitis. Based on our single-cell resolution analysis, we identified HLA-DR-expressing endothelial cells and CXCL13+ fibroblasts which are highly associated with immune regulation. We also revealed the involvement of the proinflammatory NLRP3+ macrophages in periodontitis. We further showed the increased cell-cell communication between macrophage and T/B cells in the inflammatory periodontal tissues. Our data generated an intriguing catalog of cell types and interaction networks in the human gingiva and identified new inflammation-promoting cell subtypes involved in chronic periodontitis, which will be helpful in advancing host modulation therapy.

scholarly journals Common and Distinctive Intercellular Communication Patterns in Human Obstructive and Nonobstructive Hypertrophic Cardiomyopathy

2021 ◽  
Author(s):  
Christina J. Codden ◽  
Michael T. Chin
Keyword(s):  
Extracellular Matrix ◽  
Hypertrophic Cardiomyopathy ◽  
Dendritic Cell ◽  
Differentially Expressed Genes ◽  
Intercellular Communication ◽  
Cell Communication ◽  
Cell Types ◽  
Ventricular Outflow Tract ◽  
Left Ventricular ◽  
Differentially Expressed

Hypertrophic Cardiomyopathy (HCM) is a common inherited disorder characterized by unexplained left ventricular hypertrophy, with or without left ventricular outflow tract (LVOT) obstruction. Single nuclei RNA-sequencing (snRNA-seq) of both obstructive and nonobstructive HCM patient samples have revealed alterations in communication between various cell types but a direct and integrated comparison between the two HCM phenotypes has not been reported. We performed a bioinformatic analysis of HCM snRNA-seq datasets from obstructive and nonobstructive patient samples to identify differentially expressed genes and distinctive patterns of intercellular communication. Differential gene expression analysis revealed 37 differentially expressed genes, predominantly in cardiomyocytes but also in other cell types, relevant to aging, muscle contraction, cell motility and the extracellular matrix. Intercellular communication was generally reduced in HCM, affecting the extracellular matrix, growth factor binding, integrin binding, PDGF binding and SMAD binding, but with increases in adenylate cyclase binding, calcium channel inhibitor activity, and serine-threonine kinase activity in nonobstructive HCM. Increases in neuron to leukocyte and dendritic cell communication, in fibroblast to leukocyte and dendritic cell communication and in endothelial cell communication to other cell types, largely through changes in expression of integrin-b1 and its cognate ligands, were also noted. These findings indicate both common and distinct physiological mechanisms affecting the pathogenesis of obstructive and nonobstructive HCM and provide opportunities for personalized management of different HCM phenotypes.

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 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.

scholarly journals Single-cell Polyfunctional Proteomics of CD4 Cells from Patients with AML Predicts Responses to Anti-PD-1-based therapy

2021 ◽  
Author(s):  
Hussein Abbas ◽  
Zoe Alaniz ◽  
Sean G Mackay ◽  
Matthew Cyr ◽  
Jing Zhou ◽  
...  
Keyword(s):  
Single Cell ◽  
Myeloid Leukemia ◽  
Potential Role ◽  
Cell Types ◽  
Cd4 Cells ◽  
Strength Index ◽  
Historical Cohort ◽  
Cd8 Cells ◽  
Phase 2 Clinical Trial ◽  
Functional States

Acute myeloid leukemia (AML) remains a difficult disease to treat disease. In a phase 2 clinical trial in patients with relapsed/refractory AML, combining the hypomethylating agent, azacitidine, with the PD-1 checkpoint inhibitor, nivolumab, demonstrated encouraging response rates (33%), median event-free and overall survival, compared with a historical cohort of contemporary patients treated with azacitidine-based therapies, with an acceptable safety profile. Biomarkers of response are yet to be determined. In this study, we leveraged a multiplexed immune assay to assess the functional states of CD4+ and CD8+ cells at a single-cell level in pretherapy bone marrows in 16 patients with R/R AML treated with azacitidine/nivolumab. Effector CD4+ but not CD8+ cells had distinct polyfunctional groups and were associated with responses and better outcomes. Further evaluation of the polyfunctional strength index composition across cell types revealed that IFN-g and TNF-a were the major drivers of enhanced polyfunctionality index of pretherapy CD4+ subset, while Granzyme B, IFN-g, MIP-1b and TNF-a drove the non-significantly enhanced pretreatment PSI of CD8+ subset in the responders. Single cell polyfunctional assays were predictive of response in AML and may have a potential role as a biomarker in the wider sphere of immunotherapy.

scholarly journals P020 Mapping the changing intercellular communication and its downstream effect in Ulcerative Colitis

2021 ◽  
Vol 15 (Supplement_1) ◽  
pp. S138-S139
Author(s):  
L Potari-gul ◽  
D Modos ◽  
D Turei ◽  
A Valdeolivas ◽  
M Madgwick ◽  
...  
Keyword(s):  
Ulcerative Colitis ◽  
T Cells ◽  
Regulatory T Cells ◽  
Single Cell ◽  
Intercellular Communication ◽  
Cell Types ◽  
Cell Junctions ◽  
Target Cells ◽  
Downstream Effects ◽  
Cell Cell

Abstract Background Intercellular communication is essential for growing and differentiating in multicellular organisms by transducing the signal from cell to cell. Despite its importance, the molecular background is less discovered due to the lack of data. This gap has started to be addressed with the appearance of single-cell omics approaches providing an insight among others into the gene expression of individual cells. Methods We have developed a method to predict and compare cell-cell signalling interactions using single-cell RNAseq data from colon biopsies. Transcriptomic data alone is not capable of connecting the cells, a reliable network resource is needed to mediate the signal via protein-protein interactions between the source and target cells. Here we used OmniPath - a resource providing not only intra- and intercellular interactions but also annotations of proteins involved in the interplay of cells - to reconstruct signalling networks. We examined intercellular communication among five cell-types (regulatory T cell, macrophage, dendritic cell, goblet cell and myofibroblast) in healthy colon and during Ulcerative Colitis. Results Our analysis shows that there are significant differences in the type of cell-cell communication (ligand-receptor connections, adherens junctions, etc.) between the healthy and Ulcerative Colitis (UC) conditions, and these differences lead to altered downstream effects in the signal receiving cell. In both conditions, the ligand-receptor and adhesion connections were overrepresented, however cell junctions were less abundant in UC. Regarding the communication among the five cell-types, in healthy condition, cells are tightly connected to dendritic cells while in diseased condition to regulatory T cells. Focusing on ligand-receptor interactions between myofibroblasts and regulatory T cells, our pipeline identified the MAPK, Toll-like receptor (TLR) 2/6 and TLR 7/8 pathways enriched downstream in healthy conditions. In contrast, TLR3 and TLR4 pathways were affected by the myofibroblast in Ulcerative Colitis. Conclusion We found key intercellular mechanisms leading to well-defined differential pathway activation profiles. We showed that in uninflamed UC condition myofibroblasts disrupt the anti-inflammatory effect of regulatory T cells. Our pipeline is able to predict and analyse cell-cell interactions and their downstream effects and to highlight the differences in healthy and diseased states.

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