Analysis of Single-Cell RNA-Seq Identifies Cell-Cell Communication Associated with Tumor Characteristics

The growing availability of single-cell data has sparked an increased interest in the inference of cell-cell communication from this data. Many tools have been 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 created a framework, available at https://github.com/saezlab/ligrec_decoupler, to facilitate a comparative assessment of methods for inferring cell-cell communication from single cell transcriptomics data and then compared 15 resources and 6 methods. We found few unique interactions and a varying degree of overlap among the resources, and observed uneven coverage in terms of pathways and biological categories. We analysed a colorectal cancer single cell RNA-Seq dataset using all possible combinations of methods and resources. We found major differences among the highest ranked intercellular interactions inferred by each method even when using the same resources. The varying predictions lead to fundamentally different biological interpretations, highlighting the need to benchmark resources and methods.

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P005 Cytokine mediated intercellular communication in inflammatory bowel disease

Journal of Crohn s and Colitis ◽

10.1093/ecco-jcc/jjab076.134 ◽

2021 ◽

Vol 15 (Supplement_1) ◽

pp. S127-S128

Author(s):

J P Thomas ◽

M Olbei ◽

I Hautefort ◽

D Modos ◽

T Korcsmaros

Keyword(s):

Ulcerative Colitis ◽

Inflammatory Bowel Disease ◽

Immune System ◽

Single Cell ◽

Bowel Disease ◽

Cell Communication ◽

Mucosal Immune System ◽

Rna Seq ◽

Inflammatory Bowel ◽

Cell Cell

Abstract Background During inflammatory bowel disease the mucosal immune system is altered. The mucosal immune cells are communicating through the various cytokines. Single cell and small volume RNA-seq and proteomics approaches make the investigation of cytokine networks plausible However the lack of specific resources make such efforts hard. Methods To address this need in this project, we built a cell-cell communication map, CytokineLink, which collates cytokine mediated intercellular interactions. CytokineLink collects the cytokine-cytokine receptor interactions from the OmniPath, immuneXpresso and immunoGlobe databases. We demonstrate the applicability of CytokineLink by presenting how cytokine feedback loops are built and altered during Ulcerative Colitis. We mapped single-cell RNA-seq expression data from inflamed and uninflamed Ulcerative Colitis biopsies to the interactions between cytokines and cytokine receptors, and then we compared the specific cytokine-mediated cell-cell interactions. Results Using our approach, we were able to point out major differences in cell-cell communication between inflamed and uninflamed conditions, and identify key cytokine changes. For example, the generally anti-inflammatory cytokine IL-10 is produced by regulatory T-cells in both conditions. However the IL-10 receptor positive cells are altered between the inflamed and uninflamed condition: dendritic cells and innate lymphocytes did not express the receptor in the sufficient amount. It suggests that not the cytokine level directly but the receptor level alterations are involved in ulcerative colitis. Also the chemokine CXCL12 was expressed by the inflammatory fibroblasts. This cytokine promotes the T-cell recruitment and through that inflammation. Conclusion With CytokineLink, researchers are capable to pinpoint the most important interactions in the changing mucosal immune system and propose novel therapeutic approaches. We are currently developing a website and easy to follow workflows to make CytokineLink available.

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New avenues for systematically inferring cell-cell communication: through single-cell transcriptomics data

Protein & Cell ◽

10.1007/s13238-020-00727-5 ◽

2020 ◽

Vol 11 (12) ◽

pp. 866-880 ◽

Cited By ~ 5

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.

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Single-Cell Analysis of Cell-Cell Communication of Exosome Components Derived from Melanoma Cells

ECS Meeting Abstracts ◽

10.1149/ma2020-02442825mtgabs ◽

2020 ◽

Vol MA2020-02 (44) ◽

pp. 2825-2825

Author(s):

Miyu Fukaya ◽

Tomohiro Hatakenaka ◽

Nahoko Matsuki ◽

Seiya Minagawa ◽

Mikako Saito

Keyword(s):

Single Cell ◽

Single Cell Analysis ◽

Cell Communication ◽

Melanoma Cells ◽

Cell Analysis ◽

Cell Cell

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Abstract 3391: Dissecting intratumoral cell-cell interactions in myeloid reprogramming by single cell RNA-seq

10.1158/1538-7445.sabcs18-3391 ◽

2019 ◽

Author(s):

Qianqian Song ◽

Gregory A. Hawkins ◽

Leonard Wudel ◽

Ping-Chieh Chou ◽

Elizabeth Forbes ◽

...

Keyword(s):

Single Cell ◽

Cell Interactions ◽

Rna Seq ◽

Cell Cell

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A Critical-like Collective State Leads to Long-range Cell Communication in Dictyostelium discoideum Aggregation

10.1101/086538 ◽

2016 ◽

Author(s):

Giovanna De Palo ◽

Darvin Yi ◽

Robert G. Endres

Keyword(s):

Single Cell ◽

Dictyostelium Discoideum ◽

Long Range ◽

Early Development ◽

Cell Communication ◽

Multiscale Model ◽

Wild Type ◽

Social Amoeba ◽

Range Cell ◽

Cell Cell

AbstractThe transition from single-cell to multicellular behavior is important in early development but rarely studied. The starvation-induced aggregation of the social amoeba Dictyostelium discoideum into a multicellular slug is known to result from single-cell chemotaxis towards emitted pulses of cyclic adenosine monophosphate (cAMP). However, how exactly do transient short-range chemical gradients lead to coherent collective movement at a macroscopic scale? Here, we developed a multiscale model verified by quantitative microscopy to describe wide-ranging behaviors from chemotaxis and excitability of individual cells to aggregation of thousands of cells. To better understand the mechanism of long-range cell-cell communication and hence aggregation, we analyzed cell-cell correlations, showing evidence of self-organization at the onset of aggregation (as opposed to following a leader cell). Surprisingly, cell collectives, despite their finite size, show features of criticality known from phase transitions in physical systems. By comparing wild-type and mutant cells with impaired aggregation, we found the longest cellcell communication distance in wild-type cells, suggesting that criticality provides an adaptive advantage and optimally sized aggregates for the dispersal of spores.Author SummaryCells are often coupled to each other in cell collectives, such as aggregates during early development, tissues in the developed organism, and tumors in disease. How do cells communicate over macroscopic distances much larger than the typical cell-cell distance to decide how they should behave? Here, we developed a multiscale model of social amoeba, spanning behavior from individuals to thousands of cells. We show that local cell-cell coupling via secreted chemicals may be tuned to a critical value, resulting in emergent long-range communication and heightened sensitivity. Hence, these aggregates are remarkably similar to bacterial biofilms and neuronal networks, all communicating in a pulse-like fashion. Similar organizing principles may also aid our understanding of the remarkable robustness in cancer development.

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Cell Lineage and Communication Network Inference via Optimization for Single-cell Transcriptomics

10.1101/168922 ◽

2017 ◽

Cited By ~ 1

Author(s):

Shuxiong Wang ◽

Matthew Karikomi ◽

Adam L. MacLean ◽

Qing Nie

Keyword(s):

Single Cell ◽

Network Inference ◽

Marker Gene ◽

Cell Communication ◽

Cell Lineage ◽

Marker Genes ◽

Specific Cell ◽

Mathematical Framework ◽

Similarity Matrix ◽

Cell Cell

AbstractThe use of single-cell transcriptomics has become a major approach to delineate cell subpopulations and the transitions between them. While various computational tools using different mathematical methods have been developed to infer clusters, marker genes, and cell lineage, none yet integrate these within a mathematical framework to perform multiple tasks coherently. Such coherence is critical for the inference of cell-cell communication, a major remaining challenge. Here we present similarity matrix-based optimization for single-cell data analysis (SoptSC), in which unsupervised clustering, pseudotemporal ordering, lineage inference, and marker gene identification are inferred via a structured cell-to-cell similarity matrix. SoptSC then predicts cell-cell communication networks, enabling reconstruction of complex cell lineages that include feedback or feedforward interactions. Application of SoptSC to early embryonic development, epidermal regeneration, and hematopoiesis demonstrates robust identification of subpopulations, lineage relationships, and pseudotime, and prediction of pathway-specific cell communication patterns regulating processes of development and differentiation.

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Runx2-Twist1 interaction coordinates cranial neural crest guidance of soft palate myogenesis

eLife ◽

10.7554/elife.62387 ◽

2021 ◽

Vol 10 ◽

Author(s):

Xia Han ◽

Jifan Feng ◽

Tingwei Guo ◽

Yong-Hwee Eddie Loh ◽

Yuan Yuan ◽

...

Keyword(s):

Neural Crest ◽

Single Cell ◽

Soft Palate ◽

Muscle Development ◽

Cell Interactions ◽

Lineage Commitment ◽

Rna Seq ◽

Cranial Neural Crest ◽

Marker Expression ◽

Cell Cell

Cranial neural crest (CNC) cells give rise to bone, cartilage, tendons, and ligaments of the vertebrate craniofacial musculoskeletal complex, as well as regulate mesoderm-derived craniofacial muscle development through cell-cell interactions. Using the mouse soft palate as a model, we performed an unbiased single-cell RNA-seq analysis to investigate the heterogeneity and lineage commitment of CNC derivatives during craniofacial muscle development. We show that Runx2, a known osteogenic regulator, is expressed in the CNC-derived perimysial and progenitor populations. Loss of Runx2 in CNC-derivatives results in reduced expression of perimysial markers (Aldh1a2 and Hic1) as well as soft palate muscle defects in Osr2-Cre;Runx2fl/fl mice. We further reveal that Runx2 maintains perimysial marker expression through suppressing Twist1, and that myogenesis is restored in Osr2-Cre;Runx2fl/fl;Twist1fl/+ mice. Collectively, our findings highlight the roles of Runx2, Twist1, and their interaction in regulating the fate of CNC-derived cells as they guide craniofacial muscle development through cell-cell interactions.

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Investigating immune and non-immune cell interactions in head and neck tumors by single-cell RNA sequencing

Nature Communications ◽

10.1038/s41467-021-27619-4 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Cornelius H. L. Kürten ◽

Aditi Kulkarni ◽

Anthony R. Cillo ◽

Patricia M. Santos ◽

Anna K. Roble ◽

...

Keyword(s):

Head And Neck ◽

Single Cell ◽

Rna Sequencing ◽

Immune Cell ◽

Cell Communication ◽

Cell Interactions ◽

Cell Populations ◽

Hpv Status ◽

Single Cell Rna Sequencing ◽

Cell Cell

AbstractHead and neck squamous cell carcinoma (HNSCC) is characterized by complex relations between stromal, epithelial, and immune cells within the tumor microenvironment (TME). To enable the development of more efficacious therapies, we aim to study the heterogeneity, signatures of unique cell populations, and cell-cell interactions of non-immune and immune cell populations in 6 human papillomavirus (HPV)+ and 12 HPV– HNSCC patient tumor and matched peripheral blood specimens using single-cell RNA sequencing. Using this dataset of 134,606 cells, we show cell type-specific signatures associated with inflammation and HPV status, describe the negative prognostic value of fibroblasts with elastic differentiation specifically in the HPV+ TME, predict therapeutically targetable checkpoint receptor-ligand interactions, and show that tumor-associated macrophages are dominant contributors of PD-L1 and other immune checkpoint ligands in the TME. We present a comprehensive single-cell view of cell-intrinsic mechanisms and cell-cell communication shaping the HNSCC microenvironment.

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LR Hunting: A Random Forest Based Cell–Cell Interaction Discovery Method for Single-Cell Gene Expression Data

Frontiers in Genetics ◽

10.3389/fgene.2021.708835 ◽

2021 ◽

Vol 12 ◽

Author(s):

Min Lu ◽

Yifan Sha ◽

Tiago C. Silva ◽

Antonio Colaprico ◽

Xiaodian Sun ◽

...

Keyword(s):

Single Cell ◽

Cell Communication ◽

Cell Types ◽

Cell Interaction ◽

Data Imputation ◽

Novel Approach ◽

Cell Gene Expression ◽

Minimal Depth ◽

Discovery Method ◽

Cell Cell

Cell–cell interactions (CCIs) and cell–cell communication (CCC) are critical for maintaining complex biological systems. The availability of single-cell RNA sequencing (scRNA-seq) data opens new avenues for deciphering CCIs and CCCs through identifying ligand-receptor (LR) gene interactions between cells. However, most methods were developed to examine the LR interactions of individual pairs of genes. Here, we propose a novel approach named LR hunting which first uses random forests (RFs)-based data imputation technique to link the data between different cell types. To guarantee the robustness of the data imputation procedure, we repeat the computation procedures multiple times to generate aggregated imputed minimal depth index (IMDI). Next, we identify significant LR interactions among all combinations of LR pairs simultaneously using unsupervised RFs. We demonstrated LR hunting can recover biological meaningful CCIs using a mouse cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) dataset and a triple-negative breast cancer scRNA-seq dataset.

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