Applications and analytical tools of cell communication based on ligand-receptor interactions at single cell level

Ma, Fen; Zhang, Siwei; Song, Lixia; Wang, Bozhi; Wei, Lanlan; Zhang, Fengmin

doi:10.1186/s13578-021-00635-z

Cited by 26 publications

(19 citation statements)

References 91 publications

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“…Intercellular communication through LR pairs in the tumor microenvironment underlie the poor prognosis of multiple cancers, such as pancreatic ductal adenocarcinoma ( 19 ) and colorectal cancer ( 20 ). Increasing discoveries of receptors and ligands and their interactions has encouraged the integration of the available information on ligand-receptor interactions from many databases to facilitate research ( 21 ). ConnectomeDB2020 is a database that integrates 2293 pairs of LR interactions.…”

Section: Discussionmentioning

confidence: 99%

The molecular subtypes of triple negative breast cancer were defined and a ligand-receptor pair score model was constructed by comprehensive analysis of ligand-receptor pairs

et al. 2022

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BackgroundIntercellular communication mediated by ligand-receptor interactions in tumor microenvironment (TME) has a profound impact on tumor progression. This study aimed to explore the molecular subtypes mediated by ligand-receptor (LR) pairs in triple negative breast cancer (TNBC), identify the most important LR pairs to construct a prognostic risk model, and study their effect on TNBC immunotherapy.MethodsLR pairs subclasses of TNBC were categorized by consensus clustering based on LR Pairs in METABRIC dataset. Least absolute shrinkage and selection operator (LASSO) Cox regression and stepwise Akaike information criterion (stepAIC) were conducted to build a LR pairs score model. The relationship between LR pairs score and immune cell infiltration, stromal score and immune score associated with TME was analyzed, and the prediction of drug therapy and immunotherapy efficacy by LR pairs score was evaluated.ResultsAccording to the expression pattern of 145 TNBC prognostic LR pairs, the samples were divided into three subclasses with different survival outcomes, copy number variation (CNV), TME immune cell infiltration, stromal score and immune score. The LR pairs score model constructed in the METABRIC dataset was composed of four LR pairs, and its predictive significance for TNBC prognosis was verified in GSE58812 and GSE21653 cohorts. In addition, LR pairs score was negatively correlated with several immune pathways regulating immunity and immune score, and related to the sensitivity of anti-neoplastic drugs and the effect of anti-PD-L1 therapy.ConclusionOur study confirmed the impact of LR pairs on the molecular heterogeneity of TNBC, characterized three LR pairs subtypes with different survival outcomes and TME patterns, and proposed a LR pairs score system with predictive significance for TNBC prognosis and anti-PD-L1 therapeutic effect, which provides a potential evaluation scheme for TNBC management.

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Section: Discussionmentioning

confidence: 99%

The molecular subtypes of triple negative breast cancer were defined and a ligand-receptor pair score model was constructed by comprehensive analysis of ligand-receptor pairs

et al. 2022

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“…The FANTOM5 database ( 27 ) specifically, which includes ~2,500 human ligand-receptor pairs, is referenced by nearly all of the selected algorithms in Table 2 . Further comparison of the general features, advantages, and limitations of FANTOM5 against other commonly used reference databases, including those underlying CellPhoneDB, NATMI, ICELLNET, CellChat, iTALK, SingleCellSignalR, and NicheNet, has been reviewed elsewhere ( 89 , 90 ).…”

Section: Underlying Reference Databases Play a Key Role In The Infere...mentioning

confidence: 99%

Mapping and Validation of scRNA-Seq-Derived Cell-Cell Communication Networks in the Tumor Microenvironment

Bridges

Miller‐Jensen

2022

Front. Immunol.

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Recent advances in single-cell technologies, particularly single-cell RNA-sequencing (scRNA-seq), have permitted high throughput transcriptional profiling of a wide variety of biological systems. As scRNA-seq supports inference of cell-cell communication, this technology has and continues to anchor groundbreaking studies into the efficacy and mechanism of novel immunotherapies for cancer treatment. In this review, we will highlight methods developed to infer inter- and intracellular signaling from scRNA-seq and discuss how they have contributed to studies of immunotherapeutic intervention in the tumor microenvironment (TME). However, a central challenge remains in validating the hypothesized cell-cell interactions. Therefore, this review will also cover strategies for integration of these scRNA-seq-derived interaction networks with existing experimental and computational approaches. Integration of these networks with imaging, protein secretion measurements, and network analysis and mathematical modeling tools addresses challenges that remain with scRNA-seq to enhance studies of immunosuppressive and immunotherapy-altered signaling in the TME.

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“…A typical signaling cascade begins with a single key event, such as ligand-receptor (LR) interactions, which trigger the activation of downstream signaling pathways. Finally, it affects the activities of transcription factors (TFs) and their target gene expression [ 11–13 ]. CCIs mediated by LR interactions have been the most common scenario for the computational study of CCIs in recent years [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

A systematic evaluation of the computational tools for ligand-receptor-based cell–cell interaction inference

Wang¹,

Zheng²,

Choi³

et al. 2022

Briefings in Functional Genomics

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Cell–cell interactions (CCIs) are essential for multicellular organisms to coordinate biological processes and functions. One classical type of CCI interaction is between secreted ligands and cell surface receptors, i.e. ligand-receptor (LR) interactions. With the recent development of single-cell technologies, a large amount of single-cell ribonucleic acid (RNA) sequencing (scRNA-Seq) data has become widely available. This data availability motivated the single-cell-resolution study of CCIs, particularly LR-based CCIs. Dozens of computational methods and tools have been developed to predict CCIs by identifying LR-based CCIs. Many of these tools have been theoretically reviewed. However, there is little study on current LR-based CCI prediction tools regarding their performance and running results on public scRNA-Seq datasets. In this work, to fill this gap, we tested and compared nine of the most recent computational tools for LR-based CCI prediction. We used 15 well-studied scRNA-Seq samples that correspond to approximately 100K single cells under different experimental conditions for testing and comparison. Besides briefing the methodology used in these nine tools, we summarized the similarities and differences of these tools in terms of both LR prediction and CCI inference between cell types. We provided insight into using these tools to make meaningful discoveries in understanding cell communications.

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Applications and analytical tools of cell communication based on ligand-receptor interactions at single cell level

Cited by 26 publications

References 91 publications

The molecular subtypes of triple negative breast cancer were defined and a ligand-receptor pair score model was constructed by comprehensive analysis of ligand-receptor pairs

The molecular subtypes of triple negative breast cancer were defined and a ligand-receptor pair score model was constructed by comprehensive analysis of ligand-receptor pairs

Mapping and Validation of scRNA-Seq-Derived Cell-Cell Communication Networks in the Tumor Microenvironment

A systematic evaluation of the computational tools for ligand-receptor-based cell–cell interaction inference

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