scConnect: a method for exploratory analysis of cell–cell communication based on single-cell RNA-sequencing data

Jakobsson, Jon E. T.; Spjuth, Ola; Lagerström, Malin C.

doi:10.1093/bioinformatics/btab245

Cited by 23 publications

(15 citation statements)

References 58 publications

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“…Of note, the cytokine activities and receptor proteins, presented in this work as an evaluation, could also be used to improve the confidence in predictions 56 . Similarly, other analyses such as pathway 14 or transcription factor activities 15,57 , as well as other types of cell-communication dedicated methods, including NicheNet 19 , CytoTalk 22 , and SoptSC 20 , could be utilised to provide further confidence in the predicted ligandreceptor interactions.…”

Section: Discussionmentioning

confidence: 99%

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Comparison of methods and resources for cell-cell communication inference from single-cell RNA-Seq data

et al. 2022

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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.

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Section: Discussionmentioning

confidence: 99%

“…A number of computational tools and resources have emerged that can be further classified as those that predict CCC interactions alone [4][5][6][7][8][9][10][11][12][13][14][15][16][17] , and those that additionally estimate intracellular activities related to CCC [18][19][20][21][22][23][24] . Here, we focus on the former (Table 1).…”

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confidence: 99%

Comparison of methods and resources for cell-cell communication inference from single-cell RNA-Seq data

et al. 2022

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“…Two recently released algorithms, including the CellPhoneDBv4 54 and scConnect 55 , could perform analysis of cell-cell communications mediated by some small molecules. Likely because these two algorithms were designed mainly for analysis of protein ligand-receptor analysis, only 199 small molecule-receptor partners and 8 neurotransmitter-receptor partners were included respectively.…”

Section: Discussionmentioning

confidence: 99%

MEBOCOST: Metabolite-mediated Cell Communication Modeling by Single Cell Transcriptome

Zheng

Zhang

Tsuji

et al. 2022

Preprint

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We developed MEBOCOST, a computational algorithm for quantitatively inferring metabolite-based intercellular communications using single cell RNA-seq data. MEBOCOST predicted cell-cell communication events for which metabolites, such as lipids, are secreted by one cell (sender cells) and traveled to interact with sensor proteins of another cell (receiver cells). The sensor protein on receiver cell might be cell surface receptor, cell surface transporter, and nuclear receptor. MEBOCOST relies on a curated database of metabolite-sensor partners, which we collected from the literatures and other public sources. Based on scRNA-seq data, MEBOCOST identifies cell-cell metabolite-sensor communications between cell groups, in which metabolite enzymes and sensors were highly expressed in sender and receiver cells, respectively. Applying MEBOCOST on brown adipose tissue (BAT) showed the robustness of predicting known and novel metabolite-based autocrine and paracrine communications. Additionally, MEBOCOST identified a set of intercellular metabolite-sensor communications that was regulated by cold exposure in BAT. Those predicted communicating metabolites and sensors may play important roles in thermogenesis regulation. We believe that MEBOCOST will be useful to numerous researchers to investigate metabolite-based cell-cell communications in many biological and disease models, thus will be useful to remove critical barriers impeding the development of new therapies to target these communications. MEBOCOST is freely available at https://github.com/zhengrongbin/MEBOCOST.

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“…We developed a computational pipeline followed by manual curation for a thorough literature mining to collect reported metabolite-sensor partners from the literature. The Two recently released algorithms, including the CellPhoneDBv4 55 and scConnect 56 , could perform analysis of cell-cell communications mediated by some small molecules. Likely because these two algorithms were designed mainly for analysis of protein ligand-receptor analysis, only 199 small molecule-receptor partners and 8 neurotransmitter-receptor partners were included respectively.…”

Section: Discussionmentioning

confidence: 99%

MEBOCOST: Metabolite-mediated Cell Communication Modeling by Single Cell Transcriptome

Chen

Zheng

Zhang

et al. 2022

Preprint

View full text Add to dashboard Cite

We developed MEBOCOST, an algorithm for quantitatively inferring metabolite-mediated intercellular communications using single-cell RNA-seq data. The algorithm identifies cell-cell communications in which metabolites, such as lipids, are secreted by sender cells and traveled to interact with sensor proteins of receiver cells. The sensors on the receiver cell include the cell surface receptors, transporters across the cell membrane, or nuclear receptors. MEBOCOST relies on a comprehensive database of metabolite-sensor partners, which we manually curated from the literature and other public sources. MEBOCOST defines sender and receiver cells for an extracellular metabolite based on the expression levels of the enzymes and sensors, respectively, thus identifies metabolite-sensor communications between the cells. Applying MEBOCOST to mouse brown adipose tissue (BAT) successfully recaptured known metabolite-mediated cell communications and further identified new communications. Additionally, MEBOCOST identified a set of intercellular metabolite-sensor communications regulated by cold exposure in mouse BAT. MEBOCOST will be useful to researchers for investigation of metabolite-mediated cell-cell communications in many biological and disease models. The MEBOCOST software is freely available at https://github.com/zhengrongbin/MEBOCOST.

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scConnect: a method for exploratory analysis of cell–cell communication based on single-cell RNA-sequencing data

Cited by 23 publications

References 58 publications

Comparison of methods and resources for cell-cell communication inference from single-cell RNA-Seq data

Comparison of methods and resources for cell-cell communication inference from single-cell RNA-Seq data

MEBOCOST: Metabolite-mediated Cell Communication Modeling by Single Cell Transcriptome

MEBOCOST: Metabolite-mediated Cell Communication Modeling by Single Cell Transcriptome

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