Deciphering cell–cell interactions and communication from gene expression

By Erick Armingol, Adam Officer, Olivier Harismendy, Nathan E. Lewis. Nature reviews genetics, 2020

1,074 citations recorded by the source corpus.

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.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/f557f0c92bacf92a551c35e88ed4791cc2355d7b

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Citations in the corpus

scGNN is a novel graph neural network framework for single-cell RNA-Seq analyses (2021, methodology): “We used CellChat 42 to predict potential interaction probability scores (ranging from 0 to 1; a higher score indicates the two cells are more likely to interact with each other) of ligand – receptor pairs (LRP) between any two cells.”
Integrated intra‐ and intercellular signaling knowledge for multicellular omics analysis (2020, neutral): “Or for the resulting networks used to make the conclusions it would be good to show the number of resources from which the nodes/data points were extracted, to make the case that this integrated resource is necessary for this analysis.”
An introduction to spatial transcriptomics for biomedical research (2022, background): “One form these signals can take is of cell surface-bound protein receptors and ligand pairs, the mRNA for which can be detected by transcriptomics [9].”
Robust mapping of spatiotemporal trajectories and cell–cell interactions in healthy and diseased tissues (2023, neutral): “Consequently, these methods face an inherent issue of detecting false positive interactions between cells that are located outside this biological distance limit 72 .”
Comparison of methods and resources for cell-cell communication inference from single-cell RNA-Seq data (2022, neutral): “(2) P-values†—significance identified via permutation of cell cluster labels and recalculating the probabilities”
Spatially informed clustering, integration, and deconvolution of spatial transcriptomics with GraphST (2023, neutral): “When comparing the spatial mapping of cells from adjacent normal and solid tumor sites, adjacent normal cells were mainly mapped onto the Healthy (1, 2) and Tumor edge (1, 2, 4, 5) regions (Fig.”