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Deciphering cell–cell interactions and communication from gene expression

Nature Reviews Genetics volume 22, pages 71–88 (2021)Cite this article

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Abstract

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.

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Fig. 1: Types and applications of cell–cell interactions and communication.
Fig. 2: Analysis workflow for inferring cell–cell interactions and communication from gene expression.
Fig. 3: Toy examples of using core functions to compute communication scores.
Fig. 4: Common visualization techniques for cell–cell interactions and communication.

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Acknowledgements

E.A. is supported by the Chilean Agencia Nacional de Investigación y Desarrollo through its scholarship programme DOCTORADO BECAS CHILE/2018 - 72190270 and by the Fulbright Commission. A.O. is supported by the US NLM (T15LM011271). O.H. is supported by the US NIH (U01CA196406). The authors also thank A. Perez-Lopez, C. Zuñiga, J. Tibocha-Bonilla, L. Zaramela, M. Kumar and P. Tamayo for providing meaningful feedback and discussion. This work was also supported by the US NIGMS (R35 GM119850; N.E.L.).

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Author notes

  1. These authors contributed equally: Erick Armingol, Adam Officer.

Authors and Affiliations

  1. Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA

    Erick Armingol & Nathan E. Lewis

  2. Novo Nordisk Foundation Center for Biosustainability at the University of California, San Diego, La Jolla, CA, USA

    Erick Armingol & Nathan E. Lewis

  3. Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA, USA

    Erick Armingol & Adam Officer

  4. Division of Biomedical Informatics, University of California, San Diego, La Jolla, CA, USA

    Adam Officer & Olivier Harismendy

  5. Moores Cancer Center, University of California, San Diego, La Jolla, CA, USA

    Olivier Harismendy

  6. Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA

    Nathan E. Lewis

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E.A. and A.O. researched the literature. All authors contributed to discussions of the content and wrote, reviewed and/or edited the manuscript before submission.

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Nature Reviews Genetics thanks Q. Nie; V. Soumelis; and R. Vento-Tormo, who co-reviewed with A. Arutyunyan, for their contribution to the peer review of this work.

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Related links

Ligand–receptor pair repository: https://github.com/LewisLabUCSD/Ligand-Receptor-Pairs

Supplementary information

Glossary

Cell–cell interactions

(CCIs). Physical interactions between two or more cells, which can be mediated by proteins, ligands, sugars or other biomolecules.

Receptors

Proteins that bind to other biomolecules to receive or amplify a signal. They are most commonly membrane-bound but can also be found in the cytoplasm.

Ligands

Biomolecules that bind to receptors and change the activity, conformation or other biological properties of the receptor, triggering a signalling event.

Extracellular matrix

Three-dimensional organization of biomolecules located in the extracellular space. It provides structural and functional support to neighbouring cells.

Cell–cell communication

(CCC). Subset of cell–cell interactions involving biochemical signals that are sent between or within cells and generate an intracellular effect.

Protein–protein interactions

(PPIs). Physical interaction between two proteins, often involved in structural systems, signal transduction or metabolic processes.

Network

A set of nodes with defined pairwise attributes. For example, a protein–protein interaction network would consist of proteins as nodes with attributes linking nodes that are known to interact with each other.

Communication pathways

Molecular components used for an intercellular communication event, usually corresponding to a ligand–receptor pair.

Interactome

Network of biomolecule interactions within and between cells.

False positives

In classification, a false positive occurs when a negative example is assigned a positive label. For the purpose of this Review, this means a non-interacting ligand–receptor pair is labelled as interacting.

False negatives

A false negative occurs when a positive example is assigned a negative label. For the purpose of this Review, this means a true interacting ligand–receptor pair is labelled as not interacting.

Post-translational modifications

Covalent modification of amino acid residues on a protein, commonly altering function, structure or localization. Phosphorylation, acetylation and glycosylation are among the most common.

Signalling pathways

The network of biomolecules that serve to transmit signals and induce cellular responses. Post-translational modification of proteins is the most common way signals are propagated.

Fuzzy logic

Extension of standard Boolean logic to define truth values of variables, encompassing real values between 0 and 1 both inclusive, instead of binary values.

Permutation

Random reassignment of sample labels, frequently used to compute null models in biological systems.

Differentially expressed genes

Genes identified as more highly (or lowly) expressed in one condition versus the other after comparison of their expression values between two conditions.

PageRank algorithm

Algorithm that takes as input a network and quantifies the importance of each node on the basis of centrality and connectedness to other central nodes.

Null model

Statistical model under which there is no interaction or difference between the groups being tested.

Multisubunit protein complexes

Quaternary structures of proteins involving the non-covalent interaction of two or more proteins to generate a functional unit.

Tensor

Higher N-dimensional generalizations of matrices and vectors. Vectors are tensors of rank 1, matrices are tensors of rank 2.

Tucker decomposition

Decomposing of a tensor of rank N as a product of a set of N matrices and one core tensor. Used to summarize data, similarly to principal component analysis.

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Armingol, E., Officer, A., Harismendy, O. et al. Deciphering cell–cell interactions and communication from gene expression. Nat Rev Genet 22, 71–88 (2021). https://doi.org/10.1038/s41576-020-00292-x

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