Cell–cell communication mediated by ligand–receptor complexes is critical to coordinating diverse biological processes, such as development, differentiation and inflammation. To investigate how the context-dependent crosstalk of different cell types enables physiological processes to proceed, we developed CellPhoneDB, a novel repository of ligands, receptors and their interactions. In contrast to other repositories, our database takes into account the subunit architecture of both ligands and receptors, representing heteromeric complexes accurately. We integrated our resource with a statistical framework that predicts enriched cellular interactions between two cell types from single-cell transcriptomics data. Here, we outline the structure and content of our repository, provide procedures for inferring cell–cell communication networks from single-cell RNA sequencing data and present a practical step-by-step guide to help implement the protocol. CellPhoneDB v.2.0 is an updated version of our resource that incorporates additional functionalities to enable users to introduce new interacting molecules and reduces the time and resources needed to interrogate large datasets. CellPhoneDB v.2.0 is publicly available, both as code and as a user-friendly web interface; it can be used by both experts and researchers with little experience in computational genomics. In our protocol, we demonstrate how to evaluate meaningful biological interactions with CellPhoneDB v.2.0 using published datasets. This protocol typically takes ~2 h to complete, from installation to statistical analysis and visualization, for a dataset of ~10 GB, 10,000 cells and 19 cell types, and using five threads.
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We thank K. Meyer and M. Stubbington for scientific discussions; P. Porras for advice on querying the IMEx database; L. Garcia-Alonso and K. Polanski for carefully reading the manuscript; G.J. Wright, L. Wood and G. Graham for advice on protein–protein interactions; and J. Eliasova and A. Hupalowska for help with the illustrations. We are grateful to A. Lopez and YDEVS members for their help with the webserver and the implementation of the code in GitHub, as well as to all the Teichmann lab and Vento-Tormo lab members for their fruitful advice. The project was supported by Wellcome Sanger core funding (WT206194) and a Wellcome Strategic Support Science award (211276/Z/18/Z).
The authors declare no competing interests.
Peer review information Nature Protocols thanks Evangelia Petsalaki and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Key references using this protocol
Vento-Tormo, R. et al. Nature 563, 347–353 (2018): https://doi.org/10.1038/s41586-018-0698-6
Stewart, B. et al. Science 365, 1461–1466 (2019): https://doi.org/10.1126/science.aat5031
Popescu, D. et al. Nature 574, 365–371 (2019): https://doi.org/10.1038/s41586-019-1652-y
Integrated supplementary information
a) database schema, b) protein_input/complex_input storage in the CellPhoneDB database tables. The multidata entity stores fields common to complex_input and protein_input. This makes it easier and faster for the user to perform interaction queries because interaction_table is only related to multidata_table. All non-common fields are stored in either protein_table or complex_table. Complex fields are stored in complex_composition_table. The is_complex and total_protein field are created for optimization purposes.
An example of two complex_input rows with two and four components.
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Efremova, M., Vento-Tormo, M., Teichmann, S.A. et al. CellPhoneDB: inferring cell–cell communication from combined expression of multi-subunit ligand–receptor complexes. Nat Protoc 15, 1484–1506 (2020). https://doi.org/10.1038/s41596-020-0292-x
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