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CellPhoneDB: inferring cell–cell communication from combined expression of multi-subunit ligand–receptor complexes

Nature Protocols volume 15, pages 1484–1506 (2020)Cite this article

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Abstract

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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Fig. 1: Overview of the database.
Fig. 2: Overview of the statistical method framework used to infer ligand–receptor complexes specific to two cell types from single-cell transcriptomics data.
Fig. 3: Example dataset run with CellPhoneDB and CellPhoneDB v.2.0.
Fig. 4: Diagram showing how lists are generated.
Fig. 5: Screenshot of the web portal.

Data availability

The decidua and placenta datasets can be downloaded from ArrayExpress, with experiment code E-MTAB-6701.

Code availability

The CellPhoneDB code is available at https://github.com/Teichlab/cellphonedb. It can also be downloaded from https://cellphonedb.org/downloads. The code in this paper has been peer-reviewed.

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Acknowledgements

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).

Author information

Authors and Affiliations

  1. Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK

    Mirjana Efremova, Sarah A. Teichmann & Roser Vento-Tormo

  2. YDEVS Software Development, Valencia, Spain

    Miquel Vento-Tormo

  3. Theory of Condensed Matter Group, Cavendish Laboratory, University of Cambridge, Cambridge, UK

    Sarah A. Teichmann

Authors
  1. Mirjana Efremova
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  2. Miquel Vento-Tormo
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  3. Sarah A. Teichmann
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  4. Roser Vento-Tormo
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Contributions

M.E., S.A.T. and R.V.-T. conceived and developed the protocol and wrote the manuscript. M.V.-T. developed the database, implemented the code in the webserver and GitHub and contributed to writing the manuscript.

Corresponding author

Correspondence to Roser Vento-Tormo.

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Competing interests

The authors declare no competing interests.

Additional information

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.

Related links

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

Supplementary Figure 1 Diagram of the database structure.

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.

Supplementary Figure 2 Example of complex_input components stored in CellPhoneDB.

An example of two complex_input rows with two and four components.

Supplementary information

Supplementary Information

Supplementary Figs. 1 and 2.

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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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