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  • Protocol Update
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CellPhoneDB v5: inferring cell–cell communication from single-cell multiomics data

Nature Protocols (2025)Cite this article

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Abstract

Cell–cell communication is essential for tissue development, function and regeneration. The revolution of single-cell genomics technologies offers an unprecedented opportunity to uncover how cells communicate in vivo within their tissue niches and how disruption of these niches can lead to diseases and developmental abnormalities. CellPhoneDB is a bioinformatics toolkit designed to infer cell–cell communication by combining a curated repository of bona fide ligand-receptor interactions with methods to integrate these interactions with single-cell genomics data. Here we present a protocol for the latest version of CellPhoneDB (v5), offering several new features. First, the repository has been expanded by one-third with the addition of new interactions, including ~1,000 interactions mediated by nonpeptidic ligands such as steroidogenic hormones, neurotransmitters and small G-protein-coupled receptor (GPCR)-binding ligands. Second, we outline a new way of using the database that allows users to tailor queries to their experimental designs. Third, the update incorporates novel strategies to prioritize specific cell–cell interactions, leveraging information from other modalities such as tissue microenvironments derived from spatial transcriptomics technologies or transcription factor activities derived from a single-cell assay for transposase accessible chromatin assays. Finally, we describe the new CellPhoneDBViz module to interactively visualize and share results. Altogether, CellPhoneDB v5 enhances the precision of cell–cell communication inference, offering new insights into tissue biology in physiological microenvironments. This protocol typically takes ~15 min and requires basic knowledge of python.

Key points

  • Understanding cellular communication is essential for obtaining insights into normal tissue development and physiology and the dysregulation of these processes in disease states. CellPhoneDB is a popular bioinformatics toolkit for inferring cell–cell communication combining a curated database of interactions and single-cell expression data.

  • This protocol update describes how to use the newest implementation (CellPhoneDB v5), which expands the database to nonpeptide ligands and includes several strategies to prioritize and visualize interactions, incorporating information from epigenomics and spatial transcriptomics modalities.

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Fig. 1: Overview of the database.
Fig. 2: Overview of CellPhoneDB methods.
Fig. 3: CellPhoneDB workflow and visualization via CellPhoneDBViz and Ktplots for case study 1.

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Data availability

The CellPhoneDB database is available at GitHub via https://github.com/ventolab/CellphoneDB-data (ref. 94) and the processed data used in the case examples (Supplementary Notes 1–3) and to generate Fig. 3b–f are available at GitHub via https://github.com/ventolab/CellphoneDB/tree/master/NatureProtocols2024_case_studies (ref. 95). The raw data associated with the case studies are publicly available, with access detailed in the primary research articles13,14,15,17.

Code availability

The CellPhoneDB and CellphoneDBViz codes are available at GitHub via https://github.com/ventolab/CellphoneDB (ref. 96) and https://github.com/ventolab/CellphoneDBViz (ref. 97), respectively. Kplots are available at GitHub via https://github.com/zktuong/ktplots (ref. 98).

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Acknowledgements

The authors thank J. Shilts for introducing new interactions in the CellphoneDB database, E. Armingol for insightful discussion of the manuscript, A. García from Bio-Graphics for scientific illustrations, A. Maartens for proofreading and R. Vilarrasa for her feedback on the scoring methodology. We are grateful to all the Vento-Tormo lab members for their advice. This project was supported by the Chan Zuckerberg Initiative DAF grant 2022-249429 (S.T., L.G-A. and R.V.-T.), the Wellcome Trust grant 220540/Z/20/A, (R.V.-T.) and the UK Research and Innovation (UKRI) under the UK government’s Horizon Europe funding guarantee EP/Y009924/1 (R.V.T.).

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Author notes

  1. Sarah A. Teichmann

    Present address: Department of Medicine and Cambridge Stem Cell Institute Clinical School, University of Cambridge, Cambridge, UK

Authors and Affiliations

  1. Wellcome Sanger Institute, Cambridge, UK

    Kevin Troulé, Robert Petryszak, Batuhan Cakir, James Cranley, Martin Prete, Zewen Kelvin Tuong, Sarah A. Teichmann, Luz Garcia-Alonso & Roser Vento-Tormo

  2. Ian Frazer Centre for Children’s Immunotherapy Research, Child Health Research Centre, Faculty of Medicine, The University of Queensland, Brisbane, Queensland, Australia

    Alicia Harasty & Zewen Kelvin Tuong

  3. Department of Physics, Cavendish Laboratory, University of Cambridge, Cambridge, UK

    Sarah A. Teichmann

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Contributions

K.T., L.G.-A., S.T. and R.V.-T. conceived and developed the protocol and wrote the manuscript. M.P. and R.P. implemented and optimized CellPhoneDB v5. R.P. developed CellPhoneDBViz. K.T., L.G.-A., J.C. and S.T. contributed to the manual revision of the CellphoneDB database. A.H. and Z.K.T. developed ktplots and ktplotspy. S.T., L.G.-A. and R.V.-T. cosupervised the work.

Corresponding authors

Correspondence to Luz Garcia-Alonso or Roser Vento-Tormo.

Ethics declarations

Competing interests

In the past 3 years, S.A.T. has consulted or been a member of scientific advisory boards at Roche, Genentech, Biogen, GlaxoSmithKline, Qiagen and ForeSite Labs, is an equity holder of Transition Bio and is a cofounder of Ensocell Therapeutics.

Peer review

Peer review information

Nature Protocols thanks Lucile Massenet, Vassili Soumelis and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Key references

Garcia-Alonso, L. et al. Nat. Genet. 53, 1698–1711 (2021): https://doi.org/10.1038/s41588-021-00972-2

Garcia-Alonso, L. et al. Nature 607, 540–547 (2022): https://doi.org/10.1038/s41586-022-04918-4

Marečková, M. et al. Nat. Genet 56, 1925–1937 (2024): https://doi.org/10.1038/s41588-024-01873-w

This protocol is an update to: Nat. Protoc. 15, 1484–1506 (2020): https://doi.org/10.1038/s41596-020-0292-x

Supplementary information

Supplementary Information

Supplementary Information containing Supplementary Notes, Figs. and Tables. Supplementary Notes. Three case studies using the novel features of CellPhoneDB v5, including method 3 and the three prioritization strategies CellSign, microenvironments and specificity scoring. Supplementary Note 1 describes case example 1 exploring germ-somatic communication during ovarian development, by combining method 3, CellSign and spatial microenvironments. Supplementary Note 2 describes case example 2 inferring cell–cell communication in the spatial niches of the endometrium, by combining method 3 and spatiotemporal microenvironments. Supplementary Note 3 describes case example 3, which aims to identify overexpressed cell–cell interactions between trophoblasts and maternal vessels by combining method 2 and scoring approach. Supplementary Fig. 1. CellPhoneDBViz web overview. Screenshots of CellPhoneDBViz reporting CellPhoneDB results for the case study 1 (Supplementary Note 1). a, Stankey plot and dotplot depicting cell types (second row or y axis) per microenvironment (thirds row or x axis). b, Chord plots showing the total number of interactions identified for each cell-type pair in each microenvironment (same data coils be represented as heat maps, a feature that is customizable in CellPhoneDBViz). c, Mean expression for a subset of selected interactions (y axis) per cell-type pair (x axis). If the interaction involves a receptor which downstream TF is active (CellSign) then an outer green ring is shown. The cell-type pairs are colored according to their microenvironment definition. Supplementary Table. Description of the output files (a) and the meaning of the columns (b).

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Troulé, K., Petryszak, R., Cakir, B. et al. CellPhoneDB v5: inferring cell–cell communication from single-cell multiomics data. Nat Protoc (2025). https://doi.org/10.1038/s41596-024-01137-1

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