Abstract
Ligand–receptor interactions constitute a fundamental mechanism of cell–cell communication and signaling. NicheNet is a well-established computational tool that infers ligand–receptor interactions that potentially regulate gene expression changes in receiver cell populations. Whereas the original publication delves into the algorithm and validation, this paper describes a best practices workflow cultivated over four years of experience and user feedback. Starting from the input single-cell expression matrix, we describe a ‘sender-agnostic’ approach that considers ligands from the entire microenvironment and a ‘sender-focused’ approach that considers ligands only from cell populations of interest. As output, users will obtain a list of prioritized ligands and their potential target genes, along with multiple visualizations. We include further developments made in NicheNet v2, in which we have updated the data sources and implemented a downstream procedure for prioritizing cell type–specific ligand–receptor pairs. Although a standard NicheNet analysis takes <10 min to run, users often invest additional time in making decisions about the approach and parameters that best suit their biological question. This paper serves to aid in this decision-making process by describing the most appropriate workflow for common experimental designs like case-control and cell-differentiation studies. Finally, in addition to the step-by-step description of the code, we also provide wrapper functions that enable the analysis to be run in one line of code, thus tailoring the workflow to users at all levels of computational proficiency.
Key points
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This protocol describes NicheNet, which is a computational tool to infer the ligand–receptor interactions that potentially regulate gene expression changes in receiver cell populations. This protocol produces a list of prioritized ligands and their potential target genes and includes multiple visualizations.
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Unlike many other cell–cell communication tools, which include information only about the expression of ligands and receptors, NicheNet incorporates information about the transcriptional responses triggered by these interactions.
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Data availability
The original NICHE-seq data can be accessed at the Gene Expression Omnibus with accession number GSE104054. The processed Seurat object can be downloaded on Zenodo41 (https://zenodo.org/record/3531889). NicheNet networks can be downloaded at https://zenodo.org/record/7074291/ (ref. 42).
Code availability
NicheNet is publicly available on https://github.com/saeyslab/nichenetr/ as an R package. The code shown in this paper and the code for reproducing the figures can be found on https://github.com/saeyslab/nichenet_protocol. The code in this protocol has been peer reviewed.
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Acknowledgements
C.S.-a. is funded by the Ghent University Special Research Fund (grant number BOF21-DOC-105), R.S. is funded by the Flemish Government under the Flanders AI Research Program and Y.S. is funded by Ghent University Special Research Fund (grant number BOF18-GOA-024), the Belgian Excellence of Science (EOS) program, FWO SBO (grant number S001121N) and the Leducq project “Cellular and Molecular Drivers of Acute Aortic Dissections.”
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R.B. conceptualized the NicheNet algorithm. R.B. and C.S.-a. wrote the code. R.S. and Y.S. supervised the work. C.S. wrote the manuscript. All authors edited, read and approved the manuscript.
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Key references
Browaeys, R., Saelens, W. & Saeys, Y. Nat. Methods 17, 159–162 (2020): https://doi.org/10.1038/s41592-019-0667-5
Bonnardel, J. et al. Immunity 51, 638–654.e9 (2019): https://doi.org/10.1016/j.immuni.2019.08.017
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Sang-aram, C., Browaeys, R., Seurinck, R. et al. Unraveling cell–cell communication with NicheNet by inferring active ligands from transcriptomics data. Nat Protoc 20, 1439–1467 (2025). https://doi.org/10.1038/s41596-024-01121-9
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DOI: https://doi.org/10.1038/s41596-024-01121-9
