4D-NicheNet: modeling of spatiotemporal cell-cell communication dynamics by using prior knowledge on ligand-to-target signaling paths
Problem setting
Studying intercellular communication is essential for an improved understanding of tissue biology and disease pathophysiology. Advances in single-cell and spatial transcriptomics help address this need through their ability to generate molecular profiles of cells within a tissue1. Several computational approaches have been developed to investigate cell-cell communication from these profiles2,3. Most tools infer cell-cell communication by predicting interactions between ligands (membrane-bound or secreted extracellular proteins) expressed by sender cell types and receptors expressed by receiver cell types. Whereas these methods provide a comprehensive overview of potential expressed ligand-receptor pairs, they don’t provide a functional understanding of cell-cell communication because they don’t infer the response of the receiver cell type in reaction to the ligand-receptor binding.
To address this problem, we previously developed the computational method NicheNet (https://github.com/saeyslab/nichenetr)5. NicheNet starts from human or mouse gene expression data of interacting cells and combines this with a prior model calculated through integrating prior knowledge about ligand-to-target signaling paths. Specifically, we applied graph algorithms like Personalized PageRank6 on an integrated network of known ligand-receptor, signal transduction, and gene regulatory interactions to link ligand-receptor pairs to the target genes they might influence. NicheNet then uses this general prior model to interpret the data of putatively interacting cells: it prioritizes which ligands are most likely to influence the expression in the receiver cell , which target genes are affected by each prioritized ligand, and which signaling mediators may be involved. Our benchmark5 demonstrated that NicheNet can accurately predict active ligands given an observed gene expression signature after in vitro ligand stimulation for the majority of datasets. The accuracy of ligand activity predictions is harder to systematically assess in vivo, but several studies reported experimental validation of some of the top predictions7–12.
Although NicheNet is a stepping stone in modeling downstream responses of ligand-receptor interactions, its current version still has several limitations. A first limitation is that the quality of the predictions depends on how representative the prior knowledge of ligand-to-target signaling paths is for the context/cell types under study. We can thus expect that predictions will be less accurate for less-studied ligands and less-studied receiver cell types. Another limitation concerns the types of expression data that NicheNet can be applied to. The basic version of NicheNet was developed to analyze cell-cell communication in steady-state or to study pairwise differences between conditions. This means that it cannot handle other exciting data types for studying cell-cell communication, such as longitudinal datasets. Moreover, the typical datasets analyzed now are scRNA-seq data from dissociated cells, meaning that information about the spatial structure in the tissue is lost. Because of the relevance of tissue architecture in cell-cell signaling, it is another limitation that the current NicheNet algorithm cannot handle spatially resolved transcriptomics data properly.
Aim and rationale for the approach
The goal of this research project is thus to further develop the NicheNet framework to address the limitations mentioned above. We hope that the developed approach will improve the current ability to model cell-cell communication in space and time, while using accurate and context-appropriate prior knowledge to better link ligand-receptor pairs to their downstream response.
Details of the suggested approach
The first part of the project involves improving the prior knowledge of ligand-to-target signaling paths. The first idea for this part is to continuously include additional relevant data sources to the currently integrated networks (e.g., cytokine signatures from CytoSig13). The second idea is to provide integration with context-specific data. We are currently exploring how to integrate context-specific gene regulatory networks inferred from complementary data such as (sc)ATAC-seq (e.g., by using tools like SCENIC+). In the near future, we will investigate how to include CRISPR perturbation data. We anticipate that downstream signatures obtained from (context-specific) perturbation of ligands/receptors/transcription factors might greatly improve how we link ligand-receptor interactions to downstream responses in the prior knowledge model.
The second part of the project involves extending NicheNet to take advantage of the wealth of information provided in spatial transcriptomics and longitudinal data. The main idea here is to exploit the expected covariance between ligand-receptor expression and target gene expression. For spatial data, we expect that real target genes of ligand-receptor pairs will show higher expression in cells that are physically close to cells that show high expression of the ligand-receptor pair. For temporal data, we expect to observe a delay in time between an increase in ligand-receptor expression in sender cells and an increase in target gene expression in receiver cells. Because some target genes that are induced at an early timepoint will encode for ligands or receptors that might be involved in cell-cell communication at a later timepoint, we might start inferring intercellular signaling feedback and cascade mechanisms over time. In the hope of better discriminating between merely expression correlation between ligand-receptor pairs and target genes, we will use the improved NicheNet model to find links that are supported by plausible prior knowledge.
How it will affect the broader field
We anticipate a substantial interplay between this computational framework and wet-lab experimental work. First, NicheNet generates hypotheses from omics data to guide wet-lab validation of potentially crucial cell-cell communication patterns in the system under study. In the other direction, NicheNet will greatly benefit from ongoing efforts in experimentally determining downstream effects of important mediators in cell-cell communication (e.g., through CRISPR screens). The power of the approach is that the more and better prior knowledge becomes available, the better cell-cell communication patterns will be deciphered from omics data of interacting cells. In the end, we hereby hope to contribute to an improved understanding of the role of cell-cell signaling in health and disease.
References
1. Tanay, A. & Regev, A. Scaling single-cell genomics from phenomenology to mechanism. Nature 541, 331–338 (2017).
2. Almet, A. A., Cang, Z., Jin, S. & Nie, Q. The landscape of cell-cell communication through single-cell transcriptomics. Curr. Opin. Syst. Biol. (2021) doi:10.1016/j.coisb.2021.03.007.
3. Armingol, E., Officer, A., Harismendy, O. & Lewis, N. E. Deciphering cell–cell interactions and communication from gene expression. Nat. Rev. Genet. 1–18 (2020) doi:10.1038/s41576-020-00292-x.
4. Dimitrov, D. et al. Comparison of methods and resources for cell-cell communication inference from single-cell RNA-Seq data. Nat. Commun. 13, 3224 (2022).
5. Browaeys, R., Saelens, W. & Saeys, Y. NicheNet: modeling intercellular communication by linking ligands to target genes. Nat. Methods 17, 159–162 (2020).
6. Jeh, G. & Widom, J. Scaling Personalized Web Search. in Proceedings of the 12th International Conference on World Wide Web 271–279 (ACM, 2003). doi:10.1145/775152.775191.
7. Bonnardel, J. et al. Stellate Cells, Hepatocytes, and Endothelial Cells Imprint the Kupffer Cell Identity on Monocytes Colonizing the Liver Macrophage Niche. Immunity (2019) doi:10.1016/j.immuni.2019.08.017.
8. Guilliams, M. et al. Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches. Cell 185, 379-396.e38 (2022).
9. Mair, F. et al. Extricating human tumour immune alterations from tissue inflammation. Nature 605, 728–735 (2022).
10. Hoste, E. et al. OTULIN maintains skin homeostasis by controlling keratinocyte death and stem cell identity. Nat. Commun. 12, 5913 (2021).
11. Stakenborg, M. et al. Enteric glial cells favor accumulation of anti-inflammatory macrophages during the resolution of muscularis inflammation. Mucosal Immunol. 1–13 (2022) doi:10.1038/s41385-022-00563-2.
12. Karras, P. et al. A cellular hierarchy in melanoma uncouples growth and metastasis. Nature 1–9 (2022) doi:10.1038/s41586-022-05242-7.
13. Jiang, P. et al. Systematic investigation of cytokine signaling activity at the tissue and single-cell levels. Nat. Methods 18, 1181–1191 (2021).