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The diversification of methods for studying cell–cell interactions and communication

Nature Reviews Genetics (2024)Cite this article

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Abstract

No cell lives in a vacuum, and the molecular interactions between cells define most phenotypes. Transcriptomics provides rich information to infer cell–cell interactions and communication, thus accelerating the discovery of the roles of cells within their communities. Such research relies heavily on algorithms that infer which cells are interacting and the ligands and receptors involved. Specific pressures on different research niches are driving the evolution of next-generation computational tools, enabling new conceptual opportunities and technological advances. More sophisticated algorithms now account for the heterogeneity and spatial organization of cells, multiple ligand types and intracellular signalling events, and enable the use of larger and more complex datasets, including single-cell and spatial transcriptomics. Similarly, new high-throughput experimental methods are increasing the number and resolution of interactions that can be analysed simultaneously. Here, we explore recent progress in cell–cell interaction research and highlight the diversification of the next generation of tools, which have yielded a rich ecosystem of tools for different applications and are enabling invaluable discoveries.

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Fig. 1: Methodological advancement of cell–cell interaction research.
Fig. 2: Phylogenetic tree of computational tools for inferring cell–cell interactions.
Fig. 3: New features and analyses performed by next-generation computational tools.
Fig. 4: Major approaches in next-generation experimental methods for studying cell–cell interactions.
Fig. 5: Challenges and opportunities for enhancing methods for cell–cell interaction research.

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References

  1. Sgro, A. E. et al. From intracellular signaling to population oscillations: bridging size- and time-scales in collective behavior. Mol. Syst. Biol. 11, 779 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  2. Dang, Y., Grundel, D. A. J. & Youk, H. Cellular dialogues: cell–cell communication through diffusible molecules yields dynamic spatial patterns. Cell Syst. 10, 82–98.e7 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Stent, G. S. Cellular communication. Sci. Am. 227, 43–51 (1972).

    Article  CAS  PubMed  Google Scholar 

  4. Huh, J. R. & Veiga-Fernandes, H. Neuroimmune circuits in inter-organ communication. Nat. Rev. Immunol. 20, 217–228 (2020).

    Article  CAS  PubMed  Google Scholar 

  5. Kuppe, C. et al. Spatial multi-omic map of human myocardial infarction. Nature 608, 766–777 (2022). This study performs a spatiotemporal multi-omic profiling of myocardium from patients with myocardial infarction and controls, and compares CCC between conditions.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Garcia-Alonso, L. et al. Single-cell roadmap of human gonadal development. Nature 607, 540–547 (2022). This study presents a comprehensive spatiotemporal map of human gonadal differentiation using multi-omics and provides valuable insights into CCC during gonadal development.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Armingol, E., Officer, A., Harismendy, O. & Lewis, N. E. Deciphering cell–cell interactions and communication from gene expression. Nat. Rev. Genet. 22, 71–88 (2021). This review introduces the core concepts and applications of inferring CCIs and communication from transcriptomics data.

    Article  CAS  PubMed  Google Scholar 

  8. Almet, A. A., Cang, Z., Jin, S. & Nie, Q. The landscape of cell–cell communication through single-cell transcriptomics. Curr. Opin. Syst. Biol. 26, 12–23 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Shao, X., Lu, X., Liao, J., Chen, H. & Fan, X. New avenues for systematically inferring cell–cell communication: through single-cell transcriptomics data. Protein Cell 11, 866–880 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Blencowe, M. et al. Network modeling of single-cell omics data: challenges, opportunities, and progresses. Emerg. Top. Life Sci. 3, 379–398 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Wang, S. et al. A systematic evaluation of the computational tools for ligand–receptor-based cell–cell interaction inference. Brief. Funct. Genomics 21, 339–356 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Ma, F. et al. Applications and analytical tools of cell communication based on ligand–receptor interactions at single cell level. Cell Biosci. 11, 121 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  13. Peng, L. et al. Cell–cell communication inference and analysis in the tumour microenvironments from single-cell transcriptomics: data resources and computational strategies. Brief. Bioinform. 23, bbac234 (2022).

    Article  PubMed  Google Scholar 

  14. Bridges, K. & Miller-Jensen, K. Mapping and validation of scRNA-seq-derived cell–cell communication networks in the tumor microenvironment. Front. Immunol. 13, 885267 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Wang, X., Almet, A. A. & Nie, Q. The promising application of cell–cell interaction analysis in cancer from single-cell and spatial transcriptomics. Semin. Cancer Biol. 95, 42–51 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Efremova, M., Vento-Tormo, M., Teichmann, S. A. & Vento-Tormo, R. CellPhoneDB: inferring cell–cell communication from combined expression of multi-subunit ligand–receptor complexes. Nat. Protoc. 15, 1484–1506 (2020). This protocol explains how to use CellPhoneDB, a highly used core tool to infer CCC.

    Article  CAS  PubMed  Google Scholar 

  17. Jin, S. et al. Inference and analysis of cell–cell communication using CellChat. Nat. Commun. 12, 1088 (2021). This work presents CellChat, a highly used core tool to infer CCC, and the concept of mass action to predict CCIs.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Türei, D. et al. Integrated intra- and intercellular signaling knowledge for multicellular omics analysis. Mol. Syst. Biol. 17, e9923 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  19. Wang, S., Karikomi, M., MacLean, A. L. & Nie, Q. Cell lineage and communication network inference via optimization for single-cell transcriptomics. Nucleic Acids Res. 47, e66 (2019). This work introduces SoptSC, a pioneering tool to study CCIs given the intracellular signals that are active in receiver cells. This tool also represents an early attempt to consider single-cell resolution of CCIs.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Browaeys, R., Saelens, W. & Saeys, Y. NicheNet: modeling intercellular communication by linking ligands to target genes. Nat. Methods 17, 159–162 (2019). This study introduces NicheNet, a tool based on network propagation, to rank LRIs involved in communication of cells.

    Article  PubMed  Google Scholar 

  21. Herholt, A., Sahoo, V. K., Popovic, L., Wehr, M. C. & Rossner, M. J. Dissecting intercellular and intracellular signaling networks with barcoded genetic tools. Curr. Opin. Chem. Biol. 66, 102091 (2022).

    Article  CAS  PubMed  Google Scholar 

  22. Bechtel, T. J., Reyes-Robles, T., Fadeyi, O. O. & Oslund, R. C. Strategies for monitoring cell–cell interactions. Nat. Chem. Biol. 17, 641–652 (2021). This review highlights cutting-edge experimental methods to monitor CCIs including microscopy imaging, chemical tagging and engineering-based strategies.

    Article  CAS  PubMed  Google Scholar 

  23. Yang, B. A., Westerhof, T. M., Sabin, K., Merajver, S. D. & Aguilar, C. A. Engineered tools to study intercellular communication. Adv. Sci. 8, 2002825 (2021).

    Article  CAS  Google Scholar 

  24. Manhas, J., Edelstein, H. I., Leonard, J. N. & Morsut, L. The evolution of synthetic receptor systems. Nat. Chem. Biol. 18, 244–255 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Kwon, E. & Heo, W. D. Optogenetic tools for dissecting complex intracellular signaling pathways. Biochem. Biophys. Res. Commun. 527, 331–336 (2020).

    Article  CAS  PubMed  Google Scholar 

  26. Beitz, A. M., Oakes, C. G. & Galloway, K. E. Synthetic gene circuits as tools for drug discovery. Trends Biotechnol. 40, 210–225 (2022).

    Article  CAS  PubMed  Google Scholar 

  27. Kang, M.-G. & Rhee, H.-W. Molecular spatiomics by proximity labeling. Acc. Chem. Res. 55, 1411–1422 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Norris, D. et al. Signaling heterogeneity is defined by pathway architecture and intercellular variability in protein expression. iScience 24, 102118 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Longo, S. K., Guo, M. G., Ji, A. L. & Khavari, P. A. Integrating single-cell and spatial transcriptomics to elucidate intercellular tissue dynamics. Nat. Rev. Genet. 22, 627–644 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Palla, G., Fischer, D. S., Regev, A. & Theis, F. J. Spatial components of molecular tissue biology. Nat. Biotechnol. 40, 308–318 (2022).

    Article  CAS  PubMed  Google Scholar 

  31. Walker, B. L., Cang, Z., Ren, H., Bourgain-Chang, E. & Nie, Q. Deciphering tissue structure and function using spatial transcriptomics. Commun. Biol. 5, 220 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  32. Innes, B. T. & Bader, G. D. Transcriptional signatures of cell–cell interactions are dependent on cellular context. Preprint at bioRxiv https://doi.org/10.1101/2021.09.06.459134 (2021).

  33. Armingol, E. et al. Context-aware deconvolution of cell–cell communication with Tensor-cell2cell. Nat. Commun. 13, 3665 (2022). This work presents an unsupervised method using tensor decomposition to extract patterns of CCC across multiple conditions simultaneously, going beyond pairwise comparisons that other methods only consider.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Klumpe, H. E. et al. The context-dependent, combinatorial logic of BMP signaling. Cell Syst. 13, 388–407.e10 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Villemin, J.-P. et al. Inferring ligand–receptor cellular networks from bulk and spatial transcriptomic datasets with BulkSignalR. Nucleic Acids Res. 51, 4726–4744 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Choi, H. et al. Transcriptome analysis of individual stromal cell populations identifies stroma–tumor crosstalk in mouse lung cancer model. Cell Rep. 10, 1187–1201 (2015).

    Article  CAS  PubMed  Google Scholar 

  37. Ximerakis, M. et al. Single-cell transcriptomic profiling of the aging mouse brain. Nat. Neurosci. 22, 1696–1708 (2019).

    Article  CAS  PubMed  Google Scholar 

  38. Liu, Y. et al. FlyPhoneDB: an integrated web-based resource for cell–cell communication prediction in Drosophila. Genetics 220, iyab235 (2022).

    Article  PubMed  Google Scholar 

  39. Noël, F. et al. Dissection of intercellular communication using the transcriptome-based framework ICELLNET. Nat. Commun. 12, 1089 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  40. Jin, Z. et al. InterCellDB: a user-defined database for inferring intercellular networks. Adv. Sci. 9, e2200045 (2022).

    Article  Google Scholar 

  41. Xu, C., Ma, D., Ding, Q., Zhou, Y. & Zheng, H.-L. PlantPhoneDB: a manually curated pan-plant database of ligand–receptor pairs infers cell–cell communication. Plant. Biotechnol. J. 20, 2123–2134 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Cabello-Aguilar, S. et al. SingleCellSignalR: inference of intercellular networks from single-cell transcriptomics. Nucleic Acids Res. 48, e55 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Vahid, M. R. et al. DiSiR: fast and robust method to identify ligand–receptor interactions at subunit level from single-cell RNA-sequencing data. NAR Genom. Bioinform. 5, lqad030 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  44. Raredon, M. S. B. et al. Comprehensive visualization of cell–cell interactions in single-cell and spatial transcriptomics with NICHES. Bioinformatics 39, btac775 (2023). This study introduces NICHES to study CCIs at the single-cell resolution, and presents different analyses that can be done by taking advantage of its resolution level.

    Article  CAS  PubMed  Google Scholar 

  45. Wilk, A. J., Shalek, A. K., Holmes, S. & Blish, C. A. Comparative analysis of cell–cell communication at single-cell resolution. Nat. Biotechnol. https://www.nature.com/articles/s41587-023-01782-z (2023). This work presents Scriabin to study CCIs at the single-cell resolution, and shows distinct biological applications that include the use of spatial transcriptomics.

  46. Subedi, S. & Park, Y. P. Single-cell pair-wise relationships untangled by composite embedding model. iScience 26, 106025 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Kojima, Y. et al. Single-cell colocalization analysis using a deep generative model. Preprint at bioRxiv https://doi.org/10.1101/2022.04.10.487815 (2022).

  48. McMurdie, P. J. & Holmes, S. Waste not, want not: why rarefying microbiome data is inadmissible. PLoS Comput. Biol. 10, e1003531 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  49. Armingol, E. et al. Inferring a spatial code of cell–cell interactions across a whole animal body. PLoS Comput. Biol. 18, e1010715 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Ren, X. et al. Reconstruction of cell spatial organization from single-cell RNA sequencing data based on ligand–receptor mediated self-assembly. Cell Res. 30, 763–778 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Smart, M. & Zilman, A. Emergent properties of collective gene-expression patterns in multicellular systems. Cell Rep. Phys. Sci. 4, 101247 (2023).

    Article  CAS  Google Scholar 

  52. Simsek, M. F. & Özbudak, E. M. Patterning principles of morphogen gradients. Open. Biol. 12, 220224 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Briscoe, J. & Small, S. Morphogen rules: design principles of gradient-mediated embryo patterning. Development 142, 3996–4009 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Halpern, K. B. et al. Single-cell spatial reconstruction reveals global division of labour in the mammalian liver. Nature 542, 352–356 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Dries, R. et al. Giotto: a toolbox for integrative analysis and visualization of spatial expression data. Genome Biol. 22, 78 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Palla, G. et al. Squidpy: a scalable framework for spatial omics analysis. Nat. Methods 19, 171–178 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Tanevski, J., Flores, R. O. R., Gabor, A., Schapiro, D. & Saez-Rodriguez, J. Explainable multiview framework for dissecting spatial relationships from highly multiplexed data. Genome Biol. 23, 97 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  58. Pham, D., Tan, X., Balderson, B. et al. Robust mapping of spatiotemporal trajectories and cell–cell interactions in healthy and diseased tissues. Nat. Commun. 14, 7739 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Arnol, D., Schapiro, D., Bodenmiller, B., Saez-Rodriguez, J. & Stegle, O. Modeling cell–cell interactions from spatial molecular data with spatial variance component analysis. Cell Rep. 29, 202–211.e6 (2019). This work evaluates the gene expression variability in space given the impact of CCIs.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Garcia-Alonso, L. et al. Mapping the temporal and spatial dynamics of the human endometrium in vivo and in vitro. Nat. Genet. 53, 1698–1711 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Li, D., Ding, J. & Bar-Joseph, Z. Identifying signaling genes in spatial single-cell expression data. Bioinformatics 37, 968–975 (2021).

    Article  CAS  PubMed  Google Scholar 

  62. Fischer, D. S., Schaar, A. C. & Theis, F. J. Modeling intercellular communication in tissues using spatial graphs of cells. Nat. Biotechnol. 41, 332–336 (2022). This work introduces NCEM, a regression-based model that uses spatial graphs and gene expression to study CCIs from their niches defined using spatial transcriptomics.

    Article  PubMed  PubMed Central  Google Scholar 

  63. Shao, X. et al. Knowledge-graph-based cell–cell communication inference for spatially resolved transcriptomic data with SpaTalk. Nat. Commun. 13, 4429 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Pancheva, A., Wheadon, H., Rogers, S. & Otto, T. D. Using topic modeling to detect cellular crosstalk in scRNA-seq. PLoS Comput. Biol. 18, e1009975 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Tsuchiya, T., Hori, H. & Ozaki, H. CCPLS reveals cell-type-specific spatial dependence of transcriptomes in single cells. Bioinformatics 38, 4868–4877 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Ru, B., Huang, J., Zhang, Y., Aldape, K. & Jiang, P. Estimation of cell lineages in tumors from spatial transcriptomics data. Nat. Commun. 14, 568 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Cang, Z. et al. Screening cell–cell communication in spatial transcriptomics via collective optimal transport. Nat. Methods 20, 218–228 (2023). This work introduces COMMOT, a tool using collective optimal transport to study CCC with spatial transcriptomics. This approach includes competing signals and can evaluate signalling directionality within tissues.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Rao, N. et al. Charting spatial ligand–target activity using Renoir. Preprint at bioRxiv https://doi.org/10.1101/2023.04.14.536833 (2023).

  69. Qu, F. et al. Three-dimensional molecular architecture of mouse organogenesis. Nat. Commun. 14, 4599 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Lück, N. et al. SpaCeNet: spatial cellular networks from omics data. Preprint at bioRxiv https://doi.org/10.1101/2022.09.01.506219 (2022).

  71. Cheng, J., Yan, L., Nie, Q. & Sun, X. Modeling spatial intercellular communication and multilayer signaling regulations using stMLnet. Preprint at bioRxiv https://doi.org/10.1101/2022.06.27.497696 (2022).

  72. So, E., Hayat, S., Nair, S. K., Wang, B. & Haibe-Kains, B. GraphComm: a graph-based deep learning method to predict cell–cell communication in single-cell RNAseq data. Preprint at bioRxiv https://doi.org/10.1101/2023.04.26.538432 (2023).

  73. Li, H. et al. Decoding functional cell-cell communication events by multi-view graph learning on spatial transcriptomics. Brief. Bioinform. 24, bbad359 (2023).

    Article  PubMed  Google Scholar 

  74. Li, Z., Wang, T., Liu, P. & Huang, Y. SpatialDM for rapid identification of spatially co-expressed ligand–receptor and revealing cell–cell communication patterns. Nat. Commun. 14, 3995 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Baccin, C. et al. Combined single-cell and spatial transcriptomics reveal the molecular, cellular and spatial bone marrow niche organization. Nat. Cell Biol. 22, 38–48 (2020).

    Article  CAS  PubMed  Google Scholar 

  76. Cang, Z. & Nie, Q. Inferring spatial and signaling relationships between cells from single cell transcriptomic data. Nat. Commun. 11, 2084 (2020). This work introduces SpaOTsc, a pioneering tool to study single-cell CCIs using spatial transcriptomics by using an optimal transport algorithm.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Wang, J., Li, S., Chen, L. & Li, S. C. SPROUT: spectral sparsification helps restore the spatial structure at single-cell resolution. Nar. Genom. Bioinform 4, lqac069 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  78. Ghaddar, B. & De, S. Reconstructing physical cell interaction networks from single-cell data using Neighbor-seq. Nucleic Acids Res. 50, e82 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Yuan, Y. & Bar-Joseph, Z. GCNG: graph convolutional networks for inferring gene interaction from spatial transcriptomics data. Genome Biol. 21, 300 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  80. Li, R. & Yang, X. De novo reconstruction of cell interaction landscapes from single-cell spatial transcriptome data with DeepLinc. Genome Biol. 23, 124 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  81. Tang, Z., Zhang, T., Yang, B., Su, J. & Song, Q. spaCI: deciphering spatial cellular communications through adaptive graph model. Brief. Bioinform. 24, bbac563 (2023).

    Article  PubMed  Google Scholar 

  82. Kim, H. et al. CellNeighborEX: deciphering neighbor-dependent gene expression from spatial transcriptomics data. Mol. Syst. Biol. 19, e11670 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Wu, D., Gaskins, J. T., Sekula, M. & Datta, S. Inferring cell–cell communications from spatially resolved transcriptomics data using a Bayesian Tweedie model. Genes 14, 1368 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Montesuma, E. F., Mboula, F. N. & Souloumiac, A. Recent advances in optimal transport for machine learning. Preprint at https://doi.org/10.48550/arXiv.2306.16156 (2023).

  85. Bafna, M., Li, H. & Zhang, X. CLARIFY: cell–cell interaction and gene regulatory network refinement from spatially resolved transcriptomics. Bioinformatics 39, i484–i493 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  86. Ghaddar, A. et al. Whole-body gene expression atlas of an adult metazoan. Sci. Adv. 9, eadg0506 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Liu, Z., Sun, D. & Wang, C. Evaluation of cell–cell interaction methods by integrating single-cell RNA sequencing data with spatial information. Genome Biol. 23, 218 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  88. Caviglia, S. & Ober, E. A. Non-conventional protrusions: the diversity of cell interactions at short and long distance. Curr. Opin. Cell Biol. 54, 106–113 (2018).

    Article  CAS  PubMed  Google Scholar 

  89. Metzner, C. et al. Detecting long-range interactions between migrating cells. Sci. Rep. 11, 15031 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Paul, O., Tao, J. Q., Guo, X. & Chatterjee, S. in Endothelial Signaling in Vascular Dysfunction and Disease Ch. 1 (ed. Chatterjee, S.) 3–13 (Academic, 2021).

  91. Zheng, R. et al. MEBOCOST: metabolic cell–cell communication modeling by single cell transcriptome. Preprint at bioRxiv https://doi.org/10.1101/2022.05.30.494067 (2022).

  92. Buccitelli, C. & Selbach, M. mRNAs, proteins and the emerging principles of gene expression control. Nat. Rev. Genet. 21, 630–644 (2020).

    Article  CAS  PubMed  Google Scholar 

  93. Zhao, W., Johnston, K. G., Ren, H., Xu, X. & Nie, Q. Inferring neuron–neuron communications from single-cell transcriptomics through NeuronChat. Nat. Commun. 14, 1–16 (2023). This work presents NeuronChat, a tool for studying CCIs in neuroscience that is particularly designed to study different kinds of molecules used by neurons to communicate.

    Google Scholar 

  94. Jakobsson, J. E. T., Spjuth, O. & Lagerström, M. C. scConnect: a method for exploratory analysis of cell–cell communication based on single cell RNA sequencing data. Bioinformatics 37, 3501–3508 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  95. Cui, K. et al. Epsin nanotherapy regulates cholesterol transport to fortify atheroma regression. Circ. Res. 132, e22–e42 (2023).

    Article  CAS  PubMed  Google Scholar 

  96. Lempp, M. et al. Systematic identification of metabolites controlling gene expression in E. coli. Nat. Commun. 10, 4463 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  97. Baruzzo, G., Cesaro, G. & Di Camillo, B. Identify, quantify and characterize cellular communication from single-cell RNA sequencing data with scSeqComm. Bioinformatics 38, 1920–1929 (2022).

    Article  CAS  PubMed  Google Scholar 

  98. Hu, Y., Peng, T., Gao, L. & Tan, K. CytoTalk: de novo construction of signal transduction networks using single-cell transcriptomic data. Sci. Adv. 7, eabf1356 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  99. Xin, Y. et al. LRLoop: a method to predict feedback loops in cell–cell communication. Bioinformatics 38, 4117–4126 (2022). This work leverages the strategies that use intracellular signalling pathways to incorporate the concept of feedback loops between two interacting cells to improve the predictions of CCC and produce more biological meaningful results.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  100. Cherry, C. et al. Computational reconstruction of the signalling networks surrounding implanted biomaterials from single-cell transcriptomics. Nat. Biomed. Eng. 5, 1228–1238 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  101. Jung, S., Singh, K. & Del Sol, A. FunRes: resolving tissue-specific functional cell states based on a cell–cell communication network model. Brief. Bioinform. 22, bbaa283 (2021).

    Article  PubMed  Google Scholar 

  102. Mishra, V. et al. Systematic elucidation of neuron–astrocyte interaction in models of amyotrophic lateral sclerosis using multi-modal integrated bioinformatics workflow. Nat. Commun. 11, 5579 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  103. Zhang, Y. et al. CellCall: integrating paired ligand–receptor and transcription factor activities for cell–cell communication. Nucleic Acids Res. 49, 8520–8534 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  104. Cheng, J., Zhang, J., Wu, Z. & Sun, X. Inferring microenvironmental regulation of gene expression from single-cell RNA sequencing data using scMLnet with an application to COVID-19. Brief. Bioinform. 22, 988–1005 (2021).

    Article  CAS  PubMed  Google Scholar 

  105. Lummertz da Rocha, E. et al. CellComm infers cellular crosstalk that drives haematopoietic stem and progenitor cell development. Nat. Cell Biol. 24, 579–589 (2022).

    Article  CAS  PubMed  Google Scholar 

  106. He, C., Zhou, P. & Nie, Q. exFINDER: identify external communication signals using single-cell transcriptomics data. Nucleic Acids Res. 15, e58 (2023).

    Article  Google Scholar 

  107. Cowen, L., Ideker, T., Raphael, B. J. & Sharan, R. Network propagation: a universal amplifier of genetic associations. Nat. Rev. Genet. 18, 551–562 (2017).

    Article  CAS  PubMed  Google Scholar 

  108. Shakiba, N., Jones, R. D., Weiss, R. & Del Vecchio, D. Context-aware synthetic biology by controller design: engineering the mammalian cell. Cell Syst. 12, 561–592 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  109. Palsson, B. & Zengler, K. The challenges of integrating multi-omic data sets. Nat. Chem. Biol. 6, 787–789 (2010).

    Article  PubMed  Google Scholar 

  110. Raredon, M. S. B. et al. Computation and visualization of cell–cell signaling topologies in single-cell systems data using connectome. Sci. Rep. 12, 4187 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  111. Hao, M., Zou, X. & Jin, S. Identification of intercellular signaling changes across conditions and their influence on intracellular signaling response from multiple single-cell datasets. Front. Genet. 12, 751158 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  112. Vu, R. et al. Wound healing in aged skin exhibits systems-level alterations in cellular composition and cell–cell communication. Cell Rep. 40, 111155 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  113. Lagger, C. et al. scDiffCom: a tool for differential analysis of cell–cell interactions provides a mouse atlas of aging changes in intercellular communication. Nat. Aging 3, 1446–1461 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  114. Yang, Y. et al. scTenifoldXct: a semi-supervised method for predicting cell–cell interactions and mapping cellular communication graphs. Cell Syst. 14, 302–311.e4 (2023). This work introduces scTenifoldXct, a tool that infer CCIs by combining gene-regulatory networks, gene expression and neural networks. It can infer interacting genes that are not limited to LRIs.

    Article  CAS  PubMed  Google Scholar 

  115. Cillo, A. R. et al. Immune landscape of viral- and carcinogen-driven head and neck cancer. Immunity 52, 183–199.e9 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  116. Yuan, Y. et al. CINS: cell interaction network inference from single cell expression data. PLoS Comput. Biol. 18, e1010468 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  117. Lu, H. et al. CommPath: an R package for inference and analysis of pathway-mediated cell–cell communication chain from single-cell transcriptomics. Comput. Struct. Biotechnol. J. 20, 5978–5983 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  118. Solovey, M. & Scialdone, A. COMUNET: a tool to explore and visualize intercellular communication. Bioinformatics 36, 4296–4300 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  119. Nagai, J. S., Leimkühler, N. B., Schaub, M. T., Schneider, R. K. & Costa, I. G. CrossTalkeR: analysis and visualization of ligand–receptor networks. Bioinformatics 37, 4263–4265 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  120. Mitchel, J. et al. Tensor decomposition reveals coordinated multicellular patterns of transcriptional variation that distinguish and stratify disease individuals. Preprint at bioRxiv https://doi.org/10.1101/2022.02.16.480703 (2022).

  121. Jerby-Arnon, L. & Regev, A. DIALOGUE maps multicellular programs in tissue from single-cell or spatial transcriptomics data. Nat. Biotechnol. 40, 1467–1477 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  122. Ramirez Flores, R. O., Lanzer, J. D., Dimitrov, D., Velten, B. & Saez-Rodriguez, J. Multicellular factor analysis of single-cell data for a tissue-centric understanding of disease. eLife 12, e93161 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  123. Guilliams, M. et al. Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches. Cell 185, 379–396.e38 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  124. Wang, Y. et al. iTALK: an R package to characterize and illustrate intercellular communication. Preprint at bioRxiv https://doi.org/10.1101/507871 (2019).

  125. Hou, R., Denisenko, E., Ong, H. T., Ramilowski, J. A. & Forrest, A. R. R. Predicting cell-to-cell communication networks using NATMI. Nat. Commun. 11, 5011 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  126. Tyler, S. R. et al. PyMINEr finds gene and autocrine–paracrine networks from human islet scRNA-seq. Cell Rep. 26, 1951–1964.e8 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  127. Li, D. et al. TraSig: inferring cell–cell interactions from pseudotime ordering of scRNA-seq data. Genome Biol. 23, 73 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  128. Wang, L. et al. TimeTalk uses single-cell RNA-seq datasets to decipher cell–cell communication during early embryo development. Commun. Biol. 6, 901 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  129. Browaeys, R. et al. MultiNicheNet: a flexible framework for differential cell–cell communication analysis from multi-sample multi-condition single-cell transcriptomics data. Preprint at bioRxiv https://doi.org/10.1101/2023.06.13.544751 (2023).

  130. Liu, Q., Hsu, C.-Y., Li, J. & Shyr, Y. Dysregulated ligand–receptor interactions from single-cell transcriptomics. Bioinformatics 38, 3216–3221 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  131. Wang, K. et al. Deconvolving clinically relevant cellular immune cross-talk from bulk gene expression using CODEFACS and LIRICS stratifies patients with melanoma to anti-PD-1 therapy. Cancer Discov. 12, 1088–1105 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  132. Chin, J. L., Chan, L. C., Yeaman, M. R. & Meyer, A. S. Tensor-based insights into systems immunity and infectious disease. Trends Immunol. 44, 329–332 (2023).

    Article  CAS  PubMed  Google Scholar 

  133. Armingol, E., Larsen, R. O., Cequeira, M., Baghdassarian, H. & Lewis, N. E. Unraveling the coordinated dynamics of protein- and metabolite-mediated cell–cell communication. Preprint at bioRxiv https://doi.org/10.1101/2022.11.02.514917 (2022).

  134. Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  135. Interlandi, M., Kerl, K. & Dugas, M. InterCellar enables interactive analysis and exploration of cell–cell communication in single-cell transcriptomic data. Commun. Biol. 5, 21 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  136. Zhang, Y. et al. Cellinker: a platform of ligand–receptor interactions for intercellular communication analysis. Bioinformatics https://doi.org/10.1093/bioinformatics/btab036 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  137. Moratalla-Navarro, F., Moreno, V. & Sanz-Pamplona, R. TALKIEN: crossTALK IntEraction Network. A web-based tool for deciphering molecular communication through ligand–receptor interactions. Mol. Omics 19, 688–696 (2023).

    Article  CAS  PubMed  Google Scholar 

  138. Yang, W. et al. DeepCCI: a deep learning framework for identifying cell–cell interactions from single-cell RNA sequencing data. Bioinformatics 39, btad596 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  139. Liu, S., Zhang, Y., Peng, J. & Shang, X. An improved hierarchical variational autoencoder for cell–cell communication estimation using single-cell RNA-seq data. Brief. Funct. Genomics https://doi.org/10.1093/bfgp/elac056 (2023).

    Article  PubMed  Google Scholar 

  140. 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). This study introduces LIANA, a computational tool including multiple existing strategies to infer CCIs and distinct ligand–receptor resources. In addition, LIANA implements a consensus approach across other methods to more robustly infer intercellular communication.

    Article  PubMed  PubMed Central  Google Scholar 

  141. Lu, M. et al. LR hunting: a random forest based cell–cell interaction discovery method for single-cell gene expression data. Front. Genet. 12, 708835 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  142. van Santvoort, M., Lapuente-Santana, Ó., Finotello, F., van der Hoorn, P. & Eduati, F. Mathematically mapping the network of cells in the tumor microenvironment. Preprint at bioRxiv https://doi.org/10.1101/2023.02.03.526946 (2023).

  143. Yu, A. et al. Reconstructing codependent cellular cross-talk in lung adenocarcinoma using REMI. Sci. Adv. 8, eabi4757 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  144. Li, H., Zhang, Z., Squires, M., Chen, X. & Zhang, X. scMultiSim: simulation of multi-modality single cell data guided by cell–cell interactions and gene regulatory networks. Preprint at bioRxiv https://doi.org/10.1101/2022.10.15.512320 (2022).

  145. Tsuyuzaki, K., Ishii, M. & Nikaido, I. Sctensor detects many-to-many cell–cell interactions from single cell RNA-sequencing data. BMC. Bioinformatics 24, 420 (2023).

    PubMed  PubMed Central  Google Scholar 

  146. Burdziak, C. et al. Epigenetic plasticity cooperates with cell–cell interactions to direct pancreatic tumorigenesis. Science 380, eadd5327 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  147. Peng, L. et al. Deciphering ligand–receptor-mediated intercellular communication based on ensemble deep learning and the joint scoring strategy from single-cell transcriptomic data. Comput. Biol. Med. 163, 107137 (2023).

    Article  CAS  PubMed  Google Scholar 

  148. Peng, L. et al. CellEnBoost: a boosting-based ligand–receptor interaction identification model for cell-to-cell communication inference. IEEE Trans. Nanobioscience 22, 705–715 (2023).

    Article  CAS  PubMed  Google Scholar 

  149. Zhang, C., Gao, L., Hu, Y. & Huang, Z. RobustCCC: a robustness evaluation tool for cell–cell communication methods. Front. Genet. 14, 1236956 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  150. Raghavan, V. & Ding, J. Harnessing agent-based modeling in cellagentchat to unravel cell–cell interactions from single-cell data. Preprint at bioRxiv https://doi.org/10.1101/2023.08.23.554489 (2023).

  151. Luo, J., Deng, M., Zhang, X. & Sun, X. ESICCC as a systematic computational framework for evaluation, selection, and integration of cell–cell communication inference methods. Genome Res. 33, 1788–1805 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  152. Boisset, J.-C. et al. Mapping the physical network of cellular interactions. Nat. Methods 15, 547–553 (2018).

    Article  CAS  PubMed  Google Scholar 

  153. Giladi, A. et al. Dissecting cellular crosstalk by sequencing physically interacting cells. Nat. Biotechnol. 38, 629–637 (2020).

    Article  CAS  PubMed  Google Scholar 

  154. Clark, I. C. et al. Barcoded viral tracing of single-cell interactions in central nervous system inflammation. Science 372, eabf1230 (2021). This study shows how CCI networks can be traced through RABID-seq, a novel method using engineered rabies viruses to track cell–cell contacts in the brain and their molecular mechanisms.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  155. Pasqual, G. et al. Monitoring T cell–dendritic cell interactions in vivo by intercellular enzymatic labelling. Nature 553, 496–500 (2018). This work developed LIPSTIC, SrtA-mediated cell labelling, to study dynamic CCIs both in vitro and in vivo.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  156. Ge, Y. et al. Enzyme-mediated intercellular proximity labeling for detecting cell–cell interactions. J. Am. Chem. Soc. 141, 1833–1837 (2019).

    Article  CAS  PubMed  Google Scholar 

  157. Liu, Z. et al. Detecting tumor antigen-specific T cells via interaction-dependent fucosyl-biotinylation. Cell 183, 1117–1133.e19 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  158. Oslund, R. C. et al. Detection of cell–cell interactions via photocatalytic cell tagging. Nat. Chem. Biol. 18, 850–858 (2022). This study introduces PhoTag, a photocatalytic cell tagging method, for interrogating CCC and LRIs in cell–cell contacts.

    Article  CAS  PubMed  Google Scholar 

  159. Zhang, S. et al. Monitoring of cell–cell communication and contact history in mammals. Science 378, eabo5503 (2022). This study presents an approach to trace cell–cell contacts in vivo by modifying synthetic receptor systems, and applies it to analyse endothelial cell migration, their contacts and ligand–receptor mechanisms.

    Article  CAS  PubMed  Google Scholar 

  160. Peikon, I. D. et al. Using high-throughput barcode sequencing to efficiently map connectomes. Nucleic Acids Res. 45, e115 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  161. Kebschull, J. M. et al. High-throughput mapping of single-neuron projections by sequencing of barcoded RNA. Neuron 91, 975–987 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  162. Huang, L. et al. BRICseq bridges brain-wide interregional connectivity to neural activity and gene expression in single animals. Cell 183, 2040 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  163. Wheeler, M. A. et al. Droplet-based forward genetic screening of astrocyte–microglia cross-talk. Science 379, 1023–1030 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  164. Niu, M. et al. Droplet-based transcriptome profiling of individual synapses. Nat. Biotechnol. 41, 1332–1344 (2023).

    Article  CAS  PubMed  Google Scholar 

  165. Aamodt, C. M. & Lewis, N. E. Single-cell A/B testing for cell–cell communication. Cell Syst. 14, 428–429 (2023).

    Article  CAS  PubMed  Google Scholar 

  166. Geri, J. B. et al. Microenvironment mapping via Dexter energy transfer on immune cells. Science 367, 1091–1097 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  167. Branon, T. C. et al. Efficient proximity labeling in living cells and organisms with TurboID. Nat. Biotechnol. 36, 880–887 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  168. Qiu, S. et al. Use of intercellular proximity labeling to quantify and decipher cell–cell interactions directed by diversified molecular pairs. Sci. Adv. 8, eadd2337 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  169. Nakandakari-Higa, S. et al. Universal recording of cell–cell contacts in vivo for interaction-based transcriptomics. Preprint at bioRxiv https://doi.org/10.1101/2023.03.16.533003 (2023).

  170. Martell, J. D. et al. Engineered ascorbate peroxidase as a genetically encoded reporter for electron microscopy. Nat. Biotechnol. 30, 1143–1148 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  171. Qin, W. et al. Dynamic mapping of proteome trafficking within and between living cells by TransitID. Cell 186, 3307–3324.e30 (2023).

    Article  CAS  PubMed  Google Scholar 

  172. Cho, K. F. et al. Proximity labeling in mammalian cells with TurboID and split-TurboID. Nat. Protoc. 15, 3971–3999 (2020).

    Article  CAS  PubMed  Google Scholar 

  173. Lam, S. S. et al. Directed evolution of APEX2 for electron microscopy and proximity labeling. Nat. Methods 12, 51–54 (2015).

    Article  CAS  PubMed  Google Scholar 

  174. Sears, R. M., May, D. G. & Roux, K. J. BioID as a tool for protein-proximity labeling in living cells. Methods Mol. Biol. 2012, 299–313 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  175. Cho, K. F. et al. Split-TurboID enables contact-dependent proximity labeling in cells. Proc. Natl Acad. Sci. USA 117, 12143–12154 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  176. Wintgens, J. P., Wichert, S. P., Popovic, L., Rossner, M. J. & Wehr, M. C. Monitoring activities of receptor tyrosine kinases using a universal adapter in genetically encoded split TEV assays. Cell. Mol. Life Sci. 76, 1185–1199 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  177. Saraon, P. et al. A drug discovery platform to identify compounds that inhibit EGFR triple mutants. Nat. Chem. Biol. 16, 577–586 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  178. Morsut, L. et al. Engineering customized cell sensing and response behaviors using synthetic notch receptors. Cell 164, 780–791 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  179. Huang, H. et al. Cell–cell contact-induced gene editing/activation in mammalian cells using a synNotch–CRISPR/Cas9 system. Protein Cell 11, 299–303 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  180. Malaguti, M. et al. SyNPL: synthetic Notch pluripotent cell lines to monitor and manipulate cell interactions in vitro and in vivo. Development 149, dev200226 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  181. Kumar, A., Grams, T. R., Bloom, D. C. & Toth, Z. Signaling pathway reporter screen with SARS-CoV-2 proteins identifies nsp5 as a repressor of p53 activity. Viruses 14, 1039 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  182. Jones, E. M. et al. A scalable, multiplexed assay for decoding GPCR–ligand interactions with RNA sequencing. Cell Syst. 8, 254–260.e6 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  183. Franchini, L. & Orlandi, C. in Progress in Molecular Biology and Translational Science Vol. 195 Ch. 3 (ed. Shukla, A. K.) 47–76 (Academic, 2023).

  184. Karikomi, M., Zhou, P. & Nie, Q. DURIAN: an integrative deconvolution and imputation method for robust signaling analysis of single-cell transcriptomics data. Brief. Bioinform. 23, bbac223 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  185. Jin, S., Plikus, M. V. & Nie, Q. CellChat for systematic analysis of cell–cell communication from single-cell and spatially resolved transcriptomics. Preprint at bioRxiv https://doi.org/10.1101/2023.11.05.565674 (2023).

  186. Troulé, K. et al. CellPhoneDB v5: inferring cell–cell communication from single-cell multiomics data. Preprint at https://doi.org/10.48550/arXiv.2311.04567 (2023).

  187. Dimitrov, D. et al. LIANA+: an all-in-one cell–cell communication framework. Preprint at bioRxiv https://doi.org/10.1101/2023.08.19.553863 (2023).

  188. Xie, Y. et al. A global database for modeling tumor-immune cell communication. Sci. Data 10, 444 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  189. Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  190. Danneskiold-Samsøe, N. B. et al. Rapid and accurate deorphanization of ligand–receptor pairs using AlphaFold. Preprint at bioRxiv https://doi.org/10.1101/2023.03.16.531341 (2023).

  191. Su, C. J. et al. Ligand–receptor promiscuity enables cellular addressing. Cell Syst. 13, 408–425.e12 (2022).

    Article  CAS  PubMed  Google Scholar 

  192. Ma, Q., Li, Q., Zheng, X. & Pan, J. CellCommuNet: an atlas of cell–cell communication networks from single-cell RNA sequencing of human and mouse tissues in normal and disease states. Nucleic Acids Res. https://doi.org/10.1093/nar/gkad906 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  193. Shan, N. et al. CITEdb: a manually curated database of cell–cell interactions in human. Bioinformatics 38, 5144–5148 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  194. Xie, Z., Li, X. & Mora, A. A comparison of cell–cell interaction prediction tools based on scRNA-seq data. Biomolecules 13, 1211 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  195. Shilts, J. et al. A physical wiring diagram for the human immune system. Nature 608, 397–404 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  196. Belardi, B., Son, S., Felce, J. H., Dustin, M. L. & Fletcher, D. A. Cell–cell interfaces as specialized compartments directing cell function. Nat. Rev. Mol. Cell Biol. 21, 750–764 (2020).

    Article  CAS  PubMed  Google Scholar 

  197. Castro, A. et al. Subcellular location of source proteins improves prediction of neoantigens for immunotherapy. EMBO J. 41, e111071 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  198. Hickey, J. W. et al. Spatial mapping of protein composition and tissue organization: a primer for multiplexed antibody-based imaging. Nat. Methods 19, 284–295 (2022).

    Article  CAS  PubMed  Google Scholar 

  199. Goltsev, Y. et al. Deep profiling of mouse splenic architecture with CODEX multiplexed imaging. Cell 174, 968–981.e15 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  200. Kim, K.-E. et al. Dynamic tracking and identification of tissue-specific secretory proteins in the circulation of live mice. Nat. Commun. 12, 5204 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  201. Seth, A. et al. High-resolution imaging of protein secretion at the single-cell level using plasmon-enhanced FluoroDOT assay. Cell Rep. Methods 2, 100267 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  202. Verweij, F. J. et al. Live tracking of inter-organ communication by endogenous exosomes in vivo. Dev. Cell 48, 573–589.e4 (2019).

    Article  CAS  PubMed  Google Scholar 

  203. Rittaud, B. & Heeffer, A. The pigeonhole principle, two centuries before Dirichlet. Math. Intell. 36, 27–29 (2014).

    Article  Google Scholar 

  204. Stein-O’Brien, G. L. et al. Enter the matrix: factorization uncovers knowledge from omics. Trends Genet. 34, 790–805 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  205. Baghdassarian, H., Dimitrov, D., Armingol, E., Saez-Rodriguez, J. & Lewis, N. E. Combining LIANA and Tensor-cell2cell to decipher cell–cell communication across multiple samples. Preprint at bioRxiv https://doi.org/10.1101/2023.04.28.538731 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  206. Yuan, D., Tao, Y., Chen, G. & Shi, T. Systematic expression analysis of ligand–receptor pairs reveals important cell-to-cell interactions inside glioma. Cell Commun. Signal. 17, 48 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  207. Chen, L.-X. et al. Cell–cell communications shape tumor microenvironment and predict clinical outcomes in clear cell renal carcinoma. J. Transl. Med. 21, 113 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  208. Angermueller, C., Pärnamaa, T., Parts, L. & Stegle, O. Deep learning for computational biology. Mol. Syst. Biol. 12, 878 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  209. Naveed, H. et al. A comprehensive overview of large language models. Preprint at https://doi.org/10.48550/arXiv.2307.06435 (2023).

  210. Lubiana, T. et al. Ten quick tips for harnessing the power of ChatGPT in computational biology. PLoS Comput. Biol. 19, e1011319 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  211. Ma, A. et al. Single-cell biological network inference using a heterogeneous graph transformer. Nat. Commun. 14, 964 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  212. Cui, H. et al. scGPT: towards building a foundation model for single-cell multi-omics using generative AI. Preprint at bioRxiv https://doi.org/10.1101/2023.04.30.538439 (2023).

  213. Hao, M. et al. Large scale foundation model on single-cell transcriptomics. Preprint at bioRxiv https://doi.org/10.1101/2023.05.29.542705 (2023).

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Acknowledgements

E.A. is supported by the Chilean Agencia Nacional de Investigación y Desarrollo (ANID) through its scholarship programme DOCTORADO BECAS CHILE/2018-72190270, the Fulbright Chile Commission and the Siebel Scholars Foundation. N.E.L. is supported, in part, by National Institute of General Medical Sciences (NIGMS) R35 GM119850. H.B. is supported by an Oak Ridge Institute for Science and Education (ORISE) fellowship.

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Authors and Affiliations

  1. Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA, USA

    Erick Armingol & Hratch M. Baghdassarian

  2. Department of Paediatrics, University of California, San Diego, La Jolla, CA, USA

    Erick Armingol, Hratch M. Baghdassarian & Nathan E. Lewis

  3. Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA

    Nathan E. Lewis

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E.A. and N.E.L. conceived the review. E.A. and H.B. researched the literature. All authors contributed to discussions of the content and wrote, reviewed and/or edited the manuscript before submission.

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Glossary

Autoencoder

A neural network composed of an encoder and a decoder that is trained to reconstruct its inputs, typically used for dimensionality reduction or feature learning.

Communication score

The score computed from the gene expression of a ligand and its cognate receptor in a sender and a receiver cell, respectively. The communication score depends on a tool-specific mathematical function.

Embeddings

Low-dimensional representations of data that capture essential features, enabling effective learning and similarity measurement by a given machine-learning technique.

Factorization methods

Unsupervised techniques that extract low-dimensional structure from data (that is, data decomposition), preserving essential information while reducing complexity.

Genetic algorithm

A search and/or optimization algorithm based on natural evolution in which individuals are selected by their optimal fitness to an objective function.

Intercellular feedback loops

Two-way communications in which one ligand–receptor interaction (LRI) triggers the production of a ligand by one cell. The interaction of this second ligand and its cognate receptor induces the expression of the first ligand on the other cell.

Latent features

Unobservable variables inferred from observed data to capture underlying structures.

Latent space

A term used in machine learning to refer to a lower-dimensional representation of complex data, which enables meaningful feature extraction and manipulation, and the identification of structures or patterns in data.

Loadings

A representation of the contribution of variables to principal components or factors generated by dimensionality reduction methods, revealing their significance in each factor.

Multiplets

Multiple cells inadvertently captured and sequenced together as one cell or barcode.

Network propagation

A set of probabilistic processes that model the spread of information within a network across time.

Optimal transport algorithm

A mathematical method for moving and transforming distributions from one state to another with minimum cost.

Pseudo-bulk

The resolution resulting from aggregating the gene expression of single cells into a higher group of cells, such as cluster, cell type, sub-cluster or sub-cell type.

Tensor factorization

A decomposition method designed to extract properties of a multidimensional data structure, also known as a tensor (a matrix is a tensor of two dimensions, whereas higher-order tensors have more dimensions).

True positive rate

A metric used to evaluate the performance of a model. Specifically, it measures the proportion of true positives with respect to the total actual positives. Also known as sensitivity or recall.

Zero-preserving

A property of data transformations that maintains the number or proportion of zero values observed in the original input data.

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Armingol, E., Baghdassarian, H.M. & Lewis, N.E. The diversification of methods for studying cell–cell interactions and communication. Nat Rev Genet (2024). https://doi.org/10.1038/s41576-023-00685-8

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