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Deciphering cell–cell interactions and communication from gene expression

Nature Reviews Genetics volume 22, pages 71–88 (2021)Cite this article

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Abstract

Cell–cell interactions orchestrate organismal development, homeostasis and single-cell functions. When cells do not properly interact or improperly decode molecular messages, disease ensues. Thus, the identification and quantification of intercellular signalling pathways has become a common analysis performed across diverse disciplines. The expansion of protein–protein interaction databases and recent advances in RNA sequencing technologies have enabled routine analyses of intercellular signalling from gene expression measurements of bulk and single-cell data sets. In particular, ligand–receptor pairs can be used to infer intercellular communication from the coordinated expression of their cognate genes. In this Review, we highlight discoveries enabled by analyses of cell–cell interactions from transcriptomic data and review the methods and tools used in this context.

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Fig. 1: Types and applications of cell–cell interactions and communication.
Fig. 2: Analysis workflow for inferring cell–cell interactions and communication from gene expression.
Fig. 3: Toy examples of using core functions to compute communication scores.
Fig. 4: Common visualization techniques for cell–cell interactions and communication.

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References

  1. Zhou, X. et al. Circuit design features of a stable two-cell system. Cell 172, 744–757.e17 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  2. Rouault, H. & Hakim, V. Different cell fates from cell-cell interactions: core architectures of two-cell bistable networks. Biophys. J. 102, 417–426 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  3. Bonnans, C., Chou, J. & Werb, Z. Remodelling the extracellular matrix in development and disease. Nat. Rev. Mol. Cell Biol. 15, 786–801 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  4. Bich, L., Pradeu, T. & Moreau, J.-F. Understanding multicellularity: the functional organization of the intercellular space. Front. Physiol. 10, 1170 (2019).

    PubMed  PubMed Central  Google Scholar 

  5. Rao, V. S., Srinivasa Rao, V., Srinivas, K., Sujini, G. N. & Sunand Kumar, G. N. Protein-protein interaction detection: methods and analysis. Int. J. Proteom. 2014, 1–12 (2014).

    Google Scholar 

  6. Zhou, Y. et al. Evaluation of single-cell cytokine secretion and cell-cell interactions with a hierarchical loading microwell chip. Cell Rep. 31, 107574 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. Ramilowski, J. A. et al. A draft network of ligand-receptor-mediated multicellular signalling in human. Nat. Commun. 6, 7866 (2015). This study provides a list of human ligand–receptor pairs that has been broadly used as a reference. It also evaluates the false-positive rate of using different thresholds for computing communication scores by expression thresholding.

    CAS  PubMed  Google Scholar 

  8. Mortazavi, A., Williams, B. A., McCue, K., Schaeffer, L. & Wold, B. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat. Methods 5, 621–628 (2008).

    CAS  PubMed  Google Scholar 

  9. Fend, F. et al. Immuno-LCM: laser capture microdissection of immunostained frozen sections for mRNA analysis. Am. J. Pathol. 154, 61–66 (1999).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. Tang, F. et al. mRNA-Seq whole-transcriptome analysis of a single cell. Nat. Methods 6, 377–382 (2009).

    CAS  PubMed  Google Scholar 

  11. Marx, V. A dream of single-cell proteomics. Nat. Methods 16, 809–812 (2019).

    CAS  PubMed  Google Scholar 

  12. Grün, D. et al. Single-cell messenger RNA sequencing reveals rare intestinal cell types. Nature 525, 251–255 (2015).

    PubMed  Google Scholar 

  13. Park, J.-E. et al. A cell atlas of human thymic development defines T cell repertoire formation. Science 367, eaay3224 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. Kirouac, D. C. et al. Dynamic interaction networks in a hierarchically organized tissue. Mol. Syst. Biol. 6, 417 (2010).

    PubMed  PubMed Central  Google Scholar 

  15. Qiao, W. et al. Intercellular network structure and regulatory motifs in the human hematopoietic system. Mol. Syst. Biol. 10, 741 (2014). This work reports the construction of a CCC network that enables the discovery of cellular properties associated with the production of ligands and receptors.

    PubMed  PubMed Central  Google Scholar 

  16. Yuzwa, S. A. et al. Proneurogenic ligands defined by modeling developing cortex growth factor communication networks. Neuron 91, 988–1004 (2016).

    CAS  PubMed  Google Scholar 

  17. Paik, D. T. et al. Large-scale single-cell RNA-seq reveals molecular signatures of heterogeneous populations of human induced pluripotent stem cell-derived endothelial cells. Circ. Res. 123, 443–450 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Li, G. et al. Single cell expression analysis reveals anatomical and cell cycle-dependent transcriptional shifts during heart development. Development 146, dev173476 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Schiebinger, G. et al. Optimal-transport analysis of single-cell gene expression identifies developmental trajectories in reprogramming. Cell 176, 928–943.e22 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Xue, Y. et al. A 3D atlas of hematopoietic stem and progenitor cell expansion by multi-dimensional RNA-seq analysis. Cell Rep. 27, 1567–1578.e5 (2019).

    CAS  PubMed  Google Scholar 

  21. Basson, M. A. Signaling in cell differentiation and morphogenesis. Cold Spring Harb. Perspect. Biol. 4, a008151 (2012).

    PubMed  PubMed Central  Google Scholar 

  22. Sheikh, B. N. et al. Systematic identification of cell-cell communication networks in the developing brain. iScience 21, 273–287 (2019).

    PubMed  PubMed Central  Google Scholar 

  23. Popescu, D.-M. et al. Decoding human fetal liver haematopoiesis. Nature 574, 365–371 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Camp, J. G. et al. Multilineage communication regulates human liver bud development from pluripotency. Nature 546, 533–538 (2017). This study reports the analysis of intercellular communication from gene expression during the development of an organoid.

    CAS  PubMed  Google Scholar 

  25. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Joost, S. et al. Single-cell transcriptomics reveals that differentiation and spatial signatures shape epidermal and hair follicle heterogeneity. Cell Syst. 3, 221–237.e9 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Joost, S. et al. Single-cell transcriptomics of traced epidermal and hair follicle stem cells reveals rapid adaptations during wound healing. Cell Rep. 25, 585–597.e7 (2018).

    CAS  PubMed  Google Scholar 

  28. Wang, S. et al. Single cell transcriptomics of human epidermis reveals basal stem cell transition states. Preprint at bioRxiv https://doi.org/10.1101/784579 (2019).

    Article  Google Scholar 

  29. Pavlicˇev, M. et al. Single-cell transcriptomics of the human placenta: inferring the cell communication network of the maternal-fetal interface. Genome Res. 27, 349–361 (2017). First report using gene expression for inferring CCC in the placenta.

    PubMed  PubMed Central  Google Scholar 

  30. Vento-Tormo, R. et al. Single-cell reconstruction of the early maternal-fetal interface in humans. Nature 563, 347–353 (2018). This study reports the development of CellPhoneDB, one of the most used tools for deciphering CCC and the first to consider multimeric proteins. This strategy for analysing ligand–receptor interactions was applied to the maternal–fetal interface.

    CAS  PubMed  PubMed Central  Google Scholar 

  31. Arneson, D. et al. Single cell molecular alterations reveal target cells and pathways of concussive brain injury. Nat. Commun. 9, 3894 (2018).

    PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  Google Scholar 

  33. Skelly, D. A. et al. Single-cell transcriptional profiling reveals cellular diversity and intercommunication in the mouse heart. Cell Rep. 22, 600–610 (2018).

    CAS  PubMed  Google Scholar 

  34. Wang, L. et al. Single-cell reconstruction of the adult human heart during heart failure and recovery reveals the cellular landscape underlying cardiac function. Nat. Cell Biol. 22, 108–119 (2020).

    CAS  PubMed  Google Scholar 

  35. Wu, H. et al. Single-cell transcriptomics of a human kidney allograft biopsy specimen defines a diverse inflammatory response. J. Am. Soc. Nephrol. 29, 2069–2080 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Wu, H., Kirita, Y., Donnelly, E. L. & Humphreys, B. D. Advantages of single-nucleus over single-cell RNA sequencing of adult kidney: rare cell types and novel cell states revealed in fibrosis. J. Am. Soc. Nephrol. 30, 23–32 (2019).

    CAS  PubMed  Google Scholar 

  37. Stewart, B. J. et al. Spatiotemporal immune zonation of the human kidney. Science 365, 1461–1466 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Ding, C. et al. A cell-type-resolved liver proteome. Mol. Cell. Proteom. 15, 3190–3202 (2016).

    CAS  Google Scholar 

  39. Bonnardel, J. et al. Stellate cells, hepatocytes, and endothelial cells imprint the Kupffer cell identity on monocytes colonizing the liver macrophage niche. Immunity 51, 638–654.e9 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Zepp, J. A. et al. Distinct mesenchymal lineages and niches promote epithelial self-renewal and myofibrogenesis in the lung. Cell 170, 1134–1148.e10 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. Cohen, M. et al. Lung single-cell signaling interaction map reveals basophil role in macrophage imprinting. Cell 175, 1031–1044.e18 (2018).

    CAS  PubMed  Google Scholar 

  42. Raredon, M. S. B. et al. Single-cell connectomic analysis of adult mammalian lungs. Sci. Adv. 5, eaaw3851 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Niethamer, T. K. et al. Defining the role of pulmonary endothelial cell heterogeneity in the response to acute lung injury. eLife 9, e53072 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. Suryawanshi, H., Morozov, P., Straus, A. & Sahasrabudhe, N. A single-cell survey of the human first-trimester placenta and decidua. Sci. Adv. https://doi.org/10.1126/sciadv.aau4788 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  45. Hu, Y. et al. Dissecting the transcriptome landscape of the human fetal neural retina and retinal pigment epithelium by single-cell RNA-seq analysis. PLoS Biol. 17, e3000365 (2019).

    PubMed  PubMed Central  Google Scholar 

  46. Hrvatin, S. et al. Single-cell analysis of experience-dependent transcriptomic states in the mouse visual cortex. Nat. Neurosci. 21, 120–129 (2018).

    CAS  PubMed  Google Scholar 

  47. Solé-Boldo, L. et al. Single-cell transcriptomes of the human skin reveal age-related loss of fibroblast priming. Commun. Biol. 3, 188 (2020).

    PubMed  PubMed Central  Google Scholar 

  48. Fernandez, D. M. et al. Single-cell immune landscape of human atherosclerotic plaques. Nat. Med. 25, 1576–1588 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. Martin, J. C. et al. Single-cell analysis of Crohn’s disease lesions identifies a pathogenic cellular module associated with resistance to anti-TNF therapy. Cell 178, 1493–1508.e20 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Xiong, X. et al. Landscape of intercellular crosstalk in healthy and NASH liver revealed by single-cell secretome gene analysis. Mol. Cell 75, 644–660.e5 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. Vieira Braga, F. A. et al. A cellular census of human lungs identifies novel cell states in health and in asthma. Nat. Med. 25, 1153–1163 (2019).

    CAS  PubMed  Google Scholar 

  52. Qi, F., Qian, S., Zhang, S. & Zhang, Z. Single cell RNA sequencing of 13 human tissues identify cell types and receptors of human coronaviruses. Biochem. Biophys. Res. Commun. https://doi.org/10.1016/j.bbrc.2020.03.044 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  53. 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).

    CAS  PubMed  Google Scholar 

  54. Browaeys, R., Saelens, W. & Saeys, Y. NicheNet: modeling intercellular communication by linking ligands to target genes. Nat. Methods https://doi.org/10.1038/s41592-019-0667-5 (2019). This study introduces a PageRank-based algorithm to rank ligand–receptor interactions involved in communication of cells.

    Article  PubMed  Google Scholar 

  55. Cain, M. P., Hernandez, B. J. & Chen, J. Quantitative single-cell interactomes in normal and virus-infected mouse lungs. Preprint at bioRxiv https://doi.org/10.1101/2020.02.05.936054 (2020).

    Article  Google Scholar 

  56. Chua, R. L. et al. COVID-19 severity correlates with airway epithelium–immune cell interactions identified by single-cell analysis. Nat. Biotechnol. https://doi.org/10.1038/s41587-020-0602-4 (2020).

    Article  PubMed  Google Scholar 

  57. Rieckmann, J. C. et al. Social network architecture of human immune cells unveiled by quantitative proteomics. Nat. Immunol. 18, 583–593 (2017).

    CAS  PubMed  Google Scholar 

  58. Krausgruber, T. et al. Structural cells are key regulators of organ-specific immune responses. Nature https://doi.org/10.1038/s41586-020-2424-4 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  59. Puram, S. V. et al. Single-cell transcriptomic analysis of primary and metastatic tumor ecosystems in head and neck cancer. Cell 171, 1611–1624.e24 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  60. Kumar, M. P. et al. Analysis of single-cell RNA-seq identifies cell-cell communication associated with tumor characteristics. Cell Rep. 25, 1458–1468.e4 (2018). This study is an example of using expression products for measuring intercellular communication and for finding relationships between ligand–receptor pairs and tumour phenotypes.

    CAS  PubMed  PubMed Central  Google Scholar 

  61. Cillo, A. R. et al. Immune landscape of viral- and carcinogen-driven head and neck cancer. Immunity https://doi.org/10.1016/j.immuni.2019.11.014 (2019).

    Article  Google Scholar 

  62. Zhang, Q. et al. Landscape and dynamics of single immune cells in hepatocellular carcinoma. Cell 179, 829–845.e20 (2019).

    CAS  PubMed  Google Scholar 

  63. Song, Q. et al. Dissecting intratumoral myeloid cell plasticity by single cell RNA-seq. Cancer Med. 68, 7 (2019).

    Google Scholar 

  64. Zhang, M. et al. Single cell transcriptomic architecture and intercellular crosstalk of human intrahepatic cholangiocarcinoma. J. Hepatol. https://doi.org/10.1016/j.jhep.2020.05.039 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  65. Finotello, F., Rieder, D., Hackl, H. & Trajanoski, Z. Next-generation computational tools for interrogating cancer immunity. Nat. Rev. Genet. 20, 724–746 (2019).

    CAS  PubMed  Google Scholar 

  66. Zitvogel, L. & Kroemer, G. Targeting PD-1/PD-L1 interactions for cancer immunotherapy. Oncoimmunology 1, 1223–1225 (2012).

    PubMed  PubMed Central  Google Scholar 

  67. 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).

    CAS  PubMed  Google Scholar 

  68. Yeung, T.-L. et al. Systematic identification of druggable epithelial–stromal crosstalk signaling networks in ovarian cancer. J. Natl. Cancer Inst. 111, 272–282 (2019).

    PubMed  Google Scholar 

  69. Graeber, T. G. & Eisenberg, D. Bioinformatic identification of potential autocrine signaling loops in cancers from gene expression profiles. Nat. Genet. 29, 295–300 (2001). This study is an early example of inferring communication of cancer cells from gene expression of ligand–receptor pairs. It also introduces the use of correlation as a communication score.

    CAS  PubMed  Google Scholar 

  70. Zhou, J. X., Taramelli, R., Pedrini, E., Knijnenburg, T. & Huang, S. Extracting intercellular signaling network of cancer tissues using ligand-receptor expression patterns from whole-tumor and single-cell transcriptomes. Sci. Rep. 7, 8815 (2017).

    PubMed  PubMed Central  Google Scholar 

  71. 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). This is pioneering work that trains a machine learning model to predict the prognosis of patients with glioma from the ligand–receptor pairs used by glioma cells.

    PubMed  PubMed Central  Google Scholar 

  72. Murali, T. et al. DroID 2011: a comprehensive, integrated resource for protein, transcription factor, RNA and gene interactions for Drosophila. Nucleic Acids Res. 39, D736–D743 (2011).

    CAS  PubMed  Google Scholar 

  73. Thurmond, J. et al. FlyBase 2.0: the next generation. Nucleic Acids Res. 47, D759–D765 (2019).

    CAS  PubMed  Google Scholar 

  74. Girard, L. R. et al. WormBook: the online review of Caenorhabditis elegans biology. Nucleic Acids Res. 35, D472–D475 (2007).

    CAS  PubMed  Google Scholar 

  75. Harris, T. W. et al. WormBase: a comprehensive resource for nematode research. Nucleic Acids Res. 38, D463–D467 (2010).

    CAS  PubMed  Google Scholar 

  76. Oh, E.-Y. et al. Extensive rewiring of epithelial-stromal co-expression networks in breast cancer. Genome Biol. 16, 128 (2015).

    PubMed  PubMed Central  Google Scholar 

  77. Han, X. et al. Mapping the mouse cell atlas by microwell-seq. Cell 172, 1091–1107.e17 (2018).

    CAS  PubMed  Google Scholar 

  78. Krämer, A., Green, J., Pollard, J. Jr & Tugendreich, S. Causal analysis approaches in ingenuity pathway analysis. Bioinformatics 30, 523–530 (2014).

    PubMed  Google Scholar 

  79. Jin, S. et al. Inference and analysis of cell-cell communication using CellChat. Preprint at bioRxiv https://doi.org/10.1101/2020.07.21.214387 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  80. Wrana, J. L. et al. TGFβ signals through a heteromeric protein kinase receptor complex. Cell 71, 1003–1014 (1992).

    CAS  PubMed  Google Scholar 

  81. Sato, N. & Miyajima, A. Multimeric cytokine receptors: common versus specific functions. Curr. Opin. Cell Biol. 6, 174–179 (1994).

    CAS  PubMed  Google Scholar 

  82. Buccitelli, C. & Selbach, M. mRNAs, proteins and the emerging principles of gene expression control. Nat. Rev. Genet. https://doi.org/10.1038/s41576-020-0258-4 (2020).

    Article  PubMed  Google Scholar 

  83. 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. https://doi.org/10.1038/s41596-020-0292-x (2020).

    Article  PubMed  Google Scholar 

  84. Noël, F. et al. ICELLNET: a transcriptome-based framework to dissect intercellular communication. Preprint at bioRxiv https://doi.org/10.1101/2020.03.05.976878 (2020).

    Article  Google Scholar 

  85. Komurov, K. Modeling community-wide molecular networks of multicellular systems. Bioinformatics 28, 694–700 (2012).

    CAS  PubMed  Google Scholar 

  86. Richelle, A., Joshi, C. & Lewis, N. E. Assessing key decisions for transcriptomic data integration in biochemical networks. PLoS Comput. Biol. 15, e1007185 (2019).

    PubMed  PubMed Central  Google Scholar 

  87. Richelle, A. et al. What does your cell really do? Model-based assessment of mammalian cells metabolic functionalities using omics data. Preprint at bioRxiv https://doi.org/10.1101/2020.04.26.057943 (2020).

    Article  Google Scholar 

  88. Costa-Silva, J., Domingues, D. & Lopes, F. M. RNA-Seq differential expression analysis: an extended review and a software tool. PLoS ONE 12, e0190152 (2017).

    PubMed  PubMed Central  Google Scholar 

  89. Wang, T., Li, B., Nelson, C. E. & Nabavi, S. Comparative analysis of differential gene expression analysis tools for single-cell RNA sequencing data. BMC Bioinformatics 20, 40 (2019).

    PubMed  PubMed Central  Google Scholar 

  90. Maedler, K. et al. Low concentration of interleukin-1beta induces FLICE-inhibitory protein-mediated beta-cell proliferation in human pancreatic islets. Diabetes 55, 2713–2722 (2006).

    CAS  PubMed  Google Scholar 

  91. Middendorf, T. R. & Aldrich, R. W. The structure of binding curves and practical identifiability of equilibrium ligand-binding parameters. J. Gen. Physiol. 149, 121–147 (2017).

    PubMed  PubMed Central  Google Scholar 

  92. Goldman, S. L. et al. The impact of heterogeneity on single-cell sequencing. Front. Genet. 10, 8 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  93. AlJanahi, A. A., Danielsen, M. & Dunbar, C. E. An introduction to the analysis of single-cell RNA-sequencing data. Mol. Ther. Methods Clin. Dev. 10, 189–196 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  94. Lähnemann, D. et al. Eleven grand challenges in single-cell data science. Genome Biol. 21, 31 (2020).

    PubMed  PubMed Central  Google Scholar 

  95. Aggarwal, R. & Ranganathan, P. Common pitfalls in statistical analysis: the use of correlation techniques. Perspect. Clin. Res. 7, 187–190 (2016).

    PubMed  PubMed Central  Google Scholar 

  96. Conesa, A. et al. A survey of best practices for RNA-seq data analysis. Genome Biol. 17, 13 (2016).

    PubMed  PubMed Central  Google Scholar 

  97. Luecken, M. D. & Theis, F. J. Current best practices in single-cell RNA-seq analysis: a tutorial. Mol. Syst. Biol. 15, e8746 (2019).

    PubMed  PubMed Central  Google Scholar 

  98. 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). In this pioneering work, the authors evaluated gene expression variability due to CCIs by integrating spatial transcriptomics.

    CAS  PubMed  PubMed Central  Google Scholar 

  99. Satija, R., Farrell, J. A., Gennert, D., Schier, A. F. & Regev, A. Spatial reconstruction of single-cell gene expression data. Nat. Biotechnol. 33, 495–502 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  100. Navarro, J. F., Sjöstrand, J., Salmén, F., Lundeberg, J. & Ståhl, P. L. ST Pipeline: an automated pipeline for spatial mapping of unique transcripts. Bioinformatics 33, 2591–2593 (2017).

    PubMed  Google Scholar 

  101. Rodriques, S. G. et al. Slide-seq: a scalable technology for measuring genome-wide expression at high spatial resolution. Science 363, 1463–1467 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  102. Vickovic, S. et al. High-definition spatial transcriptomics for in situ tissue profiling. Nat. Methods 16, 987–990 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  103. Moncada, R. et al. Integrating microarray-based spatial transcriptomics and single-cell RNA-seq reveals tissue architecture in pancreatic ductal adenocarcinomas. Nat. Biotechnol. https://doi.org/10.1038/s41587-019-0392-8 (2020).

    Article  PubMed  Google Scholar 

  104. Ståhl, P. L. et al. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science 353, 78–82 (2016).

    PubMed  Google Scholar 

  105. Li, D., Ding, J. & Bar-Joseph, Z. Identifying signaling genes in spatial single cell expression data. Preprint at bioRxiv https://doi.org/10.1101/2020.07.27.221465 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  108. Cang, Z. & Nie, Q. Inferring spatial and signaling relationships between cells from single cell transcriptomic data. Nat. Commun. 11, 2084 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  109. Titouan, V., Courty, N., Tavenard, R., Laetitia, C. & Flamary, R. in Proceedings of the 36th International Conference on Machine Learning vol. 97 (eds Chaudhuri, K. & Salakhutdinov, R.) 6275–6284 (PMLR, 2019).

  110. Yasukawa, H., Sasaki, A. & Yoshimura, A. Negative regulation of cytokine signaling pathways. Annu. Rev. Immunol. 18, 143–164 (2000).

    CAS  PubMed  Google Scholar 

  111. Dries, R. et al. Giotto, a pipeline for integrative analysis and visualization of single-cell spatial transcriptomic data. Preprint at bioRxiv https://doi.org/10.1101/701680 (2019).

    Article  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  113. Tsuyuzaki, K., Ishii, M. & Nikaido, I. Uncovering hypergraphs of cell-cell interaction from single cell RNA-sequencing data. Preprint at bioRxiv https://doi.org/10.1101/566182 (2019). This is the first work using tensor decomposition for inferring CCIs from gene expression.

    Article  Google Scholar 

  114. Kim, Y. & Choi, S. in 2007 IEEE Conference on Computer Vision and Pattern Recognition 1–8 (IEEE, 2007).

  115. Liu, Y., Beyer, A. & Aebersold, R. On the dependency of cellular protein levels on mRNA abundance. Cell 165, 535–550 (2016).

    CAS  PubMed  Google Scholar 

  116. Wegler, C. et al. Global variability analysis of mRNA and protein concentrations across and within human tissues. NAR Genom. Bioinform. 2, lqz010 (2019).

    PubMed  PubMed Central  Google Scholar 

  117. Grandclaudon, M. et al. A quantitative multivariate model of human dendritic cell-T helper cell communication. Cell 179, 432–447.e21 (2019).

    CAS  PubMed  Google Scholar 

  118. Gault, J. et al. Combining native and ‘omics’ mass spectrometry to identify endogenous ligands bound to membrane proteins. Nat. Methods 17, 505–508 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  119. Katzenelenbogen, Y. et al. Coupled scRNA-seq and intracellular protein activity reveal an immunosuppressive role of TREM2 in cancer. Cell https://doi.org/10.1016/j.cell.2020.06.032 (2020).

    Article  PubMed  Google Scholar 

  120. Madsen, T. D. et al. An atlas of O-linked glycosylation on peptide hormones reveals diverse biological roles. Nat. Commun. 11, 4033 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  121. Arey, B. J. in Glycosylation (ed. Petrescu, S.) (InTech, 2012).

  122. Lux, A. & Nimmerjahn, F. in Crossroads between Innate and Adaptive Immunity III 113–124 (Springer, 2011).

  123. Boscher, C., Dennis, J. W. & Nabi, I. R. Glycosylation, galectins and cellular signaling. Curr. Opin. Cell Biol. 23, 383–392 (2011).

    CAS  PubMed  Google Scholar 

  124. De Bousser, E., Meuris, L., Callewaert, N. & Festjens, N. Human T cell glycosylation and implications on immune therapy for cancer. Hum. Vaccin. Immunother. https://doi.org/10.1080/21645515.2020.1730658 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  125. Beltrao, P., Bork, P., Krogan, N. J. & van Noort, V. Evolution and functional cross-talk of protein post-translational modifications. Mol. Syst. Biol. 9, 714 (2013).

    PubMed  PubMed Central  Google Scholar 

  126. Tytgat, H. L. P. & de Vos, W. M. Sugar coating the envelope: glycoconjugates for microbe-host crosstalk. Trends Microbiol. 24, 853–861 (2016).

    CAS  PubMed  Google Scholar 

  127. Gagneux, P., Aebi, M. & Varki, A. in Essentials of Glycobiology (eds Varki, A. et al.) (Cold Spring Harbor Laboratory Press, 2017)

  128. Boisset, J.-C. et al. Mapping the physical network of cellular interactions. Nat. Methods 15, 547–553 (2018). This work introduces ProximID, a computational approach for building a cell–cell network from physical interactions and single-cell RNA-seq data.

    CAS  PubMed  Google Scholar 

  129. 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 https://doi.org/10.1007/s13238-020-00727-5 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  130. Giladi, A. et al. Dissecting cellular crosstalk by sequencing physically interacting cells. Nat. Biotechnol. https://doi.org/10.1038/s41587-020-0442-2 (2020). This work describes PIC-seq, a transcriptomics technology to study physically interacting cells.

    Article  PubMed  Google Scholar 

  131. Halpern, K. B. et al. Paired-cell sequencing enables spatial gene expression mapping of liver endothelial cells. Nat. Biotechnol. 36, 962–970 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  132. Szczerba, B. M. et al. Neutrophils escort circulating tumour cells to enable cell cycle progression. Nature 566, 553–557 (2019).

    CAS  PubMed  Google Scholar 

  133. Schapiro, D. et al. histoCAT: analysis of cell phenotypes and interactions in multiplex image cytometry data. Nat. Methods 14, 873–876 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  134. Lundberg, E. & Borner, G. H. H. Spatial proteomics: a powerful discovery tool for cell biology. Nat. Rev. Mol. Cell Biol. 20, 285–302 (2019).

    CAS  PubMed  Google Scholar 

  135. Ren, X. et al. Reconstruction of cell spatial organization from single-cell RNA sequencing data based on ligand-receptor mediated self-assembly. Cell Res. https://doi.org/10.1038/s41422-020-0353-2 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  136. Francis, K. & Palsson, B. O. Effective intercellular communication distances are determined by the relative time constants for cyto/chemokine secretion and diffusion. Proc. Natl Acad. Sci. USA 94, 12258–12262 (1997).

    CAS  PubMed  PubMed Central  Google Scholar 

  137. Atakan, B. in Molecular Communications and Nanonetworks: From Nature to Practical Systems (ed. Atakan, B.) 105–143 (Springer, 2014).

  138. Wagner, D. E. & Klein, A. M. Lineage tracing meets single-cell omics: opportunities and challenges. Nat. Rev. Genet. 21, 410–427 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  139. Zhang, J., Nie, Q. & Zhou, T. Revealing dynamic mechanisms of cell fate decisions from single-cell transcriptomic data. Front. Genet. 10, 1280 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  140. Kuchta, K. et al. Predicting proteome dynamics using gene expression data. Sci. Rep. 8, 13866 (2018).

    PubMed  PubMed Central  Google Scholar 

  141. Krieglstein, C. F. & Granger, D. N. Adhesion molecules and their role in vascular disease. Am. J. Hypertens. 14, 44S–54S (2001).

    CAS  PubMed  Google Scholar 

  142. Nourani, E., Khunjush, F. & Durmus¸, S. Computational approaches for prediction of pathogen-host protein-protein interactions. Front. Microbiol. 6, 94 (2015).

    PubMed  PubMed Central  Google Scholar 

  143. Durmus¸, S., Çakır, T., Özgür, A. & Guthke, R. A review on computational systems biology of pathogen-host interactions. Front. Microbiol. 6, 235 (2015).

    PubMed  PubMed Central  Google Scholar 

  144. Schulze, S., Henkel, S. G., Driesch, D., Guthke, R. & Linde, J. Computational prediction of molecular pathogen-host interactions based on dual transcriptome data. Front. Microbiol. 6, 65 (2015).

    PubMed  PubMed Central  Google Scholar 

  145. Mirrashidi, K. M. et al. Global mapping of the Inc-human interactome reveals that retromer restricts chlamydia infection. Cell Host Microbe 18, 109–121 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  146. Thompson, L. R. et al. A communal catalogue reveals Earth’s multiscale microbial diversity. Nature 551, 457–463 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  147. McDonald, D. et al. American gut: an open platform for citizen science microbiome research. mSystems 3, (2018).

  148. Kyrpides, N. C., Eloe-Fadrosh, E. A. & Ivanova, N. N. Microbiome data science: understanding our microbial planet. Trends Microbiol. 24, 425–427 (2016).

    CAS  PubMed  Google Scholar 

  149. Gonzalez, A. et al. Qiita: rapid, web-enabled microbiome meta-analysis. Nat. Methods 15, 796–798 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  150. Zuñiga, C. et al. Environmental stimuli drive a transition from cooperation to competition in synthetic phototrophic communities. Nat. Microbiol. 4, 2184–2191 (2019).

    PubMed  Google Scholar 

  151. Lapek, J. D. Jr et al. Defining host responses during systemic bacterial infection through construction of a murine organ proteome atlas. Cell Syst. 6, 579–592.e4 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  152. Penaranda, C. & Hung, D. T. Single-cell RNA sequencing to understand host–pathogen interactions. ACS Infect. Dis. 5, 336–344 (2019).

    CAS  PubMed  Google Scholar 

  153. Jäger, S. et al. Global landscape of HIV-human protein complexes. Nature 481, 365–370 (2011).

    PubMed  PubMed Central  Google Scholar 

  154. Batra, J. et al. Protein interaction mapping identifies RBBP6 as a negative regulator of ebola virus replication. Cell 175, 1917–1930.e13 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  155. Shah, P. S. et al. Comparative flavivirus-host protein interaction mapping reveals mechanisms of Dengue and Zika virus pathogenesis. Cell 175, 1931–1945.e18 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  156. Gordon, D. E. et al. A SARS-CoV-2 protein interaction map reveals targets for drug repurposing. Nature https://doi.org/10.1038/s41586-020-2286-9 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  157. Adli, M. The CRISPR tool kit for genome editing and beyond. Nat. Commun. 9, 1911 (2018).

    PubMed  PubMed Central  Google Scholar 

  158. Hsu, M.-N. et al. CRISPR technologies for stem cell engineering and regenerative medicine. Biotechnol. Adv. 37, 107447 (2019).

    CAS  PubMed  Google Scholar 

  159. Kwon, E. D. et al. Manipulation of T cell costimulatory and inhibitory signals for immunotherapy of prostate cancer. Proc. Natl Acad. Sci. USA 94, 8099–8103 (1997).

    CAS  PubMed  PubMed Central  Google Scholar 

  160. Xu, W., Atkins, M. B. & McDermott, D. F. Checkpoint inhibitor immunotherapy in kidney cancer. Nat. Rev. Urol. 17, 137–150 (2020).

    CAS  PubMed  Google Scholar 

  161. 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).

    PubMed  PubMed Central  Google Scholar 

  162. de Juan, D., Pazos, F. & Valencia, A. Emerging methods in protein co-evolution. Nat. Rev. Genet. 14, 249–261 (2013).

    PubMed  Google Scholar 

  163. Kuhlman, B. & Bradley, P. Advances in protein structure prediction and design. Nat. Rev. Mol. Cell Biol. 20, 681–697 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  164. Chupakhin, V., Marcou, G., Baskin, I., Varnek, A. & Rognan, D. Predicting ligand binding modes from neural networks trained on protein-ligand interaction fingerprints. J. Chem. Inf. Model. 53, 763–772 (2013).

    CAS  PubMed  Google Scholar 

  165. Fenoy, E., Izarzugaza, J. M. G., Jurtz, V., Brunak, S. & Nielsen, M. A generic deep convolutional neural network framework for prediction of receptor-ligand interactions-NetPhosPan: application to kinase phosphorylation prediction. Bioinformatics 35, 1098–1107 (2019).

    CAS  PubMed  Google Scholar 

  166. Kveler, K. et al. Immune-centric network of cytokines and cells in disease context identified by computational mining of PubMed. Nat. Biotechnol. 36, 651–659 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  167. Ashburner, M. et al. Gene ontology: tool for the unification of biology. Nat. Genet. 25, 25–29 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  168. UniProt Consortium. UniProt: a hub for protein information. Nucleic Acids Res. 43, D204–D212 (2015).

    Google Scholar 

  169. Kanehisa, M. & Goto, S. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 28, 27–30 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  170. Waterhouse, R. M., Tegenfeldt, F., Li, J., Zdobnov, E. M. & Kriventseva, E. V. OrthoDB: a hierarchical catalog of animal, fungal and bacterial orthologs. Nucleic Acids Res. 41, D358–D365 (2013).

    CAS  PubMed  Google Scholar 

  171. Reimand, J., Kull, M., Peterson, H., Hansen, J. & Vilo, J. g:profiler — a web-based toolset for functional profiling of gene lists from large-scale experiments. Nucleic Acids Res. 35, W193–W200 (2007).

    PubMed  PubMed Central  Google Scholar 

  172. Uhlén, M. et al. Tissue-based map of the human proteome. Science 347, 1260419 (2015).

    PubMed  Google Scholar 

  173. Szklarczyk, D. et al. STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 47, D607–D613 (2019).

    CAS  PubMed  Google Scholar 

  174. Stark, C. et al. BioGRID: a general repository for interaction datasets. Nucleic Acids Res. 34, D535–D539 (2006).

    CAS  PubMed  Google Scholar 

  175. Gioutlakis, A., Klapa, M. I. & Moschonas, N. K. PICKLE 2.0: a human protein-protein interaction meta-database employing data integration via genetic information ontology. PLoS ONE 12, e0186039 (2017).

    PubMed  PubMed Central  Google Scholar 

  176. Prieto, C. & De Las Rivas, J. APID: agile protein interaction DataAnalyzer. Nucleic Acids Res. 34, W298–W302 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  177. Hermjakob, H. et al. IntAct: an open source molecular interaction database. Nucleic Acids Res. 32, D452–D455 (2004).

    CAS  PubMed  PubMed Central  Google Scholar 

  178. Cerami, E. G. et al. Pathway commons, a web resource for biological pathway data. Nucleic Acids Res. 39, D685–D690 (2011).

    CAS  PubMed  Google Scholar 

  179. Pawson, A. J. et al. The IUPHAR/BPS Guide to PHARMACOLOGY: an expert-driven knowledgebase of drug targets and their ligands. Nucleic Acids Res. 42, D1098–D1106 (2014).

    CAS  PubMed  Google Scholar 

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Acknowledgements

E.A. is supported by the Chilean Agencia Nacional de Investigación y Desarrollo through its scholarship programme DOCTORADO BECAS CHILE/2018 - 72190270 and by the Fulbright Commission. A.O. is supported by the US NLM (T15LM011271). O.H. is supported by the US NIH (U01CA196406). The authors also thank A. Perez-Lopez, C. Zuñiga, J. Tibocha-Bonilla, L. Zaramela, M. Kumar and P. Tamayo for providing meaningful feedback and discussion. This work was also supported by the US NIGMS (R35 GM119850; N.E.L.).

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Author notes

  1. These authors contributed equally: Erick Armingol, Adam Officer.

Authors and Affiliations

  1. Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA

    Erick Armingol & Nathan E. Lewis

  2. Novo Nordisk Foundation Center for Biosustainability at the University of California, San Diego, La Jolla, CA, USA

    Erick Armingol & Nathan E. Lewis

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

    Erick Armingol & Adam Officer

  4. Division of Biomedical Informatics, University of California, San Diego, La Jolla, CA, USA

    Adam Officer & Olivier Harismendy

  5. Moores Cancer Center, University of California, San Diego, La Jolla, CA, USA

    Olivier Harismendy

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

    Nathan E. Lewis

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E.A. and A.O. 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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Nature Reviews Genetics thanks Q. Nie; V. Soumelis; and R. Vento-Tormo, who co-reviewed with A. Arutyunyan, for their contribution to the peer review of this work.

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Related links

Ligand–receptor pair repository: https://github.com/LewisLabUCSD/Ligand-Receptor-Pairs

Supplementary information

Glossary

Cell–cell interactions

(CCIs). Physical interactions between two or more cells, which can be mediated by proteins, ligands, sugars or other biomolecules.

Receptors

Proteins that bind to other biomolecules to receive or amplify a signal. They are most commonly membrane-bound but can also be found in the cytoplasm.

Ligands

Biomolecules that bind to receptors and change the activity, conformation or other biological properties of the receptor, triggering a signalling event.

Extracellular matrix

Three-dimensional organization of biomolecules located in the extracellular space. It provides structural and functional support to neighbouring cells.

Cell–cell communication

(CCC). Subset of cell–cell interactions involving biochemical signals that are sent between or within cells and generate an intracellular effect.

Protein–protein interactions

(PPIs). Physical interaction between two proteins, often involved in structural systems, signal transduction or metabolic processes.

Network

A set of nodes with defined pairwise attributes. For example, a protein–protein interaction network would consist of proteins as nodes with attributes linking nodes that are known to interact with each other.

Communication pathways

Molecular components used for an intercellular communication event, usually corresponding to a ligand–receptor pair.

Interactome

Network of biomolecule interactions within and between cells.

False positives

In classification, a false positive occurs when a negative example is assigned a positive label. For the purpose of this Review, this means a non-interacting ligand–receptor pair is labelled as interacting.

False negatives

A false negative occurs when a positive example is assigned a negative label. For the purpose of this Review, this means a true interacting ligand–receptor pair is labelled as not interacting.

Post-translational modifications

Covalent modification of amino acid residues on a protein, commonly altering function, structure or localization. Phosphorylation, acetylation and glycosylation are among the most common.

Signalling pathways

The network of biomolecules that serve to transmit signals and induce cellular responses. Post-translational modification of proteins is the most common way signals are propagated.

Fuzzy logic

Extension of standard Boolean logic to define truth values of variables, encompassing real values between 0 and 1 both inclusive, instead of binary values.

Permutation

Random reassignment of sample labels, frequently used to compute null models in biological systems.

Differentially expressed genes

Genes identified as more highly (or lowly) expressed in one condition versus the other after comparison of their expression values between two conditions.

PageRank algorithm

Algorithm that takes as input a network and quantifies the importance of each node on the basis of centrality and connectedness to other central nodes.

Null model

Statistical model under which there is no interaction or difference between the groups being tested.

Multisubunit protein complexes

Quaternary structures of proteins involving the non-covalent interaction of two or more proteins to generate a functional unit.

Tensor

Higher N-dimensional generalizations of matrices and vectors. Vectors are tensors of rank 1, matrices are tensors of rank 2.

Tucker decomposition

Decomposing of a tensor of rank N as a product of a set of N matrices and one core tensor. Used to summarize data, similarly to principal component analysis.

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Armingol, E., Officer, A., Harismendy, O. et al. Deciphering cell–cell interactions and communication from gene expression. Nat Rev Genet 22, 71–88 (2021). https://doi.org/10.1038/s41576-020-00292-x

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