Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Review Article
  • Published:

Integrating single-cell multi-omics and prior biological knowledge for a functional characterization of the immune system

Nature Immunology volume 25, pages 405–417 (2024)Cite this article

Subjects

Abstract

The immune system comprises diverse specialized cell types that cooperate to defend the host against a wide range of pathogenic threats. Recent advancements in single-cell and spatial multi-omics technologies provide rich information about the molecular state of immune cells. Here, we review how the integration of single-cell and spatial multi-omics data with prior knowledge—gathered from decades of detailed biochemical studies—allows us to obtain functional insights, focusing on gene regulatory processes and cell–cell interactions. We present diverse applications in immunology and critically assess underlying assumptions and limitations. Finally, we offer a perspective on the ongoing technological and algorithmic developments that promise to get us closer to a systemic mechanistic understanding of the immune system.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Cycle of biological knowledge and computational methods driven by technological progress.
Fig. 2: Mechanistic representations for single-cell data.
Fig. 3: Inference of mechanisms across levels.
Fig. 4: Limitations of current single-cell technologies.

Similar content being viewed by others

References

  1. Arunachalam, P. S. et al. Systems biological assessment of immunity to mild versus severe COVID-19 infection in humans. Science 369, 1210–1220 (2020).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  2. Shen-Orr, S. S. & Furman, D. Variability in the immune system: of vaccine responses and immune states. Curr. Opin. Immunol. 25, 542–547 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Yofe, I., Dahan, R. & Amit, I. Single-cell genomic approaches for developing the next generation of immunotherapies. Nat. Med. 26, 171–177 (2020).

    Article  CAS  PubMed  Google Scholar 

  4. Banchereau, R., Cepika, A.-M. & Pascual, V. Systems approaches to human autoimmune diseases. Curr. Opin. Immunol. 25, 598–605 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Davis, M. M., Tato, C. M. & Furman, D. Systems immunology: just getting started. Nat. Immunol. 18, 725–732 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Germain, R. N., Meier-Schellersheim, M., Nita-Lazar, A. & Fraser, I. D. C. Systems biology in immunology: a computational modeling perspective. Annu. Rev. Immunol. 29, 527–585 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Ma, S. et al. Chromatin potential identified by shared single-cell profiling of RNA and chromatin. Cell 183, 1103–1116 (2020). This work pioneered paired sequencing of chromatin accessibility and transcriptome in single cells (SHARE-seq).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Ståhl, P. L. et al. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science 353, 78–82 (2016). This work pioneered spatially resolved transcriptomics.

    Article  ADS  PubMed  Google Scholar 

  9. Regev, A. et al. The Human Cell Atlas. eLife https://doi.org/10.7554/eLife.27041 (2017).

  10. Domínguez Conde, C. et al. Cross-tissue immune cell analysis reveals tissue-specific features in humans. Science 376, eabl5197 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  11. HuBMAP Consortium. The human body at cellular resolution: the NIH Human Biomolecular Atlas Program. Nature 574, 187–192 (2019).

    Article  ADS  CAS  Google Scholar 

  12. Martin, F. J. et al. Ensembl 2023. Nucleic Acids Res. 51, D933–D941 (2023).

    Article  CAS  PubMed  Google Scholar 

  13. UniProt Consortium. Uniprot: the universal protein knowledgebase in 2023. Nucleic Acids Res. 51, D523–D531 (2023).

    Article  Google Scholar 

  14. Türei, D. et al. Integrated intra‐ and intercellular signaling knowledge for multicellular omics analysis. Mol. Syst. Biol. 17, e9923 (2021). This work introduces a comprehensive meta-resource that collects data from over a hundred databases, including, for example, protein interaction networks, ligand–receptor annotations and protein complex information.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Szklarczyk, D. et al. STITCH 5: augmenting protein-chemical interaction networks with tissue and affinity data. Nucleic Acids Res. 44, D380–D384 (2016).

    Article  CAS  PubMed  Google Scholar 

  16. Garcia-Alonso, L., Holland, C. H., Ibrahim, M. M., Turei, D. & Saez-Rodriguez, J. Benchmark and integration of resources for the estimation of human transcription factor activities. Genome Res. 29, 1363–1375 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Schubert, M. et al. Perturbation-response genes reveal signaling footprints in cancer gene expression. Nat. Commun. 9, 20 (2018).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  18. 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).

    Article  CAS  PubMed  Google Scholar 

  19. Vandereyken, K., Sifrim, A., Thienpont, B. & Voet, T. Methods and applications for single-cell and spatial multi-omics. Nat. Rev. Genet. 24, 494–515 (2023).

    Article  CAS  PubMed  Google Scholar 

  20. Baysoy, A., Bai, Z., Satija, R. & Fan, R. The technological landscape and applications of single-cell multi-omics. Nat. Rev. Mol. Cell Biol. 24, 695–713 (2023).

    Article  CAS  PubMed  Google Scholar 

  21. Bonaguro, L. et al. A guide to systems-level immunomics. Nat. Immunol. 23, 1412–1423 (2022).

    Article  CAS  PubMed  Google Scholar 

  22. Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Heumos, L. et al. Best practices for single-cell analysis across modalities. Nat. Rev. Genet. 24, 550–572 (2023).

    Article  CAS  PubMed  Google Scholar 

  24. Amezquita, R. A. et al. Orchestrating single-cell analysis with Bioconductor. Nat. Methods 17, 137–145 (2020).

    Article  CAS  PubMed  Google Scholar 

  25. Gene Ontology Consortium et al. The Gene Ontology knowledgebase in 2023. Genetics 224, iyad031 (2023).

  26. Kanehisa, M., Furumichi, M., Sato, Y., Kawashima, M. & Ishiguro-Watanabe, M. KEGG for taxonomy-based analysis of pathways and genomes. Nucleic Acids Res. 51, D587–D592 (2023).

    Article  CAS  PubMed  Google Scholar 

  27. Gillespie, M. et al. The reactome pathway knowledgebase 2022. Nucleic Acids Res. 50, D687–D692 (2022).

    Article  CAS  PubMed  Google Scholar 

  28. Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  29. Väremo, L., Nielsen, J. & Nookaew, I. Enriching the gene set analysis of genome-wide data by incorporating directionality of gene expression and combining statistical hypotheses and methods. Nucleic Acids Res. 41, 4378–4391 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  30. Badia-I-Mompel, P. et al. decoupleR: ensemble of computational methods to infer biological activities from omics data. Bioinform. Adv. 2, vbac016 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  31. Hosokawa, H. & Rothenberg, E. V. How transcription factors drive choice of the T cell fate. Nat. Rev. Immunol. 21, 162–176 (2021).

    Article  CAS  PubMed  Google Scholar 

  32. Kawasaki, T. & Kawai, T. Toll-like receptor signaling pathways. Front. Immunol. 5, 461 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  33. Saini, A., Ghoneim, H. E., Lio, C. -W. J., Collins, P. L. & Oltz, E. M. Gene regulatory circuits in innate and adaptive immune cells. Annu. Rev. Immunol. 40, 387–411 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Liu, Z.-P., Wu, C., Miao, H. & Wu, H. RegNetwork: an integrated database of transcriptional and post-transcriptional regulatory networks in human and mouse. Database 2015, bav095 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  35. Keenan, A. B. et al. ChEA3: transcription factor enrichment analysis by orthogonal omics integration. Nucleic Acids Res. 47, W212–W224 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Müller-Dott, S. et al. Expanding the coverage of regulons from high-confidence prior knowledge for accurate estimation of transcription factor activities. Nucleic Acids Res. 51, 10934–10949 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  37. Holland, C. H. et al. Robustness and applicability of transcription factor and pathway analysis tools on single-cell RNA-seq data. Genome Biol. 21, 36 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Zeitlinger, J. Seven myths of how transcription factors read the cis-regulatory code. Curr. Opin. Syst. Biol. 23, 22–31 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  39. Fiers, M. W. E. J. et al. Mapping gene regulatory networks from single-cell omics data. Brief. Funct. Genomics 17, 246–254 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Chen, S. & Mar, J. C. Evaluating methods of inferring gene regulatory networks highlights their lack of performance for single cell gene expression data. BMC Bioinformatics 19, 232 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  41. Pratapa, A., Jalihal, A. P., Law, J. N., Bharadwaj, A. & Murali, T. M. Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data. Nat. Methods 17, 147–154 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Badia-I-Mompel, P. et al. Gene regulatory network inference in the era of single-cell multi-omics. Nat. Rev. Genet. 24, 739–754 (2023).

    Article  CAS  PubMed  Google Scholar 

  43. Wayman, J. A. et al. An atlas of gene regulatory networks for memory CD4+ T cells in youth and old age. Preprint at BioRxiv https://doi.org/10.1101/2023.03.07.531590 (2023).

  44. Chowdhary, K. & Benoist, C. A variegated model of transcription factor function in the immune system. Trends Immunol. 44, 530–541 (2023).

    Article  CAS  PubMed  Google Scholar 

  45. Bravo González-Blas, C. et al. SCENIC+: single-cell multiomic inference of enhancers and gene regulatory networks. Nat. Methods 20, 1355–1367 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  46. Kramer, B. A., Sarabia Del Castillo, J. & Pelkmans, L. Multimodal perception links cellular state to decision-making in single cells. Science 377, 642–648 (2022).

    Article  ADS  CAS  PubMed  Google Scholar 

  47. Weidemüller, P., Kholmatov, M., Petsalaki, E. & Zaugg, J. B. Transcription factors: bridge between cell signaling and gene regulation. Proteomics 21, e2000034 (2021).

    Article  PubMed  Google Scholar 

  48. Deribe, Y. L., Pawson, T. & Dikic, I. Post-translational modifications in signal integration. Nat. Struct. Mol. Biol. 17, 666–672 (2010).

    Article  CAS  PubMed  Google Scholar 

  49. Lun, X. -K. & Bodenmiller, B. Profiling cell signaling networks at single-cell resolution. Mol. Cell. Proteom. 19, 744–756 (2020).

    Article  CAS  Google Scholar 

  50. Dugourd, A. & Saez-Rodriguez, J. Footprint-based functional analysis of multiomic data. Curr. Opin. Syst. Biol. 15, 82–90 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  51. Rydenfelt, M., Klinger, B., Klünemann, M. & Blüthgen, N. SPEED2: inferring upstream pathway activity from differential gene expression. Nucleic Acids Res. 48, W307–W312 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Jiang, P. et al. Systematic investigation of cytokine signaling activity at the tissue and single-cell levels. Nat. Methods 18, 1181–1191 (2021). This work introduced a comprehensive resource describing how cytokines affect gene expression, which can be used to infer cytokine activity from transcriptomics data.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Garrido-Rodriguez, M., Zirngibl, K., Ivanova, O., Lobentanzer, S. & Saez-Rodriguez, J. Integrating knowledge and omics to decipher mechanisms via large-scale models of signaling networks. Mol. Syst. Biol. 18, e11036 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  54. 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).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  55. Vento-Tormo, R. et al. Single-cell reconstruction of the early maternal-fetal interface in humans. Nature 563, 347–353 (2018). This work introduced a popular tool for cell–cell communication analysis, which they used to systematically study immuneoregulatory mechanisms in the maternal–fetal interface.

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  56. Browaeys, R., Saelens, W. & Saeys, Y. NicheNet: modeling intercellular communication by linking ligands to target genes. Nat. Methods 17, 159–162 (2020). This work introduced a broadly used tools to analyze cell–cell communication analysis by integrating intracellular signaling networks.

    Article  CAS  PubMed  Google Scholar 

  57. Zhang, Y. et al. A T cell resilience model associated with response to immunotherapy in multiple tumor types. Nat. Med. 28, 1421–1431 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Wilk, A. J., Shalek, A. K., Holmes, S. & Blish, C. A. Comparative analysis of cell–cell communication at single-cell resolution. Nat. Biotechnol. https://doi.org/10.1038/s41587-023-01782-z (2023).

    Article  PubMed  Google Scholar 

  59. Raredon, M. S. B. et al. Comprehensive visualization of cell-cell interactions in single-cell and spatial transcriptomics with NICHES. Bioinformatics 39, btac775 (2023).

    Article  CAS  PubMed  Google Scholar 

  60. Zinner, M., Lukonin, I. & Liberali, P. Design principles of tissue organisation: how single cells coordinate across scales. Curr. Opin. Cell Biol. 67, 37–45 (2020).

    Article  CAS  PubMed  Google Scholar 

  61. Luthria, G., Lauffenburger, D. & Miller, M. A. Cell–cell communication networks in tissue: toward quantitatively linking structure with function. Curr. Opin. Syst. Biol. https://doi.org/10.1016/j.coisb.2021.05.002 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  62. Graham, D. B. & Xavier, R. J. Conditioning of the immune system by the microbiome. Trends Immunol. 44, 499–511 (2023).

    Article  CAS  PubMed  Google Scholar 

  63. Zhang, B., Vogelzang, A. & Fagarasan, S. Secreted immune metabolites that mediate immune cell communication and function. Trends Immunol. 43, 990–1005 (2022).

    Article  CAS  PubMed  Google Scholar 

  64. 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).

  65. 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).

  66. Zhao, W., Johnston, K. G., Ren, H., Xu, X. & Nie, Q. Inferring neuron-neuron communications from single-cell transcriptomics through NeuronChat. Nat. Commun. 14, 1128 (2023).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  67. 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).

  68. Cui, A. et al. Dictionary of immune responses to cytokines at single-cell resolution. Nature https://doi.org/10.1038/s41586-023-06816-9 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  69. 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 

  70. 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).

    Article  Google Scholar 

  71. Armingol, E. et al. Context-aware deconvolution of cell–cell communication with Tensor-cell2cell. Nat. Commun. 13, 3665 (2022).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  72. Chua, R. L. et al. COVID-19 severity correlates with airway epithelium-immune cell interactions identified by single-cell analysis. Nat. Biotechnol. 38, 970–979 (2020).

    Article  CAS  PubMed  Google Scholar 

  73. Nasab, R. Z. et al. Deep learning in spatially resolved transcriptomics: a comprehensive technical view. Preprint at https://arxiv.org/abs/2210.04453 (2022).

  74. Li, B. et al. Benchmarking spatial and single-cell transcriptomics integration methods for transcript distribution prediction and cell type deconvolution. Nat. Methods 19, 662–670 (2022).

    Article  CAS  PubMed  Google Scholar 

  75. 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 

  76. 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 

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

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  78. Li, Z., Wang, T., Liu, P. & Huang, Y. SpatialDM: rapid identification of spatially co-expressed ligand-receptor reveals cell-cell communication patterns. Preprint at BioRxiv https://doi.org/10.1101/2022.08.19.504616 (2022).

  79. Cang, Z. et al. Screening cell-cell communication in spatial transcriptomics via collective optimal transport. Nat. Methods 20, 218–228 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Frede, A. et al. B cell expansion hinders the stroma-epithelium regenerative cross talk during mucosal healing. Immunity 55, 2336–2351 (2022).

    Article  CAS  PubMed  Google Scholar 

  81. Lin, J.-R. et al. Multiplexed 3D atlas of state transitions and immune interaction in colorectal cancer. Cell 186, 363–381 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Bock, C. et al. High-content CRISPR screening. Nat. Rev. Methods Primers 2, 9 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  83. Kustatscher, G. et al. Understudied proteins: opportunities and challenges for functional proteomics. Nat. Methods 19, 774–779 (2022).

    Article  CAS  PubMed  Google Scholar 

  84. Stražar, M. et al. HLA-II immunopeptidome profiling and deep learning reveal features of antigenicity to inform antigen discovery. Immunity 56, 1681–1698 (2023).

    Article  PubMed  Google Scholar 

  85. Huffman, R. G. et al. Prioritized mass spectrometry increases the depth, sensitivity and data completeness of single-cell proteomics. Nat. Methods 20, 714–722 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Makhmut, A. et al. A framework for ultra-low input spatial tissue proteomics. Cell Syst. https://doi.org/10.1016/j.cels.2023.10.003 (2023).

  87. Rappez, L. et al. SpaceM reveals metabolic states of single cells. Nat. Methods 18, 799–805 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Vicari, M. et al. Spatial multimodal analysis of transcriptomes and metabolomes in tissues. Nat. Biotechnol. https://doi.org/10.1038/s41587-023-01937-y (2023).

    Article  PubMed  Google Scholar 

  89. Deng, Y. et al. Spatial profiling of chromatin accessibility in mouse and human tissues. Nature 609, 375–383 (2022).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  90. Zhang, D. et al. Spatial epigenome-transcriptome co-profiling of mammalian tissues. Nature 616, 113–122 (2023).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  91. Engblom, C. et al. Spatial transcriptomics of B cell and T cell receptors reveals lymphocyte clonal dynamics. Science 382, eadf8486 (2023).

    Article  CAS  PubMed  Google Scholar 

  92. Liu, Y. et al. High-plex protein and whole transcriptome co-mapping at cellular resolution with spatial CITE-seq. Nat. Biotechnol. 41, 1405–1409 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  93. Lötstedt, B., Stražar, M., Xavier, R., Regev, A. & Vickovic, S. Spatial host-microbiome sequencing reveals niches in the mouse gut. Nat. Biotechnol. https://doi.org/10.1038/s41587-023-01988-1 (2023).

    Article  PubMed  Google Scholar 

  94. Saarenpää, S. et al. Spatial metatranscriptomics resolves host-bacteria-fungi interactomes. Nat. Biotechnol. https://doi.org/10.1038/s41587-023-01979-2 (2023).

    Article  PubMed  Google Scholar 

  95. Udani, S. et al. Secretion encoded single-cell sequencing (SEC-seq) uncovers gene expression signatures associated with high VEGF-A secretion in mesenchymal stromal cells. Preprint at BioRxiv https://doi.org/10.1101/2023.01.07.523110 (2023).

  96. Zhang, S. et al. Monitoring of cell-cell communication and contact history in mammals. Science 378, eabo5503 (2022).

    Article  CAS  PubMed  Google Scholar 

  97. Clark, I. C. et al. Barcoded viral tracing of single-cell interactions in central nervous system inflammation. Science 372, eabf1230 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. Hor, J. L. & Germain, R. N. Intravital and high-content multiplex imaging of the immune system. Trends Cell Biol. 32, 406–420 (2022).

    Article  PubMed  Google Scholar 

  99. Chen, W. et al. Live-seq enables temporal transcriptomic recording of single cells. Nature 608, 733–740 (2022).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  100. Kunes, R. Z. et al. Supervised discovery of interpretable gene programs from single-cell data. Nat. Biotechnol. https://doi.org/10.1038/s41587-023-01940-3 (2023).

  101. Qoku, A. & Buettner, F. Encoding domain knowledge in multi-view latent variable models: a bayesian approach with structured sparsity. Preprint at https://arxiv.org/abs/2204.06242 (2022).

  102. Lotfollahi, M. et al. Biologically informed deep learning to query gene programs in single-cell atlases. Nat. Cell Biol. 25, 337–350 (2023).

    CAS  PubMed  PubMed Central  Google Scholar 

  103. OpenAI. GPT-4 Technical Report. Preprint at https://arxiv.org/abs/2303.08774 (2023).

  104. Cui, H., Wang, C., Maan, H. & Wang, B. 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).

  105. Theodoris, C. V. et al. Transfer learning enables predictions in network biology. Nature 618, 616–624 (2023).

    Article  ADS  CAS  PubMed  Google Scholar 

  106. Vodovotz, Y. et al. Solving immunology? Trends Immunol. 38, 116–127 (2017).

    Article  CAS  PubMed  Google Scholar 

  107. Handel, A., La Gruta, N. L. & Thomas, P. G. Simulation modelling for immunologists. Nat. Rev. Immunol. 20, 186–195 (2020).

    Article  CAS  PubMed  Google Scholar 

  108. Bachireddy, P. et al. Mapping the evolution of T cell states during response and resistance to adoptive cellular therapy. Cell Rep. 37, 109992 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  109. Kuppe, C. et al. Spatial multi-omic map of human myocardial infarction. Nature 608, 766–777 (2022).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  110. Kamal, A. et al. GRaNIE and GRaNPA: inference and evaluation of enhancer-mediated gene regulatory networks. Mol. Syst. Biol. 19, e11627 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  111. Kamimoto, K. et al. Dissecting cell identity via network inference and in silico gene perturbation. Nature 614, 742–751 (2023). This work pioneered GRN inference from paired single-cell sequencing of chromatin accessibility and transcriptome and its use to predict perturbation effects.

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  112. Kwok, A. J. et al. Neutrophils and emergency granulopoiesis drive immune suppression and an extreme response endotype during sepsis. Nat. Immunol. 24, 767–779 (2023).

    Article  CAS  PubMed  Google Scholar 

  113. Han, A., Glanville, J., Hansmann, L. & Davis, M. M. Linking T-cell receptor sequence to functional phenotype at the single-cell level. Nat. Biotechnol. 32, 684–692 (2014). This work pioneered paired sequencing of the transcriptome and TCR alpha/beta chains in single cells in a high-throughput manner.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  114. Pai, J. A. & Satpathy, A. T. High-throughput and single-cell T cell receptor sequencing technologies. Nat. Methods 18, 881–892 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  115. Valkiers, S. et al. Recent advances in T-cell receptor repertoire analysis: bridging the gap with multimodal single-cell RNA sequencing. ImmunoInformatics https://doi.org/10.1016/j.immuno.2022.100009 (2022).

    Article  Google Scholar 

  116. Joglekar, A. V. & Li, G. T cell antigen discovery. Nat. Methods 18, 873–880 (2021).

    Article  CAS  PubMed  Google Scholar 

  117. Bradley, P. & Thomas, P. G. Using T cell receptor repertoires to understand the principles of adaptive immune recognition. Annu. Rev. Immunol. 37, 547–570 (2019).

    Article  CAS  PubMed  Google Scholar 

  118. Zhang, L. et al. Lineage tracking reveals dynamic relationships of T cells in colorectal cancer. Nature 564, 268–272 (2018).

    Article  ADS  CAS  PubMed  Google Scholar 

  119. Glanville, J. et al. Identifying specificity groups in the T cell receptor repertoire. Nature 547, 94–98 (2017).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  120. Dash, P. et al. Quantifiable predictive features define epitope-specific T cell receptor repertoires. Nature 547, 89–93 (2017).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  121. Bassez, A. et al. A single-cell map of intratumoral changes during anti-PD1 treatment of patients with breast cancer. Nat. Med. 27, 820–832 (2021).

    Article  CAS  PubMed  Google Scholar 

  122. Suo, C. et al. Dandelion uses the single-cell adaptive immune receptor repertoire to explore lymphocyte developmental origins. Nat. Biotechnol. https://doi.org/10.1038/s41587-023-01734-7 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  123. Zhang, Z., Xiong, D., Wang, X., Liu, H. & Wang, T. Mapping the functional landscape of T cell receptor repertoires by single-T cell transcriptomics. Nat. Methods 18, 92–99 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  124. Schattgen, S. A. et al. Integrating T cell receptor sequences and transcriptional profiles by clonotype neighbor graph analysis (CoNGA). Nat. Biotechnol. 40, 54–63 (2022).

    Article  CAS  PubMed  Google Scholar 

  125. An, Y., Drost, F., Theis, F., Schubert, B. & Lotfollahi, M. Integrating T-cell receptor and transcriptome for large-scale single-cell immune profiling analysis. Preprint at BioRxiv https://doi.org/10.1101/2021.06.24.449733 (2021).

  126. Zhang, Z. et al. Interpreting the B-cell receptor repertoire with single-cell gene expression using Benisse. Nat. Mach. Intell. https://doi.org/10.1038/s42256-022-00492-6 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  127. Shugay, M. et al. VDJdb: a curated database of T-cell receptor sequences with known antigen specificity. Nucleic Acids Res. 46, D419–D427 (2018).

    Article  CAS  PubMed  Google Scholar 

  128. Hudson, D., Fernandes, R. A., Basham, M., Ogg, G. & Koohy, H. Can we predict T cell specificity with digital biology and machine learning? Nat. Rev. Immunol. 23, 511–521 (2023).

    Article  CAS  PubMed  Google Scholar 

  129. Osumi-Sutherland, D. et al. Cell type ontologies of the Human Cell Atlas. Nat. Cell Biol. 23, 1129–1135 (2021).

    Article  CAS  PubMed  Google Scholar 

  130. Lotfollahi, M. et al. Mapping single-cell data to reference atlases by transfer learning. Nat. Biotechnol. 40, 121–130 (2022).

    Article  CAS  PubMed  Google Scholar 

  131. Heimberg, G. et al. Scalable querying of human cell atlases via a foundational model reveals commonalities across fibrosis-associated macrophages. Preprint at BioRxiv https://doi.org/10.1101/2023.07.18.549537 (2023).

  132. Hao, Y. et al. Integrated analysis of multimodal single-cell data. Cell 184, 3573–3587 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  133. 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 

  134. 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).

  135. 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 

  136. 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 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  138. Keren, L. et al. A structured tumor-immune microenvironment in triple negative breast cancer revealed by multiplexed ion beam imaging. Cell 174, 1373–1387 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  139. Chen, Z., Soifer, I., Hilton, H., Keren, L. & Jojic, V. Modeling multiplexed images with spatial-LDA reveals novel tissue microenvironments. J. Comput. Biol. 27, 1204–1218 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  141. Schürch, C. M. et al. Coordinated cellular neighborhoods orchestrate antitumoral immunity at the colorectal cancer invasive front. Cell 182, 1341–1359 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  142. Wang, X. Q. et al. Spatial predictors of immunotherapy response in triple-negative breast cancer. Nature 621, 868–876 (2023).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  143. Fischer, D. S., Schaar, A. C. & Theis, F. J. Modeling intercellular communication in tissues using spatial graphs of cells. Nat. Biotechnol. 41, 332–336 (2023).

    Article  CAS  PubMed  Google Scholar 

  144. 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 

  145. Wu, Z. et al. Graph deep learning for the characterization of tumour microenvironments from spatial protein profiles in tissue specimens. Nat. Biomed. Eng. 6, 1435–1448 (2022).

    Article  CAS  PubMed  Google Scholar 

  146. Sinha, S., Eisenhaber, B., Jensen, L. J., Kalbuaji, B. & Eisenhaber, F. Darkness in the human gene and protein function space: widely modest or absent illumination by the life science literature and the trend for fewer protein function discoveries since 2000. Proteomics 18, e1800093 (2018).

    Article  PubMed  Google Scholar 

  147. Lambert, S. A. et al. The human transcription factors. Cell 172, 650–665 (2018).

    Article  CAS  PubMed  Google Scholar 

  148. 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).

    Article  CAS  PubMed  Google Scholar 

  149. Ozaki, K. & Leonard, W. J. Cytokine and cytokine receptor pleiotropy and redundancy. J. Biol. Chem. 277, 29355–29358 (2002).

    Article  CAS  PubMed  Google Scholar 

  150. Korn, T. & Hiltensperger, M. Role of IL-6 in the commitment of T cell subsets. Cytokine 146, 155654 (2021).

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

P.S.L.S. has received funding from the Deutsche Forschungsgemeinschaft under grant agreement SPP 2395. D.D. is supported by the European Union’s Horizon 2020 research and innovation program (860329 Marie-Curie ITN ‘STRATEGY-CKD’). We thank R. O. R. Flores, P. Badia-i- Mompel, J. Tanevski, L. Küchenhoff, M. Garrido-Rodriguez, C. Lu and K. Mikulik for the helpful discussions.

Author information

Authors and Affiliations

  1. Institute for Computational Bioscience, Faculty of Medicine and Heidelberg University Hospital, Heidelberg University, Heidelberg, Germany

    Philipp Sven Lars Schäfer, Daniel Dimitrov & Julio Saez-Rodriguez

  2. Division of Immunology and Allergy, Department of Medicine Solna, Karolinska Institute and Karolinska University Hospital, Stockholm, Sweden

    Eduardo J. Villablanca

  3. Center of Molecular Medicine, Stockholm, Sweden

    Eduardo J. Villablanca

Authors
  1. Philipp Sven Lars Schäfer
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Daniel Dimitrov
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Eduardo J. Villablanca
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Julio Saez-Rodriguez
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

P.S.L.S., D.D. and J.S.-R. developed the concept based on starting ideas from J.S.-R. P.S.L.S. designed the figures. P.S.L.S., with assistance from D.D., wrote the original draft, with the supervision of J.S.-R. E.J.V. contributed immunological expertise. All authors reviewed and edited the manuscript.

Corresponding author

Correspondence to Julio Saez-Rodriguez.

Ethics declarations

Competing interests

J.S.-R. reports funding from GSK, Pfizer and Sanofi and fees/honoraria from Travere Therapeutics, Stadapharm, Astex, Pfizer, Owkin and Grunenthal. E.J.V. has received research grants from F. Hoffmann-La Roche. All other authors declare no competing interests.

Peer review

Peer review information

Nature Immunology thanks Jishnu Das and Yvan Saeys for their contribution to the peer review of this work. Primary Handling Editor: L. A. Dempsey, in collaboration with the Nature Immunology team.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Schäfer, P.S.L., Dimitrov, D., Villablanca, E.J. et al. Integrating single-cell multi-omics and prior biological knowledge for a functional characterization of the immune system. Nat Immunol 25, 405–417 (2024). https://doi.org/10.1038/s41590-024-01768-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41590-024-01768-2

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing