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Universal recording of immune cell interactions in vivo

Nature volume 627, pages 399–406 (2024)Cite this article

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Abstract

Immune cells rely on transient physical interactions with other immune and non-immune populations to regulate their function1. To study these ‘kiss-and-run’ interactions directly in vivo, we previously developed LIPSTIC (labelling immune partnerships by SorTagging intercellular contacts)2, an approach that uses enzymatic transfer of a labelled substrate between the molecular partners CD40L and CD40 to label interacting cells. Reliance on this pathway limited the use of LIPSTIC to measuring interactions between CD4+ T helper cells and antigen-presenting cells, however. Here we report the development of a universal version of LIPSTIC (uLIPSTIC), which can record physical interactions both among immune cells and between immune and non-immune populations irrespective of the receptors and ligands involved. We show that uLIPSTIC can be used, among other things, to monitor the priming of CD8+ T cells by dendritic cells, reveal the steady-state cellular partners of regulatory T cells and identify germinal centre-resident T follicular helper cells on the basis of their ability to interact cognately with germinal centre B cells. By coupling uLIPSTIC with single-cell transcriptomics, we build a catalogue of the immune populations that physically interact with intestinal epithelial cells at the steady state and profile the evolution of the interactome of lymphocytic choriomeningitis virus-specific CD8+ T cells in multiple organs following systemic infection. Thus, uLIPSTIC provides a broadly useful technology for measuring and understanding cell–cell interactions across multiple biological systems.

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Fig. 1: The uLIPSTIC system.
Fig. 2: uLIPSTIC labelling of cell–cell interactions in vivo.
Fig. 3: uLIPSTIC identifies cellular partners of Treg cells, TFH cells and IECs.
Fig. 4: Using uLIPSTIC for interaction-based transcriptomics.
Fig. 5: Using uLIPSTIC to dissect the early events in CD8+ T cell priming following LCMV infection.

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Data availability

Final scRNA-seq datasets are available from the Gene Expression Omnibus under the accession number GSE253000. Processed scRNA-seq data are available at https://github.com/pritykinlab/ulipstic-analysis. Source data are provided with this paper.

Code availability

The full code used to analyse the scRNA-seq data is available at https://github.com/pritykinlab/ulipstic-analysis.

References

  1. Dustin, M. L. The immunological synapse. Cancer Immunol. Res. 2, 1023–1033 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Pasqual, G. et al. Monitoring T cell-dendritic cell interactions in vivo by intercellular enzymatic labelling. Nature 553, 496–500 (2018).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  3. Greenwald, I. & Rubin, G. M. Making a difference: the role of cell-cell interactions in establishing separate identities for equivalent cells. Cell 68, 271–281 (1992).

    Article  CAS  PubMed  Google Scholar 

  4. Sudhof, T. C. & Malenka, R. C. Understanding synapses: past, present, and future. Neuron 60, 469–476 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Victora, G. D. & Nussenzweig, M. C. Germinal centers. Annu. Rev. Immunol. 30, 429–457 (2012).

    Article  CAS  PubMed  Google Scholar 

  6. Bilate, A. M. et al. T cell receptor is required for differentiation, but not maintenance, of intestinal CD4+ intraepithelial lymphocytes. Immunity 53, 1001–1014 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Niec, R. E., Rudensky, A. Y. & Fuchs, E. Inflammatory adaptation in barrier tissues. Cell 184, 3361–3375 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Mempel, T. R., Henrickson, S. E. & Von Andrian, U. H. T-cell priming by dendritic cells in lymph nodes occurs in three distinct phases. Nature 427, 154–159 (2004).

    Article  ADS  CAS  PubMed  Google Scholar 

  9. Moses, L. & Pachter, L. Museum of spatial transcriptomics. Nat. Methods 19, 534–546 (2022).

    Article  CAS  PubMed  Google Scholar 

  10. 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 

  11. Liu, D. S., Loh, K. H., Lam, S. S., White, K. A. & Ting, A. Y. Imaging trans-cellular neurexin-neuroligin interactions by enzymatic probe ligation. PLoS ONE 8, e52823 (2013).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  12. Ombrato, L. et al. Metastatic-niche labelling reveals parenchymal cells with stem features. Nature 572, 603–608 (2019).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  14. 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).

    Article  CAS  PubMed  Google Scholar 

  15. Yassin, M. et al. Upregulation of PD-1 follows tumour development in the AOM/DSS model of inflammation-induced colorectal cancer in mice. Immunology 158, 35–46 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Weizman, O. E. et al. Developing synthetic tools to decipher the tumor-immune interactome. Proc. Natl Acad. Sci. USA 120, e2306632120 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Dorr, B. M., Ham, H. O., An, C., Chaikof, E. L. & Liu, D. R. Reprogramming the specificity of sortase enzymes. Proc. Natl Acad. Sci. USA 111, 13343–13348 (2014).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  18. Guimaraes, C. P. et al. Site-specific C-terminal and internal loop labeling of proteins using sortase-mediated reactions. Nat. Protoc. 8, 1787–1799 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  19. Dustin, M. L. & Depoil, D. New insights into the T cell synapse from single molecule techniques. Nat. Rev. Immunol. 11, 672–684 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Madisen, L. et al. A robust and high-throughput Cre reporting and characterization system for the whole mouse brain. Nat. Neurosci. 13, 133–140 (2010).

    Article  CAS  PubMed  Google Scholar 

  21. Robertson, J. M., Jensen, P. E. & Evavold, B. D. DO11.10 and OT-II T cells recognize a C-terminal ovalbumin 323-339 epitope. J. Immunol. 164, 4706–4712 (2000).

    Article  CAS  PubMed  Google Scholar 

  22. Merkenschlager, J. et al. Dynamic regulation of TFH selection during the germinal centre reaction. Nature 591, 458–463 (2021).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  23. Stoll, S., Delon, J., Brotz, T. M. & Germain, R. N. Dynamic imaging of T cell-dendritic cell interactions in lymph nodes. Science 296, 1873–1876 (2002).

    Article  ADS  PubMed  Google Scholar 

  24. Frederico, B. et al. DNGR-1-tracing marks an ependymal cell subset with damage-responsive neural stem cell potential. Dev. Cell 57, 1957–1975 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Rubtsov, Y. P. et al. Stability of the regulatory T cell lineage in vivo. Science 329, 1667–1671 (2010).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  26. Aghajani, K., Keerthivasan, S., Yu, Y. & Gounari, F. Generation of CD4CreERT2 transgenic mice to study development of peripheral CD4-T-cells. Genesis 50, 908–913 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Shulman, Z. et al. T follicular helper cell dynamics in germinal centers. Science 341, 673–677 (2013).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  28. Dogan, I. et al. Multiple layers of B cell memory with different effector functions. Nat. Immunol. 10, 1292–1299 (2009).

    Article  CAS  PubMed  Google Scholar 

  29. McDonald, B. D., Jabri, B. & Bendelac, A. Diverse developmental pathways of intestinal intraepithelial lymphocytes. Nat. Rev. Immunol. 18, 514–525 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. el Marjou, F. et al. Tissue-specific and inducible Cre-mediated recombination in the gut epithelium. Genesis 39, 186–193 (2004).

    Article  CAS  PubMed  Google Scholar 

  31. Mucida, D. et al. Transcriptional reprogramming of mature CD4+ helper T cells generates distinct MHC class II-restricted cytotoxic T lymphocytes. Nat. Immunol. 14, 281–289 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. London, M., Bilate, A. M., Castro, T. B. R., Sujino, T. & Mucida, D. Stepwise chromatin and transcriptional acquisition of an intraepithelial lymphocyte program. Nat. Immunol. 22, 449–459 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Cepek, K. L. et al. Adhesion between epithelial cells and T lymphocytes mediated by E-cadherin and the alpha E beta 7 integrin. Nature 372, 190–193 (1994).

    Article  ADS  CAS  PubMed  Google Scholar 

  34. Zen, K. et al. Neutrophil migration across tight junctions is mediated by adhesive interactions between epithelial coxsackie and adenovirus receptor and a junctional adhesion molecule-like protein on neutrophils. Mol. Biol. Cell 16, 2694–2703 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Cohen, C. J. et al. The coxsackievirus and adenovirus receptor is a transmembrane component of the tight junction. Proc. Natl Acad. Sci. USA 98, 15191–15196 (2001).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  36. Pazirandeh, A. et al. Multiple phenotypes in adult mice following inactivation of the Coxsackievirus and Adenovirus Receptor (Car) gene. PLoS ONE 6, e20203 (2011).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  37. 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 

  38. Oldstone, M. B. et al. Virus and immune responses: lymphocytic choriomeningitis virus as a prototype model of viral pathogenesis. Br. Med. Bull. 41, 70–74 (1985).

    Article  CAS  PubMed  Google Scholar 

  39. Olson, M. R., McDermott, D. S. & Varga, S. M. The initial draining lymph node primes the bulk of the CD8 T cell response and influences memory T cell trafficking after a systemic viral infection. PLoS Pathog. 8, e1003054 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Jakubzick, C. et al. Minimal differentiation of classical monocytes as they survey steady-state tissues and transport antigen to lymph nodes. Immunity 39, 599–610 (2013).

    Article  CAS  PubMed  Google Scholar 

  41. Puglielli, M. T. et al. In vivo selection of a lymphocytic choriomeningitis virus variant that affects recognition of the GP33-43 epitope by H-2Db but not H-2Kb. J. Virol. 75, 5099–5107 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Stevens, A. J. et al. Programming multicellular assembly with synthetic cell adhesion molecules. Nature 614, 144–152 (2022).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  44. Sujino, T. et al. Tissue adaptation of regulatory and intraepithelial CD4+ T cells controls gut inflammation. Science 352, 1581–1586 (2016).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  45. Shin, K. S. et al. Monocyte-derived dendritic cells dictate the memory differentiation of CD8+ T cells during acute infection. Front. Immunol. 10, 1887 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Engels, B. et al. Retroviral vectors for high-level transgene expression in T lymphocytes. Hum. Gene Ther. 14, 1155–1168 (2003).

    Article  CAS  PubMed  Google Scholar 

  47. Kim, J. H. et al. High cleavage efficiency of a 2A peptide derived from porcine teschovirus-1 in human cell lines, zebrafish and mice. PLoS ONE 6, e18556 (2011).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  48. Argos, P. An investigation of oligopeptides linking domains in protein tertiary structures and possible candidates for general gene fusion. J. Mol. Biol. 211, 943–958 (1990).

    Article  CAS  PubMed  Google Scholar 

  49. Lee, P. P. et al. A critical role for Dnmt1 and DNA methylation in T cell development, function, and survival. Immunity 15, 763–774 (2001).

    Article  CAS  PubMed  Google Scholar 

  50. Schraml, B. U. et al. Genetic tracing via DNGR-1 expression history defines dendritic cells as a hematopoietic lineage. Cell 154, 843–858 (2013).

    Article  CAS  PubMed  Google Scholar 

  51. Shinnakasu, R. et al. Regulated selection of germinal-center cells into the memory B cell compartment. Nat. Immunol. 17, 861–869 (2016).

    Article  CAS  PubMed  Google Scholar 

  52. Barnden, M. J., Allison, J., Heath, W. R. & Carbone, F. R. Defective TCR expression in transgenic mice constructed using cDNA-based alpha- and beta-chain genes under the control of heterologous regulatory elements. Immunol. Cell Biol. 76, 34–40 (1998).

    Article  CAS  PubMed  Google Scholar 

  53. Danciu, C. et al. A characterization of four B16 murine melanoma cell sublines molecular fingerprint and proliferation behavior. Cancer Cell Int. 13, 75 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Sanchez, A. B. & de la Torre, J. C. Rescue of the prototypic Arenavirus LCMV entirely from plasmid. Virology 350, 370–380 (2006).

    Article  CAS  PubMed  Google Scholar 

  55. Emonet, S. F., Garidou, L., McGavern, D. B. & de la Torre, J. C. Generation of recombinant lymphocytic choriomeningitis viruses with trisegmented genomes stably expressing two additional genes of interest. Proc. Natl Acad. Sci. USA 106, 3473–3478 (2009).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  56. Iwasaki, M., Ngo, N., Cubitt, B., Teijaro, J. R. & de la Torre, J. C. General molecular strategy for development of arenavirus live-attenuated vaccines. J. Virol. 89, 12166–12177 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. van der Heide, V. et al. Functional impairment of “helpless” CD8+ memory T cells is transient and driven by prolonged but finite cognate antigen presentation. Preprint at bioRxiv https://doi.org/10.1101/2024.01.22.576725 (2024).

  58. Pasqual, G., Angelini, A. & Victora, G. D. Triggering positive selection of germinal center B cells by antigen targeting to DEC-205. Methods Mol. Biol. 1291, 125–134 (2015).

    Article  CAS  PubMed  Google Scholar 

  59. Bilate, A. M. et al. Tissue-specific emergence of regulatory and intraepithelial T cells from a clonal T cell precursor. Sci. Immunol. 1, eaaf7471 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  60. Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 19, 15 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  61. Sturm, G. et al. Scirpy: a Scanpy extension for analyzing single-cell T-cell receptor-sequencing data. Bioinformatics 36, 4817–4818 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Franzen, O., Gan, L. M. & Bjorkegren, J. L. M. PanglaoDB: a web server for exploration of mouse and human single-cell RNA sequencing data. Database 2019, baz046 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  63. Setty, M. et al. Wishbone identifies bifurcating developmental trajectories from single-cell data. Nat. Biotechnol. 34, 637–645 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Storey, J. D. & Tibshirani, R. Statistical significance for genomewide studies. Proc. Natl Acad. Sci. USA 100, 9440–9445 (2003).

    Article  ADS  MathSciNet  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  66. Emsley, P. & Cowtan, K. Coot: model-building tools for molecular graphics. Acta Crystallogr. D 60, 2126–2132 (2004).

    Article  ADS  PubMed  Google Scholar 

  67. Ko, J., Park, H., Heo, L. & Seok, C. GalaxyWEB server for protein structure prediction and refinement. Nucleic Acids Res. 40, W294–W297 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Jo, S., Kim, T., Iyer, V. G. & Im, W. CHARMM-GUI: a web-based graphical user interface for CHARMM. J. Comput. Chem. 29, 1859–1865 (2008).

    Article  CAS  PubMed  Google Scholar 

  69. Paulick, M. G. & Bertozzi, C. R. The glycosylphosphatidylinositol anchor: a complex membrane-anchoring structure for proteins. Biochemistry 47, 6991–7000 (2008).

    Article  CAS  PubMed  Google Scholar 

  70. Goddard, T. D. et al. UCSF ChimeraX: meeting modern challenges in visualization and analysis. Protein Sci. 27, 14–25 (2018).

    Article  CAS  PubMed  Google Scholar 

  71. PyMOL v.2.4.0 (Schrödinger & DeLano, 2020).

  72. Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36, 411–420 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank the Rockefeller University Transgenics and Gene Targeting facilities for generating the uLIPSTIC mouse strain; the Comparative Biosciences Center for mouse housing; T. B. R. Castro for bioinformatics assistance; C. L. Ferreira for mouse genotyping; K. Gordon and J.-P. Truman for cell sorting; all Rockefeller University staff for their continuous support; C. Reis e Sousa, T. Kurosaki, T. Okada, C.-A. Reynaud and Jean-Claude Weill for mice; J. Merkenschlager for altered peptide ligands; and T. Catarino and H. Veiga-Fernandes for preliminary contributions to the development of the uLIPSTIC system. This study was supported by the National Institutes of Health (NIH) grants DP1AI144248 (Pioneer award) and R01AI173086 to G.D.V., DP2AI171161 to Y.P., R01AR050452 and R01AR27883 to E.F., R01AI153363 to A.O.K. and V.v.d.H., and Starr Consortium grant I10-0044 to G.D.V. Work in the laboratory of G.D.V. is additionally supported by the Robertson Foundation, and work in the laboratory of Y.P. is additionally supported by the Ludwig Institute for Cancer Research. S.N.-H. was supported by a Bulgari Women & Science Fellowship, S.W. was supported by the NIH National Human Genome Research Institute training grant 5T32HG003284, M.C.C.C. was supported by the Pew Latin American Fellows Program, A.C. was supported by a Damon Runyon Postdoctoral Fellowship, and S.M.P. was supported by a Cancer Research Institute Carson Family Postdoctoral Fellowship (CRI4498). E.F. and D.M. are HHMI investigators. G.D.V. is a Burroughs Wellcome Investigator in the Pathogenesis of Infectious Disease and a Pew-Stewart Scholar.

Author information

Author notes

  1. Johanne T. Jacobsen

    Present address: Institute for Immunology and Transfusion Medicine, Rikshospitalet, University of Oslo, Oslo, Norway

  2. These authors contributed equally: Sarah Walker, Maria C. C. Canesso

Authors and Affiliations

  1. Laboratory of Lymphocyte Dynamics, The Rockefeller University, New York, NY, USA

    Sandra Nakandakari-Higa, Maria C. C. Canesso, Aleksey Chudnovskiy, Johanne T. Jacobsen, Jana Bilanovic & Gabriel D. Victora

  2. Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA

    Sarah Walker & Yuri Pritykin

  3. Department of Quantitative and Computational Biology, Princeton University, Princeton, NJ, USA

    Sarah Walker

  4. Laboratory of Mucosal Immunology, The Rockefeller University, New York, NY, USA

    Maria C. C. Canesso, Dong-Yoon Kim, Roham Parsa, Angelina M. Bilate & Daniel Mucida

  5. Marc and Jennifer Lipschultz Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Verena van der Heide & Alice O. Kamphorst

  6. Tisch Cancer Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Verena van der Heide & Alice O. Kamphorst

  7. Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Verena van der Heide & Alice O. Kamphorst

  8. Laboratory of Mammalian Cell Biology and Development, The Rockefeller University, New York, NY, USA

    S. Martina Parigi & Elaine Fuchs

  9. Laboratory of Membrane Biology and Biophysics, The Rockefeller University, New York, NY, USA

    Karol Fiedorczuk

  10. Howard Hughes Medical Institute, The Rockefeller University, New York, NY, USA

    Elaine Fuchs & Daniel Mucida

  11. Laboratory of Synthetic Immunology, Department of Surgery, Oncology and Gastroenterology, University of Padova, Padua, Italy

    Giulia Pasqual

  12. Department of Computer Science, Princeton University, Princeton, NJ, USA

    Yuri Pritykin

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  1. Sandra Nakandakari-Higa
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Contributions

S.N.-H. carried out all experimental work with assistance from M.C.C.C., A.C., J.T.J., J.B. and S.M.P. S.W. and Y.P. designed and carried out all computational analyses. V.v.d.H. and A.O.K. contributed to the design, performance and interpretation of LCMV experiments. K.F. generated the structural model of the interaction between SrtA and G5–Thy1.1. G.P. conceived the uLIPSTIC system along with G.D.V., Y.P., A.M.B., E.F., D.M. and G.D.V. supervised the work. S.N.-H., S.W., Y.P. and G.D.V. wrote the manuscript, with input from all authors.

Corresponding authors

Correspondence to Yuri Pritykin or Gabriel D. Victora.

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Competing interests

G.D.V. is an adviser for and owns stock futures in the Vaccine Company, Inc. E.F. recently served on the scientific advisory boards of L’Oreal and Arsenal Biosciences and owns stock futures in the latter company.

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Nature thanks Thorsten Mempel and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data figures and tables

Extended Data Fig. 1 Design and characterization of Rosa26uLIPSTIC mice.

(A) The uLIPSTIC cassette carrying the lox-stop-lox G5Thy1.1 followed by mSrtA-PDGFRtm fused to FLAG tag was cloned into the Ai9 Rosa26 targeting plasmid. (B) Insertion of the uLIPSTIC cassette was assessed in embryonic stem (ES) cells by Southern blotting using a 32P-labeled probe (Supplementary Table 8 and Supplementary Fig. 1 for gel source data) annealing upstream of the left arm after EcoRI digestion. ESCs carrying the insertion exhibit an extra EcoRI restriction site, resulting in a 7.4 kb fragment upon enzymatic digestion. The blot shows 2 heterozygous integrations out of 7 ES cell clones screened. (C–E) The specificity and efficiency of Rosa26uLIPSTIC recombination are determined by the Cre driver used. (C) Representative gating strategy for resident dendritic cells (rDCs; LIN–, MHC-IIint, CD11chi), migratory dendritic cells (mDCs; LIN–, MHC-IIhi, CD11c+), CD4+ T cells, CD8+ T cells, regulatory T (Treg) cells and B cells in lymph nodes. (D) SrtA expression (determined by FLAG detection) is induced by Cre recombination. Use of a constitutive Cre line (e.g., CD4-Cre) results in efficient but non-specific SrtA expression, generating T cells that can only be used in adoptive cell transfer experiments. The use of inducible Cre lines such as CD4-CreERT2 and Foxp3CreERT2 can often resolve specificity issues, enabling the implementation of uLIPSTIC in fully endogenous models. (E) SrtA expression in conventional DC subsets 1 (cDC1) and 2 (cDC2) in Rosa26uLIPSTIC/WT.Clec9aCre/WT mice.

Extended Data Fig. 2 Kinetics and sensitivity of the uLIPSTIC reaction.

(A–E) Kinetics of the uLIPSTIC reaction. (A) Experimental setup for panels (B–E). OT-II CD4+ T cells from Rosa26uLIPSTIC/WT.CD4-Cre or Rosa26WT/WT.CD4-Cre control mice were co-incubated ex vivo with Rosa26uLIPSTIC/uLIPSTIC acceptor DCs in the presence of OVA323–339 cognate peptide. LIPSTIC substrate was added during the final minutes of incubation as indicated. (B,C) Efficiency of formation of the acyl intermediate (loading of LIPSTIC substrate onto SrtA) in OT-II SrtA+ donor T cells increases gradually with time. (D,E) Transfer of LIPSTIC substrate onto the surface of interacting acceptor DCs followed similar kinetics as acyl intermediate formation. (F–J) uLIPSTIC can resolve differences in peptide concentration and affinity both in vitro and in vivo. (F) Altered peptide ligands (APLs) of the OVA323–339 peptide, when complexed with MHC-II, display decreasing affinities for the OT-II TCR. (G) In vitro co-culture of Rosa26uLIPSTIC/WT.CD4-Cre OT-II T cells with Rosa26uLIPSTIC/uLIPSTIC DCs loaded with its APLs results in a reduction in LIPSTIC labeling that aligns with both the affinity of the peptide-MHCII complex to the OT-II TCR and the peptide concentration gradients. (H) Experimental layout for panels (I,J). (I) In vivo labeling of APL-pulsed DCs show decreased uLIPSTIC labeling in accordance with the affinity to the fixed OT-II TCR. Quantified in (J). Data for all plots are for three mice per condition from one experiment. P-values were calculated using two-tailed Student’s tests.

Source Data

Extended Data Fig. 3 uLIPSTIC labeling of T cell–DC interactions in adaptive transfer models.

(A–E) mSrtA+ donor cell numbers determine the degree of uLIPSTIC labeling. (A) Experimental layout for panels (B-E). Increasing numbers (105, 3 × 105, 106, 3 × 106) of Rosa26uLIPSTIC/+.CD4-Cre OT-II CD4 + T cells were adoptively transferred into recipient Rosa26uLIPSTIC/ uLIPSTIC mice, followed by OVA/alum immunization 18 h post-transfer and LIPSTIC substrate injection one day later. The number of transferred cells (CD45.1/2) determined the proportion of donor cells in the CD4+ T cell compartment (B–C) and the corresponding percentage of labeled interacting cells in the mDC compartment (D-E). (F–I) Persistence of label on acceptor cells with time. (F) Experimental layout for panels (G-I). (G) uLIPSTIC labeling of mDCs after incremental delays between substrate injection and tissue harvest. Quantified in (H,I). Data for all plots are for three mice per condition from one experiment. P-values were calculated using two-tailed Student’s tests.

Source Data

Extended Data Fig. 4 uLIPSTIC labeling in inducible Cre lines in fully endogenous models.

(A–D) uLIPSTIC labeling of Treg cell interactions in the steady-state pLN. (A) Cellular interactome of Tregs at steady-state. Rosa26uLIPSTIC/WT.Foxp3CreERT2/Y experimental mice and Rosa26WT/WT.Foxp3CreERT2/Y controls were given tamoxifen and administered LIPSTIC substrate in the footpad 2 days later. Left, flow cytometry plots show uLIPSTIC labeling in selected immune populations in control (top) and experimental (bottom) mice. The presence of residual labeling in B cells is an artifact common to uLIPSTIC and to other flow-cytometry based methods aimed at identifying rare B cell populations, likely due to B cell receptor-dependent binding of detection components by polyclonal B cells. Right, quantification of the proportion of all labeled cells belonging to each major immune population in control (SrtA–) or experimental (SrtA+) mice. Data for three mice per condition from one experiment, bar plots show mean ± SEM. (B-D) Treg cells interact with mDCs to a greater extent than conventional CD4+ T cells. (B) To test if enhanced interaction with mDCs is a specific feature of Treg cells or a general feature of all CD4+ T cells, we titrated the dose of tamoxifen in Rosa26uLIPSTIC/+.CD4-CreERT2 mice to achieve a similar percentage of SrtA-expression among total CD4+ T cells as in Rosa26uLIPSTIC/+.Foxp3CreERT2/Y mice. (C) At a dose of 0.3 mg of tamoxifen, Rosa26uLIPSTIC/+.CD4-CreERT2 mice showed SrtA expression in a small number of Treg cells (left), with most SrtA+ cells observed in CD4+ conventional T cells (center) and overall numbers of SrtA+ cells among total CD4+ T cells that were comparable with those of Rosa26uLIPSTIC/+.Foxp3CreERT2/Y mice treated with 10 mg tamoxifen (right). (D) When numbers of Treg and CD4+ conventional donor cells are equalized, acceptor mDCs show stronger interaction with Treg cell partners. For (C) and (D), data from two independent experiments with each symbol representing one mouse, P-values were calculated using two-tailed Student’s tests. (E–G) Kinetics of tamoxifen-driven recombination of the Rosa26uLIPSTIC allele according to cell type. (E) SrtA expression in the highly proliferative mesenteric lymph node GC B cells of Rosa26uLIPSTIC/WT.AicdaCreER/WT mice was assessed at different timepoints after tamoxifen administration. The fraction of recombined cells plateaus at 24 h post-tamoxifen administration (hpt), while SrtA protein expression is still increasing by 96 hpt. (F) Labeling of GC B cell interacting partners can be detected as early as 12 hpt, increasing thereafter according to SrtA expression levels. (G) In contrast, SrtA expression in quiescent naïve (PD-1–CXCR5–CD69–) CD4+ T cells in Rosa26uLIPSTIC/WT.Cd4-CreERT2 mice increased at a slower rate than, reaching >80% positive cells only at 96 hpt. For (E), (F) and (G), each plot used three mice per condition from one experiment, P-values were calculated using two-tailed Student’s tests.

Source Data

Extended Data Fig. 5 Intraperitoneally-injected LIPSTIC substrate reaches cells in multiple tissues.

(A) Steady state Rosa26uLIPSTIC/WT.Clec9aCreER/WT or Rosa26uLIPSTIC/WT.Clec9aWT/WT control mice were injected i.p. with the LIPSTIC substrate and its loading onto DCs was achieved in all analyzed tissues. (B-C) I.p. injection of the LIPSTIC substrate reaches the brain in Rosa26uLIPSTIC/WT.Cx3xr1CreERT2/WT mice. (B) Flow cytometry gating strategy to analyze CX3CR1-expressing microglia in the brain. To discriminate resident from circulating immune cells in the brain, α-CD45 antibody was injected intravenously to mark the latter. (C) SrtA expression was detected in ~80% of CD11b+CX3CR1+ cells, 68% of which acquired i.p.-administered LIPSTIC substrate.

Extended Data Fig. 6 uLIPSTIC to study epithelial cell – immune cell interactions in the gut.

(A–C) Flow cytometry strategy for intraepithelial immune cells. (A) Representative gating strategy for γδ TCR and αβ TCR (Cd8αα+, CD8αβ+, and CD4+) IEL subsets. (B) Top, expression of SrtA (FLAG) and capture of LIPSTIC substrate by IEC donor cells and bottom, transfer of substrate onto CD45+ acceptor cells in SrtA-expressing and control mice. (C) Sorting strategy for the scRNA-seq experiment. Samples were enriched for rarer (e.g., B cell, CD4+ IEL) populations by first sorting 12,500 total cells then an additional 12,500 cells depleted of the dominant γδ, CD8αα, and CD8αβ IEL populations. Three independent samples were sorted and stained with different hashtag oligos for downstream identification. (D–L) Clustering analysis of the immune interactome of IECs in the small intestine. (D) UMAP colored by Leiden clustering of the entire scRNA-seq/uLIPSTIC dataset (n = 3,677 cells) used as an intermediate step in cell type annotations. (E) Left, UMAP colored by biological replicate. Right, bar plot indicating cluster composition by biological replicate, cluster size indicated at the right of each bar. (F-G) Further analysis of cluster 10 shows that is a composite comprising proliferating T and B cells. This co-clustering of B and T cells held true for varying number of PCs between 20 and 100 (not shown). (F) Left, Leiden cluster 10 was isolated and sub-clustered, yielding two separate clusters (UMAP). Right, normalized expression of Cd79a and Cd8a for these two sub-clusters of cluster 10 determines their annotation as either B or T cells. (G) UMAP showing the S and G2M phase cell cycle gene list scores (obtained using the ‘score_genes_cell_cycle()‘ function with lists from the Seurat package72), characterizing Leiden cluster 10 as proliferating cells, thus explaining their co-clustering. (H) UMAP showing final clustering of the entire data, with Leiden cluster 10 subdivided into clusters 10a and 10b. (I) Dendrogram representing transcriptional similarities among clusters. Differentially expressed genes were identified for each cluster (log2FC > 1, FDR < 0.05, see Methods), and normalized expression of all such genes (5,956 genes total), averaged per cluster, was used for the hierarchical clustering analysis that produced the dendrogram. Final annotation clusters shown in Fig. 4 are indicated below the Leiden cluster numbers. (J) Dot plot of marker genes indicating their level of expression in each cluster. Dot size indicates the fraction of cells in the cluster with Pearson residual normalized expression greater than 0, dot color represents level of expression. (K) Violin plot showing levels of normalized uLIPSTIC signal for each Leiden cluster. (L) UMAP showing presence of rearranged TCRα and β in each cell.

Extended Data Fig. 7 Expression of marker genes and gene signatures in the annotated scRNA-seq data.

(A) UMAP plots showing normalized gene expression levels for selected marker genes characteristic of the final annotation clus. (B) Dot plot of marker genes indicating level of expression for each cell type annotation. (C) Dot plot of scores for gene signatures of immune cell types from PanglaoDB62. For both dot plots, dot size indicates the fraction of cells in the population with values greater than 0, dot color represents level of value (Pearson residual normalized expression or gene signature scores for B and C, respectively).

Extended Data Fig. 8 Analysis of combined scRNA-seq + uLIPSTIC data for CD4+ T cells.

(A) UMAP for CD4+ T cells showing new Leiden sub-clusters and expression of selected marker genes in each cluster (n = 915). (B) Dot plot of marker genes for each annotated subset of CD4+ T cells. Dot size indicates the fraction of cells in the cluster with Pearson residual normalized expression greater than 0, dot color represents level of expression. (C) Bar plot indicating CD4 Leiden cluster composition by biological replicate, cluster size indicated at the top of each bar. (D) Spearman correlation values, in increasing order, for uLIPSTIC signal and normalized expression of a gene, calculated separately for cells from each biological replicate, indicating consistency across mice. (E) Spearman correlation values, in increasing order, for uLIPSTIC signal and normalized expression of a gene, calculated when removing Tfh-like and naïve/conventional T cells (Leiden CD4 sub-clusters 0 and 1). (F–I) Correlation between acquisition of uLIPSTIC label and expression of CD103 and selected gene signatures by CD4+ IELs. (F) Flow cytometry plots show uLIPSTIC signal and CD103 expression in one control Rosa26uLIPSTIC/WT and three Vil1-Cre.Rosa26uLIPSTIC/WT mice treated as in Fig. 3g. (G) Gene signatures from the MSigDB “canonical pathways” (M2.CP) database showing significant positive association with normalized biotin signal in scRNA-seq analysis over all CD4+ T cells. Plots show Spearman’s ρ value for each signature. (H) Correlation between acquisition of uLIPSTIC signal by CD4+ T cells (shown for all T cells and excluding Tfh-like and Naïve/Tconv clusters) and expression of the Biocarta CTL gene signature. Trend line and error are for linear regression with 95% confidence interval, Spearman’s ρ and two-sided P-value are listed. (I) Correlation between acquisition of uLIPSTIC signal by CD4+ T cells (shown for T cells excluding Tfh-like and Naïve/Tconv clusters) and expression of gene signatures up and downregulated as epithelial T cells transition from Tconv (CD4+CD103–CD8αα–) to CD4+ IEL (CD4+CD103+CD8αα+) phenotypes (signatures based on data from Bilate et al.6). Trend line and error are for linear regression with 95% confidence interval, Spearman’s ρ and two-sided P-value are listed.

Extended Data Fig. 9 Using uLIPSTIC to study CD8+ T cell priming during acute systemic LCMV infection.

(A) Left, adoptively transferred LCMV-specific P14 CD8+ T cells infiltrated the mediastinal (m)LN of LCMV-infected Rosa26uLIPSTIC/uLIPSTIC hosts as early as 36 hpi. Right, fraction of P14 cells in total lymphocytes at the indicated timepoint. Data for ten mice per timepoint from three independent experiments, bar plots show mean ± SEM. (B) uLIPSTIC labeling of the P14-interactome (“Biotin+ All” in grey) showed that DCs (“Biotin+ DCs,” in orange) make up only a fraction of all interacting cells. (C) Sorting strategy for the scRNA-seq experiment. Immune cells—excluding B cells—were sorted both in an unbiased and biased manner, enriching for biotin+ acceptor cells and Flag+ donor cells using distinct hashtag oligos for downstream classification. Two-three independent samples per timepoint were sorted and stained with different hashtag oligos for downstream identification. (D–J) scRNA-seq nalysis of the immune interactome of P14 CD8+ T cells in the mLN during acute LCMV infection. (D) UMAP colored by Leiden clustering of the entire scRNA-seq/uLIPSTIC dataset (n = 11,846 cells). (E) Left, UMAP colored by timepoint. Right, bar plot indicating cluster composition by timepoint, cluster size indicated at the right of each bar the right. (F) Left, UMAP colored by biological replicate. Right, bar plot indicating cluster composition by biological replicate, separated by whether the sample was sorted as total mLN cells or biotin-enriched mLN cells, as specified in (C). The cluster size is indicated at the right of each bar. (G) Dendrogram representing transcriptional similarities among clusters. Differentially expressed genes were identified for each cluster (log2FC > 1, FDR < 0.01, see Methods), and normalized expression of all such genes (6,484 genes total), averaged per cluster, was used for the hierarchical clustering analysis that produced the dendrogram. Final annotation clusters shown in Fig. 5 are indicated below the Leiden cluster numbers. (H) Dot plot of marker genes indicating their level of expression in each cell type annotation. Dot size indicates the fraction of cells in the cluster with Pearson residual normalized expression greater than 0, dot color represents level of expression. (I) UMAPs showing normalized gene expression levels for selected marker genes. (J) Violin plot showing levels of normalized uLIPSTIC signal for each cell type annotation, separated by timepoint and excluding P14 donor cells (high FLAG).

Source Data

Extended Data Fig. 10 Analysis of combined scRNA-seq + uLIPSTIC data for LCMV tissues (profiled at 96 hpi).

(A) UMAP colored by Leiden clustering of the entire scRNA-seq/uLIPSTIC dataset (n = 12,324 cells). (B) Left, UMAP colored by tissue type. Right, bar plot indicating cluster composition by tissue, cluster size indicated at the right of each bar. (C) Left, UMAP colored by biological replicate. Right, bar plot indicating cluster composition by biological replicate, separated by whether the sample was unsorted cells or sorted as biotin-enriched cells. The cluster size is indicated at the right of each bar. (D) Dendrogram representing transcriptional similarities among tissue Leiden clusters with annotations from the mLN data. Normalized expression of all genes in the LCMV datasets (11,558 genes total), averaged per Leiden cluster for the tissue data and averaged per annotation for the mLN data, was used for the hierarchical clustering analysis that produced the dendrogram. Final annotation clusters shown in Fig. 5 are indicated below the Leiden cluster numbers. (E) Dot plot of marker genes indicating their level of expression in each cell type annotation. Dot size indicates the fraction of cells in the population with Pearson residual normalized expression greater than 0, dot color represents level of expression. (F) UMAP plots showing normalized gene expression levels for selected marker genes characteristic of the final annotation clusters. (G) Violin plot showing levels of normalized uLIPSTIC signal for each cell type annotation, separated by tissue type and excluding P14 donor cells (high FLAG). (H) uLIPSTIC labeling of MHC-IIhi monocytes/macrophages (Mo/MΦ2) in organs of mice treated as in Fig. 5a but infected with either LCMVWT or LCMV∆P14, analyzed at 96 hpi. Data from one experiment with each symbol representing one mouse, P-values were calculated using two-tailed Student’s test.

Supplementary information

Supplementary Fig. 1

Southern blot of ESCs to screen for uLIPSTIC cassette insertion. The uLIPSTIC cassette carrying the lox-stop-lox G5Thy1.1 followed by mSrtA–PDGFRtm fused to the Flag tag was cloned into the Ai9 Rosa26-targeting plasmid and this was used as the template for homologous recombination in ESCs. Insertion of the uLIPSTIC cassette was assessed in 192 ESCs by Southern blotting using a 32P-labelled probe annealing upstream of the left arm after EcoRI digestion. WT ESCs show a 15.6-kilobase fragment, whereas ESCs carrying the insertion exhibit an extra EcoRI restriction site, resulting in a 7.4-kilobase fragment following enzymatic digestion.

Reporting Summary

Supplementary Table 1

Differentially expressed genes (DEGs) for each cluster in scRNA-seq + uLIPSTIC data from the gut interactome. DEGs were calculated by comparing all cells within a cluster with all cells outside the cluster. Cluster means are the pseudobulk raw UMI counts summed over all cells within the cluster and normalized by total amounts of UMI counts for those cells. Noncluster means are the pseudobulk raw UMI counts summed over all cells not in the cluster and normalized by total amounts of UMI counts for those cells. The log2-transformed values of these counts were taken to generate the log2[FC] column. P values were then calculated for all genes with abs(log2[FC]) greater than 0.5, using Pearson residual normalized expression values. These P values were then adjusted with Benjamini–Hochberg correction; only those with adjusted P value smaller than 0.05 are shown.

Supplementary Table 2

DEGs for each annotated cluster in scRNA-seq + uLIPSTIC data from the gut interactome. DEGs were calculated by comparing all cells within a cluster with all cells outside the cluster. Cluster means are the pseudobulk raw UMI counts summed over all cells within the cluster and normalized by total amounts of UMI counts for those cells. Noncluster means are the pseudobulk raw UMI counts summed over all cells not in the cluster and normalized by total amounts of UMI counts for those cells. The log2-transformed values of these counts were taken to generate the log2[FC] column. P values were then calculated for all genes with abs(log2[FC]) greater than 0.5, using Pearson residual normalized expression values. These P values were then adjusted with Benjamini–Hochberg correction; only those with adjusted P value smaller than 0.05 are shown.

Supplementary Table 3

DEGs for each annotated cluster in scRNA-seq + uLIPSTIC data for CD4+ T cells from the gut interactome. DEGs were calculated by comparing all cells within the cluster with all cells not in the cluster. Cluster means are the pseudobulk raw UMI counts summed over all cells within the cluster and normalized by total amounts of UMI counts for those cells. Noncluster means are the pseudobulk raw UMI counts summed over all cells not in the cluster and normalized by total amounts of UMI counts for those cells. The log2-transformed values of these counts were taken to generate the log2[FC] column. P values were then calculated for all genes with abs(log2[FC]) greater than 0.5, using Pearson residual normalized expression values. These P values were then adjusted with Benjamini–Hochberg correction; only those with adjusted P value smaller than 0.05 are shown.

Supplementary Table 4

Correlation analysis of combined scRNA-seq + uLIPSTIC data for CD4+ T cells from the gut interactome. Correlation and P value from Spearman correlation calculated between each gene’s normalized expression and normalized biotin level for all cells in the CD4+ T cell subset. P values were adjusted using Bonferroni correlation in the Padj column.

Supplementary Table 5

Targeted correlation analysis of scRNA-seq + uLIPSTIC data. Correlation between biotin acquisition and expression of genes modulated as conventional T cells develop into CD4+ IELs.

Supplementary Table 6

DEGs for each cluster in scRNA-seq + uLIPSTIC data from the P14 CD8+ T cell interactome in the mLN during acute LCMV infection. DEGs were calculated by comparing all cells within a cluster with all cells outside the cluster. Cluster means are the pseudobulk raw UMI counts summed over all cells within the cluster and normalized by total amounts of UMI counts for those cells. Noncluster means are the pseudobulk raw UMI counts summed over all cells not in the cluster and normalized by total amounts of UMI counts for those cells. The log2-transformed values of these counts were taken to generate the log2[FC] column. P values were then calculated for all genes with abs(log2[FC]) greater than 0.5, using Pearson residual normalized expression values. These P values were then adjusted with Benjamini–Hochberg correction; only those with adjusted P value smaller than 0.05 are shown.

Supplementary Table 7

DEGs for each cluster in scRNA-seq + uLIPSTIC data from the P14 CD8+ T cell interactome in the liver, lung and spleen during acute LCMV infection. DEGs were calculated by comparing all cells within a cluster with all cells outside the cluster. Cluster means are the pseudobulk raw UMI counts summed over all cells within the cluster and normalized by total amounts of UMI counts for those cells. Noncluster means are the pseudobulk raw UMI counts summed over all cells not in the cluster and normalized by total amounts of UMI counts for those cells. The log2-transformed values of these counts were taken to generate the log2[FC] column. P values were then calculated for all genes with abs(log2[FC]) greater than 0.5, using Pearson residual normalized expression values. These P -values were then adjusted with Benjamini–Hochberg correction; only those with adjusted P value smaller than 0.05 are shown.

Supplementary Table 8

Sequences for the DNA constructs and probes used throughout this publication.

Supplementary Table 9

Antibodies used for flow cytometry, CITE-seq (cellular indexing of transcriptomes and epitopes by sequencing) and in vivo blocking.

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Nakandakari-Higa, S., Walker, S., Canesso, M.C.C. et al. Universal recording of immune cell interactions in vivo. Nature 627, 399–406 (2024). https://doi.org/10.1038/s41586-024-07134-4

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