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Monitoring T cell–dendritic cell interactions in vivo by intercellular enzymatic labelling

Nature volume 553, pages 496–500 (2018)Cite this article

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Abstract

Interactions between different cell types are essential for multiple biological processes, including immunity, embryonic development and neuronal signalling. Although the dynamics of cell–cell interactions can be monitored in vivo by intravital microscopy1, this approach does not provide any information on the receptors and ligands involved or enable the isolation of interacting cells for downstream analysis. Here we describe a complementary approach that uses bacterial sortase A-mediated cell labelling across synapses of immune cells to identify receptor–ligand interactions between cells in living mice, by generating a signal that can subsequently be detected ex vivo by flow cytometry. We call this approach for the labelling of ‘kiss-and-run’ interactions between immune cells ‘Labelling Immune Partnerships by SorTagging Intercellular Contacts’ (LIPSTIC). Using LIPSTIC, we show that interactions between dendritic cells and CD4+ T cells during T-cell priming in vivo occur in two distinct modalities: an early, cognate stage, during which CD40–CD40L interactions occur specifically between T cells and antigen-loaded dendritic cells; and a later, non-cognate stage during which these interactions no longer require prior engagement of the T-cell receptor. Therefore, LIPSTIC enables the direct measurement of dynamic cell–cell interactions both in vitro and in vivo. Given its flexibility for use with different receptor–ligand pairs and a range of detectable labels, we expect that this approach will be of use to any field of biology requiring quantification of intercellular communication.

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Figure 1: Using LIPSTIC to track ligand–receptor interactions.
Figure 2: LIPSTIC labelling of CD40–CD40L interactions ex vivo.
Figure 3: LIPSTIC enables monitoring of CD40–CD40L interactions between T cells and dendritic cells in vivo.
Figure 4: Different modalities of the CD40–CD40L interaction between CD4+ T cells and dendritic cells in vivo.

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References

  1. Cahalan, M. D. & Parker, I. Choreography of cell motility and interaction dynamics imaged by two-photon microscopy in lymphoid organs. Annu. Rev. Immunol. 26, 585–626 (2008)

    Article  CAS  Google Scholar 

  2. Popp, M. W. & Ploegh, H. L. Making and breaking peptide bonds: protein engineering using sortase. Angew. Chem. Int. Ed. Engl. 50, 5024–5032 (2011)

    Article  CAS  Google Scholar 

  3. Chen, I., Dorr, B. M. & Liu, D. R. A general strategy for the evolution of bond-forming enzymes using yeast display. Proc. Natl Acad. Sci. USA 108, 11399–11404 (2011)

    Article  ADS  CAS  Google Scholar 

  4. Haswell, L. E., Glennie, M. J. & Al-Shamkhani, A. Analysis of the oligomeric requirement for signaling by CD40 using soluble multimeric forms of its ligand, CD154. Eur. J. Immunol. 31, 3094–3100 (2001)

    Article  CAS  Google Scholar 

  5. Ghiotto, M. et al. PD-L1 and PD-L2 differ in their molecular mechanisms of interaction with PD-1. Int. Immunol. 22, 651–660 (2010)

    Article  CAS  Google Scholar 

  6. van der Merwe, P. A., Bodian, D. L., Daenke, S., Linsley, P. & Davis, S. J. CD80 (B7-1) binds both CD28 and CTLA-4 with a low affinity and very fast kinetics. J. Exp. Med. 185, 393–404 (1997)

    Article  CAS  Google Scholar 

  7. Greene, J. L. et al. Covalent dimerization of CD28/CTLA-4 and oligomerization of CD80/CD86 regulate T cell costimulatory interactions. J. Biol. Chem. 271, 26762–26771 (1996)

    Article  CAS  Google Scholar 

  8. An, H.-J. et al. Crystallographic and mutational analysis of the CD40–CD154 complex and its implications for receptor activation. J. Biol. Chem. 286, 11226–11235 (2011)

    Article  CAS  Google Scholar 

  9. Singh, J. et al. The role of polar interactions in the molecular recognition of CD40L with its receptor CD40. Protein Sci. 7, 1124–1135 (1998)

    Article  CAS  Google Scholar 

  10. 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  Google Scholar 

  11. Swee, L. K., Lourido, S., Bell, G. W., Ingram, J. R. & Ploegh, H. L. One-step enzymatic modification of the cell surface redirects cellular cytotoxicity and parasite tropism. ACS Chem. Biol. 10, 460–465 (2015)

    Article  CAS  Google Scholar 

  12. Eickhoff, S. et al. Robust anti-viral immunity requires multiple distinct T cell–dendritic cell interactions. Cell 162, 1322–1337 (2015)

    Article  CAS  Google Scholar 

  13. Kretschmer, B., Kühl, S., Fleischer, B. & Breloer, M. Activated T cells induce rapid CD83 expression on B cells by engagement of CD40. Immunol. Lett. 136, 221–227 (2011)

    Article  CAS  Google Scholar 

  14. Lesley, R., Kelly, L. M., Xu, Y. & Cyster, J. G. Naive CD4 T cells constitutively express CD40L and augment autoreactive B cell survival. Proc. Natl Acad. Sci. USA 103, 10717–10722 (2006)

    Article  ADS  CAS  Google Scholar 

  15. Behrens, G. M. et al. Helper requirements for generation of effector CTL to islet b cell antigens. J. Immunol. 172, 5420–5426 (2004)

    Article  CAS  Google Scholar 

  16. 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  Google Scholar 

  17. Slavoff, S. A., Liu, D. S., Cohen, J. D. & Ting, A. Y. Imaging protein–protein interactions inside living cells via interaction-dependent fluorophore ligation. J. Am. Chem. Soc. 133, 19769–19776 (2011)

    Article  CAS  Google Scholar 

  18. Martell, J. D. et al. A split horseradish peroxidase for the detection of intercellular protein–protein interactions and sensitive visualization of synapses. Nat. Biotechnol. 34, 774–780 (2016)

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  20. 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  Google Scholar 

  21. Whitlow, M. et al. An improved linker for single-chain Fv with reduced aggregation and enhanced proteolytic stability. Protein Eng. 6, 989–995 (1993)

    Article  CAS  Google Scholar 

  22. Kawabe, T. et al. The immune responses in CD40-deficient mice: impaired immunoglobulin class switching and germinal center formation. Immunity 1, 167–178 (1994)

    Article  CAS  Google Scholar 

  23. Renshaw, B. R. et al. Humoral immune responses in CD40 ligand-deficient mice. J. Exp. Med. 180, 1889–1900 (1994)

    Article  CAS  Google Scholar 

  24. Madsen, L. et al. Mice lacking all conventional MHC class II genes. Proc. Natl Acad. Sci. USA 96, 10338–10343 (1999)

    Article  ADS  CAS  Google Scholar 

  25. 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  Google Scholar 

  26. Hadjantonakis, A. K., Macmaster, S. & Nagy, A. Embryonic stem cells and mice expressing different GFP variants for multiple non-invasive reporter usage within a single animal. BMC Biotechnol. 2, 11 (2002)

    Article  Google Scholar 

  27. Shih, T. A., Roederer, M. & Nussenzweig, M. C. Role of antigen receptor affinity in T cell-independent antibody responses in vivo. Nat. Immunol. 3, 399–406 (2002)

    Article  CAS  Google Scholar 

  28. Gu, H., Zou, Y. R. & Rajewsky, K. Independent control of immunoglobulin switch recombination at individual switch regions evidenced through Cre–loxP-mediated gene targeting. Cell 73, 1155–1164 (1993)

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  30. Yang, H. et al. One-step generation of mice carrying reporter and conditional alleles by CRISPR/Cas-mediated genome engineering. Cell 154, 1370–1379 (2013)

    Article  CAS  Google Scholar 

  31. Wang, H. et al. One-step generation of mice carrying mutations in multiple genes by CRISPR/Cas-mediated genome engineering. Cell 153, 910–918 (2013)

    Article  CAS  Google Scholar 

  32. Maruyama, T. et al. Increasing the efficiency of precise genome editing with CRISPR–Cas9 by inhibition of nonhomologous end joining. Nat. Biotechnol. 33, 538–542 (2015)

    Article  CAS  Google Scholar 

  33. Picelli, S. et al. Full-length RNA-seq from single cells using Smart-seq2. Nat. Protoc. 9, 171–181 (2014)

    Article  CAS  Google Scholar 

  34. Shalek, A. K. et al. Single-cell RNA-seq reveals dynamic paracrine control of cellular variation. Nature 510, 363–369 (2014)

    Article  ADS  CAS  Google Scholar 

  35. Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012)

    Article  CAS  Google Scholar 

  36. Li, B. & Dewey, C. N. RSEM: accurate transcript quantification from RNA-seq data with or without a reference genome. BMC Bioinformatics 12, 323 (2011)

    Article  CAS  Google Scholar 

  37. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014)

    Article  Google Scholar 

  38. 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  Google Scholar 

  39. Southern, E. Southern blotting. Nat. Protoc. 1, 518–525 (2006)

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We thank H. Ploegh for introducing us to sortase A; H. Yang, S. Markoulaki and R. Jaenisch for generating gene-targeted mice; A. Ting for NLG- and NRX-expressing constructs; and L. Mesin and C. F. Opel for technical advice. This work was funded by NIH grants DP5OD012146 and R01AI119006 to G.D.V. and a Starr Cancer Consortium grant to G.D.V. and N.H. G.P. was supported by the Swiss National Science Foundation Postdoctoral fellowship and the Cancer Research Institute Irvington Postdoctoral fellowship. G.V. is a Searle Scholar.

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Authors and Affiliations

  1. Laboratory of Lymphocyte Dynamics, The Rockefeller University, 1230 York Avenue, New York, New York, USA

    Giulia Pasqual, Aleksey Chudnovskiy, Jeroen M. J. Tas, Marianna Agudelo & Gabriel D. Victora

  2. Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA

    Lawrence D. Schweitzer, Ang Cui & Nir Hacohen

  3. Massachusetts General Hospital Department of Medicine, Center for Cancer Research, Massachusetts General Hospital, Boston, Massachusetts, USA

    Lawrence D. Schweitzer, Ang Cui & Nir Hacohen

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  1. Giulia Pasqual
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Contributions

G.P. and G.D.V. conceived the study, designed and analysed experiments and wrote the manuscript. G.P. performed all experimental work (with the exception of gene-expression analysis), with sporadic assistance from A.Ch., J.M.T. and M.A. L.D.S., A.Cu. and N.H. contributed the gene-expression profiling work, including experiments and data analysis presented in Fig. 4g and Extended Data Fig. 10 and wrote the text for these experiments.

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Correspondence to Gabriel D. Victora.

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Competing interests

G.P. and G.D.V. have submitted a US Non Provisional Patent Application for the LIPSTIC technology (US20160097773).

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Reviewer Information Nature thanks M. Dustin, A. Esser-Kahn, T. 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 Figure 1 Schematic representation of the SrtA reaction.

The SrtA enzyme recognizes the short amino acid sequence LPXTG (where X is any amino acid). Upon binding, SrtA forms a covalent acyl intermediate between the threonine of the substrate and the cysteine present in its catalytic pocket. The reaction proceeds with the formation of an amide bond between substrate threonine and an N-terminal glycine. Affinities displayed refer to engineered SrtA variants carrying P94S, D160N, and K196T mutations.

Extended Data Figure 2 Two point mutations in the mouse CD40L coding sequence impair binding to CD40.

a, Sequence alignment of human and mouse CD40L proteins. Owing to the lack of crystallographic data describing the mouse CD40–CD40L complex, we identified residues potentially engaged in CD40 binding on the basis of information available for the human CD40–CD40L complex. Residues in human CD40L sequences engaged in the interaction with CD40 based on crystallographic data are highlighted in blue. Among these, residues for which a charge reversal mutation was shown to affect CD40 binding are boxed. Filled boxes identify the residues in mouse CD40L for which a charge reversal mutation was performed (K142E and R202E). Mutations at equivalent locations in the human CD40L coding sequence (K143, R203) have also been detected in patients with hyper-IgM syndrome. CD40L with both mutations (K142E and R202E) is labelled as CD40L*. b, Binding of CD40 to CD40L–SrtA and CD40L*–SrtA. HEK293T cells were transfected with CD40L–SrtA or CD40L*–SrtA, incubated with CD40–Fc protein and analysed by flow cytometry. Histograms show severe impairment of CD40 binding to CD40L*–SrtA. c, B-cell activation by CD40L–SrtA and CD40L*–SrtA. Primary mouse B cells were cultured on a monolayer of HEK293T cells expressing CD40L–SrtA or CD40L*–SrtA. CD86 surface expression was analysed by flow cytometry 18 h later. Histograms show reduced upregulation of CD86 in B cells stimulated with CD40L*–SrtA. Data are representative of two independent experiments.

Extended Data Figure 3 Imaging of LIPSTIC labelling.

a, Experimental setup for live imaging of LIPSTIC labelling. Cd40−/− B cells and Cd40lg−/Y CD4+ T cells were transduced with G5–CD40 (Tomato reporter) or CD40L–SrtA (GFP reporter), respectively. CD40L–SrtA+ T cells were loaded with AlexaFluor647–SELPETGG, mixed with G5–CD40+ B cells on intercellular adhesion molecule (ICAM)-coated chambers to allow interactions and were immediately imaged. b, Time-series showing transfer of AlexaFluor647–SELPETGG (white) from CD40L–SrtA+ T cells (green) to G5–CD40+ B cells (red) upon interaction. Data are representative of two independent experiments.

Extended Data Figure 4 Generation of Cd40G5 and Cd40lgSrtA gene-targeted mice.

a, b, Schematic representation and CRISPR–Cas9 genome-editing strategy for the Cd40G5 (a) and Cd40lgSrtA (b) alleles. HA, homology arm; PAM, protospacer adjacent motif. c, Restriction fragment length polymorphism analysis of Cd40G5/+ mice. PCR products generated using primers surrounding the G5 insertion site were digested with KpnI and analysed by electrophoresis on an agarose gel. WT, wild type. Data are representative of at least two experiments. d, Southern blot analysis of a Cd40lgSrtA/+ mouse. Genomic DNA was extracted, digested with XbaI, and transferred onto a nitrocellulose membrane after electrophoresis on an agarose gel. Genomic DNA fragments were detected using a probe annealing between exons 4 and 5.

Extended Data Figure 5 LIPSTIC labelling ex vivo.

a, Experimental setup used in b, c to assess the influence of substrate incubation length on intercellular labelling between primary dendritic cells and CD4+ T cells. Cd40G5/G5 dendritic cells populations were separately treated with 1 μM of OVA323–339 or LCMV-GP61–80, mixed and co-cultured for 6 h with Cd40lgSrtA/Y CD4-Cre OT-II CD4+ T cells. Biotin–LPETG was added during the final 1, 5, 10, 20, 180 min of co-culture or for the entire co-culture time (360 min) at a final concentration of 10 μM, and cells were analysed by flow cytometry. b, Flow cytometric analysis of co-cultured dendritic cells incubated with biotin–LPETG for the indicated times. c, Percentage of biotin+ dendritic cells gated as in b. d, Experimental setup used in e–g to analyse intercellular labelling ex vivo between primary B cells and CD4+ T cells. Two populations of Cd40G5/G5 B cells that either carried a wild-type polyclonal B-cell receptor repertoire or expressed the B1-8hi Ig heavy chain, which when paired to an Igλ light chain confers specificity towards the hapten 4-hydroxy-3-nitrophenylacetyl (NP), were mixed and treated with the indicated concentrations of NP-OVA. Cells were then co-cultured for 18 h with Cd40lgSrtA/Y CD4-Cre OT-II CD4+ T cells. Biotin–LPETG was added during the last 30 min of co-culture at a final concentration of 100 μM, and cells analysed by flow cytometry. e, Flow cytometric analysis of B cells treated with 1 nM of NP-OVA showing preferential biotin labelling of B1-8hiλ+ B cells. f, Percentage of biotin+ B cells among polyclonal and B1-8hiλ+ populations at the indicated NP-OVA concentrations. g, Flow cytometric analysis of B cells treated with 1 nM of NP-OVA showing positive correlation between biotin labelling and expression of the activation marker CD69. Data are representative of three independent experiments.

Extended Data Figure 6 Characterization of Cd40lgSrtA/Y T cells.

a, Upregulation of CD86 on B cells by CD40L–SrtA. B1-8hi λ+ B cells were treated with the indicated concentrations of NP-OVA and co-cultured with Cd40lgSrtA/Y CD4-Cre OT-II or wild-type OT-II CD4+ T cells for 18 h. Cells were analysed by flow cytometry. The percentage of CD86+ B cells when co-cultured with Cd40lgSrtA/Y CD4-Cre OT-II or wild-type OT-II T cells in the presence of indicated concentrations of NP-OVA is shown. b, Germinal centre formation in Cd40lgSrtA/Y and Cd40lgSrtA/Y CD4-Cre mice. C57BL/6J, Cd40lg+/Y and Cd40lgSrtA/Y CD4-Cre mice were immunized subcutaneously with 20 μg of NP-OVA in alum at the base of the tail. Inguinal lymph nodes were analysed by flow cytometry 12 days after immunization. Dot plots show the absence of germinal centre formation in both Cd40lgSrtA/Y and Cd40lgSrtA/Y CD4-Cre mice, suggestive of an impaired ability of Cd40lgSrtA/Y T cells to activate B cells. A similar phenotype is observed regardless of the presence of Cre recombination, which is likely because of the addition of a translated LoxP site to the C terminus of the CD40L protein. c, In vivo expansion of Cd40lgSrtA/Y CD4-Cre OT-II CD4+ T cells. 5 × 105Cd40G5/G5 dendritic cells treated ex vivo with OVA323–339 were injected subcutaneously into the hind footpad of C57BL/6J recipients. After 18 h, 3 × 105 CFSE-labelled CD40lgSrtA/Y CD4-Cre OT-II (or wild-type OT-II as control) CD4+ T cells were transferred intravenously. PLNs were analysed by flow cytometry 72 h after T-cell transfer. Histograms show comparable expansion of both transferred T-cell populations, as indicated by CFSE dilution. Data are representative of two independent experiments.

Extended Data Figure 7 Characterization of LIPSTIC labelling in vivo.

a, CD40L–SrtA expression in Cd40lgSrtA/Y CD4-Cre OT-II CD4+ T cells in vivo after dendritic cell transfer. Mice were treated as in Fig. 3a. Flow cytometric analysis of PLN cells shows transferred Cd40lgSrtA/Y CD4-Cre OT-II CD4+ T cells expressing CD40L–SrtA as revealed by the detection of biotin staining (formation of an acyl intermediate between SrtA and biotin–LPETG). CD40L–SrtA expression requires dendritic cell presentation of cognate antigen (OVA323–339), is not affected by CD40L-blocking antibody treatment and positively correlates with the activation marker CD69. b, Detection background in major leukocyte populations. Cd40G5/G5 CD45.1/2 dendritic cells were treated with OVA323–339 and transferred subcutaneously (5 × 105 per footpad) into Cd40G5/G5 recipients. After 18 h, 3 × 105CD40lgSrtA/Y CD45.1/1 CD4-Cre OT-II (or Cd40lg+/Y CD45.1/1 OT-II lacking Cre expression as control) CD4+ T cells were transferred intravenously. Biotin–LPETG (or PBS as control) was administered subcutaneously (300 nmol per footpad) 10 to 12 h after T-cell transfer and PLN cells were analysed by flow cytometry. Plots show biotin staining among B cells, CD4+ T cells and dendritic cells. c, Efficiency of labelling of Cd40G5/G5 and Cd40+/+ dendritic cells after T cell–dendritic cell interaction in vivo. Cd40G5/G5 and Cd40+/+ dendritic cells were treated ex vivo with OVA323–339, mixed and injected subcutaneously into C57BL/6J recipients (5 × 105 per footpad). After 18 h, 3 × 105Cd40lgSrtA/Y CD4-Cre OT-II CD4+ T cells were transferred intravenously. Biotin–LPETG was administered subcutaneously (300 nmol per footpad) 10–12 h after T-cell transfer. Dot plots show flow cytometric analysis of transferred Cd40G5/G5 and Cd40+/+ dendritic cells. d, Percentage (left) and MFI (right) of biotin+ dendritic cells (gated as in c) among transferred dendritic cell populations. Labelling of Cd40+/+ dendritic cells probably reflects biotin–LPETG transfer onto endogenous N-terminal glycines. Each symbol represents one mouse; bars indicate the mean. e, Labelling of endogenous N-terminal glycines requires CD40L–CD40 interaction. Experimental setup as in c, except that a mixture of C57BL/6J and Cd40−/− dendritic cells was transferred. Dot plots show flow cytometric analysis of transferred Cd40+/+ and Cd40−/− dendritic cells. f, Percentage of biotin+ dendritic cells gated as in e among transferred dendritic cell populations. Each symbol represents one mouse; bars indicate the mean. g, Graphic representation of the experimental protocol used in h–k to determine the clearance of surface biotin labelling. Cd40G5/G5 dendritic cells were treated with OVA323–339 and transferred subcutaneously (5 × 105 per footpad) into C57BL/6J recipients. After 18 h, 3 × 105CD40lgSrtA/Y CD4-Cre OT-II CD4+ T cells were transferred intravenously, biotin–LPETG was administered subcutaneously (300 nmol per footpad) 10–12 h after T-cell transfer. PLNs were collected and analysed by flow cytometry 0, 4, 8 or 24 h after the final biotin–LPETG injection. h, Flow cytometric analysis of PLN cells showing biotin labelling of transferred Cd40G5/G5 dendritic cells at the indicated hours after biotin–LPETG administration. i, Percentage (left) and MFI (right) of biotin+ dendritic cells among transferred Cd40G5/G5 dendritic cells gated as in h. Each symbol represents one mouse; bars indicate the mean. j, Flow cytometric analysis of PLN cells showing biotin labelling of transferred Cd40lgSrtA/Y CD4-Cre OT-II CD4+ T cells at the indicated time points after biotin–LPETG administration. k, Percentage (left) and MFI (right) of biotin+ cells among transferred Cd40lgSrtA/Y CD4-Cre OT-II CD4+ T cells gated as in h. Each symbol represents one mouse; bars indicate the mean. l, CD40L–SrtA expression in Cd40lgSrtA/Y CD4-Cre OT-II CD4+ T cells in vivo after immunization. Mice were treated as in Fig. 3d. Flow cytometric analysis of PLN cells showing transferred Cd40lgSrtA/Y CD4-Cre OT-II CD4+ T cells in mice left untreated (left) or treated with CD40L-blocking antibody 4 h before PLN collection (right). Data are representative of two independent experiments.

Extended Data Figure 8 CD40–CD40L interaction between CD4+ T cells and dendritic cells in vivo can occur in an antigen-independent manner.

a, MFI of biotin+ dendritic cells 48 h after T-cell transfer in mice treated as in Fig. 4a. Each symbol represents one mouse; bars indicate the mean. Data are pooled from two independent experiments. b, MFI of biotin+ dendritic cells 48 h after T-cell transfer in mice treated as in Fig. 4d. Each symbol represents one mouse; bars indicate the mean. c, Graphic representation of the experimental protocol used in d, e. C57BL/6J mice were lethally irradiated and reconstituted with a mixture of Cd40G5/G5 (80%) and Cd40G5/G5;H2−/− (20%) bone marrow. After reconstitution, bone marrow chimaeras received 3 × 105Cd40lgSrtA/Y CD4-Cre OT-II CD4+ T cells intravenously and were immunized the following day with 10 μg of OVA in alum in the hind footpad. PLN were analysed 24, 48, 72 and 96 h after immunization. Biotin–LPETG was administered subcutaneously (300 nmol per footpad) during the last 2 h before analysis. d, Flow cytometric analysis of PLN cells showing biotin labelling of endogenous Cd40G5/G5 and Cd40G5/G5;H2−/− dendritic cells at 24 or 72 h after immunization. e, Percentage of biotin+ dendritic cells among Cd40G5/G5 and Cd40G5/G5;H2−/− dendritic cells gated as in d. Each symbol represents one mouse; bars indicate the mean. Data are pooled from two independent experiments.

Extended Data Figure 9 CD40–CD40L interaction between CD4+ T cells and dendritic cells ex vivo can occur in an antigen-independent manner.

a, Experimental setup used in b–e. Two Cd40G5/G5 dendritic cell populations were individually treated with the indicated concentrations of either OVA323–339 or LCMV-GP61–80, mixed and co-cultured for 24 h with Cd40lgSrtA/Y CD4-Cre OT-II CD4+ T cells. Biotin–LPETG was added during the last 20 min of co-culture at a final concentration of 10 μM, and cells analysed by flow cytometry. Where indicated, CD40L- or MHC-II-blocking antibodies were added at a final concentration of 150 μg ml−1 either at the beginning of co-culture (t = 0) or 2 h before analysis (t = 22). b, Flow cytometric analysis of dendritic cells treated with 1 μM peptides showing biotin labelling. c–e, Percentage of biotin+ dendritic cells gated as in b. Data are representative of three independent experiments.

Extended Data Figure 10 RNA sequencing analysis of sorted biotin+ dendritic cells.

a, Graphic representation of the protocol for dendritic cell sorting. Five hundred thousand Cd40G5/G5 CFSE-labelled dendritic cells treated ex vivo with OVA323–339 were injected subcutaneously into the hind footpad of Cd40G5/G5 recipients. After 18 h, 3 × 105CD40lgSrtA/Y CD4-Cre OT-II CD4+ T cells were transferred intravenously. Biotin–LPETG was administered subcutaneously (300 nmol per footpad) during the last 2 h before analysis. PLNs were collected 48 h after T-cell transfer and dendritic cell populations were sorted by flow cytometry and later processed for RNA-sequencing analysis. As controls, dendritic cells were also sorted from Cd40−/− mice, which were treated as above except that they received wild-type (instead of Cd40G5/G5) dendritic cells and wild-type OT-II (instead of CD40lgSrtA/Y CD4-Cre OT-II) CD4+ T cells. b, Gating strategy for sorting. Endogenous dendritic cells were first identified as B220−CD3−NK1.1−MHC-II+CD11c+CFSE−. Sorting was restricted to CD11b+XCR1− dendritic cells showing an activated phenotype (MHC-IIhi), which represent the major population involved in bystander interactions. Biotin+ and biotin− dendritic cells were gated as shown. c, Hierarchical clustering of transcriptomic profiles. Colour scheme is based upon Pearson correlation. Data are derived from a single experiment, n = 3. d, Volcano plots showing differential gene expression between biotin+ and biotin− dendritic cells. All genes used for the differential expression analysis are shown; differentially expressed genes (log2(fold change) > 1 and false-discovery rate < 0.05, see Methods) are coloured red. Data are derived from a single experiment, n = 3.

Supplementary information

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Supplementary Table 1

Differentially expressed genes between biotin+ and biotin- DC populations. This table contains a list of differentially expressed genes (log2 fold change > 1 and FDR < 0.05, see Methods section) and GSEA analysis showing selected top gene sets obtained by GO enrichment analysis with FDR indicated. Data are derived from a single experiment, n=3. (XLSX 95 kb)

Supplementary Table 2

This table contains the DNA sequences of the plasmids used in the study. (XLSX 90 kb)

Supplementary Table 3

This table contains a list of antibodies used in the study. (XLSX 10 kb)

Imaging of LIPSTIC labeling between lymphocytes in vitro

Lymphocytes were imaged as described in Extended Data Fig. 3a. The video (675X speed, total real time 90 min) shows transfer of AlexaFluor647-SELPETGG (white) from CD40L-SrtA+ T cells (green) to G5-CD40+ B cells (red) upon interaction. (AVI 21557 kb)

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Pasqual, G., Chudnovskiy, A., Tas, J. et al. Monitoring T cell–dendritic cell interactions in vivo by intercellular enzymatic labelling. Nature 553, 496–500 (2018). https://doi.org/10.1038/nature25442

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