Initiation of T cell signaling by CD45 segregation at 'close contacts' (original) (raw)

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References

  1. van der Merwe, P.A. & Dushek, O. Mechanisms for T cell receptor triggering. Nat. Rev. Immunol. 11, 47–55 (2011).
    Article CAS PubMed Google Scholar
  2. Hermiston, M.L., Xu, Z. & Weiss, A. CD45: a critical regulator of signaling thresholds in immune cells. Annu. Rev. Immunol. 21, 107–137 (2003).
    Article CAS PubMed Google Scholar
  3. Williams, A.F. & Barclay, A.N. in Handbook of Experimental Immunology 22.1–22.24 (Blackwell Scientific Publications, 1986).
  4. Fischer, E.H., Charbonneau, H., Cool, D.E. & Tonks, N.K. Tyrosine phosphatases and their possible interplay with tyrosine kinases. Ciba Found. Symp. 164, 132–140 (1992).
    CAS PubMed Google Scholar
  5. Pingel, J.T. & Thomas, M.L. Evidence that the leukocyte-common antigen is required for antigen-induced T lymphocyte proliferation. Cell 58, 1055–1065 (1989).
    Article CAS PubMed PubMed Central Google Scholar
  6. Secrist, J.P., Burns, L.A., Karnitz, L., Koretzky, G.A. & Abraham, R.T. Stimulatory effects of the protein tyrosine phosphatase inhibitor, pervanadate, on T-cell activation events. J. Biol. Chem. 268, 5886–5893 (1993).
    CAS PubMed Google Scholar
  7. Schoenborn, J.R., Tan, Y.X., Zhang, C., Shokat, K.M. & Weiss, A. Feedback circuits monitor and adjust basal Lck-dependent events in T cell receptor signaling. Sci. Signal. 4, ra59 (2011).
    Article CAS PubMed PubMed Central Google Scholar
  8. Davis, S.J. & van der Merwe, P.A. The structure and ligand interactions of CD2: implications for T-cell function. Immunol. Today 17, 177–187 (1996).
    Article CAS PubMed Google Scholar
  9. Davis, S.J. & van der Merwe, P.A. The kinetic-segregation model: TCR triggering and beyond. Nat. Immunol. 7, 803–809 (2006).
    Article CAS PubMed Google Scholar
  10. Springer, T.A. Adhesion receptors of the immune system. Nature 346, 425–434 (1990).
    Article CAS PubMed Google Scholar
  11. Burroughs, N.J., Lazic, Z. & van der Merwe, P.A. Ligand detection and discrimination by spatial relocalization: A kinase-phosphatase segregation model of TCR activation. Biophys. J. 91, 1619–1629 (2006).
    Article CAS PubMed PubMed Central Google Scholar
  12. Varma, R., Campi, G., Yokosuka, T., Saito, T. & Dustin, M.L. T cell receptor-proximal signals are sustained in peripheral microclusters and terminated in the central supramolecular activation cluster. Immunity 25, 117–127 (2006).
    Article CAS PubMed PubMed Central Google Scholar
  13. Leupin, O., Zaru, R., Laroche, T., Müller, S. & Valitutti, S. Exclusion of CD45 from the T-cell receptor signaling area in antigen-stimulated T lymphocytes. Curr. Biol. 10, 277–280 (2000).
    Article CAS PubMed Google Scholar
  14. Johnson, K.G., Bromley, S.K., Dustin, M.L. & Thomas, M.L. A supramolecular basis for CD45 tyrosine phosphatase regulation in sustained T cell activation. Proc. Natl. Acad. Sci. USA 97, 10138–10143 (2000).
    Article CAS PubMed PubMed Central Google Scholar
  15. Tan, Y.X., Zikherman, J. & Weiss, A. Novel tools to dissect the dynamic regulation of TCR signaling by the kinase Csk and the phosphatase CD45. Cold Spring Harb. Symp. Quant. Biol. 78, 131–139 (2013).
    Article PubMed PubMed Central Google Scholar
  16. Symons, A., Willis, A.C. & Barclay, A.N. Domain organization of the extracellular region of CD45. Protein Eng. 12, 885–892 (1999).
    Article CAS PubMed Google Scholar
  17. Irles, C. et al. CD45 ectodomain controls interaction with GEMs and Lck activity for optimal TCR signaling. Nat. Immunol. 4, 189–197 (2003).
    Article CAS PubMed Google Scholar
  18. James, J.R. & Vale, R.D. Biophysical mechanism of T-cell receptor triggering in a reconstituted system. Nature 487, 64–69 (2012).
    Article CAS PubMed PubMed Central Google Scholar
  19. Choudhuri, K., Wiseman, D., Brown, M.H., Gould, K. & van der Merwe, P.A. T-cell receptor triggering is critically dependent on the dimensions of its peptide-MHC ligand. Nature 436, 578–582 (2005).
    Article CAS PubMed Google Scholar
  20. Chakraborty, A.K. & Weiss, A. Insights into the initiation of TCR signaling. Nat. Immunol. 15, 798–807 (2014).
    Article CAS PubMed PubMed Central Google Scholar
  21. Barr, A.J. et al. Large-scale structural analysis of the classical human protein tyrosine phosphatome. Cell 136, 352–363 (2009).
    Article CAS PubMed PubMed Central Google Scholar
  22. Hui, E. & Vale, R.D. In vitro membrane reconstitution of the T-cell receptor proximal signaling network. Nat. Struct. Mol. Biol. 21, 133–142 (2014).
    Article CAS PubMed PubMed Central Google Scholar
  23. James, J.R. et al. The T cell receptor triggering apparatus is composed of monovalent or monomeric proteins. J. Biol. Chem. 286, 31993–32001 (2011).
    Article CAS PubMed PubMed Central Google Scholar
  24. Aricescu, A.R. et al. Structure of a tyrosine phosphatase adhesive interaction reveals a spacer-clamp mechanism. Science 317, 1217–1220 (2007).
    Article CAS PubMed Google Scholar
  25. Jentoft, N. Why are proteins O-glycosylated? Trends Biochem. Sci. 15, 291–294 (1990).
    Article CAS PubMed Google Scholar
  26. Cyster, J.G., Shotton, D.M. & Williams, A.F. The dimensions of the T lymphocyte glycoprotein leukosialin and identification of linear protein epitopes that can be modified by glycosylation. EMBO J. 10, 893–902 (1991).
    Article CAS PubMed PubMed Central Google Scholar
  27. McCall, M.N., Shotton, D.M. & Barclay, A.N. Expression of soluble isoforms of rat CD45. Analysis by electron microscopy and use in epitope mapping of anti-CD45R monoclonal antibodies. Immunology 76, 310–317 (1992).
    CAS PubMed PubMed Central Google Scholar
  28. Olveczky, B.P., Periasamy, N. & Verkman, A.S. Mapping fluorophore distributions in three dimensions by quantitative multiple angle-total internal reflection fluorescence microscopy. Biophys. J. 73, 2836–2847 (1997).
    Article CAS PubMed PubMed Central Google Scholar
  29. Evans, E.J. et al. Crystal structure and binding properties of the CD2 and CD244 (2B4)-binding protein, CD48. J. Biol. Chem. 281, 29309–29320 (2006).
    Article CAS PubMed Google Scholar
  30. Olszowy, M.W., Leuchtmann, P.L., Veillette, A. & Shaw, A.S. Comparison of p56lck and p59fyn protein expression in thymocyte subsets, peripheral T cells, NK cells, and lymphoid cell lines. J. Immunol. 155, 4236–4240 (1995).
    CAS PubMed Google Scholar
  31. Yokosuka, T. et al. Newly generated T cell receptor microclusters initiate and sustain T cell activation by recruitment of Zap70 and SLP-76. Nat. Immunol. 6, 1253–1262 (2005).
    Article CAS PubMed Google Scholar
  32. Coles, C.H. et al. Structural basis for extracellular cis and trans RPTPσ signal competition in synaptogenesis. Nat. Commun. 5, 5209 (2014).
    Article CAS PubMed Google Scholar
  33. Garcia, K.C. et al. An αβ T cell receptor structure at 2.5 Å and its orientation in the TCR-MHC complex. Science 274, 209–219 (1996).
    Article CAS PubMed Google Scholar
  34. Garboczi, D.N. et al. Structure of the complex between human T-cell receptor, viral peptide and HLA-A2. Nature 384, 134–141 (1996).
    Article CAS PubMed Google Scholar
  35. Burroughs, N.J. et al. Boltzmann energy-based image analysis demonstrates that extracellular domain size differences explain protein segregation at immune synapses. PLoS Comput. Biol. 7, e1002076 (2011).
    Article CAS PubMed PubMed Central Google Scholar
  36. van der Merwe, P.A., McNamee, P.N., Davies, E.A., Barclay, A.N. & Davis, S.J. Topology of the CD2-CD48 cell-adhesion molecule complex: implications for antigen recognition by T cells. Curr. Biol. 5, 74–84 (1995).
    Article CAS PubMed Google Scholar
  37. Parsey, M.V. & Lewis, G.K. Actin polymerization and pseudopod reorganization accompany anti-CD3-induced growth arrest in Jurkat T cells. J. Immunol. 151, 1881–1893 (1993).
    CAS PubMed Google Scholar
  38. Wang, J.H. & Reinherz, E.L. The structural basis of αβ T-lineage immune recognition: TCR docking topologies, mechanotransduction, and co-receptor function. Immunol. Rev. 250, 102–119 (2012).
    Article CAS PubMed PubMed Central Google Scholar
  39. Roncagalli, R. et al. Quantitative proteomics analysis of signalosome dynamics in primary T cells identifies the surface receptor CD6 as a Lat adaptor-independent TCR signaling hub. Nat. Immunol. 15, 384–392 (2014).
    Article CAS PubMed PubMed Central Google Scholar
  40. Irving, B.A., Alt, F.W. & Killeen, N. Thymocyte development in the absence of pre-T cell receptor extracellular immunoglobulin domains. Science 280, 905–908 (1998).
    Article CAS PubMed Google Scholar
  41. Aricescu, A.R., Lu, W. & Jones, E.Y. A time- and cost-efficient system for high-level protein production in mammalian cells. Acta Crystallogr. D Biol. Crystallogr. 62, 1243–1250 (2006).
    Article CAS PubMed Google Scholar
  42. Cockett, M.I., Bebbington, C.R. & Yarranton, G.T. High level expression of tissue inhibitor of metalloproteinases in Chinese hamster ovary cells using glutamine synthetase gene amplification. Bio/Technology 8, 662–667 (1990).
    CAS Google Scholar
  43. Zufferey, R., Nagy, D., Mandel, R.J., Naldini, L. & Trono, D. Multiply attenuated lentiviral vector achieves efficient gene delivery in vivo. Nat. Biotechnol. 15, 871–875 (1997).
    Article CAS PubMed Google Scholar
  44. Chang, V.T. et al. Glycoprotein structural genomics: solving the glycosylation problem. Structure 15, 267–273 (2007).
    Article CAS PubMed PubMed Central Google Scholar
  45. Davis, S.J. et al. Ligand binding by the immunoglobulin superfamily recognition molecule CD2 is glycosylation-independent. J. Biol. Chem. 270, 369–375 (1995).
    Article PubMed Google Scholar
  46. Walter, T.S. et al. Lysine methylation as a routine rescue strategy for protein crystallization. Structure 14, 1617–1622 (2006).
    Article CAS PubMed PubMed Central Google Scholar
  47. Dunne, P.D. et al. DySCo: quantitating associations of membrane proteins using two-color single-molecule tracking. Biophys. J. 97, L5–L7 (2009).
    Article CAS PubMed PubMed Central Google Scholar
  48. Otwinowski, Z. & Minor, W. Processing of X-ray diffraction data collected in oscillation mode. Methods Enzymol. 276, 307–326 (1997).
    Article CAS PubMed Google Scholar
  49. Winter, G. xia2: an expert system for macromolecular crystallography data reduction. J. Appl. Cryst. 43, 186–190 (2010).
    Article CAS Google Scholar
  50. Evans, P.R. & Murshudov, G.N. How good are my data and what is the resolution? Acta Crystallogr. D Biol. Crystallogr. 69, 1204–1214 (2013).
    Article CAS PubMed PubMed Central Google Scholar
  51. Kabsch, W. XDS. Acta Crystallogr. D Biol. Crystallogr. 66, 125–132 (2010).
    Article CAS PubMed PubMed Central Google Scholar
  52. Schneider, T.R. & Sheldrick, G.M. Substructure solution with SHELXD. Acta Crystallogr. D Biol. Crystallogr. 58, 1772–1779 (2002).
    Article CAS PubMed Google Scholar
  53. Vonrhein, C., Blanc, E., Roversi, P. & Bricogne, G. Automated structure solution with autoSHARP. Methods Mol. Biol. 364, 215–230 (2007).
    CAS PubMed Google Scholar
  54. Perrakis, A., Morris, R. & Lamzin, V.S. Automated protein model building combined with iterative structure refinement. Nat. Struct. Biol. 6, 458–463 (1999).
    Article CAS PubMed Google Scholar
  55. Emsley, P., Lohkamp, B., Scott, W.G. & Cowtan, K. Features and development of Coot. Acta Crystallogr. D Biol. Crystallogr. 66, 486–501 (2010).
    Article CAS PubMed PubMed Central Google Scholar
  56. Bricogne, G. et al. BUSTER version 2.11.2 (Global Phasing Ltd., 2011).
  57. Murshudov, G.N., Vagin, A.A. & Dodson, E.J. Refinement of macromolecular structures by the maximum-likelihood method. Acta Crystallogr. D Biol. Crystallogr. 53, 240–255 (1997).
    Article CAS PubMed Google Scholar
  58. Afonine, P.V. et al. Towards automated crystallographic structure refinement with phenix.refine. Acta Crystallogr. D Biol. Crystallogr. 68, 352–367 (2012).
    Article CAS PubMed PubMed Central Google Scholar
  59. McCoy, A.J. et al. Phaser crystallographic software. J. Appl. Crystallogr. 40, 658–674 (2007).
    CAS PubMed PubMed Central Google Scholar
  60. Chen, V.B. et al. MolProbity: all-atom structure validation for macromolecular crystallography. Acta Crystallogr. D Biol. Crystallogr. 66, 12–21 (2009).
    Article CAS PubMed PubMed Central Google Scholar
  61. Stuart, D.I., Levine, M., Muirhead, H. & Stammers, D.K. Crystal structure of cat muscle pyruvate kinase at a resolution of 2.6 Å. J. Mol. Biol. 134, 109–142 (1979).
    Article CAS PubMed Google Scholar
  62. Ohi, M., Li, Y., Cheng, Y. & Walz, T. Negative staining and image classification—powerful tools in modern electron microscopy. Biol. Proced. Online 6, 23–34 (2004).
    Article CAS PubMed PubMed Central Google Scholar
  63. Ludtke, S.J., Baldwin, P.R. & Chiu, W. EMAN: semiautomated software for high-resolution single-particle reconstructions. J. Struct. Biol. 128, 82–97 (1999).
    Article CAS PubMed Google Scholar
  64. van Heel, M., Harauz, G., Orlova, E.V., Schmidt, R. & Schatz, M. A new generation of the IMAGIC image processing system. J. Struct. Biol. 116, 17–24 (1996).
    Article CAS PubMed Google Scholar
  65. Frank, J. et al. SPIDER and WEB: processing and visualization of images in 3D electron microscopy and related fields. J. Struct. Biol. 116, 190–199 (1996).
    Article CAS PubMed Google Scholar
  66. Jönsson, P. et al. Hydrodynamic trapping of molecules in lipid bilayers. Proc. Natl. Acad. Sci. USA 109, 10328–10333 (2012).
    Article PubMed PubMed Central Google Scholar
  67. Edelstein, A., Amodaj, N., Hoover, K., Vale, R. & Stuurman, N. Computer control of microscopes using μManager. Curr. Protoc. Mol. Biol. 14, 14.20 (2010).
    Google Scholar
  68. Crocker, J.C. & Grier, D.G. When like charges attract: the effects of geometrical confinement on long-range colloidal interactions. Phys. Rev. Lett. 77, 1897–1900 (1996).
    Article CAS PubMed Google Scholar
  69. Weimann, L. et al. A quantitative comparison of single-dye tracking analysis tools using Monte Carlo simulations. PloS ONE 8, e64287 (2013).
    Article CAS PubMed PubMed Central Google Scholar

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Acknowledgements

The authors thank R.A. Cornall, M.L. Dustin and P.A. van der Merwe for comments on the manuscript and S. Ikemizu for useful discussions about the structure. We also thank W. Lu and T. Walter for technical support with protein expression and crystallization, the staff at Diamond Light Source beamlines I02, I03 and I04-1 (proposal mx10627) and European Synchrotron Radiation Facility beamlines ID23EH1 and ID23EH2 for assistance at the synchrotrons, G. Sutton for assistance with MALS experiments, and M. Fritzsche for advice on the calcium analysis. This work was funded by the Wellcome Trust (098274/Z/12/Z to S.J.D.; 090532/Z/09/Z to R.J.C.G.; 090708/Z/09/Z to D.K.), the UK Medical Research Council (G0700232 to A.R.A.), the Royal Society (UF120277 to S.F.L.) and Cancer Research UK (C20724/A14414 to C.S.; C375/A10976 to E.Y.J.). The Oxford Division of Structural Biology is part of the Wellcome Trust Centre for Human Genetics, Wellcome Trust Core Award Grant Number 090532/Z/09/Z. We acknowledge financial support from Instruct, an ESFRI Landmark Project. The OPIC electron microscopy facility was funded by a Wellcome Trust JIF award (060208/Z/00/Z).

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Author notes

  1. Veronica T Chang
    Present address: Present address: Division of Structural Biology, Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK.,
  2. Veronica T Chang, Ricardo A Fernandes, Kristina A Ganzinger and Steven F Lee: These authors contributed equally to this work.

Authors and Affiliations

  1. Radcliffe Department of Medicine and MRC Human Immunology Unit, John Radcliffe Hospital, University of Oxford, Oxford, UK
    Veronica T Chang, Ricardo A Fernandes, Yuan Lui, Elizabeth Huang & Simon J Davis
  2. Department of Chemistry, University of Cambridge, Cambridge, UK
    Kristina A Ganzinger, Steven F Lee, James McColl, Peter Jönsson, Matthieu Palayret & David Klenerman
  3. Division of Structural Biology, Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
    Christian Siebold, Karl Harlos, Charlotte H Coles, E Yvonne Jones, Robert J C Gilbert & A Radu Aricescu

Authors

  1. Veronica T Chang
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  2. Ricardo A Fernandes
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  3. Kristina A Ganzinger
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  4. Steven F Lee
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  5. Christian Siebold
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  6. James McColl
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  7. Peter Jönsson
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  8. Matthieu Palayret
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  9. Karl Harlos
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  10. Charlotte H Coles
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  11. E Yvonne Jones
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  12. Yuan Lui
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  13. Elizabeth Huang
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  14. Robert J C Gilbert
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  15. David Klenerman
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  16. A Radu Aricescu
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  17. Simon J Davis
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Contributions

V.T.C., R.A.F., K.A.G., S.F.L., E.Y.J., R.J.C.G., D.K., A.R.A. and S.J.D. designed experiments; V.T.C., R.A.F., K.A.G., S.F.L., J.M., P.J., M.P., K.H., C.S., C.H.C., Y.L., E.H., R.J.C.G. and A.R.A. performed experiments; R.A.F., K.A.G., S.F.L., C.S., E.Y.J., R.J.C.G., V.T.C., A.R.A., D.K. and S.J.D. drafted and/or edited the manuscript.

Corresponding authors

Correspondence toDavid Klenerman, A Radu Aricescu or Simon J Davis.

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The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Proteins used in the study and representative electron density.

(a) (Left) N-glycosylated proteins analyzed using MALS (lanes 1–3), cryo-EM (lane 4) and VA-TIRF (lanes 5–7): CD45RABC (lane 1), CD45R0 (lane 2), CD45d1–d4 (lane 3) and Sema6A-CD45R0 (lane 4), and N-terminally HaloTag-tagged forms of CD45RABC (lane 5), CD45R0 (lane 6) and CD45d1–d4 (lane 7). Top and bottom panels show 10% non-reducing (NR) and reducing (R) SDS-PAGE gels, respectively. (Right) Endo F1-deglycosylated proteins used for growing crystals: CD45d1–d4 (lane 1), CD45d1–d3 (lane 2), CD45d1d2 (lane 3) and rCD45d3d4 (lane 4). Top and bottom panels show 15% NR and R SDS-PAGE gels, respectively. (b) Electron density maps for CD45d1d2. (i) KPtCl4-SAD phased, phase-extended and density modified map from SHARP (calculated to 2.3 Å and contoured at 1.1 σ). The two CD45d1d2 molecules in the crystallographic asymmetric unit are colored yellow and magenta, respectively. (ii–iii) Close-up view of the density for the residual GlcNAc (remaining following Endo F1 cleavage of N-linked oligosaccharides) at residue N378. In (ii), a region of the experimental map in (i) is shown. In (iii), the SigmaA-weighted 2Fo-Fc map of the final refinement step from autoBUSTER, calculated to 2.3 Å resolution and contoured at 1.1 σ is shown. (c) rCD45d3d4 maps. (i) Ta6Br12-SAD phased, phase-extended and density modified map from SHARP (calculated to 2.45 Å and contoured at 1.1 σ). The two rCD45d3d4 molecules in the crystallographic asymmetric unit are colored yellow and magenta, respectively. (ii–iii) Close-up view of electron density for the residual GlcNAc at residue N502. In (ii), a region of the experimental map in (i) is shown. In (iii), the SigmaA-weighted 2Fo-Fc map of the final refinement from autoBUSTER, calculated to 2.45 Å resolution contoured at 1.1 σ is shown. (d) CD45d1–d4 maps. (i–ii) Stick representation of the final CD45d1–d4 model showing the two molecules in the asymmetric unit. (i) SigmaA-weighted 2Fo-Fc map of the final refinement step from autoBUSTER contoured at 1.0 σ. The model is in atomic colouring (oxygen: red; nitrogen: blue; sulphur: orange; carbon: yellow/red). In (ii) the two CD45d1–d4 molecules per asymmetric unit are colour-coded according to temperature factors (green, low B and red, high B).

Supplementary Figure 2 Glycosylation and conservation at the ‘top’ and base of the modular region of CD45.

(a) Thirteen animal CD45 sequences were aligned using MULTALIN (data not shown). Glycosylation sequons in these sequences were identified using NetNGlyc, and the putatively glycosylated Asn-equivalent positions mapped onto the human CD45d1–d4 crystal structure using the alignment. Sites were colored according to conservation level using the alignment. (b) Electrostatic potential calculated using PyMol (-5 KbT/ec (red) to +5 KbT/ec (blue)) is shown. This analysis identifies a surface with asymmetric charge and N-glycan distribution perhaps used to access substrates (shaded region in c). Views of the d1–d4 region along its long axis from the top (d) and bottom (e) depict: (left) residue conservation at the top and base (residues are colored according to the number of identical or conservatively substituted residues at each position in an alignment of 11 mammalian sequences (data not shown; see Online Methods for residue groupings)); (middle) the network of conserved residues forming the caps (purple, shown in stick format); and (right) the surface electrostatic potential calculated using PyMol (-5 KbT/ec (red) to +5 KbT/ec (blue)). For the sake of clarity, the middle panels in d,e are drawn slightly enlarged relative to the other panels.

Supplementary Figure 3 Electron microscopy.

(a) Examples of Sema6A-CD45R0 negative-staining EM raw images corresponding to each of the 50 classes shown in (b). Each class contained between 127 and 399 such images. (b) Sema6A-CD45R0 negative-staining EM class averages. The gallery of all fifty class-sums derived from the Sema6A-CD45R0 images (top) and their corresponding I-images (bottom). (c) Fourier Shell Correlation of the reconstruction of Sema6A-CD45R0 made using SPIDER software, showing a resolution at FSC = 0.5 of 33 Å. (d) The original class averages of the Sema6A-CD45R0 negative stain images, prior to 3D reconstruction, compared to reprojections of the reconstruction itself. In each case the class averages are grouped by reference to the reprojections they agree with best. For each block of images (1–24), the final image (marked by yellow borders) is the reprojection best matching the other image(s) shown in that block. (e) Two views (as in Figure 3c of the main text) of the reconstruction fitted with the Sema6A and CD45d1–d4 region and with the mucin linker modeled in between them. Pairs of images are shown, in which the first is the same as in Figure 3c and the second has superimposed a map calculated for the Sema6A-CD45R0 atomic model filtered to 33 Å to show that the mucin linker is unresolved.

Supplementary Figure 4 Distribution of non-chimeric and chimeric forms of Lck versus CD45 and signaling at close contacts.

(a) Additional examples of close contact formation and CD45 segregation observed for J.CaM1.6 T cells expressing mLck. (b) Mean time taken between initial cell contact with OKT3-coated glass surface and calcium release (maximum Fluo-4 fluorescence signal; lag triggering time), at 20°C. (c–f) Additional examples of close contact formation and CD45 segregation observed for J.CaM1.6 T cells expressing TMLck (c), RABCLck (d), R0Lck (e) and d1–d4Lck (f) interacting with an OKT3-coated surface. The Lck constructs were labeled via a HaloTag and HaloTag TMR (red) and CD45 was labeled using Alexa Fluor 488-tagged Gap 8.3 Fab fragments (green). Cell positions were estimated from white-light transmission images (white circles). Imaging data in (a) and (c–f) was taken at 20°C; scale bars, 5 μm. (g) Fractions of J.CaM1.6 T cells expressing the constructs shown in Figure 5a,b of the main text eliciting a calcium response upon contacting OKT3-coated glass at 37°C. Short-, but not the long-forms of Lck rescue OKT3-triggered signaling in J.CaM1.6 T cells. The shaded part of the graph is the 95% confidence interval for the J.CaM1.6 dataset defining the range of values for individual calcium responses indistinguishable from those of the J.CaM1.6 cells. (h) Calcium responses of T cells contacting glass surfaces in the presence and absence of anti-TCR antibody. Glass surfaces were coated with OKT3 antibody or bovine IgG (“IgG”). The fractions of native CD4+ (“CD4+”) and normal or mutant Jurkat T cells expressing or not expressing the TCR that elicited a calcium response upon contacting the coated surfaces at 37°C are shown. The shaded part of the graph is the 95% confidence interval for the J.RT3-T3.5 dataset defining the range of values for individual calcium responses indistinguishable from those of the J.RT3-T3.5 cells. (i) Individual calcium (Fluo-4) intensity profiles for triggered cells contacting OKT3 or IgG-coated glass surfaces at 37°C. (j) Mean intensity profiles of 100 randomly chosen cells triggered in response to contacting OKT3 or IgG-coated glass surfaces at 37°C (mean ± S.D.). The data were collected in at least three independent experiments.

Source data

Supplementary Figure 5 Principle of R2 analysis.

(a) Pixel intensities from images that are anti-correlated when plotted against each other display a poor linear fit, whereas correlated images (b) display a good fit.

Supplementary Figure 6 Close contacts formed with an SLB.

(a) Montage from a time-lapse showing the spatio-temporal organization of CD45 (labeled with Alexa Fluor 488-tagged Gap 8.3 Fab, green) during contact of a Jurkat T cell expressing a transmembrane-anchored, non-signaling, T92A-mutated form of CD48 (CD48+) and a SLB containing rCD2 (Alexa Fluor 647-tagged, red). (b) Equivalent to (a) except that the fluorophores are swapped (CD45 is labeled with Alexa Fluor 647-tagged Gap 8.3 Fab, green, and rCD2 is tagged with Alexa Fluor 488, red). (c–e) Montages show distribution of a truncated form of CD45 (HA-CD45, c), TCR (d) and mLck (e) each labeled with HaloTag TMR, red) versus CD45 (green) during contact of a CD48+ Jurkat T cell with a SLB containing rCD2. The time intervals between frames shown are 5 s; data was collected at 37°C. (f) More examples of mLck versus CD45 distribution at T cell contacts with SLBs. Examples of rarer multi-focal contacts of CD48+ Jurkat T cells with rCD2-containing SLBs showing the organization of CD45 (labeled with Alexa Fluor 488-tagged Gap 8.3 Fab, green) and mLck labeled with HaloTag TMR (red).

Supplementary Figure 7 Non-centripetal motion of Zap70 clusters.

(a) Zap70 cluster trajectories (colour-coded with time). (b) Schematic depiction of the trajectory analysis: the vector from the center of the contact to the end of a given trajectory is calculated as well as the vector from the start to the end of the trajectory. The angle between the two vectors is 0/180° for centripetal motion, and takes a random value for random motion. (c) Histogram showing the frequencies for all calculated angles γ for Zap70 trajectories observed in Jurkat and JRT3-T3.5 cells during the first 180 s post contact formation with a rCD2 bilayer. Total number of trajectories analyzed: 2234 (Jurkat), 2158 (JRT3-T3.5), total number of analyzed cells: 28 (Jurkat), 36 (JRT3-T3.5). The data were collected in at least three independent experiments.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–7 and Supplementary Tables 1 and 2 (PDF 7193 kb)

Calcium signaling in mLck-expressing J.CaM1.6 cells.

Representative movie (raw data) showing the influx of calcium (change in Fluo-4 intensity) in cells expressing mLck upon interaction with an ‘activating’ surface (OKT3-coated glass) at 20°C. Movie is part of the data presented in Figure 5g. The movie is recorded as 300 ms frames every 1 second and played at 33 frames per second. Scale bar, 10 μm. (AVI 10668 kb)

Calcium signaling in CD45 RABCLck-expressing J.CaM1.6 cells.

Representative movie (raw data) showing the influx of calcium (change in Fluo-4 intensity) in cells expressing RABCLck upon interaction with an ‘activating’ surface (OKT3-coated glass) at 20°C. Movie is part of the data presented in Figure 5g. The movie is recorded as 300 ms frames every 1 second and played at 33 frames per second. Scale bar, 10 μm. (AVI 10769 kb)

Segregation of CD45 and mLck on SLBs.

Representative movie (raw data) showing that mLck and CD45 segregate immediately upon contact of Jurkat T cells expressing CD48 with a rCD2-containing SLB. CD45 is labeled with Alexa Fluor 488-tagged Fab (green) and mLck is labeled with TMR via a HaloTag® (red). The movie is played back 10-fold faster than real-time (10 frames per second). Scale bar, 2 μm. (AVI 10420 kb)

Contact dependence of CD45 exclusion on SLBs.

Representative movie (raw data) showing that CD45 exclusion on rCD2-containing SLBs is contact dependent. Normal Jurkat T cells (i.e. cells not expressing CD48) do not form a stable cell-SLB contact: rCD2 in the SLB (left panel) does not accumulate and CD45 expressed by the cells (right panel) is evenly distributed (i.e. there is no exclusion). CD45 is labeled with Alexa Fluor 488-tagged Fab (green) and rCD2 is tagged with Alexa Fluor 647 (red). At 0.18 a white light transmission image of the same area is shown indicating the presence of multiple Jurkat T cells above the SLB. The movie is played back 10-fold faster than real-time (10 frames per second). Scale bar, 2 μm. (AVI 9961 kb)

Zap70 recruitment to contacts depleted of CD45 in Jurkat T cells.

Representative movie showing simultaneous CD45 exclusion and Zap70 cluster formation in a CD48-expressing Jurkat T cell interacting with a rCD2-containing SLB. The movie combines raw data for the CD45 channel (i.e. CD45 labeled with Alexa Fluor 488-tagged Fab (green), left) with a movie of the Zap70 channel (i.e. Zap70 labeled with HaloTag® TMR, red) prior to (raw data, middle) and post application of a bandpass filter (right; Zap70 clusters identified by the analysis software are marked with a circle and running number). The movie is played back 5-fold faster than real-time (10 frames per second). Scale bar, 2 μm. (AVI 2244 kb)

Zap70 recruitment in contacts depleted of CD45 in J.RT3-T3.5 T cells.

Movie collage analogous to Supplementary Video 5 for a CD48-expressing J.RT3-T3.5 T cell. The movie is played back 5-fold faster than real-time (10 frames per second). Scale bar, 2 μm. (AVI 2665 kb)

TCR triggering without ligands (an animation).

The animation shows the resting T cell surface and the changes in organization of signaling proteins that occur when the cell interacts with an SLB containing the cell adhesion molecule CD2, leading to ligand-independent TCR triggering. The early stages of the animation strive to portray the rapid movements of the proteins and the crowded nature of the T cell surface. The interaction of CD2 (in the SLB) with a non-signaling form of CD48 (on the T cell) results in close-contact formation and the exclusion of CD45. Because contact with the SLB is over a large area, the exclusion of CD45 results in strong TCR phosphorylation by non-excluded Lck kinase, leading to ZAP70 recruitment and calcium signaling in the absence of TCR ligands. The animation finishes with an image of an SLB-contacting Jurkat T cell from the present study showing the distribution of CD45 (green) and Lck (red; see Supplementary Fig. 6f). The molecules shown are: the TCR (terracotta; based on a model using TCRαβ PDB accession code 1AO7, CD3ɛδ PDB 1XIW, CD3ɛγ PDB 1JBJ and CD3ξξ PDB 2HAC), CD45 (light green; based on a model using the CD45 ECD structure described herein and the CD45 phosphatase domain PDB 1YGR); Lck in an active conformation (red; based on Src PDB 1Y57 and the zinc-clasp motif in PDB 1Q68); CD48 (dark green) and CD2 (yellow) modeled on human CD2 (using PDB 1HNF); CD48/CD2 complexes based on a model of the CD2/CD58 complex (using PDB 1QA9); and ZAP-70 (purple; PDB 4K2R). All transmembrane regions and CD3 cytoplasmic regions were modeled as α-helixes in the Build Structure function in UCSF Chimera (http://www.cgl.ucsf.edu/chimera/), using the native protein sequences. A probe radius of 8 Å was used to generate all model surfaces. The animation was built using Blender software (http://www.blender.org). (MP4 131031 kb)

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Chang, V., Fernandes, R., Ganzinger, K. et al. Initiation of T cell signaling by CD45 segregation at 'close contacts'.Nat Immunol 17, 574–582 (2016). https://doi.org/10.1038/ni.3392

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