Skip to main content

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

  • Article
  • Published:

T cell cytolytic capacity is independent of initial stimulation strength

Nature Immunology volume 19, pages 849–858 (2018)Cite this article

Subjects

Abstract

How cells respond to myriad stimuli with finite signaling machinery is central to immunology. In naive T cells, the inherent effect of ligand strength on activation pathways and endpoints has remained controversial, confounded by environmental fluctuations and intercellular variability within populations. Here we studied how ligand potency affected the activation of CD8+ T cells in vitro, through the use of genome-wide RNA, multi-dimensional protein and functional measurements in single cells. Our data revealed that strong ligands drove more efficient and uniform activation than did weak ligands, but all activated cells were fully cytolytic. Notably, activation followed the same transcriptional pathways regardless of ligand potency. Thus, stimulation strength did not intrinsically dictate the T cell–activation route or phenotype; instead, it controlled how rapidly and simultaneously the cells initiated activation, allowing limited machinery to elicit wide-ranging responses.

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

Access options

Buy this article

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

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

Fig. 1: A burst of transcriptional regulatory machinery characterizes early T cell activation.
Fig. 2: Early-response genes can be TCR dependent or TCR independent.
Fig. 3: Ligand potency determines activation rate.
Fig. 4: Accounting for activation status reveals minimal differential expression between cells activated with different ligands.
Fig. 5: Cell number is associated with ligand potency, but maximum division number is not.
Fig. 6: Effector T cells exhibit the same spectrum of phenotypes regardless of the strength of the primary stimulation.
Fig. 7: All activated CD8+ T cells can degranulate regardless of the affinity of their primary stimulus.

Similar content being viewed by others

References

  1. Brownlie, R. J. & Zamoyska, R. T cell receptor signalling networks: branched, diversified and bounded. Nat. Rev. Immunol. 13, 257–269 (2013).

    Article  CAS  PubMed  Google Scholar 

  2. Cantrell, D. Signaling in lymphocyte activation. Cold Spring Harb. Perspect. Biol 7, a018788 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  3. Conley, J. M., Gallagher, M. P. & Berg, L. J. T. cells and gene regulation: the switching on and turning up of genes after T cell receptor stimulation in CD8 T cells. Front. Immunol. 7, 76 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  4. Zikherman, J. & Au-Yeung, B. The role of T cell receptor signaling thresholds in guiding T cell fate decisions. Curr. Opin. Immunol. 33, 43–48 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Daniels, M. A. et al. Thymic selection threshold defined by compartmentalization of Ras/MAPK signalling. Nature 444, 724–729 (2006).

    Article  CAS  PubMed  Google Scholar 

  6. Hogquist, K. A. et al. T cell receptor antagonist peptides induce positive selection. Cell 76, 17–27 (1994).

    Article  CAS  PubMed  Google Scholar 

  7. Fu, G. et al. Themis sets the signal threshold for positive and negative selection in T-cell development. Nature 504, 441–445 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Ozga, A. J. et al. pMHC affinity controls duration of CD8+ T cell-DC interactions and imprints timing of effector differentiation versus expansion. J. Exp. Med. 213, 2811–2829 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Zehn, D., Lee, S. Y. & Bevan, M. J. Complete but curtailed T-cell response to very low-affinity antigen. Nature 458, 211–214 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Skokos, D. et al. Peptide-MHC potency governs dynamic interactions between T cells and dendritic cells in lymph nodes. Nat. Immunol. 8, 835–844 (2007).

    Article  CAS  PubMed  Google Scholar 

  11. Denton, A. E. et al. Affinity thresholds for naive CD8+ CTL activation by peptides and engineered influenza A viruses. J. Immunol. 187, 5733–5744 (2011).

    Article  CAS  PubMed  Google Scholar 

  12. King, C. G. et al. T cell affinity regulates asymmetric division, effector cell differentiation, and tissue pathology. Immunity 37, 709–720 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Palmer, E., Drobek, A. & Stepanek, O. Opposing effects of actin signaling and LFA-1 on establishing the affinity threshold for inducing effector T-cell responses in mice. Eur. J. Immunol. 46, 1887–1901 (2016).

    Article  CAS  PubMed  Google Scholar 

  14. Auphan-Anezin, N., Verdeil, G. & Schmitt-Verhulst, A. M. Distinct thresholds for CD8 T cell activation lead to functional heterogeneity: CD8 T cell priming can occur independently of cell division. J. Immunol. 170, 2442–2448 (2003).

    Article  CAS  PubMed  Google Scholar 

  15. Man, K. et al. The transcription factor IRF4 is essential for TCR affinity-mediated metabolic programming and clonal expansion of T cells. Nat. Immunol. 14, 1155–1165 (2013).

    Article  CAS  PubMed  Google Scholar 

  16. Marchingo, J. M. et al. T cell signaling. Antigen affinity, costimulation, and cytokine inputs sum linearly to amplify T cell expansion. Science 346, 1123–1127 (2014).

    Article  CAS  PubMed  Google Scholar 

  17. Navarro, M. N., Feijoo-Carnero, C., Arandilla, A. G., Trost, M. & Cantrell, D. A. Protein kinase D2 is a digital amplifier of T cell receptor-stimulated diacylglycerol signaling in naïve CD8+ T cells. Sci. Signal. 7, ra99 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  18. Preston, G. C. et al. Single cell tuning of Myc expression by antigen receptor signal strength and interleukin-2 in T lymphocytes. EMBO J. 34, 2008–2024 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Rosette, C. et al. The impact of duration versus extent of TCR occupancy on T cell activation: a revision of the kinetic proofreading model. Immunity 15, 59–70 (2001).

    Article  CAS  PubMed  Google Scholar 

  20. Yao, S. et al. Interferon regulatory factor 4 sustains CD8+ T cell expansion and effector differentiation. Immunity 39, 833–845 (2013).

    Article  CAS  PubMed  Google Scholar 

  21. Balyan, R. et al. Modulation of naive CD8 T cell response features by ligand density, affinity, and continued signaling via internalized TCRs. J. Immunol. 198, 1823–1837 (2017).

    Article  CAS  PubMed  Google Scholar 

  22. Hommel, M. & Hodgkin, P. D. TCR affinity promotes CD8+ T cell expansion by regulating survival. J. Immunol. 179, 2250–2260 (2007).

    Article  CAS  PubMed  Google Scholar 

  23. Nayar, R. et al. Graded levels of IRF4 regulate CD8+ T cell differentiation and expansion, but not attrition, in response to acute virus infection. J. Immunol. 192, 5881–5893 (2014).

    Article  CAS  PubMed  Google Scholar 

  24. Marchingo, J. M. et al. T-cell stimuli independently sum to regulate an inherited clonal division fate. Nat. Commun. 7, 13540 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. van Gisbergen, K. P. et al. The costimulatory molecule CD27 maintains clonally diverse CD8+ T cell responses of low antigen affinity to protect against viral variants. Immunity 35, 97–108 (2011).

    Article  PubMed  CAS  Google Scholar 

  26. Voisinne, G. et al. T cells integrate local and global cues to discriminate between structurally similar antigens. Cell Rep. 11, 1208–1219 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Au-Yeung, B. B. et al. IL-2 modulates the TCR signaling threshold for CD8 but not CD4 T cell proliferation on a single-cell level. J. Immunol. 198, 2445–2456 (2017).

    Article  CAS  PubMed  Google Scholar 

  28. Verdeil, G., Puthier, D., Nguyen, C., Schmitt-Verhulst, A. M. & Auphan-Anezin, N. STAT5-mediated signals sustain a TCR-initiated gene expression program toward differentiation of CD8 T cell effectors. J. Immunol. 176, 4834–4842 (2006).

    Article  CAS  PubMed  Google Scholar 

  29. Altan-Bonnet, G. & Germain, R. N. Modeling T cell antigen discrimination based on feedback control of digital ERK responses. PLoS Biol. 3, e356 (2005).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  30. Allison, K. A. et al. Affinity and dose of TCR engagement yield proportional enhancer and gene activity in CD4. T cells. eLife 5, e10134 (2016).

    Article  PubMed  Google Scholar 

  31. Haghverdi, L., Büttner, M., Wolf, F. A., Buettner, F. & Theis, F. J. Diffusion pseudotime robustly reconstructs lineage branching. Nat. Methods 13, 845–848 (2016).

    Article  CAS  PubMed  Google Scholar 

  32. Pollizzi, K. N. & Powell, J. D. Integrating canonical and metabolic signalling programmes in the regulation of T cell responses. Nat. Rev. Immunol. 14, 435–446 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Tan, T. C. J. et al. Suboptimal T-cell receptor signaling compromises protein translation, ribosome biogenesis, and proliferation of mouse CD8 Tcells.Proc. Natl. Acad. Sci. USA 114, E6117–E6126 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Moran, A. E. et al. T cell receptor signal strength in Treg and iNKT cell development demonstrated by a novel fluorescent reporter mouse. J. Exp. Med. 208, 1279–1289 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Ashouri, J. F. & Weiss, A. Endogenous Nur77 is a specific indicator of antigen receptor signaling in human T and B cells. J. Immunol. 198, 657–668 (2017).

    Article  CAS  PubMed  Google Scholar 

  36. Au-Yeung, B. B. et al. A sharp T-cell antigen receptor signaling threshold for T-cell proliferation.Proc. Natl. Acad. Sci. USA 111, E3679–E3688 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Alam, S. M. et al. Qualitative and quantitative differences in T cell receptor binding of agonist and antagonist ligands. Immunity 10, 227–237 (1999).

    Article  CAS  PubMed  Google Scholar 

  38. Lun, A. T. L., Richard, A. C. & Marioni, J. C. Testing for differential abundance in mass cytometry data. Nat. Methods 14, 707–709 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Prlic, M., Hernandez-Hoyos, G. & Bevan, M. J. Duration of the initial TCR stimulus controls the magnitude but not functionality of the CD8+ T cell response. J. Exp. Med. 203, 2135–2143 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. van Stipdonk, M. J. et al. Dynamic programming of CD8+ T lymphocyte responses. Nat. Immunol. 4, 361–365 (2003).

    Article  PubMed  CAS  Google Scholar 

  41. Yachi, P. P., Ampudia, J., Zal, T. & Gascoigne, N. R. Altered peptide ligands induce delayed CD8-T cell receptor interaction-a role for CD8 in distinguishing antigen quality. Immunity 25, 203–211 (2006).

    Article  CAS  PubMed  Google Scholar 

  42. Zahm, C. D., Colluru, V. T. & McNeel, D. G. Vaccination with high-affinity epitopes impairs antitumor efficacy by increasing PD-1 expression on CD8+ T cells. Cancer Immunol. Res. 5, 630–641 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Moreau, H. D. et al. Dynamic in situ cytometry uncovers T cell receptor signaling during immunological synapses and kinapses in vivo. Immunity 37, 351–363 (2012).

    Article  CAS  PubMed  Google Scholar 

  44. Pipkin, M. E. et al. Interleukin-2 and inflammation induce distinct transcriptional programs that promote the differentiation of effector cytolytic T cells. Immunity 32, 79–90 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Heinzel, S. et al. A Myc-dependent division timer complements a cell-death timer to regulate T cell and B cell responses. Nat. Immunol. 18, 96–103 (2017).

    Article  CAS  PubMed  Google Scholar 

  46. Tkach, K. E. et al. T cellstranslate individual, quantal activation into collective, analog cytokine responses via time-integrated feedbacks. eLife 3, e01944 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  47. Chen, J. L. et al. Ca2+ release from the endoplasmic reticulum of NY-ESO-1-specific T cells is modulated by the affinity of TCR and by the use of the CD8 coreceptor. J. Immunol. 184, 1829–1839 (2010).

    Article  CAS  PubMed  Google Scholar 

  48. Le Borgne, M. et al. Real-time analysis of calcium signals during the early phase of T cell activation using a genetically encoded calcium biosensor. J. Immunol. 196, 1471–1479 (2016).

    Article  PubMed  CAS  Google Scholar 

  49. Mayya, V. & Dustin, M. L. What Scales the T Cell Response? Trends Immunol. 37, 513–522 (2016).

    Article  CAS  PubMed  Google Scholar 

  50. Feinerman, O., Veiga, J., Dorfman, J. R., Germain, R. N. & Altan-Bonnet, G. Variability and robustness in T cell activation from regulated heterogeneity in protein levels. Science 321, 1081–1084 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  52. Liao, Y., Smyth, G. K. & Shi, W. The Subread aligner: fast, accurate and scalable read mapping by seed-and-vote. Nucleic Acids Res. 41, e108 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  53. Liao, Y., Smyth, G. K. & Shi, W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014).

    Article  CAS  PubMed  Google Scholar 

  54. Lun, A. T., McCarthy, D. J. & Marioni, J. C. A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor. F1000Res 5, 2122 (2016).

    PubMed  PubMed Central  Google Scholar 

  55. McCarthy, D. J., Campbell, K. R., Lun, A. T. & Wills, Q. F. Scater: pre-processing, quality control, normalization and visualization of single-cell RNA-seq data in R. Bioinformatics 33, 1179–1186 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  56. Lun, A. T. L., Calero-Nieto, F. J., Haim-Vilmovsky, L., Göttgens, B. & Marioni, J. C. Assessing the reliability of spike-in normalization for analyses of single-cell RNA sequencing data. Genome Res. 27, 1795–1806 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Johnson, W. E., Li, C. & Rabinovic, A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 8, 118–127 (2007).

    Article  PubMed  Google Scholar 

  58. McCarthy, D. J., Chen, Y. & Smyth, G. K. Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation. Nucleic Acids Res. 40, 4288–4297 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).

    Article  CAS  PubMed  Google Scholar 

  60. Schmeier, S., Alam, T., Essack, M. & Bajic, V. B. TcoF-DBv2: update of the database of human and mouse transcription co-factors and transcription factor interactions. Nucleic Acids Res. 45 D1, D145–D150 (2017).

    Article  CAS  Google Scholar 

  61. Parks, D. R., Roederer, M. & Moore, W. A. A new “Logicle” display method avoids deceptive effects of logarithmic scaling for low signals and compensated data. Cytom. A 69, 541–551 (2006).

    Article  Google Scholar 

  62. Lun, A. T., Chen, Y. & Smyth, G. K. It’s DE-licious: A Recipe for Differential Expression Analyses of RNA-seq Experiments Using Quasi-Likelihood Methods in edgeR. Methods Mol. Biol. 1418, 391–416 (2016).

    Article  PubMed  Google Scholar 

  63. van der Maaten, L. J. P. Accelerating t-SNE using tree-based algorithms. J. Mach. Learn. Res. 15, 3221–3245 (2014).

    Google Scholar 

Download references

Acknowledgements

This work was funded by an MRC Skills Development Fellowship (MR/P014178/1 to A.C.R.); the Wellcome Trust (grants [103930] and [100140] to G.M.G.); Cancer Research UK (core funding (A17197) to J.C.M.); EMBL (core funding to J.C.M.); the University of Cambridge; and Hutchison Whampoa Limited. Single-cell collection and analysis were supported through MRC Clinical Research Infrastructure funds for the Cambridge Single Cell Facility (MR/M008975/1). W.W.Y.L. and B.G. were supported by Bloodwise (12029) and Cancer Research UK (C1163/A12765 and C1163/A21762). This research was supported by the CIMR Flow Cytometry Core Facility. We thank R. Schulte and C. Cossetti for their advice and support in cell sorting; the CRUK-CI Flow Cytometry core, particularly M. Strzelecki and R. Grenfell, and Genomics core for their resources and assistance; the Wellcome Trust Sanger Institute Mouse Genetics Project (Sanger MGP) and its funders for the wild-type C57BL/6 mouse line (funding information, https://www.sanger.ac.uk/science/collaboration/mouse-resource-portal); and C. Gawden-Bone, J. Warland, A. Denton and G. Frazer for critical reading of the manuscript.

Author information

Author notes

  1. These authors jointly supervised this work: John C. Marioni, Gillian M. Griffiths.

Authors and Affiliations

  1. Cambridge Institute for Medical Research, University of Cambridge, Cambridge, UK

    Arianne C. Richard, Winnie W. Y. Lau, Berthold Göttgens & Gillian M. Griffiths

  2. Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK

    Arianne C. Richard, Aaron T. L. Lun & John C. Marioni

  3. Department of Haematology, Wellcome - Medical Research Council Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK

    Winnie W. Y. Lau & Berthold Göttgens

  4. European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Cambridge, UK

    John C. Marioni

  5. Wellcome Sanger Institute, Cambridge, UK

    John C. Marioni

Authors
  1. Arianne C. Richard
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Aaron T. L. Lun
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Winnie W. Y. Lau
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Berthold Göttgens
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. John C. Marioni
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Gillian M. Griffiths
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

A.C.R., J.C.M. and G.M.G. designed the study and wrote the manuscript; A.C.R. carried out the experiments and analyses under the supervision of J.C.M. and G.M.G.; A.T.L.L. designed analytical pipelines and software and advised analyses; W.W.Y.L. and B.G. advised and supervised, respectively, cell sorting and library preparation for scRNA-seq, which were optimized by W.W.Y.L.; and all authors edited and approved the final manuscript.

Corresponding authors

Correspondence to John C. Marioni or Gillian M. Griffiths.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

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

Integrated supplementary information

Supplementary Figure 1 Single-cell profiling and clustering in early CD8+ T cell activation.

a, OT-I CD8+ T cells were stimulated with high potency ovalbumin peptide (N4) at various concentrations for 4 hours before measuring CD69 and CD25 protein expression by flow cytometry. Results are representative of two independent experiments. b, We hypothesized that heterogeneity among stimulated cells might result from stochastic variation in gene expression in the unstimulated state driving certain cells to more quickly respond to stimulation. We therefore examined highly variable genes within unstimulated cells. Heatmap depicts the top 100 most biologically variable genes detected by scRNA-seq in unstimulated cells (n = 44 cells) clustered by Euclidean distance. Colored bars indicate the pseudotime designation of these cells from Fig. 1c and the experimental batch in which they were processed. No obvious structure was observed in the unstimulated population. c, Histogram of the cell distribution along diffusion pseudotime from Fig. 1d. d, Cells were clustered along diffusion pseudotime using Jenks’ natural breaks classification method. e, The hypergeometric test was used to test for enrichment of transcriptional regulators among transcripts specifically upregulated in the early activation cluster compared to both resting and late activation cells.

Supplementary Figure 2 Costimulation effects and control gene selection in early CD8+ T cell activation.

a, OT-I CD8+ T cells were stimulated as in Fig. 2 with pure peptide in the presence and absence of exogenous murine IL-2. After 6 hours, surface protein expression was examined by flow cytometry. Plots depict results from 3 separate mice; line depicts the mean. Stimulation in the presence of IL-2 primarily affected expression of the high affinity receptor CD25 in the first 6 hours, with minimal impact on other activation readouts. b, Cells were stained with a proliferation dye before stimulation as in a. After 2 days, proliferation was examined by flow cytometry, confirming previous reports that IL-2 is important for proliferation under low potency activation conditions. Results are representative of 3 separate mice. c, The Rpl39 control gene for RNA flow cytometry was identified in single-cell RNA-seq data as having low variance and minimal condition-dependence. Plots show expression in two independent scRNA-seq data sets and ANOVA p values without multiple testing correction; left, n = 46 cells for N4 6h, 47 for T4 6h, 46 for G4 6h, 46 for NP68 6h, 44 for unstimulated, 51 for N4 1h, and 64 for N4 3h; right, n = 45 cells for N4, 44 for T4, 48 for G4, and 47 for NP68. Box plots show the median, boxed interquartile range, and whiskers extending to the most extreme point up to 1.5 x the interquartile range. d, Control gene expression in an RNA flow cytometry experiment as depicted in Fig. 2. Histograms are representative of 3 independent experiments.

Supplementary Figure 3 Transcriptomic activation status correlation with CD69 protein expression and examination of early expression of specific genes.

a, OTI CD8+ T cells were stimulated with peptides of varying affinity for 6 hours before scRNA-seq. Violin plots depict the distribution of surface protein expression measurements in sequenced cells stimulated for 6 hours; n = 46 cells for N4, 47 for T4, 46 for G4, and 46 for NP68. Data is the second independent replicate of that in Fig. 3a. b, CD69 protein FACS measurements are plotted against transcriptional activation status as determined by diffusion pseudotime analysis in Fig. 3b for each of two scRNA-seq experiments. Surface protein measurements were made independently in each experiment; diffusion pseudotime analysis to determine transcriptional activation status was performed using the combined data set of both experiments. r denotes Pearson’s correlation coefficient; n = 184, left; n = 293, right. c-d, Normalized gene expression without regression on activation status for genes depicted in Fig. 2e,f is plotted by stimulation condition. e, Residual expression of Granzyme B (Gzmb) in scRNA-seq data after regression on batch and CD69 expression is plotted by stimulation condition (left). Normalized expression of Gzmb without regression on activation status is plotted by stimulation condition (right). Box plots (c-e) show the median, boxed interquartile range, and whiskers extending to the most extreme point up to 1.5 x the interquartile range. For (c, d, e (right)), n = 44 cells for unstimulated, 64 for N4 3h, 91 for N4 6h, 91 for T4 6h, 94 for G4 6h, 93 for NP68 6h; for (e (left)), n = 155 cells for N4, 91 for T4, 94 for G4.

Supplementary Figure 4 Comparison of alternative antigen presentation strategies for 2-day stimulation of CD8+ T cells.

a, OTI CD8+ T cells activated with peptide-pulsed T-depleted autologous APCs (top) or pure peptide (bottom) for 2 days were examined for death by flow cytometry. Cells were gated on singlet, CD8+, proliferation dye+ cells. Gate denotes the percentage of dead cells in each sample. Direct comparison using T cells from the same mouse is representative of 2 independent experiments. b, OT-I CD8+ T cells activated with pure peptide for 2 days were examined for proliferation and CD44 expression by flow cytometry. Results are representative of 4 separate mice in 3 independent experiments. c, OT-I CD8+ T cells were stimulated as in Fig. 5 except that wild-type splenocytes were used as APCs. After 2 days, proliferation and CD44 expression were measured by flow cytometry. For cell division plotting, cells were additionally gated on proliferation dye+ cells to exclude CD8+ T cells from wild-type splenocyte APCs. d, Protein phenotypes of cells stimulated as in c were examined by flow cytometry. Results (c-d) are representative of 2-3 independent experiments for each condition and measurement.

Supplementary Figure 5 Expression of all proteins measured by mass cytometry in phenotypic hyperspheres that were differentially abundant between stimulation conditions.

Plot as in Fig. 6c is colored by the intensity of each marker measured by mass cytometry. Labels indicate the metal tag, antibody target, and secondary antibody where appropriate.

Supplementary Figure 6 Flow cytometry validation of protein expression phenotypes observed in 2-day stimulation of CD8+ T cells.

a, Co-expression of selected proteins measured by mass cytometry in Fig. 6 was validated by flow cytometry. Each co-expression plot is representative of at least 4 separate mice in at least 3 independent experiments. b, Plots summarize replicate experiments from a quantifying the percentages of cells in each condition expressing the indicated proteins; n = 10 mice in 7 independent experiments for CD44, 9 in 6 for Granzyme B (GZMB), 8 in 5 for CTLA-4, 4 in 3 for CD62L, 4 in 3 for CD25; lines depict the mean. c, GZMB expression in GZMB+ cells is quantified by flow cytometry for 9 separate mice in 6 independent experiments; p values by two-sided Wilcoxon signed rank test. d, GZMB versus proliferation dye dilution for one representative experiment from c. e, Mass cytometry data plot as in Fig. 6b,c is colored by the relative proportion of cells from each stimulus in each hypersphere (i.e. the proportion of the cells in the hypersphere occupied by cells from each condition after normalizing for total cell count in each condition). f, Mass cytometry data were validated by flow cytometry examining the percentage of IFN-γ+ cells in each condition. Plot depicts results from 8 separate mice in 5 independent experiments; line denotes the mean. g, IFN-γ versus proliferation dye dilution for one representative experiment from f.

Supplementary Figure 7 Confirmation and summarization of degranulation phenotypes.

a, OTI CD8+ T cells were activated with pulsed wild-type splenocyte APCs for 2 days as in Supplementary Fig. 4c before assaying degranulation as in Fig. 7a. b-c, Cells in a were gated on their division number (b) or CD44 expression (c) before comparing LAMP1 MFI. We note that there were not sufficient cells that had undergone 4 divisions after G4 primary stimulation to calculate LAMP1 MFI. (a-c) are representative of 3 independent experiments. d, The 8 replicates of the degranulation assay in Fig. 7d (left) challenging day-7 CTLs with ovalbumin peptide (N4)-pulsed EL4 cells. e, As d for the 5 replicates of the degranulation assay in Fig. 7d (right) challenging day-7 CTLs with plate-bound anti-CD3ε. p values (d-e) by two-sided Wilcoxon signed rank test.

Supplementary Figure 8 Flow cytometry gating strategies.

a, Example gating strategy for single-cell sorting: cells were sorted on size, single cells, live cells, CD8+ T cells, and proliferation dye+ cells. b, Example gating strategy for RNA flow cytometry: cells were gated on size, single cells, live cells, and Rpl39+ cells before examining Nr4a1 and Fosb expression; Nr4a1, Fosb and Rpl39 positive gates were set on fluorescence-minus-one stains. c, Example gating strategy for T cell phenotyping by flow cytometry: where included, 123count eBeads were counted and excluded; cells were gated on size, singlets, live cells, CD8+ cells, and proliferation dye+ cells. Cells in each division cycle were quantified. A combination of two separate staining panels was used to examine surface and intracellular proteins. d, Example gating strategy for day-2 degranulation assays: cells were gated on size, singlets, live cells, CD8+ cells, and, where necessary, proliferation dye+ cells; further refinement was achieved by gating cells in each division cycle or CD44hi cells before comparison of LAMP1 staining. e, Gating strategy for day-7 degranulation assays: cells were gated on size, singlets, live cells, and CD8+ cells before comparison of LAMP1 staining.

Supplementary information

Supplementary Figures

Supplementary Figures 1-8

Reporting Summary

Supplementary Table 1

A differential expression analysis was performed on scRNA-seq data between cells in the early activation cluster and the resting naive T cell cluster, and between cells in the late activation cluster and the resting cluster from Fig. 1. Differentially expressed genes were tested for Gene Ontology (GO) category enrichment. Enriched categories (FDR < 0.05) are reported

Supplementary Table 2

Results from the sc-RNAseq differential expression analysis between cells stimulated with peptides of different potencies (n = 155 cells for N4, 91 for T4, 94 for G4), controlling for activation status by CD69 protein expression depicted in Fig. 4

Supplementary Table 3

A differential expression analysis was performed on scRNA-seq data between peptides of different potencies, controlling for CD69 expression as described in Fig. 4 and Supplementary Table 2. Differentially expressed genes were tested for Gene Ontology (GO) category enrichment. Enriched categories (FDR < 0.05) are reported

Supplementary Table 4

Antibodies used for mass cytometry experiment

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Richard, A.C., Lun, A.T.L., Lau, W.W.Y. et al. T cell cytolytic capacity is independent of initial stimulation strength. Nat Immunol 19, 849–858 (2018). https://doi.org/10.1038/s41590-018-0160-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41590-018-0160-9

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

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

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