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Linking T-cell receptor sequence to functional phenotype at the single-cell level

Nature Biotechnology volume 32, pages 684–692 (2014)Cite this article

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A Corrigendum to this article was published on 06 February 2015

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Abstract

Although each T lymphocyte expresses a T-cell receptor (TCR) that recognizes cognate antigen and controls T-cell activation, different T cells bearing the same TCR can be functionally distinct. Each TCR is a heterodimer, and both α- and β-chains contribute to determining TCR antigen specificity. Here we present a methodology enabling integration of information about TCR specificity with information about T cell function. This method involves sequencing of TCRα and TCRβ genes, and amplifying functional genes characteristic of different T cell subsets, in single T cells. Because this approach retains information about individual TCRα-TCRβ pairs, TCRs of interest can be expressed and used in functional studies, for antigen discovery, or in therapeutic applications. We apply this approach to study the clonal ancestry and differentiation of T lymphocytes infiltrating a human colorectal carcinoma.

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Figure 1: Strategy for single-cell TCR sequencing and phenotyping, and determination of TCR-sequencing efficiency.
Figure 2: Accuracy of phenotypic analysis.
Figure 3: TCR sequencing and phenotypic analysis of single human TILs.
Figure 4: TCR sequencing and phenotypic analysis of FOXP3+ TILs.

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Change history

  • 14 January 2015

    In the version of this article initially published, the concentration of the V-region primers in the Online Methods section was given as 0.6 μM. The correct concentration is 0.06 μM. The error has been corrected in the HTML and PDF versions of the article.

References

  1. Wills, Q.F. et al. Single-cell gene expression analysis reveals genetic associations masked in whole-tissue experiments. Nat. Biotechnol. 31, 748–752 (2013).

    Article  CAS  Google Scholar 

  2. Newell, E.W., Sigal, N., Bendall, S.C., Nolan, G.P. & Davis, M.M. Cytometry by time-of-flight shows combinatorial cytokine expression and virus-specific cell niches within a continuum of CD8+ T cell phenotypes. Immunity 36, 142–152 (2012).

    Article  CAS  Google Scholar 

  3. Shapiro, E., Biezuner, T. & Linnarsson, S. Single-cell sequencing-based technologies will revolutionize whole-organism science. Nat. Rev. Genet. 14, 618–630 (2013).

    Article  CAS  Google Scholar 

  4. Bendall, S.C., Nolan, G.P., Roederer, M. & Chattopadhyay, P.K. A deep profiler's guide to cytometry. Trends Immunol. 33, 323–332 (2012).

    Article  CAS  Google Scholar 

  5. Spurgeon, S.L., Jones, R.C. & Ramakrishnan, R. High throughput gene expression measurement with real time PCR in a microfluidic dynamic array. PLoS ONE 3, e1662 (2008).

    Article  Google Scholar 

  6. Wu, A.R. et al. Quantitative assessment of single-cell RNA-sequencing methods. Nat. Methods 11, 41–46 (2014).

    Article  CAS  Google Scholar 

  7. Newell, E.W. & Davis, M.M. Beyond model antigens: high-dimensional methods for the analysis of antigen-specific T cells. Nat. Biotechnol. 32, 149–157 (2014).

    Article  CAS  Google Scholar 

  8. Krogsgaard, M. & Davis, M.M. How T cells 'see' antigen. Nat. Immunol. 6, 239–245 (2005).

    Article  CAS  Google Scholar 

  9. Murphy, K., Travers, P., Walport, M. & Janeway, C. Janeway's Immunobiology 8th Edn. (Garland Science, 2012).

  10. Newell, E.W. et al. Combinatorial tetramer staining and mass cytometry analysis facilitate T-cell epitope mapping and characterization. Nat. Biotechnol. 31, 623–629 (2013).

    Article  CAS  Google Scholar 

  11. Birnbaum, M.E. et al. Deconstructing the peptide-MHC specificity of T cell recognition. Cell 157, 1073–1087 (2014).

    Article  CAS  Google Scholar 

  12. Hinrichs, C.S. & Restifo, N.P. Reassessing target antigens for adoptive T-cell therapy. Nat. Biotechnol. 31, 999–1008 (2013).

    Article  CAS  Google Scholar 

  13. Han, A. et al. Dietary gluten triggers concomitant activation of CD4+ and CD8+ alphabeta T cells and gammadelta T cells in celiac disease. Proc. Natl. Acad. Sci. USA 110, 13073–13078 (2013).

    Article  CAS  Google Scholar 

  14. Kim, S.M. et al. Analysis of the paired TCR alpha- and beta-chains of single human T cells. PLoS ONE 7, e37338 (2012).

    Article  CAS  Google Scholar 

  15. Dash, P. et al. Paired analysis of TCRalpha and TCRbeta chains at the single-cell level in mice. J. Clin. Invest. 121, 288–295 (2011).

    Article  CAS  Google Scholar 

  16. Gascoigne, N.R. & Alam, S.M. Allelic exclusion of the T cell receptor alpha-chain: developmental regulation of a post-translational event. Semin. Immunol. 11, 337–347 (1999).

    Article  CAS  Google Scholar 

  17. Malissen, M. et al. Regulation of TCR alpha and beta gene allelic exclusion during T-cell development. Immunol. Today 13, 315–322 (1992).

    Article  CAS  Google Scholar 

  18. Bentley, D.R. et al. Accurate whole human genome sequencing using reversible terminator chemistry. Nature 456, 53–59 (2008).

    Article  CAS  Google Scholar 

  19. Glanville, J. et al. Precise determination of the diversity of a combinatorial antibody library gives insight into the human immunoglobulin repertoire. Proc. Natl. Acad. Sci. USA 106, 20216–20221 (2009).

    Article  CAS  Google Scholar 

  20. De Rosa, S.C., Herzenberg, L.A., Herzenberg, L.A. & Roederer, M. 11-color, 13-parameter flow cytometry: identification of human naive T cells by phenotype, function, and T-cell receptor diversity. Nat. Med. 7, 245–248 (2001).

    Article  CAS  Google Scholar 

  21. Yanagi, Y., Chan, A., Chin, B., Minden, M. & Mak, T.W. Analysis of cDNA clones specific for human T cells and the alpha and beta chains of the T-cell receptor heterodimer from a human T-cell line. Proc. Natl. Acad. Sci. USA 82, 3430–3434 (1985).

    Article  CAS  Google Scholar 

  22. Nakamura, K. et al. Sequence-specific error profile of Illumina sequencers. Nucleic Acids Res. 39, e90 (2011).

    Article  CAS  Google Scholar 

  23. Law, J.P. et al. The importance of Foxp3 antibody and fixation/permeabilization buffer combinations in identifying CD4+CD25+Foxp3+ regulatory T cells. Cytometry A 75, 1040–1050 (2009).

    Article  Google Scholar 

  24. Vahedi, G., Kanno, Y., Sartorelli, V. & O'Shea, J.J. Transcription factors and CD4 T cells seeking identity: masters, minions, setters and spikers. Immunology 139, 294–298 (2013).

    Article  CAS  Google Scholar 

  25. Oestreich, K.J. & Weinmann, A.S. Master regulators or lineage-specifying? Changing views on CD4+ T cell transcription factors. Nat. Rev. Immunol. 12, 799–804 (2012).

    Article  CAS  Google Scholar 

  26. Wilson, C.B., Rowell, E. & Sekimata, M. Epigenetic control of T-helper-cell differentiation. Nat. Rev. Immunol. 9, 91–105 (2009).

    Article  CAS  Google Scholar 

  27. Collins, A., Littman, D.R. & Taniuchi, I. RUNX proteins in transcription factor networks that regulate T-cell lineage choice. Nat. Rev. Immunol. 9, 106–115 (2009).

    Article  CAS  Google Scholar 

  28. Assenmacher, M., Lohning, M. & Radbruch, A. Detection and isolation of cytokine secreting cells using the cytometric cytokine secretion assay. Curr. Protoc. Immunol. 46, 6.27 (2002).

    Google Scholar 

  29. Anderson, P. Post-transcriptional control of cytokine production. Nat. Immunol. 9, 353–359 (2008).

    Article  CAS  Google Scholar 

  30. Fontenot, J.D., Gavin, M.A. & Rudensky, A.Y. Foxp3 programs the development and function of CD4+CD25+ regulatory T cells. Nat. Immunol. 4, 330–336 (2003).

    Article  CAS  Google Scholar 

  31. Ribas, A. Tumor immunotherapy directed at PD-1. N. Engl. J. Med. 366, 2517–2519 (2012).

    Article  CAS  Google Scholar 

  32. Sliwkowski, M.X. & Mellman, I. Antibody therapeutics in cancer. Science 341, 1192–1198 (2013).

    Article  CAS  Google Scholar 

  33. Pagès, F. et al. Effector memory T cells, early metastasis, and survival in colorectal cancer. N. Engl. J. Med. 353, 2654–2666 (2005).

    Article  Google Scholar 

  34. Galon, J. et al. Type, density, and location of immune cells within human colorectal tumors predict clinical outcome. Science 313, 1960–1964 (2006).

    Article  CAS  Google Scholar 

  35. Gerlinger, M. et al. Ultra-deep T-cell receptor sequencing reveals the complexity and intratumour heterogeneity of T-cell clones in renal cell carcinomas. J. Pathol. 231, 424–432 (2013).

    Article  CAS  Google Scholar 

  36. Sherwood, A.M. et al. Tumor-infiltrating lymphocytes in colorectal tumors display a diversity of T cell receptor sequences that differ from the T cells in adjacent mucosal tissue. Cancer immunol. immunother. 62, 1453–1461 (2013).

    Article  CAS  Google Scholar 

  37. Sasada, T. & Suekane, S. Variation of tumor-infiltrating lymphocytes in human cancers: controversy on clinical significance. Immunotherapy 3, 1235–1251 (2011).

    Article  CAS  Google Scholar 

  38. deLeeuw, R.J., Kost, S.E., Kakal, J.A. & Nelson, B.H. The prognostic value of FoxP3+ tumor-infiltrating lymphocytes in cancer: a critical review of the literature. Clin. Cancer Res. 18, 3022–3029 (2012).

    Article  CAS  Google Scholar 

  39. Scurr, M., Gallimore, A. & Godkin, A. T cell subsets and colorectal cancer: discerning the good from the bad. Cell. Immunol. 279, 21–24 (2012).

    Article  CAS  Google Scholar 

  40. Tosolini, M. et al. Clinical impact of different classes of infiltrating T cytotoxic and helper cells (Th1, th2, treg, th17) in patients with colorectal cancer. Cancer Res. 71, 1263–1271 (2011).

    Article  CAS  Google Scholar 

  41. Ladoire, S., Martin, F. & Ghiringhelli, F. Prognostic role of FOXP3+ regulatory T cells infiltrating human carcinomas: the paradox of colorectal cancer. Cancer immunol. immunother. 60, 909–918 (2011).

    Article  CAS  Google Scholar 

  42. Blatner, N.R. et al. Expression of RORgammat marks a pathogenic regulatory T cell subset in human colon cancer. Sci. Transl. Med. 4, 164ra159 (2012).

    Article  Google Scholar 

  43. Gounaris, E. et al. T-regulatory cells shift from a protective anti-inflammatory to a cancer-promoting proinflammatory phenotype in polyposis. Cancer Res. 69, 5490–5497 (2009).

    Article  CAS  Google Scholar 

  44. Miyara, M. et al. Functional delineation and differentiation dynamics of human CD4+ T cells expressing the FoxP3 transcription factor. Immunity 30, 899–911 (2009).

    Article  CAS  Google Scholar 

  45. Zhou, L., Chong, M.M. & Littman, D.R. Plasticity of CD4+ T cell lineage differentiation. Immunity 30, 646–655 (2009).

    Article  CAS  Google Scholar 

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Acknowledgements

We thank members of the Davis laboratory and the Y.-H. Chien laboratory for helpful discussions. We thank E. Newell for critical reading of the manuscript and helpful suggestions. We thank C. Bolen for assistance with data analysis. We thank X. Ji for deep sequencing. Tissue was obtained through the Stanford University Tissue Bank. The Stanford Shared FACS Facility provided use of equipment and the Stanford Functional Genomics Facility provided deep sequencing services. A.H. is supported by a grant from the Simons Foundation. L.H. is supported by a fellowship from the German Research Foundation (D.F.G.). M.M.D. is funded by the US National Institutes of Health and is an investigator of the Howard Hughes Medical Institute.

Author information

Authors and Affiliations

  1. Department of Medicine, Division of Gastroenterology, Stanford University School of Medicine, Stanford, California, USA

    Arnold Han

  2. Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, California, USA

    Arnold Han, Leo Hansmann & Mark M Davis

  3. Program in Computational and Systems Immunology, Institute for Immunity, Transplantation and Infection, Stanford University School of Medicine, Stanford, California, USA

    Jacob Glanville & Mark M Davis

  4. Howard Hughes Medical Institute, Stanford University School of Medicine, Stanford, California, USA

    Mark M Davis

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Contributions

A.H. conceived the project and experiments, designed methodology and primers, performed experiments, analyzed data, assisted in the optimization of the software pipeline, generated figures and wrote the manuscript. J.G. designed the software, analyzed data and generated figures. L.H. designed and performed experiments, analyzed data and generated figures. M.M.D. conceived the project and experiments and wrote the manuscript.

Corresponding author

Correspondence to Mark M Davis.

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The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–8, Supplementary Tables 1, 3, 5, 7 and Supplementary Note (PDF 22832 kb)

Supplementary Table 2

Penotyping primers for first two PCR reactions (XLSX 57 kb)

Supplementary Table 4

TCR sequences from the TCR validation panel (XLSX 62 kb)

Supplementary Table 6

Reads counts per well of each phenotyping parameter illustrated in Fig. 2. (XLSX 119 kb)

Supplementary Table 8

Reads counts per well of each phenotyping parameter illustrated in Fig. 2. (XLSX 108 kb)

Supplementary Table 9

Paired TCR alpha/beta sequences for 309 CD4+ T cells from adjacent colon for which a TCR beta chain was obtained. (XLSX 71 kb)

Supplementary Table 10

Reads counts per well of each tumor CD4+ T cell analyzed. (XLSX 106 kb)

Supplementary Table 11

Reads counts per well of each adjacent colon CD4+ T cell analyzed. (XLSX 89 kb)

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Han, A., Glanville, J., Hansmann, L. et al. Linking T-cell receptor sequence to functional phenotype at the single-cell level. Nat Biotechnol 32, 684–692 (2014). https://doi.org/10.1038/nbt.2938

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