Transcript-indexed ATAC-seq for precision immune profiling

Abstract

T cells create vast amounts of diversity in the genes that encode their T cell receptors (TCRs), which enables individual clones to recognize specific peptide–major histocompatibility complex (MHC) ligands. Here we combined sequencing of the TCR-encoding genes with assay for transposase-accessible chromatin with sequencing (ATAC-seq) analysis at the single-cell level to provide information on the TCR specificity and epigenomic state of individual T cells. By using this approach, termed transcript-indexed ATAC-seq (T-ATAC-seq), we identified epigenomic signatures in immortalized leukemic T cells, primary human T cells from healthy volunteers and primary leukemic T cells from patient samples. In peripheral blood CD4+ T cells from healthy individuals, we identified cis and trans regulators of naive and memory T cell states and found substantial heterogeneity in surface-marker-defined T cell populations. In patients with a leukemic form of cutaneous T cell lymphoma, T-ATAC-seq enabled identification of leukemic and nonleukemic regulatory pathways in T cells from the same individual by allowing separation of the signals that arose from the malignant clone from the background T cell noise. Thus, T-ATAC-seq is a new tool that enables analysis of epigenomic landscapes in clonal T cells and should be valuable for studies of T cell malignancy, immunity and immunotherapy.

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Fig. 1: T-ATAC-seq generates open chromatin and TCR profiles in single T cells.
Fig. 2: T-ATAC-seq identifies epigenomic signatures of clonal Jurkat T cells.
Fig. 3: Epigenomic landscape of ensemble human CD4+ T cell subtypes.
Fig. 4: Single-cell epigenomic and TCR profiling of human CD4+ T cells.
Fig. 5: T-ATAC-seq identifies epigenomic signatures of clonal leukemic T cells in patient samples.

References

  1. 1.

    Davis, M. M. & Bjorkman, P. J. T cell antigen receptor genes and T cell recognition. Nature 334, 395–402 (1988).

    CAS  Article  Google Scholar 

  2. 2.

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

    CAS  Article  Google Scholar 

  3. 3.

    Gaublomme, J. T. et al. Single-cell genomics unveils critical regulators of TH17 cell pathogenicity. Cell 163, 1400–1412 (2015).

    CAS  Article  Google Scholar 

  4. 4.

    Paul, F. et al. Transcriptional heterogeneity and lineage commitment in myeloid progenitors. Cell 163, 1663–1677 (2015).

    CAS  Article  Google Scholar 

  5. 5.

    Tirosh, I. et al. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science 352, 189–196 (2016).

    CAS  Article  Google Scholar 

  6. 6.

    Han, A., Glanville, J., Hansmann, L. & Davis, M. M. Linking T cell receptor sequence to functional phenotype at the single-cell level. Nat. Biotechnol. 32, 684–692 (2014).

    CAS  Article  Google Scholar 

  7. 7.

    Buenrostro, J. D., Giresi, P. G., Zaba, L. C., Chang, H. Y. & Greenleaf, W. J. Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat. Methods 10, 1213–1218 (2013).

    CAS  Article  Google Scholar 

  8. 8.

    Buenrostro, J. D. et al. Single-cell chromatin accessibility reveals principles of regulatory variation. Nature 523, 486–490 (2015).

    CAS  Article  Google Scholar 

  9. 9.

    Corces, M. R. et al. Lineage-specific and single-cell chromatin accessibility charts human hematopoiesis and leukemia evolution. Nat. Genet. 48, 1193–1203 (2016).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  10. 10.

    Buenrostro, J. D. et al. Single-cell epigenomics maps the continuous regulatory landscape of human hematopoietic differentiation. bioRxiv http://dx.doi.org/10.1101/109843 (2017).

  11. 11.

    Schep, A. N., Wu, B., Buenrostro, J. D. & Greenleaf, W. J. chromVAR: inferring transcription-factor-associated accessibility from single-cell epigenomic data. Nat. Methods 14, 975–978 (2017).

    CAS  Article  Google Scholar 

  12. 12.

    Stubbington, M. J. T. et al. T cell fate and clonality inference from single-cell transcriptomes. Nat. Methods 13, 329–332 (2016).

    Article  Google Scholar 

  13. 13.

    Afik, S. et al. Targeted reconstruction of T cell receptor sequence from single-cell RNA-seq links CDR3 length to T cell differentiation state. Nucleic Acids Res. 45, e148 (2017).

    Article  Google Scholar 

  14. 14.

    Cusanovich, D. A. et al. Multiplex single-cell profiling of chromatin accessibility by combinatorial cellular indexing. Science 348, 910–914 (2015).

    CAS  Article  Google Scholar 

  15. 15.

    Weber, B. N. et al. A critical role for TCF-1 in T lineage specification and differentiation. Nature 476, 63–68 (2011).

    CAS  Article  Google Scholar 

  16. 16.

    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).

    CAS  Article  Google Scholar 

  17. 17.

    Morita, R. et al. Human blood CXCR5+CD4+ T cells are counterparts of T follicular cells and contain specific subsets that differentially support antibody secretion. Immunity 34, 108–121 (2011).

    CAS  Article  Google Scholar 

  18. 18.

    Fontenot, J. D., Rasmussen, J. P., Gavin, M. A. & Rudensky, A. Y. A function for interleukin 2 in Foxp3-expressing regulatory T cells. Nat. Immunol. 6, 1142–1151 (2005).

    CAS  Article  Google Scholar 

  19. 19.

    Ouyang, W., Kolls, J. K. & Zheng, Y. The biological functions of T helper 17 cell effector cytokines in inflammation. Immunity 28, 454–467 (2008).

    CAS  Article  Google Scholar 

  20. 20.

    Meller, S. et al. TH17 cells promote microbial killing and innate immune sensing of DNA via interleukin 26. Nat. Immunol. 16, 970–979 (2015).

    CAS  Article  Google Scholar 

  21. 21.

    van der Maaten, L. & Hinton, G. Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008).

    Google Scholar 

  22. 22.

    Kimmig, S. et al. Two subsets of naive T helper cells with distinct T cell receptor excision circle content in human adult peripheral blood. J. Exp. Med. 195, 789–794 (2002).

    CAS  Article  Google Scholar 

  23. 23.

    Boursalian, T. E., Golob, J., Soper, D. M., Cooper, C. J. & Fink, P. J. Continued maturation of thymic emigrants in the periphery. Nat. Immunol. 5, 418–425 (2004).

    CAS  Article  Google Scholar 

  24. 24.

    Harari, A., Vallelian, F. & Pantaleo, G. Phenotypic heterogeneity of antigen-specific CD4 T cells under different conditions of antigen persistence and antigen load. Eur. J. Immunol. 34, 3525–3533 (2004).

    CAS  Article  Google Scholar 

  25. 25.

    Zhao, C. & Davies, J. D. A peripheral CD4+ T cell precursor for naive, memory and regulatory T cells. J. Exp. Med. 207, 2883–2894 (2010).

    CAS  Article  Google Scholar 

  26. 26.

    Song, K. et al. Characterization of subsets of CD4+ memory T cells reveals early branched pathways of T cell differentiation in humans. Proc. Natl Acad. Sci. USA 102, 7916–7921 (2005).

    CAS  Article  Google Scholar 

  27. 27.

    Gattinoni, L. et al. A human memory T cell subset with stem-cell-like properties. Nat. Med. 17, 1290–1297 (2011).

    CAS  Article  Google Scholar 

  28. 28.

    Weiskopf, D. et al. Dengue virus infection elicits highly polarized CX3CR1+ cytotoxic CD4+ T cells associated with protective immunity. Proc. Natl Acad. Sci. USA 112, E4256–E4263 (2015).

    CAS  Article  Google Scholar 

  29. 29.

    Yui, M. A. & Rothenberg, E. V. Developmental gene networks: a triathlon on the course to T cell identity. Nat. Rev. Immunol. 14, 529–545 (2014).

    CAS  Article  Google Scholar 

  30. 30.

    Zheng, W. & Flavell, R. A. The transcription factor GATA-3 is necessary and sufficient for TH2 cytokine gene expression in CD4 T cells. Cell 89, 587–596 (1997).

    CAS  Article  Google Scholar 

  31. 31.

    Lohoff, M. & Mak, T. W. Roles of interferon-regulatory factors in T helper cell differentiation. Nat. Rev. Immunol. 5, 125–135 (2005).

    CAS  Article  Google Scholar 

  32. 32.

    Ivanov, I. I. et al. The orphan nuclear receptor ROR-γt directs the differentiation program of proinflammatory IL-17+ T helper cells. Cell 126, 1121–1133 (2006).

    CAS  Article  Google Scholar 

  33. 33.

    Yang, X. O. et al. T helper 17 lineage differentiation is programmed by orphan nuclear receptors ROR-α and ROR-γ. Immunity 28, 29–39 (2008).

    CAS  Article  Google Scholar 

  34. 34.

    Bauquet, A. T. et al. The co-stimulatory molecule ICOS regulates the expression of c-Maf and IL-21 in the development of follicular T helper cells and TH17 cells. Nat. Immunol. 10, 167–175 (2009).

    CAS  Article  Google Scholar 

  35. 35.

    Schraml, B. U. et al. The AP-1 transcription factor Batf controls TH17 differentiation. Nature 460, 405–409 (2009).

    CAS  Article  Google Scholar 

  36. 36.

    O’Shea, J. J., Lahesmaa, R., Vahedi, G., Laurence, A. & Kanno, Y. Genomic views of STAT function in CD4+ T helper cell differentiation. Nat. Rev. Immunol. 11, 239–250 (2011).

    Article  Google Scholar 

  37. 37.

    Rutz, S. et al. Transcription factor c-Maf mediates the TGF-β-dependent suppression of IL-22 production in TH17 cells. Nat. Immunol. 12, 1238–1245 (2011).

    CAS  Article  Google Scholar 

  38. 38.

    Ciofani, M. et al. A validated regulatory network for TH17 cell specification. Cell 151, 289–303 (2012).

    CAS  Article  Google Scholar 

  39. 39.

    Bigler, R. D., Boselli, C. M., Foley, B. & Vonderheid, E. C. Failure of anti–T cell receptor Vβ antibodies to consistently identify a malignant T cell clone in Sézary syndrome. Am. J. Pathol. 149, 1477–1483 (1996).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. 40.

    Kelemen, K., Guitart, J., Kuzel, T. M., Goolsby, C. L. & Peterson, L. C. The usefulness of CD26 in flow cytometric analysis of peripheral blood in Sézary syndrome. Am. J. Clin. Pathol. 129, 146–156 (2008).

    CAS  Article  Google Scholar 

  41. 41.

    Weng, W. K. et al. Minimal residual disease monitoring with high-throughput sequencing of T cell receptors in cutaneous T cell lymphoma. Sci. Transl. Med. 5, 214ra171 (2013).

    Article  Google Scholar 

  42. 42.

    Sufficool, K. E. et al. T cell clonality assessment by next-generation sequencing improves detection sensitivity in mycosis fungoides. J. Am. Acad. Dermatol. 73, 228–236 (2015).

    CAS  Article  Google Scholar 

  43. 43.

    Rook, A. H., Vowels, B. R., Jaworsky, C., Singh, A. & Lessin, S. R. The immunopathogenesis of cutaneous T cell lymphoma. Abnormal cytokine production by Sézary T cells. Arch. Dermatol. 129, 486–489 (1993).

    CAS  Article  Google Scholar 

  44. 44.

    Vowels, B. R. et al. TH2 cytokine mRNA expression in skin in cutaneous T cell lymphoma. J. Invest. Dermatol. 103, 669–673 (1994).

    CAS  Article  Google Scholar 

  45. 45.

    Krejsgaard, T., Odum, N., Geisler, C., Wasik, M. A. & Woetmann, A. Regulatory T cells and immunodeficiency in mycosis fungoides and Sézary syndrome. Leukemia 26, 424–432 (2012).

    CAS  Article  Google Scholar 

  46. 46.

    Ungewickell, A. et al. Genomic analysis of mycosis fungoides and Sézary syndrome identifies recurrent alterations in TNFR2. Nat. Genet. 47, 1056–1060 (2015).

    CAS  Article  Google Scholar 

  47. 47.

    Choi, J. et al. Genomic landscape of cutaneous T cell lymphoma. Nat. Genet. 47, 1011–1019 (2015).

    CAS  Article  Google Scholar 

  48. 48.

    Bernengo, M. G. et al. Prognostic factors in Sézary syndrome: a multivariate analysis of clinical, hematological and immunological features. Ann. Oncol. 9, 857–863 (1998).

    CAS  Article  Google Scholar 

  49. 49.

    Kirsch, I. R. et al. TCR sequencing facilitates diagnosis and identifies mature T cells as the cell of origin in CTCL. Sci. Transl. Med. 7, 308ra158 (2015).

    Article  Google Scholar 

  50. 50.

    Bolden, J. E., Peart, M. J. & Johnstone, R. W. Anticancer activities of histone deacetylase inhibitors. Nat. Rev. Drug Discov. 5, 769–784 (2006).

    CAS  Article  Google Scholar 

  51. 51.

    Qu, K. et al. Chromatin accessibility landscape of cutaneous T cell lymphoma and dynamic response to HDAC inhibitors. Cancer Cell 32, 27–41.e4 (2017).

    CAS  Article  Google Scholar 

  52. 52.

    Regev, A. et al. The Human Cell Atlas. Elife 6, e27041 (2017).

    Article  Google Scholar 

  53. 53.

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

    CAS  Article  Google Scholar 

  54. 54.

    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).

    CAS  Article  Google Scholar 

  55. 55.

    Letourneur, F. & Malissen, B. Derivation of a T cell hybridoma variant deprived of functional T cell receptor α- and β-chain transcripts reveals a nonfunctional α-mRNA of BW5147 origin. Eur. J. Immunol. 19, 2269–2274 (1989).

    CAS  Article  Google Scholar 

  56. 56.

    Huse, M. et al. Spatial and temporal dynamics of T cell receptor signaling with a photoactivatable agonist. Immunity 27, 76–88 (2007).

    CAS  Article  Google Scholar 

  57. 57.

    Glanville, J. et al. Identifying specificity groups in the T cell receptor repertoire. Nature 547, 94–98 (2017).

    CAS  Article  Google Scholar 

  58. 58.

    Mathelier, A. et al. JASPAR 2016: a major expansion and update of the open-access database of transcription factor binding profiles. Nucleic Acids Res. 44.D1, D110–D115 (2016). 

    CAS  Article  Google Scholar 

  59. 59.

    Jolma, A. et al. DNA-binding specificities of human transcription factors. Cell 152, 327–339 (2013).

    CAS  Article  Google Scholar 

  60. 60.

    Weirauch, M. T. et al. Determination and inference of eukaryotic transcription factor sequence specificity. Cell 158, 1431–1443 (2014).

    CAS  Article  Google Scholar 

  61. 61.

    Mumbach, M. R. et al. Enhancer connectome in primary human cells identifies target genes of disease-associated DNA elements. Nat. Genet. 49, 1602–1612 (2017).

    CAS  Article  Google Scholar 

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Acknowledgements

We thank members of the Chang, Davis and Greenleaf laboratories, including Y. Shen and K. Qu, for helpful discussions. We thank X. Ji, D. Wagh and J. Coller at the Stanford Functional Genomics Facility. This work was supported by the Parker Institute for Cancer Immunotherapy (A.T.S., H.Y.C. and M.M.D.), the US National Institutes of Health (NIH) grants P50HG007735 (H.Y.C. and W.J.G.), 5U19AI057229 (M.M.D.), and U19AI057266 (W.J.G), and the Scleroderma Research Foundation (H.Y.C.). A.T.S. was supported by a Parker Bridge Scholar Award from the Parker Institute for Cancer Immunotherapy and a Cancer Research Institute Irvington Fellowship from the Cancer Research Institute. N.S. was supported by the National Multiple Sclerosis Society Postdoctoral Fellowship. J.D.B. acknowledges the Broad Institute Fellows and Harvard Society of Fellows programs for funding. M.R.C. was supported by a grant from the Leukemia and Lymphoma Society Career Development Program. W.J.G. is a Chan Zuckerberg Biohub investigator. M.M.D. is an investigator of the Howard Hughes Medical Institute. Sequencing was performed by the Stanford Functional Genomics Facility (which is supported by NIH grant S10OD018220).

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A.T.S., N.S., M.M.D. and H.Y.C. conceived the project; A.T.S, N.S. and J.D.B. performed experiments and analyzed data; B.W. and Y.Q. performed T-ATAC-seq experiments; R.L., J.M.G., M.R.M. and D.G.G. performed ensemble ATAC-seq experiments and analyzed data; W.S.S. performed TCR-seq experiments; Y.W., A.J.R., K.R.P., C.A.L., A.N.S. and M.R.C. analyzed data; M.S.K. and Y.H.K. obtained clinical specimens; H.Y.C, M.M.D, W.J.G. and P.A.K. guided experiments and data analysis; and A.T.S, M.M.D. and H.Y.C. wrote the manuscript with input from all of the authors.

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Correspondence to Mark M. Davis or Howard Y. Chang.

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H.Y.C. and W.J.G. are founders of Epinomics and members of its scientific advisory board. H.Y.C. is a founder of Accent Therapeutics and a member of its scientific advisory board. H.Y.C. is a member of the scientific advisory board of Spring Discovery.

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Satpathy, A.T., Saligrama, N., Buenrostro, J.D. et al. Transcript-indexed ATAC-seq for precision immune profiling. Nat Med 24, 580–590 (2018). https://doi.org/10.1038/s41591-018-0008-8

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