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.

  • Resource
  • Published:

Multimodally profiling memory T cells from a tuberculosis cohort identifies cell state associations with demographics, environment and disease

Nature Immunology volume 22, pages 781–793 (2021)Cite this article

Subjects

Abstract

Multimodal T cell profiling can enable more precise characterization of elusive cell states underlying disease. Here, we integrated single-cell RNA and surface protein data from 500,089 memory T cells to define 31 cell states from 259 individuals in a Peruvian tuberculosis (TB) progression cohort. At immune steady state >4 years after infection and disease resolution, we found that, after accounting for significant effects of age, sex, season and genetic ancestry on T cell composition, a polyfunctional type 17 helper T (TH17) cell–like effector state was reduced in abundance and function in individuals who previously progressed from Mycobacterium tuberculosis (M.tb) infection to active TB disease. These cells are capable of responding to M.tb peptides. Deconvoluting this state—uniquely identifiable with multimodal analysis—from public data demonstrated that its depletion may precede and persist beyond active disease. Our study demonstrates the power of integrative multimodal single-cell profiling to define cell states relevant to disease and other traits.

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: Study design and quality control.
Fig. 2: Landscape of memory T cell states.
Fig. 3: Memory T cell state associations with demographic and environmental factors.
Fig. 4: Identification and isolation of a depleted memory T cell state in TB cases.
Fig. 5: Identifying sortable markers of C-12 for ex vivo isolation.
Fig. 6: Defining the cytokine profile of C-12 in a Boston donor.
Fig. 7: Characterizing C-12 as an IL-17+ state with reduced function in Peruvian TB cases.

Similar content being viewed by others

Data availability

Single-cell sequencing data are available on dbGaP; count matrices and metadata are available on the GEO (GSE158769). Single-cell expression and cluster abundance data can also be explored in an interactive browser at https://immunogenomics.io/tbru/. Genotype data are available on dbGaP under accession code phs002025.v1.p1.

Code availability

Custom analysis scripts are available on GitHub (https://github.com/immunogenomics/TB_Tcell_CITEseq).

References

  1. Nathan, A., Baglaenko, Y., Fonseka, C. Y., Beynor, J. I. & Raychaudhuri, S. Multimodal single-cell approaches shed light on T cell heterogeneity. Curr. Opin. Immunol. 61, 17–25 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Spitzer, M. H. & Nolan, G. P. Mass cytometry: single cells, many features. Cell 165, 780–791 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Peterson, V. M. et al. Multiplexed quantification of proteins and transcripts in single cells. Nat. Biotechnol. 35, 936–939 (2017).

    Article  CAS  PubMed  Google Scholar 

  4. Stoeckius, M. et al. Simultaneous epitope and transcriptome measurement in single cells. Nat. Methods 14, 865–868 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Carr, E. J. et al. The cellular composition of the human immune system is shaped by age and cohabitation. Nat. Immunol. 17, 461–468 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Brodin, P. et al. Variation in the human immune system is largely driven by non-heritable influences. Cell 160, 37–47 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Houben, R. M. & Dodd, P. J. The global burden of latent tuberculosis infection: a reestimation using mathematical modelling. PLoS Med. 13, e1002152 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  8. World Health Organization. Global Tuberculosis Report 2020. (WHO, 2020); https://www.who.int/tb/publications/global_report/en/

  9. Verver, S. et al. Rate of reinfection tuberculosis after successful treatment is higher than rate of new tuberculosis. Am. J. Respir. Crit. Care Med. 171, 1430–1435 (2005).

    Article  PubMed  Google Scholar 

  10. Day, C. L. et al. PD-1 expression on Mycobacterium tuberculosis-specific CD4 T cells is associated with bacterial load in human tuberculosis. Front. Immunol. 9, 1995 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  11. Adekambi, T. et al. Distinct effector memory CD4+ T cell signatures in latent Mycobacterium tuberculosis infection, BCG vaccination and clinically resolved tuberculosis. PLoS ONE 7, e36046 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Arrigucci, R. et al. Active tuberculosis is characterized by highly differentiated effector memory TH1 cells. Front. Immunol. 9, 2127 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  13. Lindestam Arlehamn, C. S. et al. Memory T cells in latent Mycobacterium tuberculosis infection are directed against three antigenic islands and largely contained in a CXCR3+CCR6+ TH1 subset. PLoS Pathog. 9, e1003130 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  14. Scriba, T. J. et al. Distinct, specific IL-17- and IL-22-producing CD4+ T cell subsets contribute to the human anti-mycobacterial immune response. J. Immunol. 180, 1961–1970 (2008).

    Article  Google Scholar 

  15. Coulter, F. et al. IL-17 production from T helper 17, mucosal-associated invariant T, and γδ cells in tuberculosis infection and disease. Front. Immunol. 8, 1252 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  16. Burel, J. G. et al. Transcriptomic analysis of CD4+ T cells reveals novel immune signatures of latent tuberculosis. J. Immunol. 200, 3283–3290 (2018).

    Article  CAS  PubMed  Google Scholar 

  17. Perreau, M. et al. Lack of Mycobacterium tuberculosis–specific interleukin-17A-producing CD4+ T cells in active disease. Eur. J. Immunol. 43, 939–948 (2013).

    Article  CAS  PubMed  Google Scholar 

  18. Becerra, M. C. et al. Transmissibility and potential for disease progression of drug resistant Mycobacterium tuberculosis: prospective cohort study. BMJ 367, l5894 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  19. Zumla, A., Raviglione, M., Hafner, C. & von Reyn, C. F. Tuberculosis. N. Engl. J. Med. 368, 745–755 (2013).

    Article  CAS  PubMed  Google Scholar 

  20. 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  PubMed  Google Scholar 

  21. Liu, W. et al. CD127 expression inversely correlates with FoxP3 and suppressive function of human CD4+ Treg cells. J. Exp. Med. 203, 1701–1711 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Gutierrez-Arcelus, M. et al. Lymphocyte innateness defined by transcriptional states reflects a balance between proliferation and effector functions. Nat. Commun. 10, 687 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Korsunsky, I. et al. Fast, sensitive, and accurate integration of single-cell data with Harmony. Nat. Methods 16, 1289–1296 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Savage, A. K. et al. The transcription factor PLZF directs the effector program of the NKT cell lineage. Immunity 29, 391–403 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Suliman, S. et al. Peripheral blood mucosal-associated invariant T cells in tuberculosis patients and healthy Mycobacterium tuberculosis-exposed controls. J. Infect. Dis. 222, 995–1007 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Sallusto, F., Lenig, D., Förster, R., Lipp, M. & Lanzavecchia, A. Two subsets of memory T lymphocytes with distinct homing potentials and effector functions. Nature 401, 708–712 (1999).

    Article  CAS  PubMed  Google Scholar 

  27. Joeckel, L. T. & Bird, P. I. Are all granzymes cytotoxic in vivo? Biol. Chem. 395, 181–202 (2014).

    Article  CAS  PubMed  Google Scholar 

  28. Maecker, H. T., McCoy, J. P. & Nussenblatt, R. Standardizing immunophenotyping for the Human Immunology Project. Nat. Rev. Immunol. 12, 191–200 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Acosta-Rodriguez, E. V. et al. Surface phenotype and antigenic specificity of human interleukin 17–producing T helper memory cells. Nat. Immunol. 8, 639–646 (2007).

    Article  CAS  PubMed  Google Scholar 

  30. Szabo, S. J. et al. A novel transcription factor, T-bet, directs TH1 lineage commitment. Cell 100, 665–669 (2000).

    Article  Google Scholar 

  31. Sallusto, F., Zielinski, C. E. & Lanzavecchia, A. Human TH17 subsets. Eur. J. Immunol. 42, 2215–2220 (2012).

  32. Wambre, E. et al. A phenotypically and functionally distinct human TH2 cell subpopulation is associated with allergic disorders. Sci. Transl. Med. 9, eaam9171 (2017).

  33. Kleinewietfeld, M. et al. CCR6 expression defines regulatory effector/memory-like cells within the CD25+CD4+ T cell subset. Blood 105, 2877–2886 (2005).

  34. Lee, J. et al. Enrichment of human CCR6+ regulatory T cells with superior suppressive activity in oral cancer. J. Immunol. 199, 467–476 (2017).

  35. Fonseka, C. Y. et al. Mixed-effects association of single cells identifies an expanded effector CD4+ T cell subset in rheumatoid arthritis. Sci. Transl. Med. 10, eaaq0305 (2018).

  36. Kaczorowski, K. J. et al. Continuous immunotypes describe human immune variation and predict diverse responses. Proc. Natl Acad. Sci. USA 114, E6097–E6106 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Hashimoto, K. et al. Single-cell transcriptomics reveals expansion of cytotoxic CD4 T cells in supercentenarians. Proc. Natl Acad. Sci. USA 116, 24242–24251 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Amadori, A. et al. Genetic control of the CD4/CD8 T cell ratio in humans. Nat. Med. 1, 1279–1283 (1995).

    Article  CAS  PubMed  Google Scholar 

  39. Kissick, H. T. et al. Androgens alter T cell immunity by inhibiting T-helper 1 differentiation. Proc. Natl Acad. Sci. USA 111, 9887–9892 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Ter Horst, R. et al. Host and environmental factors influencing individual human cytokine responses. Cell 167, 1111–1124 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  41. Nedelec, Y. et al. Genetic ancestry and natural selection drive population differences in immune responses to pathogens. Cell 167, 657–669 (2016).

    Article  CAS  PubMed  Google Scholar 

  42. Berry, M. P. et al. An interferon-inducible neutrophil-driven blood transcriptional signature in human tuberculosis. Nature 466, 973–977 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Scriba, T. J. et al. Sequential inflammatory processes define human progression from M. tuberculosis infection to tuberculosis disease. PLoS Pathog. 13, e1006687 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  44. Fritsch, R. D. et al. Stepwise differentiation of CD4 memory T cells defined by expression of CCR7 and CD27. J. Immunol. 175, 6489–6497 (2005).

    Article  CAS  PubMed  Google Scholar 

  45. Coquet, J. M. et al. The CD27 and CD70 costimulatory pathway inhibits effector function of T helper 17 cells and attenuates associated autoimmunity. Immunity 38, 53–65 (2013).

    Article  CAS  PubMed  Google Scholar 

  46. MA State Public Health Laboratory Tuberculosis Report. (Massachusetts Department of Public Health, 2020).

  47. Arlehamn, C. S., McKinney, D. M. & Carpenter, C. A quantitative analysis of complexity of human pathogen-specific CD4 T cell responses in healthy M. tuberculosis-infected South Africans. PLoS Pathog. 12, e1005760 (2016).

    Article  CAS  Google Scholar 

  48. Christophersen, A. et al. Distinct phenotype of CD4+ T cells driving celiac disease identified in multiple autoimmune conditions. Nat. Med. 25, 734–737 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Zhang, F. et al. Defining inflammatory cell states in rheumatoid arthritis joint synovial tissues by integrating single-cell transcriptomics and mass cytometry. Nat. Immunol. 20, 928–942 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  50. Smillie, C. S. et al. Intra- and inter-cellular rewiring of the human colon during ulcerative colitis. Cell 178, 714–730 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Boisson-Dupuis, S. et al. Inherited and acquired immunodeficiencies underlying tuberculosis in childhood. Immunol. Rev. 264, 103–120 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Pai, M. et al. Tuberculosis. Nat. Rev. Dis. Primers 2, 16076 (2016).

    Article  PubMed  Google Scholar 

  53. Bustamante, J., Boisson-Dupuis, S., Abel, L. & Casanova, J. L. Mendelian susceptibility to mycobacterial disease: genetic, immunological and clinical features of inborn errors of IFN-γ immunity. Semin. Immunol. 26, 454–470 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Okada, S. et al. Impairment of immunity to Candida and Mycobacterium in humans with bi-allelic RORC mutations. Science 349, 606–613 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Luo, Y. et al. Early progression to active tuberculosis is a highly heritable trait driven by 3q23 in Peruvians. Nat. Commun. 10, 3765 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  56. Cosmi, L. et al. Human interleukin 17–producing cells originate from a CD161+CD4+ T cell precursor. J. Exp. Med. 205, 1903–1916 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Bengsch, B. et al. Human TH17 cells express high levels of enzymatically active dipeptidylpeptidase IV (CD26). J. Immunol. 188, 5438–5447 (2012).

  58. Morimoto, C. & Schlossman, S. F. The structure and function of CD26 in the T cell immune response. Immunol. Rev. 161, 55–70 (1998).

    Article  CAS  PubMed  Google Scholar 

  59. Fergusson, J. R. et al. CD161 defines a transcriptional and functional phenotype across distinct human T cell lineages. Cell Rep. 9, 1075–1088 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Yamazaki, T. et al. CCR6 regulates the migration of inflammatory and regulatory T cells. J. Immunol. 181, 8391–8401 (2008).

    Article  CAS  PubMed  Google Scholar 

  61. Scriba, T. J. et al. Differential recognition of Mycobacterium tuberculosis-specific epitopes as a function of tuberculosis disease history. Am. J. Respir. Crit. Care Med. 196, 772–781 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Chowdhury, R. et al. A multi-cohort study of the immune factors associated with M. tuberculosis infection outcomes. Nature 560, 644–648 (2018).

    Article  PubMed Central  CAS  Google Scholar 

  63. Wang, M. et al. Genetic polymorphisms of IL-17A, IL-17F, TLR4 and miR-146a in association with the risk of pulmonary tuberculosis. Sci. Rep. 6, 28586 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Khader, S. A. et al. IL-23 and IL-17 in the establishment of protective pulmonary CD4+ T cell responses after vaccination and during Mycobacterium tuberculosis challenge. Nat. Immunol. 8, 369–377 (2007).

    Article  CAS  PubMed  Google Scholar 

  65. Arlehamn, C. L. et al. Transcriptional profile of tuberculosis antigen-specific T cells reveals novel multifunctional features. J. Immunol. 193, 2931–2940 (2014).

    Article  CAS  PubMed  Google Scholar 

  66. Shanmugasundaram, U. et al. Pulmonary Mycobacterium tuberculosis control associates with CXCR3- and CCR6-expressing antigen-specific Th1 and Th17 cell recruitment. JCI Insight 5, 137858 (2020).

    Article  PubMed  Google Scholar 

  67. Darrah, P. A. et al. Prevention of tuberculosis in macaques after intravenous BCG immunization. Nature 577, 95–102 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Lindenstrom, T. et al. Vaccine-induced TH17 cells are maintained long-term postvaccination as a distinct and phenotypically stable memory subset. Infect. Immun. 80, 3533–3544 (2012).

  69. Fares, A. Seasonality of tuberculosis. J. Glob. Infect. Dis. 3, 46–55 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  70. Moody, D. B. et al. CD1b-mediated T cell recognition of a glycolipid antigen generated from mycobacterial lipid and host carbohydrate during infection. J. Exp. Med. 192, 965–976 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Thornton, T. et al. Estimating kinship in admixed populations. Am. J. Hum. Genet. 91, 122–138 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. The 1000 Genomes Project Consortium. A global reference for human genetic variation. Nature 526, 68–74 (2015).

  73. Reich, D. et al. Reconstructing Native American population history. Nature 488, 370–374 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Alexander, D. H., Novembre, J. & Lange, K. Fast model-based estimation of ancestry in unrelated individuals. Genome Res. 19, 1655–1664 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. O’Connell, J. et al. A general approach for haplotype phasing across the full spectrum of relatedness. PLoS Genet. 10, e1004234 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  77. Howie, B. N., Donnelly, P. & Marchini, J. A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS Genet. 5, e1000529 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  78. Kang, H. M. et al. Multiplexed droplet single-cell RNA-sequencing using natural genetic variation. Nat. Biotechnol. 36, 89–94 (2018).

    Article  CAS  PubMed  Google Scholar 

  79. Li, H. A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data. Bioinformatics 27, 2987–2993 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. McInnes, L. & Healy, J. UMAP: uniform manifold approximation and projection for dimension reduction. Preprint at arXiv https://arxiv.org/abs/1802.03426 (2018).

  81. Gonzalez, I., Déjean, S., Martin, P. G. P. & Baccini, A. CCA: an R package to extend canonical correlation analysis. J. Stat. Softw. https://doi.org/10.18637/jss.v023.i12 (2007).

  82. Dobin, A., Davis, C. A. & Schlesinger, F. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Friedman, J. H., Hastie, T. & Tibshirani, R. Regularization paths for generalized linear models via coordinate descent. J. Stat. Softw. https://doi.org/10.18637/jss.v033.i01 (2010).

  85. Kuznetsova, A., Brockhoff, P. B. & Christensen, R. H. B. lmerTest Package: tests in linear mixed-effects models. J. Stat. Softw. https://doi.org/10.18637/jss.v082.i13 (2017).

Download references

Acknowledgements

We thank K. Slowikowski for building an interactive data browser. This work is supported in part by funding from the National Institutes of Health (U19 AI111224 to S.R., M.B.M. and D.B.M.; UH2AR067677 to S.R.; T32 HG002295 to A.N.; T32 AR007530 to A.N.; U01 HG009379 to S.R.; U19 AI111211 to J.D.E.; and R01AI049313 to D.B.M.).

Author information

Authors and Affiliations

  1. Center for Data Sciences, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA

    Aparna Nathan, Jessica I. Beynor, Yuriy Baglaenko, Kazuyoshi Ishigaki, Samira Asgari, Yang Luo & Soumya Raychaudhuri

  2. Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA

    Aparna Nathan, Jessica I. Beynor, Yuriy Baglaenko, Sara Suliman, Kazuyoshi Ishigaki, Samira Asgari, Yang Luo, Kattya Lopez, Ildiko Van Rhijn, D. Branch Moody & Soumya Raychaudhuri

  3. Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA

    Aparna Nathan, Jessica I. Beynor, Yuriy Baglaenko, Kazuyoshi Ishigaki, Samira Asgari, Yang Luo & Soumya Raychaudhuri

  4. Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Aparna Nathan, Jessica I. Beynor, Yuriy Baglaenko, Kazuyoshi Ishigaki, Samira Asgari, Yang Luo & Soumya Raychaudhuri

  5. Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA

    Aparna Nathan, Jessica I. Beynor, Yuriy Baglaenko, Kazuyoshi Ishigaki, Samira Asgari, Yang Luo & Soumya Raychaudhuri

  6. Department of Global Health and Social Medicine, Harvard Medical School, Boston, MA, USA

    Chuan-Chin Huang, Zibiao Zhang, Leonid Lecca & Megan B. Murray

  7. Division of Global Health Equity, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA

    Chuan-Chin Huang, Zibiao Zhang & Megan B. Murray

  8. Socios En Salud Sucursal Peru, Lima, Peru

    Kattya Lopez, Judith Jimenez, Roger I. Calderón & Leonid Lecca

  9. Center for Infectious Disease, La Jolla Institute for Immunology, La Jolla, CA, USA

    Cecilia S. Lindestam Arlehamn

  10. Division of Experimental Medicine, Department of Medicine, University of California, San Francisco, San Francisco, CA, USA

    Joel D. Ernst

  11. Programa Acadêmico de Tuberculose, Faculdade de Medicina, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil

    Roger I. Calderón

  12. Department of Infectious Diseases and Immunology, Faculty of Veterinary Medicine, Utrecht University, Utrecht, the Netherlands

    Ildiko Van Rhijn

  13. Centre for Genetics and Genomics Versus Arthritis, Manchester Academic Health Science Centre, University of Manchester, Manchester, UK

    Soumya Raychaudhuri

Authors
  1. Aparna Nathan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jessica I. Beynor
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yuriy Baglaenko
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sara Suliman
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Kazuyoshi Ishigaki
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Samira Asgari
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Chuan-Chin Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Yang Luo
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Zibiao Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Kattya Lopez
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Cecilia S. Lindestam Arlehamn
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Joel D. Ernst
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Judith Jimenez
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Roger I. Calderón
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Leonid Lecca
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Ildiko Van Rhijn
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. D. Branch Moody
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Megan B. Murray
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Soumya Raychaudhuri
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

S.R., D.B.M. and M.B.M. conceptualized and designed the study. A.N. and S.R. designed the statistical and computational strategy and analyzed the data. K.I., S.A. and Y.L. conducted additional statistical analyses. J.I.B., Y.B., S.S., C.S.L.A., J.D.E., A.N., I.V.R., D.B.M. and S.R. designed the immunoprofiling strategy. J.I.B., Y.B. and S.S. conducted all immunoprofiling experiments. J.J., L.L. and M.B.M. recruited, clinically phenotyped and obtained blood samples from human participants. I.V.R., K.L. and R.C. organized processing, transportation and management of PBMCs. C.-C.H., Z.Z. and M.B.M. curated and analyzed clinical phenotype data. A.N. and S.R. wrote the initial manuscript. All authors contributed to the writing and editing of the final manuscript.

Corresponding author

Correspondence to Soumya Raychaudhuri.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Immunology thanks Thomas Scriba and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available. Zoltan Fehervari was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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

Extended data

Extended Data Fig. 1 CITE-seq cell and feature quality.

a, In silico memory T cell gating. Each cell is plotted based on its normalized surface expression of CD3 and CD45RO, measured through CITE-seq. Gates are demarcated with red dashed lines. Red cells were removed. Counts represent the number of cells in each quadrant. b, UMAP representation of gated cells. Red cells were gated out in (a) and cells clustering with them in the UMAP (shown in black in (a) and (b)) were also removed. c, Normalized CD3 and CD45RO surface protein expression. d, Number of cells per sample after QC, stratified by TB progression status. P value is from a two-sided t test comparing mean cell counts in each group. e, Pearson correlation coefficient (r) was calculated between normalized mRNA and surface protein expression for each marker across cells passing QC. r is plotted against average normalized mRNA expression for each protein. f, Each cell is plotted based on normalized expression of each marker in surface protein and mRNA, both measured through CITE-seq, with density contours. We fit a best-fit line (in blue) with a linear model.

Extended Data Fig. 2 Comparing proportions of eight major T cell states between flow cytometry and CITE-seq.

a, Average percents per population in CITE-seq vs. flow cytometry. Gates in (b). Dashed line indicates the identity line. b, For each population, proportions plotted across 259 donors. Flow cytometry gating occurred after gating T cells. CITE-seq gating occurred after isolation of memory T cells. The dashed line indicates the identity line, and we calculated Pearson correlation coefficients (r) for each state.

Extended Data Fig. 3 Multimodal integration with canonical correlation analysis.

a, Correlations for the top 20 canonical dimensions used in downstream analysis. Bars represent the Pearson correlation between mRNA and protein projections for each dimension. b, Marker correlation with canonical variates (CVs). Each marker is plotted based on its mRNA and protein correlation with CV1 (left) or CV2 (right). c, Innateness scores. UMAP is colored based on a gene expression-derived cytotoxicity score defined in Gutierrez-Arcelus, et al. d, Correlation between innateness score and CV1. Each cell is plotted based on its innateness score from (c) and its CV1 projection, and we report the Pearson correlation coefficient.

Extended Data Fig. 4 Single-cell expression of surface proteins, measured with CITE-seq.

Each cell is colored according to its expression of each protein and plotted in UMAP space. Colors are scaled independently for each marker from minimum (blue) to maximum (yellow) expression.

Extended Data Fig. 5 Technical replicate consistency.

For each of the 31 multimodal clusters, we plotted its proportion in replicate 1 and in replicate 2 of each donor. We calculated the Pearson correlation coefficients (r) for each cluster.

Extended Data Fig. 6 Effects of donor covariates on memory T cell states.

Effects of age, sex, winter blood draw and proportion of European genetic ancestry in (a) model correcting for technical covariates (# UMIs/cell, % MT UMIs/cell), donor, batch and TB disease status, and (b) full model with TB disease status, age, sex, winter blood draw, proportion of European genetic ancestry, technical covariates, donor and batch. For all, n = 271 samples from 259 independent donors. For each cluster, data are presented as the MASC OR of a cell being in each cluster given the contrast covariate (95% CI error bars) and the -log(LRT p value) of the association. The dashed horizontal line corresponds to a Bonferroni p-value threshold of 0.05/31. Labeled clusters are significant at this threshold.

Extended Data Fig. 7 Unimodal clusters and associations with TB disease progression.

a–c, mRNA clusters. d–f, protein clusters. a and d, UMAPs colored by unimodal clusters. Clusters boxed in red are CD4+, purple are mixed CD4+ and CD8+, blue are CD8+, and green are CD4-CD8-. b and e, Expression of major lineage-defining surface markers measured through CITE-seq. The UMAPs are colored by the expression of five markers measured through CITE-seq. Colors are scaled independently for each marker from minimum (blue) to maximum (yellow) expression. c and f, Heatmap of overlap between unimodal and multimodal clusters. Colors indicate the proportion of the multimodal cluster (column) overlapping with the unimodal cluster (row). g, Associations between TB disease status and unimodal protein clusters. For each cluster, the data are presented as MASC ORs of a cell being in each cluster for cases vs. controls (95% CI error bars), and the -log(LRT p value) of the association (n = 271 samples from 259 independent donors). The dashed horizontal line corresponds to a Bonferroni p-value threshold of 0.05/40. Labeled clusters are significant at this threshold. h, Abundance of C-20 in 128 cases and 131 controls. P value is from an LRT with 1 d.f. Boxplots show the median (vertical bar), 25th and 75th percentiles (lower and upper bounds of box, respectively), and 1.5xIQR (or minimum/maximum if they lie within that range; end of whiskers).

Extended Data Fig. 8 Cell state signature extrapolation in public data.

a, Correlation between actual and predicted C-12 proportion, per sample in memory T cell CITE-seq study (n = 271, black) and bulk PBMC RNA-seq (n = 15, blue). Line represents the identity line and we calculated the Pearson correlation coefficients (r) for T cell samples and bulk PBMC samples separately. Predicted (b) C-12 or (c) C-11 (TH2, negative control) proportion in 3 categories of donors from Berry, et al. d, Predicted C-12 proportion in active donors at 3 time points during anti-mycobacterial treatment (0, 2, 12 months) and uninfected controls. In b–d, we calculated p values with a two-sided t test. e, Histogram of two-sided t test p values from 1,000 trials of downsampling LIMAA cohort to 7 cases and 12 controls (as in Berry, et al.) and comparing the average C-12 proportion in cases vs controls. Dashed line is the significance threshold of p = 0.05 (power = 0.15). f, Predicted C-12 proportion in active cases and latent controls at 2 pre-disease-progression time points in Scriba, et al. g, Predicted C-12 proportion in active cases and latent controls in pre- and post-disease cohorts. Pre-disease data are aggregated across 2 time points. In f and g, p-values are from a one-sided t-test (Satterthwaite’s d.f. method) of the beta estimate for TB progression status in the linear mixed model. h, Concordance of Pearson correlations between each cluster’s proportion and the C-12 score or the C-12 cluster’s proportion. Pearson correlation coefficients were computed for each cluster across 271 memory T cell samples. Each point represents one of the 31 clusters, and the dashed line is the identity line. All boxplots show the median (vertical bar), 25th and 75th percentiles (lower and upper bounds of box, respectively), and 1.5xIQR (or minimum/maximum if they lie within that range; end of whiskers).

Extended Data Fig. 9 Cytokine production in Boston donors.

Bars represent the mean and error bars show standard error of the mean across 5 Boston donors unascertained for TB. a, Per-donor percent of cells producing each cytokine in gated populations. b, Per-donor percent of total cytokine-producing memory CD4 + T cells in each gated population.

Extended Data Fig. 10 M.tb antigen-specific response in CD4+CD26+CD161+CCR6+ memory T cells.

a, Biaxial plots showing representative gating of CD3+CD4+CD45RO+CD161+CCR6+CD26+ cells in a Boston donor. b, Intracellular staining for IL-17A and IFNγ in a Boston control donor and two Peruvian TB cohort donors after either no peptide stimulation (control) or stimulation with the MTB300 megapool. c, IL-17A or IFNγ response to MTB300 stimulation in all CD3 + T cells from either Boston control donors (n = 2) or Peruvian TB cohort donors (n = 6). Each point corresponds to the percent of cells producing IL-17A or IFNγ from one donor, measured with intracellular cytokine staining. Lines connect measurements from the same donor before and after stimulation with MTB300 peptide megapool. Boxplots show the median (vertical bar), 25th and 75th percentiles (lower and upper bounds of box, respectively), and minimum/maximum (end of whiskers). P values are from a two-sided Wilcoxon signed-rank test comparing donors before and after antigen stimulation.

Supplementary information

Supplementary Information

Supplementary Figs. 1–3.

Reporting Summary

Peer Review Information

Supplementary Tables

Supplementary Tables 1–10

Supplementary Data

Weights for each gene in linear mixed model estimating C-12 proportion, trained on T cell and PBMC data.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nathan, A., Beynor, J.I., Baglaenko, Y. et al. Multimodally profiling memory T cells from a tuberculosis cohort identifies cell state associations with demographics, environment and disease. Nat Immunol 22, 781–793 (2021). https://doi.org/10.1038/s41590-021-00933-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41590-021-00933-1

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research