Resource | Published:

The cellular composition of the human immune system is shaped by age and cohabitation

Nature Immunology volume 17, pages 461468 (2016) | Download Citation


Detailed population-level description of the human immune system has recently become achievable. We used a 'systems-level' approach to establish a resource of cellular immune profiles of 670 healthy individuals. We report a high level of interindividual variation, with low longitudinal variation, at the level of cellular subset composition of the immune system. Despite the profound effects of antigen exposure on individual antigen-specific clones, the cellular subset structure proved highly elastic, with transient vaccination-induced changes followed by a return to the individual's unique baseline. Notably, the largest influence on immunological variation identified was cohabitation, with 50% less immunological variation between individuals who share an environment (as parents) than between people in the wider population. These results identify local environmental conditions as a key factor in shaping the human immune system.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.


  1. 1.

    Immunology taught by humans. Sci. Transl. Med. 4, 117fs2 (2012).

  2. 2.

    et al. Genetic variants regulating immune cell levels in health and disease. Cell 155, 242–256 (2013).

  3. 3.

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

  4. 4.

    et al. ImmVar project: Insights and design considerations for future studies of “healthy” immune variation. Semin. Immunol. 27, 51–57 (2015).

  5. 5.

    , & Age-dependent dysregulation of innate immunity. Nat. Rev. Immunol. 13, 875–887 (2013).

  6. 6.

    et al. Generation of functional thymocytes in the human adult. Immunity 10, 569–575 (1999).

  7. 7.

    et al. Maintenance of peripheral naive T cells is sustained by thymus output in mice but not humans. Immunity 36, 288–297 (2012).

  8. 8.

    , , & Peripheral selection rather than thymic involution explains sudden contraction in naive CD4 T-cell diversity with age. Proc. Natl. Acad. Sci. USA 109, 21432–21437 (2012).

  9. 9.

    et al. Global analyses of human immune variation reveal baseline predictors of postvaccination responses. Cell 157, 499–513 (2014).

  10. 10.

    & Microbiota-mediated inflammation and antimicrobial defense in the intestine. Annu. Rev. Immunol. 33, 227–256 (2015).

  11. 11.

    et al. Induction of intestinal Th17 cells by segmented filamentous bacteria. Cell 139, 485–498 (2009).

  12. 12.

    et al. Global analyses of human immune variation reveal baseline predictors of postvaccination responses. Cell 157, 499–513 (2014).

  13. 13.

    et al. Ex vivo phenotype and frequency of influenza virus-specific CD4 memory T cells. J. Virol. 78, 7284–7287 (2004).

  14. 14.

    et al. Human effector and memory CD8+ T cell responses to smallpox and yellow fever vaccines. Immunity 28, 710–722 (2008).

  15. 15.

    et al. Early patterns of gene expression correlate with the humoral immune response to influenza vaccination in humans. J. Infect. Dis. 203, 921–929 (2011).

  16. 16.

    et al. Systems biology of vaccination for seasonal influenza in humans. Nat. Immunol. 12, 786–795 (2011).

  17. 17.

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

  18. 18.

    & Through thick and thin: wasting, obesity, and TNF-α. Cell 73, 625–627 (1993).

  19. 19.

    , , , & Excess body mass is associated with T cell differentiation indicative of immune ageing in children. Clin. Exp. Immunol. 176, 246–254 (2014).

  20. 20.

    , , & Human leptin enhances activation and proliferation of human circulating T lymphocytes. Cell. Immunol. 199, 15–24 (2000).

  21. 21.

    et al. Association between anti-human heat shock protein-60 and interleukin-2 with coronary artery calcium score. Heart 101, 436–441 (2015).

  22. 22.

    & Molecular control over thymic involution: from cytokines and microRNA to aging and adipose tissue. Eur. J. Immunol. 42, 1073–1079 (2012).

  23. 23.

    et al. Premature thymic involution is independent of structural plasticity of the thymic stroma. Eur. J. Immunol. 45, 1535–1547 (2015).

  24. 24.

    et al. Aging enhances the basal production of IL-6 and CCL2 in vascular smooth muscle cells. Arterioscler. Thromb. Vasc. Biol. 32, 103–109 (2012).

  25. 25.

    et al. Age-associated proinflammatory secretory phenotype in vascular smooth muscle cells from the non-human primate Macaca mulatta: reversal by resveratrol treatment. J. Gerontol. A Biol. Sci. Med. Sci. 67, 811–820 (2012).

  26. 26.

    , , & Perivascular adipose tissue, inflammation and vascular dysfunction in obesity. Curr. Vasc. Pharmacol. 12, 403–411 (2014).

  27. 27.

    , & The varying faces of IL-6: from cardiac protection to cardiac failure. Cytokine 74, 62–68 (2015).

  28. 28.

    , , & How sex and age affect immune responses, susceptibility to infections, and response to vaccination. Aging Cell 14, 309–321 (2015).

  29. 29.

    et al. Systems analysis of sex differences reveals an immunosuppressive role for testosterone in the response to influenza vaccination. Proc. Natl. Acad. Sci. USA 111, 869–874 (2014).

  30. 30.

    et al. Global analyses of human immune variation reveal baseline predictors of postvaccination responses. Cell 157, 499–513 (2014).

  31. 31.

    et al. Individuality and variation in gene expression patterns in human blood. Proc. Natl. Acad. Sci. USA 100, 1896–1901 (2003).

  32. 32.

    et al. Human gut microbiome viewed across age and geography. Nature 486, 222–227 (2012).

  33. 33.

    et al. Cohabiting family members share microbiota with one another and with their dogs. eLife 2, e00458 (2013).

  34. 34.

    et al. Longitudinal analysis of microbial interaction between humans and the indoor environment. Science 345, 1048–1052 (2014).

  35. 35.

    et al. Shaping the oral microbiota through intimate kissing. Microbiome 2, 41 (2014).

  36. 36.

    et al. Diet dominates host genotype in shaping the murine gut microbiota. Cell Host Microbe 17, 72–84 (2015).

  37. 37.

    & Spousal concordance in health behavior change. Health Serv. Res. 43, 96–116 (2008).

  38. 38.

    & Concordance of use of alcohol and other substances among older adult couples. Addict. Behav. 24, 839–856 (1999).

  39. 39.

    et al. Hypertension status, treatment, and control among spousal pairs in a middle-aged adult cohort. Am. J. Epidemiol. 174, 790–796 (2011).

  40. 40.

    R Development Core Team. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, 2014).

  41. 41.

    , , & Consensus clustering: A resampling-based method for class discovery and visualization of gene expression microarray data. Mach. Learn. 52, 91–118 (2003).

  42. 42.

    Relative importance for linear regression in R: the package relaimpo. J. Stat. Softw. 17, 1–27 (2006).

Download references


We thank all volunteers for their participation; the Cambridge BioResource staff for help with volunteer recruitment; members of the Cambridge BioResource SAB and Management Committee for support of this study; and A. Nuygen, D. Franckaert, D. Danso-Abeam and L. Van Eyck (KUL-VIB) for technical assistance. Supported by the National Institute for Health Research Cambridge Biomedical Research Centre; the European Research Council (Start Grant IMMUNO to A.L. and Start Grant TWILIGHT to M.A.L.). the NIHR (E.J.C.), the Biotechnology and Biological Sciences Research Council (M.A.L.), Research Fund KU Leuven (OT/11/087 to A.G.), Research Foundation Flanders (G073415N to A.G.) and the Wellcome Trust (105920/Z/14/Z to J.C.L.).

Author information

Author notes

    • Edward J Carr
    •  & James Dooley

    These authors contributed equally to this work.

    • Michelle A Linterman
    •  & Adrian Liston

    These authors jointly directed this work.


  1. Lymphocyte Signaling and Development ISP, Babraham Institute, Cambridge, UK.

    • Edward J Carr
    •  & Michelle A Linterman
  2. Translational Immunology Laboratory, VIB, Leuven, Belgium.

    • James Dooley
    • , Josselyn E Garcia-Perez
    • , Vasiliki Lagou
    •  & Adrian Liston
  3. Department of Microbiology and Immunology, University of Leuven, Leuven, Belgium.

    • James Dooley
    • , Josselyn E Garcia-Perez
    • , Vasiliki Lagou
    • , Carine Wouters
    • , Isabelle Meyts
    •  & Adrian Liston
  4. Department of Neurosciences, University of Leuven, Leuven, Belgium.

    • Vasiliki Lagou
    •  & An Goris
  5. Cambridge Institute for Medical Research, University of Cambridge, Cambridge Biomedical Campus, Cambridge, UK.

    • James C Lee
  6. Department of Medicine, University of Cambridge School of Clinical Medicine, Cambridge, UK.

    • James C Lee
  7. Department of Experimental Medicine, University of Leuven, Leuven, Belgium.

    • Guy Boeckxstaens


  1. Search for Edward J Carr in:

  2. Search for James Dooley in:

  3. Search for Josselyn E Garcia-Perez in:

  4. Search for Vasiliki Lagou in:

  5. Search for James C Lee in:

  6. Search for Carine Wouters in:

  7. Search for Isabelle Meyts in:

  8. Search for An Goris in:

  9. Search for Guy Boeckxstaens in:

  10. Search for Michelle A Linterman in:

  11. Search for Adrian Liston in:


E.J.C. analyzed the data and drafted the manuscript. J.D. and J.E.G.-P. performed the experiments. V.L. analysed the vaccination cohort. J.C.L., C.W., I.M., A.N. and G.B. designed and recruited subcohorts. M.A.L. designed and supervised the vaccination study, contributed to the data analysis and the manuscript. A.L. designed and supervised the study and drafted the manuscript.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Adrian Liston.

Integrated supplementary information

Supplementary information

PDF files

  1. 1.

    Supplementary Tables and Figures

    Supplementary Figures 1–8 and Supplementary Tables 1 and 3

  2. 2.

    Supplementary Code 1: Code for resource data set

    R-compatible code for analysis of Belgian cohort (PDF format).

  3. 3.

    Supplementary Code 2: Code for vaccination data set

    R-compatible code for analysis of UK vaccination subcohort (PDF format)

Excel files

  1. 1.

    Supplementary Table 2: Immune parameter definitions and summary statistics.

    Definitions and characterization for each immune parameter. Summary statistics for each immune parameter (R2 values, P values, 95% confidence intervals).

  2. 2.

    Supplementary Data Set 1: Resource data set

    Raw data for Belgian cohort (Excel format).

  3. 3.

    Supplementary Data Set 2: Vaccination data set

    Raw data for UK vaccination subcohort (Excel format).

Zip files

  1. 1.

    Supplementary Data Set 3: Resource data set

    Raw data for Belgian cohort (RData format).

  2. 2.

    Supplementary Data Set 4: Vaccination data set

    Raw data for UK vaccination subcohort (RData format).

  3. 3.

    Supplementary Code 3: Code for resource data set

    R-compatible code for analysis of Belgian cohort (Rmd format).

  4. 4.

    Supplementary Code 4: Code for vaccination data set

    R-compatible code for analysis of UK vaccination subcohort (Rmd format).

About this article

Publication history





Further reading