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:

Early emergence of T central memory precursors programs clonal dominance during chronic viral infection

Nature Immunology volume 21, pages 1563–1573 (2020)Cite this article

Subjects

Abstract

Chronic cytomegalovirus (CMV) infection leads to long-term maintenance of extraordinarily large CMV-specific T cell populations. The magnitude of this so-called ‘memory inflation’ is thought to mainly depend on antigenic stimulation during the chronic phase of infection. However, by mapping the long-term development of CD8+ T cell families derived from single naive precursors, we find that fate decisions made during the acute phase of murine CMV infection can alter the level of memory inflation by more than 1,000-fold. Counterintuitively, a T cell family’s capacity for memory inflation is not determined by its initial expansion. Instead, those rare T cell families that dominate the chronic phase of infection show an early transcriptomic signature akin to that of established T central memory cells. Accordingly, a T cell family’s long-term dominance is best predicted by its early content of T central memory precursors, which later serve as a stem-cell-like source for memory inflation.

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: Single-cell-derived memory inflation is highly variable and not predicted by acute clonal expansion.
Fig. 2: A T cell family’s fate is determined during the acute phase of MCMV infection.
Fig. 3: Long-term dominating T cell families share an early transcriptional signature with established TCM cells.
Fig. 4: Early abundance of CD62L+CD27+ CMPs best predicts a T cell family’s later magnitude of memory inflation.
Fig. 5: T cell families without detectable CMPs are at higher risk of attrition.
Fig. 6: Single TCM cells show a stem-cell-like capacity for reconstitution of memory inflation.

Similar content being viewed by others

Data availability

RNA sequencing data generated for this study have been deposited in the Gene Expression Omnibus under accession number GSE157644. All other data generated or analyzed during this study are included in the published article (and its supplementary information files) or are available from the corresponding author upon reasonable request. Source data are provided with this paper.

Code availability

No custom code or algorithms were used in this study.

References

  1. Karrer, U. et al. Memory inflation: continuous accumulation of antiviral CD8+ T cells over time. J. Immunol. 170, 2022–2029 (2003).

    Article  CAS  PubMed  Google Scholar 

  2. Sylwester, A. W. et al. Broadly targeted human cytomegalovirus-specific CD4+ and CD8+ T cells dominate the memory compartments of exposed subjects. J. Exp. Med. 202, 673–685 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Simon, C. O. et al. CD8 T cells control cytomegalovirus latency by epitope-specific sensing of transcriptional reactivation. J. Virol. 80, 10436–10456 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Dekhtiarenko, I. et al. Peptide processing is critical for T-cell memory inflation and may be optimized to improve immune protection by CMV-based vaccine vectors. PLoS Pathog. 12, e1006072 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  5. Snyder, C. M. et al. Memory inflation during chronic viral infection is maintained by continuous production of short-lived, functional T cells. Immunity 29, 650–659 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Klenerman, P. & Oxenius, A. T cell responses to cytomegalovirus. Nat. Rev. Immunol. 16, 367–377 (2016).

    Article  CAS  PubMed  Google Scholar 

  7. Karrer, U. et al. Expansion of protective CD8+ T-cell responses driven by recombinant cytomegaloviruses. J. Virol. 78, 2255–2264 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Hansen, S. G. et al. Effector memory T cell responses are associated with protection of rhesus monkeys from mucosal simian immunodeficiency virus challenge. Nat. Med. 15, 293–299 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Hansen, S. G. et al. Profound early control of highly pathogenic SIV by an effector memory T-cell vaccine. Nature 473, 523–527 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Hansen, S. G. et al. Immune clearance of highly pathogenic SIV infection. Nature 502, 100–104 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Hansen, S. G. et al. Prevention of tuberculosis in rhesus macaques by a cytomegalovirus-based vaccine. Nat. Med. 24, 130–143 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Weekes, M. P., Wills, M. R., Mynard, K., Carmichael, A. J. & Sissons, J. G. The memory cytotoxic T-lymphocyte (CTL) response to human cytomegalovirus infection contains individual peptide-specific CTL clones that have undergone extensive expansion in vivo. J. Virol. 73, 2099–2108 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Khan, N., Cobbold, M., Keenan, R. & Moss, P. A. H. Comparative analysis of CD8+ T cell responses against human cytomegalovirus proteins pp65 and immediate early 1 shows similarities in precursor frequency, oligoclonality, and phenotype. J. Infect. Dis. 185, 1025–1034 (2002).

    Article  CAS  PubMed  Google Scholar 

  14. McHeyzer-Williams, M. G. & Davis, M. M. Antigen-specific development of primary and memory T cells in vivo. Science 268, 106–111 (1995).

    Article  CAS  PubMed  Google Scholar 

  15. Lin, M. Y. & Welsh, R. M. Stability and diversity of T cell receptor repertoire usage during lymphocytic choriomeningitis virus infection of mice. J. Exp. Med. 188, 1993–2005 (1998).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Busch, D. H. & Pamer, E. G. T cell affinity maturation by selective expansion during infection. J. Exp. Med. 189, 701–710 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. 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 

  18. Williams, M. A., Ravkov, E. V. & Bevan, M. J. Rapid culling of the CD4+ T cell repertoire in the transition from effector to memory. Immunity 28, 533–545 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Kim, C., Wilson, T., Fischer, K. F. & Williams, M. A. Sustained interactions between T cell receptors and antigens promote the differentiation of CD4+ memory T cells. Immunity 39, 508–520 (2013).

    Article  CAS  PubMed  Google Scholar 

  20. Schober, K. et al. Reverse TCR repertoire evolution toward dominant low-affinity clones during chronic CMV infection. Nat. Immunol. 21, 434–441 (2020).

    Article  CAS  PubMed  Google Scholar 

  21. Day, E. K. et al. Rapid CD8+ T cell repertoire focusing and selection of high-affinity clones into memory following primary infection with a persistent human virus: human cytomegalovirus. J. Immunol. 179, 3203–3213 (2007).

    Article  CAS  PubMed  Google Scholar 

  22. Trautmann, L. et al. Selection of T cell clones expressing high-affinity public TCRs within human cytomegalovirus-specific CD8 T cell responses. J. Immunol. 175, 6123–6132 (2005).

    Article  CAS  PubMed  Google Scholar 

  23. Buchholz, V. R. et al. Disparate individual fates compose robust CD8+ T cell immunity. Science 340, 630–635 (2013).

    Article  CAS  PubMed  Google Scholar 

  24. Gerlach, C. et al. Heterogeneous differentiation patterns of individual CD8+ T cells. Science 340, 635–639 (2013).

    Article  CAS  PubMed  Google Scholar 

  25. Cho, Y.-L. et al. TCR signal quality modulates fate decisions of single CD4+ T cells in a probabilistic manner. Cell Rep. 20, 806–818 (2017).

    Article  CAS  PubMed  Google Scholar 

  26. Buchholz, V. R., Schumacher, T. N. M. & Busch, D. H. T cell fate at the single-cell level. Annu. Rev. Immunol. 34, 65–92 (2016).

    Article  CAS  PubMed  Google Scholar 

  27. La Manno, G. et al. RNA velocity of single cells. Nature 560, 494–498 (2018).

    PubMed  PubMed Central  Google Scholar 

  28. Plumlee, C. R., Sheridan, B. S., Cicek, B. B. & Lefrançois, L. Environmental cues dictate the fate of individual CD8+ T cells responding to infection. Immunity 39, 347–356 (2013).

    Article  CAS  PubMed  Google Scholar 

  29. Tubo, N. J. et al. Single naive CD4+ T cells from a diverse repertoire produce different effector cell types during infection. Cell 153, 785–796 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Grassmann, S. et al. Distinct surface expression of activating receptor Ly49H drives differential expansion of NK cell clones upon murine cytomegalovirus infection. Immunity 50, 1391–1400.e4 (2019).

    Article  CAS  PubMed  Google Scholar 

  31. Dekhtiarenko, I., Jarvis, M. A., Ruzsics, Z. & Cicin-Sain, L. The context of gene expression defines the immunodominance hierarchy of cytomegalovirus antigens. J. Immunol. 190, 3399–3409 (2013).

    Article  CAS  PubMed  Google Scholar 

  32. Graef, P. et al. Serial transfer of single-cell-derived immunocompetence reveals stemness of CD8+ central memory T cells. Immunity 41, 116–126 (2014).

    Article  CAS  PubMed  Google Scholar 

  33. Flossdorf, M., Rössler, J., Buchholz, V. R., Busch, D. H. & Höfer, T. CD8+ T cell diversification by asymmetric cell division. Nat. Immunol. 16, 891–893 (2015).

    Article  CAS  PubMed  Google Scholar 

  34. Kretschmer, L. et al. Differential expansion of T central memory precursor and effector subsets is regulated by division speed. Nat. Commun. 11, 113 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Jeannet, G. et al. Essential role of the Wnt pathway effector Tcf-1 for the establishment of functional CD8 T cell memory. Proc. Natl Acad. Sci. USA 107, 9777–9782 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Zhou, X. et al. Differentiation and persistence of memory CD8+ T cells depend on T cell factor 1. Immunity 33, 229–240 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Shrestha, S. et al. Tsc1 promotes the differentiation of memory CD8+ T cells via orchestrating the transcriptional and metabolic programs. Proc. Natl Acad. Sci. USA 111, 14858–14863 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Miao, T. et al. Egr2 and 3 control adaptive immune responses by temporally uncoupling expansion from T cell differentiation. J. Exp. Med. https://doi.org/10.1084/jem.20160553 (2017).

  39. Jeng, M. Y. et al. Metabolic reprogramming of human CD8+ memory T cells through loss of SIRT1. J. Exp. Med. 215, 51–62 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Byrne, S. M. et al. Cathepsin B controls the persistence of memory CD8+ T lymphocytes. J. Immunol. 189, 1133–1143 (2012).

    Article  CAS  PubMed  Google Scholar 

  41. Yeh, J.-H., Sidhu, S. S. & Chan, A. C. Regulation of a late phase of T cell polarity and effector functions by Crtam. Cell 132, 846–859 (2008).

    Article  CAS  PubMed  Google Scholar 

  42. Im, S. J. et al. Defining CD8+ T cells that provide the proliferative burst after PD-1 therapy. Nature 537, 417–421 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. van der Windt, G. J. W. et al. Mitochondrial respiratory capacity is a critical regulator of CD8+ T cell memory development. Immunity 36, 68–78 (2012).

    Article  CAS  PubMed  Google Scholar 

  44. Gulati, G. S. et al. Single-cell transcriptional diversity is a hallmark of developmental potential. Science 367, 405–411 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Joshi, N. S. et al. Inflammation directs memory precursor and short-lived effector CD8+ T cell fates via the graded expression of T-bet transcription factor. Immunity 27, 281–295 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Sarkar, S. et al. Functional and genomic profiling of effector CD8 T cell subsets with distinct memory fates. J. Exp. Med. 205, 625–640 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Obar, J. J. & Lefrançois, L. Early signals during CD8 T cell priming regulate the generation of central memory cells. J. Immunol. 185, 263–272 (2010).

    Article  CAS  PubMed  Google Scholar 

  48. Lin, W.-H. W. et al. CD8+ T lymphocyte self-renewal during effector cell determination. Cell Rep. 17, 1773–1782 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Welten, S. P. M. et al. Tcf1+ cells are required to maintain the inflationary T cell pool upon MCMV infection. Nat. Commun. 11, 2295 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Davenport, M. P., Fazou, C., McMichael, A. J. & Callan, M. F. C. Clonal selection, clonal senescence, and clonal succession: the evolution of the T cell response to infection with a persistent virus. J. Immunol. 168, 3309–3317 (2002).

    Article  CAS  PubMed  Google Scholar 

  51. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

    PubMed  PubMed Central  Google Scholar 

  52. Young, M. D., Wakefield, M. J., Smyth, G. K. & Oshlack, A. Gene ontology analysis for RNA-seq: accounting for selection bias. Genome Biol. 11, R14 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  53. Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 19, 15 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  54. McCarthy, D. J., Campbell, K. R. & Lun, A. 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 

  55. DeTomaso, D. & Yosef, N. FastProject: a tool for low-dimensional analysis of single-cell RNA-Seq data. BMC Bioinformatics 17, 315 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  56. Bergen, V., Lange, M., Peidli, S., Wolf, F. A. & Theis, F. J. Generalizing RNA velocity to transient cell states through dynamical modeling. Nat. Biotechnol. https://doi.org/10.1038/s41587-020-0591-3 (2020).

  57. 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 

Download references

Acknowledgements

We thank thank J. Sun and N. Adams as well as all members of the Buchholz laboratory for discussion and support. This work was supported by the Else Kröner-Fresenius-Stiftung – EKFS 2019_A91 (‘Dissecting the induction of inflationary CD8+ T cell memory on the single-cell level’) to V.R.B., the DFG – SFB 1054 (project no. 210592381) to V.R.B. and D.H.B., the TUM Seed Funds (SCIMAP) to V.R.B., the BMBF project Quan-T-cell (e:Med initiative on Systems Medicine, FKZ 01ZX1505) to M.F., the BMBF project TIDY (Computational Life Sciences, FKZ 031L0170A) to T.H., the Helmholtz association project PIE-008 to L.C.S. and the DFG – TRR 179 (project number 031L0170A) to M.S.

Author information

Author notes

  1. These authors contributed equally: Simon Grassmann, Lorenz Mihatsch.

Authors and Affiliations

  1. Institute for Medical Microbiology, Immunology and Hygiene, Technical University of Munich (TUM), Munich, Germany

    Simon Grassmann, Lorenz Mihatsch, Jonas Mir, Atefeh Kazeroonian, Roza Rahimi, Sophie Flommersfeld, Kilian Schober, Inge Hensel, Justin Leube, Ludwig O. Pachmayr, Lorenz Kretschmer, Matthias Schiemann, Dirk H. Busch, Michael Flossdorf & Veit R. Buchholz

  2. Immunology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Simon Grassmann

  3. Division of Theoretical Systems Biology, German Cancer Research Center (DKFZ), Heidelberg, Germany

    Qin Zhang, Adrien Jolly & Thomas Höfer

  4. BioQuant Center, University of Heidelberg, Heidelberg, Germany

    Qin Zhang, Adrien Jolly & Thomas Höfer

  5. Department of Vaccinology and Applied Microbiology, Helmholtz Centre for Infection Research, Braunschweig, Germany

    M. Zeeshan Chaudhry & Luka Cicin-Sain

  6. German Center for Infection Research (DZIF), Partner Site Hannover-Braunschweig, Braunschweig, Germany

    M. Zeeshan Chaudhry & Luka Cicin-Sain

  7. German Center for Infection Research (DZIF), Partner Site-Munich, Munich, Germany

    Dirk H. Busch & Veit R. Buchholz

Authors
  1. Simon Grassmann
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lorenz Mihatsch
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jonas Mir
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Atefeh Kazeroonian
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Roza Rahimi
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Sophie Flommersfeld
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Kilian Schober
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Inge Hensel
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Justin Leube
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Ludwig O. Pachmayr
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Lorenz Kretschmer
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Qin Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Adrien Jolly
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. M. Zeeshan Chaudhry
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Matthias Schiemann
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Luka Cicin-Sain
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Thomas Höfer
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Dirk H. Busch
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Michael Flossdorf
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Veit R. Buchholz
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

S.G. and V.R.B. designed the study. S.G., L.M., R.R., S.F., I.H., J.L., L.K. and L.O.P. performed experiments. S.G., L.M., J.M., A.K., A.J. and M.F. performed data analysis. Q.Z., A.J. and T.H. provided sample processing and RNA sequencing. M.Z.C. and L.C.-S. generated virus stocks. M.S. provided cell sorting. K.S. and D.H.B. contributed to study design and critically read the manuscript. S.G., L.M. and V.R.B. wrote the manuscript.

Corresponding author

Correspondence to Veit R. Buchholz.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information 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 Virus schematic, gating strategy and tracking of T cell families until day 412 post infection.

a, Schematic of MCMV-ie2-SIINFEKL virus. The SIINFEKL peptide sequence was inserted at the 3’ end of the ie2 gene. b, Gating strategy for single-cell fate mapping. Following doublet and dead-cell exclusion, transferred cells are identified by expression of congenic markers. Further gating depends on the method used for single-cell transfer (exemplified in Figs. 2b and 4b, respectively). T cell families are characterized phenotypically using antibodies directed against CD27 and CD62L. c, Longitudinal quantification of T cell family sizes in blood, measured as percent of living leukocytes, for a cohort of T cell families from Fig. 1e traced until day 412 p.i. n = 15 T cell families.

Source data

Extended Data Fig. 2 Gene ontology term enrichment analysis of early gene signature in long-term dominators (sc-derived RNA-seq, day 8 p.i.).

a–c, Gene ontology (GO) term enrichment analysis of early gene signature in long-term dominating T cell families acquired via sc-derived RNA-seq at day 8 p.i. a, Top 10 GO terms that were underrepresented in long-term dominators (n = 2) compared to all remaining T cell families (n = 14). b,c, Top 10 GO terms that were significantly underrepresented (b) or overrepresented (c) in long-term dominators (n = 2) compared to acutely dominating T cell families (n = 2). d, GSEA analysis of genes differentially expressed in long-term dominating vs. acutely dominating T cell families associated with cellular senescence (GO:0090398). Genes up- (red) and down-regulated (blue) in long-term dominators vs. acutely dominating T cell families. P values were determined based on the default method in the goseq (a–c) and fgsea packages (d). See methods for details.

Source data

Extended Data Fig. 3 Expression of genes enriched in TCM, TEM and TE clusters (scRNA-seq, day 60 p.i.).

a,b, Analysis of scRNA-seq data derived from an established inflationary T cell population in spleen at day 60 p.i. a, Dimensional reduction using Uniform Manifold Approximation and Projection (UMAP) showing Leiden clusters and expression of genes enriched in the corresponding TCM, TEM and TE clusters. b, Cell cycle scores for S and G2M-phase projected onto UMAP embedding. These scores indicate both a weak proliferative activity and a uniform distribution of scores across different Leiden clusters. Scores were computed based on lists for S- and G2M-phase associated genes using the software package SCANPY.

Extended Data Fig. 4 Retrogenic color-barcoding and single-cell transfer.

a, Schematic of the generation of retrogenic color-barcoded OT-1 T cells: Bone marrow of CD45.1+ OT-1 Rag1–/– mice was harvested and enriched for hematopoietic stem cells (HSCs) by flow cytometric sorting for Sca1+ cells. These were transduced retrovirally with different combinations of fluorescent proteins (GFP, YFP, BFP, CFP and T-sapphire) and transplanted into sublethally irradiated C57BL/6 mice. More than 6 weeks after HSC engraftment, naive color-barcoded OT-1 T cells were harvested for experiments from peripheral blood of retrogenic chimeras. b, Exemplary gating strategy for identification of color-barcoded naive OT-1 T-cells. c, Sort and transfer of single naive color-barcoded OT-1 T cells (see methods section for details).

Extended Data Fig. 5 Temporal dynamics of single-cell-derived memory inflation correlated to early abundance of TEs, EMPs and CMPs.

a, Scatter plots comparing the amount of TEs (top row), EMPs (middle row) and CMPs (bottom row) in each T cell family on day 8 p.i. to the total size of the respective T cell families at various time points between day 8 and day 140 p.i. The amount of CMPs, EMPs and TEs and the total size of T cell families is measured as % of living leukocytes. Top-10 long-term dominating T cell families are marked (blue circles). P values from left to right: TE: p < 0.0001, p < 0.0001, p = 0.009, p = 0.95, p = 0.91, p = 0.97. EMP: p < 0.0001, p < 0.0001, p = 0.003, p = 0.88, p = 0.30, p = 0.48. CMP: p = 0.001, p = 0.001, p = 0.007, p = 0.0018, p = 0.0008, p = 0.0007. b, Scatter plots comparing the relative amount of TEs (top row), EMPs (middle row) and CMPs (bottom row) in each T cell family on day 8 p.i. to the total size of the respective T cell families at various time points between day 8 and day 140 p.i. The relative amount of CMPs, EMPs and TEs is measured as % of the corresponding T cell family’s overall size. Top-10 long-term dominating T cell families are marked (blue circles). P values from left to right: TE: p = 0.99, p = 0.07, p = 0.15, p = 0.13, p = 0.006, p = 0.002. EMP: p = 0.59, p = 0.73, p = 0.90, p = 0.82, p = 0.39, p = 0.10. CMP: p < 0.0001, p = 0.58, p = 0.57, p = 0.42, p = 0.0008, p = 0.0004. ‘r’ indicates Spearman’s correlation coefficient. Significances were measured using a two-sided Mann-Whitney test. * p < 0.05, ** p <0.01, *** p <0.001, **** p < 0.0001. n = 104 T cell families (zero values are not shown and not used for determining Spearman’s correlation coefficients and respective p values).

Source data

Extended Data Fig. 6 CMPs constitute only a minute fraction of MPECs and are enriched for non-cytolytic T cells.

a–d, Adoptive transfer of 100 congenically labelled OT-1 T cells followed by immunization with MCMV-ie2-SIINFEKL. a,b, Analysis of CD27, CD127, KLRG-1 and CD62L expression in expanded T cell populations (n = 6) at day 8 p.i. in spleen. a, Percent of CD62L+CD27+ CMPs among all responding T cells or among KLRG-1–CD27+ MPECs b, Percent of CD62L+CD27+ CMPs among all responding T cells or KLRG-1–CD127hi MPECs or KLRG-1+CD127lo SLECs. c,d, Analysis of Gzmb expression in T cell subsets defined as in (a) measured in expanded T cell populations (n = 5) at day 8 p.i. c, Percent of Gzmb– cells among MPECs or SLECs (d) Percent of Gzmb– cells among CMPs, EMPs or TEs. e,f, Adoptive transfer of 100 congenically labelled OT-1 T cells followed by immunization with 5000 cfu L.m.-OVA. Analysis of Gzmb expression in T cell subsets defined as in (a) in expanded T cell populations (n = 4) at day 8 p.i. e, Percent of Gzmb– cells among MPECs or SLECs (f) Percent of Gzmb– cells among CMPs, EMPs or TEs. Stacked bars in 6a–f show mean, error bars indicate SD. g,h, Further analysis of experiments described in Fig. 4: Scatter plots comparing the amount of CD27+ Memory precursors (MPs) (g) or the percentage of MPs in each T cell family (h) on day 8 p.i. to the total size of the respective T cell families at various time points between day 8 and day 140 p.i. The amount of MPs and the total size of T cell families is measured as % of living leukocytes. The relative amount of MPs is measured as % of the corresponding T cell family’s overall size. Top-10 long-term dominating T cell families are marked (blue circles). P values from left to right: (g): TE: p < 0.0001, p < 0.0001, p = 0.008, p = 0.69, p = 0.16, p = 0.30. (h): p = 0.14, p = 0.21, p = 0.27, p = 0.19, p = 0.01, p = 0.002. ‘r’ indicates Spearman’s correlation coefficient. Significances were measured using a two-sided Mann-Whitney test. * p < 0.05, ** p <0.01, *** p <0.001, **** p < 0.0001. n = 104 T cell families (zero values are not shown and not used for determining Spearman’s correlation coefficients and respective p values).

Source data

Supplementary information

Source data

Source Data Fig. 1

Statistical source data.

Source Data Fig. 2

Statistical source data.

Source Data Fig. 3

Statistical source data and lists of differentially expressed genes.

Source Data Fig. 4

Statistical source data.

Source Data Fig. 5

Statistical source data.

Source Data Fig. 6

Statistical source data.

Source Data Extended Data Fig. 1

Statistical source data.

Source Data Extended Data Fig. 2

Lists of differentially expressed genes.

Source Data Extended Data Fig. 5

Statistical source data.

Source Data Extended Data Fig. 6

Statistical source data.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Grassmann, S., Mihatsch, L., Mir, J. et al. Early emergence of T central memory precursors programs clonal dominance during chronic viral infection. Nat Immunol 21, 1563–1573 (2020). https://doi.org/10.1038/s41590-020-00807-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41590-020-00807-y

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