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Transcriptome dynamics of CD4+ T cells during malaria maps gradual transit from effector to memory

Nature Immunology volume 21, pages 1597–1610 (2020)Cite this article

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Abstract

The dynamics of CD4+ T cell memory development remain to be examined at genome scale. In malaria-endemic regions, antimalarial chemoprevention protects long after its cessation and associates with effects on CD4+ T cells. We applied single-cell RNA sequencing and computational modelling to track memory development during Plasmodium infection and treatment. In the absence of central memory precursors, two trajectories developed as T helper 1 (TH1) and follicular helper T (TFH) transcriptomes contracted and partially coalesced over three weeks. Progeny of single clones populated TH1 and TFH trajectories, and fate-mapping suggested that there was minimal lineage plasticity. Relationships between TFH and central memory were revealed, with antimalarials modulating these responses and boosting TH1 recall. Finally, single-cell epigenomics confirmed that heterogeneity among effectors was partially reset in memory. Thus, the effector-to-memory transition in CD4+ T cells is gradual during malaria and is modulated by antiparasitic drugs. Graphical user interfaces are presented for examining gene-expression dynamics and gene–gene correlations (http://haquelab.mdhs.unimelb.edu.au/cd4_memory/).

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Fig. 1: Plasmodium-specific TCR transgenic PbTII cells develop memory during infection.
Fig. 2: A lack of PbTII central memory precursors at peak infection.
Fig. 3: Temporal mixture modelling of scRNA-seq data maps effector-to-memory transit.
Fig. 4: Transcriptome dynamics reveal gene signatures that were preserved during memory development.
Fig. 5: Memory fate of naive clones is set during the first week of infection.
Fig. 6: IAT boosts development of GC TFH and memory CD4+ T cells during malaria.
Fig. 7: scATAC-seq reveals partial epigenomic resetting and homogenization during memory development.

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Data availability

All relevant data from this study, including raw flow-cytometry data, are available from the corresponding author upon request. Raw single-cell RNA sequencing data from our previous publication have been deposited in the ArrayExpress under accession number E-MTAB-4388. Raw scRNA-seq data, bulk ATAC-seq data and scATAC-seq data generated from the current manuscript have been deposited in the ArrayExpress under accession numbers E-MTAB-9317 (10x Genomics scRNAseq data), E-MTAB-9393 (bulk ATAC-seq data), E-MTAB-9403 (Smart-seq2 data), and E-MTAB-9402 (scATAC-seq data). JASPAR 2016 database (version 1.14.0) was used for transcription factor motif analyses.

References

  1. Harrington, L. E., Janowski, K. M., Oliver, J. R., Zajac, A. J. & Weaver, C. T. Memory CD4 T cells emerge from effector T-cell progenitors. Nature 452, 356–360 (2008).

    Article  CAS  PubMed  Google Scholar 

  2. Pepper, M., Pagan, A. J., Igyarto, B. Z., Taylor, J. J. & Jenkins, M. K. Opposing signals from the Bcl6 transcription factor and the interleukin-2 receptor generate T helper 1 central and effector memory cells. Immunity 35, 583–595 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Hale, J. S. et al. Distinct memory CD4+ T cells with commitment to T follicular helper- and T helper 1-cell lineages are generated after acute viral infection. Immunity 38, 805–817 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Marshall, H. D. et al. Differential expression of Ly6C and T-bet distinguish effector and memory TH1 CD4+ cell properties during viral infection. Immunity 35, 633–646 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Tubo, N. J. et al. Most microbe-specific naive CD4+ T cells produce memory cells during infection. Science 351, 511–514 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Opata, M. M. et al. Early effector cells survive the contraction phase in malaria infection and generate both central and effector memory T cells. J. Immunol. 194, 5346–5354 (2015).

    Article  CAS  PubMed  Google Scholar 

  7. Ciucci, T. et al. The emergence and functional fitness of memory CD4+ T cells require the transcription factor Thpok. Immunity 50, 91–105.e4 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Waickman, A. T. et al. Dissecting the heterogeneity of DENV vaccine-elicited cellular immunity using single-cell RNA sequencing and metabolic profiling. Nat. Commun. 10, 3666 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  9. Akondy, R. S. et al. Origin and differentiation of human memory CD8 T cells after vaccination. Nature 552, 362–367 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Arroyo, E. N. & Pepper, M. B cells are sufficient to prime the dominant CD4+ TFH response to Plasmodium infection. J. Exp. Med. 217, e20190849 (2020).

  11. WHO Global Malaria Programme: World Malaria Report 2019 (World Health Organization, 2019).

  12. Langhorne, J., Ndungu, F. M., Sponaas, A. M. & Marsh, K. Immunity to malaria: more questions than answers. Nat. Immunol. 9, 725–732 (2008).

    Article  CAS  PubMed  Google Scholar 

  13. Muhindo, M. K. et al. Intermittent preventive treatment with dihydroartemisinin-piperaquine and risk of malaria following cessation in young Ugandan children: a double-blind, randomised, controlled trial. Lancet Infect. Dis. 19, 962–972 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Schellenberg, D. et al. Intermittent preventive antimalarial treatment for Tanzanian infants: follow-up to age 2 years of a randomised, placebo-controlled trial. Lancet 365, 1481–1483 (2005).

    Article  CAS  PubMed  Google Scholar 

  15. Jagannathan, P. et al. Effective antimalarial chemoprevention in childhood enhances the quality of CD4+ T cells and limits their production of immunoregulatory interleukin 10. J. Infect. Dis. 214, 329–338 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Butler, N. S. et al. Therapeutic blockade of PD-L1 and LAG-3 rapidly clears established blood-stage Plasmodium infection. Nat. Immunol. 13, 188–195 (2011).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  17. Suss, G., Eichmann, K., Kury, E., Linke, A. & Langhorne, J. Roles of CD4- and CD8-bearing T lymphocytes in the immune response to the erythrocytic stages of Plasmodium chabaudi. Infect. Immun. 56, 3081–3088 (1988).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Lonnberg, T. et al. Single-cell RNA-seq and computational analysis using temporal mixture modelling resolves TH1/TFH fate bifurcation in malaria. Sci. Immunol. 2, eaal2192 (2017).

  19. Fernandez-Ruiz, D. et al. Development of a novel CD4+ TCR transgenic line that reveals a dominant role for CD8+ dendritic cells and CD40 signaling in the generation of helper and CTL responses to blood-stage malaria. J. Immunol. 199, 4165–4179 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Meding, S. J., Cheng, S. C., Simon-Haarhaus, B. & Langhorne, J. Role of gamma interferon during infection with Plasmodium chabaudi chabaudi. Infect. Immun. 58, 3671–3678 (1990).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Perez-Mazliah, D. et al. Follicular helper T cells are essential for the elimination of Plasmodium infection. EBioMedicine 24, 216–230 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  22. Obeng-Adjei, N. et al. Circulating TH1-cell-type TFH cells that exhibit impaired B cell help are preferentially activated during acute malaria in children. Cell Rep. 13, 425–439 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Street, K. et al. Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics. BMC Genomics 19, 477 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  25. Lopez, R., Regier, J., Cole, M. B., Jordan, M. I. & Yosef, N. Deep generative modeling for single-cell transcriptomics. Nat. Methods 15, 1053–1058 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Svensson, V., Teichmann, S. A. & Stegle, O. SpatialDE: identification of spatially variable genes. Nat. Methods 15, 343–346 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Huynh, J. P. et al. Bhlhe40 is an essential repressor of IL-10 during Mycobacterium tuberculosis infection. J. Exp. Med. 215, 1823–1838 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Yu, F. et al. The transcription factor Bhlhe40 is a switch of inflammatory versus antiinflammatory TH1 cell fate determination. J. Exp. Med. 215, 1813–1821 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  30. Cusanovich, D. A. et al. A single-cell atlas of in vivo mammalian chromatin accessibility. Cell 174, 1309–1324.e18 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. White, M. T. et al. The relationship between RTS,S vaccine-induced antibodies, CD4+ T cell responses and protection against Plasmodium falciparum infection. PLoS ONE 8, e61395 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Olotu, A. et al. Circumsporozoite-specific T cell responses in children vaccinated with RTS,S/AS01E and protection against P falciparum clinical malaria. PLoS ONE 6, e25786 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Pinzon-Charry, A. et al. Low doses of killed parasite in CpG elicit vigorous CD4+ T cell responses against blood-stage malaria in mice. J. Clin. Invest. 120, 2967–2978 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Ryg-Cornejo, V. et al. Severe malaria infections impair germinal center responses by inhibiting T follicular helper cell differentiation. Cell Rep. 14, 68–81 (2016).

    Article  CAS  PubMed  Google Scholar 

  36. 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 

  37. Lee, J. Y. et al. The transcription factor KLF2 restrains CD4+ T follicular helper cell differentiation. Immunity 42, 252–264 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Yasuda, K. et al. Satb1 regulates the effector program of encephalitogenic tissue TH17 cells in chronic inflammation. Nat. Commun. 10, 549 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Siddiqui, I. et al. Intratumoral Tcf1+PD-1+CD8+ T cells with stem-like properties promote tumor control in response to vaccination and checkpoint blockade immunotherapy. Immunity 50, 195–211.e10 (2019).

    Article  CAS  PubMed  Google Scholar 

  40. Chen, Z. et al. TCF-1-centered transcriptional network drives an effector versus exhausted CD8 T cell-fate decision. Immunity 51, 840–855.e5 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Nish, S. A. et al. CD4+ T cell effector commitment coupled to self-renewal by asymmetric cell divisions. J. Exp. Med. 214, 39–47 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Lin, W. 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 

  43. Scott-Browne, J. P. et al. Dynamic changes in chromatin accessibility occur in CD8+ T cells responding to viral infection. Immunity 45, 1327–1340 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Akter, J. et al. Plasmodium-specific antibodies block in vivo parasite growth without clearing infected red blood cells. PLoS Pathog. 15, e1007599 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. James, K. R. et al. IFN regulatory factor 3 balances TH1 and T follicular helper immunity during nonlethal blood-stage plasmodium infection. J. Immunol. 200, 1443–1456 (2018).

    Article  CAS  PubMed  Google Scholar 

  46. Haque, A. et al. Type I IFN signaling in CD8– DCs impairs TH1-dependent malaria immunity. J. Clin. Invest. 124, 2483–2496 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Couper, K. N. et al. IL-10 from CD4+CD25–Foxp3–CD127– adaptive regulatory T cells modulates parasite clearance and pathology during malaria infection. PLoS Pathog. 4, e1000004 (2008).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Li, H. Aligning sequence reads, clone sequences and assembly contigs with BWA–MEM. Preprint at https://arxiv.org/abs/1303.3997 (2013).

  50. Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Yu, G., Wang, L. G. & He, Q. Y. ChIPseeker: an R/Bioconductor package for ChIP peak annotation, comparison and visualization. Bioinformatics 31, 2382–2383 (2015).

    Article  CAS  PubMed  Google Scholar 

  52. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    Article  CAS  PubMed  Google Scholar 

  53. 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 

  54. Senabouth, A. et al. Comparative performance of the BGI and Illumina sequencing technology for single-cell RNA-sequencing. NAR Genom. Bioinform. 2, lqaa034 (2020).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  55. Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  56. Lun, A. T., Bach, K. & Marioni, J. C. Pooling across cells to normalize single-cell RNA sequencing data with many zero counts. Genome Biol. 17, 75 (2016).

    Article  PubMed  CAS  Google Scholar 

  57. Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36, 411–420 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Santos, A., Wernersson, R. & Jensen, L. J. Cyclebase 3.0: a multi-organism database on cell-cycle regulation and phenotypes. Nucleic Acids Res. 43, D1140–D1144 (2015).

    Article  CAS  PubMed  Google Scholar 

  59. Wherry, E. J. & Kurachi, M. Molecular and cellular insights into T cell exhaustion. Nat. Rev. Immunol. 15, 486–499 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Carlson, M. org.Mm.eg.db: Genome wide annotation for Mouse. R package v.3.8.2 (2019).

  61. Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).

    Article  CAS  PubMed  Google Scholar 

  62. Chen, X., Miragaia, R. J., Natarajan, K. N. & Teichmann, S. A. A rapid and robust method for single cell chromatin accessibility profiling. Nat. Commun. 9, 5345 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Neph, S. et al. BEDOPS: high-performance genomic feature operations. Bioinformatics 28, 1919–1920 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Pagès, H., Aboyoun, P., Gentleman, R., DebRoy, S. Biostrings: efficient manipulation of biological strings. R package v.2.52.0 (2019).

  65. Tan, G. & Lenhard, B. TFBSTools: an R/bioconductor package for transcription factor binding site analysis. Bioinformatics 32, 1555–1556 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Tan, G. JASPAR2016: data package for JASPAR 2016. R package v.1.12.0 (2019).

  67. Yu, G. & He, Q. Y. ReactomePA: an R/Bioconductor package for reactome pathway analysis and visualization. Mol. Biosyst. 12, 477–479 (2016).

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

This study was funded by the Australian National Health & Medical Research Council: Project Grant 1126399 (awarded to A.H. and S.A.T.). Smart-seq2 work was funded by ERC Consolidator grant ThDEFINE (awarded to S.A.T.). We are grateful to J. Moehrle at Medicines for Malaria Venture for providing sodium artesunate. We acknowledge the expertise and assistance of several QIMR Berghofer Medical Research Institute Core Staff: the Flow Cytometry and Microscopy Core, particularly M. Rist and T. Hong Nguyen, for single-cell sorting; the Histology Core, including C. Winterford; the Animal Facility, including all technicians involved with animal husbandry; the Next-Generation-Sequencing Core, including P. Collins for assistance with droplet-based scRNA-seq and Illumina sequencing. We acknowledge the Single-Cell Genomics Core Facility and sequencing pipeline at the Wellcome Sanger Institute for plate-based Smart-seq2 and scATAC-seq processing.

Author information

Author notes

  1. These authors contributed equally: Megan S. F. Soon, Hyun Jae Lee, Jessica A. Engel.

  2. These authors jointly supervised this work: Sarah A. Teichmann, Ashraful Haque.

Authors and Affiliations

  1. QIMR Berghofer Medical Research Institute, Herston, Brisbane, Queensland, Australia

    Megan S. F. Soon, Hyun Jae Lee, Jessica A. Engel, Jasmin Straube, Bryce S. Thomas, Clara P. S. Pernold, Lachlan S. Clarke, Pawat Laohamonthonkul, Rohit N. Haldar, Cameron G. Williams, Lianne I. M. Lansink, Lambros T. Koufariotis, Scott Wood, Steven W. Lane, Christian R. Engwerda & Ashraful Haque

  2. Department of Microbiology and Immunology, University of Melbourne, located at The Peter Doherty Institute for Infection and Immunity, Parkville, Victoria, Australia

    Hyun Jae Lee, Cameron G. Williams, Lianne I. M. Lansink, Marcela L. Moreira, Michael Bramhall & Ashraful Haque

  3. Department of Biology, South University of Science and Technology of China, Shenzhen, China

    Xi Chen

  4. Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK

    Xi Chen, Kylie R. James & Sarah A. Teichmann

  5. Cavendish Laboratory, University of Cambridge, Cambridge, UK

    Xi Chen, Kylie R. James & Sarah A. Teichmann

  6. Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland

    Tapio Lönnberg

  7. The University of Queensland Diamantina Institute, The University of Queensland, Brisbane, Queensland, Australia

    Gabrielle T. Belz

  8. Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia

    Gabrielle T. Belz

  9. Department of Medical Biology, University of Melbourne, Parkville, Australia

    Gabrielle T. Belz

  10. Kirby Institute, University of New South Wales, Kensington, Sydney, New South Wales, Australia

    David S. Khoury & Miles P. Davenport

  11. Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA

    Valentine Svensson

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  1. Megan S. F. Soon
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Contributions

A.H. conceived the study with T.L., K.R.J. and S.A.T. A.H. led efforts to acquire funding and managed the project in consultation with S.A.T. M.S.F.S., H.J.L. and J.A.E. ran the study equally, managing different aspects of cellular immunology, single-cell genomics and computational biology—first authorship order is determined by workload. B.S.T., C.P.S.P., L.S.C., P.L., R.N.H., X.C., K.R.J. and L.I.M.L. carried out experimentation in consultation with M.S.F.S., H.J.L., J.A.E., S.A.T. and A.H. G.T.B., C.R.E. and S.W.L. assisted in experimental design and data analysis and interpretation. M.S.F.S., H.J.L., J.A.E., J.S., C.G.W., M.L.M., M.B., L.T.K., S.W. and D.S.K. conducted analysis in consultation with A.H., S.A.T., V.S. and M.P.D. M.S.F.S., H.J.L., J.A.E. and J.S. interpreted results in consultation with A.H., S.A.T. and K.R.J. A.H., M.S.F.S., H.J.L. and J.A.E. wrote the manuscript.

Corresponding authors

Correspondence to Sarah A. Teichmann or Ashraful Haque.

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The authors declare no competing interests.

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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 PbTII cells localise to T cell zones, B cell follicles and GCs during infection.

a, Representative FACS plots for surface CXCR6 and CXCR5 expression on PbTII cells at various timepoints during infection. Graphs showing proportion of CXCR6+ or CXCR5+ PbTII cells over time. Data are pooled from 3 independent experiments (n=5 mice per group, per individual timepoint for each independent experiment) and presented as mean +/- SEM. Statistical analysis was performed between saline and IAT groups for each individual timepoint using two-tailed Mann-Whitney test. p-values where indicated are **p< 0.01, ***p<0.001, ****p<0.0001. b, eGFP+ PbTII cells were distributed throughout splenic B cell follicles and T cell zones at D7 p.i. (n=4). Graph showing proportion of PbTII cells localised within B cell (IgD) or T cell (CD3) zones. Scale bar, 2mm. c, Standard microanatomical structures observed in saline-treated mice at D21 p.i. White arrows indicate eGFP+ PbTII cells. Scale bar, 100µm. d, Localisation of PbTII cells within GCs and IgD+ naive B cell follicle regions of mice at D21 p.i. in the presence or absence of IAT. Graph showing proportion of PbTII cells localised within GCs or follicular regions of saline- (n=6) or IAT- (n=5) treated mice. Scale bar, 2mm. Images were acquired on an Aperio FL slide scanner (b, d) or a Zeiss 780-NLO point scanning confocal microscope (c).

Extended Data Fig. 2 PbTII cells exhibit functional memory characteristics.

a, b, Number of (a) IFNγ-expressing or (b) IL-10-expressing splenic PbTII cells after restimulation with PMA and ionomycin for 3 hours in vitro over time, in the presence or absence of IAT. Data are presented as mean +/- SEM. Data are representative of 2 independent experiments (n=5/ group, per individual timepoint for each experiment). Statistical test performed using two-tailed Mann-Whitney test. c, Average number of reads for ATAC-seq peaks within enhancer regions enriched for D7 p.i. sample. Bulk ATAC-seq experiments were performed as 2 independent experiments. Data are derived using overlapping peaks shared between biological replicates from each experiment for individual timepoints. Error bars represent mean +/- SD. Statistical test performed using two-sided Wilcoxon rank-sum test. d, Mean ATAC-seq peak coverage at Ifng, Tbx21, Cxcr5 and Il21 gene loci with a scale of 0-45 for all tracks. Boxes represent peaks called using MACS2. Data is shown for a representative experiment out of 2 independent biological repeats. e, Representative FACS plots and graph showing proliferative marker Ki67 expression for memory (green) or naive (grey) PbTII cells at 17 hours post-rechallenge. Data are pooled from 2 independent experiments (n= 5 mice per group, per independent experiment). Statistical test performed using paired two-way ANOVA with Tukey’s multiple comparison test. f, Representative FACS plots and graph showing expression of early activation marker, CD69 for memory (green) or naive (grey) PbTII cells at 17 hours post-rechallenge. Data are pooled from 2 independent experiments (n= 5/ group, per independent experiment). Statistical test performed using paired two-way ANOVA with Tukey’s multiple comparison test. p-values are indicated where *p< 0.05, **p< 0.01, ****p<0.0001. Statistical analysis was performed between saline- and IAT-treated groups for each individual timepoint (a, b).

Extended Data Fig. 3 PbTII cells at peak effector stage display mixed expression of canonical TH1 and TFH markers.

a, Visualisation of the expression of common markers used for TH1 and Tfh lineage tracing experiments on UMAP representation of PbTII cells at D7 p.i. assessed using the droplet-based 10x Genomics platform. b, (top) UMAP representation as in (a) showing the individual clusters identified by unsupervised clustering analysis. (bottom) Violin plots showing the expression of genes described in (a) within each cluster. The expression value for naive (D0) PbTII cells is shown for each gene for reference.

Extended Data Fig. 4 Quality control checks for scRNA-seq assessment of memory PbTII cell differentiation.

a, FACS gating strategy for isolation of naive (CD62L+ CD44-) donor PbTII cells for transfer into recipients, 24 hours prior to infection. A subset of these cells was also used for scRNA-seq assessment for PbTII responses at D0 p.i. Cells were then either sorted as single-cells onto 384-well plates for Smart-seq2 assessment or sorted as single-cells using the 10X Genomics platform. b, FACS gating strategy for isolation of PbTII cells from D7 p.i. onwards for scRNA-seq assessment for either Smart-seq2 or 10X Genomics assessment. c, Distribution of PbTII cells from the Smart-Seq2 dataset after filtering for number of genes (1000<nGene< 5000), mapped counts (> 100,000) and percentage of mitochondrial content (<0.35). d, The current Smart-seq2 (384) PbTII dataset of D0, D7, D10*, D14*, D17*, D21* and D28* p.i. cells were combined with our previous datasets18 containing PbTII cells isolated from D0 to D7 p.i. (SMARTer batch: D0, D2, D3, D4, D7 p.i.; Smart-seq2 (96) batch: D0, D4, D7 p.i.) PCA plots showing the entire time series, with shapes denoting the different experimental batches, (left) before and (right) after batch effect correction as described in methods. (*) = samples were isolated either from saline or IAT groups.

Extended Data Fig. 5 Multiple computational approaches for trajectory inference of PbTII scRNA-seq data.

a, (left) UMAP representation of batch-corrected Smart-seq2 PbTII dataset superimposed with trajectory inferences calculated using Slingshot. (right) Visualising Cxcr6 and Cxcr5 expression on UMAP representations as described previously. b, Grid-view of RNA velocities for each cell from the Smart-seq2 PbTII dataset (only D4-D28 p.i.) visualised on 2D bGPLVM representations. c, (left) Integration of the three PbTII datasets (Smart-seq2(96/ 384) and SMARTer) using scVI represented on a UMAP plot. (right) Visualising Cxcr6 and Cxcr5 on a UMAP representation of the scVI-integrated dataset.

Extended Data Fig. 6 Gene expression and protein validation of multiple memory-associated genes in PbTII cells.

a, Visualisation of the expression of various memory-associated genes identified using GPfates modelling on 2D bGPLVM representations. LV, latent variable. b–d, Representative FACS plots and histograms showing protein level expression of various memory-associated markers over time, in the absence or presence of IAT. Graphs showing kinetics of Id2, TCF1, cMaf and CCL5 protein expression over time. Data are representative of 3 independent experiments (n=3 for naive and n=6 for day 7 and day 28 saline- or IAT-treated groups, per individual timepoint). For the box plot shown, the centre line indicates the median, box limits indicate the upper and lower quartiles, and the whiskers indicate the maximum and minimum measures. Statistical analysis was performed using one-way ANOVA between all groups. TCF1 staining was performed after stimulation with PMA/ Ionomycin. e, Representative FACS plot showing co-expression of CXCR3 with either CXCR6 or CXCR5 on PbTII cells at D28 p.i. during IAT. f, Representative FACS plots, overlaid histograms and graph showing the relationship between promoter activity of Id3 from Id3GFP reporter mice with either CXCR6 or CXCR5. Data are representative of 2 independent experiments (n= 6 mice per group, per indepdent experiment). Statistical analysis performed using two-tailed Wilcoxon matched-pairs signed-rank test. g, Expression of TCF1 in PbTII cells from individual PbTIIcre-ERT2 Tcf7wt/wt and PbTIIcre-ERT2 Tcf7fl/fl donor mice on the day of transfer, 7 days post-tamoxifen treatment. h, Representative FACS plots and graphs showing expression of various TH1 and Tfh-associated markers at D7 p.i. in Tcf7wt/wt and Tcf7fl/fl PbTIIs. Experiment performed once (n=7 mice per group). Data are presented as mean+/- SEM. Statistical test performed using two-tailed Mann-Whitney test. p-values are indicated where *p<0.05, **p<0.01, ***p<0.001, ****p<0.0001.

Extended Data Fig. 7 Clonal analysis of memory fate and lineage plasticity testing with and without IAT.

a, Families sharing endogenous TCRα and TCRβ sequences displaying no crossover between timepoints or treatments. b, Lineage tracing of effector to memory transition of PbTIIs with and without the presence of IAT. Representative FACS plots and graphs showing expression of CXCR5 and CXCR6 in splenic PbTIIs at D28 p.i. Experiment performed once (n=5 for Reference and n=4 for CXCR5+ Transfer in the IAT group, and n=6 for Reference and n=6 for CXCR5+ Transfer in the Saline group). Statistical test performed using two-tailed Mann-Whitney test. c, Representative FACS plots and graphs showing co-expression of two TH1-markers in splenic PbTIIs at D28 p.i. Experiment performed once (n=5 for Reference and n=4 for CXCR5+ Transfer in the IAT group, and n=6 for Reference and n=6 for CXCR5+ Transfer in the Saline group). Statistical test performed using two-tailed Mann-Whitney test. Data are presented as mean +/- SEM (b, c). p-values are indicated where * p<0.05, ** p<0.01.

Extended Data Fig. 8 Assessing relationships of TH1 and Tfh-lineages with TCM, GC Tfh and Tr1 subsets in the GPfates model.

a, Visualisation of Sell and Ccr7 expression on 2D bGPLVM representations overlaid with OMGP trajectories representing TH1 or Tfh branch. b, Co-expression of Sell and Ccr7 along pseudotime, for all cells annotated for the Tfh (left) or TH1 (right) branch as outlined in methods. Kernel density estimation failed to estimate a population of Sell+ Ccr7+ cells, hence a threshold was estimated where cells express both Sell ≥ 4.7 and Ccr7 ≥ 3.75 corrected gene values. c, Representative FACS plots and graph showing expression of TH1-markers (CCL5 and CXCR6) on PbTIIs at D28 p.i. isolated from the spleen and inguinal lymph nodes of the IAT group. Experiment performed once (n=6 mice). Statistical test performed using Wilcoxon matched-pairs signed-rank test. p-value where indicated is *p<0.05. d, Visualisation of Pdcd1 expression on 2D bGPLVM representations overlaid with OMGP trajectories representing TH1 or Tfh branch. e, Co-expression of Pdcd1 and Ccr7 along pseudotime, for all cells annotated for the Tfh branch. Kernel density estimation failed to estimate a dense population of Pdcd1+ Ccr7+ cells, hence a threshold was estimated where cells express both Pdcd1 ≥ 4.0 and Ccr7 ≥ 3.50 corrected gene values. f, Visualisation of Il10 and Ifng expression on 2D bGPLVM representations overlaid with OMGP trajectories representing TH1 or Tfh branch. g, Co-expression of Il10 and Ifng along pseudotime for all cells annotated for TH1. Tr1 cells are annotated as those cells expressing Il10 and Ifng above the threshold drawn (cells expressing both Il10 ≥ 4.9 and Ifng ≥ 3.75 corrected gene values), according to kernel density estimation.

Extended Data Fig. 9 Changes in availability of transcription factor binding motifs in memory PbTII cells via scATAC-seq.

Changes in scATAC-seq peaks for cells from naive (D0), saline and IAT groups at D32 p.i. associated with different transcription factors. Error bars represent mean+/- SD. Statistical test was performed using two-sided Wilcoxon rank-sum test. p-values are indicated where ***p<0.001.

Extended Data Fig. 10 A conceptual view for development of memory CD4+ T cells during malaria.

A broad overview of transcriptome dynamics accompanying transition of CD4+ T cells from naivety to memory. CD4+ T cells branch into effector TH1 and Tfh after undergoing clonal expansion, and exhibit partial retention of effector phenotypes as they transition to memory. TH1 effector cells give rise to Tr1 cells and TH1 phenotype TEM memory cells. Tfh effector cells give rise to TCM cells, GC Tfh cells, or memory Tfh cells. Genes correlating strongly with memory development are summarised in the box (right). Numbers in boxes denote the average number of genes detected by high-resolution scRNA-seq at different stages of CD4+ T cell differentiation.

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Soon, M.S.F., Lee, H.J., Engel, J.A. et al. Transcriptome dynamics of CD4+ T cells during malaria maps gradual transit from effector to memory. Nat Immunol 21, 1597–1610 (2020). https://doi.org/10.1038/s41590-020-0800-8

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