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:

Human germinal centres engage memory and naive B cells after influenza vaccination

Nature volume 586, pages 127–132 (2020)Cite this article

Subjects

Abstract

Influenza viruses remain a major public health threat. Seasonal influenza vaccination in humans primarily stimulates pre-existing memory B cells, which differentiate into a transient wave of circulating antibody-secreting plasmablasts1,2,3. This recall response contributes to ‘original antigenic sin’—the selective increase of antibody species elicited by previous exposures to influenza virus antigens4. It remains unclear whether such vaccination can also induce germinal centre reactions in the draining lymph nodes, where diversification and maturation of recruited B cells can occur5. Here we used ultrasound-guided fine needle aspiration to serially sample the draining lymph nodes and investigate the dynamics and specificity of germinal centre B cell responses after influenza vaccination in humans. Germinal centre B cells that bind to influenza vaccine could be detected as early as one week after vaccination. In three out of eight participants, we detected vaccine-binding germinal centre B cells up to nine weeks after vaccination. Between 12% and 88% of the responding germinal centre B cell clones overlapped with B cells detected among early circulating plasmablasts. These shared B cell clones had high frequencies of somatic hypermutation and encoded broadly cross-reactive monoclonal antibodies. By contrast, vaccine-induced B cell clones detected only in the germinal centre compartment exhibited significantly lower frequencies of somatic hypermutation and predominantly encoded strain-specific monoclonal antibodies, which suggests a naive B cell origin. Some of these strain-specific monoclonal antibodies recognized epitopes that were not targeted by the early plasmablast response. Thus, influenza virus vaccination in humans can elicit a germinal centre reaction that recruits B cell clones that can target new epitopes, thereby broadening the spectrum of vaccine-induced protective antibodies.

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: Robust peripheral B cell response to influenza virus vaccination.
Fig. 2: Defining influenza virus vaccine-induced GC B cell response in humans.
Fig. 3: Clonally diverse GC B cell response to influenza virus vaccine.
Fig. 4: Functionally diverse GC and PB responses to influenza virus vaccine.

Similar content being viewed by others

Data availability

Raw fastq files and associated RNA sequencing data have been uploaded to the NCBI Gene Expression Omnibus (GEO) database under identifiers SRP251458 and GSE148633. Antibody sequences are deposited at GenBank under the following accession numbers: MT200037-MT200636, KEB[P-Z]00000000, KEC[A-Z]00000000, KED[A-Z]00000000, and KEE[A-F]00000000, available from GenBank/EMBL/DDBJ. The template switch sequences, constant region primers, and isotype-specific internal constant region sequences that were used in these studies are available at https://bitbucket.org/kleinstein/immcantation/src/master/protocols/AbSeq/. The 3D reconstructions are deposited to the Electron Microscopy Data Bank (EMDB) under the following accession numbers: EMD-21703, EMD-21710, EMD-21733, EMD-21734, EMD-21737, EMD-21765, EMD-21809, EMD-21768, EMD-21776, EMD-21778, EMD-21779, EMD-21780, EMD-21782, EMD-21783 (participant 4 polyclonal immune complexes); EMD-21739, EMD-21741, EMD-21745, EMD-21746, EMD-21750, EMD-21784, EMD-21785, EMD-21786, EMD-21787, EMD-21788, EMD-21792, EMD-21793, EMD-21797, EMD-21798, EMD-21799, EMD-21800, EMD-21802 (participant 5 polyclonal immune complexes); EMD-21714, EMD-21715, EMD-21717, EMD-21719, EMD-21722, EMD-21723, EMD-21726, EMD-21729, EMD-21730, EMD-21731, EMD-21751, EMD-21752, EMD-21754, EMD-21803, EMD-21804, EMD-21806, EMD-21808 (participant 11 polyclonal immune complexes); EMD-21757, EMD-21758, EMD-21759, EMD-21760, EMD-21761, EMD-21762, EMD-21763 (monoclonal immune complexes). Other relevant data are available from the corresponding author upon reasonable request. Source data are provided with this paper.

References

  1. Wrammert, J. et al. Rapid cloning of high-affinity human monoclonal antibodies against influenza virus. Nature 453, 667–671 (2008).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  2. Corti, D. et al. A neutralizing antibody selected from plasma cells that binds to group 1 and group 2 influenza A hemagglutinins. Science 333, 850–856 (2011).

    Article  ADS  CAS  PubMed  Google Scholar 

  3. Corti, D. et al. Heterosubtypic neutralizing antibodies are produced by individuals immunized with a seasonal influenza vaccine. J. Clin. Invest. 120, 1663–1673 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Francis, T. On the doctrine of original antigenic sin. Proc. Am. Phil. Soc. 104, 572–578 (1953).

    Google Scholar 

  5. Victora, G. D. & Nussenzweig, M. C. Germinal centers. Annu. Rev. Immunol. 30, 429–457 (2012).

    Article  CAS  PubMed  Google Scholar 

  6. Iuliano, A. D. et al. Estimates of global seasonal influenza-associated respiratory mortality: a modelling study. Lancet 391, 1285–1300 (2018).

    Article  PubMed  Google Scholar 

  7. Cirelli, K. M. et al. Slow delivery immunization enhances HIV neutralizing antibody and germinal center responses via modulation of immunodominance. Cell 177, 1153–1171 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Havenar-Daughton, C. et al. Direct probing of germinal center responses reveals immunological features and bottlenecks for neutralizing antibody responses to HIV Env trimer. Cell Rep. 17, 2195–2209 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Havenar-Daughton, C. et al. Normal human lymph node T follicular helper cells and germinal center B cells accessed via fine needle aspirations. J. Immunol. Methods 479, 112746 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Ellebedy, A. H. et al. Defining antigen-specific plasmablast and memory B cell subsets in human blood after viral infection or vaccination. Nat. Immunol. 17, 1226–1234 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. O’Rahilly, R., Müller, F., Carpenter, S. & Swenson, R. Basic Human Anatomy: a Regional Study of Human Structure; https://www.dartmouth.edu/~humananatomy/part_2/chapter_7.html (2008).

  12. Carati, C., Gannon, B. & Piller, N. Anatomy and physiology in relation to compression of the upper limb and thorax. J. Lymphoedema 5, 58–67 (2010).

    Google Scholar 

  13. Wrammert, J. et al. Broadly cross-reactive antibodies dominate the human B cell response against 2009 pandemic H1N1 influenza virus infection. J. Exp. Med. 208, 181–193 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Stadlbauer, D. et al. Broadly protective human antibodies that target the active site of influenza virus neuraminidase. Science 366, 499–504 (2019).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  15. Gupta, N. T. et al. Hierarchical clustering can identify B cell clones with high confidence in Ig repertoire sequencing data. J. Immunol. 198, 2489–2499 (2017).

    Article  CAS  PubMed  Google Scholar 

  16. Zhou, J. Q. & Kleinstein, S. H. Cutting edge: Ig H chains are sufficient to determine most B cell clonal relationships. J. Immunol. 203, 1687–1692 (2019).

    Article  CAS  PubMed  Google Scholar 

  17. Ellebedy, A. H. & Ahmed, R. Re-engaging cross-reactive memory B cells: the influenza puzzle. Front. Immunol. 3, 53 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  18. Meade, P., Latorre-Margalef, N., Stallknecht, D. E. & Krammer, F. Development of an influenza virus protein microarray to measure the humoral response to influenza virus infection in mallards. Emerg. Microbes Infect. 6, e110 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  19. Meade, P. et al. Influenza virus infection induces a narrow antibody response in children but a broad recall response in adults. MBio 11, e03243-19 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  20. Bianchi, M. et al. Electron-microscopy-based epitope mapping defines specificities of polyclonal antibodies elicited during HIV-1 BG505 envelope trimer immunization. Immunity 49, 288–300.e8 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Nogal, B. et al. Mapping polyclonal antibody responses in non-human primates vaccinated with HIV Env trimer subunit vaccines. Cell Rep. 30, 3755–3765 (2019).

    Article  CAS  Google Scholar 

  22. Dogan, I. et al. Multiple layers of B cell memory with different effector functions. Nat. Immunol. 10, 1292–1299 (2009).

    Article  CAS  PubMed  Google Scholar 

  23. Pape, K. A., Taylor, J. J., Maul, R. W., Gearhart, P. J. & Jenkins, M. K. Different B cell populations mediate early and late memory during an endogenous immune response. Science 331, 1203–1207 (2011).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  24. Zuccarino-Catania, G. V. et al. CD80 and PD-L2 define functionally distinct memory B cell subsets that are independent of antibody isotype. Nat. Immunol. 15, 631–637 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Mesin, L. et al. Restricted clonality and limited germinal center reentry characterize memory B cell reactivation by boosting. Cell 180, 92–106 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Kuraoka, M. et al. Complex antigens drive permissive clonal selection in germinal centers. Immunity 44, 542–552 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Moyer, T. J. et al. Engineered immunogen binding to alum adjuvant enhances humoral immunity. Nat. Med. 26, 430–440 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Lingwood, D. et al. Structural and genetic basis for development of broadly neutralizing influenza antibodies. Nature 489, 566–570 (2012).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  29. Margine, I., Palese, P. & Krammer, F. Expression of functional recombinant hemagglutinin and neuraminidase proteins from the novel H7N9 influenza virus using the baculovirus expression system. J. Vis. Exp. 81, 51112 (2013).

    Google Scholar 

  30. Smith, K. et al. Rapid generation of fully human monoclonal antibodies specific to a vaccinating antigen. Nat. Protocols 4, 372–384 (2009).

    Article  CAS  PubMed  Google Scholar 

  31. Nachbagauer, R. et al. Broadly reactive human monoclonal antibodies elicited following pandemic H1N1 influenza virus exposure protect mice against highly pathogenic H5N1 challenge. J. Virol. 92, 1–17 (2018).

    Article  Google Scholar 

  32. Vander Heiden, J. A. et al. pRESTO: a toolkit for processing high-throughput sequencing raw reads of lymphocyte receptor repertoires. Bioinformatics 30, 1930–1932 (2014).

    Article  CAS  Google Scholar 

  33. Camacho, C. et al. BLAST+: architecture and applications. BMC Bioinformatics 10, 421 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  34. Ye, J., Ma, N., Madden, T. L. & Ostell, J. M. IgBLAST: an immunoglobulin variable domain sequence analysis tool. Nucleic Acids Res. 41, W34-40 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  35. Giudicelli, V., Chaume, D. & Lefranc, M. P. IMGT/GENE-DB: a comprehensive database for human and mouse immunoglobulin and T cell receptor genes. Nucleic Acids Res. 33, D256–D261 (2005).

    Article  CAS  PubMed  Google Scholar 

  36. Gupta, N. T. et al. Change-O: a toolkit for analyzing large-scale B cell immunoglobulin repertoire sequencing data. Bioinformatics 31, 3356–3358 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Lefranc, M. P. et al. IMGT unique numbering for immunoglobulin and T cell receptor variable domains and Ig superfamily V-like domains. Dev. Comp. Immunol. 27, 55–77 (2003).

    Article  CAS  PubMed  Google Scholar 

  38. Gadala-Maria, D., Yaari, G., Uduman, M. & Kleinstein, S. H. Automated analysis of high-throughput B-cell sequencing data reveals a high frequency of novel immunoglobulin V gene segment alleles. Proc. Natl Acad. Sci. USA 112, E862–E870 (2015).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  39. Gu, Z., Gu, L., Eils, R., Schlesner, M. & Brors, B. circlize Implements and enhances circular visualization in R. Bioinformatics 30, 2811–2812 (2014).

    Article  CAS  PubMed  Google Scholar 

  40. Hoehn, K. B., Lunter, G. & Pybus, O. G. A phylogenetic codon substitution model for antibody lineages. Genetics 206, 417–427 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Hoehn, K. B. et al. Repertoire-wide phylogenetic models of B cell molecular evolution reveal evolutionary signatures of aging and vaccination. Proc. Natl Acad. Sci. USA 116, 22664–22672 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Durinck, S. et al. BioMart and Bioconductor: a powerful link between biological databases and microarray data analysis. Bioinformatics 21, 3439–3440 (2005).

    Article  CAS  PubMed  Google Scholar 

  43. Yates, A. D. et al. Ensembl 2020. Nucleic Acids Res. 48, D682–D688 (2020).

    Article  CAS  PubMed  Google Scholar 

  44. Treutlein, B. et al. Reconstructing lineage hierarchies of the distal lung epithelium using single-cell RNA-seq. Nature 509, 371–375 (2014).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  45. Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Vallania, F. et al. Leveraging heterogeneity across multiple datasets increases cell-mixture deconvolution accuracy and reduces biological and technical biases. Nat. Commun. 9, 4735 (2018).

    Article  ADS  PubMed  PubMed Central  CAS  Google Scholar 

  47. Suloway, C. et al. Automated molecular microscopy: the new Leginon system. J. Struct. Biol. 151, 41–60 (2005).

    Article  CAS  PubMed  Google Scholar 

  48. Lander, G. C. et al. Appion: an integrated, database-driven pipeline to facilitate EM image processing. J. Struct. Biol. 166, 95–102 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Scheres, S. H. W. RELION: implementation of a Bayesian approach to cryo-EM structure determination. J. Struct. Biol. 180, 519–530 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Zivanov, J. et al. New tools for automated high-resolution cryo-EM structure determination in RELION-3. eLife 7, e42166 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  51. Pettersen, E. F. et al. UCSF Chimera—a visualization system for exploratory research and analysis. J. Comput. Chem. 25, 1605–1612 (2004).

    Article  CAS  PubMed  Google Scholar 

  52. Gilchuk, I. M. et al. Influenza H7N9 virus neuraminidase-specific human monoclonal antibodies inhibit viral egress and protect from lethal influenza infection in mice. Cell Host Microbe 26, 715–728.e8 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank K. Hoehn for discussion on phylogenetic analysis, A. Boon and H. Harastani for providing A/California/04/2009 E3 (H1N1) virus; E. Lantelme for facilitating sorting; L. Kessels, M. Royal and the staff of the Infectious Diseases Clinical Research Unit at Washington University School of Medicine for assistance with vaccination and sample collection; P. G. Thomas and A. Sant for critically reading the manuscript; the Yale Center for Research Computing for use of high-performance computing infrastructure; the Genome Technology Access Center (GTAC) in the Department of Genetics at Washington University School of Medicine and the Yale Center for Genome Analysis for help with genomic analysis. The GTAC is partially supported by NCI Cancer Center Support Grant P30 CA91842 to the Siteman Cancer Center and by ICTS/CTSA Grant UL1 TR000448 from the NCRR. The Ellebedy laboratory was supported by NIAID grants R21 AI139813 and U01 AI141990, and NIAID Centers of Excellence for Influenza Research and Surveillance (CEIRS) contract HHSN272201400006C. The Kleinstein laboratory was supported by NIH grant R01AI10473 and HIPC-CEIRS grant U19AI089992. The Ward laboratory was supported by Collaborative Influenza Vaccine Innovation Centers contract 75N93019C00051-0-9999-1. The Krammer laboratory was supported by NIAID CEIRS contract HHSN272201400008C and NIAID grants AI117287 and AI128821. J.S.T. was supported by NIAID 5T32CA009547. J.H. was supported by NIAID 2 T32 AI007244-36. The WU321 study was reviewed and approved by the Washington University Institutional Review Board (approval no. 201808171). The manuscript was edited by the Scientific Editing Service of the Institute of Clinical and Translational Sciences at Washington University, which is supported by an NIH Clinical and Translational Science Award (UL1 TR002345).

Author information

Author notes

  1. These authors contributed equally: Jackson S. Turner, Julian Q. Zhou, Julianna Han

Authors and Affiliations

  1. Department of Pathology and Immunology, Washington University School of Medicine, St Louis, MO, USA

    Jackson S. Turner, Aaron J. Schmitz, Amena A. Rizk, Wafaa B. Alsoussi, Tingting Lei, Mostafa Amor, Katherine M. McIntire, Rafael I. Brent & Ali H. Ellebedy

  2. Interdepartmental Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA

    Julian Q. Zhou & Steven H. Kleinstein

  3. Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, USA

    Julianna Han, Sara T. Richey, Yuhe R. Yang & Andrew B. Ward

  4. Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Philip Meade, Shirin Strohmeier & Florian Krammer

  5. Graduate School of Biomedical Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Philip Meade

  6. Clinical Trials Unit, Washington University School of Medicine, St Louis, MO, USA

    Alem Haile & Michael K. Klebert

  7. Department of Radiology, Washington University School of Medicine, St Louis, MO, USA

    Teresa Suessen & Sharlene Teefey

  8. Department of Internal Medicine-Infectious Diseases, Washington University School of Medicine, St Louis, MO, USA

    Rachel M. Presti

  9. Department of Pathology and Department of Immunobiology, Yale School of Medicine, New Haven, CT, USA

    Steven H. Kleinstein

  10. The Andrew M. and Jane M. Bursky Center for Human Immunology and Immunotherapy Programs, Washington University School of Medicine, St Louis, MO, USA

    Ali H. Ellebedy

Authors
  1. Jackson S. Turner
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Julian Q. Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Julianna Han
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Aaron J. Schmitz
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Amena A. Rizk
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Wafaa B. Alsoussi
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Tingting Lei
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Mostafa Amor
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Katherine M. McIntire
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Philip Meade
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Shirin Strohmeier
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Rafael I. Brent
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Sara T. Richey
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Alem Haile
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Yuhe R. Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Michael K. Klebert
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Teresa Suessen
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Sharlene Teefey
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Rachel M. Presti
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Florian Krammer
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Steven H. Kleinstein
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. Andrew B. Ward
    View author publications

    You can also search for this author in PubMed Google Scholar

  23. Ali H. Ellebedy
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

A.H.E. conceived and designed the study. J.S.T. and A.H.E. composed the manuscript. A.H.E., R.M.P., M.K.K. and A.H. wrote and maintained the IRB protocol, recruited, vaccinated and phlebotomized participants and coordinated sample collection. T.S. performed the FNA under the supervision of S.T. J.S.T., J.Q.Z., J.H., F.K., A.B.W., S.H.K. and A.H.E. designed experiments. J.S.T. and A.H.E. collected and analysed the flow cytometry data. J.S.T. performed fluorescence-activated cell sorting. A.J.S. performed RNA extractions and library preparation for BCR bulk sequencing. J.Q.Z. and S.H.K. performed scRNA-seq and BCR repertoire data analysis. J.S.T., A.J.S., A.A.R., W.B.A., T.L., M.A., K.M.M. and R.I.B. expressed, purified and characterized the monoclonal antibodies and Fab fragments. S.S. expressed the recombinant proteins used in the ELISA and ELISpot analyses. P.M. and F.K. performed and analysed the antigen microarray analysis. J.H., S.T.R., Y.R.Y. and A.B.W. performed and analysed the EMPEM and other EM structural analyses. All authors reviewed the manuscript.

Corresponding author

Correspondence to Ali H. Ellebedy.

Ethics declarations

Competing interests

A.H.E. is a consultant for InBios and Fimbrion Therapeutics. The Ellebedy laboratory received funding unrelated to this work under sponsored research agreements from Emergent BioSolutions. All other authors declare no competing interests.

Additional information

Peer review information Nature thanks Antonio Lanzavecchia and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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

Extended data figures and tables

Extended Data Fig. 1 Robust peripheral B cell response to influenza virus vaccination.

a, ELISpot quantification of QIV-binding IgG-, IgM- and IgA-secreting PBs in blood 1 week after vaccination. Each symbol represents one participant (n = 8). b–e, Flow cytometry (b, c) and sorting (d, e) gating strategies for PBMCs (b, d) and FNA (c, e). Population counts per ml of blood and frequencies are presented in f and in Figs. 1d, 2f and Extended Data Fig. 2c, m. f, Kinetics of HA-binding PBs (CD20lo HA+, open triangles) and activated B cells (ABCs) (CD20+ HA+, closed circles) in PBMCs, gated as in Fig. 1c, pre-gated IgDlo CD19+ CD4− live singlet lymphocytes as in b. Symbols at each time point represent one sample (n = 8). g, h, IgG plasma antibody titres against QIV and Tetanus/Diphtheria vaccine measured by ELISA (g) and haemagglutination inhibition titres against QIV-constituent viruses (h) before and 4 weeks after vaccination. Symbols at each time point represent one sample (n = 8). Horizontal lines in a, g and h represent means. Dotted lines represent limit of detection. P values were determined by paired two-sided Student’s t-tests.

Extended Data Fig. 2 Defining influenza virus vaccine-induced GC B cell response in humans.

a, Cortical thickness measurements of axillary lymph nodes before each FNA collection. b, FNA cell yields for each participant at the indicated time point. Symbols at each time point represent one sample (n = 7). c, Participant 02 percentages of CD19+ CD4− B cells (left), CD14− CD4+ T cells (middle), and CD14+ CD4− monocytes or granulocytes (right) of CD45+ in PBMCs (red) and FNA (blue) from one set of paired samples, representative of five FNA samples. d, Unsupervised clustering via tSNE based on scRNA-seq gene expression of all cells pooled from all samples and time points from participant 05. Each dot represents a cell, coloured by phenotype as defined by gene expression profile. e, Dot plot showing the average log-normalized expression of a set of marker genes and the fraction of cells expressing the genes in each unsupervised cluster. f, g, Annotated tSNE clusters of all cells from all scRNA-seq samples (f) and IgDlo enriched B cells from PBMC scRNA-seq samples (g) pooled from all time points from participant 05. Total number of cells is below clusters. h, Dot plot for annotated clusters. i, Representative flow cytometry gating of BCL6 expression within CD20hi CD38int cells in PBMC and FNA. Cells pre-gated IgDlo CD19+ CD4− live singlet lymphocytes. j, Representative histographs (top) and median fluorescence intensity (bottom) of the indicated markers on GC B cells (IgDlo CD20hi CD38int) compared to PBs (IgDlo CD20− CD38+), memory B cells (IgDlo CD27+ CD38−), and naive B cells (IgD+ CD27−). All populations pre-gated CD19+ CD4− live singlet lymphocytes. MFIs from 2- or 4-week FNA samples from participants 04, 05, 07, 08, 09 and 11. Lines represent medians. k, Representative gating of HA+ GC B cells. Cells pre-gated CD20hi CD38int IgDlo CD19+ CD4− live singlet lymphocytes. l, Kinetics of HA-binding percent of GC B cells measured by flow cytometry in participants 04, 05 and 11. m, Kinetics of HA+ CD38+ CD20lo PBs (open triangles) and HA+ CD38− CD20+ activated B cells (closed circles) in FNA, as gated in Extended Data Fig. 1c. Symbols at each time point represent one sample (n = 7). Daggers denote samples excluded from analysis due to low cell recovery or blood contamination.

Extended Data Fig. 3 GC B cell response to influenza virus vaccine is clonally diverse.

a, Schematic of single-cell monoclonal antibody cloning and expression. Paired heavy and light chain genes were amplified from singly sorted PBs or GC B cells. Variable portions of heavy chains were cloned into a Cγ1 expression vector and variable portions of κ and λ light chains were cloned into respective expression vectors. Paired heavy and light chain expression vectors were co-transfected into 293F cells, and monoclonal antibodies were purified from culture supernatant by protein A affinity chromatography, then screened for QIV specificity by ELISA. b, c, Minimum positive concentrations of clonally unique monoclonal antibodies generated from singly sorted PBs (b) and GC B cells (c) from the indicated participants as determined by QIV ELISA; positive binding defined as greater than 3× background. d,Distance-to-nearest-neighbour plots for choosing a distance threshold for inferring clones via hierarchical clustering. After partitioning sequences based on common V and J genes and junction length, the nucleotide Hamming distance of a junction to its nearest non-identical neighbour from the same participant within its partition was calculated and normalized by junction length (blue histogram). For reference, the distance to the nearest non-identical neighbour from other participants was calculated (green histogram). A clustering threshold of 0.1 (dashed black line) was chosen via manual inspection and kernel density estimate (dashed purple line) to separate the two modes of the within-participant distance distribution representing, respectively, sequences that were probably clonally related and unrelated. e, Clonal overlap of sequences from monoclonal antibody cloning and bulk repertoire analysis between PBs sorted from PBMCs 1 week after vaccination and GC B cells from the indicated time point among total (top) and only QIV-binding (bottom) sequences. Purple chords link overlapping GC and PB clones; black chords link GC clones found at multiple time points that did not participate in the early PB response. Chord width corresponds to clonal population size. Percentages are of GC sequences overlapping with PBs. f, Clonal rank-abundance distributions of GC B cells from indicated time points (left) and of early blood PBs (right). The number of GC B cells or early blood PBs in a clone as a percentage of the total GC or early blood PB repertoire (y axis) is plotted against the abundance rank of that clone (x axis). Solid lines represent the estimated clonal abundance curves, with shaded bands representing the 95% confidence intervals from 200 bootstraps. g, tSNE clusters of B cells from FNA scRNA-seq samples from participant 05. Each dot represents a cell, coloured by phenotype as defined by gene expression profile. Total numbers of cells are given below clusters. GC percentages are indicated in blue. h, IGHV mutation frequency of naive B cells pooled from all time points (left) and the indicated populations at the indicated time point (right) from scRNA-seq of whole and memory B cell-enriched PBMC and FNA samples from participant 05. Horizontal lines represent medians. P values were determined by two-sided Dunn’s multiple comparisons test.

Extended Data Fig. 4 B cell clustering for participant 05.

a, tSNE plot showing unsupervised clusters based on scRNA-seq gene expression of cells in the ‘B cell’ cluster of Extended Data Fig. 2f, pooled from all samples and time points from participant 05. b, Dot plot showing the average log-normalized expression of a set of marker genes and the fraction of cells expressing the genes in each unsupervised cluster. c, tSNE plot showing BCR availability. d, tSNE plot showing interim annotated clusters. e, Dot plot for interim annotated clusters. f, tSNE plot showing final annotated clusters. g, Dot plot for final annotated clusters. h, tSNE plot showing IHGV mutation frequency in BCRs. Total numbers of cells are given below clusters. i, Bar plots showing isotype usage in annotated B cell clusters. Numbers of cells per cluster are in Extended Data Table 1.

Extended Data Fig. 5 GC and PB responses to influenza virus vaccine are functionally diverse.

a–c, Influenza virus protein microarray binding of H1- (a), H3- (b) and influenza B/HA- (c) binding monoclonal antibodies generated from singly sorted PBs and GC B cells that overlapped clonally (purple) or did not overlap (red and blue) from the indicated participant. Scale bar represents median fluorescence intensity. Asterisks denote HAI+ monoclonal antibodies. Vaccine strains in bold type; underlined strains circulated in humans in participants’ lifetimes. d, Percentages of monoclonal antibodies that bound two or more HA strains from participants 04, 05 and 11 from GC clones that did not participate in the early PB response (blue), clones that participated in both GC and early PB responses (purple), and from PB clones not found in GCs (red). Numbers of monoclonal antibodies are indicated in the middle of the charts. e, Polyclonal epitopes of Fabs from plasma at indicated time points from participants 04, 05 and 11 with HA from A/Singapore/INFIMH-16-0019/2016. Epitopes were determined by 3D reconstructions and/or 2D class averages (images to bottom right of 3D reconstructions). HA proteins shown in grey; Fabs shown in multiple colours; Fabs with dashed outlines have predicted epitopes due to limited particle representation. f, g, Example 2D class averages of immune complexes from participants 04, 05 and 11 plasma with HA from A/Michigan/45/2015 (f) and A/Singapore/INFIMH-16-0019/2016 (g). h, i, Monoclonal and polyclonal epitopes of immune complexes with HA from A/Michigan/45/2015 (h) or A/Singapore/INFIMH-16-0019/2016 (i) and Fabs generated from indicated GC monoclonal antibodies in blue or purple mesh and plasma polyclonal antibodies in red. Fabs with dashed outlines have predicted epitopes due to limited particle representation. j, k, Example 2D class averages of immune complexes from the indicated mAb with HA from A/Michigan/45/2015 (j) and A/Singapore/INFIMH-16-0019/2016 (k).

Extended Data Table 1 Cell counts in overall and B cell clusters based on single-cell gene expression (participant 05)
Full size table
Extended Data Table 2 Cell counts of FNA populations based on flow cytometry
Full size table
Extended Data Table 3 Processing of bulk sequencing BCR reads and number of nested PCR sequences and QIV+ clonally distinct monoclonal antibody sequences
Full size table
Extended Data Table 4 Processing of 10x Genomics single-cell BCR and 5′ gene expression data (participant 05)
Full size table
Extended Data Table 5 HA strains for influenza virus protein microarrays
Full size table

Supplementary information

Source data

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Turner, J.S., Zhou, J.Q., Han, J. et al. Human germinal centres engage memory and naive B cells after influenza vaccination. Nature 586, 127–132 (2020). https://doi.org/10.1038/s41586-020-2711-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41586-020-2711-0

This article is cited by

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

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