Pre-existing immunity and vaccine history determine hemagglutinin-specific CD4 T cell and IgG response following seasonal influenza vaccination

Effectiveness of seasonal influenza vaccination varies between individuals and might be affected by vaccination history among other factors. Here we show, by monitoring frequencies of CD4 T cells specific to the conserved hemagglutinin epitope HA118-132 and titres of IgG against the corresponding recombinant hemagglutinin protein, that antigen-specific CD4 T cell and antibody responses are closely linked to pre-existing immunity and vaccine history. Upon immunization, a strong early reaction is observed in all vaccine naïve participants and also in vaccine experienced individuals who have not received the respective seasonal vaccine in the previous year. This response is characterized by HA118-132 specific CD4 T cells with a follicular helper T cell phenotype and by ascending titers of hemagglutinin-specific antibodies from baseline to day 28 following vaccination. This trend was observed in only a proportion of those participants who received the seasonal vaccine the year preceding the study. Regardless of history, levels of pre-existing antibodies and CD127 expression on CD4 T cells at baseline were the strongest predictors of robust early response. Thus, both pre-existing immunity and vaccine history contribute to the response to seasonal influenza vaccines.

The exact sample size (n) for each experimental group/condition, given as a discrete number and unit of measurement A statement on whether measurements were taken from distinct samples or whether the same sample was measured repeatedly The statistical test(s) used AND whether they are one-or two-sided Only common tests should be described solely by name; describe more complex techniques in the Methods section.
A description of all covariates tested A description of any assumptions or corrections, such as tests of normality and adjustment for multiple comparisons A full description of the statistical parameters including central tendency (e.g. means) or other basic estimates (e.g. regression coefficient) AND variation (e.g. standard deviation) or associated estimates of uncertainty (e.g. confidence intervals) For null hypothesis testing, the test statistic (e.g. F, t, r) with confidence intervals, effect sizes, degrees of freedom and P value noted Give P values as exact values whenever suitable.

For Bayesian analysis, information on the choice of priors and Markov chain Monte Carlo settings
For hierarchical and complex designs, identification of the appropriate level for tests and full reporting of outcomes Estimates of effect sizes (e.g. Cohen's d, Pearson's r), indicating how they were calculated Our web collection on statistics for biologists contains articles on many of the points above.

Software and code
Policy information about availability of computer code Data collection All software used to perform data collection are described in the methods section of the manuscript. Multiparametric Flow cytometry data was collected by FACSDiva Software (Becton Dickinson). Vaccine-specific IgG ELISA data were acquired and calculated using SparkControl magellan software 2.2. HA-specific IgG ELISA data were acquired using BioTek Plate Reader(Winooski, VT, USA).

Data analysis
All codes and software versions used to perform bioinformatic analyses are described in the methods section of the manuscript or provided in in a github repository. Multiparametric Flow cytometry data was analyzed using FlowJo software version 10.0.7 (LLC, BD Life Sciences, Ashland, OR, USA). Heatmap analysis with unbiased clustering was done with R version 3.6.3 using the ComplexHeatmap package (Gu ZG, Eils R, Schlesner M (2016)). R code to reproduce the analyses of multiparametric flow-cytometry data is available online: https://github.com/teximmed2-fr/fluspecific-CD4-T-cells. A workflow including dimension reduction using tSNE and FlowSOM clustering analysis was implemented in Omiq (Omiq, Inc.). Visualization and statistical analysis was performed using GraphPad 8 software.
For manuscripts utilizing custom algorithms or software that are central to the research but not yet described in published literature, software must be made available to editors and reviewers. We strongly encourage code deposition in a community repository (e.g. GitHub). See the Nature Research guidelines for submitting code & software for further information.

April 2020
Data Policy information about availability of data All manuscripts must include a data availability statement. This statement should provide the following information, where applicable: -Accession codes, unique identifiers, or web links for publicly available datasets -A list of figures that have associated raw data -A description of any restrictions on data availability Raw data in this study are provided in the Source data. Additional supporting data are available from the corresponding authors upon reasonable request (response within two weeks). All requests for raw and analyzed data and materials will be reviewed by the corresponding authors to verify if the request is subject to any intellectual property or confidentiality obligations. Donor-related data not included in the paper were generated as part of clinical examination and may be subject to patients confidentiality. Any data and materials that can be shared will be released via a Material Transfer Agreement. Code availability: R code to reproduce the analyses of multiparametric flow-cytometry data is available online: https://github.com/teximmed2-fr/flu-specific-CD4-Tcells. The R code is citable with following DOI: 10.5281/zenodo.5541093.

Field-specific reporting
Please select the one below that is the best fit for your research. If you are not sure, read the appropriate sections before making your selection.

Life sciences Behavioural & social sciences Ecological, evolutionary & environmental sciences
For a reference copy of the document with all sections, see nature.com/documents/nr-reporting-summary-flat.pdf

Life sciences study design
All studies must disclose on these points even when the disclosure is negative. Randomization Donors were selected based on availability and HLA-typing (DRB1*0101). The covariates age and gender are well-documented at Table 1.

Blinding
Blinding was not applied. Non-objective parameters were not included in the study design. Due to standardized analyses of the flow cytometric data set, biased analysis can be excluded.

Reporting for specific materials, systems and methods
We require information from authors about some types of materials, experimental systems and methods used in many studies. Here, indicate whether each material, system or method listed is relevant to your study. If you are not sure if a list item applies to your research, read the appropriate section before selecting a response.  CD4, clone RPA-T4 : antibody titration on PBMCs; control clones SK3; using B cells as negative control CCR7, clone 3D12 : antibody titration on PBMCs; control clone G043H7; validated with respect to differential expression of naïve and non-naïve T cell subpopulations CXCR5, clone RF8B2 and J252D4: antibody titration on PBMCs; control clone MU5UBEE; validated with respect to differential expression of naïve and non-naïve T cell subpopulations PD-1, clone EH12.2H7: antibody titration on PBMCs; control clones eBioJ105; validated with respect to differential expression of naïve and non-naïve T cell subpopulations CD134, clone ACT-35: antibody titration on PBMCs; control clones L106; validated with respect to differential expression of naïve and non-naïve T cell subpopulations CD38, clone HB7: antibody titration on PBMCs; control clone HIT2; validated with respect to differential expression of naïve and nonnaïve T cell subpopulations ICOS, clone DX29: antibody titration on PBMCs; control clone ISA-3; validated with respect to differential expression of naïve and non-naïve T cell subpopulations CD127, clone HIL-7R-M21 and A019D5: antibody titration on PBMCs; control clone eBioRDR5; validated with respect to differential April 2020 expression of naïve and non-naïve T cell subpopulations CCR6, clone 11A9: antibody titration on PBMCs; validated with respect to differential expression of naïve and non-naïve T cell subpopulations T-bet, clone O4-46: antibody titration on PBMCs; control clones 4B10; validated with respect to differential expression of naïve and non-naïve T cell subpopulations CXCR3, clone G025H7: antibody titration on PBMCs; control clone 1C6/CXCR3; validated with respect to differential expression of activated and non-activated T cell subpopulations CD27, clone M-T271: antibody titration on PBMCs; control clone O323; validated with respect to differential expression of naïve and non-naïve T cell subpopulations Ki67, clone Ki67: antibody titration on PBMCs; control clone B56; validated with respect to differential expression of naïve and nonnaïve T cell subpopulations TCF-1, clone C63D9: antibody titration on PBMCs; control clone 7F11A10; validated with respect to differential expression of naïve and non-naïve T cell subpopulations CD14 , clone 61D3: antibody titration on PBMCs; control clones M5E2 and MφP9; using T cell populations as negative control CD19, clone HIB19: antibody titration on PBMCs; control clone SJ25C1; using T cell populations as negative control Viability Dye was titrated on PBMCs; validated with respect to differential staining of live and dead cell populations TOX1, clone TRX10: antibody titration on PBMCs; control clone REA473; validated with respect to differential expression of naïve and non-naïve T cell subpopulations CD45RA, clone HI100: antibody titration on PBMCs; validated with respect to differential expression of naïve and non-naïve T cell subpopulations 2-NBDG: antibody titration on PBMCs; validated with respect to differential expression of naïve and non-naïve T cell subpopulations

Recruitment
Healthy donors were recruited at the University Hospital Freiburg. Self-selection bias or other biases can be excluded since several people were included in the recruitment. Samples were banked and retrospectively selected according to the following inclusion criteria: HLA-DRB1*0101. Additional donors independent on HLA typing were used to measure influenzaspecific IgG (vaccine and HA-specific IgG).

Ethics oversight
Written informed consent was obtained from all participants and the study was conducted according to federal guidelines, local ethics committee regulations (Albert-Ludwigs-Universität, Freiburg, Germany; vote #: 322/20) and the Declaration of Helsinki (1975).
Note that full information on the approval of the study protocol must also be provided in the manuscript.

Flow Cytometry
Plots Confirm that: The axis labels state the marker and fluorochrome used (e.g. CD4-FITC).
The axis scales are clearly visible. Include numbers along axes only for bottom left plot of group (a 'group' is an analysis of identical markers).
All plots are contour plots with outliers or pseudocolor plots.
A numerical value for number of cells or percentage (with statistics) is provided.

Sample preparation
Cryopreserved isolated human PBMCs were thawed and prepared for flow cytometry decribed in the methods section. Plasma was thawed for vaccine-specific and HA-specific IgG ELISA. Tick this box to confirm that a figure exemplifying the gating strategy is provided in the Supplementary Information.