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Prior vaccination promotes early activation of memory T cells and enhances immune responses during SARS-CoV-2 breakthrough infection

Nature Immunology volume 24, pages 1711–1724 (2023)Cite this article

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Abstract

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection of vaccinated individuals is increasingly common but rarely results in severe disease, likely due to the enhanced potency and accelerated kinetics of memory immune responses. However, there have been few opportunities to rigorously study early recall responses during human viral infection. To better understand human immune memory and identify potential mediators of lasting vaccine efficacy, we used high-dimensional flow cytometry and SARS-CoV-2 antigen probes to examine immune responses in longitudinal samples from vaccinated individuals infected during the Omicron wave. These studies revealed heightened spike-specific responses during infection of vaccinated compared to unvaccinated individuals. Spike-specific cluster of differentiation (CD)4 T cells and plasmablasts expanded and CD8 T cells were robustly activated during the first week. In contrast, memory B cell activation, neutralizing antibody production and primary responses to nonspike antigens occurred during the second week. Collectively, these data demonstrate the functionality of vaccine-primed immune memory and highlight memory T cells as rapid responders during SARS-CoV-2 infection.

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Fig. 1: Spike-specific plasmablast expansion during the first week of breakthrough infection precedes an increase in neutralizing antibodies during the second week.
Fig. 2: Vaccination promotes larger spike-specific memory B cell responses that become activated, expand and class switch during the second week of breakthrough infection.
Fig. 3: Spike-specific CD4 T cells expand during the first week of breakthrough infection while T cell responses to nonspike SARS-CoV-2 antigens peak at day 15.
Fig. 4: Vaccination promotes greater spike-specific CD8 T cell responses that are activated during the first week of breakthrough infection without impeding primary responses to nonspike antigens.
Fig. 5: Vaccination promotes activation of central memory CD8 T cells, which peaks during the first week of breakthrough infection.
Fig. 6: Rapid memory T cell activation and pre-existing antibodies represent the early systemic adaptive immune responses during SARS-CoV-2 breakthrough infection.

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Data availability

All Source Data and data files are available from the authors upon request. Source Data files for all figures in the manuscript are published in Supplementary Information. Source data are provided with this paper.

Code availability

All source code and data files are available from the authors upon request. Source Data files for all figures in the manuscript are published in Supplementary Information.

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Acknowledgements

We would like to thank the study participants for their generosity in making the study possible. We also thank the Penn Cytomics and Cell Sorting Resource Laboratory for access to instruments, S. Park for assistance with HLA-I tetramers and members of the Wherry Lab for helpful discussions and feedback. This work was supported by the National Institute of Health (NIH) under grants AI105343, AI082630, AI108545, AI155577 and AI149680 (to E.J.W.); AI152236 (to P.B.); HL143613 (to J.R.G.); T32 AR076951-01 (to S.A.A.); T32 CA009140 (to J.R.G. and D.M.); T32-GM007170 (to A.N.S.); U19AI082630 (to S.E.H. and E.J.W.) and K08CA230157 (to A.C.H.), funding from the Allen Institute for Immunology (to S.A.A. and E.J.W.), Cancer Research Institute-Mark Foundation Fellowship (to J.R.G.), Chen Family Research Fund (to S.A.A.), the Parker Institute for Cancer Immunotherapy (to J.R.G. and E.J.W.), the Penn Center for Research on Coronavirus and Other Emerging Pathogens (to P.B.), the University of Pennsylvania Perelman School of Medicine COVID Fund (to R.R.G. and E.J.W.), the Damon Runyon Clinical Investigator Award (to A.C.H.), the Doris Duke Clinical Scientist Development Award (to A.C.H.) and a philanthropic gift from J. Lurie, J. Embiid, J. Harris and D. Blitzer (to S.E.H.). Work in the Wherry Lab is supported by the Parker Institute for Cancer Immunotherapy. This work was also supported by NIH under contract 75N9301900065 (to D.W. and A.S.). This project has been funded in part with Federal funds from the National Institute of Allergy and Infectious Diseases, NIH, Department of Health and Human Services, under contract 75N93021C00015. We acknowledge the Penn Medicine BioBank (PMBB) for providing data and thank the patient-participants of Penn Medicine who consented to participate in this research program. The PMBB is approved under IRB protocol 813913 and supported by Perelman School of Medicine at the University of Pennsylvania, a gift from the Smilow family, and the National Center for Advancing Translational Sciences of the NIH under CTSA award UL1TR001878.

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  1. A full list of members appears in the Supplementary Information.

Authors and Affiliations

  1. Institute for Immunology, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA

    Mark M. Painter, Timothy S. Johnston, Rishi R. Goel, Sokratis A. Apostolidis, Divij Mathew, Bria Fulmer, Amy E. Baxter, Josephine R. Giles, Ashwin N. Skelly, Maura McLaughlin, Oliva Kuthuru, Alexander C. Huang, Allison R. Greenplate & E. John Wherry

  2. Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA

    Mark M. Painter, Rishi R. Goel, Sokratis A. Apostolidis, Divij Mathew, Bria Fulmer, Amy E. Baxter, Josephine R. Giles, Maura McLaughlin, Oliva Kuthuru, Allison R. Greenplate & E. John Wherry

  3. Immune Health, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA

    Mark M. Painter, Juliana S. Qin, Rishi R. Goel, Sokratis A. Apostolidis, Justine C. Williams, Michelle L. McKeague, Ajinkya Pattekar, Ahmad Goode, Sean Nasta, Jeanette Dougherty, Sharon Adamski, Sherea Long, Macy Kee, Cynthia Clendenin, Allison R. Greenplate & E. John Wherry

  4. Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA

    Timothy S. Johnston

  5. Immunology Graduate Group, University of Pennsylvania, Philadelphia, PA, USA

    Timothy S. Johnston

  6. Department of Microbiology, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA

    Kendall A. Lundgreen, Jefferson J. S. Santos, Scott E. Hensley & Paul Bates

  7. Division of Rheumatology, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA

    Sokratis A. Apostolidis

  8. Parker Institute for Cancer Immunotherapy, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA

    Josephine R. Giles, Alexander C. Huang & E. John Wherry

  9. Department of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Ashwin N. Skelly, Anurag Verma & Theodore Drivas

  10. Division of Infectious Disease, Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA

    Laura E. Felley

  11. Department of Genetics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA

    Joellen Weaver, Marylyn D. Ritchie, JoEllen Weaver, Meghan Livingstone, Shefali S. Verma, Yuki Bradford & Daniel J. Rader

  12. Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology (LJI), La Jolla, CA, USA

    Ricardo da Silva Antunes, Alba Grifoni, Daniela Weiskopf & Alessandro Sette

  13. Department of Medicine, Division of Infectious Diseases and Global Public Health, University of California, San Diego (UCSD), La Jolla, CA, USA

    Alessandro Sette

  14. Division of Hematology/Oncology, Department of Medicine, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA

    Alexander C. Huang

  15. Penn Medicine Biobank, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA

    Marylyn D. Ritchie, Michael D. Feldman, JoEllen Weaver, Nawar Naseer, Afiya Poindexter, Ashlei Brock, Khadijah Hu-Sain, Yi-An Ko, Meghan Livingstone, Fred Vadivieso, Ashley Kloter, Stephanie DerOhannessian, Teo Tran, Linda Morrel, Ned Haubein, Joseph Dunn, Anurag Verma, Colleen Morse, Marjorie Risman, Renae Judy, Shefali S. Verma, Yuki Bradford, Scott Dudek, Theodore Drivas & Daniel J. Rader

  16. Department of Pathology and Laboratory Medicine, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA

    Michael D. Feldman & Fred Vadivieso

  17. Laboratory Information Services, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA

    Ned Haubein & Joseph Dunn

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  1. Mark M. Painter
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Consortia

Penn Medicine BioBank

  • Daniel J. Rader
  • , Marylyn D. Ritchie
  • , Michael D. Feldman
  • , JoEllen Weaver
  • , Nawar Naseer
  • , Afiya Poindexter
  • , Ashlei Brock
  • , Khadijah Hu-Sain
  • , Yi-An Ko
  • , Meghan Livingstone
  • , Fred Vadivieso
  • , Ashley Kloter
  • , Stephanie DerOhannessian
  • , Teo Tran
  • , Linda Morrel
  • , Ned Haubein
  • , Joseph Dunn
  • , Anurag Verma
  • , Colleen Morse
  • , Marjorie Risman
  • , Renae Judy
  • , Shefali S. Verma
  • , Yuki Bradford
  • , Scott Dudek
  •  & Theodore Drivas

Contributions

M.M.P. and E.J.W. conceived the study. M.M.P., T.S.J., K.A.L. and J.J.S.S. carried out experiments. M.M.P. and T.J. analyzed data and wrote the manuscripts. J.C.W., O.K., J.D., S.L., M.K. and C.C. were involved in clinical recruitment. J.R.G. and D.M. provided input on statistical analyses. D.M., A.E.B., R.R.G., S.A.A., K.A.L., J.J.S.S. and A.C.H. contributed to the methodology. M.M.P., D.M., R.R.G., S.A.A., B.F., J.C.W., M.L.M., A.P., A.N.S., L.E.F., A.G. and S.N. processed peripheral blood samples. J.C.W. and S.A. managed sample storage. M.M.P., J.C.W., M.K. and C.C. managed the sample database. M.M.P., R.R.G., O.K., J.D., S.L. and M.K. performed phlebotomy. J.S.Q., J.W., M.M., A.G., D.W., R.S.A., S.E.H., P.B., A.S. and D.J.R. provided data and materials. A.R.G. and E.J.W. supervised the study. All authors participated in data analysis and interpretation. M.M.P. and E.J.W. wrote the manuscript. All authors provided input and edits to the manuscript.

Corresponding author

Correspondence to E. John Wherry.

Ethics declarations

Competing interests

E.J.W. is a member of the Parker Institute for Cancer Immunotherapy which supported this study. S.E.H. has received consultancy fees from Sanofi Pasteur, Lumen, Novavax and Merk for work unrelated to this report. E.J.W. is an advisor for Danger Bio, Janssen, New Limit, Marengo, Pluto Immunotherapeutics Related Sciences, Santa Ana Bio, Synthekine and Surface Oncology. E.J.W. is a founder of and holds stock in Surface Oncology, Danger Bio and Arsenal Biosciences. A.S. is a consultant for Gritstone Bio, Flow Pharma, Moderna, AstraZeneca, Qiagen, Fortress, Gilead, Sanofi, Merck, RiverVest, MedaCorp, Turnstone, NA Vaccine Institute, Emervax, Gerson Lehrman Group and Guggenheim. La Jolla Institute for Immunology has filed for patent protection for various aspects of T cell epitope and vaccine design work. The remaining authors declare no competing interests.

Peer review

Peer review information

Nature Immunology thanks Rafick Sekaly and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: L. A. Dempsey, in collaboration with the Nature Immunology editorial team.

Additional information

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Extended data

Extended Data Fig. 1 Cohort information, gating strategies and supporting analyses for studies of antibody and B cell variant reactivity.

a) Correlation of neutralizing antibody titers and binding antibody endpoint titers at all pre- and post-infection timepoints from the longitudinal cohort for D614G (left) and BA.1.1 (right). Correlation statistics were calculated using Spearman rank correlation and are shown with Pearson trend lines for visualization. b) Days between positive test and sample collection (left) and age (right) for the vaccinated and unvaccinated individuals in the cross-sectional cohort (N = 31 unvaccinated, 59 vaccinated). c) Representative flow cytometric gating strategy for identifying plasmablasts, memory B cells, and memory B cell phenotypes. RM = resting memory, AM = activated memory. d) Frequency of Nucleocapsid and Spike-specific plasmablasts during Omicron breakthrough infection. e) Flow cytometric data depicting the frequency of total B cells that are antigen-specific plasmablasts binding Spike or Nucleocapsid during SARS-CoV-2 infection of vaccinated or unvaccinated individuals in the cross-sectional cohort. f) Representative flow cytometric gating strategy for identifying B cells specifically binding Omicron subvariant RBDs with or without conserved binding to WT RBD, gated directly from Decoy-negative B cells without prior gating on cells binding the WT Spike probe. g) Frequency of Omicron-specific plasmablasts (left) and memory B cells (right), defined as cells binding to both the BA.1 RBD and BA.5 RBD probes without binding the WT RBD probe. h) Frequency of total B cells that are Nucleocapsid-specific memory B cells during SARS-CoV-2 infection of previously vaccinated and unvaccinated individuals. i) Variant cross-binding of WT RBD-binding memory B cells during SARS-CoV-2 infection of unvaccinated individuals, divided by dominant variant at the time of positive test. j) Variant cross-binding of WT RBD-binding memory B cells during Omicron breakthrough infection. Boxplots represent median with interquartile range, whiskers indicate range. Statistics calculated using two-sided Wilcoxon test with Benjamini-Hochberg correction for multiple comparisons. ns indicates p > 0.05, * indicates p < 0.05, ** indicates p < 0.01, *** indicates p < 0.001, **** indicates p < 0.0001.

Source data

Extended Data Fig. 2 Memory B cell activation and class-switching occur during the second week.

a) Frequency of total B cells that are activated (CD71+) memory B cells and bind Spike or Nucleocapsid antigens during Omicron breakthrough infection. b) Percent activation (CD71+) of antigen-specific memory B cells. c) Percent of activated memory (AM) Spike-binding memory B cells targeting the S2, NTD, or RBD domains during SARS-CoV-2 infection, used as an alternative to CD71 but supporting the same conclusions. d) Percent of WT RBD-binding CD71+ memory B cells that cross-bind the indicated variant RBDs during SARS-CoV-2 breakthrough infection separated by vaccination status and the dominant circulating variant at the time of positive test. e) Frequency of total B cells that are IgA+ memory B cells and bind Spike or Nucleocapsid antigens during Omicron breakthrough infection. f) Frequency of total B cells that are IgG+ memory B cells and bind Spike or Nucleocapsid antigens during Omicron breakthrough infection. g) Percent of WT and BA.1 RBD-binding B cells that are IgG+ during breakthrough infection. h) Frequency of total B cells that are IgA+ memory B cells and specific for the indicated domains of Spike during SARS-CoV-2 infection in previously vaccinated or unvaccinated individuals. Statistics calculated using two-sided Wilcoxon test with Benjamini-Hochberg correction for multiple comparisons. ns indicates p > 0.05, * indicates p < 0.05, ** indicates p < 0.01, *** indicates p < 0.001, **** indicates p < 0.0001.

Source data

Extended Data Fig. 3 Stimulation assays reveal the dynamics of SARS-CoV-2-specific T cell responses during breakthrough infection and booster vaccination.

a) Representative flow cytometric gating strategy for identifying non-naïve CD4 and CD8 T cells and CD4 T cell subsets. b) Representative gating showing the kinetics of bulk CD4 T cell activation during Omicron breakthrough infection. c) Bulk activation of CD4 T cells during Omicron breakthrough infection calculated as percent of total T cells that are CD4 T cells expressing at least 2 of 5 activation markers as in Extended Data Fig. 4c. d) Percent of non-naïve CD4 T cells that are Spike-specific Th2 (CXCR5-, CXCR3-, CCR6-) or Th17 (CXCR5-, CXCR3-, CCR6+) cells. e) Comparison of total Spike- and Non-Spike-specific CD8 T cell responses during Omicron infection. f) Comparison of non-Spike-specific CD8 T cell responses detected using CD4-RE and CD8-RE peptide megapools on a subset of samples collected during Omicron breakthrough infection. g-h) Correlation (g) and comparison (h) of CD8 T cell responses detected from paired samples stimulated with CD4-RE and CD8-RE, excluding samples with undetectable responses to either megapool. Dotted line indicates a hypothetical 1:1 relationship in the detection of CD8 T cells specific for non-Spike epitopes using each megapool. i) Frequency of Spike-specific CD4 T cells, Th1, and cTfh-like cells before and after a 3rd dose of mRNA vaccine (cohort from Fig. 1a). Statistics calculated using two-sided Wilcoxon test with Benjamini-Hochberg correction for multiple comparisons. ns indicates p > 0.05, * indicates p < 0.05, ** indicates p < 0.01, *** indicates p < 0.001, **** indicates p < 0.0001.

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Extended Data Fig. 4 Multiplexed HLA-I/peptide tetramer assays demonstrate SARS-CoV-2-specific T cell activation during breakthrough infection.

a) Representative flow cytometric gating strategy for identifying total CD8 T cells for MHC-I tetramer staining and phenotyping. b) Representative flow cytometric gating strategy for identifying MHC-I tetramer-binding cells from total CD8 T cells. Cells are pre-gated to not fluoresce in the two mismatched tetramer channels before identifying antigen-specific cells based on dual-fluorescence as depicted in Fig. 4a. c, d) Representative gating of activated (c) bulk and (d) Spike-specific CD8 T cells on day 2 and day 7 of breakthrough infection. Cells defined as Ki67+ fall in any gate co-expressing Ki67 and another activation marker (blue), and cells defined as activated fall in any gate co-expressing two activation markers (green). e) Percent of total CD8 T cells that are antigen-specific and express at least two activation markers, supporting Fig. 4j with an alternative method for defining activated cells. f) Percent of Spike-specific CD8 T cells that express at least two activation markers from left) all pre-infection samples and right) a subset of individuals with samples collected at days 2-3 post-symptom onset. g) Percent of total CD8 T cells that are antigen-specific and express Ki67 and at least one other activation marker during SARS-CoV-2 infection in previously vaccinated or unvaccinated individuals, supporting Fig. 4k with an alternative method for defining activated cells (vaccinated HLA-A2, N = 31; HLA-A3, N = 11; unvaccinated HLA-A2 N = 11; HLA-A3 N = 3). Boxplots represent median with interquartile range, whiskers indicate range. Statistics calculated using two-sided Wilcoxon test with Benjamini-Hochberg correction for multiple comparisons. Statistics without brackets are in comparison to day 0. ns indicates p > 0.05, * indicates p < 0.05, ** indicates p < 0.01, *** indicates p < 0.001, **** indicates p < 0.0001.

Source data

Extended Data Fig. 5 SARS-CoV-2 infection does not durably alter the T cell landscape.

Percent of total T cells attributed to various bulk CD4 or CD8 T cell populations or Spike-specific CD8 T cell populations during Omicron breakthrough infection, highlighting transient activation of CD4 and CD8 T cells but no lasting alterations to the T cell landscape by 45 days after mild SARS-CoV-2 infection. Points represent individual subjects and are colored by cohort. Thin lines indicate individual subjects sampled longitudinally. Horizontal bars and thick trend lines represent means. Statistics calculated using two-sided Wilcoxon test with Benjamini-Hochberg correction for multiple comparisons. Statistics are in comparison to day 0. ns indicates p > 0.05, * indicates p < 0.05, ** indicates p < 0.01, *** indicates p < 0.001, **** indicates p < 0.0001.

Source data

Extended Data Fig. 6 SARS-CoV-2 infection does not negatively impact future T cell responses to SARS-CoV-2.

a) Primary mRNA vaccine cohort of SARS-CoV-2 naïve and recovered individuals as described in previous publications (Goel et al., Science11). b) HLA-I/peptide tetramer assay as described in Fig. 4 to assess the frequency of Spike-specific CD8 T cells (left) and activated Spike-specific CD8 T cells (right) defined as cells expressing at least two of five activation markers. c) Overview of mRNA booster vaccine cohort of SARS-CoV-2 naïve and recovered individuals sampled before and after a 3rd or 4th dose of mRNA vaccine for D) and E). d) AIM assay for Spike-specific CD4 (left, CD200+ CD40L+) and CD8 (right, IFNγ+ 41BB+) T cells after stimulation with Spike peptide megapool as in Fig. 3. e) HLA-I/peptide tetramer assay to assess the frequency of Spike-specific CD8 T cells (left), Ki67+ Spike-specific CD8 T cells (center, cells expressing Ki67 and at least one other activation marker), and activated Spike-specific CD8 T cells (right, cells expressing at least two activation markers) before and after mRNA booster vaccination. f) Longitudinal Omicron breakthrough infection cohort to assess Spike-specific T cell proliferative potential. g) Experimental design for assessing Spike-specific T cell proliferative potential. CTV = CellTrace Violet. h) Representative plots from a single donor showing dilution of CTV after 96 hours of stimulation with the Spike megapool or paired unstimulated controls for CD4 (left) and CD8 (right) T cells. i) Frequency of CTVlow CD4 (left) and CD8 (right) T cells for the two stimulation conditions before and after infection. j) Percent CTVlow above background for CD4 and CD8 T cells comparing pre- (Day 0) and post-infection (Day 45) timepoints. Points represent individual subjects. Thin lines indicate individual subjects sampled longitudinally. Horizontal bars and thick trend lines represent means. Statistics calculated using two-sided Wilcoxon test with Benjamini-Hochberg correction for multiple comparisons. Statistics without brackets are comparing SARS-CoV-2 Naïve and Recovered individuals at the same timepoint (B). ns indicates p > 0.05, * indicates p < 0.05, ** indicates p < 0.01, *** indicates p < 0.001, **** indicates p < 0.0001.

Source data

Extended Data Fig. 7 Dimensionality reduction and manual gating classify distinct CD8 T cell differentiation states from flow cytometric data.

a) Expression in UMAP space of each of the 16 input parameters used to generate the UMAP in Fig. 5b. b) Heat map depicting average expression of each marker in the manually gated populations as defined in Fig. 5a. The dendrogram at the left clusters populations based on the similarity of their average expression across all 16 parameters. c) Subset distribution of Spike-specific CD8 T cells at pre-infection baseline for eleven subjects with paired measurements at baseline and day 7, as in Fig. 5h. d) The abundance of Spike-specific CD8 T cells in each subset during SARS-CoV-2 infection of previously vaccinated or unvaccinated individuals, calculated as the percent of total CD8 T cells. Vaccinated HLA-A2, N = 31; HLA-A3, N = 11; unvaccinated HLA-A2 N = 11; HLA-A3 N = 3. Boxplots represent median with interquartile range, whiskers indicate range. Statistics calculated using two-sided Wilcoxon test with Benjamini-Hochberg correction for multiple comparisons. ns indicates p > 0.05, * indicates p < 0.05, ** indicates p < 0.01, *** indicates p < 0.001, **** indicates p < 0.0001.

Source data

Extended Data Fig. 8 CD8 T cell differentiation states are associated with distinct activation dynamics during SARS-CoV-2 breakthrough infection.

a) The magnitude of the activated Spike-specific CD8 T cell response in each subset during breakthrough infection as a percent of total CD8 T cells, where activated cells express at least two activation markers. Supports Fig. 5e using a different method for defining activated cells. b) The percent activation of Spike-specific CD8 T cells in each subset, where activated cells express at least two activation markers. Supports Fig. 5g using a different method for defining activated cells. c) Summary data for the subset distribution of activated Spike-specific CD8 T cells at day 7 post-symptom onset. Supports Fig. 5i using a different method for defining activated cells (N = 16). d) Correlations within Spike-specific CD8 T cell responses. Left) day 7 total Spike-specific CD8 T cells and day 7 total Ki67+ Spike-specific CD8 T cells. Right) day 7 total Spike-specific CM cells and day 7 total Ki67+ Spike-specific CD8 T cells. Correlation statistics were calculated using Spearman rank correlation and are shown with Pearson trend lines for visualization. e) Percent of total CD8 T cells that are antigen-specific, in the indicated subset, and express at least two activation markers from left) all pre-infection samples and right) a subset of individuals with samples collected at days 2-3 post-symptom onset. Boxplots represent median with interquartile range, whiskers indicate range. Statistics calculated using two-sided Wilcoxon test with Benjamini-Hochberg correction for multiple comparisons. Statistics without brackets are in comparison to day 0. ns indicates p > 0.05, * indicates p < 0.05, ** indicates p < 0.01, *** indicates p < 0.001, **** indicates p < 0.0001.

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Supplementary information

Supplementary Information

Supplementary Tables 1–3 and Penn Medicine BioBank banner author list and contribution statements.

Reporting Summary

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Antibody titers and flow cytometry data.

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Mean responses normalized to peak.

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Antibody titers and flow cytometry data.

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Painter, M.M., Johnston, T.S., Lundgreen, K.A. et al. Prior vaccination promotes early activation of memory T cells and enhances immune responses during SARS-CoV-2 breakthrough infection. Nat Immunol 24, 1711–1724 (2023). https://doi.org/10.1038/s41590-023-01613-y

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