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Variants and vaccines impact nasal immunity over three waves of SARS-CoV-2

Nature Immunology volume 26, pages 294–307 (2025)Cite this article

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Abstract

Viral variant and host vaccination status impact infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), yet how these factors shift cellular responses in the human nasal mucosa remains uncharacterized. We performed single-cell RNA sequencing (scRNA-seq) on nasopharyngeal swabs from vaccinated and unvaccinated adults with acute Delta and Omicron SARS-CoV-2 infections and integrated with data from acute infections with ancestral SARS-CoV-2. Patients with Delta and Omicron exhibited greater similarity in nasal cell composition driven by myeloid, T cell and SARS-CoV-2hi cell subsets, which was distinct from that of ancestral cases. Delta-infected samples had a marked increase in viral RNA, and a subset of PER2+EGR1+GDF15+ epithelial cells was enriched in SARS-CoV-2 RNA+ cells in all variants. Prior vaccination was associated with increased frequency and activation of nasal macrophages. Expression of interferon-stimulated genes negatively correlated with coronavirus disease 2019 (COVID-19) severity in patients with ancestral and Delta but not Omicron variants. Our study defines nasal cell responses and signatures of disease severity across SARS-CoV-2 variants and vaccination.

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Fig. 1: SARS-CoV-2 induces changes in the cellular composition of nasal mucosa in patients infected during the ancestral, Delta and Omicron waves.
Fig. 2: Delta and Omicron cases share nasal cell ecosystems that are distinct from those of ancestral cases.
Fig. 3: SARS-CoV-2 variants have unique patterns of viral RNA distribution among nasal cells.
Fig. 4: Prior vaccination is associated with nasal macrophage recruitment and activation during SARS-CoV-2 infection.
Fig. 5: COVID-19 severity correlates with cellular and transcriptional phenotypes in a variant-specific manner.

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Data availability

scRNA-seq data are publicly available for download and visualization via the Broad Institute Single Cell Portal (accession code SCP2593). Custom reference FASTA and GTF for SARS-CoV-2 are available for download: https://github.com/ShalekLab/SARSCoV2-genome-reference.

Code availability

Code was written using R version 4.2.3 and publicly available packages. No new algorithms or functions were created, and code used in-built functions in listed packages available on CRAN. All code generated and used to analyze the data reported in this paper is available on GitHub in the jo-m-lab repository: https://github.com/jo-m-lab/SARSCoV2-Variants-Vaccines-Paper.

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Acknowledgements

We thank the study participants and their families for enabling this research, the clinical support staff at the UMMC for assistance with sample collection and members of the Ordovas-Montanes laboratory (Boston Children’s Hospital, Harvard Medical School, Broad Institute, Ragon Institute), the Shalek laboratory (MIT, Broad Institute, Ragon Institute), the Horwitz laboratory (Boston Children’s Hospital) and the Glover laboratory (UMMC, Tulane University) for thoughtful discussion and feedback. We sincerely thank A. Sharpe and all members of the Sharpe laboratory (Harvard Medical School) for insightful discussion and feedback as well. We thank J. Gould, K. Siddle, B. Li, S. Fleming and the Broad Institute viral-ngs and Cumulus teams for assistance with computational pipelines. This project was made possible in part by grant number 2020-216949 from the Chan Zuckerberg Initiative DAF, an advised fund of the Silicon Valley Community Foundation to A.K.S. and J.O.-M. Work performed through the UMMC Molecular and Genomics Facility is supported, in part, by funds from the NIGMS, including the Molecular Center of Health and Disease (P20GM144041, M.R.G.), the Mississippi INBRE (P20GM103476, A. Flynt) and Obesity, Cardiorenal and Metabolic Diseases—COBRE (P30GM149404, J. Hall). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. J.O-.M. is a New York Stem Cell Foundation—Robertson Investigator. J.O.-M. was supported by the AbbVie–Harvard Medical School Alliance, the Richard and Susan Smith Family Foundation, the AGA Research Foundation’s AGA–Takeda Pharmaceuticals Research Scholar Award in IBD (AGA2020-13-01), the HDDC Pilot and Feasibility (P30 DK034854), the Leona M. and Harry B. Helmsley Charitable Trust, the Pew Charitable Trusts Biomedical Scholars, the Broad Next Generation Award, the Chan Zuckerberg Initiative Pediatric Networks, the Mathers Foundation, the New York Stem Cell Foundation, NIH R01 HL162642, NIH R01 DE031928 and the Cell Discovery Network, a collaborative funded by the Manton Foundation and the Warren Alpert Foundation at Boston Children’s Hospital. J.M.L.W. was supported by NIH Training Grant 5TL1TR002543, S.W.K. was supported by the Cancer Research Institute’s Irvington Postdoctoral Fellowship, and A.K.S. was supported by the Bill and Melinda Gates Foundation, a Sloan Fellowship in Chemistry, the NIH (5U24AI118672) and the Ragon Institute of MGH, MIT and Harvard.

Author information

Author notes

  1. These authors contributed equally: Jaclyn M. L. Walsh, Vincent N. Miao, Anna H. Owings, Ying Tang, Joshua D. Bromley.

  2. These authors jointly supervised this work: Alex K. Shalek, Bruce H. Horwitz, Sarah C. Glover, Jose Ordovas-Montanes.

Authors and Affiliations

  1. Division of Gastroenterology, Hepatology and Nutrition, Boston Children’s Hospital, Boston, MA, USA

    Jaclyn M. L. Walsh, Ying Tang, Samuel W. Kazer, Kyle Kimler, Chelsea Asare, Bruce H. Horwitz & Jose Ordovas-Montanes

  2. Department of Immunology, Harvard Medical School, Boston, MA, USA

    Jaclyn M. L. Walsh & Samuel W. Kazer

  3. Program in Immunology, Harvard Medical School, Boston, MA, USA

    Jaclyn M. L. Walsh, Alex K. Shalek, Bruce H. Horwitz & Jose Ordovas-Montanes

  4. Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Jaclyn M. L. Walsh, Vincent N. Miao, Joshua D. Bromley, Samuel W. Kazer, Kyle Kimler, Chelsea Asare, Carly G. K. Ziegler, Samira Ibrahim, Tasneem Jivanjee, Micayla George, Andrew W. Navia, Riley S. Drake, Alex K. Shalek & Jose Ordovas-Montanes

  5. Ragon Institute of MGH, MIT and Harvard, Cambridge, MA, USA

    Vincent N. Miao, Joshua D. Bromley, Samuel W. Kazer, Kyle Kimler, Carly G. K. Ziegler, Samira Ibrahim, Tasneem Jivanjee, Micayla George, Andrew W. Navia, Riley S. Drake, Alex K. Shalek & Jose Ordovas-Montanes

  6. Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA

    Vincent N. Miao, Joshua D. Bromley, Kyle Kimler, Carly G. K. Ziegler, Samira Ibrahim, Tasneem Jivanjee, Micayla George, Andrew W. Navia, Riley S. Drake & Alex K. Shalek

  7. Program in Health Sciences and Technology, Harvard Medical School and MIT, Boston, MA, USA

    Vincent N. Miao, Carly G. K. Ziegler & Alex K. Shalek

  8. Department of Medicine, University of Mississippi Medical Center, Jackson, MS, USA

    Anna H. Owings, Benjamin C. Billingsley, Spurthi Tarugu, Hannah Laird, T. Grant Wichman, Neha S. Dhaliwal, Jessie Harvey & Michael R. Garrett

  9. Division of Digestive Diseases, University of Mississippi Medical Center, Jackson, MS, USA

    Anna H. Owings, Adam Parker, Paul Dotherow, Sai K. Kota, Hannah Laird, Yesenia T. Davis, Neha S. Dhaliwal, Yilianys Pride, Tanya O. Robinson & Sarah C. Glover

  10. Graduate Program in Microbiology, Massachusetts Institute of Technology, Cambridge, MA, USA

    Joshua D. Bromley

  11. Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA

    Kyle Kimler, Andrew W. Navia & Alex K. Shalek

  12. Division of Pediatric Hematology/Oncology, Boston Children’s Hospital, Boston, MA, USA

    Kyle Kimler

  13. Harvard Graduate Program in Biophysics, Cambridge, MA, USA

    Carly G. K. Ziegler & Alex K. Shalek

  14. Division of Pulmonary, Critical Care and Sleep Medicine, University of Mississippi Medical Center, Jackson, MS, USA

    Yanglin Guo & Michal Senitko

  15. Department of Cell and Molecular Biology, University of Mississippi Medical Center, Jackson, MS, USA

    John T. Bates, Michael R. Garrett, D. Ashley Robinson & Sarah C. Glover

  16. Department of Oral Immunology and Infectious Diseases, University of Louisville School of Dentistry, Louisville, KY, USA

    Gill Diamond

  17. Department of Pathology, University of Mississippi Medical Center, Jackson, MS, USA

    I. J. Frame

  18. Division of Allergy and Immunology, Department of Medicine, University of California San Diego, La Jolla, CA, USA

    Jonathan J. Lyons

  19. Veterans Affairs San Diego Healthcare System, La Jolla, CA, USA

    Jonathan J. Lyons

  20. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA

    Alex K. Shalek

  21. Division of Emergency Medicine, Boston Children’s Hospital, Boston, MA, USA

    Bruce H. Horwitz

  22. Department of Medicine, Section of Gastroenterology and Hepatology, Tulane University, New Orleans, LA, USA

    Sarah C. Glover

  23. Harvard Stem Cell Institute, Cambridge, MA, USA

    Jose Ordovas-Montanes

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  1. Jaclyn M. L. Walsh
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  2. Vincent N. Miao
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  35. Alex K. Shalek
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  36. Bruce H. Horwitz
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  37. Sarah C. Glover
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  38. Jose Ordovas-Montanes
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Conceptualization, J.O.-M., A.K.S., S.C.G., B.H.H.; methodology, J.M.L.W., V.N.M., A.H.O., Y.T., J.D.B., S.W.K., K.K., C.A., C.G.K.Z., A.W.N., S.C.G., J.O.-M., A.K.S., B.H.H.; software, K.K., S.W.K.; formal analysis, J.M.L.W., V.N.M., A.H.O., Y.T., J.D.B., S.W.K., K.K; investigation, J.M.L.W., V.N.M., A.H.O., Y.T., J.D.B., C.G.K.Z., A.W.N., S.I., T.J., M.G., R.S.D., J.T.B., B.C.B., D.A.R.; resources, S.C.G., A.H.O.; data curation, A.H.O, Y.T., N.S.D., T.O.R., J.M.L.W.; writing (original draft), J.M.L.W., J.O.-M., A.K.S., S.C.G., B.H.H.; writing (review and editing), J.M.L.W., V.N.M., A.H.O., Y.T., J.D.B., S.W.K., K.K., C.A., C.G.K.Z., S.I., T.J., M.G., A.W.N., R.S.D., A.P., B.C.B., P.D., S.T., S.K.K., H.L., T.G.W., Y.T.D., N.S.D., Y.P., Y.G., M.S., J.H., J.T.B., G.D., M.R.G., D.A.R., I.J.F., J.J.L., T.O.R., J.O.-M., A.K.S., S.C.G., B.H.H.; visualization, J.M.L.W., Y.T.; supervision, J.O.-M., A.K.S., S.C.G., B.H.H., T.O.R.; project administration, Y.P., T.O.R.; funding acquisition, J.O.-M., A.K.S., S.C.G., B.H.H.

Corresponding author

Correspondence to Jose Ordovas-Montanes.

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Competing interests

V.N.M. reports compensation from MPM Capital and RA Capital Management unrelated to this work. S.W.K. reports compensation for consulting services with Monopteros Therapeutics, Flagship Pioneering and Radera Biosciences. M.S. reports compensation for consulting services with GE Precision Healthcare. J.J.L. reports compensation for consulting services with Blueprint Medicines and Human Immunology Biosciences. J.O.-M. reports compensation for consulting services with Cellarity, Tessel Biosciences and Radera Biotherapeutics. A.K.S. reports compensation for consulting and/or SAB membership from Merck, Honeycomb Biotechnologies, Cellarity, Hovione, Ochre Bio, Third Rock Ventures, Relation Therapeutics, Dahlia Biosciences, Bio-Rad Laboratories, IntrECate Biotherapeutics, FogPharma and Passkey Therapeutics. C.G.K.Z., V.N.M., A.H.O., A.W.N., Y.T., J.D.B., A.K.S., S.C.G., B.H.H. and J.O.-M. are co-inventors on a provisional patent application relating to methods of stratifying and treating viral infections. The other authors declare no competing interests.

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Peer review information

Nature Immunology thanks Daniel Altmann and Martijn Nawijn for their contribution to the peer review of this work. Peer reviewer reports are available. Primary Handling Editor: Ioana Staicu, in collaboration with the Nature Immunology team.

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Extended data

Extended Data Fig. 1 Participant metadata across variant and vaccination groups.

a-b) Stacked bar charts showing fraction of Delta or Omicron cases in SARS-CoV-2 variant (a) and lineage (b) groups as identified by targeted sequencing of viral RNA from supernatant collected during nasopharyngeal swab processing (restricted to participants with SARS-CoV-2 S CT value < 30, Delta: n = 22, Omicron: n = 12). ‘Undetermined’: CT values > 30, sample not sequenced (Delta: n = 8, Omicron: n = 9). SARS-CoV-2 variant (a) and lineages (b) were determined using DRAGEN COVIDSeq Test software (see Methods, ‘Viral Variant Sequencing’). c) Comparison of COVID-19 severity score (WHO score of respiratory support) at time of nasopharyngeal swab collection (‘Swab’) and peak disease severity (‘Peak’) in all ancestral (n = 32), Delta (n = 33), and Omicron (n = 21) cases. d) Length of time in days from positive SARS-CoV-2 PCR test to nasopharyngeal swab collection (all cases), from reported symptom onset to nasopharyngeal swab collection (Ancestral: n = 30, Delta: n = 31, Omicron: n = 18, Delta vs. Ancestral: p = 0.031), and from reported symptom onset to peak COVID-19 severity score (Ancestral: n = 30, Delta: n = 31, Omicron: n = 18). e) SARS-CoV-2 plasma serology across variant groups. Plasma samples were taken from a subset of participants (Ancestral: n = 24, Delta: n = 30, Omicron: n = 17) on the same day as the nasopharyngeal swab used for scRNA-seq. Dotted lines indicate limits of detection. RBD-W: original Washington strain receptor binding domain; RBD-D: Delta strain receptor binding domain; NP: nucleoprotein. f) Comparison of COVID-19 severity score as in (c) across vaccination groups (Delta unvaccinated (‘Unvax’): n = 19, Delta vaccinated (‘Vax’): n = 14, Omicron unvaccinated: n = 10, Omicron vaccinated: n = 11). g) Length of time in days between first vaccine dose or most recent vaccine dose and positive SARS-CoV-2 PCR test in vaccinated Delta (n = 14) and Omicron (n = 10) cases. h) Disease course timing as in (d) across vaccination groups (All cases for PCR test to sample collection, for symptom onset data Delta unvaccinated: n = 19, Delta vaccinated: n = 12, Omicron unvaccinated: n = 7, Omicron vaccinated: n = 10. i) SARS-CoV-2 plasma serology as in (e) across vaccination groups (Delta unvaccinated: n = 18, Delta vaccinated: n = 12, Omicron unvaccinated: n = 7, Omicron vaccinated: n = 10, Delta vaccinated vs Delta unvaccinated NP IgG: p = 0.049). d,e,h,i, statistical test represents two-sided Kruskal-Wallis test results with Benjamini-Hochberg correction for multiple comparison. *Dunn post hoc test, p < 0.05.

Extended Data Fig. 2 scRNA-seq Quality Metrics and Cell Type Distributions.

a) Single cell quality metrics (total number of cells, number of unique UMI (unique molecular identifiers) per cell, number of unique genes per cell, and percent mitochondrial transcripts) for each participant (bars or violins) after filtering for low quality cells. b) UMAP of 48,730 cells from all participants (n = 112) colored by variant group (left), UMAP of 23,987 cells from Delta and Omicron participants (n = 54) colored by vaccination status (center), and UMAP of 48,730 cells from all participants (n = 112) colored by WHO peak respiratory support score (right). c) Stacked bar chart showing frequency of major cell types across variant (control: n = 26, ancestral, n = 32, Delta, n = 33, Omicron, n = 21) and vaccination (Delta unvaccinated (‘Unvax’): n = 19, Delta vaccinated (‘Vax’): n = 14, Omicron unvaccinated: n = 10, Omicron vaccinated: n = 11) groups. d) Frequency of secretory cells (ancestral vs. control: p = 4.8*10−4), deuterosomal cells (ancestral vs. control: p = 5.6*10−6, Delta vs. control: p = 0.054), ciliated cells (Delta vs. control: p = 0.011, Omicron vs. control: p = 0.031), SARS-CoV-2hi cells (ancestral vs. control: p = 0.013, Delta vs. control: p = 0.03, Omicron vs. control: p = 5*10−3), T cells, and macrophages as a fraction of all cells in that lineage (epithelial or immune) in that sample from all controls (n = 26) and all ancestral (n = 32), Delta (n = 33), and Omicron (n = 21) cases. Box plots represent median (center line), upper and lower quartiles (box limits); and 1.5x the inter-quartile range (whiskers). Compare to Fig. 1f. Statistical test represents two-sided Kruskal-Wallis test with Benjamini-Hochberg correction for multiple comparisons. *Dunn’s post hoc test, p < 0.05.

Extended Data Fig. 3 Epithelial diversity in the nasal mucosa during SARS-CoV-2 infection.

a) UMAP of 43,322 epithelial cells from all participants (n = 112) colored by major epithelial cell type as identified in Fig. 1d. b-c) UMAPs of detailed subsets within (b) basal/secretory cells (n = 112 participants, 9,106 cells) and (c) squamous cells (n = 112 participants, 3,913 cells). Cells from all participants belonging to that cell type were sub-clustered with louvain clustering. See Supplementary Tables 3, 4 for full lists of subset marker genes. d) Violin plot of marker genes for basal, secretory, goblet, squamous, and ionocyte subsets. e–g) UMAPs of detailed subsets within (e) ciliated cells (n = 112 participants, 20,683 cells), (f) MThi cells (n = 112 participants, 5,930 cells) and (g) SARS-CoV-2 RNAhi cells (n = 112 participants, 2,551 cells). h) Violin plot of marker genes for ciliated, deuterosomal, MThi, and SARS-CoV-2 RNAhi subsets.

Extended Data Fig. 4 Immune cell diversity in the nasal mucosa during SARS-CoV-2 infection.

a) UMAP of 5,408 immune cells from all participants colored by major immune cell type as identified in Fig. 1d. b-c) UMAPs of detailed subclusters within (b) T cells (n = 112 participants, 2,705 cells) and (c) myeloid cells (n = 112 participants, 2,535 cells). Cells from all participants belonging to that cell type were sub-clustered with louvain clustering. See Supplementary Table 5 for full lists of subset marker genes. d) Violin plot of marker genes for each immune subset.

Extended Data Fig. 5 Non-negative matrix factorization (NMF) Analysis.

a) Gene loadings of ciliated factors 8, 10, 13, and 21 identified by running NMF on all ciliated cells (n = 20,683) from all participants (n = 112). Complete list of genes contributing to all 25 factors is provided in Supplementary Table 7. b) Average score of ciliated factor 8 (Delta vs. control: p = 0.012, Omicron vs. control: p = 8.46*10−3), ciliated factor 10 (ancestral vs. control: p = 7.54*10−6, Delta vs. control, p = 3.32*10−3, Omicron vs. control: p = 0.011), ciliated factor 13 (ancestral vs. control: p = 3.89*10−3, Delta vs. control: p = 1.98*10−5, Omicron vs. control: p = 3*10−3), and ciliated factor 21 (ancestral vs. control: p = 1.08*10−4, Delta vs. control, p = 4.51*10−4, Omicron vs. control: p = 4.35*10−3) within pseudobulk ciliated cells for all controls (n = 26) and ancestral (n = 32), Delta (n = 33), and Omicron (n = 21) cases. c) Gene loadings of secretory factors 6, 11, 5, and 20 identified by running NMF on all basal and secretory cells (n = 9,106) from all participants (n = 112). Complete list of genes contributing to all 23 factors is provided in Supplementary Table 7. d) Average factor score of secretory factor 6 (ancestral vs. control: p = 0.033, Delta vs. control, p = 5.1*10−4, Omicron vs. control: p = 0.035) and secretory factor 11 (ancestral vs. control: p = 2.72*10−3) within pseudobulk secretory cells for all controls (n = 26) and ancestral (n = 32), Delta (n = 33), and Omicron (n = 21) cases. e) Violin plot of scores for secretory factors 6, 11, 5, and 20 across all basal, goblet, and secretory subsets in all participants (n = 112). b,d Box plots represent median (center line), upper and lower quartiles (box limits), and 1.5x the inter-quartile range (whiskers). Statistical tests represent two-sided Kruskal-Wallis test with Benjamini-Hochberg correction for multiple comparisons. *Dunn’s post hoc test, p < 0.05.

Extended Data Fig. 6 Detection and assignment of SARS-CoV-2 RNA+ cells.

a) Schematic of method for SARS-CoV-2 RNA+ assignment. scRNA-seq data was aligned to a joint human and SARS-CoV-2 genome. The amount of ambient RNA in each cell was estimated using Cellbender. Cells were assigned SARS-CoV-2 RNA+ if the amount of viral RNA was greater than the expected ambient amount (see Methods). b) Total SARS-CoV-2 transcripts per cell in a subset of ancestral (n = 30), Delta (n = 31), and Omicron (n = 18) cases, divided by time from symptom onset. c) Spearman correlation (two-tailed test) between the total number of transcripts (UMI) per cell and the total number of SARS-CoV-2 transcripts per cell in all ancestral (n = 32), Delta (n = 33), and Omicron (n = 21) cases. Schematic in a created with BioRender.com.

Extended Data Fig. 7 Frequency of Major Cell Types Across Vaccination Groups.

a) Frequency of B cells, dendritic cells, macrophages, and T cells as a percentage of all cells in the sample in control participants. Control (‘Ctrl’) unvaccinated (‘Unvax’) (n = 13) samples were collected during the Ancestral wave, and control vaccinated (‘Vax’) (n = 13) samples were collected during the Delta and Omicron waves. b) Frequency of epithelial cell types (basal cells, ciliated cells, deuterosomal cells, goblet cells, ionocytes, MThi cells, SARS-CoV-2hi cells (Ctrl Vax vs Ctrl Unvax: p = 0.041), secretory cells, and squamous cells) as a percentage of all cells in the sample in unvaccinated (‘Unvax’) controls (n = 13), vaccinated (‘Vax’) controls (n = 13), unvaccinated Delta (n = 19), vaccinated Delta (n = 14), unvaccinated Omicron (n = 10), and vaccinated Omicron (n = 11) cases. c) Spearman correlation (two-tailed test) between time from most recent SARS-CoV-2 vaccine dose and frequency of immune cell types (B cells, dendritic cells, macrophages, and T cells) in all vaccinated Delta (n = 14) and Omicron (n = 11) cases. Error bands represent 95% confidence interval. a,b Box plots represent median (center line), upper and lower quartiles (box limits); and 1.5x the inter-quartile range (whiskers). Statistical tests represent two-sided Kruskal-Wallis test with Benjamini-Hochberg correction for multiple comparisons. *Dunn’s post hoc test, p < 0.05.

Extended Data Fig. 8 Expression of ISG Modules Across COVID-19 Severity.

a-b) Violin plots of IFN alpha (a) and IFN gamma (b) response gene module scores in ciliated (n = 15,180 cells), secretory (n = 6,656), and T cells (n = 2,181) from all ancestral (n = 32), Delta (n = 33), and Omicron (n = 21) cases. Cells are separated by COVID-19 severity score (WHO range 0–8). Genes uses for module score were derived from previously published results of stimulating human nasal basal cells with IFN alpha or IFN gamma (Supplementary Table 10)70. Lines represent local regression using locally estimated scatterplot smoothing. Error bands represent normal confidence interval.

Extended Data Fig. 9 Association of clinical metadata and nasal cell type frequencies.

Heatmap of spearman correlation (two-tailed test) between clinical parameters and demographic information (rows) and frequency of major cell types (as identified in Fig. 1) and SARS-CoV-2 RNA+ cells (as identified in Fig. 3) (columns) in all participants (n = 112). Color indicates spearman R, *FDR < 0.05.

Extended Data Table 1 Composition of participant vaccination groups
Full size table

Supplementary information

Reporting Summary

Peer Review File

Supplementary Table 1

Demographic and clinical metadata of all participants.

Supplementary Table 2

Clustering and removal of low-quality cells: marker genes and annotations.

Supplementary Table 3

Epithelial clustering round 1 marker genes and annotations.

Supplementary Table 4

Epithelial clustering round 2–3 marker genes and annotations.

Supplementary Table 5

Immune clustering marker genes and annotations.

Supplementary Table 6

Abundance of cell subsets for each participant (normalized to 500 cells per participant).

Supplementary Table 7

Gene loadings for NMF analysis within ciliated and secretory cells.

Supplementary Table 8

DE genes in macrophages and T cells between vaccinated and unvaccinated Delta-infected participants.

Supplementary Table 9

GO terms in genes upregulated in macrophages from vaccinated Delta cases compared to macrophages from unvaccinated Delta cases.

Supplementary Table 10

Human nasal basal cell cytokine stimulation response genes (Ziegler et al.70).

Supplementary Table 11

Significant correlations between cell type frequencies and participant metadata.

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Walsh, J.M.L., Miao, V.N., Owings, A.H. et al. Variants and vaccines impact nasal immunity over three waves of SARS-CoV-2. Nat Immunol 26, 294–307 (2025). https://doi.org/10.1038/s41590-024-02052-z

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