Abstract
We present a pilot, single-subject longitudinal analysis of plasma antibodies and proteome dynamics across 310 days spanning COVID-19 vaccination and subsequent SARS-CoV-2 infection. Serial plasma samples were analyzed by DIA-LC-MS/MS, identifying 1502 proteins. Comparative analyses across key intervals (pre-vaccination, post-dose 1, post-dose 2, and post-infection) showed minimal proteomic shifts after vaccination but distinct, coordinated expression changes after SARS-CoV-2 infection.
Similar content being viewed by others
Data availability
The raw and processed mass spectrometry data have been deposited in JPOST (JPST003696) and registered with ProteomeXchange (PXD062242).
References
Shuai, H. et al. Attenuated replication and pathogenicity of SARS-CoV-2 B.1.1.529 Omicron. Nature 603, 693–699 (2022).
Suzuki, R. et al. Attenuated fusogenicity and pathogenicity of SARS-CoV-2 Omicron variant. Nature 603, 700–705 (2022).
Marcelin, J. R. et al. COVID-19 Vaccines and SARS-CoV-2 Transmission in the Era of New Variants: A Review and Perspective. Open. Forum Infect. Dis. 9, ofac124 (2022).
Silva, S. J. R., Kohl, A., Pena, L. & Pardee, K. Recent insights into SARS-CoV-2 omicron variant. Rev. Med. Virol. 33, e2373 (2023).
Zhang, F., Ge, W., Ruan, G., Cai, X. & Guo, T. Data-Independent Acquisition Mass Spectrometry-Based Proteomics and Software Tools: A Glimpse in 2020. Proteomics 20, e1900276 (2020).
Demichev, V., Messner, C. B., Vernardis, S. I., Lilley, K. S. & Ralser, M. DIA-NN: neural networks and interference correction enable deep proteome coverage in high throughput. Nat. Methods. 17, 41–44 (2020).
Jiang, N. et al. A data-independent acquisition (DIA)-based quantification workflow for proteome analysis of 5000 cells. J. Pharm. Biomed. Anal. 216, 114795 (2022).
Gotti, C. et al. DIA proteomics data from a UPS1-spiked. Data Brief. 41, 107829 (2022).
Kaneko, Y. et al. The serological diversity of serum IgG/IgA/IgM against SARS-CoV-2 nucleoprotein, spike, and receptor-binding domain and neutralizing antibodies in patients with COVID-19 in Japan. Health Sci. Rep. 5, e572. https://doi.org/10.1002/hsr2.572 (2022).
Yokoyama, T. et al. Comprehensive proteome analysis of longitudinal plasma samples from a single donor after SARS-CoV-2 infection. PLoS One. 16, e0247711. https://doi.org/10.1371/journal.pone.0247711 (2021).
Nakamura, N., Kobashi, Y. & Kim, K. S. Modeling and predicting individual variation in COVID-19 vaccine-elicited antibody response in the general population. PLoS Digit. Health. 3, e0000497. https://doi.org/10.1371/journal.pdig.0000497 (2024).
Yoshida, M., Kobashi, Y. & Kawamura, T. Factors associated with COVID-19 vaccine booster hesitancy: a retrospective cohort study. Fukushima Vaccination Community Surv. Vaccines. 10, 515. https://doi.org/10.3390/vaccines10040515 (2022).
Saito, H., Yoshimura, H. & Yoshida, M. Antibody profiling of microbial antigens in the blood of COVID-19 mRNA vaccine recipients using microbial protein microarrays. Vaccines 11, 1694 (2023).
Obi, N. et al. Development of drug discovery screening system by molecular interaction kinetics-mass spectrometry. Rapid Commun. Mass. Spectrom. 32, 665–671 (2018).
Fukuda, T. et al. An improvement on mass spectrometry-based epigenetic analysis of large histone-derived peptides by using the Ionization Variable Unit interface. Anal. Biochem. 486, 14–16 (2015).
Kawamura, T. et al. Proteomic analysis of laser-microdissected paraffin-embedded tissues: (1) Stage-related protein candidates upon non-metastatic lung adenocarcinoma. J. Proteom. 73, 1089–1099 (2010).
Nishimura, T. et al. Proteomic analysis of laser-microdissected paraffin-embedded tissues: (2) MRM assay for stage-related proteins upon non-metastatic lung adenocarcinoma. J. Proteom. 73, 1100–1110 (2010).
Fukuda, T. et al. A selected reaction monitoring mass spectrometric assessment of biomarker candidates diagnosing large-cell neuroendocrine lung carcinoma by the scaling method using endogenous references. PLoS One. 12, e0176219 (2017).
Wang, J., Luzum, J. A., Phelps, M. A. & Kitzmiller, J. P. Liquid chromatography-tandem mass spectrometry assay for the simultaneous quantification of simvastatin, lovastatin, atorvastatin, and their major metabolites in human plasma. J. Chromatogr. B Analyt Technol. Biomed. Life Sci. 983–984, 18–25 (2015).
Kosuge, H. et al. Proteomic identification and validation of novel interactions of the putative tumor suppressor PRELP with membrane proteins including IGFI-R and p75NTR. J. Biol. Chem. 296, 100278 (2021).
Mosquim Junior, S., Siino, V., Rydén, L., Vallon-Christersson, J. & Levander, F. Choice of high-throughput proteomics method affects data integration with transcriptomics and the potential use in biomarker discovery. Cancers 14, 5761 (2022).
Demichev, V. et al. dia-PASEF data analysis using FragPipe and DIA-NN for deep proteomics of low sample amounts. Nat. Commun. 13, 3944 (2022).
Kuleshov, M. V. et al. Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res. 44 (W1), W90–W97 (2016).
Gene Ontology Consortium. The Gene Ontology resource: enriching a GOld mine. Nucleic Acids Res. 49, D325–D334 (2021).
Kanehisa, M. & Goto, S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28, 27–30 (2000).
Gray, J. P. et al. The ribosomal protein rpL11 associates with and inhibits the transcriptional activity of peroxisome proliferator-activated receptor-alpha. Toxicol. Sci. 89, 535–546 (2006).
Zhu, Q. et al. Up-regulated 60S ribosomal protein L18 in PEDV N protein-induced S-phase arrested host cells promotes viral replication. Virus Res. 321, 198916 (2022).
Khalid, Z. et al. Identification of novel therapeutic candidates against SARS-CoV-2 infections: An application of RNA sequencing toward mRNA-based nanotherapeutics. Front. Microbiol. 13, 901848 (2022).
Luan, Y. et al. Deficiency of ribosomal proteins reshapes the transcriptional and translational landscape in human cells. Nucleic Acids Res. 50, 6601–6617 (2022).
Chakraborty, A. et al. Cross talk between TP53 and c-Myc in the pathophysiology of Diamond-Blackfan anemia: Evidence from RPL11-deficient in vivo and in vitro models. Biochem. Biophys. Res. Commun. 495, 1839–1845 (2018).
Devlin, J. R. et al. Combination therapy targeting ribosome biogenesis and mRNA translation synergistically extends survival in MYC-driven lymphoma. Cancer Discov. 6, 59–70 (2016).
Cao, P. et al. Genomic gain of RRS1 promotes hepatocellular carcinoma through reducing the RPL11-MDM2-p53 signaling. Sci. Adv. 7 (35), eabf4304. https://doi.org/10.1126/sciadv.abf4304 (2021).
Li, H. et al. Loss of RPS27a expression regulates the cell cycle, apoptosis, and proliferation via the RPL11-MDM2-p53 pathway in lung adenocarcinoma cells. J. Exp. Clin. Cancer Res. 41, 33 (2022).
Will, C. L., Schneider, C., Reed, R. & Luhrmann, R. Identification of both shared and distinct proteins in the major and minor spliceosomes. Science 284, 2003–2005 (1999).
Will, C. L. et al. The human 18S U11/U12 snRNP contains a set of novel proteins not found in the U2-dependent spliceosome. RNA 10, 929–941 (2004).
Niemela, E. H., Verbeeren, J., Singha, P., Nurmi, V. & Frilander, M. J. Evolutionarily conserved exon definition interactions with U11 snRNP mediate alternative splicing regulation on U11-48K and U11/U12-65K genes. RNA Biol. 12, 1256–1264 (2015).
Hall, S. L. & Padgett, R. A. Requirement of U12 snRNA for in vivo splicing of a minor class of eukaryotic nuclear pre-mRNA introns. Science 271, 1716–1718 (1996).
Shen, B. et al. Proteomic and metabolomic characterization of COVID-19 patient sera. Cell 182, 59–72e15 (2020).
Messner, C. B. et al. Ultra-high-throughput clinical proteomics reveals classifiers of SARS-CoV-2 infection. Cell. Syst. 11, 11–24e4 (2020).
Del Valle, D. M. et al. An inflammatory cytokine signature predicts COVID-19 severity and survival. Nat. Med. 26, 1636–1643 (2020).
Viode, A. et al. Longitudinal plasma proteomic analysis of 1117 hospitalized patients with COVID-19 identifies features associated with severity and outcomes. Sci. Adv. 10, eadl5762 (2024).
Wang, Y. et al. Longitudinal proteomic profiling of COVID-19 vaccination. Protein Cell. 14, 668–682 (2023).
Acknowledgements
We would like to express our sincere appreciation to Ms. Akiko Fukuda (Laboratory for Systems Biology and Medicine, Isotope Science Center, The University of Tokyo) for her invaluable assistance in conducting the antibody titer measurements, and to Ms. Noriko Kagi (Biotage Japan Ltd., Tokyo, Japan) for her expert technical support with the LV-200 experiments.
Funding
This study was supported by Medical & Biological Laboratories Co., Ltd., and Shenzhen YHLO Biotech Co., Ltd. (the distributor and manufacturer of the antibody measurement system, iFlash 3000); by grants from Peace Winds Japan, Kowa Co., and the Research Center for Advanced Science and Technology at the University of Tokyo; and by the Japan Agency for Medical Research and Development (AMED) under the Grant-in-Aid for the Development of Vaccines for the Novel Coronavirus Disease (Grant Number: 22nf0101638h0002).
Author information
Authors and Affiliations
Contributions
Author ContributionsT.F. performed plasma sample preprocessing, DIA-LC–MS analysis, data evaluation, and verification of analytical accuracy and validity. He also prepared all tables and figures and wrote the main text of the manuscript.A.N. conducted antibody titer measurements and performed DIA-LC–MS sample measurements.Y.C. examined the DIA-LC–MS results and analyzed the data using DIA-NN.Y.B. supervised the experimental application of DIA-NN and the S-Trap method.T.K. verified the accuracy and validity of antibody assays and DIA-LC–MS analysis, and supervised the overall progress of the study.All authors reviewed and approved the final manuscript.
Corresponding authors
Ethics declarations
Declaration of generative AI and AI-assisted technologies in the writing process
During the preparation of this work, the author used ChatGPT (OpenAI) to improve the clarity and readability of the manuscript. After using this tool, the authors reviewed and edited the content as needed and took full responsibility for the content of the publication.
Competing interests
The authors declare no competing interests.
Ethical approval and consent to participate
This study was approved by the Institutional Ethics Committee of The University of Tokyo (approval number: 23–230). Informed consent was obtained from all individual participants included in the study.
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Below is the link to the electronic supplementary material.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
About this article
Cite this article
Fukuda, T., Nakayama, A., Chikaoka, Y. et al. Antibody profiling and plasma proteomics in SARS-CoV-2 infection: a pilot study. Sci Rep (2026). https://doi.org/10.1038/s41598-026-48765-z
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s41598-026-48765-z
