Fig. 1

The characteristics of SARS-CoV-2-specific T cell and TCR repertoire. A Principal Component Analysis (PCA) visualization of TCR sequences obtained from the ImmuneACCESS and ImmuneCODE databases, comparing healthy donors (n = 54) with patients at different stages of infection (acute n = 103, transition n = 90, and convalescent n = 108). B Levenshtein distances depict TCR clone similarities between acute and transition groups and diversity in TCR clones between acute and healthy groups. C Rank-abundance curve illustrating TCR diversity. D Machine learning framework for analyzing TCR sequences from COVID-19 patients and healthy individuals. E Performance evaluation of machine learning models for predicting SARS-CoV-2 infection. F Bar graphs representing the proportion of peptide-specific CD8⁺ T cells in HLA-A*02⁺ healthy donors before and after immunization. G Representative flow plot showing the percentage of specific T cells (CD8⁺XG2⁺) after peptide stimulation. H Cytotoxic activities of XG2⁺ T cells against BEAS-2B-spike cells assessed at different effector/target (E/T) ratios. I Quantification of lysis rates when co-cultured with BEAS-2B-spike cells (n = 4). J Top 5 CDR3 amino acid sequences predicted by IR-seq and DLpTCR. K Cytotoxic activities of TCR-T cells against BEAS-2B-spike cells assessed by flow cytometry. L Quantification of TCR-T cell lysis rates when co-cultured with BEAS-2B-spike cells (n = 8). M Comparison of phospho-ZAP70, ZAP70, phospho-AKT, AKT, and β-Tubulin expression in TCR-T cells by immunoblot analysis (n = 3). Data are representative of at least three independent experiments