The T-cell receptor (TCR) repertoires exhibits distinct signatures associated with COVID-19 severity. However, the precise identification of vaccine-induced SARS-CoV-2-specific TCRs and T-cell immunity mechanisms are unknown. We developed a machine-learning model that can differentiate COVID-19 patients from healthy individuals based on TCR sequence features with an accuracy of 95.7%. Additionally, we identified SARS-CoV-2-specific T cells and TCR in HLA-A*02 vaccinated individuals by peptide stimulation. The SARS-CoV-2-specific T cells exhibited higher cytotoxicity and prolonged survival when targeting spike-pulsed cells in vitro or in vivo. The top-performing TCR was further tested for its affinity and cytotoxic effect against SARS-CoV-2-associated epitopes. Furthermore, single-cell RNA sequencing (scRNA-seq), immune repertoire sequencing (IR-seq) and flow cytometry were used to access vaccine-induced cellular immunity, which demonstrated that robust T cell responses (T cell activation, tissue-resident memory T cell (Trm) generation, and TCR clonal expansion) could be induced by intranasal vaccination. In summary, we identified the SARS-CoV-2-associated TCR repertoires profile, specific TCRs and T cell responses. This study provides a theoretical basis for developing effective immunization strategies.
Keyphrases
- sars cov
- single cell
- regulatory t cells
- respiratory syndrome coronavirus
- rna seq
- machine learning
- flow cytometry
- genome wide
- high glucose
- high throughput
- endothelial cells
- gene expression
- patient safety
- induced apoptosis
- cell proliferation
- dna methylation
- drug induced
- binding protein
- mass spectrometry
- cell death
- amino acid
- pi k akt