Multiple vaccines have been approved to control COVID-19 pandemic, with Pfizer/BioNTech (BNT162b2) being widely used. We conducted a longitudinal analysis of the immune response elicited after three doses of the BNT162b2 vaccine in individuals who have previously experienced SARS-CoV-2 infection and in unexperienced ones. We conducted immunological analyses and single-cell transcriptomics of circulating T and B lymphocytes, combined to CITE-seq or LIBRA-seq, and VDJ-seq. We found that antibody levels against SARS-CoV-2 Spike, NTD and RBD from wild-type, delta and omicron VoCs show comparable dynamics in both vaccination groups, with a peak after the second dose, a decline after six months and a restoration after the booster dose. The antibody neutralization activity was maintained, with lower titers against the omicron variant. Spike-specific memory B cell response was sustained over the vaccination schedule. Clonal analysis revealed that Spike-specific B cells were polyclonal, with a partial clone conservation from natural infection to vaccination. Spike-specific T cell responses were oriented towards effector and effector memory phenotypes, with similar trends in unexperienced and experienced individuals. The CD8 T cell compartment showed a higher clonal expansion and persistence than CD4 T cells. The first two vaccinations doses tended to induce new clones rather than promoting expansion of pre-existing clones. However, we identified a fraction of Spike-specific CD8 T cell clones persisting from natural infection that were boosted by vaccination and clones specifically induced by vaccination. Collectively, our observations revealed a moderate effect of the second dose in enhancing the immune responses elicited after the first vaccination. Differently, we found that a third dose was necessary to restore comparable levels of neutralizing antibodies and Spike-specific T and B cell responses in individuals who experienced a natural SARS-CoV-2 infection.

Single-cell RNA sequencing (scRNA-seq) has revolutionised our ability to explore the transcriptional landscape of complex tissues and uncover novel cell types and biological functions. However, the task of identifying and classifying cells from scRNA-seq datasets remains a major challenge. To address this issue, we developed a new computational tool called CIA (Cluster Independent Annotation) that can accurately identify cell types across different datasets without the need for a training dataset or complex machine learning processes. Based on predefined cell type signatures, CIA provides a highly user-friendly and practical solution to functional annotation of single cells. Our results demonstrate that CIA outperforms other state-of-the-art approaches, while also having significantly lower computational running times. Overall, CIA simplifies the process of obtaining graphical representations of signature enrichment scores and classification results, providing researchers with a powerful tool to explore the complex transcriptional landscape of single cells. For further details, see tutorials (). ### Competing Interest Statement The authors have declared no competing interest.