Transcriptome landscape of high and low responders to an inactivated COVID-19 vaccine after 4 months using single-cell sequencing

BackgroundVariability in antibody responses among individuals following vaccination is a universal phenomenon. Single-cell transcriptomics offers a potential avenue to understand the underlying mechanisms of these variations and improve our ability to evaluate and predict vaccine effectiveness. ObjectiveThis study aimed to explore the potential of single-cell transcriptomic data in understanding the variability of antibody responses post-vaccination and its correlation with transcriptomic changes. MethodsBlood samples were collected from 124 individuals on day 21 post COVID-19 vaccination. These samples were categorized based on antibody titers (high, medium, low). On day 135, PBMCs from 27 donors underwent single-cell RNA sequencing to depict the transcriptome atlas. ResultsDifferentially expressed genes (DEGs) affecting antibody expression in various cell types were identified. We found that innate immunity, B cell, and T cell population each had a small set of common DEGs (MT-CO1, HLA-DQA2, FOSB, TXNIP, and JUN), and Macrophages and Th1 cells exhibited the largest number of DEGs. Pathway analysis highlighted the dominant role of the innate immune cell population in antibody differences among populations, with a significant impact from the interferon pathway. Furthermore, protein complexes analysis revealed that alterations in the ribosome complex, primarily regulated by DC cells, may play a crucial role in regulating antibody differences. Combining these findings with previous research we proposed a potential regulatory mechanism model of DC cells on B cell antibody production. ConclusionWhile direct prediction of specific antibody levels using single-cell transcriptomic data remains technically and data-wise challenging, our study demonstrated the vast potential of single-cell transcriptomics in understanding the mechanisms underlying antibody responses induced by vaccines.