[POS] Immunology

[POS-23] Estimation and mathematical modeling of immune cell dynamics in COVID-19 patients using single cell analysis

*Setsu Kunieda¹, Risa Yokokawa¹, Shinji Nakaoka¹ (1. Hokkaido University (Japan))

Keywords:

COVID-19,Immune cells,Single-cell analysis

The recent COVID-19 pandemic has highlighted the urgent need for robust vaccine strategies and a deeper understanding of long-term immunity against emerging infectious diseases. Although mRNA vaccines provide strong initial protection, antibody titers decline over time (Levin et al., 2021, NEJM), underscoring the importance of investigating the mechanisms behind durable immune responses. In this study, we aim to use single-cell RNA sequencing (scRNA-seq) to map the dynamic landscape of immune cell interactions following coronavirus infection. Using publicly available data from Ren et al. (2021, Cell), we will analyze gene expression at single-cell resolution to quantify the number and strength of immune cell interactions over the course of infection. By stratifying patients based on the number of days since disease onset, we will characterize how these interactions evolve during each phase of infection. Furthermore, by comparing immune responses between patients with moderate and severe disease, we will identify interaction patterns that may contribute to immune suppression or exacerbation. To better understand these processes, we will also construct a mathematical model to simulate immune cell dynamics—particularly focusing on T and B cells, which play a central role in antibody production—based on the scRNA-seq data. The findings from this research are expected to deepen our understanding of immune response mechanisms, guide the design of more effective vaccines, and inform the optimization of immunotherapies. Moreover, the dynamic modeling framework developed here could be adapted for other infectious diseases, thereby reinforcing the scientific foundation for future pandemic preparedness.