Time-series transcriptome analysis of peripheral blood mononuclear cells obtained from individuals who received the...

Messenger ribonucleic acid (mRNA) vaccination against coronavirus disease 2019 (COVID-19) is an effective prevention strategy, despite a limited understanding of the molecular mechanisms underlying the host immune system and individual heterogeneity of the variable effects of mRNA vaccination. We assessed the time-series changes in the comprehensive gene expression profiles of 200 vaccinated healthcare workers by performing bulk transcriptome and bioinformatics analyses, including dimensionality reduction utilizing the uniform manifold approximation and projection (UMAP) technique. For these analyses, blood samples, including peripheral blood mononuclear cells (PBMCs), were collected from 214 vaccine recipients before vaccination (T1) and on Days 22 (T2, after second dose), 90, 180 (T3, before a booster dose), and 360 (T4, after a booster dose) after receiving the first dose of BNT162b2 vaccine (UMIN000043851). UMAP successfully visualized the main cluster of gene expression at each time point in PBMC samples (T1-T4). Through differentially expressed gene (DEG) analysis, we identified genes that showed fluctuating expression levels and gradual increases in expression levels from T1 to T4, as well as genes with increased expression levels at T4 alone. We also succeeded in dividing these cases into five types based on the changes in gene expression levels. High-throughput and temporal bulk RNA-based transcriptome analysis is a useful approach for inclusive, diverse, and cost-effective large-scale clinical studies.