bioRxivImmunologyPreprint
Integrated single-cell sequencing analysis reveals peripheral immune landscape across the human lifespan
R. Tong, R. Li, Y. Zhao, L. Luo, J. Zhong, Z. Li, L. Wei, Y. Chen, J. Shi, Y. Gao, M. Sun, Y. Wang, Y. Lyu, A. Yuan, L. SuniD, Y. Guo, H. Wang, H. Chen, L. Chen, B. Li, W. Lin, F. Wang, L. Wang, J. Pu
Abstract
Systematic understanding of immune dynamics across the entire human lifespan at single-cell resolution is currently lacking. Here, we performed single-cell RNA sequencing (scRNA-seq) and single-cell T cell receptor (TCR)/B cell receptor (BCR) sequencing (scTCR/BCR-seq) on over 380,000 peripheral blood mononuclear cells collected from 45 healthy participants aged 0 to over 90 years. We revealed that the functions of T cell subsets were most susceptible to senescence among all PBMCs, featured by increased NF-{kappa}B signaling and IFN-{gamma} responses pathways, while reduced telomere maintenance and energy metabolism. We subsequently explored the rewiring of cell-cell interactions among different immune cells across the lifespan and revealed the major alteration of immune checkpoints in T cells within the cellular interaction networks. By combined analysis of scRNA-seq and scTCR-seq, we revealed that 1) GNLY+ CD8 Effector T cells exhibited a high clonal expansion with distinct functional signatures in children and the elderly; 2) Naive CD4+ T and naive CD8+ T cells displayed different aging patterns in both transcriptomes and immune repertoires; and 3) CD8+ MAIT cells showed a higher cell abundance and clonal diversity in adolescents. Furthermore, we identified a unique cytotoxic B cell subset enriched in children by scRNA-seq and scBCR-seq analysis and experimental validations. In summary, our work provided valuable insights and rich resources for understanding the development and aging of the human immune system across the lifespan.
- organism: human
- assay: scRNA-seq
- assay: scATAC-seq
- research focus: cell type annotation
- research focus: clustering
- research focus: differential expression
- approach: deep learning
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