Background:The Coronavirus disease-19 (COVID-19) pandemic has posed a serious threat to global health. Thymosin α1 (Tα1) was considered to be applied in COVID-19 therapy. However, the data remains limited.
Methods:Participants with or without Tα1 treatment were recruited. Single cell RNA-sequencing (scRNA-seq) and T cell receptor-sequencing (TCR-seq) of the peripheral blood mononuclear cell (PBMC) samples were done to analyze immune features. The differential expression analysis and functional enrichment analysis were performed to explore the mechanism of Tα1 therapy.
Results:33 symptomatic SARS-CoV-2-infected individuals (COV) and 11 healthy controls (HC) were enrolled in this study. The proportion of CD3+ KLRD1+ NKT, TBX21+ CD8+ NKT was observed to increase in COVID-19 patients with Tα1 treatment (COVT) than those without Tα1 (COV) (p = 0.024; p = 0.010). These two clusters were also significantly higher in Health controls with Tα1 treatment (HCT) than those without Tα1 (HC) (p = 0.016; p = 0.031). Besides, a series of genes and pathways related to immune responses were significantly higher enriched in Tα1 groups TBX21+ CD8+ NKT, such as KLRB1, PRF1, natural killer cell-mediated cytotoxicity pathway, chemokine signaling pathway, JAK-STAT signaling pathway. The increased TRBV9-TRBJ1-1 pair existed in both HCs and COVID-19 patients after Tα1 treatment. 1389 common complementarity determining region 3 nucleotides (CDR 3 nt) were found in COV and HC, while 0 CDR 3 nt was common in COVT and HCT.
Conclusions:Tα1 increased CD3+ KLRD1+ NKT, TBX21+ CD8+ NKT cell proportion and stimulated the diversity of TCR clones in COVT and HCT. And Tα1 could regulate the expression of genes associated with NKT activation or cytotoxicity to promote NKT cells. These data support the use of Tα1 in COVID-19 patients.
Keywords:COVID-19; NKT cells; PBMC; Single-cell RNA sequencing; T cell receptor-sequencing; Thymosin α1.
Thymosin α1 modulated the immune landscape of COVID-19 patients revealed by single-cell RNA and TCR sequencing
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组织水平:空间多组学、多重荧光免疫组化、免疫组化、免疫荧光
数据分析:流式数据分析、组化数据分析、多因子数据分析
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