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Objective: Using single cell sequencing to analyze the characteristics of the immune repertoire of SARS-CoV-2 patients at different infection stages, and to explore the possible pathogenesis. Methods: Obtaining data in the NCBI database, and healthy control, progression and convalescence groups were set up. Analysis of single cell sequencing data by RStudio, Origin, Hipliot and Excel software. Results: TCR plays an important role in antigen recognition and virus clearance. Characteristics of the three immune repertoires are very different. The number of clones exceeded 26.92%. The length distribution of α chain CDR3 was concentrated on 14 amino acids, while β-chain was concentrated on 15 amino acids. The frequency of gene fragments in V and J regions were different, and the frequency of TRAV13-1, TRAJ20, TRBV20-1 and TRBJ2-1 were statistically significant (P<0.05). Further V-J gene combination analysis showed that there were significant differences in the frequency of single chain V-J between the control group and the progression group, the control group and the rehabilitation group (P<0.05). The highest frequency of αβ double chain V-J in the control group was TRAV19-J34-TRBV4-1-J2-1, the highest frequency of αβ double-stranded V-J in the progression group was TRAV12-1-J30-TRBV19-1-J2-1, and the highest frequency of αβ double chain V-J in the convalescence group was TRAV12-2-J52-TRBV7-9-J1-5. Conclusion: This study analyzes the global characteristics of T cells in the immune repertoire, which is helpful to understand the pathogenesis of SARS-CoV-2 patients, timely and effective treatment of patients in the early stages of infection. [ABSTRACT FROM AUTHOR]

Objective: To analyze the differentially expressed genes (DEGs) and immune cell infiltration patterns in hypertensive nephropathy (HN). Methods: Downloaded gene chip dataset of HN and healthy human renal tubulointerstitial tissue samples from GEO database, and used R software to analyze DEGs. Metascape online analysis platform was used to analyze functional enrichment of DEGs. Three machine learning algorithms, LASSO, SVM-RFE and RF, were used to screen feature genes and verify the diagnostic value. Finally, CIBERSORT algorithm was used to analyze immune cell infiltration. Results: Total 277 DEGs were obtained. Functional enrichment analysis showed that they were related to many immune-related processes, such as innate immune response, humoral immune response and response to cytokines. Three characteristic genes, CISH, GADD45A and ZFP36, were obtained by machine learning algorithm, which had good diagnostic value. Immune cell infiltration analysis showed that Tregs and macrophages M1 infiltrated more. And there was a correlation among many kinds of immune cells. Conclusion: Pathogenesis of HN may be related to a variety of immune-related processes, Tregs and macrophages M1 may play a key role in pathogenesis of HN. CISH, GADD45A and ZFP36 are potential diagnostic markers of HN. [ABSTRACT FROM AUTHOR]