| Objective: During the development of COVID-19 caused by SARS-Co V-2 infection from mild disease to severe disease,it can trigger a series of complications and stimulate a strong cellular and humoral immune response.It has been reported that immune cells play a key role in the occurrence and development of COVID-19.However,the precise identification of blood immune cell response dynamics and the relevance to disease progression in COVID-19 patients remains unclear.Further understanding of the mechanisms of immune cell action during disease progression in COVID-19 patients can provide new therapeutic directions for COVID-19 treatment.In this study,we analyzed a dynamic change of immune cells in the peripheral blood of COVID-19 patients with the aggravation of the patient’s disease from the single-cell transcriptome level.In addition,unlike previous cell nomenclature,for the first time,we used the quantitative dynamics of immune cell responses to viruses during disease development to name cells,KEGG and GO analysis was performed by finding unique genes and common genes in each subgroup.And find out the mechanisms of antiviral immune regulation by different immune cell types in COVID-19 patients during the disease progression.Finally,we also combined relevant nucleic acid samples to validate the differential expression of relevant pathway genes in COVID-19 patients,and to explore their clinical implications in COVID-19 treatment.Methods: We investigated the dynamic changes in blood immune cell responses in healthy,mild,and severe patients at the single-cell transcriptome level.First,peripheral blood single-cell transcriptome data of COVID-19 patients were downloaded from the GEO database,and data from different platforms were screened and integrated.Then,the data were quality controlled by Cell Ranger upstream data analysis software.Using the Seurat R package to perform downstream data quality control dimensionality reduction and clustering on single-cell data,and use Seurat’s default Wilcox method to identify significantly highly expressed differentially expressed genes,and then identify cell types based on cell type-specific expression marker genes.Next,we counted the proportion of each cell and found that T cells and NK cells accounted for the largest proportion.We conducted a subgroup analysis of T cells and NK cells.Different from the previous subgroup cell nomenclature,we propose for the first time to use changes in cell numbers to establish new subgroups,which were divided into four groups: first from high to low cell number(H＿L＿Group),first from low to high(L＿H＿Group),continuously high(H＿Group),and continuously low(L＿Group).Next,we used the Cluster Profiler R package for studying the pathway enrichment analysis of GO and KEGG,and we performed GSEA analysis for single-cell subgroups and metabolic pathway activity analysis of cell subgroups.Finally,we obtained nucleic acid samples of COVID-19 patients and healthy people from the Luzhou Center for Disease Control and Prevention and validate the expression levels of related genes by q PCR.Results: It was found that in the course of disease development.We found that T cells increased first and then decreased,while NK cells continued to decrease.In the T cell subgroup,the immune response is mainly concentrated in the H＿L＿Group cell type,and the complications are mainly in the L＿H＿Group cell type.In the NK cell subgroup,the moderate patients are mainly related to cellular immunity,and the severe patients are mainly caused by the disease,while severe patients are mainly related to complications caused by diseases.It is also worth noting that changes in oxidative phosphorylation were the main contributor to metabolic heterogeneity.Conclusion: In summary,our study provides a dynamic response of immune cells in human blood during SARS-Co V-2 infection and the first subgroup analysis using dynamic changes in cell numbers,providing a new direction for clinical treatment of COVID-19. |
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