Single-Cell Transcriptome Profiling Reveals Neutrophil Heterogeneity and Functional Multiplicity in the Early Stage of Severe Burn Patients
The pathophysiological mechanisms, especially the roles of immune cells, underlying early stages of severe burn injury have not yet been fully clarified. Here, we analyzed circulating neutrophils (PMNs) in healthy donors and early burned patients by single-cell RNA sequencing to provide a comprehensive transcriptional landscape of PMNs in heterogeneity and functional multiplicity. Circulating PMNs in the healthy donors and burned groups were divided into five subgroups (G3, G4, G5a, G5b, G5c) with different functions. The dominant subsets of PMNs in homeostasis and burn injury significantly differed between groups. In addition, cells in the same subpopulation had the same core identity markers but performed different functions in healthy and burned states. Under burned conditions, PMN activation was very evident and accompanied by clear degranulation and metabolic abnormalities. Interestingly, was found that PMN activation, degranulation, chemotaxis, phagocytosis and reactive oxygen species (ROS) production in burned patients significantly differed between day 1 and days 2 or 3, thus providing a theoretical basis for PMN interventions in early burn stages. Significantly, previously undescribed transcription factors were also identified, including ZNF-787, ZNF-467, ZNF-189, ZNF-770, ZNF-262. In conclusion, this study conducted for the first time a detailed analysis of the heterogeneity and functional multiplicity of PMNs in early stages of severe burn injuries. Our findings attempted to clarify the influence of PMN heterogeneity on the pathophysiology and related mechanisms of burn injuries, which can provide new ideas for further research in burn intervention. Keywords: burn; function; heterogeneity; neutrophil; single-cell transcriptome profiling.
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基因水平:PCR Array、RT-PCR、PCR、单细胞测序
蛋白水平:MSD、Luminex、CBA、Elispot、Antibody Array、ELISA、Sengenics
细胞水平:细胞染色、细胞分选、细胞培养、细胞功能
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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