Shaoxiong Jin Huazhi Zhang Qingjiang Lin Jinfeng Yang Rongyao Zeng Zebo Xu Wendong Sun ^*

• The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian Province, China

Background: Metabolic dysregulation following sepsis can significantly compromise patient prognosis by altering immune-inflammatory responses. Despite its clinical relevance, the exact mechanisms of this perturbation are not yet fully understood.Methods: Single-cell RNA sequencing (scRNA-seq) was utilized to map the immune cell landscape and its association with metabolic pathways during sepsis. This study employed cell-cell interaction and phenotype profiling from scRNA-seq data, along with pseudotime trajectory analysis, to investigate neutrophil differentiation and heterogeneity. By integrating scRNA-seq with Weighted Gene Co-expression Network Analysis (WGCNA) and machine learning techniques, key genes were identified. These genes were used to develop and validate a risk score model and nomogram, with their efficacy confirmed through Receiver Operating Characteristic (ROC) curve analysis. The model's practicality was further reinforced through enrichment and immune characteristic studies based on the risk score and in vivo validation of a critical gene associated with sepsis.The complex immune landscape and neutrophil roles in metabolic disturbances during sepsis were elucidated by our in-depth scRNA-seq analysis.Pronounced neutrophil interactions with diverse cell types were revealed in the analysis of intercellular communication, highlighting pathways that differentiate between proximal and core regions within atherosclerotic plaques. Insight into the evolution of neutrophil subpopulations and their differentiation within the plaque milieu was provided by pseudotime trajectory mappings. Diagnostic markers were identified with the assistance of machine learning, resulting in the discovery of PIM1, HIST1H1C, and IGSF6. The identification of these markers culminated in the development of the risk score model, which demonstrated remarkable precision in sepsis prognosis. The model's capability to categorize patient profiles based on immune characteristics was confirmed, particularly in identifying individuals at high risk with suppressed immune cell activity and inflammatory responses. The role of PIM1 in modulating the immuneinflammatory response during sepsis was further confirmed through experimental validation, suggesting its potential as a therapeutic target.The understanding of sepsis immunopathology is improved by this research, and new avenues are opened for novel prognostic and therapeutic approaches.