Analysis of immunogenic cell death in atherosclerosis based on scRNA-seq and bulk RNA-seq data

Significance of neutrophil extracellular traps-related gene in the diagnosis and classification of atherosclerosis

Atherosclerosis (AS) is a pathological process associated with various cardiovascular diseases. Upon different stimuli, neutrophils release reticular complexes known as neutrophil extracellular traps (NETs). Numerous researches have indicated a strong correlation between NETs and AS. However, its role in cardiovascular disease requires further investigation. By utilizing a machine learning algorithm, we examined the genes associated with NETs that were expressed differently in individuals with AS compared to normal controls. As a result, we identified four distinct genes. A nomogram model was built to forecast the incidence of AS. Additionally, we conducted analysis on immune infiltration, functional enrichment and consensus clustering in AS samples. The findings indicated that individuals with AS could be categorized into two groups, exhibiting notable variations in immune infiltration traits among the groups. Furthermore, to measure the NETs model, the principal component analysis algorithm was developed and cluster B outperformed cluster A in terms of NETs. Additionally, there were variations in the expression of multiple chemokines between the two subtypes. By studying AS NETs, we acquired fresh knowledge about the molecular patterns and immune mechanisms implicated, which could open up new possibilities for AS immunotherapy.

Single cells and TRUST4 reveal immunological features of the HFRS transcriptome

The etiology of hemorrhagic fever with renal syndrome (HFRS) is significantly impacted by a variety of immune cells. Nevertheless, the existing techniques for sequencing peripheral blood T cell receptor (TCR) or B cell receptor (BCR) libraries in HFRS are constrained by both limitations and high costs. In this investigation, we utilized the computational tool TRUST4 to generate TCR and BCR libraries utilizing comprehensive RNA-seq data from peripheral blood specimens of HFRS patients. This facilitated the examination of clonality and diversity within immune libraries linked to the condition. Despite previous research on immune cell function, the underlying mechanisms remain intricate, and differential gene expression across immune cell types and cell-to-cell interactions within immune cell clusters have not been thoroughly explored. To address this gap, we performed clustering analysis on 11 cell subsets derived from raw single-cell RNA-seq data, elucidating characteristic changes in cell subset proportions under disease conditions. Additionally, we utilized CellChat, a tool for cell-cell communication analysis, to investigate the impact of MIF family, CD70 family, and GALECTIN family cytokines-known to be involved in cell communication-on immune cell subsets. Furthermore, hdWGCNA analysis identified core genes implicated in HFRS pathogenesis within T cells and B cells. Trajectory analysis revealed that most cell subsets were in a developmental stage, with high expression of transcription factors such as NFKB and JUN in Effector CD8+ T cells, as well as in Naive CD4+ T cells and Naive B cells. Our findings provide a comprehensive understanding of the dynamic changes in immune cells during HFRS pathogenesis, identifying specific V genes and J genes in TCR and BCR that contribute to advancing our knowledge of HFRS. These insights offer potential implications for the diagnosis and treatment of this autoimmune disease.