Xiaosan Chen ^* Zhidong Zhang Gang Qiao Zhigang Sun Wei Lu

• Fuwai Central China Cardiovascular Hospital, Zhengzhou, China

Abstract Background: Research into the immunological heterogeneity associated with atherosclerosis is limited, resulting in an insufficient theoretical foundation for the development of personalized immune therapies targeting this condition. Methods: Single-cell sequencing (scRNA-seq) analysis was employed to delineate the immune cell-type landscape within atherosclerotic plaques, followed by assessments of cell-cell interactions and phenotype characteristics using the scRNA-seq datasets. Subsequently, pseudotime trajectory analysis was utilized to unravel the heterogeneity in cell fate and differentiation among macrophages. Through integrated approaches encompassing single-cell sequencing, Weighted Gene Co-expression Network Analysis (WGCNA), and machine learning techniques, we identified hallmark genes. We developed and validated a risk score model and a corresponding nomogram utilizing these genes, confirmed through Receiver Operating Characteristic (ROC) curve analysis. Additionally, our study incorporated enrichment and immune characteristic analyses predicated on the risk score model. The model's applicability was further corroborated by in vitro and in vivo validation of a distinct gene implicated in atherosclerosis. Result: This comprehensive scRNA-seq analysis has shed new light on the intricate immune landscape and the role of macrophages in atherosclerotic plaques. Our results highlighted the presence of diverse immune cell populations, particularly enriched macrophage. Macrophage heterogeneity was intricately characterized, presenting four distinct subtypes with varying functional attributes that underscore their complex role in atherosclerotic pathology. Intercellular communication analysis revealed robust macrophage interactions with multiple cell types and detailed pathways that differ between proximal adjacent and atherosclerotic core groups. Furthermore, pseudotime trajectories have charted the developmental course of macrophage subpopulations, offering insights into their differentiation fates within the plaque microenvironment. The usage of machine learning to sieve out potential diagnostic markers, culminating in identifying RNASE1 and CD14. The riskScore model based on these biomarkers exhibited high accuracy in diagnosing atherosclerosis. Immune characteristic analysis validated the riskScore model's efficacy in defining patient profiles, distinguishing high-risk individuals with pronounced immune cell activities. Finally, experimental validation affirmed RNASE1's involvement in atherosclerotic progression, intimating its potential as a therapeutic target. Conclusion: Our findings have advanced our understanding of atherosclerosis immunopathology and paved the way for novel diagnostic and therapeutic strategies. Keywords: atherosclerotic plaques, single-cell sequencing, macrophages, immuno-inflammatory responses, riskScore model