Single-cell Characterization Of The Immune Microenvironment Of Abdominal Aortic Aneurysm
Description
Background: Abdominal aortic aneurysm (AAA), characterized by localized dilation of the abdominal aorta, involves an immune microenvironment that plays a crucial role in disease development. This study aimed to investigate immune cell heterogeneity and functional dynamics in AAA using single-cell RNA sequencing (scRNA-seq) analysis of AAA content and healthy aorta samples. Methods: By analyzing scRNA-seq datasets from AAA content and healthy aorta samples, along with bulk datasets comprising AAA and healthy aortic tissues, we comprehensively profiled the immune microenvironment. Results: The analysis revealed five major cell types in both AAA contents and healthy aortic tissues. Further integrative analysis identified 18 distinct subpopulations, unraveling specific immune cell subtypes within AAA. These subtypes were distinguished based on tissue infiltration, functional variations, immune dynamics, metabolic changes, and communication patterns. Notably, B cells and plasma cells were found to contribute to AAA and thrombus formation. Additionally, cytotoxic CD8+ T cells showed enrichment in cytokine and chemokine receptor interactions, indicating their involvement in AAA development. Two pro-inflammatory macrophage subtypes (LY6E+ and CCL5+) and two pro-inflammatory neutrophil subtypes specific to AAA emerged as significant findings. Particularly, MME+ neutrophils exhibited heightened immune receptor activity and infiltrated AAA tissues, as supported by bulk dataset analysis. Metabolically active, these neutrophils played a vital role in regulating CXCL signaling, enabling effective cell-to-cell communication among immune cells. Conclusions: This study provides a comprehensive understanding of the diverse immune cell populations within AAA and healthy aorta tissues. The findings shed light on their functional characteristics, developmental trajectories, metabolic differences, and intercellular communication patterns, significantly advancing our knowledge of AAA formation and progression.
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The raw reads underwent initial processing using fastQC and fastp to eliminate low-quality reads. Removal of poly-A tails and adaptor sequences was carried out using cutadapt. Following quality control, the reads were aligned to the reference genome GRCh38 through the utilization of STAR. The featureCounts software was employed to obtain gene counts and UMI counts. To facilitate further analysis, expression matrix files were created using the obtained gene counts and UMI counts.