Deep Learning and Single‐Cell Sequencing Analyses Unveiling Key Molecular Features in the Progression of Carotid Atherosclerotic Plaque

• Published in: Journal of cellular and molecular medicine 2024-11, Vol.28 (22), p.e70220-n/a
• Main Authors: Zhang, Han, Wang, Yixian, Liu, Mingyu, Qi, Yao, Shen, Shikai, Gang, Qingwei, Jiang, Han, Lun, Yu, Zhang, Jian
• Format: Article
• Language: English
• Subjects: Aged; Algorithms; Arteriosclerosis; Artificial intelligence; Atherosclerosis; Bioinformatics; Biomarkers; Carotid Artery Diseases - genetics; Carotid Artery Diseases - metabolism; Carotid Artery Diseases - pathology; carotid atherosclerotic plaques; Datasets; Deep Learning; diagnostic biomarker; Disease; Disease Progression; Female; Gene expression; Gene Expression Profiling - methods; Humans; Infiltration; Inflammation; Ischemia; Ligands; Machine learning; Male; Metabolism; Microenvironments; Middle Aged; Monocytes; Monocytes - metabolism; Monocytes - pathology; Morbidity; Neural networks; Original; Plaque, Atherosclerotic - genetics; Plaque, Atherosclerotic - pathology; Plaques; Sensitivity analysis; Sequence Analysis, RNA - methods; Single-Cell Analysis - methods; single‐cell; Tumor necrosis factor-TNF
• ISSN: 1582-1838; 1582-4934; 1582-4934
• DOI: 10.1111/jcmm.70220

ABSTRACT Rupture of advanced carotid atherosclerotic plaques increases the risk of ischaemic stroke, which has significant global morbidity and mortality rates. However, the specific characteristics of immune cells with dysregulated function and proven biomarkers for the diagnosis of atherosclerotic plaque progression remain poorly characterised. Our study elucidated the role of immune cells and explored diagnostic biomarkers in advanced plaque progression using single‐cell RNA sequencing and high‐dimensional weighted gene co‐expression network analysis. We identified a subcluster of monocytes with significantly increased infiltration in the advanced plaques. Based on the monocyte signature and machine‐learning approaches, we accurately distinguished advanced plaques from early plaques, with an area under the curve (AUC) of 0.899 in independent external testing. Using microenvironment cell populations (MCP) counter and non‐negative matrix factorisation, we determined the association between monocyte signatures and immune cell infiltration as well as the heterogeneity of the patient. Finally, we constructed a convolutional neural network deep learning model based on gene‐immune correlation, which achieved an AUC of 0.933, a sensitivity of 92.3%, and a specificity of 87.5% in independent external testing for diagnosing advanced plaques. Our findings on unique subpopulations of monocytes that contribute to carotid plaque progression are crucial for the development of diagnostic tools for clinical diseases.