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<p>Carotid and femoral plaques exhibit varying degrees of stability; however, the relationships of different genes/cell types with plaque embolism are poorly understood. We evaluated differential gene/cell expression and investigated the cells/genes associated with carotid and femoral artery plaque embolism. sc-RNA-seq and bulk RNA data were obtained to identify differentially expressed genes (DEGs). Seven machine learning models were trained, and the top 10 DEGs across all models were selected. The most disturbed cells in carotid and femoral artery plaques were identified using Augur, while the genes and cells in the carotid plaque associated with embolism were analyzed through scPagwas. The differences in most disturbed cells and embolism-related cells were further analyzed. Compared with femoral plaques, carotid plaques had 80 downregulated and 90 upregulated genes. Machine learning identified the key DEGs between carotid and femoral plaques were predominantly from the HOX gene family. Natural Killer (NK) cells were the most significantly disturbed cells between carotid and femoral plaques, and they may be most strongly associated with plaque embolism. Among the differential genes in NK cells, <i>CD2</i> was most associated with embolism. Our research may offer new insights into atherosclerosis at different locations.</p>

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Single-cell transcriptome integrated with genome-wide association study reveals heterogeneity of carotid and femoral plaques and its association with plaque stability

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年4月7日

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  • Xinhuang Hou,
  • Zhipeng Li,
  • Jun Lin,
  • Wei Lin,
  • Luyao Li,
  • Xiaoqi Zheng,
  • Xiaoling Lai,
  • Lin Zhu,
  • Pingfan Guo,
  • Fanggang Cai,
  • Jinchi Zhang,
  • Wanglong Li,
  • Changwei Yang,
  • Yiquan Dai

摘要

Carotid and femoral plaques exhibit varying degrees of stability; however, the relationships of different genes/cell types with plaque embolism are poorly understood. We evaluated differential gene/cell expression and investigated the cells/genes associated with carotid and femoral artery plaque embolism. sc-RNA-seq and bulk RNA data were obtained to identify differentially expressed genes (DEGs). Seven machine learning models were trained, and the top 10 DEGs across all models were selected. The most disturbed cells in carotid and femoral artery plaques were identified using Augur, while the genes and cells in the carotid plaque associated with embolism were analyzed through scPagwas. The differences in most disturbed cells and embolism-related cells were further analyzed. Compared with femoral plaques, carotid plaques had 80 downregulated and 90 upregulated genes. Machine learning identified the key DEGs between carotid and femoral plaques were predominantly from the HOX gene family. Natural Killer (NK) cells were the most significantly disturbed cells between carotid and femoral plaques, and they may be most strongly associated with plaque embolism. Among the differential genes in NK cells, CD2 was most associated with embolism. Our research may offer new insights into atherosclerosis at different locations.