Related Experiment Video
Updated: May 26, 2026
Isolation of Endothelial Cells from the Lumen of Mouse Carotid Arteries for Single-Cell Multi-Omics Experiments
Published on: October 4, 2021
Multi-Omics and Machine Learning Integration Identifies Key Endothelial Modules in Carotid Artery Stenosis
Jiayi Wu1, Chunguang Guo2, Linfeng Zhang3
1Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Pathology, Peking University Cancer Hospital and Institute, Beijing, China.
Background:
Carotid artery stenosis (CAS) is a major cause of ischemic stroke, yet reliable molecular biomarkers for early identification remain limited.
Methods:
We integrated single-cell RNA sequencing (scRNA-seq), bulk transcriptomics, and in-house multi-omics data, applying WGCNA and machine learning to identify endothelial cell-derived diagnostic biomarkers, validated across independent GEO and ZZ cohorts at single-cell, transcriptomic, and proteomic levels.
Results:
scRNA-seq identified endothelial enrichment in CAS and yielded 836 markers. Integrative analysis (DEGs + WGCNA) defined 80 candidates, with NRP1 and XAF1 selected by machine learning. Both were consistently upregulated and validated across multi-omics datasets, showing strong diagnostic performance.
Conclusion:
NRP1 and XAF1 represent novel endothelial cell-derived biomarkers with potential utility for early CAS screening and clinical diagnosis.
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