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Updated: Jan 11, 2026
Electrophysiological Assessment of Murine Atria with High-Resolution Optical Mapping
Published on: February 22, 2018
Integrated bulk sequencing and single-cell transcriptomic profiling implicates neutrophil-driven hypoxia in atrial
Huanjie Huang1, Huai Wang1, Yaozong Guan1
1Department of Cardiology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, China.
Background:
The precise role of inflammatory cells in atrial fibrillation (AF) pathogenesis remains incompletely understood. This study sought to characterize the inflammatory infiltration landscape distinguishing AF from sinus rhythm (SR) patients and to establish a machine learning-based diagnostic model.
Methods:
GSE79768 and GSE41177 datasets were retrieved from public database (Gene Expression Omnibus, https://www.ncbi.nlm.nih.gov/geo/). Following batch effect correction, differentially expressed genes (DEGs) between AF and SR patients were identified. Functional enrichment analysis was subsequently performed on these DEGs. Immune cell infiltration patterns in AF versus SR samples were characterized using CIBERSORT, single-sample gene set enrichment analysis (ssGSEA), and xCell algorithms. Weighted gene co-expression network analysis (WGCNA) was employed to identify co-expression modules associated with neutrophil infiltration, from which neutrophil-related genes (NRGs) were derived. A predictive support vector machine (SVM) classifier was initially developed based on the intersection between DEGs and NRGs. Ultimately, a single-cell transcriptome dataset (GSE224959) was utilized to delineate neutrophil-mediated mechanisms underlying AF pathogenesis.
Results:
We identified 334 DEGs between 46 AF samples and 18 SR samples. Enrichment analysis revealed these DEGs were significantly associated with cytokine signaling, neutrophil activation, and chemotaxis. Immune infiltration analysis demonstrated marked enrichment of inflammatory cells, particularly neutrophils, in AF samples. A SVM-based predictive model, constructed from the intersection of 334 DEGs and 401 NRGs, exhibited strong discriminative power in the training set [area under the curve (AUC) =1.0] and validation set (AUC =0.969). Single-cell RNA sequencing further indicated that neutrophil accumulation in AF may promote arrhythmogenesis by inducing a hypoxic microenvironment.
Conclusions:
Our integrative analysis implicates neutrophil-driven hypoxia in AF pathogenesis, while the neutrophil-based diagnostic model demonstrates robust AF and SR discrimination. However, the validity of our findings awaits confirmation through in vivo, in vitro, and external validation studies.
