A subscription to JoVE is required to view this content. Sign in or start your free trial.
Research Article
* These authors contributed equally
Here, we present a protocol to identify endoplasmic reticulum stress-related immune signatures in atrial fibrillation by integrating public bulk transcriptomics, machine learning, immune infiltration analysis, and single-cell transcriptomics for reproducible biomarker prioritization and cell-type localization.
This study describes a reproducible computational workflow for identifying endoplasmic reticulum stress (ERS)-related gene signatures in atrial fibrillation (AF) by integrating bulk transcriptomics, machine learning, immune infiltration analysis, and single-cell transcriptomics. Public bulk transcriptomic datasets were retrieved from the Gene Expression Omnibus (GEO), followed by phenotype harmonization, normalization, batch-effect correction, and differential expression analysis. Weighted gene co-expression network analysis (WGCNA) was combined with ERS-related gene sets to identify candidate ERS-associated genes. A multi-algorithm machine-learning framework was then used to compare feature-selection and model-fitting strategies. The selected model was evaluated in an independent external validation cohort (GSE115574) and further assessed in an additional cohort (GSE14975), with discriminatory performance quantified by receiver operating characteristic (ROC) analysis and the area under the curve (AUC). Using this workflow, 22 ERS-related core genes were identified, and an 18-gene Elastic Net (Enet) model showed the highest overall discriminatory performance across the training and validation cohorts. SHapley Additive exPlanations (SHAP) analysis highlighted the major contribution of genes such as RPS11, NCF2, and S100A4 to model prediction. Immune deconvolution and single-cell transcriptomic analysis further mapped the ERS-related signature predominantly to the monocyte-macrophage lineage, suggesting its potential involvement in AF-associated immune remodeling. This workflow provides a reproducible strategy for linking disease-associated transcriptomic signatures to specific immune cell populations and can be adapted to other disease contexts with suitable bulk and single-cell datasets.
Atrial fibrillation (AF) is the most common sustained arrhythmia in clinical practice, characterized by disordered electrical activity in the atria and loss of mechanical function. It significantly increases the risks of stroke, heart failure, and all-cause mortality, becoming a major global public health burden1. Current clinical management of AF faces significant challenges: traditional antiarrhythmic drugs have limited long-term efficacy in maintaining sinus rhythm, and their adverse effects, such as arrhythmias and cardiotoxicity, limit their continuous use2,3. Catheter ablation als....
Access restricted. Please log in or start a trial to view this content.
In accordance with the Measures for Ethical Review of Life Science and Medical Research Involving Human Subjects promulgated in China on February 18, 2023, research using publicly available data may meet the criteria for exemption from ethical review. This study used only publicly available, de-identified secondary transcriptomic data and did not involve new human participant recruitment, human sample collection, or animal experiments. Therefore, additional institutional ethical approval was not required. No animal experiments were performed in this study. Therefore, approval from the institutional animal care and use committee was not applicable.
Access restricted. Please log in or start a trial to view this content.
Identification of differentially expressed genes in AF
To improve comparability across cohorts, two AF-related transcriptomic datasets (GSE41177 and GSE79768) were integrated, and batch effects were corrected on the merged expression matrix. Figure 2A,B shows boxplots of global expression distributions before and after batch-effect correction, which were used to evaluate whether expression intensity distributions were comparable across samples.
Access restricted. Please log in or start a trial to view this content.
This study integrated bulk and single-cell transcriptomes to investigate the role of ERS in AF. An ERS-related gene signature with favorable cross-cohort discrimination was derived, and these signals were mapped predominantly to the monocyte-macrophage lineage and were associated with extensive intercellular communication. Collectively, the findings suggest that ERS-associated programs are coupled to immune-cell-centric remodeling in AF. Compared with bulk-only biomarker screening, this workflow provides a multi-layered .......
Access restricted. Please log in or start a trial to view this content.
The authors report no conflicts of interest in this work. During the revision of this manuscript, ChatGPT from OpenAI was used only to assist with English-language polishing. The authors reviewed, verified, and edited all AI-assisted text and take full responsibility for the accuracy and integrity of the final manuscript. No AI-assisted tools were used to generate research ideas, perform data analysis, interpret results, create figures or tables, or draw scientific conclusions. The work reported in the article has been performed by the authors. F.T, PR.W: Writing-original draft, Software, Methodology,Visualization, Validation, and Data curation. SY.T: Investigation and Methodology. FF.B: Supervision. QR.L: Supervision, Methodology, Data curation. XY.J, YX.X: Literature retrieval and data interpretation.
| Name | Company | Catalog Number | Comments |
|---|---|---|---|
| AddModuleScore | Seurat function | version 4.4.0 | NA |
| AUCell | Bioconductor | version 1.32.0 | RRID:SCR_021327 |
| caret | CRAN | version 7.0.1 | RRID:SCR_022524 |
| celda / decontX | Bioconductor | version 1.24.0 | NA |
| CellChat | GitHub / CellChat | version 2.2.0 | RRID:SCR_021946 |
| CIBERSORT / LM22 signature matrix | CIBERSORT | LM22 | NA |
| clusterProfiler | Bioconductor | version 4.12.6 | RRID:SCR_016884 |
| Cytoscape | Cytoscape Consortium | version 3.10 | RRID:SCR_003032 |
| DoubletFinder | GitHub / McGinnis Lab | version 2.0.4 | NA |
| e1071 | CRAN | version 1.7.16 | NA |
| gbm | CRAN | version 2.2.2 | NA |
| Gene Expression Omnibus (GEO) database | National Center for Biotechnology Information (NCBI) | GSE41177 | NA |
| Gene Expression Omnibus (GEO) database | NCBI | GSE79768 | NA |
| Gene Expression Omnibus (GEO) database | NCBI | GSE115574 | NA |
| Gene Expression Omnibus (GEO) database | NCBI | GSE14975 | NA |
| Gene Expression Omnibus (GEO) database | NCBI | GSE165838 | NA |
| glmnet | CRAN | version 4.1.8 | NA |
| Harmony | CRAN | version 1.2.4 | NA |
| limma | Bioconductor | version 3.60.6 | RRID:SCR_010943 |
| MASS | CRAN | version 7.3.61 | NA |
| mboost | CRAN | version 2.9.11 | NA |
| Monocle | Bioconductor | version 2.38.0 | RRID:SCR_016339 |
| org.Hs.eg.db | Bioconductor | version 3.19.1 | NA |
| plsRglm | CRAN | version 1.5.1 | NA |
| pROC | CRAN | version 1.18.5 | RRID:SCR_024286 |
| R statistical software | R Foundation for Statistical Computing | version 4.4.2 | RRID:SCR_001905 |
| randomForest | CRAN | version 4.7.1.2 | RRID:SCR_015718 |
| RStudio | Posit Software, PBC | version 2024.4.1.748 | RRID:SCR_000432 |
| Seurat | CRAN / Satija Lab | version 4.4.0 | RRID:SCR_016341 |
| shapviz | CRAN | version 0.10.2 | NA |
| sva | Bioconductor | version 3.52.0 | NA |
| WGCNA | CRAN | version 1.73 | RRID:SCR_003302 |
| xgboost | CRAN | version 1.7.8.1 | NA |
Access restricted. Please log in or start a trial to view this content.