Related Experiment Video
Updated: Aug 14, 2026
Preparation of a Non-Cardiomyocyte Cell Suspension for Single-Cell RNA Sequencing from a Post-Myocardial Infarction Adult Mouse Heart
Published on: February 3, 2023
Single-cell RNA sequencing pseudobulk analysis and machine learning identify candidate biomarkers for ischemic
Xianhua Ye1, Guoxiang Wu1, Jialan Xie2
1Department of Cardiology, Nanping First Hospital affiliated to Fujian Medical University, Nanping, Fujian, China.
Insights
Researchers identified four key genes (GFOD1, MLLT3, COLEC12, RARRES1) and two microRNAs (miR-195-5p, miR-5680) as promising biomarkers for ischemic cardiomyopathy (ICM). These findings offer potential for improved diagnosis and treatment strategies for ICM.
Area of Science:
- Cardiovascular Biology
- Molecular Biology
- Genomics
Background:
- Ischemic cardiomyopathy (ICM) results from reduced blood flow to the heart, causing myocardial damage and impaired cardiac function.
- Identifying reliable biomarkers is crucial for understanding ICM mechanisms and developing targeted therapies.
Purpose of the Study:
- To identify novel biomarkers and elucidate regulatory networks associated with ischemic cardiomyopathy (ICM).
- To provide a foundation for further research into ICM pathogenesis and therapeutic interventions.
Main Methods:
- Analysis of single-cell RNA sequencing (scRNA-seq) data to identify cell subpopulations and differentially expressed genes (DEGs).
- Application of machine learning algorithms with Boruta feature selection to identify key disease-characteristic genes from an external ICM dataset.
- Reconstruction of regulatory networks by predicting transcription factors (TFs) and microRNAs (miRNAs).
- Validation of identified biomarkers in an *in vivo* rat model of ICM.
Main Results:
- Four hub genes (MLLT3, GFOD1, COLEC12, RARRES1) were identified as key signatures for ICM, with a combined signature achieving an AUC of 93.0%.
- CTCF was identified as a shared transcription factor regulating these genes.
- Computational analysis predicted 329 miRNAs, with miR-195-5p and miR-5680 showing significant differential expression.
- *In vivo* validation confirmed reduced expression of signature genes and CTCF, and elevated levels of miR-195-5p and miR-5680 in ICM samples.
Conclusions:
- GFOD1, MLLT3, COLEC12, RARRES1, miR-195-5p, and miR-5680 are proposed as promising biomarkers for ischemic cardiomyopathy.
- CTCF is identified as a potential regulatory transcription factor in ICM.
- These findings highlight novel molecular targets for ICM diagnosis and therapy.
Background:
Ischemic cardiomyopathy (ICM) is a condition characterized by inadequate blood supply to the coronary arteries, resulting in myocardial damage and decreased cardiac functionality. This study aimed to identify potential biomarkers and regulatory networks in ICM, providing a foundation for further mechanistic and therapeutic investigations.
Methods:
We analyzed public single-cell RNA sequencing (scRNA-seq) data to identify cell subpopulations through dimensionality reduction clustering followed by manual annotation. Differentially expressed genes (DEGs) were derived using the pseudobulk method. Subsequently, we employed three machine learning algorithms combined with the Boruta feature selection approach to screen for disease-characteristic genes in an external ICM dataset. Potential regulatory networks were reconstructed by predicting transcription factors (TFs) and microRNAs (miRNAs). Finally, we validated the expression levels of signature genes, TFs, and miRNAs in an in vivo ICM rat model.
Results:
The pseudobulk analysis identified 168 DEGs, with machine learning selecting four hub genes as key signatures demonstrating acceptable ICM discriminative power. Their area under the curve (AUC) values were as follows: MLLT3 (76.7%), GFOD1 (78.6%), COLEC12 (77.8%), and RARRES1 (79.9%). Notably, when combined into a four-gene signature, a substantially higher AUC of 93.0% was achieved for the discrimination of ICM. Transcription factor analysis delineated that GFOD1, MLLT3, RARRES1, and COLEC12 were regulated by 17, 7, 7, and 3 TFs, respectively. The CTCF was found to be a shared transcription factor. Computational miRNA analysis retrieved 329 miRNAs. In vivo validation studies confirmed significantly reduced expression of four signature genes and CTCF, along with elevated levels of miR-195-5p and miR-5680 in ICM samples compared to those in sham controls.
Conclusion:
Our study characterized GFOD1, MLLT3, COLEC12, RARRES1, miR-195-5p, and miR-5680 as promising biomarkers for ischemic cardiomyopathy, with CTCF acting as a candidate transcription factor.
