Guest Editors
Prof. Chunguang Guo
Email: guochunguang0405@163.com
Affiliation: Chinese Academy of Medical Sciences Fuwai Hospital, National Center for Cardiovascular Diseases, Beijing, China
Homepage:
Research Interests: single-cell sequencing, multi-omics, precision medicine, artificial intelligence, pediatric cardiology
Dr. Hao Chi
Email: chihao@hawaii.edu
Affiliation: Burns School of Medicine, University of Hawaii at Manoa, Honolulu, Hawaii, USA
Homepage:
Research Interests: genetics, precision medicine
Dr. Taoyuan Lu
Email: lutaoyuan97@163.com
Affiliation: Xuanwu Hospital, Capital Medical University, Beijing, China
Homepage:
Research Interests: bioinformatics, multi-omics analysis, biomarker discovery
Summary
Congenital heart disease (CHD) remains the most common birth defect worldwide and a leading cause of infant morbidity and mortality. Despite substantial advances in surgical correction and perioperative management, many patients still experience delayed diagnosis, heterogeneous clinical outcomes, residual lesions, heart failure, arrhythmia, or lifelong complications. Current diagnostic and prognostic approaches rely primarily on imaging and anatomical classification, which often fail to capture the underlying molecular and cellular heterogeneity driving disease severity and long-term outcomes.
Recent breakthroughs in single-cell sequencing, spatial transcriptomics, proteomics, metabolomics, and integrative computational modeling provide unprecedented opportunities to dissect cardiac development, identify pathogenic cell populations, and uncover molecular signatures associated with early disease onset and postoperative prognosis. These technologies enable the discovery of clinically actionable biomarkers for early detection, prenatal screening, surgical risk assessment, and personalized follow-up strategies.
In parallel, machine learning and artificial intelligence approaches facilitate the integration of multi-modal datasets, including genomics, omics, imaging, and clinical variables, into interpretable prediction models that can guide clinical decision-making.
This Special Issue aims to showcase cutting-edge translational research applying single-cell and multi-omics technologies to congenital heart disease. We particularly encourage studies that bridge molecular discoveries with clinical implementation, including biomarker validation, multi-center cohorts, and risk prediction tools for real-world practice.By integrating systems biology with pediatric cardiology, this Special Issue seeks to accelerate precision diagnosis, prevention, and personalized care for patients with CHD.
Suggested Topics
1. Single-cell atlases of cardiac development and congenital malformations
2. Cellular heterogeneity and lineage tracing in CHD pathogenesis
3. Spatial omics mapping of cardiac tissue architecture and remodeling
4. Multi-omics identification of diagnostic or prognostic biomarkers
5. Prenatal and neonatal molecular screening strategies
6. Genetic and epigenetic mechanisms underlying congenital heart defects
7. Integrative models combining omics, imaging (echo/CT/MRI), and clinical data
8. AI/ML-based prediction of surgical risk, complications, and long-term outcomes
9. Biomarkers for postoperative remodeling, heart failure, or arrhythmia
10. Liquid biopsy approaches (cfDNA, exosomes, circulating biomarkers)
11. Multi-center validation of omics-derived risk scores
12. Translational pipelines from discovery to clinical assays
13. Precision prevention and early intervention strategies
14. Longitudinal multi-omics tracking of pediatric CHD patients
15. Reproducible computational pipelines and open datasets for CHD research
Keywords
congenital heart disease, single-cell sequencing, multi-omics, biomarker discovery, prenatal diagnosis, risk stratification, precision medicine, artificial intelligence, pediatric cardiology
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