Table 1 Overcoming Challenges and Shaping Future Directions in Neuroblastoma.
Research aspect | Key challenges | Potential solutions |
|---|---|---|
Technical limitations | Loss of cell viability, sample contamination, and high sequencing cost | Optimize tissue handling and cell preservation techniques; employ microdissection-based enrichment and low-input sequencing strategies |
Data reliability | Insufficient sequencing depth and high intercellular variability | Increase sequencing depth; integrate temporal and spatial multi-omics data to improve normalization and consistency |
Tumor heterogeneity | Complex microenvironmental and intercellular diversity | Develop cross-layer multi-omics frameworks to capture tumor–microenvironment interactions |
Multi-omics integration | Limited application beyond scRNA-seq | Expand multi-omics coverage and develop hierarchical data integration algorithms |
Computational resources | High computational demand and limited scalability of algorithms | Incorporate AI/ML-assisted high-throughput computing platforms and construct interpretable analytical frameworks |
Translational validation | Lack of robust clinical cohorts and functional validation experiments | Establish longitudinal clinical cohorts integrating spatial omics and experimental models for mechanistic validation |
