Single-cell multi-omics and machine learning for dissecting stemness in cancer.

Area of Science:

• Oncology
• Molecular Biology
• Genomics

Background:

• Cancer stem cells (CSCs) are rare tumor subpopulations driving malignancy.
• CSCs contribute significantly to therapeutic resistance and disease relapse.
• Understanding CSC heterogeneity is crucial for effective cancer treatment.

Purpose of the Study:

• To review the paradigm shift in CSC definition from static to dynamic.
• To explore how single-cell technologies redefine stemness.
• To highlight emerging platforms for targeting CSC vulnerabilities.

Main Methods:

• Single-cell RNA sequencing (scRNA-seq) for high-resolution profiling.
• Trajectory inference and spatial transcriptomics for dynamic modeling.
• Artificial intelligence, multi-omics, and CRISPR screens for vulnerability discovery.

Main Results:

• Single-cell technologies reveal functional heterogeneity in rare CSCs.
• Dynamic-state modeling redefines stemness based on context.
• Emerging platforms offer new avenues for CSC research.

Conclusions:

• Advanced single-cell approaches are transforming CSC understanding.
• Disrupting CSC plasticity and adaptation is key for new therapies.
• Precision medicine strategies targeting CSCs hold promise for improved outcomes.