Integrative Single-Cell and Spatial Multi-Omics Analysis of Cancer Stem Cell Niches and Plasticity: Methodological Framework and Research Advances

Cancer stem cells (CSCs) are closely associated with intratumoral heterogeneity, therapy resistance, relapse, and metastasis. Increasing evidence suggests that CSCs should not be regarded solely as a fixed population defined by a small set of markers, but rather as dynamic cellular states shaped by the tumor microenvironment. Single-cell RNA sequencing (scRNA-seq) resolves transcriptional heterogeneity and cellular composition, whereas spatial transcriptomics (ST) preserves in situ tissue organization. Their integration therefore provides a powerful strategy for dissecting CSC niches and plasticity. This review summarizes the major computational routes for integrative scRNA-seq and ST analysis, including malignant-cell and CSC-state identification, cell-cell communication inference, pseudotime and RNA-velocity analysis, spatial mapping, and cellular-neighborhood modeling. Representative tools and their assumptions, strengths, and limitations are compared. We further propose a standardized workflow from single-cell characterization to spatial validation and dynamic modeling, and discuss representative applications in hepatocellular carcinoma, small-cell lung cancer, and endometrial carcinoma. Key challenges include unpaired samples, spatial-resolution limitations, prior-database bias, the non-causal nature of trajectory inference, and the difficulty of localizing rare CSC states. Future directions include single-cell/subcellular spatial technologies, multimodal integration, graph-based deep learning, and causal modeling.