Integrative Single-Cell and Spatial Multi-Omics Analysis of Cancer Stem Cell Niches and Plasticity: Methodological Framework and Research Advances
Year: 2026
Type: article
Abstract: 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.
Source: Theoretical and Natural Science
Author: Yixin Ye
Institution: Hubei University
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: hybrid
OpenAlex ID: W7225745639
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