Spatial Transcriptomics in Cancer Research: Technologies, Computational Advances, Clinical Applications, Challenges, and Future Perspectives

Spatial transcriptomics (ST) has rapidly emerged as a powerful technology for studying gene expression within its native tissue context, bridging the long-standing gap between transcriptomics and histopathology. Unlike conventional bulk RNA sequencing and single-cell RNA sequencing, which require tissue dissociation and therefore lose spatial information, ST preserves the spatial organization of tissues while enabling genome-wide transcriptomic profiling. This unique capability has provided new opportunities to investigate cellular heterogeneity, tissue architecture, and intercellular communication, leading to a deeper understanding of normal tissue function as well as the molecular basis of complex diseases, particularly cancer. In this review, we summarize the development of spatial transcriptomics, describe its underlying principles, and discuss the major technological platforms currently available, including sequencing-based, imaging-based, and in situ sequencing approaches. We compare the strengths and limitations of these technologies and highlight their suitability for different biological and clinical applications. We also review the computational methods that have become essential for analyzing spatial transcriptomic data, covering preprocessing, quality control, cell segmentation, cell-type annotation, cell-type deconvolution, spatial domain identification, cell–cell communication analysis, multimodal data integration, and emerging artificial intelligence (AI)-based analytical approaches. A major focus of this review is the application of spatial transcriptomics in cancer research. By preserving the spatial relationships among malignant, immune, stromal, and vascular cells, ST has provided valuable insights into intratumoral heterogeneity, the organization of the tumor microenvironment, immune-cell interactions, metastatic progression, mechanisms of therapeutic resistance, and the discovery of clinically relevant biomarkers. The integration of spatial transcriptomics with single-cell sequencing, spatial proteomics, and digital pathology has further improved our understanding of tumor biology and is contributing to the development of more precise and personalized therapeutic strategies. Despite these advances, several challenges still limit the broader adoption of spatial transcriptomics in clinical practice. Current platforms face trade-offs between spatial resolution and transcriptome coverage, incomplete RNA capture, relatively high experimental costs, demanding computational requirements, and the absence of universally standardized analytical workflows. Addressing these limitations will require continued technological innovation, improved computational tools, greater standardization across laboratories, and close collaboration between molecular biologists, clinicians, computational scientists, and engineers. Looking ahead, the integration of spatial transcriptomics with spatial multi-omics, advanced imaging technologies, artificial intelligence, machine learning, and digital pathology is expected to further expand its impact on biomedical research and precision medicine. As both experimental and computational methods continue to mature, spatial transcriptomics is likely to become an essential platform for biomarker discovery, disease diagnosis, therapeutic target identification, and personalized treatment. Overall, this review highlights the growing role of spatial transcriptomics in modern biology and oncology while discussing the opportunities and challenges that will shape its future development and clinical translation.

کلیدواژه ها:

Spatial transcriptomics ، Spatial biology ، Cancer ، Tumor microenvironment ، Single-cell RNA sequencing ، Spatial multi-omics ، Bioinformatics ، Artificial intelligence ، Precision oncology ، Biomarker discovery