Advances in Spatial Transcriptomics and Single-Cell Sequencing in Oncology Research

Spatial transcriptomics enables the in situ resolution of gene expression spatial localization within tissues, providing oncology research with a high-precision tool for dissecting pathological samples.In studies of the tumor microenvironment, Seurat, as a representative platform for single-cell data analysis, lays a foundation for resolving tumor heterogeneity through its hierarchical data structure and user-friendly operational functions, though its capabilities heavily rely on specialized interfaces.Complementing this, Giotto, the first widely validated general-purpose toolbox applicable to various spatial omics technologies, offers comprehensive analytical algorithms for dissecting cellular composition and interaction patterns in the tumor microenvironment via its modular design and flexible, robust framework.Meanwhile, BayesSpace employs a fully Bayesian statistical framework to achieve resolution enhancement and precise clustering of spatial transcriptomics data without requiring independent single-cell data, facilitating the identification of low-abundance but functionally critical cell subpopulations and their spatial organization within tumor tissues.The synergistic development of these tools provides crucial technical support for integrating single-cell and spatial transcriptomics data, deeply resolving tumor microenvironment heterogeneity, and elucidating mechanisms of tumor initiation and progression.