This course provides a comprehensive introduction to the core technologies and data analysis methods of spatial transcriptomics. The first part compares spatial and single‑cell transcriptomics, summarizes mainstream technologies (10X Visium, Slide‑seq, Stereo‑seq, etc.) in terms of resolution, coverage, and applicable tissue types, and presents representative studies in plants (orchid, soybean) and animals (psoriasis, renal cancer). The second part focuses on the experimental principles of the 10X Visium platform (spatially barcoded capture spots, UMI tagging, library structure) and the Space Ranger quantification pipeline, with detailed explanations of output files (web_summary quality reports, expression matrices, tissue images). Basic analysis is performed with the Seurat package, covering data loading, SCT normalization, spatial visualization of marker genes, unsupervised clustering, and identification of spatially variable genes (FindSpatiallyVariable). The third part explains strategies for spatial data annotation, highlighting anchor‑based integration with single‑cell RNA‑seq references to deconvolute cell types for each spot. The fourth part discusses multi‑sample merging (merge) and batch‑effect considerations. Throughout, the course emphasizes the importance of spatial location information for understanding tissue microenvironments, cell‑cell interactions, and developmental trajectories, providing a complete analytical framework for tumor microenvironment, developmental biology, and plant organogenesis studies.