Spatial transcriptomics is transforming how gene expression is studied, providing both transcript identity and spatial context within intact tissue. By providing biologically contextualized gene expression data, it enables applications such as tissue atlasing, biomarker discovery, tumor microenvironment analysis, and cell type classification. Recent advances have expanded spatial technologies beyond array-based methods to include high-resolution, sequencing-based methodologies capable of capturing transcriptomes across large tissue areas. While these innovations open new discovery pathways, they also introduce technical and logistical challenges that require careful planning.
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