Spatial transcriptomics is a set of methods that measure gene expression while preserving the spatial organization of cells within intact tissue. Traditional RNA sequencing requires dissociating tissues, which destroys positional information and obscures how cell types are arranged and interact. Spatial transcriptomics overcomes this by combining microscopy with spatially barcoded capture of RNA so that each transcript can be mapped back to its original location.

Early approaches used arrays of barcoded spots on glass slides to capture mRNA from thin tissue sections, producing tissue wide maps at spot resolutions of tens of micrometers. Later refinements increased spatial resolution, approaching single cell or subcellular scales. Imaging based methods such as multiplexed in situ hybridization and in situ sequencing detect transcripts directly in the tissue using repeated rounds of fluorescent labeling and imaging, enabling mapping of hundreds to thousands of genes at near single molecule resolution.

These technologies reveal how heterogeneous cell types form organized structures, how gene expression varies across microenvironments and how cell cell interactions are encoded in space. They have been applied to brain, tumors, developing embryos and other organs, uncovering novel cell states, developmental gradients and spatially restricted signaling niches. Integrative analyses combine spatial transcriptomics with single cell RNA sequencing, using the detailed transcriptomes of dissociated cells to annotate spatial maps and to deconvolve mixed spots.

Current research focuses on improving resolution, sensitivity, throughput and the number of genes measured simultaneously, as well as developing computational tools for spatial clustering, trajectory inference and ligand receptor interaction analysis. Spatial transcriptomics is becoming central for building comprehensive tissue atlases and for understanding mechanisms of disease in their native context.