Spatial transcriptomics is a set of methods that measure gene expression while preserving the precise locations of cells within intact tissues. Traditional single cell RNA sequencing captures which genes are active in each cell but loses spatial context because tissues must be dissociated. Spatial approaches overcome this by combining microscopy with molecular barcodes or in situ chemistry so that each transcript can be mapped back to its original position.

Several main technological strategies have emerged. In situ hybridization and in situ sequencing techniques directly image RNA molecules in fixed tissue slices, often using rounds of fluorescent labeling and imaging to identify many genes. Array based capture methods place tissues on slides patterned with thousands of spatially barcoded spots that capture RNA, followed by sequencing and computational reconstruction of spatial gene expression maps. Newer approaches increase resolution toward single cell or even subcellular scales, and some integrate protein detection or chromatin information alongside RNA.

These methods reveal how cell types are organized and interact within their native microenvironments. In cancer, spatial transcriptomics identifies tumor subclones, immune cell niches and gradients of hypoxia or drug response. In neuroscience, it maps neuronal subtypes, circuits and regional gene expression patterns. In developmental biology, it tracks how tissues and organs are patterned over time.

Current challenges include balancing resolution, sensitivity and throughput, as well as computationally integrating spatial data with single cell sequencing and imaging. Ongoing developments aim at whole organ coverage, 3D reconstructions and multimodal measurements to build comprehensive atlases of tissue architecture and function.