Spatial transcriptomics is a set of methods that measure gene expression while preserving information about where each transcript is located inside tissues. Traditional RNA sequencing averages signals across many cells and loses spatial context. In contrast, spatial approaches reveal how cell types are arranged, how they interact and how local microenvironments shape gene activity.

Several technological strategies have been developed. One major class uses arrays or barcoded spots on slides. Tissue sections are placed on these arrays so that RNA molecules capture a positional barcode as they are sequenced, allowing reconstruction of expression maps across tissue regions. Another class relies on in situ hybridization or in situ sequencing, where RNA molecules are directly labeled and read within the intact tissue, often at subcellular resolution. These imaging based approaches can resolve fine structures but are typically limited to predefined gene panels or require complex imaging workflows.

Combining spatial transcriptomics with single cell RNA sequencing allows more detailed deconvolution of cell types and states. Profiles from dissociated cells can be computationally mapped back onto spatial maps, producing high resolution atlases of tissues such as brain, tumors or developing organs. This integration helps uncover spatially organized cell states, gradients and niche specific gene programs.

Applications span basic and translational research. In cancer, spatial maps reveal how tumor cells, immune infiltrates and stroma are arranged and communicate, informing prognosis and therapy response. In neuroscience and developmental biology, spatial patterns clarify how cell lineage, position and signaling shape tissue architecture. Ongoing efforts focus on increasing resolution, throughput, number of detectable molecules and integrating spatial transcriptomics with proteomics and epigenomics.