The applications of single-cell and spatial transcriptomics in neuroscience and brain disorders

Spatial transcriptomics extends traditional transcriptomic methods by quantifying gene expression within intact tissues while preserving each cell's precise spatial context. This technology also captures gene expression under physiological conditions, including interactions with the surrounding microenvironment, thereby enhancing our understanding of cellular states in both health and disease. Rapid recent advances have improved throughput, transcript capture, accuracy, and overall data quality. In this review, we summarize the major spatial transcriptomics platforms and outline their strengths and limitations. We also highlight key applications in neuroscience, including brain cell-type identification, structure-function relationships, and developmental processes. Additionally, we examine spatial gene-expression patterns in psychiatric and neurodegenerative disorders such as Alzheimer's disease and depression. Finally, we discuss emerging directions, including spatial multi-omics integration and the potential for artificial intelligence to advance brain research. Collectively, this work provides a foundation for future studies in neuroscience and brain disorders.