Spatial transcriptomics enables the mapping of gene expression at single or near single-cell resolution in tissue, providing insights into cellular organization and interactions in health and disease. This review discusses current and emerging spatial transcriptomic methods, their applications in kidney biology, and challenges in their implementation. Spatial transcriptomic technologies integrate spatial information with RNA sequencing data to localize mRNA expression in tissue, offering potential for understanding cellular and molecular dynamics in healthy and diseased states. These technologies include sequencing-based methods like in situ capture (ISC) and in situ sequencing (ISS), as well as imaging-based approaches such as spatially resolved transcript amplicon readout mapping (STARmap) and fluorescent in situ sequencing (FISSEQ). These methods allow for the identification of cell types, spatially variable genes, and functionally relevant neighbourhoods, which are critical for understanding kidney biology and disease pathogenesis. Integration with other omics modalities, such as proteomics and spatial epigenetics, enhances the generation of comprehensive molecular atlases and provides insights into molecular interactions in homeostasis and disease. Challenges include the high cost of technologies, limited spatial resolution, and the need for standardized protocols and quality control measures. Despite these challenges, spatial transcriptomics is increasingly being used to study kidney disease, including the identification of cell types, injury states, and interactions between immune and epithelial cells. The integration of spatial transcriptomics with histopathology and other modalities is essential for interpreting cellular and molecular data in the context of disease. Future developments in spatial transcriptomics, including improved resolution, multiplexing, and integration with other omics, will further advance our understanding of kidney biology and clinical applications.Spatial transcriptomics enables the mapping of gene expression at single or near single-cell resolution in tissue, providing insights into cellular organization and interactions in health and disease. This review discusses current and emerging spatial transcriptomic methods, their applications in kidney biology, and challenges in their implementation. Spatial transcriptomic technologies integrate spatial information with RNA sequencing data to localize mRNA expression in tissue, offering potential for understanding cellular and molecular dynamics in healthy and diseased states. These technologies include sequencing-based methods like in situ capture (ISC) and in situ sequencing (ISS), as well as imaging-based approaches such as spatially resolved transcript amplicon readout mapping (STARmap) and fluorescent in situ sequencing (FISSEQ). These methods allow for the identification of cell types, spatially variable genes, and functionally relevant neighbourhoods, which are critical for understanding kidney biology and disease pathogenesis. Integration with other omics modalities, such as proteomics and spatial epigenetics, enhances the generation of comprehensive molecular atlases and provides insights into molecular interactions in homeostasis and disease. Challenges include the high cost of technologies, limited spatial resolution, and the need for standardized protocols and quality control measures. Despite these challenges, spatial transcriptomics is increasingly being used to study kidney disease, including the identification of cell types, injury states, and interactions between immune and epithelial cells. The integration of spatial transcriptomics with histopathology and other modalities is essential for interpreting cellular and molecular data in the context of disease. Future developments in spatial transcriptomics, including improved resolution, multiplexing, and integration with other omics, will further advance our understanding of kidney biology and clinical applications.