Spatial transcriptomics meets diabetic kidney disease: Illuminating the path to precision medicine.

Abstract

Diabetic kidney disease (DKD), a primary cause of end-stage renal disease, results from progressive tissue remodeling and loss of kidney function. While single-cell RNA sequencing has significantly accelerated our understanding of cellular diversity and dynamics in DKD, its lack of spatial resolution limits insights into tissue-specific dysregulation and the microenvironment. Spatial transcriptomics (ST) is an innovative technology that combines gene expression with spatial localization, offering a powerful approach to dissect the molecular mechanisms of DKD. This mini-review introduces how ST has transformed DKD research by enabling spatially resolved analysis of cell interactions and identifying localized molecular alterations in glomeruli and tubules. ST has revealed dynamic intercellular communication within the renal microenvironment, lesion-specific gene expression patterns, and immune infiltration profiles. For example, Slide-seqV2 has highlighted disease-specific cellular neighborhoods and associated signaling networks. Furthermore, ST has pinpointed key genes implicated in disease progression, such as fibrosis-related proteins and transcription factors in tubular damage. By integration of ST with computational tools such as machine learning and network-based analysis can help uncover gene regulatory mechanisms and potential therapeutic targets. However, challenges remain in limited spatial resolution, high data complexity, and computational demands. Addressing these limitations is essential for advancing precision medicine in DKD.