Spatial transcriptomics meets diabetic kidney disease: Illuminating the path to precision medicine - PubMed (original) (raw)

Review

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

Dan-Dan Liu et al. World J Diabetes. 2025.

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.

Keywords: Computational biology; Diabetic kidney disease; Precision medicine; Renal microenvironment; Single-cell RNA sequencing; Spatial transcriptomics.

©The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved.

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Conflict of interest statement

Conflict-of-interest statement: All the Authors have no conflict of interest related to this manuscript.

Figures

Figure 1

Figure 1

The experimental workflow of spatial transcriptomics in diabetic kidney disease. Kidney tissue is first sectioned and stained with hematoxylin and eosin to retain histological context. Prepared slides undergo spatial barcoding and cDNA synthesis, followed by library construction and sequencing. The resulting data are used to reconstruct spatial gene expression patterns, identify cellular neighborhoods, and infer cell–cell interactions within intact renal tissue.

Figure 2

Figure 2

Applications of spatial transcriptomics in diabetic kidney disease. Spatial transcriptomics (ST) has been applied to diabetic kidney disease to investigate a range of spatially resolved pathological features. The representative examples include the analysis of cell-cell interactions within the renal microenvironment, the identification of region-specific gene expression signatures, and the characterization of immune infiltration in glomerular lesions-particularly involving M2 macrophages and mast cells. In addition, ST enables the detection of localized molecular alterations, such as lipid accumulation and mitochondrial damage in tubular epithelial cells, as well as spatial variation in signaling activity, including alterations in the phosphoinositol-3 kinase pathway. DKD: Diabetic kidney disease.

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