Liu DD, Hu HY, Li FF, Hu QY, Liu MW, Hao YJ, Li B. Spatial transcriptomics meets diabetic kidney disease: Illuminating the path to precision medicine. World J Diabetes 2025; 16(9): 107663 [DOI: 10.4239/wjd.v16.i9.107663]
Corresponding Author of This Article
Bo Li, PhD, Associate Professor, College of Life Sciences, Chongqing Normal University, No. 37 University City Middle Road, Shapingba District, Chongqing 401331, China. libcell@cqnu.edu.cn
Research Domain of This Article
Cell Biology
Article-Type of This Article
Minireviews
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
World J Diabetes. Sep 15, 2025; 16(9): 107663 Published online Sep 15, 2025. doi: 10.4239/wjd.v16.i9.107663
Spatial transcriptomics meets diabetic kidney disease: Illuminating the path to precision medicine
Dan-Dan Liu, Han-Yue Hu, Fei-Fei Li, Qiu-Yue Hu, Ming-Wei Liu, You-Jin Hao, Bo Li
Dan-Dan Liu, Han-Yue Hu, Fei-Fei Li, Qiu-Yue Hu, You-Jin Hao, Bo Li, College of Life Sciences, Chongqing Normal University, Chongqing 401331, China
Ming-Wei Liu, College of Laboratory Medicine, Chongqing Medical University, Chongqing 400016, China
Author contributions: Liu DD drafted the original manuscript; Liu DD and Liu MW handled resources and visualization; Liu DD, Hao YJ, and Li B were responsible for conceptualization and data curation; Hu HY, Li FF, and Hu QY provided the critical review and editorial input; Hao YJ and Li B supervised this project and secured funding; all authors reviewed and approved the final version of this manuscript.
Supported by Science and Technology Research Program of Chongqing Municipal Education Commission, No. KJQN202100538; and Talent Innovation Project in Life Sciences of Chongqing Normal University, No. CSSK2023-04.
Conflict-of-interest statement: All the Authors have no conflict of interest related to this manuscript.
Open Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Bo Li, PhD, Associate Professor, College of Life Sciences, Chongqing Normal University, No. 37 University City Middle Road, Shapingba District, Chongqing 401331, China. libcell@cqnu.edu.cn
Received: March 28, 2025 Revised: May 23, 2025 Accepted: August 15, 2025 Published online: September 15, 2025 Processing time: 167 Days and 9.5 Hours
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.
Core Tip: This mini-review highlights the emerging role of spatial transcriptomics (ST) in diabetic kidney disease (DKD) research. ST enables high-resolution mapping of gene expression within intact tissues, offering novel insights into cellular interactions, lesion-specific transcriptional changes, and immune infiltration. The mini-review further discusses the integration of ST with computational tools such as machine learning and network analysis, and its potential in precision diagnostics and therapy. Despite challenges in spatial resolution and data complexity, ST is poised to transform DKD research by bridging molecular discovery with clinical application.
