Application progress of spatial transcriptomics in the pathological research of kidney transplantation
10.12464/j.issn.1674-7445.2026069
- VernacularTitle:空间转录组学在肾移植病理研究中的应用进展
- Author:
Can CHEN
1
;
Jiqiu WEN
1
Author Information
1. Transplant Medicine Research Institute of the Second Affiliated Hospital of Guangxi Medical University, Nanning 530000, China.
- Publication Type:TransplantationForefront
- Keywords:
Spatial transcriptomics;
Kidney transplantation;
Pathological change;
Targeted therapy;
End-stage renal disease;
Tissue spatial heterogeneity;
Spatial distribution map;
Multi-omics integration
- From:
Organ Transplantation
2026;17(5):748-756
- CountryChina
- Language:Chinese
-
Abstract:
Kidney transplantation is the preferred treatment option for patients with end-stage renal disease. Although the short-term survival rate has significantly improved, factors such as rejection, ischemia-reperfusion injury (IRI), delayed graft function and renal fibrosis remain important factors affecting the long-term survival of transplanted kidneys. These pathological changes have clear spatial heterogeneity. Traditional transcriptome sequencing techniques lose cellular in situ spatial information and the communication information between cells, while spatial transcriptomics techniques can maintain the integrity of tissue structure and achieve high-resolution molecular analysis. They can precisely map the spatial distribution maps of gene expression and cell types, compensating for the limitations of traditional techniques. This review systematically summarizes the application progress of spatial transcriptomics techniques in kidney transplantation pathology research, focusing on the research findings in key pathological processes such as allogeneic rejection, xenotransplant rejection, IRI, delayed graft function and renal fibrosis. It also discusses the value of the techniques in analyzing tissue spatial heterogeneity, revealing cell interactions and dynamic evolution patterns, and their potential significance in early diagnosis, targeted therapy development and efficacy evaluation. It also analyzes the current challenges faced by the technologies, such as cost, resolution balance, data processing and limitations of sample spatial information. Finally, it looks forward to future directions such as multi-omics integration, artificial intelligence-driven analysis and dynamic longitudinal studies.
