空间转录组学在肾移植病理研究中的应用进展

Application progress of spatial transcriptomics in the pathological research of kidney transplantation

  • 摘要: 肾移植是终末期肾病患者的首选治疗方案,尽管短期存活率已显著提升,但排斥反应、缺血-再灌注损伤(IRI)、移植物功能延迟恢复及肾脏纤维化等因素仍是影响移植肾长期存活的重要因素,且这些病理改变具有明确空间异质性。传统转录组测序技术丢失细胞原位空间信息及细胞间通讯信息,而空间转录组学技术在维持组织结构完整性的同时,可实现高分辨率分子分析,精准绘制基因表达及细胞类型的空间分布图谱,弥补了传统技术的局限。本综述系统总结空间转录组学技术在肾移植病理研究中的应用进展,重点阐述其在同种异体排斥反应、异种移植排斥反应、IRI、移植物功能延迟恢复及肾纤维化等关键病理过程中的研究发现,梳理该技术在解析组织空间异质性、揭示细胞间互作及动态演变规律方面的价值,探讨其在早期诊断、靶向治疗开发及疗效评估中的潜在意义,并分析当前技术面临的成本、分辨率平衡、数据处理及样本空间信息局限性等挑战,最后对未来多组学整合、人工智能驱动分析及动态纵向研究等发展方向进行展望。

     

    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.

     

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