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从多组学到可视化及其他:在CAR-T细胞疗法中连接微观与宏观见解

From Multi-Omics to Visualization and Beyond: Bridging Micro and Macro Insights in CAR-T Cell Therapy.

作者信息

Gong Yuting, Fei Peng, Zhang Yicheng, Xu Yang, Wei Jia

机构信息

Department of Hematology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, China.

Immunotherapy Research Center for Hematologic Diseases of Hubei Province, Wuhan, Hubei, 430030, China.

出版信息

Adv Sci (Weinh). 2025 May;12(20):e2501095. doi: 10.1002/advs.202501095. Epub 2025 May 11.

Abstract

Chimeric antigen receptor T (CAR-T) cell therapies, a cornerstone of immunotherapy, have demonstrated remarkable efficacy in treating hematological malignancies and have more recently expanded into applications for solid tumors and autoimmune diseases. Emerging multidimensional profiling technologies offer promising solutions for enhancing CAR-T efficacy, overcoming resistance, and facilitating the development of novel CAR-T constructs. The integration of genomics, epigenomics, transcriptomics, proteomics, metabolomics, and microbiomics enables a comprehensive understanding of the intrinsic mechanisms underlying CAR-T therapy, while single-cell and spatial omics significantly improve data resolution and analytical depth. Coupled with advances in biomedical engineering, visualization technologies form the foundation for omics data generation by bridging microscopic and macroscopic scales and enabling dynamic, 3D in vivo monitoring of CAR-T behavior. Artificial intelligence (AI) further supports this framework by enabling the analysis of complex, high-dimensional datasets. This review highlights recent advances in the integration of multidimensional omics within CAR-T therapy and explores cutting-edge developments in visualization technologies and AI applications. The full convergence of multi-omics, visualization tools, and AI is poised to deliver transformative insights into the mechanisms governing CAR-T cell therapy.

摘要

嵌合抗原受体T(CAR-T)细胞疗法是免疫疗法的基石,已在治疗血液系统恶性肿瘤方面显示出显著疗效,最近还扩展到实体瘤和自身免疫性疾病的应用领域。新兴的多维分析技术为提高CAR-T疗效、克服耐药性以及促进新型CAR-T构建体的开发提供了有前景的解决方案。基因组学、表观基因组学、转录组学、蛋白质组学、代谢组学和微生物组学的整合能够全面了解CAR-T治疗的内在机制,而单细胞和空间组学则显著提高了数据分辨率和分析深度。结合生物医学工程的进展,可视化技术通过弥合微观和宏观尺度并实现对CAR-T行为的动态三维体内监测,构成了组学数据生成的基础。人工智能(AI)通过对复杂的高维数据集进行分析,进一步支持了这一框架。本综述重点介绍了CAR-T治疗中多维组学整合的最新进展,并探讨了可视化技术和AI应用的前沿发展。多组学、可视化工具和AI的全面融合有望为CAR-T细胞治疗的机制提供变革性见解。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9e64/12120725/500eac4d5e2c/ADVS-12-2501095-g005.jpg

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