阿尔茨海默病、卒中、抑郁症等神经精神疾病的发生发展与脑组织局部代谢失衡密切相关，而传统代谢组学因缺乏空间分辨率，难以揭示中药在特定脑区或细胞微环境中的多靶点调控作用。空间代谢组学凭借其原位、高分辨率的质谱成像技术，实现了对代谢物空间分布的可视化分析，突破了这一技术瓶颈。系统综述空间代谢组学的关键技术原理，包括基质辅助激光解吸电离、二次离子质谱及解吸电喷雾电离等质谱成像平台。目前空间代谢组学技术在中药治疗神经精神疾病研究中已有应用：单体成分层面，丹参酮Ⅱ_A调控血脑屏障鞘脂代谢，石杉碱甲调节突触胆碱能动态；提取物层面，银杏叶提取物对缺血皮层的抗氧化空间调控及丹参多组分的协同保护机制；在复方层面，安宫牛黄丸、六味地黄丸、补阳还五汤等经典方剂对卒中后能量代谢、阿尔茨海默病自噬通路及缺血后神经修复的脑区特异性调控作用。空间代谢组学技术面临着低丰度代谢物检测灵敏度不足、中药复杂成分的质谱干扰等挑战，同时人工智能辅助大数据解析、类器官模型联用及多组学整合等也是未来发展方向。

The pathogenesis and progression of neuropsychiatric disorders, such as Alzheimer's disease, stroke, and depression, are closely associated with localized metabolic imbalances in brain tissue. However, conventional metabolomics lacks spatial resolution, making it difficult to elucidate the multi-target regulatory effects of traditional Chinese medicine（TCM） within specific brain regions or cellular microenvironments. Spatial metabolomics, leveraging in situ, high-resolution mass spectrometry imaging（MSI） techniques, enables the visualization of metabolite spatial distribution, thereby overcoming this technological bottleneck. This review systematically outlines the key technical principles of spatial metabolomics, including MSI platforms such as matrix-assisted laser desorption/ionization（MALDI）, secondary ion mass spectrometry（SIMS）, and desorption electrospray ionization（DESI）. Currently, spatial metabolomics has been applied in TCM research for neuropsychiatric disorders. At the level of active components, studies have explored tanshinone Ⅱ_A in regulating sphingolipid metabolism at the blood-brain barrier and huperzine A in modulating cholinergic dynamics at synapses. At the extract level, research has focused on the spatially-targeted antioxidant regulation of Ginkgo biloba extract in the ischemic cortex and the synergistic protective mechanisms of multi-component Salvia miltiorrhiza extracts. For compound formulas, classical prescriptions such as Angong Niuhuang Pills, Liuwei Dihuang Pills, and Buyang Huanwu Decoction have demonstrated region-specific regulatory effects on post-stroke energy metabolism, autophagy pathways in Alzheimer's disease, and post-ischemic neural repair. Despite its potential, spatial metabolomics faces challenges, including insufficient sensitivity for detecting low-abundance metabolites and spectral interference from complex TCM components. Future directions include artificial intelligenceassisted big data analysis, integration with organoid models, and multi-omics approaches.

R285.5

国家自然科学基金委员会青年项目(82301725); 大学生创新创业训练计划项目(20240375); 中国博士后科学基金第73批面上资助项目(2023M732155); 山西省基础研究计划项目(202203021212028); 山西省留学回国人员科技活动择优资助项目(20250047); 山西省卫生健康委中医药科研项目(2023ZYYC2034); 山西省高等学校科技创新项目(2022L138); 经方扶阳山西省重点实验室开放课题(CPSY202301); 国家临床重点专科建设项目(2025-ZZ-010)