Mao Qiao, Huang Shiren, Luo Xinqun, Liu Ping, Wang Xiaoping, Wang Kesheng, Zhang Yong, Chen Bin, Luo Xingguang
Department of Psychosomatic Medicine, People's Hospital of Deyang City, Deyang, Sichuan 618000, China.
Department of Neurology, Jiading Branch of Shanghai General Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.
EC Psychol Psychiatr. 2023 Jun 1;12(6):1-5. Epub 2023 Jun 19.
The aim of this study is to provide a comprehensive overview of spatial multiomics analysis, including its definition, processes, applications, significance and relevant research in psychiatric disorders. To achieve this, a literature search was conducted, focusing on three major spatial omics techniques and their application to three common psychiatric disorders: Alzheimer's disease (AD), schizophrenia, and autism spectrum disorders. Spatial genomics analysis has revealed specific genes associated with neuropsychiatric disorders in certain brain regions. Spatial transcriptomics analysis has identified genes related to AD in areas such as the hippocampus, olfactory bulb, and middle temporal gyrus. It has also provided insight into the response to AD in mouse models. Spatial proteogenomics has identified autism spectrum disorder (ASD)-risk genes in specific cell types, while schizophrenia risk loci have been linked to transcriptional signatures in the human hippocampus. In summary, spatial multiomics analysis offers a powerful approach to understand AD pathology and other psychiatric diseases, integrating multiple data modalities to identify risk genes for these disorders. It is valuable for studying psychiatric disorders with high or low cellular heterogeneity and provides new insights into the brain nucleome to predict disease progression and aid diagnosis and treatment.
本研究的目的是全面概述空间多组学分析,包括其定义、流程、应用、意义以及在精神疾病方面的相关研究。为实现这一目标,我们进行了文献检索,重点关注三种主要的空间组学技术及其在三种常见精神疾病中的应用:阿尔茨海默病(AD)、精神分裂症和自闭症谱系障碍。空间基因组学分析揭示了某些脑区中与神经精神疾病相关的特定基因。空间转录组学分析在海马体、嗅球和颞中回等区域鉴定出了与AD相关的基因。它还为小鼠模型中对AD的反应提供了见解。空间蛋白质基因组学在特定细胞类型中鉴定出自闭症谱系障碍(ASD)风险基因,而精神分裂症风险位点则与人海马体中的转录特征相关。总之,空间多组学分析为理解AD病理学和其他精神疾病提供了一种强大的方法,整合多种数据模式以识别这些疾病的风险基因。它对于研究细胞异质性高或低的精神疾病很有价值,并为脑核组提供了新的见解,以预测疾病进展并辅助诊断和治疗。