Wang Shengran, Greenbaum Jonathan, Qiu Chuan, Swerdlow Russell H, Haeri Mohammad, Gong Yun, Shen Hui, Xiao Hongmei, Deng Hongwen
Reproductive Medicine Center, The Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, Guangdong 510120, China.
Center for System Biology, Data Sciences and Reproductive Health, School of Basic Medical Science, Central South University, Changsha, Hunan 410008, China.
Genes Dis. 2024 May 22;11(6):101337. doi: 10.1016/j.gendis.2024.101337. eCollection 2024 Nov.
Recent studies have explored the spatial transcriptomics patterns of Alzheimer's disease (AD) brain by spatial sequencing in mouse models, enabling the identification of unique genome-wide transcriptomic features associated with different spatial regions and pathological status. However, the dynamics of gene interactions that occur during amyloid-β accumulation remain largely unknown. In this study, we performed analyses on ligand-receptor communication, transcription factor regulatory network, and spot-specific network to reveal the dependence and the dynamics of gene associations/interactions on spatial regions and pathological status with mouse and human brains. We first used a spatial transcriptomics dataset of the knock-in AD and wild-type mouse model. We revealed 17 ligand-receptor pairs with opposite tendencies throughout the amyloid-β accumulation process and showed the specific ligand-receptor interactions across the hippocampus layers at different extents of pathological changes. We then identified nerve function related transcription factors in the hippocampus and entorhinal cortex, as well as genes with different transcriptomic association degrees in AD versus wild-type mice. Finally, another independent spatial transcriptomics dataset from different AD mouse models and human single-nuclei RNA-seq data/AlzData database were used for validation. This is the first study to identify various gene associations throughout amyloid-β accumulation based on spatial transcriptomics, establishing the foundations to reveal advanced and in-depth AD etiology from a novel perspective based on the comprehensive analyses of gene interactions that are spatio-temporal dependent.
最近的研究通过对小鼠模型进行空间测序，探索了阿尔茨海默病（AD）大脑的空间转录组学模式，从而能够识别与不同空间区域和病理状态相关的独特全基因组转录组特征。然而，在淀粉样β蛋白积累过程中发生的基因相互作用动态在很大程度上仍然未知。在本研究中，我们对配体-受体通讯、转录因子调控网络和斑点特异性网络进行了分析，以揭示基因关联/相互作用对小鼠和人类大脑空间区域和病理状态的依赖性及动态变化。我们首先使用了敲入型AD和野生型小鼠模型的空间转录组学数据集。我们揭示了在整个淀粉样β蛋白积累过程中具有相反趋势的17对配体-受体，并展示了在不同病理变化程度下海马各层之间特定的配体-受体相互作用。然后，我们在海马体和内嗅皮质中鉴定了与神经功能相关的转录因子，以及AD小鼠与野生型小鼠中转录组关联程度不同的基因。最后，使用来自不同AD小鼠模型的另一个独立空间转录组学数据集以及人类单核RNA测序数据/AlzData数据库进行验证。这是第一项基于空间转录组学识别淀粉样β蛋白积累过程中各种基因关联的研究，为从基于时空依赖性基因相互作用的综合分析这一全新视角揭示AD病因的深入研究奠定了基础。
