Zhou Yadi, Xu Jielin, Hou Yuan, Bekris Lynn, Leverenz James B, Pieper Andrew A, Cummings Jeffrey, Cheng Feixiong
Genomic Medicine Institute Lerner Research Institute Cleveland Clinic Cleveland Ohio USA.
Department of Molecular Medicine Cleveland Clinic Lerner College of Medicine Case Western Reserve University Cleveland Ohio USA.
Alzheimers Dement (N Y). 2022 Oct 13;8(1):e12350. doi: 10.1002/trc2.12350. eCollection 2022.
Recent advances in generating massive single-cell/nucleus transcriptomic data have shown great potential for facilitating the identification of cell type-specific Alzheimer's disease (AD) pathobiology and drug-target discovery for therapeutic development.
We developed The Alzheimer's Cell Atlas (TACA) by compiling an AD brain cell atlas consisting of over 1.1 million cells/nuclei across 26 data sets, covering major brain regions (hippocampus, cerebellum, prefrontal cortex, and so on) and cell types (astrocyte, microglia, neuron, oligodendrocytes, and so on). We conducted nearly 1400 differential expression comparisons to identify cell type-specific molecular alterations (e.g., case vs healthy control, sex-specific, apolipoprotein E () ε4/ε4, and TREM2 mutations). Each comparison was followed by protein-protein interaction module detection, functional enrichment analysis, and omics-informed target and drug (over 700,000 perturbation profiles) screening. Over 400 cell-cell interaction analyses using 6000 ligand-receptor interactions were conducted to identify the cell-cell communication networks in AD.
All results are integrated into TACA (https://taca.lerner.ccf.org/), a new web portal with cell type-specific, abundant transcriptomic information, and 12 interactive visualization tools for AD.
We envision that TACA will be a highly valuable resource for both basic and translational research in AD, as it provides abundant information for AD pathobiology and actionable systems biology tools for drug discovery.
We compiled an Alzheimer's disease (AD) brain cell atlas consisting of more than 1.1 million cells/nuclei transcriptomes from 26 data sets, covering major brain regions (cortex, hippocampus, cerebellum) and cell types (e.g., neuron, oligodendrocyte, astrocyte, and microglia).We conducted over 1400 differential expression (DE) comparisons to identify cell type-specific gene expression alterations. Major comparison types are (1) AD versus healthy control; (2) sex-specific DE, (3) genotype-driven DE (i.e., apolipoprotein E [] ε4/ε4 vs ε3/ε3; TREM2 vs common variants) analysis; and (4) others. Each comparison was further followed by (1) human protein-protein interactome network module analysis, (2) pathway enrichment analysis, and (3) gene-set enrichment analysis.For drug screening, we conducted gene set enrichment analysis for all the comparisons with over 700,000 drug perturbation profiles connecting more than 10,000 human genes and 13,000 drugs/compounds.A total of over 400 analyses of cell-cell interactions against 6000 experimentally validated ligand-receptor interactions were conducted to reveal the disease-relevant cell-cell communications in AD.
在生成大规模单细胞/细胞核转录组数据方面的最新进展显示出巨大潜力，有助于识别细胞类型特异性的阿尔茨海默病（AD）病理生物学特征，并发现用于治疗开发的药物靶点。
我们通过汇编一个AD脑细胞图谱开发了阿尔茨海默病细胞图谱（TACA），该图谱包含来自26个数据集的超过110万个细胞/细胞核，覆盖主要脑区（海马体、小脑、前额叶皮层等）和细胞类型（星形胶质细胞、小胶质细胞、神经元、少突胶质细胞等）。我们进行了近1400次差异表达比较，以识别细胞类型特异性的分子改变（例如病例与健康对照、性别特异性、载脂蛋白E（ApoE）ε4/ε4以及TREM2突变）。每次比较后都进行蛋白质-蛋白质相互作用模块检测、功能富集分析以及基于组学的靶点和药物（超过70万个扰动图谱）筛选。使用6000种配体-受体相互作用进行了400多次细胞-细胞相互作用分析，以识别AD中的细胞-细胞通讯网络。
所有结果都整合到TACA（https://taca.lerner.ccf.org/）中，这是一个新的网站门户，具有细胞类型特异性的丰富转录组信息以及12个用于AD的交互式可视化工具。
我们设想TACA将成为AD基础研究和转化研究的极有价值的资源，因为它为AD病理生物学提供了丰富信息，并为药物发现提供了可操作的系统生物学工具。
我们汇编了一个阿尔茨海默病（AD）脑细胞图谱，该图谱包含来自26个数据集的超过110万个细胞/细胞核转录组，覆盖主要脑区（皮层、海马体、小脑）和细胞类型（例如神经元、少突胶质细胞、星形胶质细胞和小胶质细胞）。我们进行了1400多次差异表达（DE）比较，以识别细胞类型特异性的基因表达改变。主要比较类型包括：（1）AD与健康对照；（2）性别特异性DE；（3）基因型驱动的DE（即载脂蛋白E [ApoE] ε4/ε4与ε3/ε3；TREM2与常见变体）分析；以及（4）其他。每次比较之后还进一步进行了：（1）人类蛋白质-蛋白质相互作用组网络模块分析；（2）通路富集分析；以及（3）基因集富集分析。对于药物筛选，我们对所有比较进行了基因集富集分析，使用了超过70万个连接10000多个人类基因和13000种药物/化合物的药物扰动图谱。针对6000种经过实验验证的配体-受体相互作用，总共进行了400多次细胞-细胞相互作用分析，以揭示AD中与疾病相关的细胞-细胞通讯。
