Tang Shizhen, Liu Shihan, Buchman Aron S, Bennett David A, De Jager Philip L, Yang Jingjing, Hu Jian
Center for Computational and Quantitative Genetics, Department of Human Genetics, Emory University School of Medicine, Atlanta, GA 30322, USA; Department of Biostatistics and Bioinformatics, Emory University School of Public Health, Atlanta, GA 30322, USA.
Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL 60612, USA.
HGG Adv. 2025 May 5;6(3):100447. doi: 10.1016/j.xhgg.2025.100447.
Spatial transcriptomics (ST) data provide spatially informed gene expression profiles. However, power is limited for spatially informed differential gene expression (DGE) of complex diseases such as Alzheimer disease (AD), due to small sample sizes of ST data. Conversely, single-nucleus RNA sequencing (snRNA-seq) data offer larger sample sizes for cell-type-specific (CTS) analyses but lack spatial information. Here, we integrated ST and snRNA-seq data to enhance the power of spatially informed CTS DGE analysis of AD-related phenotypes. We first utilized the CeLEry tool to infer six cortical layers of ∼1.5 million cells in the snRNA-seq data that were profiled from the dorsolateral prefrontal cortex (DLPFC) tissue of 436 postmortem brains. Then, we conducted cortical layer- and cell-type-specific (LCS) and CTS DGE analyses based on the linear mixed model, for β-amyloid, tangle density, and cognitive decline. We identified 138 LCS significant genes with false discovery rate (FDR) q <0.05, including 103 for β-amyloid, 24 for tangle density, and 25 for cognitive decline. The majority of these LCS significant genes, including known AD risk genes such as APOE, KCNIP3, and CTSD, cannot be detected by CTS analyses. We also identified 2 genes shared across all 3 phenotypes and 10 shared between 2 phenotypes. Gene set enrichment analyses with the LCS DGE results of microglia in cortical layer 6 of β-amyloid identified 12 significant AD-related pathways. In conclusion, incorporating spatial information with snRNA-seq data enhanced the power of spatially informed DGE analyses. These identified LCS significant genes not only help illustrate the pathogenesis of AD but they also provide potential targets for developing therapeutics of AD.
空间转录组学（ST）数据提供了具有空间信息的基因表达谱。然而，由于ST数据的样本量较小，对于阿尔茨海默病（AD）等复杂疾病进行具有空间信息的差异基因表达（DGE）分析的效能有限。相反，单细胞核RNA测序（snRNA-seq）数据为细胞类型特异性（CTS）分析提供了更大的样本量，但缺乏空间信息。在这里，我们整合了ST和snRNA-seq数据，以提高对AD相关表型进行具有空间信息的CTS DGE分析的效能。我们首先利用CeLEry工具在snRNA-seq数据中推断出约150万个细胞的六个皮质层，这些细胞来自436个死后大脑的背外侧前额叶皮质（DLPFC）组织。然后，我们基于线性混合模型对β-淀粉样蛋白、缠结密度和认知衰退进行了皮质层和细胞类型特异性（LCS）以及CTS DGE分析。我们鉴定出138个错误发现率（FDR）q<0.05的LCS显著基因，其中103个与β-淀粉样蛋白有关，24个与缠结密度有关，25个与认知衰退有关。这些LCS显著基因中的大多数，包括已知的AD风险基因如APOE、KCNIP3和CTSD，无法通过CTS分析检测到。我们还鉴定出在所有三种表型中都存在的2个基因以及在两种表型之间共享的10个基因。对β-淀粉样蛋白的第6层皮质小胶质细胞的LCS DGE结果进行基因集富集分析，确定了12条与AD相关的显著途径。总之，将空间信息与snRNA-seq数据相结合提高了具有空间信息的DGE分析的效能。这些鉴定出的LCS显著基因不仅有助于阐明AD的发病机制，还为开发AD治疗方法提供了潜在靶点。
