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整合空间转录学和snRNA-seq数据可以增强AD相关表现型的差异性基因表达分析结果

Shizhen Tang¹, Shihan Liu¹, Aron S Buchman²

¹Center 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.

|May 7, 2025

概括

将空间转录学与单核RNA测序集成,可以增强对阿尔茨海默病 (AD) 现型的差异性基因表达分析. 这种方法识别了与AD相关的新型基因和途径,改善了理解和潜在的治疗点.

科学领域:

神经科学是一个神经科学.
基因组学就是基因组学.
生物信息学是一种生物信息学.

背景情况:

空间转录学 (ST) 提供了空间知情的基因表达,但由于样本规模小,在阿尔茨海默病 (AD) 等复杂疾病中对差异基因表达 (DGE) 的能力有限.
单核RNA测序 (snRNA-seq) 为细胞类型特定 (CTS) 分析提供了更大的样本大小,但缺乏空间上下文.
整合ST和snRNA-seq数据可以克服用于增强疾病特异性DGE分析的单个方法的局限性.

研究的目的:

整合ST和snRNA-seq数据,以提高对AD相关表型的细胞类型特异性 (CTS) 差异性基因表达 (DGE) 分析的空间信息.
通过利用空间和细胞类型信息,识别与AD病变发生相关的新基因和途径.
通过确定层和细胞类型特定的基因表达变化来发现AD的潜在治疗点.

主要方法:

利用CeLEry工具从436个死后背侧前额皮层 (DLPFC) 大脑的snRNA-seq数据中推断出六个皮层层,从约150万个细胞中推断出六个皮层.
使用线性混合模型对β-粉样蛋白,结密度和认知衰退进行了层和细胞类型特定 (LCS) 和CTS DGE分析.
对已识别的LCS DGE结果进行基因组丰富分析,特别是与β-粉样蛋白相关的皮质层6中的微质.

主要成果:

确定了138个显著的LCS基因 (FDR q <0.05),包括103个beta-amyloid,24个纠密度和25个认知衰退.
大多数已识别的LCS基因,包括AD风险基因如APOE,KCNIP3和CTSD,仅通过CTS分析无法检测到.
发现了所有三种表型中共享的2个基因和两个表型之间共享的10个基因. 基因组丰富分析确定了微质中的12个重要的AD相关途径.

结论:

将空间信息与snRNA-seq数据集成显著增强了对AD等复杂疾病的空间知情DGE分析的力量.
已识别的LCS基因为AD病原体提供了关键的见解,并突出了潜在的新疗法标.
这种综合的方法可以更好地理解AD的分辨率,将空间和细胞信息结合起来,实现强大的生物发现.

关键词:

阿尔茨海默病是阿尔茨海默氏症的疾病.粉样蛋白-β-β 的存在.认知能力下降认知能力下降不同的基因表达.这就是 snRNA-seqq.空间转录学空间转录学纠结密度纠结密度纠结密度

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