■单细胞RNA测序(scRNA-Seq)为研究不同细胞类型与生物体之间的相互作用提供了新的视角和思路。通过将scRNA-seq与新的计算框架或特定技术集成,可以开发更好的阿尔茨海默病(AD)治疗方法。
■单细胞测序数据集GSE158234从GEO数据库获得。预处理,质量控制,降维聚类,并对其进行注释以识别细胞类型。RNA-seq分析数据集GSE238013用于确定不同样品中特定细胞亚群的组分。OMIM中包含的一组基因,基因卡,CTD,和DisGeNET数据库被选择为高度可信的AD相关基因。然后,使用KO组中显著表达的基因作为hub基因创建ROC曲线以预测诊断价值。将上述基因映射到Coremine医学数据库,以预测未来的治疗中药,并建立了“中药-成分-靶标”网络,以筛选潜在的治疗靶标。最后一步是进行孟德尔随机化研究,以确定全基因组关联研究中关键基因IL1B和AD之间的因果关系。
■使用scRNA-seq数据集,发现了五个独特的细胞簇。使用标记基因将这些簇进一步细分为四种不同的细胞类型。与WT组相比,KO组显示出更实质性的巨噬细胞差异亚组。通过使用可用的数据集和PPI网络分析,发现了54个常见基因。使用MCODE方法确定了四个集群,相关分析表明,4个基因簇中的7个基因与巨噬细胞呈显著负相关。四组中的六个基因呈显著正相关。五种基因在WT和KO组中具有不同的表达水平。String数据库用于鉴定四个基因(IL10,CX3CR1,IL1B,和IL6),最终被选为ADhub基因。筛选鉴定出干预AD转化过程的潜在中药,包括丹参,人参,灵芝,甘草,黄连,黄芩,除了有希望的治疗目标,例如PTGS1、PTGS2和RXRA。最后,表明IL1B与AD中的免疫细胞浸润直接相关。在方差倒数加权中,我们发现IL1B与AD的高风险相关,OR为1.003(95%CI=1.001-1.006,p=0.038)。
■我们的研究结合了网络药理学和scRNA-seq计算框架,以揭示相关的枢纽基因和潜在的中药治疗AD的潜在目标。这些发现可能有助于理解AD基因背后的分子过程以及治疗该疾病的新型药物的开发。
UNASSIGNED: Single-cell RNA sequencing (scRNA-Seq) provides new perspectives and ideas to investigate the interactions between different cell types and organisms. By integrating scRNA-seq with new computational frameworks or specific technologies, better Alzheimer\'s disease (AD) treatments may be developed.
UNASSIGNED: The single-cell sequencing dataset GSE158234 was obtained from the GEO database. Preprocessing, quality control, dimensionality-reducing clustering, and annotation to identify cell types were performed on it. RNA-seq profiling dataset GSE238013 was used to determine the components of specific cell subpopulations in diverse samples. A set of genes included in the OMIM, Genecards, CTD, and DisGeNET databases were selected as highly plausible AD-related genes. Then, ROC curves were created to predict the diagnostic value using the significantly expressed genes in the KO group as hub genes. The genes mentioned above were mapped to the Coremine Medical database to forecast prospective therapeutic Chinese medicines, and a \"Chinese medicine-ingredient-target\" network was constructed to screen for potential therapeutic targets. The last step was to undertake Mendelian randomization research to determine the causal link between the critical gene IL1B and AD in the genome-wide association study.
UNASSIGNED: Using the scRNA-seq dataset, five unique cell clusters were discovered. These clusters were further subdivided into four distinct cell types using marker genes. The KO group showed a more substantial differential subgroup of macrophages than the WT group. By using the available datasets and PPI network analysis, 54 common genes were discovered. Four clusters were identified using the MCODE approach, and correlation analysis showed that seven genes in those four clusters had a significantly negative correlation with macrophages. Six genes in four sets had a significantly positive correlation. Five genes had different levels of expression in the WT and KO groups. The String database was used to identify the regulatory relationships between the four genes (IL10, CX3CR1, IL1B, and IL6) that were finally selected as AD hub genes. Screening identified potential traditional Chinese medicine to intervene in the transformation process of AD, including Radix Salviae, ginseng, Ganoderma, licorice, Coptidis Rhizoma, and Scutellariae Radix, in addition to promising therapeutic targets, such as PTGS1, PTGS2, and RXRA. Finally, it was shown that IL1B directly correlated with immune cell infiltration in AD. In inverse variance weighting, we found that IL1B was associated with a higher risk of AD, with an OR of 1.003 (95% CI = 1.001-1.006, p = 0.038).
UNASSIGNED: Our research combined network pharmacology and the scRNA-seq computational framework to uncover pertinent hub genes and prospective traditional Chinese medicine potential therapeutic targets for AD. These discoveries may aid in understanding the molecular processes behind AD genes and the development of novel medications to treat the condition.