感谢 Jessica S Sadick、Michael R O'Dea、Philip Hasel 等人提供GSE167490数据集。作者对Faten A Sayed、Lay Kodama、Li Fan等人提供的GSE183068数据集表示赞赏。作者感谢 Shuqing Liu 在数据分析方面的帮助，感谢 温 Yang 提供的数据分析平台。本研究得到国家自然科学基金（82174511）、成都中医药大学杏树林学者资助、学科人才研究提升计划（QJJJ2022001）、辽宁振兴人才计划（XLYC 1807083）、四川省行政管理局中药材基金（2023MS578）、国家大学生创新创业训练项目（202310633003X）和科研实践创新课题成都中医药大学大学生教育（ky-2023100）。Hanjie Liu 和 Hui Yang 参与了研究的设计、数据的收集、解释以及手稿的起草和修订。刘淑清和李思宇参与了研究的设计、数据的收集和手稿的起草。温杨和安瓦尔·阿耶莎负责数据的收集和解释。辛潭准备了数字和/或表格。岑江、刘毅和谢陆霜构思了这项研究，并审阅/编辑了手稿。所有作者都为本文做出了贡献，并批准了提交的版本。

分析的第 2 步至第 9 步使用 R 软件实施（参见 补充图 1 和 补充文件 1），其余步骤在在线平台上执行。 材料清单中提供了本协议中使用的数据库（以及网络链接）的详细信息。
1. 数据采集
<ol><li>访问美国国家生物技术信息中心（National Center of Biotechnology Information）的公开可用的 基因表达综合（GEO） 数据库。</li>
	<li>在搜索框中搜索名为 “阿尔茨海默病” 的 GEO 数据。</li>
	<li>在右侧选择 排名靠前的生物 为 智人 。 注：搜索结果是智人中阿尔茨海默病的数据。</li>
	<li>过滤搜索到的信息后，下载 GSE167490 和 GSE183068 数据文件，其中包含每个单独的单个细胞核样本的 features.tsv、barcode.tsv 和 matrix.mtx。数据集包括 34 个源自额叶皮层的 AD 样本，其中 17 个男性样本和 17 个女性样本均等分布（补充表 1）。</li>
</ol>2. 样本合并
<ol><li>在计算机上相应地配置数据路径和样本名称。导入 34 个下载的样本，并使用函数名称为样本分配特定于性别的名称。</li>
	<li>使用函数 list 和 Read10X 以批处理方式为所有样本生成 Seurat 对象，将参数指定为 min.cells = 3 和 min.features = 200。</li>
	<li>使用 RenameCells 函数将样本 ID 作为前缀添加到单元条形码中，以在合并过程中保留单元条形码。这确保了每个细胞都保留了其唯一身份，并且在合并后可以追溯到其原始样本源。</li>
</ol>3. 质量控制（QC）
<ol><li>使用 PercentageFeatureSet 函数计算每个细胞的线粒体基因比率、红细胞基因比率和核糖体基因比率。</li>
	<li>使用 [[ ]] 运 算符将这些计算出的比率存储在元数据中，以将此信息直接附加到每个单元格的元数据。</li>
	<li>利用 子集 函数进行细胞过滤，指定参数为 nFeature_RNA > 200、nFeature_RNA < 10000、nCount_RNA < 60000、percent.mt < 10、percent.rb < 5 和 percent。HB < 75.</li>
	<li>从分析中排除GSM5106107。</li>
</ol>4. 批量效果检查
<ol><li>执行数据处理。<ol><li>使用 NormalizeData 函数对数据进行规范化。</li>
		<li>使用 FindVariableFeatures 函数识别数据集中排名前 2000 的变量特征。</li>
		<li>使用 RunPCA 对数据进行主成分分析 （PCA）17，保留 50 个主成分。</li>
		<li>使用 ElbowPlot 函数生成弯头图，以确定后续分析的最佳维数。考虑前 50 个维度。</li>
		<li>使用 ScaleData 缩放数据，以确保所有要素都处于可比的规模上。</li>
		<li>使用基于 30 个维度的 FindNeighbors 识别最近邻。</li>
		<li>使用 RunUMAP 应用 UMAP 算法，将数据的维度减少到 30 个维度。</li>
	</ol></li>
	<li>使用 DimPlot 函数可视化已处理的数据，将 reduction 参数设置为 umap ，将 group.by 参数设置为 orig.ident。 注意：此步骤可以生成一个图，该图在缩小的 UMAP 空间中可视化数据，并按原始单元格身份分组。在检查UMAP图后，很明显存在批量效应。基于细胞批次或实验来源的不同细胞聚集或分离表明，实验批次影响了基因表达谱。</li>
</ol>5. 数据集成
<ol><li>使用 SCTransform 函数对数据进行规范化和标准化。</li>
	<li>应用和谐算法18 对剩余的 33 个单核数据进行积分。使用 SCT 分析进行积分，并将最大和谐迭代次数设置为 20。</li>
	<li>使用分辨率参数设置为 0.07 的 FindClusters 函数来识别数据中的不同聚类。</li>
	<li>使用具有指定维数 （dims = 30） 的 RunUMAP 函数来进一步降低数据的维数，并在较低维空间中可视化聚类。</li>
</ol>6. 单元格类型注释
<ol><li>通过对现有文献的广泛回顾来收集细胞的标记基因（补充表2）。</li>
	<li>在鉴定出细胞簇异质性之后，通过特异性表达的标记基因对每个簇细胞的类型进行分类。</li>
	<li>使用 ggplot2 包通过 UMAP 可视化 呈现各种细胞类型，其中少突胶质细胞用颜色代码 突出显示 #DB7093，兴奋性神经元用 #FF69B4 突出显示，星形胶质细胞用 #1874CD 高亮，小胶质细胞用 #63B8FF、少突胶质细胞前体细胞用 #DB7093、抑制性神经元用 #FFC0CB 和内皮细胞用 #FF69B4。</li>
	<li>计算按性别分层的每种细胞类型的比例。</li>
</ol>7. 神经胶质细胞数据提取
<ol><li>使用子集函数从集成的批量数据中提取星形胶质细胞数据。</li>
	<li>使用子集函数从集成的批量数据中提取小胶质细胞数据。</li>
	<li>使用子集函数从集成的批量数据中提取少突胶质细胞数据。</li>
</ol>8. 神经胶质性别特异性差异表达基因 （DEGs） 捕获
<ol><li>使用 FindMarkers 函数（ident.1 = 男性，ident.2 = 女性，group.by = group.sum，测定 = RNA）识别星形胶质细胞的性别特异性 DEG，阈值： p 值 < 0.05 和 |avg_log2FC|> 30.将上调的 DEG 标记为 Up，将下调的 DEG 标记为 Down，将其余的标记为 Stable。<ol><li>使用 ggplot 函数可视化 DEG，其中 x 轴表示两个条件 （pct.1 - pct.2） 之间的百分比差异，y 轴表示avg_log2FC。上调的基因使用 颜色PaleVioletRed突出显示，下调基因使用 Pink，稳定基因使用 DodgerBlue3。</li>
	</ol></li>
	<li>使用 FindMarkers 函数（ident.1 = 男性，ident.2 = 女性，group.by = group.sum，测定 = RNA）识别小胶质细胞的性别特异性 DEG，阈值： p 值 < 0.05 和 |avg_log2FC|> 1.将上调的 DEG 标记为 Up，将下调的 DEG 标记为 Down，将其余的标记为 Stable。<ol><li>使用 ggplot 函数可视化 DEG，其中 x 轴表示两个条件 （pct.1 - pct.2） 之间的百分比差异，y 轴表示avg_log2FC。使用颜色 OrangeRed 突出显示了上调的基因，使用 LightSalmon 突出显示了下调的基因，使用 SteelBlue1 突出显示了稳定基因。</li>
	</ol></li>
	<li>使用 FindMarkers 函数（ident.1 = 男性，ident.2 = 女性，group.by = group.sum，测定 = RNA）识别少突胶质细胞的性别特异性 DEG，阈值： p 值 < 0.05 和 |avg_log2FC|> 10.将上调的 DEG 标记为 Up，将下调的 DEG 标记为 Down，将其余的标记为 Stable。<ol><li>使用 ggplot 函数可视化 DEG，其中 x 轴表示两个条件 （pct.1 - pct.2） 之间的百分比差异，y 轴表示avg_log2FC。上调基因使用 DeepPink高调，下调基因使用 HotPink，稳定基因使用 DeepSkyBlue3。</li>
	</ol></li>
</ol>9. 性别特异性DEGs的功能富集分析
<ol><li>使用 enrichGO 函数对每种神经胶质细胞类型的性别特异性 DEG 进行基因本体 （GO） 富集分析。设置以下参数：OrgDb = org。Hs.eg.db，keyType = SYMBOL，ont = ALL，pAdjustMethod = BH，pvalueCutoff = 0.01，qvalueCutoff = 0.05。</li>
	<li>使用函数 bitr 将基因符号转换为相应的基因 ID。使用 enrichKEGG 函数对每种神经胶质细胞类型的性别特异性 DEG 进行京都基因和基因组百科全书 （KEGG） 富集分析。按如下方式调整设置：organism = has、keyType = kegg、pAdjustMethod = BH、pvalueCutoff = 0.01 和 qvalueCutoff = 0.05。</li>
</ol>10. go和kegg通路中胶质DEGs的频率统计、各胶质细胞性别特异性DEGs的维恩图、PPI网络构建
<ol><li>使用频率直方图计算 GO 和 KEGG 通路中神经胶质性别特异性 DEG 的频率。</li>
	<li>访问STRING数据库，构建PPI网络。</li>
	<li>选择 多种蛋白质。在搜索框中搜索 “名称列表 ”。将“生物体”设置为智 人。</li>
	<li>查看从搜索中获得的蛋白质列表。单击“ 继续 ”以继续。</li>
	<li>通过选择 下载 选项导出 PPI 网络，最好是分辨率更高的 PNG 格式。</li>
	<li>通过使用维恩图可视化顶级性别特异性基因的共表达分布。</li>
	<li>根据维恩图分析，将共享基因确定为研究中的关键基因。</li>
</ol>11. 多因素监管网络建设
<ol><li>访问 NetworkAnalyst。</li>
	<li>单击“基因列表输入”并将生物体指定为智人（人类）。将 ID 类型设置为官方基因符号。在搜索字段中输入基因名称，然后单击“上传并继续”。</li>
	<li>选择 Gene-miRNA Interactions ，然后选择 miRTarBase v8.0。单击“ 确定”确认选择。</li>
	<li>继续进行 TF-基因相互作用 并选择 ENCODE 数据库。单击“ 确定 ”以确认选择。</li>
	<li>接下来，导航到 TF-miRNA 共调控网络 ， 然后单击确定继续 。</li>
	<li>最后，选择 “继续 ”以生成包含基因-miRNA 相互作用和 TF-基因相互作用的多因素调控网络。</li>
</ol>12. 基因和靶点中药分析
<ol><li>访问 Coremine Medical 在线数据库。</li>
	<li>在“探索”部分下的搜索框中输入特定基因名称，然后选择后缀为“基因/蛋白质”的相应基因。</li>
	<li>浏览 “药物” 部分并确定与搜索药物相关的中药。 注：具有统计学意义的药物用蓝色标记。</li>
	<li>根据中药作为治疗性中药的“显著性”价值，确定前五大中药。</li>
</ol>13. 中药成分靶向关键基因的研究综述
<ol><li>接入中医结合药理学研究平台（TCMIP）和中药系统药理学数据库和分析平台（TCMSP）。在搜索栏中输入草药的名称以检索其相应的成分。</li>
	<li>检索 PubMed 数据库中的成分，时间限制至 2023 年 4 月 10日。使用的检索词包括TCMSP中的 分子名称 和TCMIP中的 化学成分 作为检索词，并且仅限于以英文发表的文章。</li>
	<li>总结分析作用于AD的草药及其相应成分。</li>
</ol>14. 确认靶向中医治疗在AD性别特异性中的治疗功能
<ol><li>将草药导入 TCMIP 并导航到相应的描述页面。</li>
	<li>利用导出数据功能并选择CSV格式，下载GO - 生物过程、GO - 细胞成分、GO - 分子功能和 反应组通路的富集术语。</li>
	<li>使用条形图可视化每种草药的下载富集术语。</li>
</ol>

阿尔茨海默病中的性别特异性神经胶质生物标志物：转录组分析

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此手稿不存在利益冲突，所有作者均已批准发表。

AD19 的流行病学、病理学和临床表现均已确定具有性别特异性。在这里，我们证实了“激素-突触-神经元轴”在AD患者中性别特异性神经胶质基因和相关通路的潜在病理机制。NLGN4Y是3个神经胶质细胞中唯一的共享基因，被选为AD性别特异性的生物标志物，调控NLGN4Y的TF和miRNAs与性别差异和神经系统的发育密切相关。此外，靶向中药昆布和麻黄被认为可能影响“激素-突触-神经元”轴...

阿尔茨海默病（Alzheimer's disease，AD）是一种全球性高发疾病，占痴呆病例的60%-80%1。尽管AD的发病率很高，但AD的机制发病机制尚未明确，到目前为止还没有有效的治疗方法2。AD的主要病理为神经元萎缩和病理碎片积累，主要是微管相关蛋白Tau和β-淀粉样蛋白（Aβ）3,4。AD的发病机制与异常自噬、氧化应激、线粒体功能障碍、炎症和能量代谢紊乱有关5。患病率调查证明，三分之二的AD患者是女性6.AD在病因、临床表现、预防和治疗方面存在性别特异性差异。因此，揭示导致AD性别特异性差异的生物学机制，并针对中医（TCM）可能为理解AD的发病机制提供更全面的理论框架，并进一步指导准确的治疗策略。
神经胶质细胞，尤其是小胶质细胞、星形胶质细胞和少突胶质细胞，可能有助于 AD 的发病机制。在 AD 中，小胶质细胞被激活并发生基因改变，这有助于炎症反应、吞噬作用和 Aβ 清除 7,8;星形胶质细胞发生基因改变，影响突触活动、离子稳态以及能量和脂质代谢9;少突胶质细胞具有性别特异性的基因改变，可导致神经元丢失、神经原纤维缠结和白质病变10,11。
在这项研究中，我们采用单核RNA测序（snRNA-seq）作为一种卓越的技术。与单细胞 RNA 测序 （scRNA-seq） 相比，snRNA-seq 在样本丰富度、细胞类型完整性和数据可靠性方面具有优势12,13。SnRNA-seq 已被广泛用于专注于 AD 和探索神经胶质细胞作用的研究 14,15,16。它在这些研究领域的广泛采用凸显了其在为AD中神经胶质细胞的转录特征提供宝贵见解方面的有效性。通过利用snRNA-seq的优势，研究人员已经能够揭示有关神经胶质细胞参与AD病理学的关键信息，并确定潜在的治疗靶点。为了探索AD中性别特异性神经胶质细胞转录特征以及中医对AD性别特异性的潜在影响，本研究分析了NCBI GEO公共数据库中AD患者额叶皮层的snRNA-seq数据。进一步分析性别特异性差异表达基因（DEGs）、基因本体论（GO）、京都基因和基因组百科全书（KEGG）、蛋白质-蛋白质相互作用（PPI）网络和基因-TF-miRNA网络，揭示关键生物标志物和潜在发病机制。最后，提出了潜在的中药，并通过检索Coremine Medical、TCMIP和TCMSP数据库，以表格形式展示了其有效成分。

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Database</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Coremine Medical database</td>
			<td>Jointly developed by Norway, the Chinese Academy of Sciences, the Chinese Academy of Medical Sciences, the National Medical Library of the United States and other institutions</td>
			<td></td>
			<td>When you explore concepts in CoreMine Medical you access a database that is structured to relate important concepts, ranked by statistical relevance, to your topic. For example, if you type in &#34;Alzheimer disease,&#34; in addition to retrieving documents and resources that discuss the disease, you will be able to view networks and lists that show how your query concept is related to other bio-medical concepts. This provides an overview of concepts that relate to your search as well as being an interface for navigating information on these concepts. 
			Weblink: https://coremine.com/medical/</td>
		</tr>
		<tr>
			<td>Gene Expression Omnibus (GEO)</td>
			<td>National Center for Biotechnology Information in the United States (NCBI)</td>
			<td></td>
			<td>GEO is a public functional genomics data repository supporting MIAME-compliant data submissions. Array- and sequence-based data are accepted. Tools are provided to help users query and download experiments and curated gene expression profiles. 
			Weblink: https://www.ncbi.nlm.nih.gov/geo/</td>
		</tr>
		<tr>
			<td>Integrative Pharmacology-based Research Platform of Traditional Chinese Medicine (TCMIP, version: 2.0)</td>
			<td>None</td>
			<td></td>
			<td>Introduction to the Integrated Pharmacology Based Network Computational Research Platform for Traditional Chinese Medicine [TCMIP v2.0], http://www.tcmip.cn/ ) It is an intelligent data mining platform based on the online database of the Encyclopedia of Traditional Chinese Medicine (ETCM), which integrates medical big data management and pharmacological computing services. It aims to reveal the scientific connotation of traditional Chinese medicine theory and the scientific value of original thinking in traditional Chinese medicine, summarize and pass on the experience of famous doctors, control the quality of traditional Chinese medicine, explain the principles of traditional Chinese medicine action, research and development of new Chinese medicine, especially the discovery and optimization of modern drug combinations, Provide a strong data foundation and analytical tools. Based on TCMIP v1.0, a comprehensive upgrade is implemented, including five major databases and seven functional modules. Through system integration and module integration, a comprehensive analysis of the multi-level correlation of the &#34;disease syndrome prescription&#34; interaction network can be quickly achieved. As an intelligent data mining platform, TCMIP v2.0 will provide a strong data foundation and analysis platform for revealing the scientific connotation of traditional Chinese medicine theory and the scientific value of original thinking in traditional Chinese medicine, summarizing and inheriting the experience of famous doctors, quality control of traditional Chinese medicine, elucidating the principles of traditional Chinese medicine action, research and development of new traditional Chinese medicine drugs, especially modern drug combination discovery and optimization. 
			Weblink: http://www.tcmip.cn/TCMIP&#160;</td>
		</tr>
		<tr>
			<td>NetworkAnalyst</td>
			<td>None</td>
			<td></td>
			<td>Networkanalyze is an online visualization analysis platform for gene expression analysis and meta-analysis. It can perform comparative, quantitative, differential and enrichment analysis of gene expression, protein-protein interaction analysis, integration analysis of multiple datasets, and can also draw high-value images such as PCA, protein-protein interaction network diagram, heatmap, volcano diagram, Wayne diagram, etc. 
			Weblink: https://www.networkanalyst.ca/NetworkAnalyst/</td>
		</tr>
		<tr>
			<td>PubMed database</td>
			<td>National Center for Biotechnology Information in the United States (NCBI)</td>
			<td></td>
			<td>The Pubmed database is a biomedical literature database maintained by the National Library of Medicine (NLM) in the United States, aimed at providing the latest medical research results to scientists, doctors, researchers, and students worldwide. This database collects biomedical literature from around the world, including journal articles, papers, books, etc. As of now, the Pubmed database has collected over 30 million articles and is continuously updated every week. 
			Weblink: https://pubmed.ncbi.nlm.nih.gov/</td>
		</tr>
		<tr>
			<td>R software</td>
			<td>Ross Ihaka and Robert Gentleman</td>
			<td></td>
			<td>R is a language and environment for statistical computing and graphics. It is a GNU project which is similar to the S language and environment which was developed at Bell Laboratories (formerly AT&#38;T, now Lucent Technologies) by John Chambers and colleagues. R can be considered as a different implementation of S. There are 
			some important differences, but much code written for S runs unaltered under R. 
			Weblink: https://www.r-project.org/</td>
		</tr>
		<tr>
			<td>STRING database (STRING, version 11.0)&#160;</td>
			<td>Swiss Institute of Bioinformatics</td>
			<td></td>
			<td>STRING is a database of known and predicted protein interactions. The interactions include direct (physical) and indirect (functional) associations 
			Weblink: https://string-db.org/</td>
		</tr>
		<tr>
			<td>Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP)</td>
			<td>Zhejiang Jiuwei Health Co., Ltd</td>
			<td></td>
			<td>TCMSP is not only a data repository, but also an analysis platform for users to comprehensively study Traditional Chinese Medicines (TCM): including identification of active components, screening of drug targets and generation of compounds-targets-diseases networks, as well as the detailed drug pharmacokinetic information involving drug-likeness (DL), oral bioavailability (OB), blood-brain barrier (BBB),intestinal epithelial permeability (Caco-2), ALogP,fractional negative surface area (FASA-) and number of &#160;H-bond&#160;donor/acceptor &#160;(Hdon/Hacc). So far, TCMSP has attracted broad attentions and several groups have published more than 10 papers by using our TCMSP database within about one year. 
			Weblink: https://tcmsp-e.com</td>
		</tr>
	</tbody>
</table>

biomarker identification for gender specificity of alzheimer's disease based on the glial transcriptome profiles

最近在阿尔茨海默病 （AD） 中发现了许多性别特异性生物标志物;然而，脑胶质细胞的报道很少。这项研究分析了 GEO 数据库中 33 名 AD 个体额叶皮层的 220,095 个单核转录组。在神经胶质细胞中鉴定出性别特异性差异表达基因（DEGs），其中星形胶质细胞为243个，小胶质细胞为1,154个，少突胶质细胞为572个。基因本体论 （GO） 功能注释分析和京都基因和基因组百科全书 （KEGG） 通路富集分析揭示了突触、神经和激素相关通路的功能集中。蛋白-蛋白相互作用网络（PPI）鉴定出星形胶质细胞中的MT3、CALM2、DLG2、KCND2、PAKACB、CAMK2D和NLGN4Y，小胶质细胞中的TREM2、FOS、APOE、APP、NLGN4Y，以及少突胶质细胞中的GRIN2A、ITPR2、GNAS和NLGN4Y为关键基因。NLGN4Y是3个神经胶质细胞唯一共享的基因，被鉴定为AD性别特异性的生物标志物。 基因-转录因子（TF）-miRNA协同调控网络确定了NLGN4Y及其靶点中药的关键调控因子。 鉴定了Ecklonia kurome Okam（Kunbu）和Herba Ephedrae（麻黄），并展示了活性成分对AD的影响。最后，对昆布和马黄的富集分析表明，它们可能作为AD性别特异性的治疗候选者。

Alzheimer&#39;s disease (AD) is a global disease with high incidence, and it accounts for 60%-80% of dementia1. Despite its high incidence, the mechanistic pathogenesis of AD is not clearly delineated, and there have been no effective therapeutics until now2. The main pathologies in AD were identified as neuronal atrophy and the accumulation of pathological debris, mainly microtubule-associated protein Tau, and &#946;-amyloid (A&#946;)3,4. The pathogenesis of AD is associated with abnormal autophagy, oxidative stress, mitochondrial dysfunction, inflammation, and energy metabolism disorder5. Prevalence surveys proved that two-thirds of AD patients were women6. Sex-specific differences in AD exist in the etiology, clinical manifestations, prevention, and treatment. Thus, revealing the biological mechanism that causes sex-specific differences in AD and targeting traditional Chinese medicine (TCM) can potentially provide a more comprehensive theoretical framework to understand the pathogenesis of AD, and to further guide accurate treatment strategy.
Neuroglial cells, especially microglia, astrocytes, and oligodendrocytes, potentially contribute to the pathogenesis of AD. In AD, microglia are activated and genetically altered, which contribute to inflammatory response, phagocytosis, and A&#946; clearance7,8; astrocyte is genetically altered, which affects synaptic activity, ion homeostasis, and energy and lipid metabolism9; oligodendrocyte is genetically altered with sex specificity, which contributes to neuronal loss, neurofibrillary tangles, and white matter lesions10,11.
In this study, we employed single-nuclei RNA sequencing (snRNA-seq) as a superior technique. Compared to single-cell RNA sequencing (scRNA-seq), snRNA-seq offers advantages in terms of sample richness, cell type integrity, and data reliability12,13. SnRNA-seq has been extensively utilized in studies focusing on AD and exploring the role of glial cells14,15,16. Its wide adoption in these research areas highlights its effectiveness in providing valuable insights into the transcriptional characteristics of glial cells in AD. By leveraging the advantages of snRNA-seq, researchers have been able to uncover crucial information regarding the involvement of glial cells in AD pathology and identify potential therapeutic targets. In order to explore sex-specific neuroglial transcriptional characteristics in AD and potential TCMs for sex specificity of AD, this study analyzed snRNA-seq data from the frontal cortex of AD patients from the NCBI GEO public database. Sex-specific differentially expressed genes (DEGs), Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Protein-protein interaction (PPI) network, and gene-TF-miRNA network are further analyzed to reveal key biomarkers and potential pathogenesis. Finally, potential TCMs were suggested, and their active ingredients were displayed with tables by searching the Coremine Medical, TCMIP, and TCMSP databases.

作者聚焦：探索性别特异性神经胶质细胞特征和阿尔茨海默病的治疗线索

基于神经胶质转录组谱的阿尔茨海默病性别特异性生物标志物鉴定

Our research aims to identify gender-specific genes in glial cells, explore associated pathways, and evaluate the potential therapeutic effects of TCM on Alzheimer's disease. The present protocol analyzes a large number of single-nuclei transcriptomes from the GEO database through bioinformatics analysis. Our protocol offers advantages, including single-cell resolution, CTAP identification of biomarker discovery, and comparison of growth in different genders.Our laboratory will focus on the field of neuroscience with a parameter focus on utilizing acupuncture to enhance the treatment of neurological disorders.

额叶胶质胶质转录组谱的SnRNA-seq分析和细胞类型的注释 总共在17名男性AD和17名女性AD的额叶皮层中获得了220,095个细胞核和32,077个基因（图1A）。UMAP图可视化了总单核额叶转录组，在降维分析后显示了不同类型的细胞核（图1B）。显示了按性别捕获的注释细胞核总数，其中包括58,902个星形胶质细胞，14,265个小胶质细胞，77,466个少突胶质细?...

这项研究分析了 33 名阿尔茨海默病 （AD） 患者的单核转录组，揭示了神经胶质细胞中的性别特异性 DEG。功能富集分析突出了突触、神经和激素相关通路。确定了关键基因，即NLGN4Y及其调控因子，并提出了性别特异性AD的潜在治疗候选药物。

Many sex-specific biomarkers have been recently revealed in Alzheimer&#39;s disease (AD); however, cerebral glial cells were rarely reported. This study ...

我们的研究旨在鉴定神经胶质细胞中的性别特异性基因，探索相关途径，并评估中药对阿尔茨海默病的潜在治疗效果。本方案通过生物信息学分析分析了GEO数据库中的大量单核转录组。我们的方案具有优势，包括单细胞分辨率、生物标志物发现的 CTAP 鉴定以及不同性别生长的比较。我们的实验室将专注于神经科学领域，其参数重点是利用针灸来增强神经系统疾病的治疗。

biomarker-identification-for-gender-specificity-alzheimer-s-disease

Research

JoVE Journal

Neuroscience

Biomarker Identification for Gender Specificity of Alzheimer's Disease Based on the Glial Transcriptome Profiles

565 次观看.  Chengdu University of Traditional Chinese Medicine. 我们的研究旨在鉴定神经胶质细胞中的性别特异性基因，探索相关途径，并评估中药对阿尔茨海默病的潜在治疗效果。本方案通过生物信息学分析分析了GEO数据库中的大量单核转录组。我们的方案具有优势，包括单细胞分辨率、生物标志物发现的 CTAP 鉴定以及不同性别生长的比较。我们的实验室将专注于神经科学领域，其参数重点是利用针灸来增强神经系统疾病的治疗?...