目的·通过单细胞核转录组分析，探讨多发性硬化症（multiple sclerosis，MS）中小胶质细胞在灰质与白质的基因差异性表达及其在疾病进展中的作用，鉴定疾病相关的关键转录调控网络。方法·从基因表达数据库（Gene Expression Omnibus，GEO）中获取MS和对照冷冻人脑组织样本单细胞核转录组测序（single nucleus RNA sequencing，snRNA-seq）数据。使用R软件和Seurat软件等，利用特定的细胞标志物对数据进行细胞类型的鉴定。从鉴定的细胞群中提取小胶质细胞，根据其解剖来源将其分为灰质和白质小胶质细胞；利用降维聚类技术，获得具有差异性的小胶质细胞亚群。使用Seurat分析得到MS组与对照组在亚群层面上的差异表达基因（differentially expressed genes，DEGs）。对DEGs进行基因本体论（Gene Ontology，GO）分析与京都基因与基因组百科全书（Kyoto Encyclopedia of Genes and Genomes，KEGG）分析，进一步探究这些差异的生物学意义。使用Monocle3进行拟时序分析，研究疾病进展中的细胞亚群动态变化。使用单细胞调控网络推理和聚类（single cell regulatory network inference and clustering，SCENIC）方法分析转录因子（transcription factor，TF）调控网络，寻找可能参与MS调控的关键转录调控网络。结果·对数据进行质量控制后共保留了149 062个细胞核。对snRNA-seq数据进行降维聚类分析后，以DOCK8、CSF1R、P2RY12、CD74作为小胶质细胞的关键标志物鉴定得到了12 238个小胶质细胞。GO和KEGG分析结果表明，灰质小胶质细胞在疾病过程中内吞作用、离子稳态、脂质定位等功能下调，白质小胶质细胞在疾病过程中蛋白质折叠、细胞质翻译、温度刺激响应等功能上调。SCENIC分析显示MS疾病中FLI1、MITF、FOXP1等TF的表达上调。结论·小胶质细胞在MS的发展中具有重要作用，白质小胶质细胞受到MS的影响比灰质小胶质细胞更为明显。FLI1、MITF、FOXP1等是参与MS调控的关键TF，这些转录调控网络在疾病调控中发挥核心作用。

Objective ·To investigate the differential gene expression of microglia in the gray and white matter of multiple sclerosis (MS) using single-nucleus transcriptomic analysis, aiming to explore their roles in disease progression, and identify key transcriptional regulatory networks associated with the disease. Methods ·snRNA-seq data of frozen human brain tissue samples from MS patients and control individuals were obtained from the Gene Expression Omnibus (GEO) database. R language, along with R packages such as Seurat, was employed to identify cell types based on specific cell markers. Microglia were extracted from the identified cell populations and classified based on their anatomical origin, either gray matter or white matter. Dimensionality reduction and clustering techniques were utilized to identify distinct microglial subpopulations with differential characteristics. Differentially expressed genes (DEGs) between the MS and control groups at the subpopulation level were analyzed by using the Seurat package. Gene set enrichment analysis of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) was conducted on the DEGs to further explore the biological significance of these differences. Monocle3 was used for pseudotime analysis to study dynamic changes in microglia subpopulations during disease progression. Single cell regulatory network inference and clustering (SCENIC) method was applied to analyze transcription factor (TF) regulatory networks, aiming to identify key transcription factors potentially involved in MS regulation. Results ·After quality control, a total of 149 062 nuclei were retained for analysis. Following dimensional reduction and clustering, 12 238 microglia were identified by using key markers, including DOCK8, CSF1R, P2RY12, and CD74. The results of GO and KEGG pathway analysis showed that in gray matter microglia, functions such as endocytosis, ion homeostasis, and lipid localization were downregulated during disease progression, while in white matter microglia, functions such as protein folding, cytoplasmic translation, and response to thermal stimuli were upregulated. SCENIC analysis revealed that the expression of transcription factors such as FLI1, MITF, and FOXP1 was upregulated in MS. Conclusion ·Microglia play a critical role in MS, with white matter microglia being more significantly impacted by MS than their gray matter counterparts. Transcription factors such as FLI1, MITF, and FOXP1 are identified as key regulators involved in disease modulation, with their associated transcriptional regulatory networks playing a central role in disease modulation.