【作者】 郑凯；

【导师】 郝峻巍；

【作者基本信息】 天津医科大学 ， 神经病学， 2021， 博士

【摘要】 研究背景及目的:缺血性脑卒中(ischemic stroke,IS)是一种危害极大的神经系统疾病,目前有效的治疗方法有限。中枢神经系统内纷繁复杂的细胞组成存在广泛的异质性,如何在单细胞水平确定不同脑细胞亚群在缺血性脑卒中的发病和进展中的作用一直是一个挑战。尽管既往对一种或多种纯化的细胞进行了转录组研究,然而,群体细胞平均水平的辨析度可能掩盖了细胞亚型组成比例和特定细胞类型中成分的潜在改变,特别是数量罕见的细胞类型。近年来,随着单细胞测序技术的兴起,神经系统内细胞类型的异质性和免疫微环境中细胞亚群的调控网络也逐渐被研究发现。本研究基于单细胞转录组测序技术(Single-cell RNA sequencing,sc RNA-seq),通过对缺血性脑卒中后脑组织中的成千上万的每一个细胞进行单细胞水平的RNA测序,详细描述缺血性脑损伤后独特的单细胞水平的转录表达谱的改变,揭示了细胞类型特异性和共享的差异表达基因,以及探索与缺血性脑损伤特异性相关的细胞亚群,为研究缺血性脑卒中的发病机制提供更为详实的细胞分子学基础。研究方法:构建小鼠大脑中动脉闭塞模型和对应的假手术组,缺血再灌注24小时后,分别取出缺血侧脑半球和假手术组对应的脑半球,通过酶消化法,制备脑组织的单细胞悬液。对单细胞进行细胞活性染料(Zombie染料)的染色孵育后,通过流式仪器(BD FACS Aria III)分选活性较高的细胞,最后用台盼蓝染色来进一步区分死活细胞,然后用细胞计数仪进行细胞活率检测和计数统计后,若细胞活率大于80%,则将细胞浓度调整到合适的浓度(大约700-1200个/μL。)将制备好的细胞悬浮液利用10×Genomics微流控芯片,将带有细胞标签序列(cell Barcode)的凝胶珠(bead)和细胞包裹在液滴中。在液滴中,细胞破裂,释放的m RNA与凝胶珠上的细胞标签序列相连,形成单细胞GEMs结构(Gel Bead in Emulsions)。细胞的m RNA在液滴中进行逆转录反应形成c DNA,随后破乳,之后带有标签的c DNA将被混合然后扩增再进行文库构建。文库构建完成后,需要进行初步的定量(Qubit 2.0荧光定量仪),然后对文库的插入片段DNA进行检测(安捷伦Agilent 2100生物分析仪),插入片段大小符合预期后,需要对文库的有效浓度(一般为2 n M)进行准确的定量(q PCR方法),以此来确保文库质量。库检合格后,进行Illumina Hi Seq4000测序。下机后我们进一步处理原始数据base calling转化为序列数据(raw reads),然后结合10X Genomics单细胞转录组测序的文库结构特点,对属于Reads的Barcode、UMI和插入片段部分进行有效的拆分,然后将插入片段部分与参考基因组进行比对,通过统计比对到每个区域的比例,然后进行表达量的计算;最后通过基于表达量的高低,对细胞进行降维和聚类、分化发育轨迹推断和单细胞基因调控网络分析等;根据差异基因表达的情况,进行GO、KEGG等富集分析,细胞间通讯分析等。最后,利用免疫荧光染色和流式细胞术对差异表达基因进行蛋白水平的验证。研究结果:数据经过质量控制后,本研究总共捕获到58528个细胞,经标准化后平均每个细胞有92207个reads,平均每个细胞检测到的基因中位数是1295个。结合已知的细胞类型特异性的标记基因(marker gene)和本研究的基因表达聚类情况,我们大致鉴定出14种主要细胞类型,在缺血性脑损伤后每种细胞类型的细胞比例发生了不同程度的改变,以单核来源的细胞变化最为显著,细胞比例由2%变为16%。然后分别对来自假手术组和MCAO组的每种细胞类型进行差异表达基因(DEGs)分析,发现在小胶质细胞中差异表达基因最多,共275个DEGs(P<0.05)。另外,通过对单细胞水平和群体细胞平均水平的差异基因重叠度分析,使用单细胞类型识别的差异基因中,约有80%在群体细胞平均水平上未被检测到。此外,在群体细胞平均水平和单细胞水平之间的共有的DEGs中约50%与小胶质细胞DEGs广泛的重叠。其中,小胶质细胞特异性表达157个DEGs,位居第一。此外,在所有细胞类型中,小胶质细胞和中枢神经系统边界相关的巨噬细胞(CAMs)在缺血性损伤后重叠的共同差异表达基因数目最多。然后,我们通过脑组织免疫荧光染色和流式细胞术来进一步证实单细胞RNA测序的结果是否也在蛋白水平上发生变化,与假手术组相比,缺血性脑损伤后GPD1在少突胶质细胞、CCL11在周细胞、CD72在小胶质细胞和LILRB4A在巨噬细胞/小胶质细胞中的特异性表达上调。我们进一步利用Seurat软件对细胞类型的潜在亚型进行深入研究,在本研究共鉴定了五种不同的小胶质细胞亚群、六种中枢神经系统边界相关的巨噬细胞亚群、七种单核/巨噬细胞亚群、四种不同的中性粒细胞亚群、六种淋巴细胞亚群、六种血管内皮细胞亚群、三种周细胞亚群和六种血管平滑肌细胞亚群等,并通过流式细胞术对关键细胞亚型进行了验证。细胞间通讯分析结果显示,假手术组细胞间相互作用的预测分析发现了一系列生长因子相关的信号通路;在MCAO组,则以小胶质细胞和中枢神经系统边界相关的巨噬细胞主导的细胞间通讯作用为主;值得注意的是,小胶质细胞与其他免疫细胞、星形胶质细胞、周细胞和少突胶质细胞的相互作用连接数量明显增加,主要包括趋化因子、细胞因子和肿瘤坏死因子等。研究结论:总之,本研究首次采用单细胞转录组测序的方法绘制了缺血性脑卒中后单细胞的水平神经炎症过程中较为精确的转录图谱,探索了缺血缺氧情况下较为敏感的细胞亚型的异质性,细胞类型特异性和共享的差异表达基因,以及细胞间复杂的相互作用网络关系。为基于细胞亚型特异性分子探索缺血性脑卒中发病机制和药物潜在靶点的发现提供了一定的数据理论基础。更多还原

【Abstract】 Background and Objective: Ischemic stroke(IS)is a detrimental neurological disease with limited treatments options.It has been challenging to define the roles of brain cell subsets in IS onset and progression due to cellular heterogeneity in the CNS.Although transcriptome studies have been conducted on one or more purified cell types,RNA-sequencing analysis in bulk-tissue-level may mask potential changes in the proportion of cell subtypes and composition in specific cell types,especially in rare cell types.In recent years,with the development of single cell sequencing technology,the heterogeneity of cell types in tissues and the regulatory network of cell subsets in immune microenvironment have been gradually discovered.The purpose of this study was to explore transcriptome expression differences among brain cells after ischemic stroke based on single-cell RNA sequencing,reveal cell-type-specific and shared gene-expression perturbations,disease-associated cellular subpopulations,and provide a blueprint for interrogating the molecular and cellular basis of ischemic stroke.Methods: Here,the mouse model of middle cerebral artery occlusion and the corresponding sham operation group were established.After 24 hours of ischemia reperfusion,the cerebral hemispheres of the ischemic side and the sham operation group were extracted respectively.The single-cell suspension of the brain tissue was prepared by enzymatic digestion method.The single cells were stained and fixed with cellular reactive dye,and the cells with higher activity were sorted by flow cytometry.After trypan blue staining for cell count and cell viability detection,the cell concentration was adjusted to 700-1200 cells /μL if the cell viability was over 80%.The cell suspension was prepared by using 10×Genomics microfluidic chip,and the gel bead with cell Barcode and the cells were wrapped in the droplet,where the cells burst,releasing m RNAs that connect to cell tag sequences on the gel bead,forming GEMS(Gel Bead in Emulsions).The m RNA of the cells was reverse transcriptional reaction in the droplet to form c DNA,and then demulsification was performed,after which tagged c DNA is mixed and amplified for library construction.After the library construction was completed,Qubit 2.0 was used for preliminary quantification,then Agilent 2100 was used to detect the INSERT DNA of the library.After the INSERT size met the expectation,the effective concentration(2 n M)of the library was accurately quantified by q PCR method to ensure the quality of the library.After passing the library test,Illumina Hi Seq4000 sequencing was performed.The Raw data off the plane was called Raw reads,then we split Barcode,UMI and the part of embedding clips of the reads according to 10 x Genomics single-cell transcriptome sequencing unique library structure.After aligning the insert part segment to reference genome,we then count the statistical comparison to each area ratio and the expression quantity.Based on the results of expression levels,cell clustering,cell time trajectory prediction and gene regulatory network analysis were performed.Based on the results of differential gene expression,GO,KEGG enrichment analysis and inter-cell communication analysis was performed.Finally,immunofluorescence staining and flow cytometry was used to verify the protein levels of the differentially expressed genes.Results: After data quality control,a total of 58,528 cells were captured in this study,with an average of 92,207 reads per cell after standardization,and an average of1,295 median genes detected per cell.According to the known cell type specific marker genes and gene expression pattern of this study,we identified roughly 14 kinds of major cell types.After ischemic brain injury,each cell type ratio had different degree of change,and the cell rate of mononuclear derived cells changed most significantly,from 2% to 16%.Most strikingly,a total of 275 DEGs between MCAO and Sham samples with p-value <0.05 were identified in microglia,ranking microglia at the top of the list.Moreover,the overlap DEGs analysis between the single-cell and bulk tissue revealed that approximately 80% of DEGs identified using single cell type were undetected at the whole brain level,and the shared DEGs between bulk tissue and the single cell displayed a broad spectrum of microglial DEGs(>50%).Meanwhile,microglia harbored 157 unique DEGs among all cell types and were located at the top of the list,followed by Md Cs,oligodendrocyte,endothelial cells,and CAMs.Besides,microglia and CAMs shared the most common DEGs post MCAO among all cell types.Additionally,to further determine if our transcriptomic approach faithfully captured changes at the protein level,we performed immunofluorescence staining.We indeed observed the specific ischemic injury-related upregulation of GPD1 in oligodendrocyte,CCL11 in pericyte,CD72 in microglia and LILRB4 A in macrophages/microglia.To dissect cell-type heterogeneity,we next sub-clustered each major cell type,resulting in 4 microglia(MG),6CNS-associated macrophages(CAMs),7 monocytes/macrophages(Mon/Mφ),4neutrophils,6 lymphocytes,6 vascular endothelial cells(ECs),3 pericytes(PCs)and6 smooth muscle cells(SMCs)sub-clusters.Major cell subtypes were verified by flow cytometry.Analysis of the predicted cell–cell interactions between brain cells in sham controls revealed a wealth of growth factor signaling pathways;the cell–cell communication landscape in the MCAO groups is dominated by microglia and CNS border associated macrophages(CAMs).Microglia have increased predicted interactions with other immune cells,astrocytes,pericytes and oligodendrocytes including chemokines,cytokines,and tumor necrosis factor(TNF)families.Conclusions: Overall,sc RNA-seq revealed the precise transcriptional changes in ischemic stroke during neuroinflammation at the single-cell level.We identified the heterogeneity of sensitive cell subtypes under ischemia and hypoxia,cell type specific and shared differentially expressed genes,and complex intercellular interaction networks,which open up a new field for exploration of the disease mechanisms and drug discovery in stroke based on the cell-subtype specific molecules.更多还原