Single-Cell RNA-Seq Reveals The Heterogeneity Of Brain Cells In Cerebral Ischemic Injury

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.