Single-Cell RNA-Seq Reveals Changes in Cell Subsets in The Cortical Microenvironment During Acute Phase of Ischemic Stroke Rats

Single-Cell RNA-Seq Reveals Changes in Cell Subsets in The Cortical Microenvironment During Acute Phase of Ischemic Stroke Rats | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Single-Cell RNA-Seq Reveals Changes in Cell Subsets in The Cortical Microenvironment During Acute Phase of Ischemic Stroke Rats Yijin Zhao, Chongwu Xiao, Hui Chen, Rui Zhu, Meimei Zhang, Haining Liu, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2200870/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Ischemic stroke, the most common type, has threatened human life and health. The treatment options for ischemic stroke are limited due to the complexity of the pathological process and cellular information. Therefore, acute ischemic stroke rats were established by middle cerebral artery occlusion (MCAO), and the cell populations in the cortex of MCAO rats were identified utilizing single-cell RNA sequencing (scRNA-seq). We identified 21 brain clusters with cell-type specific gene expression patterns and cell subpopulations, as well as 42 marker genes representing different cell subpopulations. The number of cells in clusters 0–3 increased significantly in the MCAO group compared to the sham group, and nine cell subpopulations exhibited remarkable differences in the number of genes. Subsequently, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed on the top 40 differentially expressed genes (DEGs) in the six cell subpopulations with significant differences. The results indicated that the biological processes and signaling pathways are involved in different cell subpopulations. In conclusion, scRNA-seq revealed the diversity of cell differentiation and the unique information of cell subpopulations in the cortex of rats with acute ischemic stroke, providing a novel insight for exploring the pathological process and drug discovery in the stroke. ischemic stroke cerebral ischemia-reperfusion injury single-cell RNA-seq MCAO cellular heterogeneity Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Stroke, which has high morbidity, mortality, and disability rates, has threatened human life and health while also imposing a significant economic burden on families and society(Collaborators 2021 ). The systematic analysis for the Global Burden of Disease Study 2019 indicates that stroke accounts for 11.6% of all deaths globally and remains the second leading cause of death(Collaborators 2021 ). Meanwhile, ischemic stroke accounts for approximately 80% of stroke patients(Xu et al. 2020 ). Ischemic stroke is a neurological dysfunction caused by focal brain, spinal cord, or retinal infarction(Sacco et al. 2013 ), including limb paralysis(Hong et al. 2018 ), spasm(Pundik et al. 2019 ), dysphagia(Bath, Lee and Everton 2018 ), aphasia(Stefaniak, Halai and Lambon Ralph 2020 ), dysarthria(Chiaramonte, Pavone and Vecchio 2020 ), and depression(Das and G 2018). Currently, ischemic stroke treatment mainly relies on intravenous thrombolysis and endovascular thrombectomy to achieve reperfusion of cerebral blood flow(Sun et al. 2018 ). Although rapid blood flow reperfusion can effectively reduce the brain cell death caused by ischemia, the resulting cerebral ischemia-reperfusion injury (CIRI) may trigger a series of cascade events such as necrotic apoptosis, free radical injury, and neuroinflammation to aggravate the damage of ischemic brain tissue, worsening patient prognosis(Sun et al. 2018 , Przykaza 2021 , Liao et al. 2020 ). Therefore, exploring better prevention and treatment methods based on the pathological mechanism of CIRI is one of the focuses of ischemic stroke management. Presently, microglia or astrocytes are classified as M1/M2 or A1/A2 types, similar to macrophages(Xiong, Liu and Yang 2016 , Kanazawa et al. 2017 , Liu et al. 2020 ). Increasing research indicates that this method of cell classification based on phenotype and markers cannot reveal the complex cellular state in brain tissue after stroke. This may also be the main reason for almost all the failures of interventions in immunomodulating immune cells in the brain after stroke(Zera and Buckwalter 2020 ). Simultaneously, this highlights the importance of understanding the deleterious or beneficial effects of different immune cell subpopulations after stroke and the urgency of uncovering changes in cell subpopulations and key genes after stroke. It is difficult to identify the cell subpopulations after stroke due to the complex structure and diverse brain cell types. However, applying single-cell RNA-sequencing (scRNA-seq) technology has effectively alleviated this problem(Mickelsen et al. 2019 ). ScRNA-seq is a powerful tool to identify individual cells(Shalek et al. 2013 , Butler et al. 2018 , Yamada and Nomura 2020 ). We can rapidly determine the gene expression pattern of cells by measuring the individual cell gene expression and analyzing the heterogeneity of genetic information within a cell subtype. Recently, scRNA-seq has been widely used in several fields and provides new insights into the heterogeneity of cell subpopulations in different tissues(Mickelsen et al. 2019 , Cohen et al. 2018 ). Therefore, this study aims to reveal the heterogeneity and biological function of cell subpopulations in the cerebral cortex of acute ischemic rats via applying scRNA-seq, providing a novel scheme and theoretical basis for the prevention and treatment of ischemic stroke. 2. Materials And Methods 2.1 Experimental animals and groups The experimental animal protocol was approved by the Animal Experiment Ethics Committee of Zhujiang Hospital of Southern Medical University and carried out following the regulations of experimental animal management of Zhujiang Hospital of Southern Medical University. In this study, Specific pathogen-free (SPF) healthy male Sprague Dawley (SD) rats (weighing 280–300 g and 6–8 weeks old) were provided by the Laboratory Animal Center of Southern Medical University. The rats were fed and drank water normally. They were raised in an environment with a temperature of 24 ± 1°C, a humidity of 40 ± 5%, and a 12 h light-dark cycle. Six rats were randomly divided into the Sham and middle cerebral artery occlusion (MCAO) groups, with three in each. 2.2 Establishment of rat models of cerebral ischemia-reperfusion injury The rats were weighed and intraperitoneally injected with Avertin (0.2 mL/10 g, reagent center of Southern Medical University)(Wang et al. 2020 ). After anesthesia, the rats were subjected to MCAO according to Longa’s method(Longa et al. 1989 ). Briefly, after exposing the common carotid artery (CCA), external carotid artery (ECA), and internal carotid artery (ICA) on the right side of the rat, a silicone-coated nylon monofilament with a diameter of 0.3 mm was inserted into the ECA and passed through the ICA until the middle cerebral artery was blocked. After ischemia for 1.5 h, the suture was slowly removed, and the wound was disinfected and sutured. The rats in the sham group were operated on according to the above procedure, but no suture was inserted. 2.3 2, 3, 5-Triphenyl-2H-Tetrazolium Chloride (TTC) staining A rat was randomly selected from each group 24h after reperfusion for euthanasia(Hawkins et al. 2016 ). After removing their brains, five 2 mm thick slices were serially transected in the brain matrix device. The brain slices were placed in 2% TTC solution and incubated at 37°C for 15 min in the dark. Finally, sections were photographed, with red representing normal brain tissue and infarcted areas in white. 2.4 Preparation of single-cell samples from rat cortex The rats in the Sham and MCAO groups were euthanized, and the brains were removed to obtain cerebral cortical cells for scRNA-seq. One brain tissue specimen that met the study requirement was selected from each group. The isolated cerebral cortex was prepared into a single-cell suspension. After gently mixing 10 µL of cell suspension and 10 µL of 0.4% Trypan Blue Stain (T10282, Thermo Fisher, USA), 10 µL of the mixture was quickly added to Countess® II Cell Counting. They were counted using Countess® II Automated Cell Counter (C10228, Thermo Fisher, USA). The cell concentration in the sham group was 1.65×10 6 cells/µL, and 86% were living cells, while the cell concentration in the MCAO group was 1.43×10 6 cells/µL and 88% were living cells. If the proportion of viable cells was greater than 80%, it was considered a qualified sample, and subsequent experiments were performed after adjusting the cell concentration to 1000 cells/µL. 2.5 Single-cell RNA sequencing (scRNA-seq) Single-cell samples of rat cerebral cortex were sequenced using the BD Rhapsody Single Cell Analysis System(Fan, Fu and Fodor 2015 , Birey et al. 2017 ). The prepared single-cell suspension was added to the 20W + microwell honeycomb plate. The beads with a unique molecular identifier (UMI) and cell barcode in the microplate were combined with cells to capture and identify single cells. Following cell capture, total RNA was extracted from each well and reverse transcribed into cDNA. The constructed cDNA library was sequenced on the Illumina Hiseq sequencing platform to obtain sequencing data. The count ≥ 1 was considered to indicate that the gene was expressed. After excluding the genes expressed in less than three cells in the sequencing results, the unqualified cells were excluded according to the following requirements: (1) Remove cells with more than 6000 or less than 200 genes. (2) Remove cells with a mitochondrial gene ratio greater than 10%. (3) Remove cells with a hemoglobin gene ratio of more than 0.1%. 2.6 Bioinformatics analysis After processing the sequencing results, the Wilcox algorithm was used to compare the cell subpopulation differences among the samples. Differentially expressed genes (DEGs) with fold change (FC) > 2 and P -value < 0.05 were obtained in cell subpopulations. The top 40 DEGs were selected for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes ( KEGG) analysis to clarify cell subpopulations’ biological processes and functions, according to the order of P -value from small to large. 2.7 Statistical analysis The DESeq2 package was used to determine the read counts for differentially expressed genes. A Student’s two-tailed t-test was used for comparison between the two groups. All statistics were performed using SPSS 20.0 software, and figures were drawn using GraphPad Prism 5.0 and Photoshop CC2019. A P -value < 0.05 was defined as statistically significant. 3. Results 3.1 Preliminary analysis of scRNA-seq results The rat models of MCAO were constructed and evaluated with TTC staining using scRNA-seq to detect the changes in cell subpopulations in the cerebral cortex of acute ischemic rats. Figure 1 A shows that the cerebral infarction area of the MCAO rat was white, and the normal tissue was red, indicating that the MCAO rat model was successfully constructed. Then, we isolated the cortex of rats in the sham and MCAO groups and prepared cell suspensions for scRNA-seq (Figure. 1B). After sequencing, the results were preprocessed and normalized utilizing the Seurat R package. Unqualified cells were removed (Figs. 1 B–D). The results showed that 4871 cells in the Sham group and 6836 cells in the MCAO group were identified using scRNA-seq (Figs. 1 B–D). The filtered cells were subjected to cell cycle analysis. The results indicated that the cells from the two groups of samples were evenly distributed in each cell cycle in this experiment, proving that the cell cycle effect had no significant effect on the subsequent analysis (Figs. 1 E–H). 3.2 Identification of cell subpopulations in the rat cerebral cortex with scRNA-Seq In this study, the brain cortex cells of sham and MCAO groups were identified using scRNA-seq based on the BD Rhapsody system. After unbiased clustering analysis, the distribution of cells derived from the cerebral cortex of rats in the sham and MCAO groups in each cell cluster was identified and visualized using t-Distributed stochastic neighbor embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP) dimensionality reduction algorithms, respectively (Figs. 1 A and C). Subsequently, cell types were annotated with SingleR, and 21-cell clusters with unique gene expression patterns were defined (Figs. 1 B and D). Figure 2 E illustrates that although the number of cells and gene expression levels are different in each cell cluster in the two groups, the cell clusters are similar in the two groups. Notably, these 21-cell clusters were divided into 11 major cell lineages (Figure. 1F), including microglia (clusters 0, 1, and 16), astrocytes (cluster 2), oligodendrocytes (clusters 3, 9, and 10), endothelial cells (clusters 4, 6, 11, 12, and 17), macrophages (cluster 5), ependymal cells (clusters 7 and 20), T cells (cluster 8), fibroblasts (clusters 13 and 19), monocytes (cluster 14), granulocytes/monocytes (cluster 15) and fibroblasts activated (cluster18). In conclusion, the scRNA-seq data identified 21-cell clusters with high intercellular heterogeneity in the rat cortex. 3.3 Identification and analysis of marker genes of different cell subpopulations in the cerebral cortex We performed marker gene analysis on the 21-cell clusters to achieve further insight into the heterogeneity of gene expression of each cell subpopulation in the MCAO rat cortex. The Wilcoxon rank-sum test analyzed the genes in all cell clusters and then scored the genes with One-vs-Res(Deng et al. 2014 ). The genes with high specific expression, logFC > 0.25, and expression in at least 20% of cells in each cell cluster were selected as the significant marker genes of cell subpopulations. Subsequently, the top two marker genes in each cell cluster were drawn into a heat map to more intuitively display the marker gene expression in different cell clusters (Fig. 3 A). Figure 3 A displays that each cell cluster is separated according to the marker gene expression, indicating that the selected marker genes can accurately represent the cell clusters. Table 1 indicates the marker genes of each cell cluster. Additionally, t-SNE plots and violin plots of the marker genes of clusters 4, 6, 11, 12, and 17, with significant differences between the sham and MCAO groups in the 21 cell clusters, were drawn to demonstrate the expression differences of the marker gene in different cell clusters (Figs. 3 B–K). In conclusion, 42 marker genes were identified from the rat cortex, elucidating the gene expression heterogeneity in each cell subpopulation. Table 1 The top two marker genes in each cell cluster. Cell cluster Cell lineage Marker genes Cluster 0 Microglia Lyz2 and Ccl4 Cluster 1 Microglia Olr1 and Bag3 Cluster 2 Astrocytes Gja1 and Gpr37l1 Cluster 3 Oligodendrocytes Mal and Plp1 Cluster 4 Endothelial cells Cldn5 and Flt1 Cluster 5 Macrophages Cxcl1 and Cxcl2 Cluster 6 Endothelial cells Abcc9 and Igfbp7 Cluster 7 Ependymal cells Nnat and Terf2ip Cluster 8 T cells Cxcr4 and Ccl5 Cluster 9 Oligodendrocytes Ugt8 and Apod Cluster 10 Oligodendrocytes Pdgfra and Vcan Cluster 11 Endothelial cells Hbb and Hba.a2 Cluster 12 Endothelial cells Vtn and Abcc9 Cluster 13 Fibroblasts Tagln and Acta2 Cluster 14 Monocytes RT1.Da and RT1.Ba Cluster 15 Granulocytes/monocytes S100a8 and S100a9 Cluster 16 Microglia RT1.Ba and Cd74 Cluster 17 Endothelial cells Vwf and Mt2A Cluster 18 Fibroblasts activated Igf2 and Col1a1 Cluster 19 Fibroblasts Igfbp5 and Ahcyl2 Cluster 20 Ependymal cells Folr1 and Enpp2 3.4 Changes of cell subpopulations in rat cerebral cortex after MCAO. The number of cells in each cell cluster was analyzed in the cortex of rats, and it was discovered that the number of cells in clusters 0–3 increased significantly after MCAO (Fig. 4 A). Moreover, the cell proportion of clusters 0–3 after MCAO increased dramatically (Fig. 4 B). The Heatmap of the cell proportion of 21 cell clusters revealed that clusters 0–3 were significantly different in the sham and MCAO groups. Cluster 20, one of the ependymal cell subsets, was not obtained in the MCAO group (Fig. 4 C). These results indicated that microglia, astrocytes, and oligodendrocytes proliferated in the rat cerebral cortex after MCAO, especially microglia. As the most abundant resident immune effector cells in the central nervous system (CNS), the significant microglia proliferation demonstrated the importance of microglia-based immunomodulatory interventions in the prevention and treatment of ischemic stroke. Then, a comparison of the number of genes in each cell cluster between sham and MCAO groups revealed a significant difference in 9 cell subsets between the two groups (Figs. 4 D and E), including cluster 1 (microglia), cluster 2 (astrocytes), cluster 3 (oligodendrocytes), cluster 4 (endothelial cells), cluster 5 (macrophages), cluster 6 (endothelial cells), cluster 7 (ependymal cells), cluster 10 (oligodendrocytes), and cluster 19 (fibroblasts). 3.5 GO analysis revealed the biological process of significantly changed cell subpopulations in the cerebral cortex after MCAO. We selected the top 40 DEGs with the smallest P -value in the cell subpopulations for GO analysis to elucidate the biological processes involved in the six cell subpopulations that changed significantly after MCAO. Our analysis revealed that the biological process of enrichment of the top 40 DEGs in cluster 1 was primarily related to the stress response after hypoxia (Fig. 5 A), while Cluster 2 may maintain protein homeostasis after MCAO mainly through autophagy (Fig. 5 B). Cell clusters 3 and 6 displayed similar biological processes, such as small GTPases-mediated signal transduction (Figs. 5 C and D). Cluster 7 may be related to synapse formation (Fig. 5 E), where cluster 10 was mainly involved in cell proliferation (Fig. 5 F). In conclusion, the GO analysis demonstrated the biological processes involved cell subpopulations significantly altered in the rat cerebral cortex after MCAO. 3.6 KEGG analysis revealed the signaling pathways of significantly changed cell subpopulations in the cerebral cortex after MCAO. In addition to GO analysis, we performed KEGG analysis on the top 40 DEGs in cell subpopulations significantly altered after MCAO to further indicate the involved signaling pathways. Figure 6 illustrates that KEGG analysis reveals the signaling pathways involved in clusters 1, 2, 3, 6, 7, and 10 after MCAO. Notably, GO analysis reveals that the stress response after hypoxia in cluster 1 may be related to the HIF-1 signaling pathway, while autophagy in cluster 2 may be related to the mTOR signaling pathway. In conclusion, KEGG analysis further clarified the signaling pathways involved in DEGs in significantly altered cell subpopulations after MCAO. 4. Discussion The complexity of the central nervous system is due to the existence of multiple cell types with specific gene expression patterns. Single-cell level transcriptome sequencing technology can accurately identify different cell types in the central nervous system. Therefore, it has great potential to better understand cell types in the central nervous system. Although single-cell sequencing is still in its infancy compared to traditional sequencing technologies such as genomics and conventional transcriptome sequencing, previous studies have demonstrated that single-cell RNA sequencing can be employed to identify different cell types in the central nervous system. Single-cell sequencing identified 39 cell subpopulations with different transcripts from the transcriptome of 44808 mouse retinal cells, resulting in a molecular map of gene expression for known retinal and new candidate cell subtypes(Macosko et al. 2015 ). Similarly, another study analyzed the diversity of retinal bipolar cells via single-cell sequencing, and identified 15 types of retinal bipolar cells, including all known and two novel cell subtypes(Shekhar et al. 2016 ). Additionally, other researchers revealed the diversity of cell types in the primary visual cortex of mice using single-cell sequencing techniques, including multiple non-neuronal cell subpopulations(Tasic et al. 2016 ). Meanwhile, the study discovered that cell subpopulations with different transcriptomes exhibit specific and differential electrophysiological and axonal projection characteristics, suggesting that single-cell transcriptome signatures may be associated with specific cellular properties(Tasic et al. 2016 ). Therefore, the cell subpopulations in cortical tissues of MCAO and normal rats were identified via scRNA-seq in this experiment. The results identified 21 cell clusters, including cell cluster 0 (microglia), cell cluster 1 (microglia), cell cluster 2 (astrocytes/qNSCs), cell cluster 3 (oligendrocytes), cell cluster 4 (endothelial cells), cell cluster 5 (macrophages), cell cluster 6 (endothelial cells), cell cluster 7 (ependymal), cell cluster 8 (T cells), cell cluster 9 (oligodendrocytes), cell cluster 10 (oligodendrocytes), cell cluster 11 (endothelial cells), cell cluster 12 (endothelial cells), cell cluster 13 (fibroblasts), cell cluster 14 (monocytes), cell cluster 15 (granulocytes/monocytes), cell cluster 16 (microglia), cell cluster 17 (endothelial cells), cell cluster 18 ( fibroblasts activated), cell cluster 19 (fibroblasts), cell cluster 20 (ependymal). scRNA-seq technology can distinguish between different cell types and different subtypes. In this experiment, we discovered that the same cell types clustered into different cell subgroups. Microglia were divided into three subpopulations, oligodendrocytes into three subpopulations, endothelial cells into five subpopulations, and ependymal cells and fibroblasts into two subpopulations. Simultaneously, we noticed that microglia had the highest number of cells after MCAO, consistent with previous studies(Xu et al. 2020 , Colonna and Butovsky 2017 , Rajan et al. 2019 ). When cerebral ischemia occurs, glial cells activated first, especially microglia(Xu et al. 2020 ). However, whether microglia have significant polarization within 24 h of cerebral ischemia-reperfusion has been controversial(Al-Ahmady et al. 2019 ). Our scRNA-seq data revealed that microglia differentiated into three cell subtypes: cell clusters 0, 1, and 16 at 24 h after MCAO. This differs from traditional cognition. Previous studies often divide microglia into two types, M1 and M2 types(Ma et al. 2017 ). In this experiment, we discovered the third type of microglia, which may represent an intermediate transition body in the transition process of M1 and M2 microglia. Microglia, as a “double-edged sword,” promote neuroinflammation or injury repair in stroke development(Hu et al. 2015 ). Therefore, there is an urgent need to inhibit the polarization of microglia to the pro-inflammatory type and drive its polarization to the protective type during the acute phase of ischemic stroke. Our sequencing results provide important information for identifying therapeutic targets for ischemic stroke and characterizing early cell changes by mining the heterogeneity of cell types in the rat cerebral cortex. In this experiment, we indicated that the microenvironment of rat cortical tissue changed compared to the gene expression of different cell subpopulations in normal cortical tissue after MCAO, mainly reflecting in nine cell subpopulations, including cell cluster 0 (microglia), cell cluster 1 (microglia), cell cluster 2 (astrocytes/qNSCs), cell cluster 3 (oligodendrocytes), cell cluster 4 (endothelial cells), cell cluster 5 (macrophages), cell cluster 6 (endothelial cells), cell cluster 7 (ependymal), cell cluster 10 (oligodendrocytes) and cell cluster 19 (fibroblasts). Moreover, the Wilcox algorithm revealed that cell clusters 1, 2, 3, 6, 7, and 10 exhibited statistically significant differences between the two samples. GO and KEGG analyses of the top 40 DEGs in cell subpopulations with significant differences revealed that the participation of cluster 1 in the stress response after hypoxia may be related to the HIF-1 signaling pathway, while the autophagy involved in cluster 2 may be related to mTOR signaling pathway. These pathways may be the key regulators of microglia and astrocytes after MCAO and provide a new perspective for future research. This study has some limitations. The first limitation lies in the experimental subjects. Although we simulated cerebral ischemia-reperfusion injury in rats, the brain microenvironment blueprint derived from experimental animal data does not perfectly represent humans. The second limitation is that our findings are based on scRNA-seq data and the low sample size due to the high cost of scRNA-seq technology. Therefore, further verification is needed. 5. Conclusion In this study, 21-cell clusters were identified utilizing scRNA- seq to detect the cerebral cortex of normal and MCAO rats, including three types of microglia, three types of oligodendrocytes, five types of endothelial cell, two types of ependymal cells, two types of fibroblasts, two types of monocytes, neurons, astrocytes, T cells and macrophages. Simultaneously, 42 marker genes in different cell subsets were defined. GO and KEGG analyses of the top 40 DEGs in six cell subpopulations with significant differences revealed the biological processes and signaling pathways of different cell subsets. In conclusion, this study revealed the diversity of cortical cell differentiation and the unique information of cell subsets in rats with acute ischemic stroke, providing a new perspective for the study of the pathological process of ischemic stroke. Declarations 6. Author Contributions Guozhi Huang and Qing Zeng designed and supervised the study. Yijin Zhao, Meimei Zhang, Haining Liu established the MCAO rat model and drew Figure 1. Yijin Zhao, Chongwu Xiao, Rui Zhu, and Hui Chen analyzed the single-cell RNA sequencing results and prepared Figures 2-6. Chongwu Xiao and Xiaofeng Zhang for Statistical analysis Guozhi Huang and Yijin Zhao wrote the manuscript. All authors reviewed and approved the manuscript. 7. Conflict of Interest The authors state that the study was conducted in the absence of any business or financial relationships that could be construed as potential conflicts of interest. 8. Funding This study was supported by the National Natural Science Foundation of China (82072528, 81874032, 82002380). 9. Acknowledgements We would like to thank the other members of our department, especially Yilei Zhang, and Chen Li, for their discussions and help. References Al-Ahmady, Z. S., D. Jasim, S. S. Ahmad, R. Wong, M. Haley, G. Coutts, I. Schiessl, S. M. Allan & K. 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Kasner, J. P. Broderick, L. R. Caplan, J. J. Connors, A. Culebras, M. S. Elkind, M. G. George, A. D. Hamdan, R. T. Higashida, B. L. Hoh, L. S. Janis, C. S. Kase, D. O. Kleindorfer, J. M. Lee, M. E. Moseley, E. D. Peterson, T. N. Turan, A. L. Valderrama, H. V. Vinters, C. o. C. S. American Heart Association Stroke Council, Anesthesia, R. Council on Cardiovascular, Intervention, C. Council on, N. Stroke, E. Council on, Prevention, D. Council on Peripheral Vascular, P. A. Council on Nutrition & Metabolism (2013) An updated definition of stroke for the 21st century: a statement for healthcare professionals from the American Heart Association/American Stroke Association. Stroke , 44, 2064–2089. https://doi.org/10.1161/STR.0b013e318296aeca Shalek, A. K., R. Satija, X. Adiconis, R. S. Gertner, J. T. Gaublomme, R. Raychowdhury, S. Schwartz, N. Yosef, C. Malboeuf, D. Lu, J. J. Trombetta, D. Gennert, A. Gnirke, A. Goren, N. Hacohen, J. Z. Levin, H. Park & A. Regev (2013) Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells. Nature, 498, 236–240. https://doi.org/10.1038/nature12172 Shekhar, K., S. W. Lapan, I. E. Whitney, N. M. Tran, E. Z. Macosko, M. Kowalczyk, X. Adiconis, J. Z. Levin, J. Nemesh, M. Goldman, S. A. McCarroll, C. L. Cepko, A. Regev & J. R. Sanes (2016) Comprehensive Classification of Retinal Bipolar Neurons by Single-Cell Transcriptomics. Cell , 166, 1308–1323 e1330.https://doi.org/10.1016/j.cell.2016.07.054 Stefaniak, J. D., A. D. Halai & M. A. Lambon Ralph (2020) The neural and neurocomputational bases of recovery from post-stroke aphasia. Nat Rev Neurol, 16, 43–55. https://doi.org/10.1038/s41582-019-0282-1 Sun, M. S., H. Jin, X. Sun, S. Huang, F. L. Zhang, Z. N. Guo & Y. Yang (2018) Free Radical Damage in Ischemia-Reperfusion Injury: An Obstacle in Acute Ischemic Stroke after Revascularization Therapy. Oxid Med Cell Longev , 2018, 3804979. https://doi.org/10.1155/2018/3804979 Tasic, B., V. Menon, T. N. Nguyen, T. K. Kim, T. Jarsky, Z. Yao, B. Levi, L. T. Gray, S. A. Sorensen, T. Dolbeare, D. Bertagnolli, J. Goldy, N. Shapovalova, S. Parry, C. Lee, K. Smith, A. Bernard, L. Madisen, S. M. Sunkin, M. Hawrylycz, C. Koch & H. Zeng (2016) Adult mouse cortical cell taxonomy revealed by single cell transcriptomics. Nat Neurosci, 19, 335–346. https://doi.org/10.1038/nn.4216 Wang, D., L. Chen, Y. Fu, Q. Kang, X. Wang, X. Ma, X. Li & J. Sheng (2020) Avertin affects murine colitis by regulating neutrophils and macrophages. Int Immunopharmacol, 80, 106153. https://doi.org/10.1016/j.intimp.2019.106153 Xiong, X. Y., L. Liu & Q. W. Yang (2016) Functions and mechanisms of microglia/macrophages in neuroinflammation and neurogenesis after stroke. Prog Neurobiol, 142, 23–44. https://doi.org/10.1016/j.pneurobio.2016.05.001 Xu, S., J. Lu, A. Shao, J. H. Zhang & J. Zhang (2020) Glial Cells: Role of the Immune Response in Ischemic Stroke. Front Immunol, 11, 294. https://doi.org/10.3389/fimmu.2020.00294 Yamada, S. & S. Nomura (2020) Review of Single-Cell RNA Sequencing in the Heart. Int J Mol Sci, 21 .https://doi.org/10.3390/ijms21218345 Zera, K. A. & M. S. Buckwalter (2020) The Local and Peripheral Immune Responses to Stroke: Implications for Therapeutic Development. Neurotherapeutics, 17, 414–435. https://doi.org/10.1007/s13311-020-00844-3 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2200870","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":148991596,"identity":"ee43bd37-1adb-4626-8677-6d24ade165f7","order_by":0,"name":"Yijin Zhao","email":"","orcid":"","institution":"Southern Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yijin","middleName":"","lastName":"Zhao","suffix":""},{"id":148991597,"identity":"afdd3119-3519-4e7e-9b71-98c18a6a3540","order_by":1,"name":"Chongwu Xiao","email":"","orcid":"","institution":"Southern Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Chongwu","middleName":"","lastName":"Xiao","suffix":""},{"id":148991598,"identity":"b7317226-2127-4d81-a5bd-02407b7e39de","order_by":2,"name":"Hui Chen","email":"","orcid":"","institution":"Southern Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Hui","middleName":"","lastName":"Chen","suffix":""},{"id":148991599,"identity":"bfdb2885-d637-4cc3-ba34-83ae825ca60e","order_by":3,"name":"Rui Zhu","email":"","orcid":"","institution":"First Affiliated Hospital of Gannan Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Rui","middleName":"","lastName":"Zhu","suffix":""},{"id":148991600,"identity":"e1b65f9e-0d05-49cf-b966-7165397d6d5e","order_by":4,"name":"Meimei Zhang","email":"","orcid":"","institution":"Affiliated Hospital of Jining Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Meimei","middleName":"","lastName":"Zhang","suffix":""},{"id":148991601,"identity":"97b818d2-0175-41a3-ba1f-12c73c7ac818","order_by":5,"name":"Haining Liu","email":"","orcid":"","institution":"Southern Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Haining","middleName":"","lastName":"Liu","suffix":""},{"id":148991602,"identity":"21438f5c-8920-445f-ad03-8ef2425c8f18","order_by":6,"name":"Xiaofeng Zhang","email":"","orcid":"","institution":"Southern Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xiaofeng","middleName":"","lastName":"Zhang","suffix":""},{"id":148991603,"identity":"9698c753-9ca8-45c4-b7b1-004894eff5cd","order_by":7,"name":"Qing Zeng","email":"","orcid":"","institution":"Southern Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Qing","middleName":"","lastName":"Zeng","suffix":""},{"id":148991604,"identity":"95a66358-7f7a-4be6-9f02-d666a40dd53b","order_by":8,"name":"Guozhi Huang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAx0lEQVRIiWNgGAWjYDCCAzxgSo6fvbHx4QdStBhL9hxuNpYgRUuiwYz0NgEeYnTw3T57TOLnjtoEA8mHbQwSDHZyug0EtEiey0uT7D1zPM9cOrHtQQFDsrHZAQJaDM7wmEnwth0rtpyd2G4gwXAgcRsxWiT/th1L3HDzYJsED7FapHnbahI33GAkUovkGR5ja9m2A8BATgQGsgERfuE7w2N4821bHTAqjz98+KHCTo6gFig4DHMnccpBoI54paNgFIyCUTDyAACZd0SbfR5sJQAAAABJRU5ErkJggg==","orcid":"","institution":"Southern Medical University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Guozhi","middleName":"","lastName":"Huang","suffix":""}],"badges":[],"createdAt":"2022-10-25 05:59:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2200870/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2200870/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":28685223,"identity":"01f3d8c4-cd8f-470f-8e87-e69f18212140","added_by":"auto","created_at":"2022-11-04 21:19:12","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":84547,"visible":true,"origin":"","legend":"\u003cp\u003eExperimental scheme and data preprocessing. (A) TTC staining results after 24 h of MCAO. The cerebral infarct area was stained white, and the normal tissue was red, indicating that the model was successfully established. (B) Schematic diagram of the scRNA-seq workflow. (C) Violin diagram of the number of genes, the proportion of mitochondrial genes, and the proportion of hemoglobin genes in the samples of the sham group and (D) MCAO group. (E–F) Principal component analysis (PCA) of cell cycle-related genes and all genes in the Sham group. (G–H) PCA of cell cycle-related genes and all genes in the MCAO group. In the PCA diagram, red dots represent cells in the G1 phase, green dots represent cells in G2 or M phase, and blue dots represent cells in the S phase.\u003c/p\u003e","description":"","filename":"f1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2200870/v1/02811f6abe4561b31bd2f472.jpg"},{"id":28684747,"identity":"8c6bf3be-0145-494a-a336-0f0919707014","added_by":"auto","created_at":"2022-11-04 21:11:12","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":96925,"visible":true,"origin":"","legend":"\u003cp\u003eTwenty-one cell clusters identified in the sham and MCAO groups in the rat cortex. (A) t-SNE plot of cells in the sham and MCAO groups, where red dots represent cells derived from the sham group, and green dots show cells derived from the MCAO group (n = 1). (B) t-SNE plot of 4871 cells in the sham group and the t-SNE plot of 6836 cells in the MCAO group. The labels on the right were 21-cell clusters with corresponding colors, of which 0 to 20 represent microglia, microglia, astrocytes, oligodendrocytes, endothelial cells, macrophages, and endothelial cells, ependymal cells, T cells, oligodendrocytes, oligodendrocytes, endothelial cells, endothelial cells, fibroblasts, monocytes, granulocytes/monocytes, microglia, endothelial cells, Activated fibroblasts, fibroblasts, and ependymal cells, respectively. (C) UMAP diagram of cells in the sham and MCAO groups, where red dots represent cells derived from the sham group, and green dots show cells derived from the MCAO group (n = 1). (D) UMAP diagram of 4871 cells in the sham group and UMAP diagram of 6836 cells in the MCAO group. (E) t-SNE plot of 21 cell clusters in the sham and MCAO groups. (F) t-SNE plot of 11 major cell lineages.\u003c/p\u003e","description":"","filename":"f2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2200870/v1/5b283bd43c62d7b7341e448e.jpg"},{"id":28684746,"identity":"7b2034ce-36de-4447-9038-00da1a326737","added_by":"auto","created_at":"2022-11-04 21:11:12","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":96220,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of marker genes. (A) Heatmap plots of the top two marker genes in each cell cluster. (B–F) The tSNE plot depicted selected marker gene expression in clusters 4, 6, 11, 12, and 17. (G–K) Violin plots of selected marker gene expression of clusters 4, 6, 11, 12, and 17 in various cell clusters.\u003c/p\u003e","description":"","filename":"f3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2200870/v1/37bd6e1799fd2ff8f6916100.jpg"},{"id":28684745,"identity":"80f728a5-c127-42e5-a623-9cf352c6c2d5","added_by":"auto","created_at":"2022-11-04 21:11:12","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":60196,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of differences between sham and MCAO groups. (A) The number of cells per cluster in the sham and MCAO groups. (B) Pie charts depicting the cell proportion of each cell cluster in the sham and MCAO groups. (C) Heatmap presenting the cell proportion of each cell cluster in the sham and MCAO groups. (D) Gene numbers of cell clusters in rat cerebral cortex. (E) Histogram of the number of genes of cell clusters in the sham and MCAO groups, where blue represents the MCAO group and red represents the sham group.\u003c/p\u003e","description":"","filename":"f4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2200870/v1/fc0b11ace89e95a608363861.jpg"},{"id":28684750,"identity":"25da4ef3-9ad6-42f6-9cbb-1ffc350a3222","added_by":"auto","created_at":"2022-11-04 21:11:12","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":76267,"visible":true,"origin":"","legend":"\u003cp\u003eGo analysis of DEGs in cell subpopulations. We performed GO analysis of DEG in cell clusters 1, 2, 3, 6, 7, and 10 to further clarify the biological function of significantly changed cell subsets after MCAO. (A) Bubble plot of GO analysis of the top 40 DEGs in cell cluster 1. (B) Bubble plot of GO analysis of the top 40 DEGs in cell cluster 2. (C) Bubble plot of GO analysis of the top 40 DEGs in cell cluster 3. (D) Bubble plot of GO analysis of the top 40 DEGs in cell cluster 6. (E) Bubble plot of GO analysis of the top 40 DEGs in cell cluster 7. (F) Bubble plot of GO analysis of the top 40 DEGs in cell cluster 10. The bubble size represents the number of DEGs enriched in the biological process, and the color represents the \u003cem\u003eP\u003c/em\u003e-value.\u003c/p\u003e","description":"","filename":"f5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2200870/v1/c5d3fad0f72d7755e26770ad.jpg"},{"id":28685338,"identity":"d763bcb8-6713-4aac-9391-9dd1940c51a0","added_by":"auto","created_at":"2022-11-04 21:27:12","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":88155,"visible":true,"origin":"","legend":"\u003cp\u003eKEGG analysis of DEGs in cell subpopulations. KEGG analysis was performed on DEGs in cell clusters 1, 2, 3, 6, 7, and 10. (A) Bubble plot of KEGG analysis of the top 40 DEGs in cell cluster 1. (B) Bubble plot of KEGG analysis of the top 40 DEGs in cell cluster 2. (C) Bubble plot of KEGG analysis of the top 40 DEGs in cell cluster 3. (D) Bubble plot of KEGG analysis of the top 40 DEGs in cell cluster 6. (E) Bubble plot of KEGG analysis of the top 40 DEGs in cell cluster 7. (F) Bubble plot of KEGG analysis of the top 40 DEGs in cell cluster 10. The bubble size represents the number of DEGs enriched in the biological process, and the color represents the \u003cem\u003eP\u003c/em\u003e-value.\u003c/p\u003e","description":"","filename":"f6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2200870/v1/1bf8b598095c5a15f6b7bd1d.jpg"},{"id":29224150,"identity":"39f43a8b-f63c-4eb9-af66-910f3287d5a3","added_by":"auto","created_at":"2022-11-18 06:14:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":883661,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2200870/v1/31118d9d-5f79-4559-9275-d58bb4470b43.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Single-Cell RNA-Seq Reveals Changes in Cell Subsets in The Cortical Microenvironment During Acute Phase of Ischemic Stroke Rats","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eStroke, which has high morbidity, mortality, and disability rates, has threatened human life and health while also imposing a significant economic burden on families and society(Collaborators \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The systematic analysis for the Global Burden of Disease Study 2019 indicates that stroke accounts for 11.6% of all deaths globally and remains the second leading cause of death(Collaborators \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Meanwhile, ischemic stroke accounts for approximately 80% of stroke patients(Xu et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Ischemic stroke is a neurological dysfunction caused by focal brain, spinal cord, or retinal infarction(Sacco et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), including limb paralysis(Hong et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), spasm(Pundik et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), dysphagia(Bath, Lee and Everton \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), aphasia(Stefaniak, Halai and Lambon Ralph \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), dysarthria(Chiaramonte, Pavone and Vecchio \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and depression(Das and G 2018). Currently, ischemic stroke treatment mainly relies on intravenous thrombolysis and endovascular thrombectomy to achieve reperfusion of cerebral blood flow(Sun et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Although rapid blood flow reperfusion can effectively reduce the brain cell death caused by ischemia, the resulting cerebral ischemia-reperfusion injury (CIRI) may trigger a series of cascade events such as necrotic apoptosis, free radical injury, and neuroinflammation to aggravate the damage of ischemic brain tissue, worsening patient prognosis(Sun et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, Przykaza \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Liao et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Therefore, exploring better prevention and treatment methods based on the pathological mechanism of CIRI is one of the focuses of ischemic stroke management.\u003c/p\u003e \u003cp\u003ePresently, microglia or astrocytes are classified as M1/M2 or A1/A2 types, similar to macrophages(Xiong, Liu and Yang \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, Kanazawa et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, Liu et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Increasing research indicates that this method of cell classification based on phenotype and markers cannot reveal the complex cellular state in brain tissue after stroke. This may also be the main reason for almost all the failures of interventions in immunomodulating immune cells in the brain after stroke(Zera and Buckwalter \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Simultaneously, this highlights the importance of understanding the deleterious or beneficial effects of different immune cell subpopulations after stroke and the urgency of uncovering changes in cell subpopulations and key genes after stroke. It is difficult to identify the cell subpopulations after stroke due to the complex structure and diverse brain cell types. However, applying single-cell RNA-sequencing (scRNA-seq) technology has effectively alleviated this problem(Mickelsen et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eScRNA-seq is a powerful tool to identify individual cells(Shalek et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2013\u003c/span\u003e, Butler et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, Yamada and Nomura \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). We can rapidly determine the gene expression pattern of cells by measuring the individual cell gene expression and analyzing the heterogeneity of genetic information within a cell subtype. Recently, scRNA-seq has been widely used in several fields and provides new insights into the heterogeneity of cell subpopulations in different tissues(Mickelsen et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, Cohen et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Therefore, this study aims to reveal the heterogeneity and biological function of cell subpopulations in the cerebral cortex of acute ischemic rats via applying scRNA-seq, providing a novel scheme and theoretical basis for the prevention and treatment of ischemic stroke.\u003c/p\u003e"},{"header":"2. Materials And Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Experimental animals and groups\u003c/h2\u003e \u003cp\u003e The experimental animal protocol was approved by the Animal Experiment Ethics Committee of Zhujiang Hospital of Southern Medical University and carried out following the regulations of experimental animal management of Zhujiang Hospital of Southern Medical University. In this study, Specific pathogen-free (SPF) healthy male Sprague Dawley (SD) rats (weighing 280\u0026ndash;300 g and 6\u0026ndash;8 weeks old) were provided by the Laboratory Animal Center of Southern Medical University. The rats were fed and drank water normally. They were raised in an environment with a temperature of 24\u0026thinsp;\u0026plusmn;\u0026thinsp;1\u0026deg;C, a humidity of 40\u0026thinsp;\u0026plusmn;\u0026thinsp;5%, and a 12 h light-dark cycle. Six rats were randomly divided into the Sham and middle cerebral artery occlusion (MCAO) groups, with three in each.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Establishment of rat models of cerebral ischemia-reperfusion injury\u003c/h2\u003e \u003cp\u003eThe rats were weighed and intraperitoneally injected with Avertin (0.2 mL/10 g, reagent center of Southern Medical University)(Wang et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). After anesthesia, the rats were subjected to MCAO according to Longa\u0026rsquo;s method(Longa et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1989\u003c/span\u003e). Briefly, after exposing the common carotid artery (CCA), external carotid artery (ECA), and internal carotid artery (ICA) on the right side of the rat, a silicone-coated nylon monofilament with a diameter of 0.3 mm was inserted into the ECA and passed through the ICA until the middle cerebral artery was blocked. After ischemia for 1.5 h, the suture was slowly removed, and the wound was disinfected and sutured. The rats in the sham group were operated on according to the above procedure, but no suture was inserted.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 2, 3, 5-Triphenyl-2H-Tetrazolium Chloride (TTC) staining\u003c/h2\u003e \u003cp\u003eA rat was randomly selected from each group 24h after reperfusion for euthanasia(Hawkins et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). After removing their brains, five 2 mm thick slices were serially transected in the brain matrix device. The brain slices were placed in 2% TTC solution and incubated at 37\u0026deg;C for 15 min in the dark. Finally, sections were photographed, with red representing normal brain tissue and infarcted areas in white.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e2.4 Preparation of single-cell samples from rat cortex\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eThe rats in the Sham and MCAO groups were euthanized, and the brains were removed to obtain cerebral cortical cells for scRNA-seq.\u0026nbsp;One brain tissue specimen that met the study requirement was selected from each group. The isolated cerebral cortex was prepared into a single-cell suspension. After gently mixing 10 \u0026micro;L of cell suspension and 10 \u0026micro;L of 0.4% Trypan Blue Stain (T10282, Thermo Fisher, USA), 10 \u0026micro;L of the mixture was quickly added to Countess\u0026reg; II Cell Counting. They were counted using Countess\u0026reg; II Automated Cell Counter (C10228, Thermo Fisher, USA). The cell concentration in the sham group was 1.65\u0026times;10\u003csup\u003e6\u003c/sup\u003e cells/\u0026micro;L, and 86% were living cells, while the cell concentration in the MCAO group was 1.43\u0026times;10\u003csup\u003e6\u003c/sup\u003e cells/\u0026micro;L and 88% were living cells. If the proportion of viable cells was greater than 80%, it was considered a qualified sample, and subsequent experiments were performed after adjusting the cell concentration to 1000 cells/\u0026micro;L.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e2.5 Single-cell RNA sequencing (scRNA-seq)\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eSingle-cell samples of rat cerebral cortex were sequenced using the BD Rhapsody Single Cell Analysis System(Fan, Fu and Fodor \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, Birey et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The prepared single-cell suspension was added to the 20W\u0026thinsp;+\u0026thinsp;microwell honeycomb plate. The beads with a unique molecular identifier (UMI) and cell barcode in the microplate were combined with cells to capture and identify single cells. Following cell capture, total RNA was extracted from each well and reverse transcribed into cDNA. The constructed cDNA library was sequenced on the Illumina Hiseq sequencing platform to obtain sequencing data. The count\u0026thinsp;\u0026ge;\u0026thinsp;1 was considered to indicate that the gene was expressed. After excluding the genes expressed in less than three cells in the sequencing results, the unqualified cells were excluded according to the following requirements: (1) Remove cells with more than 6000 or less than 200 genes. (2) Remove cells with a mitochondrial gene ratio greater than 10%. (3) Remove cells with a hemoglobin gene ratio of more than 0.1%.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e2.6 Bioinformatics analysis\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eAfter processing the sequencing results, the Wilcox algorithm was used to compare the cell subpopulation differences among the samples. Differentially expressed genes (DEGs) with fold change (FC)\u0026thinsp;\u0026gt;\u0026thinsp;2 and \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were obtained in cell subpopulations. The top 40 DEGs were selected for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes \u003cb\u003e(\u003c/b\u003eKEGG) analysis to clarify cell subpopulations\u0026rsquo; biological processes and functions, according to the order of \u003cem\u003eP\u003c/em\u003e-value from small to large.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Statistical analysis\u003c/h2\u003e \u003cp\u003eThe DESeq2 package was used to determine the read counts for differentially expressed genes. A Student\u0026rsquo;s two-tailed t-test was used for comparison between the two groups. All statistics were performed using SPSS 20.0 software, and figures were drawn using GraphPad Prism 5.0 and Photoshop CC2019. A \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was defined as statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Preliminary analysis of scRNA-seq results\u003c/h2\u003e \u003cp\u003eThe rat models of MCAO were constructed and evaluated with TTC staining using scRNA-seq to detect the changes in cell subpopulations in the cerebral cortex of acute ischemic rats. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA shows that the cerebral infarction area of the MCAO rat was white, and the normal tissue was red, indicating that the MCAO rat model was successfully constructed. Then, we isolated the cortex of rats in the sham and MCAO groups and prepared cell suspensions for scRNA-seq (Figure. 1B). After sequencing, the results were preprocessed and normalized utilizing the Seurat R package. Unqualified cells were removed (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB\u0026ndash;D). The results showed that 4871 cells in the Sham group and 6836 cells in the MCAO group were identified using scRNA-seq (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB\u0026ndash;D). The filtered cells were subjected to cell cycle analysis. The results indicated that the cells from the two groups of samples were evenly distributed in each cell cycle in this experiment, proving that the cell cycle effect had no significant effect on the subsequent analysis (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE\u0026ndash;H).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Identification of cell subpopulations in the rat cerebral cortex with scRNA-Seq\u003c/h2\u003e \u003cp\u003eIn this study, the brain cortex cells of sham and MCAO groups were identified using scRNA-seq based on the BD Rhapsody system. After unbiased clustering analysis, the distribution of cells derived from the cerebral cortex of rats in the sham and MCAO groups in each cell cluster was identified and visualized using t-Distributed stochastic neighbor embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP) dimensionality reduction algorithms, respectively (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA and C). Subsequently, cell types were annotated with SingleR, and 21-cell clusters with unique gene expression patterns were defined (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB and D). Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE illustrates that although the number of cells and gene expression levels are different in each cell cluster in the two groups, the cell clusters are similar in the two groups. Notably, these 21-cell clusters were divided into 11 major cell lineages (Figure. 1F), including microglia (clusters 0, 1, and 16), astrocytes (cluster 2), oligodendrocytes (clusters 3, 9, and 10), endothelial cells (clusters 4, 6, 11, 12, and 17), macrophages (cluster 5), ependymal cells (clusters 7 and 20), T cells (cluster 8), fibroblasts (clusters 13 and 19), monocytes (cluster 14), granulocytes/monocytes (cluster 15) and fibroblasts activated (cluster18). In conclusion, the scRNA-seq data identified 21-cell clusters with high intercellular heterogeneity in the rat cortex.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Identification and analysis of marker genes of different cell subpopulations in the cerebral cortex\u003c/h2\u003e \u003cp\u003eWe performed marker gene analysis on the 21-cell clusters to achieve further insight into the heterogeneity of gene expression of each cell subpopulation in the MCAO rat cortex. The Wilcoxon rank-sum test analyzed the genes in all cell clusters and then scored the genes with One-vs-Res(Deng et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The genes with high specific expression, logFC\u0026thinsp;\u0026gt;\u0026thinsp;0.25, and expression in at least 20% of cells in each cell cluster were selected as the significant marker genes of cell subpopulations. Subsequently, the top two marker genes in each cell cluster were drawn into a heat map to more intuitively display the marker gene expression in different cell clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA displays that each cell cluster is separated according to the marker gene expression, indicating that the selected marker genes can accurately represent the cell clusters. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e indicates the marker genes of each cell cluster. Additionally, t-SNE plots and violin plots of the marker genes of clusters 4, 6, 11, 12, and 17, with significant differences between the sham and MCAO groups in the 21 cell clusters, were drawn to demonstrate the expression differences of the marker gene in different cell clusters (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB\u0026ndash;K). In conclusion, 42 marker genes were identified from the rat cortex, elucidating the gene expression heterogeneity in each cell subpopulation.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe top two marker genes in each cell cluster.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCell cluster\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCell lineage\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMarker genes\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCluster 0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMicroglia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLyz2 and Ccl4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCluster 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMicroglia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOlr1 and Bag3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCluster 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAstrocytes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGja1 and Gpr37l1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCluster 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOligodendrocytes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMal and Plp1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCluster 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEndothelial cells\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCldn5 and Flt1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCluster 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMacrophages\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCxcl1 and Cxcl2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCluster 6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEndothelial cells\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAbcc9 and Igfbp7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCluster 7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEpendymal cells\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNnat and Terf2ip\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCluster 8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT cells\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCxcr4 and Ccl5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCluster 9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOligodendrocytes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUgt8 and Apod\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCluster 10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOligodendrocytes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePdgfra and Vcan\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCluster 11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEndothelial cells\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHbb and Hba.a2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCluster 12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEndothelial cells\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVtn and Abcc9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCluster 13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFibroblasts\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTagln and Acta2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCluster 14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMonocytes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRT1.Da and RT1.Ba\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCluster 15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGranulocytes/monocytes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS100a8 and S100a9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCluster 16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMicroglia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRT1.Ba and Cd74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCluster 17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEndothelial cells\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVwf and Mt2A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCluster 18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFibroblasts activated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIgf2 and Col1a1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCluster 19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFibroblasts\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIgfbp5 and Ahcyl2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCluster 20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEpendymal cells\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFolr1 and Enpp2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e3.4 Changes of cell subpopulations in rat cerebral cortex after MCAO.\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eThe number of cells in each cell cluster was analyzed in the cortex of rats, and it was discovered that the number of cells in clusters 0\u0026ndash;3 increased significantly after MCAO (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Moreover, the cell proportion of clusters 0\u0026ndash;3 after MCAO increased dramatically (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). The Heatmap of the cell proportion of 21 cell clusters revealed that clusters 0\u0026ndash;3 were significantly different in the sham and MCAO groups. Cluster 20, one of the ependymal cell subsets, was not obtained in the MCAO group (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). These results indicated that microglia, astrocytes, and oligodendrocytes proliferated in the rat cerebral cortex after MCAO, especially microglia. As the most abundant resident immune effector cells in the central nervous system (CNS), the significant microglia proliferation demonstrated the importance of microglia-based immunomodulatory interventions in the prevention and treatment of ischemic stroke. Then, a comparison of the number of genes in each cell cluster between sham and MCAO groups revealed a significant difference in 9 cell subsets between the two groups (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD and E), including cluster 1 (microglia), cluster 2 (astrocytes), cluster 3 (oligodendrocytes), cluster 4 (endothelial cells), cluster 5 (macrophages), cluster 6 (endothelial cells), cluster 7 (ependymal cells), cluster 10 (oligodendrocytes), and cluster 19 (fibroblasts).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e3.5 GO analysis revealed the biological process of significantly changed cell subpopulations in the cerebral cortex after MCAO.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWe selected the top 40 DEGs with the smallest \u003cem\u003eP\u003c/em\u003e-value in the cell subpopulations for GO analysis to elucidate the biological processes involved in the six cell subpopulations that changed significantly after MCAO. Our analysis revealed that the biological process of enrichment of the top 40 DEGs in cluster 1 was primarily related to the stress response after hypoxia (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA), while Cluster 2 may maintain protein homeostasis after MCAO mainly through autophagy (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Cell clusters 3 and 6 displayed similar biological processes, such as small GTPases-mediated signal transduction (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC and D). Cluster 7 may be related to synapse formation (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE), where cluster 10 was mainly involved in cell proliferation (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF). In conclusion, the GO analysis demonstrated the biological processes involved cell subpopulations significantly altered in the rat cerebral cortex after MCAO.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e3.6 KEGG analysis revealed the signaling pathways of significantly changed cell subpopulations in the cerebral cortex after MCAO.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eIn addition to GO analysis, we performed KEGG analysis on the top 40 DEGs in cell subpopulations significantly altered after MCAO to further indicate the involved signaling pathways. Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e illustrates that KEGG analysis reveals the signaling pathways involved in clusters 1, 2, 3, 6, 7, and 10 after MCAO. Notably, GO analysis reveals that the stress response after hypoxia in cluster 1 may be related to the HIF-1 signaling pathway, while autophagy in cluster 2 may be related to the mTOR signaling pathway. In conclusion, KEGG analysis further clarified the signaling pathways involved in DEGs in significantly altered cell subpopulations after MCAO.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe complexity of the central nervous system is due to the existence of multiple cell types with specific gene expression patterns. Single-cell level transcriptome sequencing technology can accurately identify different cell types in the central nervous system. Therefore, it has great potential to better understand cell types in the central nervous system.\u003c/p\u003e \u003cp\u003eAlthough single-cell sequencing is still in its infancy compared to traditional sequencing technologies such as genomics and conventional transcriptome sequencing, previous studies have demonstrated that single-cell RNA sequencing can be employed to identify different cell types in the central nervous system. Single-cell sequencing identified 39 cell subpopulations with different transcripts from the transcriptome of 44808 mouse retinal cells, resulting in a molecular map of gene expression for known retinal and new candidate cell subtypes(Macosko et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Similarly, another study analyzed the diversity of retinal bipolar cells via single-cell sequencing, and identified 15 types of retinal bipolar cells, including all known and two novel cell subtypes(Shekhar et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Additionally, other researchers revealed the diversity of cell types in the primary visual cortex of mice using single-cell sequencing techniques, including multiple non-neuronal cell subpopulations(Tasic et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Meanwhile, the study discovered that cell subpopulations with different transcriptomes exhibit specific and differential electrophysiological and axonal projection characteristics, suggesting that single-cell transcriptome signatures may be associated with specific cellular properties(Tasic et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Therefore, the cell subpopulations in cortical tissues of MCAO and normal rats were identified via scRNA-seq in this experiment. The results identified 21 cell clusters, including cell cluster 0 (microglia), cell cluster 1 (microglia), cell cluster 2 (astrocytes/qNSCs), cell cluster 3 (oligendrocytes), cell cluster 4 (endothelial cells), cell cluster 5 (macrophages), cell cluster 6 (endothelial cells), cell cluster 7 (ependymal), cell cluster 8 (T cells), cell cluster 9 (oligodendrocytes), cell cluster 10 (oligodendrocytes), cell cluster 11 (endothelial cells), cell cluster 12 (endothelial cells), cell cluster 13 (fibroblasts), cell cluster 14 (monocytes), cell cluster 15 (granulocytes/monocytes), cell cluster 16 (microglia), cell cluster 17 (endothelial cells), cell cluster 18 ( fibroblasts activated), cell cluster 19 (fibroblasts), cell cluster 20 (ependymal).\u003c/p\u003e \u003cp\u003escRNA-seq technology can distinguish between different cell types and different subtypes. In this experiment, we discovered that the same cell types clustered into different cell subgroups. Microglia were divided into three subpopulations, oligodendrocytes into three subpopulations, endothelial cells into five subpopulations, and ependymal cells and fibroblasts into two subpopulations. Simultaneously, we noticed that microglia had the highest number of cells after MCAO, consistent with previous studies(Xu et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Colonna and Butovsky \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, Rajan et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). When cerebral ischemia occurs, glial cells activated first, especially microglia(Xu et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, whether microglia have significant polarization within 24 h of cerebral ischemia-reperfusion has been controversial(Al-Ahmady et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Our scRNA-seq data revealed that microglia differentiated into three cell subtypes: cell clusters 0, 1, and 16 at 24 h after MCAO. This differs from traditional cognition. Previous studies often divide microglia into two types, M1 and M2 types(Ma et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In this experiment, we discovered the third type of microglia, which may represent an intermediate transition body in the transition process of M1 and M2 microglia. Microglia, as a \u0026ldquo;double-edged sword,\u0026rdquo; promote neuroinflammation or injury repair in stroke development(Hu et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Therefore, there is an urgent need to inhibit the polarization of microglia to the pro-inflammatory type and drive its polarization to the protective type during the acute phase of ischemic stroke. Our sequencing results provide important information for identifying therapeutic targets for ischemic stroke and characterizing early cell changes by mining the heterogeneity of cell types in the rat cerebral cortex.\u003c/p\u003e \u003cp\u003eIn this experiment, we indicated that the microenvironment of rat cortical tissue changed compared to the gene expression of different cell subpopulations in normal cortical tissue after MCAO, mainly reflecting in nine cell subpopulations, including cell cluster 0 (microglia), cell cluster 1 (microglia), cell cluster 2 (astrocytes/qNSCs), cell cluster 3 (oligodendrocytes), cell cluster 4 (endothelial cells), cell cluster 5 (macrophages), cell cluster 6 (endothelial cells), cell cluster 7 (ependymal), cell cluster 10 (oligodendrocytes) and cell cluster 19 (fibroblasts). Moreover, the Wilcox algorithm revealed that cell clusters 1, 2, 3, 6, 7, and 10 exhibited statistically significant differences between the two samples. GO and KEGG analyses of the top 40 DEGs in cell subpopulations with significant differences revealed that the participation of cluster 1 in the stress response after hypoxia may be related to the HIF-1 signaling pathway, while the autophagy involved in cluster 2 may be related to mTOR signaling pathway. These pathways may be the key regulators of microglia and astrocytes after MCAO and provide a new perspective for future research.\u003c/p\u003e \u003cp\u003eThis study has some limitations. The first limitation lies in the experimental subjects. Although we simulated cerebral ischemia-reperfusion injury in rats, the brain microenvironment blueprint derived from experimental animal data does not perfectly represent humans. The second limitation is that our findings are based on scRNA-seq data and the low sample size due to the high cost of scRNA-seq technology. Therefore, further verification is needed.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn this study, 21-cell clusters were identified utilizing scRNA- seq to detect the cerebral cortex of normal and MCAO rats, including three types of microglia, three types of oligodendrocytes, five types of endothelial cell, two types of ependymal cells, two types of fibroblasts, two types of monocytes, neurons, astrocytes, T cells and macrophages. Simultaneously, 42 marker genes in different cell subsets were defined. GO and KEGG analyses of the top 40 DEGs in six cell subpopulations with significant differences revealed the biological processes and signaling pathways of different cell subsets. In conclusion, this study revealed the diversity of cortical cell differentiation and the unique information of cell subsets in rats with acute ischemic stroke, providing a new perspective for the study of the pathological process of ischemic stroke.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e6.\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGuozhi Huang and Qing Zeng designed and supervised the study. Yijin Zhao, Meimei Zhang, Haining Liu established the MCAO rat model and drew Figure 1. Yijin Zhao, Chongwu Xiao, Rui Zhu, and Hui Chen analyzed the single-cell RNA sequencing results and prepared Figures 2-6. Chongwu Xiao and Xiaofeng Zhang for Statistical analysis Guozhi Huang and Yijin Zhao wrote the manuscript. All authors reviewed and approved the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e7.\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors state that the study was conducted in the absence of any business or financial relationships that could be construed as potential conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e8.\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Funding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the National Natural Science Foundation of China (82072528, 81874032, 82002380).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e9.\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank the other members of our department, especially Yilei Zhang, and Chen Li, for their discussions and help.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAl-Ahmady, Z. 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Neurotherapeutics, 17, 414\u0026ndash;435.\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s13311-020-00844-3\u003c/span\u003e\u003cspan address=\"10.1007/s13311-020-00844-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"ischemic stroke, cerebral ischemia-reperfusion injury, single-cell RNA-seq, MCAO, cellular heterogeneity","lastPublishedDoi":"10.21203/rs.3.rs-2200870/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2200870/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIschemic stroke, the most common type, has threatened human life and health. The treatment options for ischemic stroke are limited due to the complexity of the pathological process and cellular information. Therefore, acute ischemic stroke rats were established by middle cerebral artery occlusion (MCAO), and the cell populations in the cortex of MCAO rats were identified utilizing single-cell RNA sequencing (scRNA-seq). We identified 21 brain clusters with cell-type specific gene expression patterns and cell subpopulations, as well as 42 marker genes representing different cell subpopulations. The number of cells in clusters 0\u0026ndash;3 increased significantly in the MCAO group compared to the sham group, and nine cell subpopulations exhibited remarkable differences in the number of genes. Subsequently, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed on the top 40 differentially expressed genes (DEGs) in the six cell subpopulations with significant differences. The results indicated that the biological processes and signaling pathways are involved in different cell subpopulations. In conclusion, scRNA-seq revealed the diversity of cell differentiation and the unique information of cell subpopulations in the cortex of rats with acute ischemic stroke, providing a novel insight for exploring the pathological process and drug discovery in the stroke.\u003c/p\u003e","manuscriptTitle":"Single-Cell RNA-Seq Reveals Changes in Cell Subsets in The Cortical Microenvironment During Acute Phase of Ischemic Stroke Rats","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-11-04 21:11:07","doi":"10.21203/rs.3.rs-2200870/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a9b53519-abd5-427f-aa72-732a6249a911","owner":[],"postedDate":"November 4th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2022-11-18T06:14:37+00:00","versionOfRecord":[],"versionCreatedAt":"2022-11-04 21:11:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2200870","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2200870","identity":"rs-2200870","version":["v1"]},"buildId":"cBFmMYwuxLRRLfASyISRj","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}