Single-cell RNA sequencing reveals the evolution of the immune landscape during perihematomal edema progression after intracerebral hemorrhage

Single-cell RNA sequencing reveals the evolution of the immune landscape during perihematomal edema progression after intracerebral hemorrhage | 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 sequencing reveals the evolution of the immune landscape during perihematomal edema progression after intracerebral hemorrhage Peng Zhang, Cong Gao, Qiang Guo, Dongxu Yang, Guangning Zhang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3996729/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Background Perihematomal edema (PHE) after post-intracerebral hemorrhage (ICH) has complex pathophysiological mechanisms that are poorly understood. The complicated immune response in the post-ICH brain constitutes a crucial component of PHE pathophysiology. In this study, we aimed to characterize the transcriptional profiles of immune cell populations in human PHE tissues and explore the microscopic differences between different types of immune cells. Methods ScRNA sequencing (scRNA-seq) was used to map immune cell populations within comprehensively resected PHE samples collected from patients at different stages after ICH. Results We established, for the first time, a comprehensive landscape of diverse immune cell populations in human PHE tissue at a single-cell level. Our study identified 12 microglial and five neutrophil subsets in human PHE tissue. What’s more, we discovered that the SPP1 pathway served as the basis for self-communication between microglia subclusters during the progression of PHE. Additionally, we traced the trajectory branches of different neutrophil subtypes. We also demonstrated that microglia-produced OPN could regulate the immune environment in PHE by interacting with CD44 cells. Conclusions As a result of our research, we have gained valuable insight into the immunomicroenvironment within PHE tissue, which could potentially be used to develop novel treatment modalities for ICH. intracerebral hemorrhage stroke single cell sequencing inflammatory cells microglia neutrophils Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Introduction Intracerebral hemorrhage (ICH) is one of the most critical and severe illnesses, posing a significant threat to global health due to its high rates of disability and mortality ( 1 – 3 ). ICH initially causes injury through the physical disruption of the initial hemorrhage and hematoma expansion, and it also leads to secondary brain injury by the development of perihematomal edema (PHE) ( 4 ). The formation of PHE occurs within 1–4 hours after ICH, with subsequent progression for up to 2–3 weeks after that ( 5 ). However, the specific pathophysiological mechanisms of PHE formation are complex and poorly understood. Previous studies have shown that PHE enhances the mass effect induced by initial hematoma and causes direct damage to brain tissue via blood-brain barrier (BBB) dysfunction and imbalanced osmotic gradients, leading to neurological deterioration ( 6 ). Furthermore, PHE progression is closely related to adverse clinical outcomes and prognosis of patients with ICH ( 7 ). Therefore, ICH remains inadequately controlled by current treatments. However, the emergence of immunomodulatory medications has reignited the hope for ICH treatment. Considering PHE's role in secondary brain injury, developing effective immunomodulatory treatments targeting PHE might offer a new therapeutic modality for ICH. Three randomized controlled trials have demonstrated that immunomodulatory drugs, such as Minocycline and Deferoxamine, aimed at targeting immune cells in ICH, have been unsuccessful in clinical settings ( 8 – 10 ). The increasingly clear consensus is that we need to have a deeper understanding of the different roles of different immune subgroups in PHE, whether beneficial or harmful. Nevertheless, experimental research on immune cells related to stroke largely relies on previous knowledge, such as markers and phenotypes of immune cell populations defined in other research fields. This approach is fundamentally flawed for identifying unique immune cell subclusters specific to ICH and for an unbiased exploration of the complex immune cell states induced by ICH. Therefore, a comprehensive understanding of the diversity within immune cells in PHE is crucial for developing effective and targeted immunotherapeutic modalities. In this study, we aimed to analyze the immune cell composition in PHE tissue through single-cell RNA sequencing (scRNA-seq). Currently, ScRNA-seq has been widely used to identify immune cell subsets specific to certain pathological conditions in the brain, including multiple sclerosis, epilepsy, as well as Alzheimer's disease ( 11 – 13 ). Correspondingly, little is known about the heterogeneity of immune cells in ICH, especially when it arising justnow. Given that the immune cell landscape at the single-cell level in PHE tissues during the progression of ICH still needs futher characterization. Therefore, this study aims to use scRNA seq to de novo characterize the evolution process of immune cells in PHE tissues using transcriptional profiling of human immune cells. Results Single-cell transcriptome profiling reveals the heterogeneity of immune and non-immune cells after ICH. In order to reveal the changes in immune cells during the progression of ICH, we collected 10×Genomics scRNA-seq datasets from fresh PHE samples, which were resected from brain tissue in the vicinity of hematoma during the evacuation of the hematoma. We constructed a multi-stage profile including Group1 (n = 3, 0–6 hours after ICH, G1), Group2 (n = 3, 6–24 hours after ICH, G2), and Group3 (n = 3, 24–48 hours after ICH, G3) (Fig. 1 A). After quality control, a total of 3152–5823 cells were retained, with an average of 2639–4716 genes per cell and an average of 9535–24244 unique molecular identifiers (UMIs) per cell for the subsequent analysis. Following that, we clustered and visualized various cell types refering to their relative gene expression levels using uniform manifold approximation and projection (UMAP), an unsupervised nonlinear dimensionality reduction algorithm. Graph-based Louvain clustering algorithms were used to cluster all cells into subsets resulting in 19 clusters (Fig. 1 B). Based on marker gene expression levels of microglia ( AIF1 , CSF1R , TMEM119 , CX3CR1 ), clusters 5, 6, 7 and 13 were identified as microglial cells (Fig. 1 C and 1 E). Clusters 8 and 16 expressed genes ( ALDH1L1 , ATP1B2 and AQP4 ) specific to astrocytes (Fig. 1 C and 1 E). Within the infiltrating immune cell subsets, cluster 3 had neutrophil marker genes ( CSF3R , S100A8 , CXCR2 and FCGR3B ) and cluster 9 had monocyte marker genes ( CD300E and VCAN ) (Fig. 1 C and 1 E). Cluster 11 expressed genes ( CD3D , CD3E , NKG7 , GZMA and GZMB ) specific to cells of NK/T cells, while cluster 19 expressed genes ( CD79A , CD79B and MS4A1 ) specific to B cells (Fig. 1 C and 1 E). Additionally, clusters 1, 2, 4, 10, 14, 15 and 17 expressed genes ( MOG , SOX10 , CNP and HAPLN2 ) specific to oligodendrocytes, and cluster 12 expressed genes ( CSPG4 and PDGFRA ) specific to neural progenitor cells (Fig. 1 C and 1 E). cluster 18 expressed endothelial cell marker genes ( CLDN5 , VWF , RGS5 and EGFL7 ). Therefore, we identified this cluster as endothelial cells (Fig. 1 C and 1 E). Furthermore, neutrophils, NK/T cells, and monocyte cells were present at all time points and increased to reach their highest level at G3 (Fig. 1 D), a timepoint that appears crucial to investigating ICH-induced immune cell changes ( 14 ). Microglia and neutrophils, as the most important immune cell populations in the central nervous system and peripheral immune system, respectively, were essential in the pathophysiology of ICH ( 1 , 3 ). Therefore, we next focused on analyzing the transcriptional profiles of these two cell types and investigated the crosstalk between central and peripheral immune cells during ICH progression. In addition, the number of B cells was too small for further bioinformatic analyses, so we excluded them from subsequent analyses. Single-cell RNA sequencing reveals the complexity of microglia states during ICH progression. As a result of cluster analysis of the 8353 microglia-like cells, 12 clusters were identified (Fig. 2 A) characterized by differentially expressed genes (DEGs). We determined the enrichment of biological pathways in each cluster via gene set enrichment analysis (GSEA) (Fig. 2 D). There was almost no overlap in the DEGs defining each cluster, supporting the unique nature of each microglia cluster (Fig. 2 D, Additional file 1: Fig. S1 ). First, we identified annotated clusters of microglia phenotypically similar to those previously described in the human brain. Cluster-enriched sets of transcriptional regulators and transcription factors were observed in some clusters ( 1 , 2 , 4 , 5 , 6 , 7 , 8 , 10 , 11 , and 12 ) but not in others (3 and 9) (Fig. 3 A) ( 15 ). Besides, some selected cell-surface marker genes were not observed in cluster 9 (Fig. 3 B). The absence of detectable unique cell-surface markers and on-off transcription factors among clusters 3 or 9 may represent homeostatic microglia, whereas the other clusters differed from them through the upregulation of specific genes. As an additional finding, we identified cluster 9 as an enriched cluster for homeostatic genes, owing to its high levels of P2RY12 and CX3CR1 expression (Additional file 1: Fig. S2 A, Additional file 1: Fig. S3 A) ( 11 , 16 – 18 ). However, the higher expression of homeostatic markers was not found in cluster 3 (Additional file 1: Fig. S2 A). Therefore, we annotated cluster 9 as homeostatic microglia (HM) cluster. HM was established as a comparative basis for evaluating DEGs from other microglia clusters (Table 1), referring to the approach in previous literature ( 15 , 17 , 18 ). Initially, we identified annotated clusters of microglia phenotypically similar to those previously characterized in the human brain. Notably, Cluster Micro3 was characterized by the downregulation of homeostatic genes (Additional File 1: Fig. S3 A), such as checkpoint genes ( TMEM119 and CX3CR1 ) and purinergic receptors ( P2RY12 ), and by the upregulation of encoding many metabolic genes ( APOC1, VIM, LDHA, RPS2, RPS6, RPS10, RPS19, and RPL12 ), predominantly ribosomal subunits genes (Fig. 2 C). In summary, cluster Micro3 reflects a degenerative phenotype of microglia, consistent with the responses of microglia to aging. Remarkably, Cluster Micro5 predominantly expressed genes characteristic of disease-associated microglia (DAMs), such as metabolic genes LPL and FABP5 (Fig. 2 C) ( 19 ). DAM subtype is novel microglia associated with neurodegenerative diseases such as Alzheimer's ( 16 ). The pathway analysis of the Micro5 genes highlighted associations with "Alzheimer's disease" and "Huntington's disease" (Fig. 2 D, Additional File 1: Fig. S1 B). Hence, we annotated this cluster as "DAM-like" microglia. Cluster Micro6 and Micro10 were defined by genes and pathways involved in canonical inflammatory phenotype. GSEA indicated that these two clusters were enriched in Toll-like receptor (TLR) signaling, Nod-like receptor (NLR) signaling, and chemokine signaling pathway, suggesting inflammatory responses of downstream effectors to stimuli (Fig. 2 D, Additional File 1: Fig. S1 A). Cluster Micro11 was characterized by anti-inflammatory and repair-related genes ( HTR7 , PDLIM7 , and LGALS3 ) and proinflammatory genes ( KCNN4 and ITGB7 ) ( 20 – 22 ), indicating that this cluster was an intermediate state in the polarization of microglia (Fig. 2 C). Cluster Micro12 was defined by expression of genes involved in DNA repair and cell cycle regulation, including MKI67 , SKA1 , E2F2 and E2F8 (Fig. 2 C). Furthermore, Micro12 was enriched for pathways involved in DNA replication and the cell cycle (Fig. 1 D, Additional File 1: Fig. S1 B). Micro1 was defined by genes ( XIST , VEGFA , KLF4 ) involved in microglial M1 polarization (Fig. 2 C) ( 23 – 26 ). Moreover, we found that cluster Micro1 was between HM cluster (Micro9) and canonical inflammatory phenotype (Micro6), suggesting that this cluster could be the intermediate transition status from HM microglia to proinflammatory microglia. Subsequently, we identified four microglial clusters—Micro2, Micro4, Micro7, and Micro8—that had not previously been characterized in human brain studies. These subclusters were distinguished by the enrichment for the pathway of DEGs relative to HM microglia. The significant DEGs of Micro7 were involved in the neurotrophin signaling pathway and Fc gamma receptor-mediated phagocytosis (Fig. 2 D, Additional File 1: Fig. S1 B). Micro4 showed gene enrichment for complement and coagulation cascades and the PPAR signaling pathway (Additional File 1: Fig. S1 A), partially sharing a subset of DEGs with Micro7 (Fig. 2 C). The activation of the PPAR signaling pathway has been demonstrated to attenuate proinflammatory responses and increase neurotrophic factors in patients with ICH. Accordingly, we annotated these two clusters as tissue repair phenotypes. Additionally, the pathway significantly enriched Micro8 in antigen processing and presentation, suggesting this subcluster of microglia is active in antigen processing and presentation for immune response (Additional File 1: Fig. S1 A). Furthermore, the pathways enriched in Micro2 confirm the relative increase of genes involved in oxidative phosphorylation and glycolysis/gluconeogenesis and decreased chemokine and endocytosis genes (Additional File 1: Fig. S1 A). This finding coincides with previous studies that persistent glycolysis exerts adverse effects on microglial functions: the activation of glycolytic metabolism impairs phagocytosis and chemotaxis of microglia ( 27 , 28 ). Overall, while future research will likely refine our understanding of microglial subtypes, our study significantly advances the knowledge of microglial heterogeneity in human PHE tissue. Microglia subclusters predominantly exhibit proinflammatory phenotypes after ICH Previous studies of single-cell transcriptomics have shown diverse subclusters of microglia, which are considered to reflect their different functions. In this study, we employed scRNA-seq to investigate the biological pathways present in microglia within PHE tissue following ICH. We observed that common microglial marker genes such as AIF1, TREM2 , and CSF1R were widely expressed across all microglial subclusters (Additional File 1: Fig. S3 A). However, other specific marker genes of microglia ( ITGAM , P2RY12 , and CX3CR1 ) indicated differential expression across clusters (Additional File 1: Fig. S3 A). Interestingly, the transcriptome of PHE tissue was almost dominated by proinflammatory pathways (Additional File 1: Figs. S3A-B). Our findings indicated a lack of significant activation of anti-inflammatory pathways within the first 48 hours post-ICH (Additional File 1: Fig. S3 B). Proinflammatory genes such as CCL2, CCL4 , and IL1B were among the most prevalently expressed cytokine and chemokine genes in ICH-associated microglia (Additional File 1: Fig. S3 A-B). ICH microglial clusters 1, 3, 5, 6, 10, and 11 were characterized by high gene expression levels of HLA-DQA , HLA-DPB1 , and HLA-DRA and low gene expression levels of P2RY12 and CX3CR1 (Additional File 1: Fig. S3 A), suggesting their involvement in the primary immune response to ICH. Complement pathway-related genes (C3, C1QB, and C1QC) also maintained high levels of expression in all microglial cell clusters (Additional File 1: Fig. S3 B). Overall, most microglial clusters displayed proinflammatory phenotype within 48 h of ICH, further corroborating the notion that a proinflammatory response is a key pathogenic mechanism in ICH. Purinergic receptor P2RY12, the cell-surface proteins of microglia, play key roles in mediating neuroinflammatory responses ( 29 ). Our results indicated reduced expression of the P2RY12 gene in microglia clusters that had higher IL1B expression levels (Additional file 1: Fig. S2 A). This finding was consistent with the previous studies that the expression of P2RY12 was gradually decreased accompanied by microglia activation following inflammatory stimulation ( 30 ). Further analysis was performed by comparing the differentially expressed genes in IL1B-expressing clusters (cluster 6 and cluster 10) with those in P2RY12-expressing clusters (cluster 7 and cluster 9). A significant difference in gene expression was found between IL1B-expressing microglia and P2RY12-expressing microglia, with 882 genes notably downregulated and 415 genes notably upregulated (adjusted P value 1.5) (Additional file 1: Fig. S2 B, Additional file 2: Tab. S2). A significant increase in chemokine and pro-inflammatory cytokines was observed in microglial cluster cells expressing IL1B (Additional file 1: Fig. S2 C). (Additional file 1: Fig. S2 C). Additionally, CX3CR1 expression was also higher in cluster cells expressing P2RY12 than in cluster cells expressing IL1B (Additional file 1: Fig. S2 B). In addition, Gene Ontology term enrichment analysis indicated genes enriched for protein binding, extracellular exosome, focal adhesion, cytokine-mediated signaling pathway, and inflammatory response (Additional file 1: Fig. S2 D). Furthermore, the Kyoto Encyclopedia of Genes and Genomes term enrichment analysis suggested genes enriched for apoptosis, NF − kappa B signaling pathway, IL − 17 signaling pathway, and Toll − like receptor signaling pathway (Additional file 1: Fig. S2 E). According to DEGs analysis, pro-inflammatory microglia expressing IL1B are structurally and functionally different from those expressing P2RY12. According to these findings, as well as previous studies, PHE tissue removed from patients with ICH contains an immune pathogenic microenvironment that attracts and induces non-specific and specific immunity rapidly. Hence, we emphasized on the characterization of immune cells infiltrating in PHE tissues. Microglia cluster-specific transcription factor regulatory networks. To explore the regulatory networks of the microglia clusters in the dataset, we also applied SCENIC analysis to identify the top transcription factor-driven networks (regulons) controlling gene expression in each of these 12 microglia clusters (Fig. 4 A, Additional File 1: Fig. S4 A). Each microglia cluster was characterized by a specific set of regulons (Fig. 4 B). This supports the theory that transcriptional regulation mechanisms are key determinants of the unique gene expression profiles observed in each microglia cluster. For example, Micro1 showed higher activity levels of POLR2A , NFKB2 , GTF2B , and BCLAF1 (Fig. 4 A, Additional File 1: Fig. S4 A). Micro3 showed higher activity levels of SOX8 , SOX10 , IRF7 , and STAT1 (Fig. 4 A, Additional File 1: Fig. S4 A); The activity of transcription factors, such as MAFB , SPI1 , DDIT3 , and XBP1 , was higher in Micro11, while high activity levels of E2F1 , TFDP1 , and BRCA1 were associated with Micro12 (Fig. 4 A, Additional File 1: Fig. S4 A). Furthermore, our analysis revealed that MAFB , a regulon governed by transcription factors commonly linked with the anti-inflammatory polarization of human microglia, was prominently featured in the Micro11 cluster (Fig. 4 B, Additional File 1: Fig. S4 C). This is consistent with the finding that these cells experience a phenotypic polarization of microglia of M2. In this study, the NFKB1 regulon, associated with canonical inflammatory responses, was identified in Micro10 (Fig. 4 B, Additional File 1: Fig. S4 C). Conversely, Micro6 exhibited the RELB regulon, linked to non-canonical inflammatory responses (Fig. 4 B, Additional File 1: Fig. S4 C). In Micro9, the high specificity of FOXP2 (Additional File 1: Figs. S4C-D), a regulon unique to human microglia and crucial for brain development, was observed, aligning with previous research identifying this subtype as an HM cluster ( 31 ). The top three regulons in other microglia clusters also showed distinct variations (Fig. 4 B, Additional File 1: Figs. S4B-C). These inferred transcription factor regulons provide insight into the diversity and difference within microglial clusters, suggesting novel potential regulatory targets for future research The SPP1 signaling pathway was the fundamental bridge to self-communication among microglia subclusters Along with the PHE progression, microglial subtypes also changed accordingly. The use of cell-cell communication networks between microglia subpopulations could contribute to a better characterization of microglia function. Interestingly, the interaction strength of the SPP1 pathway increased gradually with the progression of PHE (Fig. 5 A, Additional File 1: Fig. S5 ). Moreover, the SPP1 pathway exhibited the strongest interaction strength, irrespective of incoming or outgoing signaling pathways (Fig. 5 B). It indicates that the SPP1 signaling pathway could be responsible for self-communication between microglia subclusters. Regarding the incoming signaling, SPP1 was emitted by different microglial subclusters at different stages. At post-ICH in G1, G2, and G3, the strongest SPP1 signaling cell types were Micro6, Micro1, and Micro11, respectively (Fig. 5 B). Regarding outgoing SPP1 signaling, the Micro6 subtype was also strongest at G1 after ICH. In ICH patients at G2 and G3, Micro11 showed strong SPP1 signals (Fig. 5 B). Additionally, we visualized the crosstalk between each microglia subcluster in the SPP1 signaling pathway. We explored the specific receptor ligands and found that the SPP1 -( ITGAV + ITGB1 ) ligand-receptor pair was the fundamental bridge of self-communication among microglia subclusters (Fig. 5 C). Collectively, our findings demonstrated that the signaling pathway of SPP1 is the fundamental bridge mediating self communication between subclusters of microglia during the progression of ICH. Time-dependent transcriptional heterogeneity of neutrophils in the human brain after ICH. To analyze neutrophil transcriptional heterogeneity during ICH progression, we performed a multiplexed time series of scRNA-seq analyses combining transcriptomics. By comparing the gene expression patterns of all neutrophils at every time points, we depicted five different transcriptional cell clusters in PHE tissue after ICH, exhibiting a time-independent appearance. Due to the fact that the number of cells sampled at each time point (G1: 1312 cells; G2: 1132 cells; G3: 2109 cells) cannot reflect the true level of neutrophils in ICH PHE tissues (Fig. 6 B), we calculated the proportion represented by each cluster at different time points (Fig. 6 C). The majority of neutrophils at grade 1 (68.6%) segregated in cluster Neutro2 ( CFD , CDKN2D , HSPA1B , S100A4 , D100A6 , S100A9 , S100A12 ), a profile significantly reduced by half at later time points (< 30% at grade 2 and 3). At grade 2, Neutro3 cells (86.9% of total neutrophils; FOLR3 , SLC8A1 , AOAH , SYNE2 ) were predominant. Cluster Neutro5 ( DPYD , KIFC3 , BMP2K , ABHD5 , SPDYA ) was present at all time points and its levels significantly increased from 0.4% of cells at grade 1 to 10.4% at grade 3. Cluster Neutro1 also increased to reach its highest level at grade 3 (61.5% of all neutrophils; MCEMP1 , TNFAIP3 , IER3 , TANK ). Finally, cluster Neutro4 was characterized by highly specific expression of some transcripts ( PLNA , PFN1 , HMGA1 ), and represented 26.8%, 1.9% and 5.0% of all neutrophils at grade 1, 2 and 3 post-ICH, respectively (Fig. 6 C, Additional file 1: Fig. S6 A). The unique gene signatures and the top 20 significantly DEGs of each neutrophil subcluster were delineated (Additional file 1: Fig. S7 A). In addition, the Gene Set Variation Analysis (GSVA) was performed to functionally annotate the neutrophil subcluster (Fig. 6 D). Neutro1 showed gene enrichment for steroid biosynthesis, ABC transporters, and PPAR signaling pathway, partially sharing a subset of significantly differentially expressed genes with Neutro5 (Fig. 6 D). Neutro2 was characterized by the chemokine signaling pathway and antigen processing and presentation (Fig. 6 D), indicating this subtype of cells is active in antigen processing and presentation for immune response. The significantly DEGs of Neutro3 were involved in the folate biosynthesis and the metabolism-related pathways including histidine metabolism, alpha-linolenic acid metabolism, and ascorbate metabolism (Fig. 6 D). Neutro4 exhibited higher expression of genes associated with glycolysis and gluconeogenesis, and extracellular matrix (ECM)-receptor interaction (Fig. 6 D). Neutrophils can carry preexisting matrix from nearby tissue to reestablish new ECM scaffold in the early stages of tissue repair, suggesting that these cells may represent a repair phenotype ( 32 ). Furthermore, Neutro5 was defined by the expression of genes involved in RNA polymerase and TCA cycle (Fig. 6 D). Neutrophil cluster-specific transcription factor regulatory networks. We further employed SCENIC analysis to characterize the underlying molecular mechanisms driving the differentiation of different neutrophil phenotypes. A different transcription factor network was predicted to be expressed by neutrophils from different subtypes in this analysis. For instance, CEBPB and HIVEP2 regulons were upregulated in Neutro1 (Fig. 6 E). Consistent with the findings that the CEBPB regulon is critical for the emergency granulopoietic response, this mechanism fulfills the increased demand for neutrophils during the innate immune response to inflammation ( 33 ). Additionally, there was a notable increase in the Neutro1 population, peaking at G3 (Fig. 6 C). This suggests a substantial consumption of neutrophils at this stage, prompting the hematopoietic system to respond to the heightened demand through emergency granulopoiesis quickly. Neutro2 also upregulated networks driven by ZNF107 , IRF1 , and SPI1 (Fig. 6 E). In the Neutro3 cluster, there was a noticeable predominance of the activity of regulons, particularly those linked to ZBTB16 and ELF1 (Fig. 6 E). Some regulons showed preferential activity in Neutro4 ( MECP2 , SREBF1 , HIF1A ; Fig. 6 E). Neutro5 showed highly active transcription factors related to cell proliferation ( FOXO1 , CEBPZ , YY1 ; Fig. 6 E). By calculating the connection specificity index (CSI), we obtained a regulatory network composed of four modules and 34 regulons (Fig. 6 F). Transcription factors of CEBPB/FOSL2/HIVEP2 in module 2 displayed the strongest activity in Neutro1 (Figs. 6 F-G) and were related to emergency granulopoiesis. Trajectory analysis of neutrophils in PHE tissue during ICH progression In order to explore the dynamic transition of neutrophils from peripheral blood to PHE tissue, we constructed a pseudotime map of the neutrophil state trajectory using monocle2 (Figs. 7 A, 7 B, and 7 D). Neutrophils are also known as short-lived cells that can mobilize rapidly from the bone marrow in response to tissue damage ( 34 ). Accordingly, we performed a 'bone marrow proximity score' of neutrophils based on the expression of a set of genes previously characterized in transcriptome analyses of neutrophils at different stages ( 35 ). Cluster Neutro2 (mainly in G1) had the highest BM proximity score (p < 0.0001 versus all other clusters) (Additional File 1: Fig. S6 B). In our analysis, the progression trajectory of neutrophils was established, starting with Neutro2 (Fig. 7 C). This was followed by Neutro3, serving as a transitional state between Neutro2 and Neutro4, then moving through an intermediate infiltrating phase represented by Neutro1, and culminating in the terminally differentiated states of Neutro4 and Neutro5. Further examination of the single-cell transcriptomes of neutrophils along this trajectory identified 4043 significantly altered genes, classified into four distinct expression patterns (Figs. 7 E-F): Module 1 included genes that showed increased expression levels along one trajectory. Pathway enrichment analysis suggested that these genes participated in the chemokine signaling pathway, NOD-like receptor signaling pathway, NF-kappa B signaling pathway, and TNF signaling pathway (Fig. 7 G). Module 2 contained genes activated in the later stages of another trajectory, with enrichment in mitophagy and HIF-1 signaling pathways (Fig. 7 G). Additionally, the genes in module 3 were associated with ribosome function, Fc gamma R-mediated phagocytosis, endocytosis, regulation of actin cytoskeleton, and leukocyte transendothelial migration (Additional File 1: Fig. S6 C). Finally, module 4 genes, upregulated in the early stages, were associated with necroptosis, apoptosis, and cellular senescence (Additional File 1: Fig. S6 C). OPN-mediated microglia-monocyte interaction was essential for the crosstalk between central and peripheral immune cells We previously discovered a mixed cluster (cluster 11) by immune cell analysis (Fig. 1 E). Cluster 11 clearly showed CD3D , CD3E , NKG7 , GAMA , and GAMB gene expression, indicating doublets of T cells and NK cells (Fig. 1 E). Cluster NK/T cell was present at all time points and its levels gradually increased during ICH progression (Fig. 1 D). We re-clustered cluster 11 doublet cells using major lineage gene expression markers and obtained four clusters (Fig. 8 A). Cluster phenotypes were identified using gene expression levels (Fig. 8 D). A CD8 + T cell cluster ( CD8A , CD8B , LAG3 ; cluster 1 and cluster 2) was the main cluster found in PHE tissue (Figs. 8 C-D). We also observed CD4 + T cells ( CD4 , CCR7 , LEF1 , SELL , IL2RA ; cluster 4) and NK cells ( FCER1G , NCR1 , NCR3 , CCL3 , KLRC1 , FCGR3A ; cluster 3) (Fig. 8 D). By inferring the paired ligand-receptor pairs based on CellChat analysis, we first depicted the overall connectivity patterns between peripheral immune cells and central immune cells in PHE. The number of cell-cell interactions between immune cells changed significantly in the context of the progression of ICH (Figs. 8 E-F). However, the strength interaction involving microglia, monocytes, and CD8 + T cells is consistently higher in the progression of ICH (Fig. 8 G). Intriguingly, monocytes exhibited more extensive communications with microglia than other immune cell types apart from CD8 + T cells and NK cells (Fig. 8 G). We then extracted highly expressed interactions engaging microglia during ICH progression and uncovered underlying interactions with monocytes (Fig. 9 A, Additional File 1: Fig. S8 ). Notably, we found interactions between SPP1 , which encodes the pleiotropic cytokine OPN, and CD44 , ITGAV , ITGA4 , ITGA5 , ITGA9 , ITGB1 , ITGB3 , and ITGB5 , which encode the OPN receptor, were prominent during microglia-monocyte interactions (Fig. 9 A). Furthermore, the pair of OPN-CD44 stands out among all interaction pairs that mediate the crosstalk between microglia and monocytes and displays the highest score (Figs. 9 A-B, Additional File 1: Fig. S8 ). Additionally, we found that the SPP1 gene was primarily expressed in the microglia rather than in other immune cells. In contrast, the CD44 gene was mainly expressed in the monocytes (Fig. 9 C). This finding suggests that microglia-secreted OPN could regulate the immune environment of PHE by interacting with CD44 on monocytes. In summary, these findings indicate that OPN-mediated microglia-monocyte interaction is essential for communicating between central and peripheral immune cells. IF To verify our conclusion, we extrally collected a group of adjacent hematoma tissues from patients with ICH for immunofluorescence experiments. Iba-1 (surface marker of microglia), CD44 and OPN were labeled with different colors of fluorescence for immunofluorescence co staining. Afterwards, we used a Zeiss Imager Z2 confocal microscope to observe the stained tissue sections, such image was captured under a microscope: co localization existing between osteopontin and microglia (Iba-1), and highly spatially close to CD44 (Fig. 10 ). This also coincides with the conclusion drawn from our previous analysis: 1. Osteopontin is secreted by microglia; 2. Osteopontin as a mediator participates in the activation of microglia and CD44 cells, especially monocytes. Discussion In this study, we characterized the immune cell landscape of PHE tissue in patients with ICH tissues, uncovering the predominant proinflammatory microenvironment shaped by microglia. Using SCENIC analysis, we observed that each cluster was driven by different transcription factors, further supporting the functional diversity of clusters. Trajectory inference analysis revealed transitions and putative relationships between neutrophil phenotypes. Cell-cell communication networks analysis indicated that the SPP1 signaling pathway was the fundamental bridge of self-communication among microglia subclusters. Furthermore, we identified that microglia-derived OPN was an essential mediator in the communication between microglia and monocytes. Our study used snRNA-seq analyses to accurately distinguish between immune and non-immune cells in the human brain, identifying their specific types and biological functions. Our study suggested microglia as a major contributor to inducing a proinflammatory immune microenvironment, mainly through producing proinflammatory mediators and reducing microglial purinergic receptor-mediated signaling. Recent studies have shown that the expression level of P2RY12 in microglia is correlated with the phenotype of microglia ( 30 ). Specifically, it is less expressed by activated proinflammatory microglia, and highly expressed by activated non-inflammatory microglia ( 36 , 37 ). Reductions in P2RY12-expressing microglia indicated the presence of a broad proinflammatory milieu in PHE tissue. Furthermore, it has been demonstrated in numerous preclinical studies and clinical trials that modulating microglial polarization (M1/M2) can reduce inflammation and thus exert a protective effect in ICH ( 4 , 38 ). However, none of the immunomodulators that target neuroinflammation of PHE tissue have been applied in clinical practice. Recently, there has been increasing evidence that the M1/ M2 dichotomy of microglial polarization has been oversimplified, highlighting the need for unbiased high-throughput methods to comprehensively investigate microglial heterogeneity ( 39 ). A single-cell transcriptome study identified nine microglial subclusters in human brain samples from neurosurgical procedures, revealing phenotypes beyond the conventional M1/M2-like patterns ( 13 ). Our study did not observe a complete overlap between the microglia subtypes and classical M1 or M2 subtypes after ICH at the single-cell level. Conversely, we identified 12 distinct microglial subclusters in PHE tissue. Similarly, another scRNA-seq study in a mouse ischemic stroke model identified six distinct microglial subclusters; none fully conformed to the M1 or M2 microglia marker gene ( 40 ). Moreover, our SCENIC analysis demonstrated the diversity of the identified microglial clusters and suggested potential regulatory targets for future research, highlighting the complexity and nuance of microglial roles in the ICH context. As the first activated immune cell cluster, microglia undergo various phenotypic and functional changes depending on the stimuli involved after ICH ( 41 ). Our study has revealed the significance of the OPN-CD44 receptor axis in the interaction between microglia and monocytes. OPN, a multifunctional phosphoglycoprotein, bridges innate and adaptive immune responses under pathological conditions ( 42 ). Previous studies demonstrated that OPN is involved in neuroimmune responses in diverse central nervous system diseases, including ICH, ischemic stroke, and AD ( 43 – 45 ). Furthermore, recent evidence suggests that OPN is highly expressed by microglia, which plays a significant role in immunomodulatory effects ( 46 , 47 ). Cell-cell communication analyses predicted strong interactions between microglia and monocytes through OPN and CD44 receptors. These interactions need to be experimentally validated by a range of in vitro and in vivo investigations in the future. Prior studies indicated that peripheral monocytes are also cellular sources of OPN but with lower expression than microglia ( 48 ). Moreover, recent single-cell studies have noted an increased transcription of SPP1 , the gene encoding OPN, in monocytes in the brains of mice with AD ( 45 ). Therefore, we speculated that microglia act as initiators of OPN production in monocytes, which could be crucial for monocyte infiltration. However, it is important to note that our study could not determine whether additional OPN-independent mechanisms are involved in the communication between microglia and monocytes. Conclusion In conclusion, we generated the first scRNA-seq data set for immune cells in the human PHE tissue and provided novel insights into post-ICH microglia and neutrophil heterogeneity in the brain. We discovered that the SPP1 signaling pathway was the fundamental bridge of self-communication among microglia subtypes during ICH progression. Additionally, OPN has been identified as a mediator between monocytes and microglia. Our findings, therefore, are considerable in developing a more comprehensive understanding of immune cell diversity, which will drive the development of immunomodulators targeted neuroinflammation post-ICH. Therefore, our findings are of great significance for a more comprehensive understanding of immune cell diversity, which may contribute to exploit immunomodulators targeted neuroinflammation post-ICH. Declarations Conflict of Interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Ethics Statement The studies involving human participants were reviewed and approved by Affiliated Hospital of Jining Medical University. Funding This work was supported by grants from the Qilu Health and Outstanding Young Talents (2021-QLJQ-003), Jining Medical University (JYGC2022FKJ012) and Key R&D Plan of Jining City (2022YXNS082). Author Contribution PZ and CG designed and performed experiments, prepared figures, analyzed data, and wrote, edited, and proofread the manuscript. QG and DY analyzed and interpreted data, prepared figures, and wrote, edited, and proofread the manuscript. GZ performed experiments. HL conceptualized the project and wrote, edited, and proofread the manuscript. DL conceptualized and directed the overall project. Availability of data and materials The data sets used and/or analyzed during the current study are available from the corresponding author on reasonable request. References Magid-Bernstein J, Girard R, Polster S, Srinath A, Romanos S, Awad IA, et al. Cerebral Hemorrhage: Pathophysiology, Treatment, and Future Directions. Circ Res. 2022;130(8):1204–29. Keep RF, Hua Y, Xi G. Intracerebral haemorrhage: mechanisms of injury and therapeutic targets. Lancet Neurol. 2012;11(8):720–31. Li X, Chen G. CNS-peripheral immune interactions in hemorrhagic stroke. J Cereb Blood Flow Metab. 2023;43(2):185–97. Xue M, Yong VW. Neuroinflammation in intracerebral haemorrhage: immunotherapies with potential for translation. 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Identification of kidney injury released circulating osteopontin as causal agent of respiratory failure. Sci Adv. 2022;8(8):eabm5900. Materials and methods Human brain tissues The brain tissues utilized in our research were sourced exclusively from cerebral hemorrhagic patients who visiting Emergency Stroke Department and Neurology Department of Jining Medical University Affiliated Hospital and immediate in need surgical intervention to extract the hematoma. Strict inclusion and exclusion criteria are set for screening eligible participants. Specifically, the enrolled individuals were required to be at least 18 years of age and exhibit a hemorrhage volume ranging from 50 to 100 ml, as determined through computed tomography. Exclusion criteria encompass patients who exhibit pre-existing brain diseases, infections, autoimmune diseases, pathologically confirmed amyloid angiopathy or cerebral arteriovenous malformations, in addition to those undergoing immunosuppressive or immunomodulatory therapy. Finally, we recruited total 9 eligible patients to attend this study, whose detailed information on the age, brain sampling area, histopathology, and clinical diagnosis is registered in Supplementary Table 1. Following the acquisition of written informed consent from each patient (or their legal representative) and implementation of suitable protective measures, a minute quantity of brain tissue situated in the edema region surrounding the hematoma was procured from the participants during the surgical procedure. † Our research complies with all relevant ethical regulations and was approved by the institutional review board at the Affiliated Hospital of Jining Medical University. Single-cell RNA-seq data preprocessing The FASTQ files underwent processing and alignment to the GRCh38 human reference genome using Cell Ranger software (version 7.0.1) from 10x Genomics. The resulting unique molecular identifier (UMI) counts were then summarized for each barcode. Subsequently, the UMI count matrix was analyzed using the Seurat (1) (version 4.0.0) R package. Wherein，to eliminate low-quality cells and potential multiplet captures, a series of criteria were applied: (1) filtering cells based on gene numbers (gene numbers < 200), (2) filtering cells based on UMI counts (UMI < 1000), and (3) filtering cells based on log10GenesPerUMI values (log10GenesPerUMI < 0.7). In order to acquire the normalized gene expression data, the NormalizeData function was employed to perform library size normalization. More specifically, the gene expression measurements for each cell were normalized using the global-scaling normalization method known as "LogNormalize". This involved dividing the total expression by a scaling factor (typically set at 10,000 by default), and subsequently applying a logarithmic transformation to the obtained values. The Seurat function FindVariableGenes (mean.function=FastExpMean, dispersion.function=FastLogVMR) was utilized to calculate the top 2000 highly variable genes (HVGs). Subsequently, dimensionality reduction was performed through Principal-component analysis (PCA) using the RunPCA function. To cluster cells based on their gene expression profile, graph-based clustering was conducted employing the FindClusters function. Finally, the visualization of cells was achieved using a 2-dimensional Uniform Manifold Approximation and Projection (UMAP) algorithm with the RunUMAP function. The identification of marker genes for each cluster was conducted using the FindAllMarkers function (test.use = presto). Differentially expressed genes (DEGs) were selected using the FindMarkers function (test.use = presto). A significance threshold of P value 0.58 was applied to determine significantly differential expression. GO enrichment and KEGG pathway enrichment analyses of the DEGs were performed using R (version 4.0.3) based on the hypergeometric distribution. Gene Set Variation Analysis (GSVA) The Gene Set Variation Analysis was conducted by utilizing the GSEABase package (version 1.44.0) to import the gene set file obtained from the KEGG database (https://www.kegg.jp/), which was subsequently processed. Pathway activity estimates were assigned to individual cells through the application of GSVA (2) using default parameters, as implemented in the GSVA package (version 1.30.0). The discrepancies in pathway activities per cell were determined using the LIMMA package (version 3.38.3). Gene Set Enrichment Analysis (GSEA) The "Bone marrow proximity score" was assigned using the AddModuleScore function in Seurat v3, incorporating the genes MMP8, MMP25, LCN2, OLFM4, ITGB2, FPR1, LTF, and CAMP as features (4). To control for the aggregated expression of the feature set, the average expression levels of the relevant cluster were subtracted. The genes under analysis were categorized into bins based on their average expression, and control characteristics were randomly chosen from each bin. SCENIC Analysis The SCENIC analysis was conducted utilizing the motifs database for RcisTarget and GRNboost (SCENIC (5) version 1.2.4, corresponding to RcisTarget version 1.10.0 and AUCell version 1.12.0) with the default parameters. Specifically, the RcisTarget package was employed to identify transcription factor (TF) binding motifs that were over-represented on a gene list. The AUCell package (version 1.12.0) was utilized to score the activity of each group of regulons in each cell. In order to assess the cell type specificity of each predicted regulon, we computed the regulon specificity score (RSS) using the Jensen-Shannon divergence (JSD), a metric that quantifies the similarity between two probability distributions. More specifically, we calculated the JSD between each binary regulon activity vector and the assignment of cells to a particular cell type (6). Additionally, we determined the connection specificity index (CSI) for all regulons using the scFunctions package available at https://github.com/FloWu-enne/scFunctions/. Monocle2 Pseudotime Analysis The Monocle2 (7) package (version 2.9.0) was employed to ascertain the developmental pseudotime. Initially, the raw count was transformed from a Seurat object into a CellDataSet object using the importCDS function in Monocle. Subsequently, the differentialGeneTest function from the Monocle2 package was utilized to identify ordering genes (qval < 0.01) that were deemed informative in the arrangement of cells along the pseudotime trajectory. The dimensional reduction clustering analysis was conducted using the reduceDimension function, followed by trajectory inference employing the orderCells function with default parameters. Gene expression was visualized using the plot_genes_in_pseudotime function to monitor alterations across pseudo-time. Cell–Cell Communication Analysis The analysis of cell communication was conducted utilizing the CellChat (8) (version 1.1.3) R package. Initially, the normalized expression matrix was imported to generate the cellchat object through the createCellChat function. Subsequently, the data underwent preprocessing employing the identifyOverExpressedGenes, identifyOverExpressedInteractions, and projectData functions with the default parameters. The functions computeCommunProb, filterCommunication (with a minimum of 10 cells) and computeCommunProbPathway were subsequently employed to ascertain any plausible ligand-receptor interactions. Ultimately, the cell communication network was consolidated via the aggregateNet function. Reference 1. McGinnis CS, Murrow LM, Gartner ZJ. DoubletFinder: Doublet Detection in Single-Cell RNA Sequencing Data Using Artificial Nearest Neighbors. Cell Syst. 2019;8(4):329-37 e4. 2. Hanzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics. 2013;14:7. 3. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A. 2005;102(43):15545-50. 4. Evrard M, Kwok IWH, Chong SZ, Teng KWW, Becht E, Chen J, et al. Developmental Analysis of Bone Marrow Neutrophils Reveals Populations Specialized in Expansion, Trafficking, and Effector Functions. Immunity. 2018;48(2):364-79 e8. 5. Aibar S, Gonzalez-Blas CB, Moerman T, Huynh-Thu VA, Imrichova H, Hulselmans G, et al. SCENIC: single-cell regulatory network inference and clustering. Nat Methods. 2017;14(11):1083-6. 6. Suo S, Zhu Q, Saadatpour A, Fei L, Guo G, Yuan GC. Revealing the Critical Regulators of Cell Identity in the Mouse Cell Atlas. Cell Rep. 2018;25(6):1436-45 e3. 7. Trapnell C, Cacchiarelli D, Grimsby J, Pokharel P, Li S, Morse M, et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat Biotechnol. 2014;32(4):381-6. 8. Jin S, Guerrero-Juarez CF, Zhang L, Chang I, Ramos R, Kuan CH, et al. Inference and analysis of cell-cell communication using CellChat. Nat Commun. 2021;12(1):1088. Additional Declarations No competing interests reported. Supplementary Files Fig.S1.pdf Fig. S1. Microglia states have diverse biological pathway correlates. (A) GSEA analysis of genes that differentiate each cluster from cluster 9 ('homeostatic microglia') suggests distinct biological pathways. (B) GSEA shows top enriched pathways in some microglial subclusters. Fig.S2.pdf Fig. S2. IL-1B expressing microglia genes expression profile compared with P2RY12 expressing microglia. (A)Dotplot shows the normalized expression of P2RY12 and IL1B in microglia clusters of PHE tissue in patients with ICH. Dot size reflects the percentage of cells that showed genes, while color shows the expression levels of genes. (B)Heatmap of genes significantly modulated in IL-1B expressing clusters compared to P2RY12 expressing clusters. For the heatmap, 100 randomly sampled cells were shown for both IL1B and P2RY12 clusters. (C) P2RY12, IL1B clusters marker, chemokine CCL4, CCL3L1, and proinflammatory nuclear transcription factor NFKB1 were shown as violin plots. (D) GO gene set enrichment analysis results were shown as a bar plot where the x-axis -log of FDR adjusted p-value for GO terms was shown. The top 30 GO terms are shown in this figure. (E) KEGG gene set enrichment analysis results were shown as bubble plots. The top 20 KEGG terms are shown in the figure. Fig.S3.pdf Fig. S3. Characterizing the gene expression profile of selected microglia-specific, inflammatory, and activation marker genes. (A-B) The x-axis shows microglia subclusters, and the y-axis shows normalized expression levels. Fig.S4.pdf Fig. S4. Transcription factor regulatory networks are specific to microglia phenotypes. (A) SCENIC workflow identified transcription factor-regulated networks associated with different phenotypic clusters of microglia. Heatmap of each microglia subtype's inferred regulon activity score (RAS) in cluster levels. (B) Ranking plot of regulon specificity score (RSS). The higher RSS of the regulon may be specific to the subtypes. (C) Heatmap of regulon specificity score (RSS). The higher RSS of the regulon may be specific to the subtypes. Fig.S5.pdf Fig. S5. Signaling changes of microglia subcluster during PHE tissue progression. Cell ligand-receptor inference analysis of microglial subtypes during PHE tissue progression (G3 vs. G2). Fig.S6.pdf Fig. S6. The heterogeneity of neutrophils during PHE progression. (A) Differential expression analysis comparing each cluster to all others demonstrates distinct gene expression profiles. The top 20 genes from each cluster are displayed with gene names annotated on the right. (B) Violin plot showing the scores of functional modules for each neutrophil subcluster, using the AddModuleScore function. (C)Enriched KEGG terms for gene sets from four modules (modules 3 and 4) were represented on the left side. Fig.S7.pdf Fig. S7. Cell ligand-receptor inference analysis of immune cells during PHE progression. Bubble plot of the significant differentially expressed ligand–receptor pairs during PHE progression. Dot color reflects communication probabilities, and dot size represents computed p-values. Empty space means the communication probability is zero. The p-values were computed from a two-sided permutation test. Table1..xlsx Table2.xlsx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 29 Mar, 2024 Reviews received at journal 06 Mar, 2024 Reviewers agreed at journal 05 Mar, 2024 Reviewers invited by journal 05 Mar, 2024 Editor assigned by journal 29 Feb, 2024 Submission checks completed at journal 29 Feb, 2024 First submitted to journal 28 Feb, 2024 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. 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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-3996729","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":275608090,"identity":"e9da4eba-89e4-4b86-94e4-a0aa1091ac2b","order_by":0,"name":"Peng Zhang","email":"","orcid":"","institution":"Jining Medical University","correspondingAuthor":false,"prefix":"","firstName":"Peng","middleName":"","lastName":"Zhang","suffix":""},{"id":275608091,"identity":"879bb5b3-9047-41e1-9c7f-5d1dbc5169cb","order_by":1,"name":"Cong Gao","email":"","orcid":"","institution":"Jining Medical University","correspondingAuthor":false,"prefix":"","firstName":"Cong","middleName":"","lastName":"Gao","suffix":""},{"id":275608092,"identity":"8f69512c-1d90-44b8-b977-d5fe385bdb33","order_by":2,"name":"Qiang Guo","email":"","orcid":"","institution":"Affiliated Hospital of Jining Medical University","correspondingAuthor":false,"prefix":"","firstName":"Qiang","middleName":"","lastName":"Guo","suffix":""},{"id":275608093,"identity":"6e645f18-1bbe-4cdf-8b31-e322e2894713","order_by":3,"name":"Dongxu Yang","email":"","orcid":"","institution":"Affiliated Hospital of Jining Medical University","correspondingAuthor":false,"prefix":"","firstName":"Dongxu","middleName":"","lastName":"Yang","suffix":""},{"id":275608094,"identity":"e155ffa2-f622-47d2-81da-cae4f68a6c8a","order_by":4,"name":"Guangning Zhang","email":"","orcid":"","institution":"Affiliated Hospital of Jining Medical University","correspondingAuthor":false,"prefix":"","firstName":"Guangning","middleName":"","lastName":"Zhang","suffix":""},{"id":275608095,"identity":"f61b9d96-7b05-41f6-9c41-c9d2df5da21d","order_by":5,"name":"Hao Lu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwklEQVRIiWNgGAWjYBACPgYeEMXMzMDe2PjwAzFa2OBaeA43G0uQooWBQSK9TYCHKC0SuQc/fPhjzW4u+bCNQYLBTk63gaCWvGTJmW3pzJazE9seFDAkG5sdIKglx0Cat+Ews8HtxHYDCYYDiduI0GL8m+cPUMvNg20SPERqMZPmYQNqucFIrBaeN2aWIL8YnEkEBrIBEX7hZ88xvgEMsWSD48cfPvxQYSdHUAsMJEMoAyKVg4AdCWpHwSgYBaNgpAEAxKo71xpNO64AAAAASUVORK5CYII=","orcid":"","institution":"Affiliated Hospital of Jining Medical University","correspondingAuthor":true,"prefix":"","firstName":"Hao","middleName":"","lastName":"Lu","suffix":""},{"id":275608096,"identity":"28c78b92-f3c2-4fcb-934e-44f8c8a851cb","order_by":6,"name":"Daojing Li","email":"","orcid":"","institution":"Affiliated Hospital of Jining Medical University","correspondingAuthor":false,"prefix":"","firstName":"Daojing","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2024-02-28 13:14:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3996729/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3996729/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":52026656,"identity":"297d9a5e-a719-4585-ae1c-deb678d58d77","added_by":"auto","created_at":"2024-03-05 15:54:07","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":658972,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSingle-cell profiling of diverse immune cells from three groups\u003c/strong\u003e. \u003cstrong\u003e(A)\u003c/strong\u003e Overview of the study workflow. \u003cstrong\u003e(B)\u003c/strong\u003e Position of clusters on the UMAP map. Color represents the cluster ID. \u003cstrong\u003e(C)\u003c/strong\u003e Phenotype of clusters on the UMAP map. Different colors represent 9 clusters (cell types), including microglia, astrocytes, oligodendrocytes, neural progenitors, monocytes, neutrophils, NK/T cells, B cells, and endothelial cells. \u003cstrong\u003e(D)\u003c/strong\u003e Proportions of all cell types in each group during PHE progression. \u003cstrong\u003e(E)\u003c/strong\u003e Representative cell type marker genes (y-axis) with the percent of cells that express a gene (size of dot) in each cluster (distributed along the x-axis) and the average expression level (color intensity) are shown for microglia (\u003cem\u003eAIF1\u003c/em\u003e, \u003cem\u003eCSF1R\u003c/em\u003e, \u003cem\u003eTMEM119\u003c/em\u003e, and \u003cem\u003eCX3CR1\u003c/em\u003e), astrocytes (\u003cem\u003eAQP4\u003c/em\u003e, \u003cem\u003eATP1B2\u003c/em\u003e, and \u003cem\u003eALDH1L1\u003c/em\u003e), oligodendrocyte (\u003cem\u003eMOG\u003c/em\u003eand \u003cem\u003eSOX10\u003c/em\u003e), neural progenitor (\u003cem\u003ePDGFRA\u003c/em\u003e), monocyte (\u003cem\u003eVCAN\u003c/em\u003e and \u003cem\u003eCD300E\u003c/em\u003e), neutrophil (\u003cem\u003eCSF3R\u003c/em\u003e and \u003cem\u003eS100A8\u003c/em\u003e), NK/T cell (\u003cem\u003eGZMA\u003c/em\u003e, \u003cem\u003eNKG7\u003c/em\u003e, \u003cem\u003eCD3D\u003c/em\u003e, and \u003cem\u003eCD3E\u003c/em\u003e), B cell (\u003cem\u003eCD79B\u003c/em\u003e and \u003cem\u003eMS4A1\u003c/em\u003e) and endothelial cells (\u003cem\u003eVWF\u003c/em\u003e and \u003cem\u003eCLDN5\u003c/em\u003e) for each cluster.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3996729/v1/65cfbac5bffd15a53160893d.png"},{"id":52026657,"identity":"f16ec72d-4706-4186-abbd-e6e965bf9fbf","added_by":"auto","created_at":"2024-03-05 15:54:07","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":322072,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMicroglia states have diverse gene expression and biological pathway correlates\u003c/strong\u003e. \u003cstrong\u003e(A)\u003c/strong\u003e UMAP of unbiased clustering on the cells from the four sorted clusters (5, 6, 7, and 13, shown in Fig. 1E) meeting criteria for microglia from the 9-sample dataset contains 12 microglia clusters. \u003cstrong\u003e(B) \u003c/strong\u003eProportions of all microglia subclusters among three groups. \u003cstrong\u003e(C) \u003c/strong\u003eDifferential expression analysis comparing each cluster to others demonstrates distinct gene expression profiles. The top 25 genes from each cluster are displayed with gene names annotated on the right. \u003cstrong\u003e(D)\u003c/strong\u003e GSEA analysis of genes that differentiate each cluster from cluster 9 ('homeostatic microglia') suggests distinct biological pathways.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3996729/v1/a04d120df4cdf9a6d7b66df1.png"},{"id":52027318,"identity":"19cc4231-4c1c-4fd2-801d-261b00187fe4","added_by":"auto","created_at":"2024-03-05 16:02:08","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":231288,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentifying potential functional marker genes for the microglial clusters. (A) \u003c/strong\u003eMicroglial clusters are visualized in columns, and rows represent selected transcription regulators that are differentially expressed in specific clusters. As revealed in the key code at the right of the panel, the size of each dot represents the fraction of cells in a given cluster in which the gene was detected, and the color of the dot represents the average expression levels for the cells belonging to that cluster. \u003cstrong\u003e(B)\u003c/strong\u003e Representative membrane-associated proteins (y-axis) with the percent of cells that express a gene (size of the dot) in each subcluster (distributed along the x-axis) and the average expression level (color intensity) are shown for microglial subtypes.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-3996729/v1/2f020799b42e6f432f7e58d4.png"},{"id":52026658,"identity":"cdf0702c-d5c1-44df-9673-7fefc1595f0d","added_by":"auto","created_at":"2024-03-05 15:54:08","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":670523,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTranscription factor regulatory networks are specific to microglia phenotypes\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003e SCENIC workflow identified transcription factor-regulated networks associated with different phenotypic clusters of microglia. Heatmap of each microglia subtype's inferred regulon activity score (RAS) in cell levels. \u003cstrong\u003e(B)\u003c/strong\u003eRanking plot of regulon specificity score (RSS). The higher RSS of the regulon may be specific to the subtypes.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-3996729/v1/e1de532f5147e85e5e8d8a14.png"},{"id":52026659,"identity":"412f02b9-5112-4884-b2d2-d3e2c8ffc3a9","added_by":"auto","created_at":"2024-03-05 15:54:08","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":471149,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSignaling changes of microglia subcluster during PHE tissue progression\u003c/strong\u003e.\u003cstrong\u003e (A)\u003c/strong\u003e Cell ligand-receptor inference analysis of microglial subtypes during PHE tissue progression (G2 vs. G1). \u003cstrong\u003e(B)\u003c/strong\u003e Heatmaps demonstrate the differences in crosstalk strength of the \u003cem\u003eSPP1\u003c/em\u003e signaling pathway in microglial subclusters between different groups (G1, G2, and G3). The contribution of inferred ligand-receptor pairs in \u003cem\u003eSPP1\u003c/em\u003esignaling between different groups (G1, G2, and G3).\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-3996729/v1/9d658cbdf02c7c07a4bd60b7.png"},{"id":52026673,"identity":"7c8566e6-1c3d-407e-b74d-af22592a3562","added_by":"auto","created_at":"2024-03-05 15:54:09","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":750793,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe heterogeneity and transcription factor regulatory networks of neutrophils during PHE progression. (A)\u003c/strong\u003e UMAP of unbiased clustering on the cells for neutrophils from the 9-sample dataset contains five neutrophil clusters. \u003cstrong\u003e(B)\u003c/strong\u003e Distribution of neutrophils from different groups (G1, G2, and G3) on a UMAP plot. \u003cstrong\u003e(C)\u003c/strong\u003e Proportions of all neutrophil subclusters among three groups. \u003cstrong\u003e(D) \u003c/strong\u003eGSVA analysis indicates enriched pathways of each subset of neutrophils. \u003cstrong\u003e(E)\u003c/strong\u003e Heatmap of each neutrophil subtype's inferred regulon activity score (RAS) in cluster levels. \u003cstrong\u003e(F)\u003c/strong\u003e The connection specificity index (CSI) matrix highlights the regulon-to-regulon correlation across all neutrophil subtypes from different groups. Hierarchical clustering of regulons identifies four distinct regulon modules. The heatmap shows the regulation activity of each module. \u003cstrong\u003e(G)\u003c/strong\u003e Heatmap of the CSI matrix across all neutrophil subtypes. The color key from blue to yellow indicates the activity levels from low to high.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-3996729/v1/179aed0612a4da1eb9ce48ba.png"},{"id":52026663,"identity":"e9b2e817-1d05-4247-b381-0265674d4d6d","added_by":"auto","created_at":"2024-03-05 15:54:08","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":684827,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExploring the transition of neutrophils based on pseudo-time analysis using Monocle2. (A)\u003c/strong\u003e Trajectory of five clusters along pseudo-time in a two-dimensional state-space defined by Monocle2. Each point corresponds to a single cell, and each color represents a neutrophil cluster.\u003cstrong\u003e(B)\u003c/strong\u003e The pseudo-time trajectory plots demonstrate the sample distribution along the trajectory. The dot color represents the group.\u003cstrong\u003e (C)\u003c/strong\u003e The developmental pseudo-time of neutrophils was inferred by Monocle analysis. The dark to bright color key indicates cell differentiation from early to late.\u003cstrong\u003e (D)\u003c/strong\u003eThe pseudo-time trajectory plots demonstrate the sample distribution along the trajectory. Each dot color represents a neutrophil cluster.\u003cstrong\u003e (E)\u003c/strong\u003e The heatmap displays the significantly differential expression genes during the trajectory. The blue-to-red color key indicates low to high relative expression levels. \u003cstrong\u003e(F)\u003c/strong\u003e Averaged expression patterns of gene sets from four modules along pseudo-time.\u003cstrong\u003e (G)\u003c/strong\u003e Enriched KEGG terms for gene sets from two modules (modules 1 and 2) were represented on the left side.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-3996729/v1/f54e2a40c16bcca9babddaf4.png"},{"id":52026670,"identity":"588e5f4b-b558-47e5-b684-46b43daf6dc6","added_by":"auto","created_at":"2024-03-05 15:54:08","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":511271,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIntercellular ligand-receptor prediction among immune cells revealed by CellChat analysis. (A)\u003c/strong\u003e The UMAP plot displays four subclusters of NK/T cells. \u003cstrong\u003e(B)\u003c/strong\u003e Distribution of NK/T cells from different groups (G1, G2, and G3) on a UMAP plot. \u003cstrong\u003e(C)\u003c/strong\u003e Representative cell type marker genes (y-axis) in each cluster (distributed along the x-axis) are shown for CD8+T cells (\u003cem\u003eCD8A\u003c/em\u003e, \u003cem\u003eCD8B\u003c/em\u003e, \u003cem\u003eLAG3\u003c/em\u003e), CD4+T cell (\u003cem\u003eCD4\u003c/em\u003e, \u003cem\u003eCCR7\u003c/em\u003e, \u003cem\u003eLEF1\u003c/em\u003e, \u003cem\u003eSELL\u003c/em\u003e, \u003cem\u003eIL2RA\u003c/em\u003e), and NK cell (\u003cem\u003eFCER1G\u003c/em\u003e, \u003cem\u003eNCR1\u003c/em\u003e, \u003cem\u003eNCR3\u003c/em\u003e, \u003cem\u003eCCL3\u003c/em\u003e, \u003cem\u003eKLRC1\u003c/em\u003e, \u003cem\u003eFCGR3A\u003c/em\u003e). \u003cstrong\u003e(D) \u003c/strong\u003eProportions of CD4+ T cell, CD8+ T cell, and NK cell among three groups. \u003cstrong\u003e(E)\u003c/strong\u003e Bar plot showing the number and strength of intercellular interactions during PHE progression. Heatmaps of differential number (\u003cstrong\u003eF\u003c/strong\u003e) and strength (\u003cstrong\u003eG\u003c/strong\u003e) of intercellular interactions during PHE progression.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-3996729/v1/a15789f37abaf26074d5560a.png"},{"id":52027317,"identity":"d23b07cb-2747-44fb-8574-e000835779c1","added_by":"auto","created_at":"2024-03-05 16:02:08","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":584990,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCell ligand-receptor inference analysis of immune cells during PHE progression\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A) \u003c/strong\u003eBubble plot (excerpted in the red dotted box in\u003cstrong\u003eAdditional File 1: Fig. S7\u003c/strong\u003e) of the significant differentially expressed ligand-receptor pairs during PHE progression. Dot color reflects communication probabilities, and dot size represents computed p-values. Empty space means the communication probability is zero. P-values are computed from a two-sided permutation test. \u003cstrong\u003e(B)\u003c/strong\u003eThe contribution of inferred ligand-receptor pairs in \u003cem\u003eSPP1\u003c/em\u003e signaling between groups (G1, G2, and G3).\u003cstrong\u003e (C)\u003c/strong\u003e Violin plots of expression distribution of signaling pathway-related genes.\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-3996729/v1/cda2b6e65f61f02165dd7d4e.png"},{"id":52026661,"identity":"03ba088d-9d01-4cc4-a727-51832d1512d8","added_by":"auto","created_at":"2024-03-05 15:54:08","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":1299838,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCo expression of osteopontin and CD44 in the tissue adjacent to cerebral hemorrhage hematoma.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe following pictures are all taken under the Zeiss Z2 laser confocal microscope, showing co staining and individual staining of several molecules, respectivelyThe surface markers Iba-1 of microglia, CD44 of monocytes, and osteopontin are labeled with green, red, and pink fluorescent signals respectively, while blue is the labeling of the nucleus. Microglia are surrounded by a large amount of osteopontin, and there is a high degree of spatial co localization between osteopontin and CD44.\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-3996729/v1/ddcdc269119f9c88e2c36aea.png"},{"id":52027646,"identity":"627f65c3-59c2-42b4-aaf2-08ce415e09e7","added_by":"auto","created_at":"2024-03-05 16:10:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4829522,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3996729/v1/11d83214-530c-40e3-a0db-a336c3be956f.pdf"},{"id":52026655,"identity":"c440d8d9-4c37-443e-ad96-70ef5aba63df","added_by":"auto","created_at":"2024-03-05 15:54:07","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1955361,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig. S1. Microglia states have diverse biological pathway correlates. (A)\u003c/strong\u003e GSEA analysis of genes that differentiate each cluster from cluster 9 ('homeostatic microglia') suggests distinct biological pathways.\u003cstrong\u003e (B)\u003c/strong\u003e GSEA shows top enriched pathways in some microglial subclusters.\u003c/p\u003e","description":"","filename":"Fig.S1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3996729/v1/46d4e85783df2adfd0eedf8b.pdf"},{"id":52026660,"identity":"2b2ccc6e-2a7e-478e-925b-9b678d55797d","added_by":"auto","created_at":"2024-03-05 15:54:08","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":866571,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig. S2. IL-1B expressing microglia genes expression profile compared with P2RY12 expressing microglia\u003c/strong\u003e. \u003cstrong\u003e(A)\u003c/strong\u003eDotplot shows the normalized expression of P2RY12 and IL1B in microglia clusters of PHE tissue in patients with ICH. Dot size reflects the percentage of cells that showed genes, while color shows the expression levels of genes. \u003cstrong\u003e(B)\u003c/strong\u003eHeatmap of genes significantly modulated in IL-1B expressing clusters compared to P2RY12 expressing clusters. For the heatmap, 100 randomly sampled cells were shown for both IL1B and P2RY12 clusters. \u003cstrong\u003e(C)\u003c/strong\u003e P2RY12, IL1B clusters marker, chemokine CCL4, CCL3L1, and proinflammatory nuclear transcription factor \u003cem\u003eNFKB1\u003c/em\u003e were shown as violin plots. \u003cstrong\u003e(D)\u003c/strong\u003e GO gene set enrichment analysis results were shown as a bar plot where the x-axis -log of FDR adjusted p-value for GO terms was shown. The top 30 GO terms are shown in this figure. \u003cstrong\u003e(E)\u003c/strong\u003e KEGG gene set enrichment analysis results were shown as bubble plots. The top 20 KEGG terms are shown in the figure.\u003c/p\u003e","description":"","filename":"Fig.S2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3996729/v1/8e04007e2a0190cdc3d8a2bb.pdf"},{"id":52026665,"identity":"e5c72c29-d829-4428-81c1-4fb8818fc103","added_by":"auto","created_at":"2024-03-05 15:54:08","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":7774015,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig. S3. Characterizing the gene expression profile of selected microglia-specific, inflammatory, and activation marker genes\u003c/strong\u003e. \u003cstrong\u003e(A-B)\u003c/strong\u003e The x-axis shows microglia subclusters, and the y-axis shows normalized expression levels.\u003c/p\u003e","description":"","filename":"Fig.S3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3996729/v1/31d13dd1fe64c1fc6cce6317.pdf"},{"id":52026672,"identity":"1d1925cd-101d-494f-a6fd-ae1f88c5b982","added_by":"auto","created_at":"2024-03-05 15:54:09","extension":"pdf","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":579315,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig. S4.\u003c/strong\u003e \u003cstrong\u003eTranscription factor regulatory networks are specific to microglia phenotypes\u003c/strong\u003e.\u003cstrong\u003e (A)\u003c/strong\u003e SCENIC workflow identified transcription factor-regulated networks associated with different phenotypic clusters of microglia. Heatmap of each microglia subtype's inferred regulon activity score (RAS) in cluster levels. \u003cstrong\u003e(B)\u003c/strong\u003e Ranking plot of regulon specificity score (RSS). The higher RSS of the regulon may be specific to the subtypes.\u003cstrong\u003e (C)\u003c/strong\u003e Heatmap of regulon specificity score (RSS). The higher RSS of the regulon may be specific to the subtypes.\u003c/p\u003e","description":"","filename":"Fig.S4.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3996729/v1/83c27762b8a11520203933a2.pdf"},{"id":52027319,"identity":"191853d3-14bb-49a8-bfc3-0b3725ebe4c1","added_by":"auto","created_at":"2024-03-05 16:02:08","extension":"pdf","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":552002,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig. S5. Signaling changes of microglia subcluster during PHE tissue progression\u003c/strong\u003e.\u003cstrong\u003e \u003c/strong\u003eCell ligand-receptor inference analysis of microglial subtypes during PHE tissue progression (G3 vs. G2).\u003c/p\u003e","description":"","filename":"Fig.S5.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3996729/v1/ffdcd7bd4cc94dea5407e62c.pdf"},{"id":52026674,"identity":"66474e9b-03d8-44b1-9720-ab8481a5b41f","added_by":"auto","created_at":"2024-03-05 15:54:09","extension":"pdf","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":8349905,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig. S6. The heterogeneity of neutrophils during PHE progression.\u003c/strong\u003e \u003cstrong\u003e(A)\u003c/strong\u003e Differential expression analysis comparing each cluster to all others demonstrates distinct gene expression profiles. The top 20 genes from each cluster are displayed with gene names annotated on the right. \u003cstrong\u003e(B)\u003c/strong\u003e Violin plot showing the scores of functional modules for each neutrophil subcluster, using the AddModuleScore function. \u003cstrong\u003e(C)\u003c/strong\u003eEnriched KEGG terms for gene sets from four modules (modules 3 and 4) were represented on the left side.\u003c/p\u003e","description":"","filename":"Fig.S6.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3996729/v1/94f61e24b3154a292e34ca9d.pdf"},{"id":52026669,"identity":"74ccb608-adff-4f2e-a696-bcdc987dc9a3","added_by":"auto","created_at":"2024-03-05 15:54:08","extension":"pdf","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":1035608,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig. S7. Cell ligand-receptor inference analysis of immune cells during PHE progression\u003c/strong\u003e. Bubble plot of the significant differentially expressed ligand–receptor pairs during PHE progression. Dot color reflects communication probabilities, and dot size represents computed p-values. Empty space means the communication probability is zero. The p-values were computed from a two-sided permutation test.\u003c/p\u003e","description":"","filename":"Fig.S7.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3996729/v1/05764fc17076c1783a415e28.pdf"},{"id":52026666,"identity":"11c1ad84-b8ff-4b48-9ab0-3584e50b4d02","added_by":"auto","created_at":"2024-03-05 15:54:08","extension":"xlsx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":10989,"visible":true,"origin":"","legend":"","description":"","filename":"Table1..xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3996729/v1/34bc2631221ce7d4fef203fe.xlsx"},{"id":52026671,"identity":"d041ec63-1df4-49b5-b09a-d160dd3d7db0","added_by":"auto","created_at":"2024-03-05 15:54:08","extension":"xlsx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":3763797,"visible":true,"origin":"","legend":"","description":"","filename":"Table2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3996729/v1/89cd742d586577b36afaf3a1.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Single-cell RNA sequencing reveals the evolution of the immune landscape during perihematomal edema progression after intracerebral hemorrhage","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIntracerebral hemorrhage (ICH) is one of the most critical and severe illnesses, posing a significant threat to global health due to its high rates of disability and mortality (\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). ICH initially causes injury through the physical disruption of the initial hemorrhage and hematoma expansion, and it also leads to secondary brain injury by the development of perihematomal edema (PHE) (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). The formation of PHE occurs within 1\u0026ndash;4 hours after ICH, with subsequent progression for up to 2\u0026ndash;3 weeks after that (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). However, the specific pathophysiological mechanisms of PHE formation are complex and poorly understood. Previous studies have shown that PHE enhances the mass effect induced by initial hematoma and causes direct damage to brain tissue via blood-brain barrier (BBB) dysfunction and imbalanced osmotic gradients, leading to neurological deterioration (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Furthermore, PHE progression is closely related to adverse clinical outcomes and prognosis of patients with ICH (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Therefore, ICH remains inadequately controlled by current treatments. However, the emergence of immunomodulatory medications has reignited the hope for ICH treatment. Considering PHE's role in secondary brain injury, developing effective immunomodulatory treatments targeting PHE might offer a new therapeutic modality for ICH.\u003c/p\u003e \u003cp\u003eThree randomized controlled trials have demonstrated that immunomodulatory drugs, such as Minocycline and Deferoxamine, aimed at targeting immune cells in ICH, have been unsuccessful in clinical settings (\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). The increasingly clear consensus is that we need to have a deeper understanding of the different roles of different immune subgroups in PHE, whether beneficial or harmful. Nevertheless, experimental research on immune cells related to stroke largely relies on previous knowledge, such as markers and phenotypes of immune cell populations defined in other research fields. This approach is fundamentally flawed for identifying unique immune cell subclusters specific to ICH and for an unbiased exploration of the complex immune cell states induced by ICH. Therefore, a comprehensive understanding of the diversity within immune cells in PHE is crucial for developing effective and targeted immunotherapeutic modalities.\u003c/p\u003e \u003cp\u003eIn this study, we aimed to analyze the immune cell composition in PHE tissue through single-cell RNA sequencing (scRNA-seq). Currently, ScRNA-seq has been widely used to identify immune cell subsets specific to certain pathological conditions in the brain, including multiple sclerosis, epilepsy, as well as Alzheimer's disease (\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Correspondingly, little is known about the heterogeneity of immune cells in ICH, especially when it arising justnow. Given that the immune cell landscape at the single-cell level in PHE tissues during the progression of ICH still needs futher characterization. Therefore, this study aims to use scRNA seq to de novo characterize the evolution process of immune cells in PHE tissues using transcriptional profiling of human immune cells.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cb\u003eSingle-cell transcriptome profiling reveals the heterogeneity of immune and non-immune cells after ICH.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eIn order to reveal the changes in immune cells during the progression of ICH, we collected 10\u0026times;Genomics scRNA-seq datasets from fresh PHE samples, which were resected from brain tissue in the vicinity of hematoma during the evacuation of the hematoma. We constructed a multi-stage profile including Group1 (n\u0026thinsp;=\u0026thinsp;3, 0\u0026ndash;6 hours after ICH, G1), Group2 (n\u0026thinsp;=\u0026thinsp;3, 6\u0026ndash;24 hours after ICH, G2), and Group3 (n\u0026thinsp;=\u0026thinsp;3, 24\u0026ndash;48 hours after ICH, G3) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). After quality control, a total of 3152\u0026ndash;5823 cells were retained, with an average of 2639\u0026ndash;4716 genes per cell and an average of 9535\u0026ndash;24244 unique molecular identifiers (UMIs) per cell for the subsequent analysis. Following that, we clustered and visualized various cell types refering to their relative gene expression levels using uniform manifold approximation and projection (UMAP), an unsupervised nonlinear dimensionality reduction algorithm. Graph-based Louvain clustering algorithms were used to cluster all cells into subsets resulting in 19 clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Based on marker gene expression levels of microglia (\u003cem\u003eAIF1\u003c/em\u003e, \u003cem\u003eCSF1R\u003c/em\u003e, \u003cem\u003eTMEM119\u003c/em\u003e, \u003cem\u003eCX3CR1\u003c/em\u003e), clusters 5, 6, 7 and 13 were identified as microglial cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC and \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). Clusters 8 and 16 expressed genes (\u003cem\u003eALDH1L1\u003c/em\u003e, \u003cem\u003eATP1B2\u003c/em\u003e and \u003cem\u003eAQP4\u003c/em\u003e) specific to astrocytes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC and \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). Within the infiltrating immune cell subsets, cluster 3 had neutrophil marker genes (\u003cem\u003eCSF3R\u003c/em\u003e, \u003cem\u003eS100A8\u003c/em\u003e, \u003cem\u003eCXCR2\u003c/em\u003e and \u003cem\u003eFCGR3B\u003c/em\u003e) and cluster 9 had monocyte marker genes (\u003cem\u003eCD300E\u003c/em\u003e and \u003cem\u003eVCAN\u003c/em\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC and \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). Cluster 11 expressed genes (\u003cem\u003eCD3D\u003c/em\u003e, \u003cem\u003eCD3E\u003c/em\u003e, \u003cem\u003eNKG7\u003c/em\u003e, \u003cem\u003eGZMA\u003c/em\u003e and \u003cem\u003eGZMB\u003c/em\u003e) specific to cells of NK/T cells, while cluster 19 expressed genes (\u003cem\u003eCD79A\u003c/em\u003e, \u003cem\u003eCD79B\u003c/em\u003e and \u003cem\u003eMS4A1\u003c/em\u003e) specific to B cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC and \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). Additionally, clusters 1, 2, 4, 10, 14, 15 and 17 expressed genes (\u003cem\u003eMOG\u003c/em\u003e, \u003cem\u003eSOX10\u003c/em\u003e, \u003cem\u003eCNP\u003c/em\u003e and \u003cem\u003eHAPLN2\u003c/em\u003e) specific to oligodendrocytes, and cluster 12 expressed genes (\u003cem\u003eCSPG4\u003c/em\u003e and \u003cem\u003ePDGFRA\u003c/em\u003e) specific to neural progenitor cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC and \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). cluster 18 expressed endothelial cell marker genes (\u003cem\u003eCLDN5\u003c/em\u003e, \u003cem\u003eVWF\u003c/em\u003e, \u003cem\u003eRGS5\u003c/em\u003e and \u003cem\u003eEGFL7\u003c/em\u003e). Therefore, we identified this cluster as endothelial cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC and \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). Furthermore, neutrophils, NK/T cells, and monocyte cells were present at all time points and increased to reach their highest level at G3 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD), a timepoint that appears crucial to investigating ICH-induced immune cell changes (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Microglia and neutrophils, as the most important immune cell populations in the central nervous system and peripheral immune system, respectively, were essential in the pathophysiology of ICH (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Therefore, we next focused on analyzing the transcriptional profiles of these two cell types and investigated the crosstalk between central and peripheral immune cells during ICH progression. In addition, the number of B cells was too small for further bioinformatic analyses, so we excluded them from subsequent analyses.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eSingle-cell RNA sequencing reveals the complexity of microglia states during ICH progression.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAs a result of cluster analysis of the 8353 microglia-like cells, 12 clusters were identified (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA) characterized by differentially expressed genes (DEGs). We determined the enrichment of biological pathways in each cluster via gene set enrichment analysis (GSEA) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). There was almost no overlap in the DEGs defining each cluster, supporting the unique nature of each microglia cluster (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD, Additional file 1: Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). First, we identified annotated clusters of microglia phenotypically similar to those previously described in the human brain. Cluster-enriched sets of transcriptional regulators and transcription factors were observed in some clusters (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, and \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e) but not in others (3 and 9) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA) (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Besides, some selected cell-surface marker genes were not observed in cluster 9 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). The absence of detectable unique cell-surface markers and on-off transcription factors among clusters 3 or 9 may represent homeostatic microglia, whereas the other clusters differed from them through the upregulation of specific genes. As an additional finding, we identified cluster 9 as an enriched cluster for homeostatic genes, owing to its high levels of P2RY12 and CX3CR1 expression (Additional file 1: Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eA, Additional file 1: Fig. \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003eA) (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). However, the higher expression of homeostatic markers was not found in cluster 3 (Additional file 1: Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eA). Therefore, we annotated cluster 9 as homeostatic microglia (HM) cluster. HM was established as a comparative basis for evaluating DEGs from other microglia clusters (Table\u0026nbsp;1), referring to the approach in previous literature (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eInitially, we identified annotated clusters of microglia phenotypically similar to those previously characterized in the human brain. Notably, Cluster Micro3 was characterized by the downregulation of homeostatic genes (Additional File 1: Fig. \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003eA), such as checkpoint genes (\u003cem\u003eTMEM119 and CX3CR1\u003c/em\u003e) and purinergic receptors (\u003cem\u003eP2RY12\u003c/em\u003e), and by the upregulation of encoding many metabolic genes (\u003cem\u003eAPOC1, VIM, LDHA, RPS2, RPS6, RPS10, RPS19, and RPL12\u003c/em\u003e), predominantly ribosomal subunits genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). In summary, cluster Micro3 reflects a degenerative phenotype of microglia, consistent with the responses of microglia to aging. Remarkably, Cluster Micro5 predominantly expressed genes characteristic of disease-associated microglia (DAMs), such as metabolic genes \u003cem\u003eLPL\u003c/em\u003e and \u003cem\u003eFABP5\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC) (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). DAM subtype is novel microglia associated with neurodegenerative diseases such as Alzheimer's (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). The pathway analysis of the Micro5 genes highlighted associations with \"Alzheimer's disease\" and \"Huntington's disease\" (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD, Additional File 1: Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eB). Hence, we annotated this cluster as \"DAM-like\" microglia. Cluster Micro6 and Micro10 were defined by genes and pathways involved in canonical inflammatory phenotype. GSEA indicated that these two clusters were enriched in Toll-like receptor (TLR) signaling, Nod-like receptor (NLR) signaling, and chemokine signaling pathway, suggesting inflammatory responses of downstream effectors to stimuli (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD, Additional File 1: Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA). Cluster Micro11 was characterized by anti-inflammatory and repair-related genes (\u003cem\u003eHTR7\u003c/em\u003e, \u003cem\u003ePDLIM7\u003c/em\u003e, and \u003cem\u003eLGALS3\u003c/em\u003e) and proinflammatory genes (\u003cem\u003eKCNN4\u003c/em\u003e and \u003cem\u003eITGB7\u003c/em\u003e) (\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e), indicating that this cluster was an intermediate state in the polarization of microglia (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). Cluster Micro12 was defined by expression of genes involved in DNA repair and cell cycle regulation, including \u003cem\u003eMKI67\u003c/em\u003e, \u003cem\u003eSKA1\u003c/em\u003e, \u003cem\u003eE2F2\u003c/em\u003e and \u003cem\u003eE2F8\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). Furthermore, Micro12 was enriched for pathways involved in DNA replication and the cell cycle (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD, Additional File 1: Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eB). Micro1 was defined by genes (\u003cem\u003eXIST\u003c/em\u003e, \u003cem\u003eVEGFA\u003c/em\u003e, \u003cem\u003eKLF4\u003c/em\u003e) involved in microglial M1 polarization (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC) (\u003cspan additionalcitationids=\"CR24 CR25\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Moreover, we found that cluster Micro1 was between HM cluster (Micro9) and canonical inflammatory phenotype (Micro6), suggesting that this cluster could be the intermediate transition status from HM microglia to proinflammatory microglia.\u003c/p\u003e \u003cp\u003eSubsequently, we identified four microglial clusters\u0026mdash;Micro2, Micro4, Micro7, and Micro8\u0026mdash;that had not previously been characterized in human brain studies. These subclusters were distinguished by the enrichment for the pathway of DEGs relative to HM microglia. The significant DEGs of Micro7 were involved in the neurotrophin signaling pathway and Fc gamma receptor-mediated phagocytosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD, Additional File 1: Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eB). Micro4 showed gene enrichment for complement and coagulation cascades and the PPAR signaling pathway (Additional File 1: Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA), partially sharing a subset of DEGs with Micro7 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). The activation of the PPAR signaling pathway has been demonstrated to attenuate proinflammatory responses and increase neurotrophic factors in patients with ICH. Accordingly, we annotated these two clusters as tissue repair phenotypes. Additionally, the pathway significantly enriched Micro8 in antigen processing and presentation, suggesting this subcluster of microglia is active in antigen processing and presentation for immune response (Additional File 1: Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA). Furthermore, the pathways enriched in Micro2 confirm the relative increase of genes involved in oxidative phosphorylation and glycolysis/gluconeogenesis and decreased chemokine and endocytosis genes (Additional File 1: Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA). This finding coincides with previous studies that persistent glycolysis exerts adverse effects on microglial functions: the activation of glycolytic metabolism impairs phagocytosis and chemotaxis of microglia (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Overall, while future research will likely refine our understanding of microglial subtypes, our study significantly advances the knowledge of microglial heterogeneity in human PHE tissue.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eMicroglia subclusters predominantly exhibit proinflammatory phenotypes after ICH\u003c/h2\u003e \u003cp\u003ePrevious studies of single-cell transcriptomics have shown diverse subclusters of microglia, which are considered to reflect their different functions. In this study, we employed scRNA-seq to investigate the biological pathways present in microglia within PHE tissue following ICH. We observed that common microglial marker genes such as \u003cem\u003eAIF1, TREM2\u003c/em\u003e, and \u003cem\u003eCSF1R\u003c/em\u003e were widely expressed across all microglial subclusters (Additional File 1: Fig. \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003eA). However, other specific marker genes of microglia (\u003cem\u003eITGAM\u003c/em\u003e, \u003cem\u003eP2RY12\u003c/em\u003e, and \u003cem\u003eCX3CR1\u003c/em\u003e) indicated differential expression across clusters (Additional File 1: Fig. \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003eA). Interestingly, the transcriptome of PHE tissue was almost dominated by proinflammatory pathways (Additional File 1: Figs. S3A-B). Our findings indicated a lack of significant activation of anti-inflammatory pathways within the first 48 hours post-ICH (Additional File 1: Fig. \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003eB). Proinflammatory genes such as \u003cem\u003eCCL2, CCL4\u003c/em\u003e, and \u003cem\u003eIL1B\u003c/em\u003e were among the most prevalently expressed cytokine and chemokine genes in ICH-associated microglia (Additional File 1: Fig. \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003eA-B). ICH microglial clusters 1, 3, 5, 6, 10, and 11 were characterized by high gene expression levels of \u003cem\u003eHLA-DQA\u003c/em\u003e, \u003cem\u003eHLA-DPB1\u003c/em\u003e, and \u003cem\u003eHLA-DRA\u003c/em\u003e and low gene expression levels of \u003cem\u003eP2RY12\u003c/em\u003e and \u003cem\u003eCX3CR1\u003c/em\u003e (Additional File 1: Fig. \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003eA), suggesting their involvement in the primary immune response to ICH. Complement pathway-related genes (C3, C1QB, and C1QC) also maintained high levels of expression in all microglial cell clusters (Additional File 1: Fig. \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003eB). Overall, most microglial clusters displayed proinflammatory phenotype within 48 h of ICH, further corroborating the notion that a proinflammatory response is a key pathogenic mechanism in ICH.\u003c/p\u003e \u003cp\u003ePurinergic receptor P2RY12, the cell-surface proteins of microglia, play key roles in mediating neuroinflammatory responses (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Our results indicated reduced expression of the P2RY12 gene in microglia clusters that had higher IL1B expression levels (Additional file 1: Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eA). This finding was consistent with the previous studies that the expression of P2RY12 was gradually decreased accompanied by microglia activation following inflammatory stimulation (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Further analysis was performed by comparing the differentially expressed genes in IL1B-expressing clusters (cluster 6 and cluster 10) with those in P2RY12-expressing clusters (cluster 7 and cluster 9). A significant difference in gene expression was found between IL1B-expressing microglia and P2RY12-expressing microglia, with 882 genes notably downregulated and 415 genes notably upregulated (adjusted \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05, log2 (fold change)\u0026thinsp;\u0026gt;\u0026thinsp;1.5) (Additional file 1: Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eB, Additional file 2: Tab. S2). A significant increase in chemokine and pro-inflammatory cytokines was observed in microglial cluster cells expressing IL1B (Additional file 1: Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eC). (Additional file 1: Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eC). Additionally, CX3CR1 expression was also higher in cluster cells expressing P2RY12 than in cluster cells expressing IL1B (Additional file 1: Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eB). In addition, Gene Ontology term enrichment analysis indicated genes enriched for protein binding, extracellular exosome, focal adhesion, cytokine-mediated signaling pathway, and inflammatory response (Additional file 1: Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eD). Furthermore, the Kyoto Encyclopedia of Genes and Genomes term enrichment analysis suggested genes enriched for apoptosis, NF\u0026thinsp;\u0026minus;\u0026thinsp;kappa B signaling pathway, IL\u0026thinsp;\u0026minus;\u0026thinsp;17 signaling pathway, and Toll\u0026thinsp;\u0026minus;\u0026thinsp;like receptor signaling pathway (Additional file 1: Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eE). According to DEGs analysis, pro-inflammatory microglia expressing IL1B are structurally and functionally different from those expressing P2RY12. According to these findings, as well as previous studies, PHE tissue removed from patients with ICH contains an immune pathogenic microenvironment that attracts and induces non-specific and specific immunity rapidly. Hence, we emphasized on the characterization of immune cells infiltrating in PHE tissues.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMicroglia cluster-specific transcription factor regulatory networks.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo explore the regulatory networks of the microglia clusters in the dataset, we also applied SCENIC analysis to identify the top transcription factor-driven networks (regulons) controlling gene expression in each of these 12 microglia clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, Additional File 1: Fig. \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003eA). Each microglia cluster was characterized by a specific set of regulons (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). This supports the theory that transcriptional regulation mechanisms are key determinants of the unique gene expression profiles observed in each microglia cluster. For example, Micro1 showed higher activity levels of \u003cem\u003ePOLR2A\u003c/em\u003e, \u003cem\u003eNFKB2\u003c/em\u003e, \u003cem\u003eGTF2B\u003c/em\u003e, and \u003cem\u003eBCLAF1\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, Additional File 1: Fig. \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003eA). Micro3 showed higher activity levels of \u003cem\u003eSOX8\u003c/em\u003e, \u003cem\u003eSOX10\u003c/em\u003e, \u003cem\u003eIRF7\u003c/em\u003e, and \u003cem\u003eSTAT1\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, Additional File 1: Fig. \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003eA); The activity of transcription factors, such as \u003cem\u003eMAFB\u003c/em\u003e, \u003cem\u003eSPI1\u003c/em\u003e, \u003cem\u003eDDIT3\u003c/em\u003e, and \u003cem\u003eXBP1\u003c/em\u003e, was higher in Micro11, while high activity levels of \u003cem\u003eE2F1\u003c/em\u003e, \u003cem\u003eTFDP1\u003c/em\u003e, and \u003cem\u003eBRCA1\u003c/em\u003e were associated with Micro12 (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, Additional File 1: Fig. \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003eA). Furthermore, our analysis revealed that \u003cem\u003eMAFB\u003c/em\u003e, a regulon governed by transcription factors commonly linked with the anti-inflammatory polarization of human microglia, was prominently featured in the Micro11 cluster (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e4\u003c/span\u003eB, Additional File 1: Fig. \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003eC). This is consistent with the finding that these cells experience a phenotypic polarization of microglia of M2. In this study, the \u003cem\u003eNFKB1\u003c/em\u003e regulon, associated with canonical inflammatory responses, was identified in Micro10 (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e4\u003c/span\u003eB, Additional File 1: Fig. \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003eC). Conversely, Micro6 exhibited the \u003cem\u003eRELB\u003c/em\u003e regulon, linked to non-canonical inflammatory responses (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e4\u003c/span\u003eB, Additional File 1: Fig. \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003eC). In Micro9, the high specificity of \u003cem\u003eFOXP2\u003c/em\u003e (Additional File 1: Figs. S4C-D), a regulon unique to human microglia and crucial for brain development, was observed, aligning with previous research identifying this subtype as an HM cluster (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). The top three regulons in other microglia clusters also showed distinct variations (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e4\u003c/span\u003eB, Additional File 1: Figs. S4B-C). These inferred transcription factor regulons provide insight into the diversity and difference within microglial clusters, suggesting novel potential regulatory targets for future research\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eThe SPP1 signaling pathway was the fundamental bridge to self-communication among microglia subclusters\u003c/h2\u003e \u003cp\u003eAlong with the PHE progression, microglial subtypes also changed accordingly. The use of cell-cell communication networks between microglia subpopulations could contribute to a better characterization of microglia function. Interestingly, the interaction strength of the \u003cem\u003eSPP1\u003c/em\u003e pathway increased gradually with the progression of PHE (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e5\u003c/span\u003eA, Additional File 1: Fig. \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e). Moreover, the \u003cem\u003eSPP1\u003c/em\u003e pathway exhibited the strongest interaction strength, irrespective of incoming or outgoing signaling pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). It indicates that the SPP1 signaling pathway could be responsible for self-communication between microglia subclusters. Regarding the incoming signaling, \u003cem\u003eSPP1\u003c/em\u003e was emitted by different microglial subclusters at different stages. At post-ICH in G1, G2, and G3, the strongest \u003cem\u003eSPP1\u003c/em\u003e signaling cell types were Micro6, Micro1, and Micro11, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Regarding outgoing \u003cem\u003eSPP1\u003c/em\u003e signaling, the Micro6 subtype was also strongest at G1 after ICH. In ICH patients at G2 and G3, Micro11 showed strong \u003cem\u003eSPP1\u003c/em\u003e signals (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Additionally, we visualized the crosstalk between each microglia subcluster in the \u003cem\u003eSPP1\u003c/em\u003e signaling pathway. We explored the specific receptor ligands and found that the \u003cem\u003eSPP1\u003c/em\u003e-( \u003cem\u003eITGAV\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003eITGB1\u003c/em\u003e) ligand-receptor pair was the fundamental bridge of self-communication among microglia subclusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). Collectively, our findings demonstrated that the signaling pathway of SPP1 is the fundamental bridge mediating self communication between subclusters of microglia during the progression of ICH.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eTime-dependent transcriptional heterogeneity of neutrophils in the human brain after ICH.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo analyze neutrophil transcriptional heterogeneity during ICH progression, we performed a multiplexed time series of scRNA-seq analyses combining transcriptomics. By comparing the gene expression patterns of all neutrophils at every time points, we depicted five different transcriptional cell clusters in PHE tissue after ICH, exhibiting a time-independent appearance. Due to the fact that the number of cells sampled at each time point (G1: 1312 cells; G2: 1132 cells; G3: 2109 cells) cannot reflect the true level of neutrophils in ICH PHE tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e6\u003c/span\u003eB), we calculated the proportion represented by each cluster at different time points (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). The majority of neutrophils at grade 1 (68.6%) segregated in cluster Neutro2 (\u003cem\u003eCFD\u003c/em\u003e, \u003cem\u003eCDKN2D\u003c/em\u003e, \u003cem\u003eHSPA1B\u003c/em\u003e, \u003cem\u003eS100A4\u003c/em\u003e, \u003cem\u003eD100A6\u003c/em\u003e, \u003cem\u003eS100A9\u003c/em\u003e, \u003cem\u003eS100A12\u003c/em\u003e), a profile significantly reduced by half at later time points (\u0026lt;\u0026thinsp;30% at grade 2 and 3). At grade 2, Neutro3 cells (86.9% of total neutrophils; \u003cem\u003eFOLR3\u003c/em\u003e, \u003cem\u003eSLC8A1\u003c/em\u003e, \u003cem\u003eAOAH\u003c/em\u003e, \u003cem\u003eSYNE2\u003c/em\u003e) were predominant. Cluster Neutro5 (\u003cem\u003eDPYD\u003c/em\u003e, \u003cem\u003eKIFC3\u003c/em\u003e, \u003cem\u003eBMP2K\u003c/em\u003e, \u003cem\u003eABHD5\u003c/em\u003e, \u003cem\u003eSPDYA\u003c/em\u003e) was present at all time points and its levels significantly increased from 0.4% of cells at grade 1 to 10.4% at grade 3. Cluster Neutro1 also increased to reach its highest level at grade 3 (61.5% of all neutrophils; \u003cem\u003eMCEMP1\u003c/em\u003e, \u003cem\u003eTNFAIP3\u003c/em\u003e, \u003cem\u003eIER3\u003c/em\u003e, \u003cem\u003eTANK\u003c/em\u003e). Finally, cluster Neutro4 was characterized by highly specific expression of some transcripts (\u003cem\u003ePLNA\u003c/em\u003e, \u003cem\u003ePFN1\u003c/em\u003e, \u003cem\u003eHMGA1\u003c/em\u003e), and represented 26.8%, 1.9% and 5.0% of all neutrophils at grade 1, 2 and 3 post-ICH, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e6\u003c/span\u003eC, Additional file 1: Fig. \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe unique gene signatures and the top 20 significantly DEGs of each neutrophil subcluster were delineated (Additional file 1: Fig. \u003cspan refid=\"MOESM7\" class=\"InternalRef\"\u003eS7\u003c/span\u003eA). In addition, the Gene Set Variation Analysis (GSVA) was performed to functionally annotate the neutrophil subcluster (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). Neutro1 showed gene enrichment for steroid biosynthesis, ABC transporters, and PPAR signaling pathway, partially sharing a subset of significantly differentially expressed genes with Neutro5 (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). Neutro2 was characterized by the chemokine signaling pathway and antigen processing and presentation (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e6\u003c/span\u003eD), indicating this subtype of cells is active in antigen processing and presentation for immune response. The significantly DEGs of Neutro3 were involved in the folate biosynthesis and the metabolism-related pathways including histidine metabolism, alpha-linolenic acid metabolism, and ascorbate metabolism (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). Neutro4 exhibited higher expression of genes associated with glycolysis and gluconeogenesis, and extracellular matrix (ECM)-receptor interaction (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). Neutrophils can carry preexisting matrix from nearby tissue to reestablish new ECM scaffold in the early stages of tissue repair, suggesting that these cells may represent a repair phenotype (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). Furthermore, Neutro5 was defined by the expression of genes involved in RNA polymerase and TCA cycle (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e6\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eNeutrophil cluster-specific transcription factor regulatory networks.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWe further employed SCENIC analysis to characterize the underlying molecular mechanisms driving the differentiation of different neutrophil phenotypes. A different transcription factor network was predicted to be expressed by neutrophils from different subtypes in this analysis. For instance, \u003cem\u003eCEBPB\u003c/em\u003e and \u003cem\u003eHIVEP2\u003c/em\u003e regulons were upregulated in Neutro1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e6\u003c/span\u003eE). Consistent with the findings that the CEBPB regulon is critical for the emergency granulopoietic response, this mechanism fulfills the increased demand for neutrophils during the innate immune response to inflammation (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Additionally, there was a notable increase in the Neutro1 population, peaking at G3 (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). This suggests a substantial consumption of neutrophils at this stage, prompting the hematopoietic system to respond to the heightened demand through emergency granulopoiesis quickly. Neutro2 also upregulated networks driven by \u003cem\u003eZNF107\u003c/em\u003e, \u003cem\u003eIRF1\u003c/em\u003e, and \u003cem\u003eSPI1\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e6\u003c/span\u003eE). In the Neutro3 cluster, there was a noticeable predominance of the activity of regulons, particularly those linked to \u003cem\u003eZBTB16\u003c/em\u003e and \u003cem\u003eELF1\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e6\u003c/span\u003eE). Some regulons showed preferential activity in Neutro4 (\u003cem\u003eMECP2\u003c/em\u003e, \u003cem\u003eSREBF1\u003c/em\u003e, \u003cem\u003eHIF1A\u003c/em\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e6\u003c/span\u003eE). Neutro5 showed highly active transcription factors related to cell proliferation (\u003cem\u003eFOXO1\u003c/em\u003e, \u003cem\u003eCEBPZ\u003c/em\u003e, \u003cem\u003eYY1\u003c/em\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e6\u003c/span\u003eE). By calculating the connection specificity index (CSI), we obtained a regulatory network composed of four modules and 34 regulons (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e6\u003c/span\u003eF). Transcription factors of CEBPB/FOSL2/HIVEP2 in module 2 displayed the strongest activity in Neutro1 (Figs.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e6\u003c/span\u003eF-G) and were related to emergency granulopoiesis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eTrajectory analysis of neutrophils in PHE tissue during ICH progression\u003c/h2\u003e \u003cp\u003eIn order to explore the dynamic transition of neutrophils from peripheral blood to PHE tissue, we constructed a pseudotime map of the neutrophil state trajectory using monocle2 (Figs.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e7\u003c/span\u003eA, \u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e7\u003c/span\u003eB, and \u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e7\u003c/span\u003eD). Neutrophils are also known as short-lived cells that can mobilize rapidly from the bone marrow in response to tissue damage (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). Accordingly, we performed a 'bone marrow proximity score' of neutrophils based on the expression of a set of genes previously characterized in transcriptome analyses of neutrophils at different stages (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). Cluster Neutro2 (mainly in G1) had the highest BM proximity score (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001 versus all other clusters) (Additional File 1: Fig. \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn our analysis, the progression trajectory of neutrophils was established, starting with Neutro2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e7\u003c/span\u003eC). This was followed by Neutro3, serving as a transitional state between Neutro2 and Neutro4, then moving through an intermediate infiltrating phase represented by Neutro1, and culminating in the terminally differentiated states of Neutro4 and Neutro5. Further examination of the single-cell transcriptomes of neutrophils along this trajectory identified 4043 significantly altered genes, classified into four distinct expression patterns (Figs.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e7\u003c/span\u003eE-F): Module 1 included genes that showed increased expression levels along one trajectory. Pathway enrichment analysis suggested that these genes participated in the chemokine signaling pathway, NOD-like receptor signaling pathway, NF-kappa B signaling pathway, and TNF signaling pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e7\u003c/span\u003eG). Module 2 contained genes activated in the later stages of another trajectory, with enrichment in mitophagy and HIF-1 signaling pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e7\u003c/span\u003eG). Additionally, the genes in module 3 were associated with ribosome function, Fc gamma R-mediated phagocytosis, endocytosis, regulation of actin cytoskeleton, and leukocyte transendothelial migration (Additional File 1: Fig. \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003eC). Finally, module 4 genes, upregulated in the early stages, were associated with necroptosis, apoptosis, and cellular senescence (Additional File 1: Fig. \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003eC).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eOPN-mediated microglia-monocyte interaction was essential for the crosstalk between central and peripheral immune cells\u003c/h2\u003e \u003cp\u003eWe previously discovered a mixed cluster (cluster 11) by immune cell analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). Cluster 11 clearly showed \u003cem\u003eCD3D\u003c/em\u003e, \u003cem\u003eCD3E\u003c/em\u003e, \u003cem\u003eNKG7\u003c/em\u003e, \u003cem\u003eGAMA\u003c/em\u003e, and \u003cem\u003eGAMB\u003c/em\u003e gene expression, indicating doublets of T cells and NK cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). Cluster NK/T cell was present at all time points and its levels gradually increased during ICH progression (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). We re-clustered cluster 11 doublet cells using major lineage gene expression markers and obtained four clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e8\u003c/span\u003eA). Cluster phenotypes were identified using gene expression levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e8\u003c/span\u003eD). A CD8\u0026thinsp;+\u0026thinsp;T cell cluster (\u003cem\u003eCD8A\u003c/em\u003e, \u003cem\u003eCD8B\u003c/em\u003e, \u003cem\u003eLAG3\u003c/em\u003e; cluster 1 and cluster 2) was the main cluster found in PHE tissue (Figs.\u0026nbsp;\u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e8\u003c/span\u003eC-D). We also observed CD4\u0026thinsp;+\u0026thinsp;T cells (\u003cem\u003eCD4\u003c/em\u003e, \u003cem\u003eCCR7\u003c/em\u003e, \u003cem\u003eLEF1\u003c/em\u003e, \u003cem\u003eSELL\u003c/em\u003e, \u003cem\u003eIL2RA\u003c/em\u003e; cluster 4) and NK cells (\u003cem\u003eFCER1G\u003c/em\u003e, \u003cem\u003eNCR1\u003c/em\u003e, \u003cem\u003eNCR3\u003c/em\u003e, \u003cem\u003eCCL3\u003c/em\u003e, \u003cem\u003eKLRC1\u003c/em\u003e, \u003cem\u003eFCGR3A\u003c/em\u003e; cluster 3) (Fig.\u0026nbsp;\u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e8\u003c/span\u003eD). By inferring the paired ligand-receptor pairs based on CellChat analysis, we first depicted the overall connectivity patterns between peripheral immune cells and central immune cells in PHE. The number of cell-cell interactions between immune cells changed significantly in the context of the progression of ICH (Figs.\u0026nbsp;\u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e8\u003c/span\u003eE-F). However, the strength interaction involving microglia, monocytes, and CD8\u0026thinsp;+\u0026thinsp;T cells is consistently higher in the progression of ICH (Fig.\u0026nbsp;\u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e8\u003c/span\u003eG). Intriguingly, monocytes exhibited more extensive communications with microglia than other immune cell types apart from CD8\u0026thinsp;+\u0026thinsp;T cells and NK cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e8\u003c/span\u003eG). We then extracted highly expressed interactions engaging microglia during ICH progression and uncovered underlying interactions with monocytes (Fig.\u0026nbsp;\u003cspan refid=\"Fig16\" class=\"InternalRef\"\u003e9\u003c/span\u003eA, Additional File 1: Fig. \u003cspan refid=\"MOESM8\" class=\"InternalRef\"\u003eS8\u003c/span\u003e). Notably, we found interactions between \u003cem\u003eSPP1\u003c/em\u003e, which encodes the pleiotropic cytokine OPN, and \u003cem\u003eCD44\u003c/em\u003e, \u003cem\u003eITGAV\u003c/em\u003e, \u003cem\u003eITGA4\u003c/em\u003e, \u003cem\u003eITGA5\u003c/em\u003e, \u003cem\u003eITGA9\u003c/em\u003e, \u003cem\u003eITGB1\u003c/em\u003e, \u003cem\u003eITGB3\u003c/em\u003e, and \u003cem\u003eITGB5\u003c/em\u003e, which encode the OPN receptor, were prominent during microglia-monocyte interactions (Fig.\u0026nbsp;\u003cspan refid=\"Fig16\" class=\"InternalRef\"\u003e9\u003c/span\u003eA). Furthermore, the pair of OPN-CD44 stands out among all interaction pairs that mediate the crosstalk between microglia and monocytes and displays the highest score (Figs.\u0026nbsp;\u003cspan refid=\"Fig16\" class=\"InternalRef\"\u003e9\u003c/span\u003eA-B, Additional File 1: Fig. \u003cspan refid=\"MOESM8\" class=\"InternalRef\"\u003eS8\u003c/span\u003e). Additionally, we found that the \u003cem\u003eSPP1\u003c/em\u003e gene was primarily expressed in the microglia rather than in other immune cells. In contrast, the \u003cem\u003eCD44\u003c/em\u003e gene was mainly expressed in the monocytes (Fig.\u0026nbsp;\u003cspan refid=\"Fig16\" class=\"InternalRef\"\u003e9\u003c/span\u003eC). This finding suggests that microglia-secreted OPN could regulate the immune environment of PHE by interacting with CD44 on monocytes. In summary, these findings indicate that OPN-mediated microglia-monocyte interaction is essential for communicating between central and peripheral immune cells.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eIF\u003c/h3\u003e\n\u003cp\u003eTo verify our conclusion, we extrally collected a group of adjacent hematoma tissues from patients with ICH for immunofluorescence experiments. Iba-1 (surface marker of microglia), CD44 and OPN were labeled with different colors of fluorescence for immunofluorescence co staining. Afterwards, we used a Zeiss Imager Z2 confocal microscope to observe the stained tissue sections, such image was captured under a microscope: co localization existing between osteopontin and microglia (Iba-1), and highly spatially close to CD44 (Fig.\u0026nbsp;\u003cspan refid=\"Fig17\" class=\"InternalRef\"\u003e10\u003c/span\u003e). This also coincides with the conclusion drawn from our previous analysis: 1. Osteopontin is secreted by microglia; 2. Osteopontin as a mediator participates in the activation of microglia and CD44 cells, especially monocytes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we characterized the immune cell landscape of PHE tissue in patients with ICH tissues, uncovering the predominant proinflammatory microenvironment shaped by microglia. Using SCENIC analysis, we observed that each cluster was driven by different transcription factors, further supporting the functional diversity of clusters. Trajectory inference analysis revealed transitions and putative relationships between neutrophil phenotypes. Cell-cell communication networks analysis indicated that the \u003cem\u003eSPP1\u003c/em\u003e signaling pathway was the fundamental bridge of self-communication among microglia subclusters. Furthermore, we identified that microglia-derived OPN was an essential mediator in the communication between microglia and monocytes.\u003c/p\u003e \u003cp\u003eOur study used snRNA-seq analyses to accurately distinguish between immune and non-immune cells in the human brain, identifying their specific types and biological functions. Our study suggested microglia as a major contributor to inducing a proinflammatory immune microenvironment, mainly through producing proinflammatory mediators and reducing microglial purinergic receptor-mediated signaling. Recent studies have shown that the expression level of P2RY12 in microglia is correlated with the phenotype of microglia (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Specifically, it is less expressed by activated proinflammatory microglia, and highly expressed by activated non-inflammatory microglia (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). Reductions in P2RY12-expressing microglia indicated the presence of a broad proinflammatory milieu in PHE tissue. Furthermore, it has been demonstrated in numerous preclinical studies and clinical trials that modulating microglial polarization (M1/M2) can reduce inflammation and thus exert a protective effect in ICH (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). However, none of the immunomodulators that target neuroinflammation of PHE tissue have been applied in clinical practice. Recently, there has been increasing evidence that the M1/ M2 dichotomy of microglial polarization has been oversimplified, highlighting the need for unbiased high-throughput methods to comprehensively investigate microglial heterogeneity (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). A single-cell transcriptome study identified nine microglial subclusters in human brain samples from neurosurgical procedures, revealing phenotypes beyond the conventional M1/M2-like patterns (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Our study did not observe a complete overlap between the microglia subtypes and classical M1 or M2 subtypes after ICH at the single-cell level. Conversely, we identified 12 distinct microglial subclusters in PHE tissue. Similarly, another scRNA-seq study in a mouse ischemic stroke model identified six distinct microglial subclusters; none fully conformed to the M1 or M2 microglia marker gene (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). Moreover, our SCENIC analysis demonstrated the diversity of the identified microglial clusters and suggested potential regulatory targets for future research, highlighting the complexity and nuance of microglial roles in the ICH context.\u003c/p\u003e \u003cp\u003eAs the first activated immune cell cluster, microglia undergo various phenotypic and functional changes depending on the stimuli involved after ICH (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). Our study has revealed the significance of the OPN-CD44 receptor axis in the interaction between microglia and monocytes. OPN, a multifunctional phosphoglycoprotein, bridges innate and adaptive immune responses under pathological conditions (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). Previous studies demonstrated that OPN is involved in neuroimmune responses in diverse central nervous system diseases, including ICH, ischemic stroke, and AD (\u003cspan additionalcitationids=\"CR44\" citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). Furthermore, recent evidence suggests that OPN is highly expressed by microglia, which plays a significant role in immunomodulatory effects (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e). Cell-cell communication analyses predicted strong interactions between microglia and monocytes through OPN and CD44 receptors. These interactions need to be experimentally validated by a range of \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e investigations in the future. Prior studies indicated that peripheral monocytes are also cellular sources of OPN but with lower expression than microglia (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e). Moreover, recent single-cell studies have noted an increased transcription of \u003cem\u003eSPP1\u003c/em\u003e, the gene encoding OPN, in monocytes in the brains of mice with AD (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). Therefore, we speculated that microglia act as initiators of OPN production in monocytes, which could be crucial for monocyte infiltration. However, it is important to note that our study could not determine whether additional OPN-independent mechanisms are involved in the communication between microglia and monocytes.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, we generated the first scRNA-seq data set for immune cells in the human PHE tissue and provided novel insights into post-ICH microglia and neutrophil heterogeneity in the brain. We discovered that the \u003cem\u003eSPP1\u003c/em\u003e signaling pathway was the fundamental bridge of self-communication among microglia subtypes during ICH progression. Additionally, OPN has been identified as a mediator between monocytes and microglia. Our findings, therefore, are considerable in developing a more comprehensive understanding of immune cell diversity, which will drive the development of immunomodulators targeted neuroinflammation post-ICH. Therefore, our findings are of great significance for a more comprehensive understanding of immune cell diversity, which may contribute to exploit immunomodulators targeted neuroinflammation post-ICH.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eConflict of Interest\u003c/h2\u003e \u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e \u003ch2\u003eEthics Statement\u003c/h2\u003e \u003cp\u003eThe studies involving human participants were reviewed and approved by Affiliated Hospital of Jining Medical University.\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by grants from the Qilu Health and Outstanding Young Talents (2021-QLJQ-003), Jining Medical University (JYGC2022FKJ012) and Key R\u0026amp;D Plan of Jining City (2022YXNS082).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003ePZ and CG designed and performed experiments, prepared figures, analyzed data, and wrote, edited, and proofread the manuscript. QG and DY analyzed and interpreted data, prepared figures, and wrote, edited, and proofread the manuscript. GZ performed experiments. HL conceptualized the project and wrote, edited, and proofread the manuscript. DL conceptualized and directed the overall project.\u003c/p\u003e\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e \u003cp\u003eThe data sets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMagid-Bernstein J, Girard R, Polster S, Srinath A, Romanos S, Awad IA, et al. Cerebral Hemorrhage: Pathophysiology, Treatment, and Future Directions. Circ Res. 2022;130(8):1204\u0026ndash;29.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKeep RF, Hua Y, Xi G. Intracerebral haemorrhage: mechanisms of injury and therapeutic targets. 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Cancers (Basel). 2020;12(11).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGong L, Manaenko A, Fan R, Huang L, Enkhjargal B, McBride D, et al. Osteopontin attenuates inflammation via JAK2/STAT1 pathway in hyperglycemic rats after intracerebral hemorrhage. Neuropharmacology. 2018;138:160\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLadwig A, Walter HL, Hucklenbroich J, Willuweit A, Langen KJ, Fink GR, et al. Osteopontin Augments M2 Microglia Response and Separates M1- and M2-Polarized Microglial Activation in Permanent Focal Cerebral Ischemia. Mediators Inflamm. 2017;2017:7189421.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRentsendorj A, Sheyn J, Fuchs DT, Daley D, Salumbides BC, Schubloom HE, et al. A novel role for osteopontin in macrophage-mediated amyloid-beta clearance in Alzheimer's models. Brain Behav Immun. 2018;67:163\u0026ndash;80.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShen X, Qiu Y, Wight AE, Kim HJ, Cantor H. Definition of a mouse microglial subset that regulates neuronal development and proinflammatory responses in the brain. Proc Natl Acad Sci U S A. 2022;119(8).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNilsson G, Baburamani AA, Rutherford MA, Zhu C, Mallard C, Hagberg H, et al. White matter injury but not germinal matrix hemorrhage induces elevated osteopontin expression in human preterm brains. Acta Neuropathol Commun. 2021;9(1):166.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhamissi FZ, Ning L, Kefaloyianni E, Dun H, Arthanarisami A, Keller A, et al. Identification of kidney injury released circulating osteopontin as causal agent of respiratory failure. Sci Adv. 2022;8(8):eabm5900.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e\u003cstrong\u003eHuman brain tissues\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe brain tissues utilized in our research were sourced exclusively from cerebral hemorrhagic patients who visiting Emergency Stroke Department and Neurology Department of Jining Medical University Affiliated Hospital and immediate in need surgical intervention to extract the hematoma. Strict inclusion and exclusion criteria are set for screening eligible participants. Specifically, the enrolled individuals were required to be at least 18 years of age and exhibit a hemorrhage volume ranging from 50 to 100 ml, as determined through computed tomography. Exclusion criteria encompass patients who exhibit pre-existing brain diseases, infections, autoimmune diseases, pathologically confirmed amyloid angiopathy or cerebral arteriovenous malformations, in addition to those undergoing immunosuppressive or immunomodulatory therapy. Finally, we recruited \u0026nbsp;total 9 eligible patients to attend this study, whose detailed information on the age, brain sampling area, histopathology, and clinical diagnosis is registered in Supplementary Table 1. Following the acquisition of written informed consent from each patient (or their legal representative) and implementation of suitable protective measures, a minute quantity of brain tissue situated in the edema region surrounding the hematoma was procured from the participants during the surgical procedure.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003csup\u003e†\u0026nbsp;\u003c/sup\u003e\u003c/strong\u003eOur research complies with all relevant ethical regulations and was approved by the institutional review board at the Affiliated Hospital of Jining Medical University.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSingle-cell RNA-seq data preprocessing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe FASTQ files underwent processing and alignment to the GRCh38 human reference genome using Cell Ranger software (version 7.0.1) from 10x Genomics. The resulting unique molecular identifier (UMI) counts were then summarized for each barcode. Subsequently, the UMI count matrix was analyzed using the Seurat (1) (version 4.0.0) R package. Wherein，to eliminate low-quality cells and potential multiplet captures, a series of criteria were applied: (1) filtering cells based on gene numbers (gene numbers \u0026lt; 200), (2) filtering cells based on UMI counts (UMI \u0026lt; 1000), and (3) filtering cells based on log10GenesPerUMI values (log10GenesPerUMI \u0026lt; 0.7). In order to acquire the normalized gene expression data, the NormalizeData function was employed to perform library size normalization. More specifically, the gene expression measurements for each cell were normalized using the global-scaling normalization method known as \"LogNormalize\". This involved dividing the total expression by a scaling factor (typically set at 10,000 by default), and subsequently applying a logarithmic transformation to the obtained values.\u003c/p\u003e\n\u003cp\u003eThe Seurat function FindVariableGenes (mean.function=FastExpMean, dispersion.function=FastLogVMR) was utilized to calculate the top 2000 highly variable genes (HVGs). Subsequently, dimensionality reduction was performed through Principal-component analysis (PCA) using the RunPCA function. To cluster cells based on their gene expression profile, graph-based clustering was conducted employing the FindClusters function. Finally, the visualization of cells was achieved using a 2-dimensional Uniform Manifold Approximation and Projection (UMAP) algorithm with the RunUMAP function. The identification of marker genes for each cluster was conducted using the FindAllMarkers function (test.use = presto). Differentially expressed genes (DEGs) were selected using the FindMarkers function (test.use = presto). A significance threshold of P value \u0026lt; 0.05 and |log2foldchange| \u0026gt; 0.58 was applied to determine significantly differential expression. GO enrichment and KEGG pathway enrichment analyses of the DEGs were performed using R (version 4.0.3) based on the hypergeometric distribution.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGene Set Variation Analysis (GSVA)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Gene Set Variation Analysis was conducted by utilizing the GSEABase package (version 1.44.0) to import the gene set file obtained from the KEGG database (https://www.kegg.jp/), which was subsequently processed. Pathway activity estimates were assigned to individual cells through the application of GSVA (2) using default parameters, as implemented in the GSVA package (version 1.30.0). The discrepancies in pathway activities per cell were determined using the LIMMA package (version 3.38.3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGene Set Enrichment Analysis (GSEA)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe \"Bone marrow proximity score\" was assigned using the AddModuleScore function in Seurat v3, incorporating the genes MMP8, MMP25, LCN2, OLFM4, ITGB2, FPR1, LTF, and CAMP as features (4). To control for the aggregated expression of the feature set, the average expression levels of the relevant cluster were subtracted. The genes under analysis were categorized into bins based on their average expression, and control characteristics were randomly chosen from each bin.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSCENIC Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe SCENIC analysis was conducted utilizing the motifs database for RcisTarget and GRNboost (SCENIC (5) version 1.2.4, corresponding to RcisTarget version 1.10.0 and AUCell version 1.12.0) with the default parameters. Specifically, the RcisTarget package was employed to identify transcription factor (TF) binding motifs that were over-represented on a gene list. The AUCell package (version 1.12.0) was utilized to score the activity of each group of regulons in each cell. In order to assess the cell type specificity of each predicted regulon, we computed the regulon specificity score (RSS) using the Jensen-Shannon divergence (JSD), a metric that quantifies the similarity between two probability distributions. More specifically, we calculated the JSD between each binary regulon activity vector and the assignment of cells to a particular cell type (6). Additionally, we determined the connection specificity index (CSI) for all regulons using the scFunctions package available at https://github.com/FloWu-enne/scFunctions/.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMonocle2 Pseudotime Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Monocle2 (7) package (version 2.9.0) was employed to ascertain the developmental pseudotime. Initially, the raw count was transformed from a Seurat object into a CellDataSet object using the importCDS function in Monocle. Subsequently, the differentialGeneTest function from the Monocle2 package was utilized to identify ordering genes (qval \u0026lt; 0.01) that were deemed informative in the arrangement of cells along the pseudotime trajectory. The dimensional reduction clustering analysis was conducted using the reduceDimension function, followed by trajectory inference employing the orderCells function with default parameters. Gene expression was visualized using the plot_genes_in_pseudotime function to monitor alterations across pseudo-time.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCell–Cell Communication Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe analysis of cell communication was conducted utilizing the CellChat (8) (version 1.1.3) R package. Initially, the normalized expression matrix was imported to generate the cellchat object through the createCellChat function. Subsequently, the data underwent preprocessing employing the identifyOverExpressedGenes, identifyOverExpressedInteractions, and projectData functions with the default parameters. The functions computeCommunProb, filterCommunication (with a minimum of 10 cells) and computeCommunProbPathway were subsequently employed to ascertain any plausible ligand-receptor interactions. Ultimately, the cell communication network was consolidated via the aggregateNet function.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eReference\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e1. \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; McGinnis CS, Murrow LM, Gartner ZJ. DoubletFinder: Doublet Detection in Single-Cell RNA Sequencing Data Using Artificial Nearest Neighbors. Cell Syst. 2019;8(4):329-37 e4.\u003c/p\u003e\n\u003cp\u003e2. \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Hanzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics. 2013;14:7.\u003c/p\u003e\n\u003cp\u003e3. \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A. 2005;102(43):15545-50.\u003c/p\u003e\n\u003cp\u003e4. \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Evrard M, Kwok IWH, Chong SZ, Teng KWW, Becht E, Chen J, et al. Developmental Analysis of Bone Marrow Neutrophils Reveals Populations Specialized in Expansion, Trafficking, and Effector Functions. Immunity. 2018;48(2):364-79 e8.\u003c/p\u003e\n\u003cp\u003e5. \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Aibar S, Gonzalez-Blas CB, Moerman T, Huynh-Thu VA, Imrichova H, Hulselmans G, et al. SCENIC: single-cell regulatory network inference and clustering. Nat Methods. 2017;14(11):1083-6.\u003c/p\u003e\n\u003cp\u003e6. \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Suo S, Zhu Q, Saadatpour A, Fei L, Guo G, Yuan GC. Revealing the Critical Regulators of Cell Identity in the Mouse Cell Atlas. Cell Rep. 2018;25(6):1436-45 e3.\u003c/p\u003e\n\u003cp\u003e7. \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Trapnell C, Cacchiarelli D, Grimsby J, Pokharel P, Li S, Morse M, et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat Biotechnol. 2014;32(4):381-6.\u003c/p\u003e\n\u003cp\u003e8. \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Jin S, Guerrero-Juarez CF, Zhang L, Chang I, Ramos R, Kuan CH, et al. Inference and analysis of cell-cell communication using CellChat. Nat Commun. 2021;12(1):1088.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-neuroinflammation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jneu","sideBox":"Learn more about [Journal of Neuroinflammation](http://jneuroinflammation.biomedcentral.com)","snPcode":"12974","submissionUrl":"https://submission.nature.com/new-submission/12974/3","title":"Journal of Neuroinflammation","twitterHandle":"@bmc","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"intracerebral hemorrhage, stroke, single cell sequencing, inflammatory cells, microglia, neutrophils","lastPublishedDoi":"10.21203/rs.3.rs-3996729/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3996729/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003ePerihematomal edema (PHE) after post-intracerebral hemorrhage (ICH) has complex pathophysiological mechanisms that are poorly understood. The complicated immune response in the post-ICH brain constitutes a crucial component of PHE pathophysiology. In this study, we aimed to characterize the transcriptional profiles of immune cell populations in human PHE tissues and explore the microscopic differences between different types of immune cells.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eScRNA sequencing (scRNA-seq) was used to map immune cell populations within comprehensively resected PHE samples collected from patients at different stages after ICH.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eWe established, for the first time, a comprehensive landscape of diverse immune cell populations in human PHE tissue at a single-cell level. Our study identified 12 microglial and five neutrophil subsets in human PHE tissue. What\u0026rsquo;s more, we discovered that the SPP1 pathway served as the basis for self-communication between microglia subclusters during the progression of PHE. Additionally, we traced the trajectory branches of different neutrophil subtypes. We also demonstrated that microglia-produced OPN could regulate the immune environment in PHE by interacting with CD44 cells.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eAs a result of our research, we have gained valuable insight into the immunomicroenvironment within PHE tissue, which could potentially be used to develop novel treatment modalities for ICH.\u003c/p\u003e","manuscriptTitle":"Single-cell RNA sequencing reveals the evolution of the immune landscape during perihematomal edema progression after intracerebral hemorrhage","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-05 15:54:02","doi":"10.21203/rs.3.rs-3996729/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-03-29T05:44:51+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-03-06T17:57:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"26a8a5f0-162a-4b6a-b2eb-178307de0534","date":"2024-03-05T14:17:20+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-03-05T11:18:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-02-29T13:12:38+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-02-29T08:55:09+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Neuroinflammation","date":"2024-02-28T12:55:16+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-neuroinflammation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jneu","sideBox":"Learn more about [Journal of Neuroinflammation](http://jneuroinflammation.biomedcentral.com)","snPcode":"12974","submissionUrl":"https://submission.nature.com/new-submission/12974/3","title":"Journal of Neuroinflammation","twitterHandle":"@bmc","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d4406516-d9cd-4ebf-a8c2-b246605e7085","owner":[],"postedDate":"March 5th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-04-26T11:58:29+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-05 15:54:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3996729","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3996729","identity":"rs-3996729","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}