RESEARCH
Open Acce
Peng Zhang¹†, Cong Gao¹†, Qiang Guo², Dongxu Yang², Guangning Zhang³, Hao Lu², Liman Zhang⁴, Guorong Zhang 5 and Daojing Li 5*
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 tissue and explore the microscopic differences between different types of immune cells. Methods  9 patients with basal ganglia intracerebral hemorrhage (hematoma volume 50-100 ml) were enrolled in this study. A multi-stage profile was developed, comprising Group1 ( n = 3, 0–6 h post-ICH, G1), Group2 ( n = 3, 6–24 h post-ICH, G2), and Group3 ( n = 3, 24–48 h post-ICH, G3). A minimal quantity of edematous tissue surrounding the hematoma was preserved during hematoma evacuation. Single cell RNA 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 microglia subsets and 5 neutrophil subsets in human PHE tissue. What's more, we discovered that the secreted phosphoprotein-1 (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. Finally, we also demonstrated that microglia-produced osteopontin (OPN) could regulate the immune environment in PHE tissue by interacting with CD44-positive cells. Conclusions  As a result of our research, we have gained valuable insight into the immune-microenvironment within PHE tissue, which could potentially be used to develop novel treatment modalities for ICH. Keywords  Intracerebral hemorrhage, Single cell RNA sequencing, Perihematomal edema, Immune cell, Osteopontin †Peng Zhang and Cong Gao contributed equally to this work. *Correspondence: Daojing Li lidaojing0415@163.com Full list of author information is available at the end of the article © The Author(s) 2024. Open Access  This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this ticle are included in the articles Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The ive Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
Introduction
Intracerebral hemorrhage (ICH) is one of the most criti- cal and severe illnesses, posing a significant threat to global health due to its high rates of disability and mor- tality [ 1 – 3 ]. ICH initially causes injury through the physi- cal 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 h after ICH, with subsequent progression for up to 2–3 weeks after that [ 5 ]. However, the specific pathophysiological mecha- nisms of PHE formation are complex and poorly under- stood. 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 out- comes and prognosis of patients with ICH [ 7 ]. Therefore, ICH remains inadequately controlled by current treat- ments. However, the emergence of immunomodulatory medications has reignited the hope for ICH treatment. Considering PHE's role in secondary brain injury, devel- oping 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 various immune subgroups in PHE, whether beneficial or harm- ful. Nevertheless, experimental research on immune cells related to stroke largely relies on previous knowledge, such as markers and phenotypes of immune cell popula- tions defined in other research fields [ 11 ]. Conventional experimental techniques, such as immunofluorescent staining and flow cytometry, may present limitations in accurately identifying distinct immune cell subpopula- tions and conducting an impartial examination of the intricate immune cell phenotypes elicited by ICH. There- fore, 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 [12–14]. However, less is known about the heterogeneity of immune cells in ICH, especially in the setting of the human fresh brain. Given that the immune cell landscape at the single-cell level in PHE tissues during the progres- sion of ICH still needs futher characterization. Therefore, this study aims to use scRNA-seq to de novo character- ize the evolution process of immune cells in PHE tissues using transcriptional profiling of human immune cells.
Materials and methods
Patient inclusion and sample collection
The human brain tissue collection and research protocols were approved by the Affiliated Hospital of Jining Medi- cal University and the Institutional Review Board (ethics committee, ethical code 2023-06-C004). Eligible sub¬ jects gave informed written consent. After quality con- trol, a total of 9 samples were included in the analyses. The median age of the patients was 56.9 years, and the cohort consisted of 4 females and 5 males, whose detailed information was included in Table 1 . Patient inclusion and exclusion criteria were as follows: (1)>18 years old, (2) Intracerebral hemorrhage in the basal ganglia requires hematoma evacuation, (3) Hemorrhage volume: 50 to 100 ml, (4) With a history of hypertension. Exclusion criteria: (1) Patients who exhibiting pre-existing brain diseases, such as infections, traumatic cerebral hemor- rhage, cerebrovascular malformations, aneurysms, caver- nomas, (2) Pathologically confirmed amyloid angiopathy, (3) Patients undergoing immunosuppressive or immu- nomodulatory therapy, (4) Patients reported any cancer site. Finally, we recruited total 9 eligible participants and a multi-stage profile was developed, comprising Group1 (n=3, 0–6 h post-ICH, G1), Group2 (n=3, 6–24 h post- ICH, G2), and Group3 (n=3, 24–48 h post-ICH, G3) [ 15 , 16 ]. The surgical technique employed was the trans- sylvian-transinsular approach for hematoma removal. Fresh brain tissues of perihematomal regions were col- lected from patients undergoing hematoma evacuation after onset [ 17 – 19 ]. The brain tissue utilized in this study was obtained from the peristomal region adjacent to the hematoma cavity, with a mass of approximately 200 mg to conduct subsequent scRNA sequencing analyses.
ScRNA sequencing
According to the manufacture's introduction, Single-cell RNA-seq libraries were constructed using Single Cell 3' Library and Gel Bead Kit V3.1. The libraries were finally sequenced using an Illumina Novaseq6000 sequencer with a sequencing depth of at least 100,000 reads per cell with pair-end 150 bp (PE150) strategy (performed by CapitalBio Technology, Beijing). After data preprocess- ing, the sequencing results were used to conduct Gene Set  Variation  Analysis  (GSVA),  Gene  Set  Enrichment Analysis (GSEA), single-cell regulatory network infer- ence and clustering (SCENIC) Analysis, Monocle2 Pseu- dotime Analysis, Cell-Cell Communication Analysis. The details of post sequencing analyses are recorded in the Supplementary file “Materials and Methods”.
Immunofluorescence staining
Immunohistochemistry was performed on 6-mm frozen sections. After the slides were brought to room tempera- ture for 20 min, the tissue was fixed in 4% paraformalde- hyde for 10 min and permeabilized in 0.3% Triton X for 5 min. After blocking with 5% bovine serum albumin for 30 min, the sections were incubated with primary anti- bodies overnight at 4 °C. The next day they were washed in PBS and then incubated for 60 min at room tempera- ture with species-appropriate fluorochrome-conjugated secondary antibodies. The primary antibodies used were goat-anti Iba1 (1:200; Abcam ab5076); rabbit-anti OPN (1:250; Abcam ab63856); rat-anti CD44 (1:200, Abcam ab119348). After elution with PBS, they were incubated with donkey anti rabbit, donkey anti rat and donkey anti goat secondary antibodies. Finally, the anti fluorescence quenching agent containing DAPI is used for sealing.
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 dur- ing the progression of ICH, we collected 10×Genom- ics scRNA-seq datasets from fresh PHE samples, which were resected from brain tissue in the vicinity of hema- toma during the evacuation of the hematoma. We con- structed a multi-stage profile including Group1 (n=3, 0–6 h after ICH, G1), Group2 (n=3, 6–24 h after ICH, G2), and Group3 ( n =3, 24–48 h after ICH, G3) (Fig. 1A). After quality control, a total of 3152–5823 cells were retained, with an average of 2639–4716 genes per cell and an average of 9535–24,244 unique molecular identifiers (UMIs) per cell for the subsequent analysis. Following that, we clustered and visualized various cell types refer- ing to their relative gene expression levels using uniform manifold approximation and projection (UMAP), an unsupervised nonlinear dimensionality reduction algo- rithm. Graph-based Louvain clustering algorithms (with a resolution value of 0.4) were used to cluster all cells into subsets resulting in 19 clusters (Fig. 1B). Based on marker gene expression levels of microglia ( AIF1, CSF1R, TMEM119, CX3CR1 ), clusters 5, 6, 7 and 13 were iden- tified as microglial cells (Fig. 1C and E). Clusters 8 and 16 expressed genes ( ALDH1L1, ATP1B2 and AQP4 ) spe- cific to astrocytes (Fig. 1C and E). Within the infiltrating immune cell subsets, cluster 3 had neutrophil marker genes ( CSF3R, S100A8, CXCR2 and FCGR3B ) and clus- ter 9 had monocyte marker genes ( CD300E and VCAN ) (Fig. 1C and 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. 1C and E). Additionally, clusters 1, 2, 4, 10, 14, 15 and 17 expressed genes (MOG, SOX10, CNP and HAPLN2) specific to oli- godendrocytes, and cluster 12 expressed genes (CSPG4 and PDGFRA ) specific to neural progenitor cells (Fig. 1C and E). Cluster 18 expressed endothelial cell marker genes ( CLDN5, VWF, RGS5 and EGFL7 ). Therefore, we identified this cluster as endothelial cells (Fig. 1C and E). Furthermore, neutrophils, NK/T cells, and mono- cyte cells were present at all time points and increased to reach their highest level at G3 (Fig. 1D), a timepoint that appears crucial to investigating ICH-induced immune cell changes [ 20 ]. Microglia and neutrophils, as the most important immune cell populations in the central ner- vous system and peripheral immune system, respec- tively, were essential in the pathophysiology of ICH [ 1 , 3 ]. Therefore, we next focused on analyzing the transcrip- tional 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. 2A) characterized by differentially expressed genes (DEGs). We determined the enrichment of biological pathways in each clus- ter via gene set enrichment analysis (GSEA) (Fig. 2D). There was almost no overlap in the DEGs defining each cluster, supporting the unique nature of each microglia cluster (Fig. 2D, Supplementary file: 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 the others (3 and 9) (Fig. 3A) [ 12 ]. Besides, some selected cell-surface marker genes were not observed in cluster 9 (Fig. 3B). 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 expres- sion (Supplementary file: Fig. S2A, Supplementary file: Fig. S3A) [ 13 , 14 , 21 , 22 ]. However, the higher expres- sion of homeostatic markers was not found in cluster 3 (Supplementary file: Fig. S2A). Therefore, we annotated cluster 9 as homeostatic microglia (HM) cluster. HM was established as a comparative basis for evaluating DEGs from other microglia clusters (Supplementary file: Table 2 ), referring to the approach in previous literature [12, 21, 22]. Initially, we identified annotated clusters of microglia phenotypically similar to those previously characterized in the human brain. Notably, Cluster Micro3 was char- acterized by the downregulation of homeostatic genes (Supplementary file: Fig. S3A), 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. 2C). In summary, cluster Micro3 reflects a degenerative phenotype of microglia, consis- tent with the responses of microglia to aging. Remark- ably, Cluster Micro5 predominantly expressed genes characteristic of disease-associated microglia (DAMs), such as metabolic genes LPL and FABP5 (Fig. 2C) [23]. DAM subtype is novel microglia associated with neu- rodegenerative diseases such as Alzheimer's [ 13 ]. The pathway analysis of the Micro5 genes highlighted associ- ations with "Alzheimer's disease" and "Huntington's dis- ease" (Fig. 2D, Supplementary file: Fig. S1B). Hence, we annotated this cluster as "DAM-like" microglia. Cluster Micro6 and Micro10 were defined by genes and path- ways 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, sug- gesting inflammatory responses of downstream effectors to stimuli (Fig. 2D, Supplementary file: Fig. S1A). Clus- ter Micro11 was characterized by anti-inflammatory and repair-related genes ( HTR7, PDLIM7, and LGALS3 ) and proinflammatory genes (KCNN4 and ITGB7) [24–26], indicating that this cluster was an intermediate state in the polarization of microglia (Fig. 2C). Cluster Micro12 was defined by expression of genes involved in DNA repair and cell cycle regulation, including MKI67, SKA1, E2F2 and E2F8 (Fig. 2C). Furthermore, Micro12 was enriched for pathways involved in DNA replication and the cell cycle (Fig. 1D, Supplementary file: Fig. S1B). Micro1 was defined by genes ( XIST, VEGFA, KLF4 ) involved in microglial M1 polarization (Fig. 2C) [ 27 – 30 ]. Moreover, we found that cluster Micro1 was between HM cluster (Micro9) and canonical inflammatory phe- notype (Micro6), suggesting that this cluster could be the intermediate transition status from HM microglia to pro- inflammatory 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 enrich- ment for the pathway of DEGs relative to HM microg- lia. e significant DEGs of Micro7 were involved in the neurotrophin signaling pathway and Fc gamma receptor-mediated phagocytosis (Fig. 2D, Supplemen- tary file: Fig. S1B). Micro4 showed gene enrichment for complement and coagulation cascades and the PPAR signaling pathway (Supplementary file: Fig. S1A), par- tially sharing a subset of DEGs with Micro7 (Fig. 2C). 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 tis- sue repair phenotypes. Additionally, the pathway sig- nificantly enriched Micro8 in antigen processing and presentation, suggesting this subcluster of microglia is active in antigen processing and presentation for immune response (Supplementary file: Fig. S1A). Fur- thermore, the pathways enriched in Micro2 confirm the relative increase of genes involved in oxidative phosphorylation and glycolysis/gluconeogenesis and decreased chemokine and endocytosis genes (Supple- mentary file: Fig. S1A). This finding coincides with pre- vious studies that persistent glycolysis exerts adverse effects on microglial functions: the activation of glyco- lytic metabolism impairs phagocytosis and chemotaxis of microglia [ 31 , 32 ]. Overall, while future research will likely refine our understanding of microglial sub- types, 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 con- sidered to reflect their different functions. In this study, we employed scRNA-seq to investigate the biological pathways present in microglia within PHE tissue fol- lowing ICH. We observed that common microglial marker genes such as AIF1, TREM2, and CSF1R were widely expressed across all microglial subclusters (Supplementary file: Fig. S3A). However, other spe- cific marker genes of microglia ( ITGAM, P2RY12, and CX3CR1) indicated differential expression across clus- ters (Supplementary file: Fig. S3A). Interestingly, the transcriptome of PHE tissue was almost dominated by proinflammatory pathways (Supplementary file: Figs. S3A-B). Our findings indicated a lack of signifi- cant activation of anti-inflammatory pathways within the first 48 h post-ICH (Supplementary file: Fig. S3B). Proinflammatory genes such as CCL2, CCL4, and IL1B were among the most prevalently expressed cytokine and chemokine genes in ICH-associated microglia (Supplementary file: Fig. S3A-B). ICH microglial clus- ters 1, 3, 5, 6, 10, and 11 were characterized by high gene expression levels of HLA-DQA1, HLA-DPB1, and HLA-DRA and low gene expression levels of P2RY12 and CX3CR1 (Supplementary file: Fig. S3A), 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 (Supplementary file: Fig. S3B). Overall, most microglial clusters displayed proin¬ flammatory phenotype within 48 h of ICH, further cor- roborating 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 neuroinflam- matory responses [ 33 ]. Our results indicated reduced expression of the P2RY12 gene in microglia clusters that had higher IL1B expression levels (Supplemen- tary file: Fig. S2A). This finding was consistent with the previous studies that the expression of P2RY12 was gradually decreased accompanied by microglia activa- tion following inflammatory stimulation [34]. Further analysis was performed by comparing the differentially expressed genes in IL1B-expressing clusters (cluster 6 and cluster 10) with those in P2RY12-expressing clus- ters (cluster 7 and cluster 9). A significant difference in gene expression was found between IL1B-express- ing microglia and P2RY12-expressing microglia, with 882 genes notably downregulated and 415 genes nota- bly upregulated (adjusted P value<0.05, log2 (fold change)>1.5) (Supplementary file: Fig. S2B, Supple- mentary file: Table 3 ). A significant increase in chemo- kine and pro-inflammatory cytokines was observed in microglial cluster cells expressing IL1B (Supplementary file: Fig. S2C). Additionally, CX3CR1 expression was also higher in cluster cells expressing P2RY12 than in cluster cells expressing IL1B (Supplementary file: Fig. S2B). In addition, Gene Ontology term enrichment analysis indicated genes enriched for protein binding, extracellular exosome, focal adhesion, cytokine-medi- ated signaling pathway, and inflammatory response (Supplementary file: Fig. S2D). Furthermore, the Kyoto Encyclopedia of Genes and Genomes term enrich- ment analysis suggested genes enriched for apopto- sis, NF–kappa B signaling pathway, IL–17 signaling pathway, and Toll–like receptor signaling pathway (Supplementary file: Fig. S2E). 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 microenvi- ronment 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 clus- ters 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. 4A, Supplementary file: Fig. S4A). Each microglia cluster was characterized by a specific set of regulons (Fig. 4B). This supports the theory that tran- scriptional 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. 4A, Supplementary file: Fig. S4A). Micro3 showed higher activity levels of SOX8, SOX10, IRF7, and STAT1 (Fig. 4A, Supplementary file: Fig. S4A); e activity of transcription factors, such as MAFB, SPI1, DDIT3, and XBP1, was higher in Micro11, while high activity lev¬ els of E2F1, TFDP1, and BRCA1 were associated with Micro12 (Fig. 4A, Supplementary file: Fig. S4A). Fur- thermore, 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. 4B, Supplementary file: Fig. S4C). This is consistent with the finding that these cells experience a phenotypic polariza- tion of microglia of M2. In this study, the NFKB1 regulon, associated with canonical inflammatory responses, was identified in Micro10 (Fig. 4B, Supplementary file: Fig. S4C). Conversely, Micro6 exhibited the RELB regulon, linked to non-canonical inflammatory responses (Fig. 4B, Supplementary file: Fig. S4C ). In Micro9, the high speci- ficity of FOXP2 (Supplementary file: Figs. S4C ), a regulon unique to human microglia and crucial for brain devel- opment, was observed, aligning with previous research identifying this subtype as an HM cluster [ 35 ]. The top three regulons in other microglia clusters also showed distinct variations (Fig. 4B, Supplementary file: 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. e use of cell-cell commu- nication networks between microglia subpopulations could contribute to a better characterization of microg- lia function. Interestingly, the interaction strength of the SPP1 pathway increased gradually with the progression of PHE (Fig. 5A, Supplementary file: Fig. S5 ). Moreover, the SPP1 pathway exhibited the strongest interaction strength, irrespective of incoming or outgoing signaling pathways (Fig. 5B). 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 sub- clusters at different stages. At post-ICH in G1, G2, and G3, the strongest SPP1 signaling cell types were Micro6, Micro1, and Micro11, respectively (Fig. 5B). 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. 5B). Addition- ally, 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 fundamen- tal bridge of self-communication among microglia sub- clusters (Fig. 5C). Collectively, our findings demonstrated that the signaling pathway of SPP1 is the fundamental bridge mediating self communication between subclus- ters 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 tran- scriptomics. By comparing the gene expression pat- terns of all neutrophils at every time points, we depicted five different transcriptional cell clusters in PHE tis- sue after ICH, exhibiting a time-independent appear- ance. 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. 6B), we calculated the propor- tion represented by each cluster at different time points (Fig. 6C). Surprisingly, we found that cluster Neutro1 (MECMP1, TNFAIP3, IER3, TANK) increased to reach its highest level at G3 (P<0.05, G3 vs. G1 and G3 vs. G2, Supplementary file: Table 4 ). The majority of neu- trophils at G1 (68.6%) segregated in cluster Neutro2 (CFD, CDKN2D, HSPA1B, S100A4, D100A6, S100A9, S100A12), a profile reduced by half at later time points (<30% at G2 and G3). At G2, Neutro3 cells (FOLR3, SLC8A1, AOAH, SYNE2; 86.9% of total neutrophils) 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 G3. 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 G1, 2 and 3 post-ICH, respectively (Fig. 6C, Supplementary file: Fig. S6A). The unique gene signatures and the top 20 signifi- cantly DEGs of each neutrophil subcluster were delin- eated (Supplementary file: Fig. S6A). In addition, the Gene Set Variation Analysis (GSVA) was performed to functionally annotate the neutrophil subcluster (Fig. 6D). Neutro1 showed gene enrichment for ste- roid biosynthesis, ABC transporters, and PPAR signal- ing pathway, partially sharing a subset of significantly differentially expressed genes with Neutro5 (Fig. 6D). Neutro2 was characterized by the chemokine signal- ing pathway and antigen processing and presenta- tion (Fig. 6D), indicating this subtype of cells is active in antigen processing and presentation for immune response. e significantly DEGs of Neutro3 were involved in the folate biosynthesis and the metabo- lism-related pathways including histidine metabolism, alpha-linolenic acid metabolism, and ascorbate metab- olism (Fig. 6D). Neutro4 exhibited higher expression of genes associated with glycolysis and gluconeogenesis, and extracellular matrix (ECM)-receptor interaction (Fig. 6D). 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 [ 36 ]. Further- more, Neutro5 was defined by the expression of genes involved in RNA polymerase and TCA cycle (Fig. 6D).
Neutrophil cluster-specific transcription factor regulatory
networks
We further employed SCENIC analysis to characterize the underlying molecular mechanisms driving the dif- ferentiation of different neutrophil phenotypes. A dif- ferent transcription factor network was predicted to be expressed by neutrophils from different subtypes in this analysis. For instance, CEBPB and HIVEP2 regu- lons were upregulated in Neutro1 (Fig. 6E). 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 [ 37 ]. Addition- ally, there was a notable increase in the Neutro1 popula- tion, peaking at G3 (Fig. 6C). This suggests a substantial consumption of neutrophils at this stage, prompting the hematopoietic system to respond to the heightened demand through emergency granulopoiesis quickly. Neu- tro2 also upregulated networks driven by ZNF107 , IRF1, and SPI1 (Fig. 6E). In the Neutro3 cluster, there was a noticeable predominance of the activity of regulons, particularly those linked to ZBTB16 and ELF1 (Fig. 6E). Some regulons showed preferential activity in Neutro4 (MECP2, SREBF1, HIF1A; Fig. 6E). Neutro5 showed highly active transcription factors related to cell prolif- eration (FOXO1, CEBPZ, YY1; Fig. 6E). By calculating the connection specificity index (CSI), we obtained a regu- latory network composed of four modules and 34 regu- lons (Fig. 6F). Transcription factors of CEBPB/FOSL2/ HIVEP2 in module 2 displayed the strongest activity in Neutro1 (Fig. 6F-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 (Fig. 7A and B, and 7D). Neutrophils are also known as short-lived cells that can mobilize rapidly from the bone marrow in response to tissue damage [38]. Accordingly, we performed a 'bone marrow proximity score’ of neutrophils based on the expression of a set of genes previously characterized in transcriptome analy- ses of neutrophils at different stages [ 39 ]. Cluster Neu- tro2 (mainly in G1) had the highest BM proximity score ( p<0.0001 versus all other clusters) (Supplementary file: Fig. S6B). In our analysis, the progression trajectory of neutro- phils was established, starting with Neutro2 (Fig. 7C). 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 differen- tiated states of Neutro4 and Neutro5. Further examina- tion of the single-cell transcriptomes of neutrophils along this trajectory identified 4043 significantly altered genes, classified into four distinct expression patterns (Fig. 7E- F): Module 1 included genes that showed increased expression levels along one trajectory. Pathway enrich- ment 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. 7G). Module 2 contained genes activated in the later stages of another trajectory, with enrichment in mitophagy and HIF-1 signaling path- ways (Fig. 7G). Additionally, the genes in module 3 were associated with ribosome function, Fc gamma R-medi- ated phagocytosis, endocytosis, regulation of actin cytoskeleton, and leukocyte transendothelial migration (Supplementary file: Fig. S6C). Finally, module 4 genes, upregulated in the early stages, were associated with necroptosis, apoptosis, and cellular senescence (Supple- mentary file: Fig. S6C).
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. 1E). Cluster 11 clearly showed CD3D, CD3E, NKG7, GZMA, and GZMB gene expression, indicating doublets of T cells and NK cells (Fig. 1E). Cluster NK/T cell was present at all time points and its levels gradually increased during ICH progression (Fig. 1D and Fig. 8B). We re-clustered clus- ter 11 doublet cells using major lineage gene expression markers and obtained four clusters (Fig. 8A). Clus- ter phenotypes were identified using gene expression levels (Fig. 8C). A CD8+T cell cluster (CD8A, CD8B, LAG3; cluster 1 and cluster 2) was the main cluster found in PHE tissue (Fig. 8C-D). We also observed CD4+T cells (CD4, CCR7, LEF1, SELL, IL2RA; clus- ter 4) and NK cells (FCER1G, NCR1, NCR3, CCL3, KLRC1, FCGR3A ; cluster 3) (Fig. 8C). By inferring the paired ligand-receptor pairs based on CellChat analy- sis, we first depicted the overall connectivity patterns between peripheral immune cells and central immune cells in PHE. e number of cell-cell interactions between immune cells changed significantly in the con- text of the progression of ICH (Fig. 8E-F). However, the strength interaction involving microglia, monocytes, and CD8+T cells is consistently higher in the progres- sion of ICH (Fig. 8G). Intriguingly, monocytes exhibited more extensive communications with microglia than other immune cell types apart from CD8+T cells and NK cells (Fig. 8G). We then extracted highly expressed interactions engaging microglia during ICH progression and uncovered underlying interactions with monocytes (Fig. 9A, Supplementary file: Fig. S7 ). Notably, we found interactions between SPP1 , which encodes the pleiotro- pic cytokine OPN, and CD44, ITGAV, ITGA4, ITGA5, ITGA9, ITGB1, ITGB3, and ITGB5, which encode the OPN receptor, were prominent during microglia-mono- cyte interactions (Fig. 9A). 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 (Fig. 9A-B, Supplemen- tary file: Fig. S7 ). 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. 9C). This finding suggests that microglia-secreted OPN could regulate the immune environment of PHE by interact- ing with CD44 on monocytes. In summary, these find- ings indicate that OPN-mediated microglia-monocyte interaction is essential for communicating between cen- tral and peripheral immune cells.
Immunofluorescence staining
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 differ- ent 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 OPN 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) OPN as a medi- ator 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, uncovering the pre- dominant 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. Fur- thermore, we identified that microglia-derived OPN was an essential mediator in the communication between microglia and monocytes. Our study used scRNA-seq analyses to accurately dis- tinguish between immune and non-immune cells in the human brain, identifying their specific types and biologi- cal functions. Our study suggested microglia as a major contributor to inducing a proinflammatory immune microenvironment, mainly through producing proin- flammatory mediators and reducing microglial purinergic receptor-mediated signaling. Recent studies have shown that the expression level of P2RY12 in microglia is cor- related with the phenotype of microglia [ 34 ]. Specifically, it is less expressed by activated proinflammatory microg- lia, and highly expressed by activated non-inflammatory microglia [40, 41]. Reductions in P2RY12-expressing microglia indicated the presence of a broad proinflam- matory milieu in PHE tissue. Furthermore, it has been demonstrated in numerous preclinical studies and clini- cal trials that modulating microglial polarization (M1/ M2) can reduce inflammation and thus exert a protective effect in ICH [ 4 , 42 ]. However, none of the immunomod- ulators that targeting 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, high- lighting the need for unbiased high-throughput methods to comprehensively investigate microglial heterogene- ity [43]. A single-cell transcriptome study identified nine microglial subclusters in human brain samples from neu- rosurgical procedures, revealing phenotypes beyond the conventional M1/M2-like patterns [44] . 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 [45] . Moreover, our SCENIC analysis demonstrated the diver- sity of the identified microglial clusters and suggested potential regulatory targets for future research, highlight- ing the complexity and nuance of microglial roles in the ICH context. As the first activated immune cell cluster, microg- lia undergo various phenotypic and functional changes depending on the stimuli involved after ICH [46]. Our study has revealed the significance of the OPN-CD44 receptor axis in the interaction between microglia and monocytes. OPN, a multifunctional phosphoglycopro- tein, bridges innate and adaptive immune responses under pathological conditions [ 47 ]. Previous studies demonstrated that OPN is involved in neuroimmune responses in diverse central nervous system diseases, including ICH, ischemic stroke, and AD [48–50]. Fur- thermore, recent evidence suggests that OPN is highly expressed by microglia, which plays a significant role in
Conclusion
Supplementary Information
Limitations
Funding

immunomodulatory effects [ 51 , 52 ]. Cell-cell communi- cation analyses predicted strong interactions between microglia and monocytes through OPN and CD44 receptors. ese interactions need to be experimentally validated by a range of in vitro and in vivo investiga- tions in the future. Prior studies indicated that periph- eral monocytes are also cellular sources of OPN but with lower expression than microglia [ 53 ]. Moreover, recent single-cell studies have noted an increased transcrip- tion of SPP1 , the gene encoding OPN, in monocytes in the brains of mice with AD [50]. Therefore, we specu- lated that microglia act as initiators of OPN production in monocytes, which could be crucial for monocyte infil- tration. However, it is important to note that our study could not determine whether additional OPN-indepen- dent mechanisms are involved in the communication between microglia and monocytes. note that the brain tissue samples used in this study did not differentiate between white matter and gray matter, and the inherent variability in clinical bio-samples and the dissociation process in scRNA-seq may result in the loss or bias towards certain cell types, rather than provid- ing a comprehensive representation of the cellular com¬ position of the original sample. The online version contains supplementary material available at https://doi. org/10.1186/s12974-024-03113-8. 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 find- ings, 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 comprehen- sive understanding of immune cell diversity, which may contribute to exploit immunomodulators targeted neuro- inflammation post-ICH. There were several limitations in our study. Firstly, the study is limited by a small sample size consisting solely of individuals of Asian descent, potentially restricting the generalizability of the findings. Additionally, the absence of control trials within the experimental group under- mines the strength of the conclusions drawn. Thirdly, the inability to fully differentiate between gray and white matter in the sampled tissue, coupled with the reli- ance solely on immunofluorescence for validation with- out comparison to a control group, highlights the need for more thorough and rigorous scientific verification. Fourthly, The SCENIC analysis utilizing single-cell tran- scriptome data serves as a bioinformatics tool for pre- dicting potential activation levels of transcription factors and their regulation of target genes. However, further experimental validation is necessary to confirm the iden- tified transcriptional regulators. Lastly, it is important to Acknowledgements We sincerely appreciate the technical support provided the Capitalbio, article polishing services provided by the technology platform Home for Researchers, the analytical services provided by OEbiotech. Author contributions PZ and CG designed and performed experiments, prepared figures, analyzed data, and wrote, edited, and proofread the manuscript. QG, DY, GZ performed experiments and tissue harvesting. HL conceptualized the project and wrote, edited, and proofread the manuscript. LZ and GZ revised the manuscript. DL conceptualized and directed the overall project. This work was supported by grants from the Qilu Health and Outstanding Young Talents (2021-QLJQ-003), Jining Medical University (JYGC2022FKJ001 and JYGC2022FKJ012) and Key Research and Development program of Jining (2022YXNS082). Data availability Our scRNA-seq data from human brains is available in the form of a browsable platform. The raw data our study is available at the Gene Expression Omnibus under accession code. The accession number for the sequencing data reported in this paper is GEO:GSE 266873. Source data are provided with this paper.
Declarations
Ethics approval and consent to participate The studies involving human participants were reviewed and approved by Affiliated Hospital of Jining Medical University (2023-06-C004). Competing 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 1 Department of Clinical Medicine, Jining Medical University, Jining, China ²Department of Emergency Stroke, Affiliated Hospital of Jining Medical University, Jining, China ³Department of Neurosurgery, Affiliated Hospital of Jining Medical University, Jining, China 4Department of Pathology, Affiliated Hospital of Jining Medical University, Jining, China 5 Department of Neurology, Affiliated Hospital of Jining Medical University, Jining, China Received: 28 February 2024 / Accepted: 26 April 2024 Published online: 28 May 2024 References Magid-Bernstein J, Girard R, Polster S, Srinath A, Romanos S, Awad IA, Sansing 1. LH. 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