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Dynamic changes in lactate-related genes in microglia and their role in immune cell interactions after ischemic stroke

  • Jinzhong Yao , Huan Deng , Peng Wang , Bo Li EMAIL logo and Zaisheng Qin EMAIL logo
Published/Copyright: April 15, 2025
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Open Medicine
From the journal Open Medicine Volume 20 Issue 1

Abstract

Objectives

This study aims to elucidate the dynamic changes in lactate-related genes (LRGs) in microglia following ischemic stroke (IS) and their associations with immune cells.

Methods

We performed differential expression analysis on bulk-sequencing (GSE30655 and GSE35338) and scRNA-seq data (GSE174574) to identify differentially expressed genes. These genes were intersected with lactate genes from MSigDB to identify post-stroke LRGs. We used t-SNE to visualize LRG distribution across cell types and selected microglia for cell–cell communication, pseudo time, and functional enrichment analyses. These findings were integrated with the GSE225948 scRNA-seq dataset to examine LRG trends in the chronic phase of IS. Finally, CIBERSORT was used to explore immune cell infiltration changes and LRG-immune cell associations post-IS.

Results

Nine LRGs were identified, including Spp1, Per2, Col4a1, Sfxn4, C1qbp, Myc, Apln, Cdo1, and Cav1, with Spp1, C1qbp, and Myc highly expressed in microglia. C1qbp and Myc are crucial in the acute phase, while Spp1 impacts both acute and chronic phases of IS. Microglia subcluster analysis revealed four subclusters (MG0-MG3). Immune cell infiltration analysis showed significant associations between these genes and immune cells.

Conclusion

In summary, Spp1, C1qbp, and Myc are LRGs that are predominantly expressed in microglia and play regulatory roles in various stages of IS.

Keywords: ischemic stroke; microglia; single-cell RNA sequencing; lactate metabolism

1 Introduction

Ischemic stroke (IS) is a prevalent and devastating condition that leads to significant disability and poses a substantial health burden, with many survivors facing long-term impairments and diminished quality of life [1]. Among the various types of strokes, IS constitutes approximately 80% and is caused by sudden blockage of the cerebral artery, often due to a blood clot or thrombus [2]. This blockage leads to ischemic damage, where the affected brain tissue does not receive adequate blood supply, causing cell death and permanent neurological impairment [3].

Microglia are crucial for the initiation and perpetuation of cerebral infarction through their adoption of distinct polarization states following ischemia, thereby exerting a substantial impact on the neuroinflammatory response and subsequent neuronal damage [4]. Following ischemia, microglia rapidly polarize into two distinct states: the pro-inflammatory M1 phenotype and the anti-inflammatory M2 phenotype [5]. The polarization of microglia is governed by a complex interplay of factors such as peroxisome proliferator-activated receptor γ (PPARγ) [6], the interferon regulatory factor (IRF) family [7], and Toll-like receptor 4 (TLR4) [8], which are key regulators of microglial polarization.

During cerebral ischemic acidosis, substantial lactate accumulation occurs intracellularly as the primary mode of energy metabolism shifts from aerobic to glycolytic processes to maintain ATP supply [9]. Furthermore, the permeation of lactate from peripheral blood into the brain, facilitated by a compromised blood–brain barrier, exacerbates lactate accumulation within the brain [10]. Lactate, once viewed solely as a metabolic byproduct, is now recognized for its pivotal role in cellular function and its potential as a therapeutic target, supported by recent research into lactate metabolism and its disease treatment implications [11]. The role of lactate in modulating cellular function is further underscored by its link to synaptic plasticity, a key area of investigation in central nervous system (CNS) pathologies [12]. Additionally, lactate serves as a precursor that promotes histone lactylation and influences histone lysine lactylation (Kla) levels [13]. Lactylation of histone lysine residues, a novel post-translational modification (PTM), can stimulate gene transcription within chromatin, enhance the expression of homeostatic genes like arginase 1 (Arg1), and induce a phenotypic shift in macrophages from M1 to M2 [14]. Recent single-cell RNA sequencing (scRNA-seq) analyses have demonstrated that microglia are capable of rapid metabolic reprogramming and the use of various bioenergetic substrates, suggesting a regulatory role for lactate in microglial function [15]. Moreover, the research underscores the influence of lactate on microglial function, potentially harnessed to modulate neuroinflammation and enhance brain health, a process that may be attributed to the reprogramming of microglial glycolysis [15,16]. In mice and microglia, the H3K9 lactylation site is a key site involved in histone lactylation, which further promotes glycolysis and induces neuronal injury [17]. Furthermore, lactate can modulate the microglia inflammatory responses and alleviate cerebral ischemia injury by inhibiting the CCL7/NF-κB signaling pathway induced by HIF-1α [18]. Overall, it is evident that lactate accumulation due to ischemia can impact the biological functions of microglia. Identifying the LRGs in microglia post-IS will aid in understanding which LRGs are involved in the reprogramming process of microglia.

The advent of scRNA-seq has marked a significant paradigm shift in our understanding of cellular heterogeneity and gene expression dynamics, particularly in the context of IS [19]. This technology enables detailed profiling of gene expression patterns at the single-cell level, elucidating cellular response diversity, identifying rare cell types, and revealing complex transcriptional programs that underpin biological processes [20]. Most importantly, scRNA-seq offers unique insights into cell subpopulations and their functions in pathophysiological processes, particularly in the context of IS [21].

The primary objective of this study was to identify LRGs by integrating bulk sequencing and scRNA-seq analyses. Furthermore, this study aimed to elucidate the role of LRGs in modulating microglial responses to IS and identify potential gene targets for manipulation to enhance neuroprotection or mitigate ischemic injury. This approach could pave the way for the development of innovative therapies that target metabolic pathways in microglia, potentially reducing the severity of ischemic injury and promoting neurological recovery in patients with stroke.

2 Materials and methods

2.1 Data acquisition

The research flow diagram is presented in Figure 1. The datasets utilized for our analysis were procured from the Gene Expression Omnibus (GEO) repository [22]. This public database archives and distributes high-throughput gene expression data. Specifically, we accessed two Series Matrix Files, GSE30655 and GSE35338, which were instrumental in our comparative genomic studies. Additionally, to facilitate the scRNA-seq analysis, we employed two single-cell data files identified as GSE174574 [23] and GSE225948 [24]. The overall description of these datasets is listed in Table 1. A total of 318 lactate-related genes (LRGs) were obtained from the Molecular Signatures Database [25] (http://www.gsea-msigdb.org/gsea/index.jsp).

Figure 1 Flowchart of this study.
Figure 1

Flowchart of this study.

Table 1

Detailed information of the datasets used in this study

GEO datasets Platform Sample source Stroke cases Control cases Cohort type
GSE30655 GPL1261 Brain (Mus musculus) 7 3 Bulk RNA sequencing
GSE35338 GPL1261 Brain (Mus musculus) 5 4 Bulk RNA sequencing
GSE174574 GPL21103 Brain (Mus musculus) 3 3 Single-cell RNA sequencing
GSE225948 GPL9057 Brain (Mus musculus) 4 4 Single-cell RNA sequencing

2.2 Data integration and batch effect correction

Normalization of the datasets GSE30655 and GSE35228 was conducted via the “limma” R package [26] to merge into a unified, comprehensive dataset. To address potential batch effects, which could introduce bias into our analysis, we employed the Combat method facilitated by the “sva” R package [27]. This method was specifically designed to adjust for unwanted variations owing to batch effects, thus enhancing the accuracy and validity of our findings.

2.3 Differential expression analysis and visualization

Differential expression analysis was applied to identify genes with significant changes in expression between stroke groups and sham groups using the “limma” package in R [26]. Genes were considered significantly differentially expressed if they exhibited an absolute log2 fold change (|logFC|) greater than 0.5 and an adjusted p-value less than 0.05. The results were visualized through volcano plot via “ggplot2” and heatmap via “pheatmap” R package, respectively.

2.4 Single-cell data processing and cell annotation

We utilized the GEO datasets GSE174574 and GSE225948 for single-cell RNA sequencing analysis. Following rigorous quality control to exclude low-quality cells and those with excessive mitochondrial DNA content, we implemented the “LogNormalize” method to normalize gene expression values and stabilize variance across the dataset. The “vst” method in Seurat was employed to identify highly variable genes, which were subsequently subjected to principal component analysis for dimensionality reduction and to elucidate major sources of variation. Clustering was conducted based on transcriptomic profiles, and batch effects were mitigated utilizing the harmony algorithm. The “FindAllMarkers” function in Seurat, corroborated with the previous published literature, facilitated the annotation of cell types within each cluster. Finally, t-SNE visualization offers a comprehensive two-dimensional representation of the cellular landscape, highlighting distinct cellular populations.

2.5 Ligand–receptor interaction analysis (CellChat)

The CellChat R package was utilized to analyze ligand–receptor interactions based on normalized gene expression profiles, which facilitated the quantification of communication strengths between distinct cell types and the identification of key cellular communicators. The constructed cell–cell communication network was visualized through various graphical representations to illustrate the interactions and their intensities, thereby providing insights into potential signaling pathways.

2.6 Microglial subtype identification

To refine the classification of microglial subtypes and track the expression patterns of Spp1, C1qbp, and Myc, we analyzed their distribution across subtypes and compared their expression levels between stroke groups and sham groups. The spatial distribution of these genes was visualized using t-SNE/UMAP, and violin plots were used to represent their expression profiles.

2.7 Gene set variation analysis (GSVA)

GSVA is a non-parametric, unsupervised method employed to evaluate gene set enrichment dynamics across samples within an expression dataset. For this analysis, genesets were extracted from the Molecular Signatures Database (MSigDB) database using the “msigdbr” R package. Subsequently, lactate-related genesets were identified by filtering the comprehensive gene set with the keyword “lactate.” All four microglial subclusters were analyzed using LRGs to elucidate the biological function of each microglial type in lactate metabolism.

2.8 Gene set enrichment analysis (GSEA) pathway enrichment analysis

To elucidate the biological functions of Spp1, C1qbp, and Myc, we utilized bulk sequencing data for GSEA. Initially, we stratified the expression matrix into high- and low-expression groups based on the variation in the expression levels of these three genes and subsequently performed differential analysis using the limma package. Following ENTREZID conversion, we conducted an analysis using the clusterProfiler package and ultimately selected the top five pathways by normalized enrichment score for visualization.

2.9 Functional enrichment analysis of microglia MG1

To explore the biological functions and pathways of microglia MG1, we performed a functional enrichment analysis of the top 50 differentially expressed genes (DEGs) from these cells. Utilizing the ClusterProfiler R package, we conducted a Gene Ontology (GO) analysis to identify significantly enriched GO terms, which were visualized as a bar plot where the height and color of each bar represent the enrichment scores and gene counts, respectively. For pathway analysis, we investigated the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways using the same package, and the results were depicted in a bubble chart with bubble sizes indicating the number of associated genes and color intensity reflecting the significance of enrichment. Additionally, we employed a chord diagram to visually map the interactions between key genes.

2.10 Pseudotime trajectory analysis

Pseudotime trajectory analysis was conducted via Monocle 2 (version 2.32) to explore the microglial subclusters and examine the expression dynamics of Spp1, C1qbp, and Myc within these subtypes across stroke and sham groups. Through pseudotime trajectory analysis, microglial cells were ordered along a developmental sequence from less mature to more mature states based on the expression of key marker genes. This analysis allowed us to track changes in the expression of Spp1, C1qbp, and Myc during cellular development, visualized through UMAP plots colored by pseudotime values.

2.11 Immune cell infiltration analysis

To study the disease immune microenvironment, we used the R package “CIBERSORT” to calculate immune cell infiltration based on the merged bulk-seq dataset. The results were visualized in stacked bar plots to illustrate the distribution of immune cells across samples and box plots to assess their variability. Spearman correlation coefficients were calculated to measure LRGs and immune cell correlations, and the results were visualized using a correlation heatmap. Finally, correlation lollipop chart were used to specifically analyze the associations between LRGs and 22 immune cells.

3 Results

3.1 Identification of DEGs in stroke mice

Box-plot analysis of the raw data demonstrated that the gene expression levels displayed heterogeneity across samples within both datasets (Figure 2a). This batch effect was subsequently mitigated through quantile normalization to ensure a more accurate comparison (Figure 2b). Using the selection criteria of |log2FC| > 0.5 and adjusted p-value <0.05, 682 DEGs were identified, comprising 482 upregulated and 200 downregulated genes (Figure 2c). The top ten upregulated and downregulated genes were selected for visualization using a volcano plot (Figure 2d).

Figure 2 Datasets normalization and differential analysis (a) and (b) Comparison of GSE30655 and GSE35388 before and after batch effect correction. (c) Heatmap of the DEGs between sham and MCAO samples; red represents up-regulated genes, and blue represents down-regulated genes. (d) The volcano plot illustrates the distribution of DEGs, with the top 10 significantly up-regulated (blue) and down-regulated (red) genes marked for emphasis.
Figure 2

Datasets normalization and differential analysis (a) and (b) Comparison of GSE30655 and GSE35388 before and after batch effect correction. (c) Heatmap of the DEGs between sham and MCAO samples; red represents up-regulated genes, and blue represents down-regulated genes. (d) The volcano plot illustrates the distribution of DEGs, with the top 10 significantly up-regulated (blue) and down-regulated (red) genes marked for emphasis.

3.2 Cell subpopulation annotation of single-cell data and lactate-related DEG identification

In the present study, we conducted a comprehensive scRNA-seq analysis to discern diverse cell populations within both the stroke and sham groups. The t-SNE plot shows the distribution and heterogeneity among the identified cell types (Figure 3a). Sixteen major cell types were annotated based on their specific marker genes and in accordance with previous studies [23], including astrocytes, capillary endothelial cells, CNS border-associated macrophages, ependymocytes, lymphocytes, microglia, monocytes, neural progenitor cells, oligodendrocytes, oligodendrocyte progenitor cells, and smooth muscle cells. The stacked bar plots (Figure 3b) depict the proportional changes in each cell type within the brains of the subjects who experienced IS. In the control group, capillary endothelial cells and microglial subset 1 were predominant, constituting >50% of the total cellular composition. In contrast, in the stroke groups, there was a significant upregulation in the expression of venous endothelial cells, microglia, and astrocytes. Subsequently, we performed differential expression analysis to compare the gene expression across each cell type within the two groups (Figure S1). Further intersecting these DEGs with bulk sequencing data, scRNA-seq results, and LRGs enabled us to successfully identify nine lactate-related differentially expressed genes (LR-DEGs) (Figure 3c). The t-SNE plot (Figure 3d) and bubble plot (Figure 3e) visualized the distribution and expression levels of the nine LR-DEGs across the 14 cell types. The box plot illustrates that in the stroke group, the expression levels of Spp1 and Cav1 were significantly elevated, whereas the expression level of Per2 was reduced (Figure 3f).

Figure 3 Cell subpopulation annotation of single-cell data and LR-DEG identification. (a) Cell annotation of 16 clusters, 16 clusters annotated into 14 cell types, astrocyte, capillary endothelial cells, CNS border-associated macrophages, ependymocytes, lymphocytes, microglia, monocyte-derived cells, neural progenitor cells, neutrophils, oligodendrocytes, pericytes, smooth muscle cells, perivascular fibroblast-like cells, and venous endothelial cells. (b) The cell ratio between sham MCAO groups. (c) Venn diagram displaying nine LR-DEGs in IS that overlapped bulk RNA sequencing analysis, single-cell RNA sequencing analysis, and LRGs. (d) and (e) The distribution and expression level of the nine LR-DEGs in cells; blue represents high expression in tSNE, and black represents low expression. The size of the circle represents the percentage it occupies. (f) The gene expression level of the nine LR-DEGs.
Figure 3

Cell subpopulation annotation of single-cell data and LR-DEG identification. (a) Cell annotation of 16 clusters, 16 clusters annotated into 14 cell types, astrocyte, capillary endothelial cells, CNS border-associated macrophages, ependymocytes, lymphocytes, microglia, monocyte-derived cells, neural progenitor cells, neutrophils, oligodendrocytes, pericytes, smooth muscle cells, perivascular fibroblast-like cells, and venous endothelial cells. (b) The cell ratio between sham MCAO groups. (c) Venn diagram displaying nine LR-DEGs in IS that overlapped bulk RNA sequencing analysis, single-cell RNA sequencing analysis, and LRGs. (d) and (e) The distribution and expression level of the nine LR-DEGs in cells; blue represents high expression in tSNE, and black represents low expression. The size of the circle represents the percentage it occupies. (f) The gene expression level of the nine LR-DEGs.

3.3 Intercellular communication analysis and signaling pathway analysis

To elucidate the intercellular relationships among the diverse cell types, we conducted an intercellular communication analysis. Figure 4a and 4b presents the cell–cell interaction network, with each node representing a distinct cell type. The edge interconnecting nodes denote the strength and frequency of interactions, with line thickness and density indicative of the extent of communication. Thicker lines imply more intense or frequent interactions. Notably, endothelial cells, microglia, and monocyte-derived cells occupy central positions within the network. Subsequently, we focused on intercellular communication between microglia and other cell types (Figure 4c). This analysis revealed significant interactions between microglia, monocyte-derived cells, and neutrophils, as evidenced by the robust edges connecting the corresponding nodes within the cell–cell interaction network. Figure S2 displays the overall communication conditions for all cell clusters in terms of quantity and strength. To reveal the biological functions associated with each cell type, we performed a comprehensive analysis of signaling pathways. The heatmap and circular plot (Figure 4d–f) illustrate the central role of microglia as primary signal emitters in the SPP1 pathway. Conversely, monocyte-derived cells have been identified as key signal recipients, underscoring their receptive roles in this biological interaction.

Figure 4 Intercellular communication analysis and signaling pathway analysis. (a) and (b) Circos diagrams illustrate the density of interactions between various pairs of cell types. The thickness of a line in the Circos plot corresponds to the intensity of interactions among distinct cell types. (c) Microglia communication with a diverse range of cell types. (d) and (e) The major signaling inputs and outputs among different cell types. (f) The circos diagram of Spp1 signaling pathway between different cell types.
Figure 4

Intercellular communication analysis and signaling pathway analysis. (a) and (b) Circos diagrams illustrate the density of interactions between various pairs of cell types. The thickness of a line in the Circos plot corresponds to the intensity of interactions among distinct cell types. (c) Microglia communication with a diverse range of cell types. (d) and (e) The major signaling inputs and outputs among different cell types. (f) The circos diagram of Spp1 signaling pathway between different cell types.

3.4 Microglia subcluster analysis

To gain a comprehensive understanding of the regulatory functions of microglia at the onset of IS, we performed a subcluster analysis of the microglial population. Our findings demonstrated that microglia can be classified into four distinct subclusters (Figure 5a). In the sham group, MG0 was predominant, indicating that the majority of the cells were in a resting state. Conversely, in the MCAO group, MG1 was markedly increased and emerged as the predominant subcluster. This increase suggests the transition of these cells into an activated state, acting as effector cells that are critical for the pathogenesis of IS (Figure 5b). Additionally, the MCAO group showed elevated expression levels of Spp1, C1qbp, and Myc, which were primarily localized within MG1. Notably, C1qbp expression remained relatively stable across MG1 in both MCAO and sham groups 1-day post-stroke (Figure 5c). This observation implies that C1qbp may play a crucial role in the regulation of microglial resilience. Subsequently, we used the GSE225948 dataset to further explore the dynamic change of LRGs 14 days post-stroke. In this dataset, we observed a significant decrease in LRG expression levels at 14 days post-stroke compared to 1-day post-stroke, with C1qbp and Myc showing the most marked reductions, nearly returning to sham group levels (Figure S3c). Our results suggest that C1qbp and Myc are primarily active during the acute phase of IS. In contrast, the expression of Spp1 remains elevated in MG2 and MG3 at 14 days post-stroke, with levels similar to those on day 1 (Figure S3d–e). This indicates that Spp1 plays a key role in both the acute and chronic phases of IS. In summary, C1qbp and Myc are primarily involved in the acute response to IS, while Spp1 exerts neuroregulatory effects during both the acute and chronic phases of IS. GSVA analysis revealed that MG1 was highly enriched in lactate transmembrane transport and lactate transmembrane transporter activity, indicating that MG1 plays a vital role in lactate metabolism (Figure 5d).

Figure 5 Microglia subclusters analysis and functional enrichment analysis of cluster 1. (a) The four subclusters of microglia. (b) Microglia density changes by groups. (c) Distribution of the three key LR-DEGs in microglia and the expression level of these genes. (d) GSVA analysis of four microglia subclusters.
Figure 5

Microglia subclusters analysis and functional enrichment analysis of cluster 1. (a) The four subclusters of microglia. (b) Microglia density changes by groups. (c) Distribution of the three key LR-DEGs in microglia and the expression level of these genes. (d) GSVA analysis of four microglia subclusters.

3.5 Functional enrichment analysis of the microglia MG1 and GSEA analysis of LRGs

GO (Figure 6a) and KEGG (Figure 6b) pathway analyses were performed to elucidate the biological functions specific to microglial MG1. Biological process (BP) analysis indicated that genes within this subcluster are primarily associated with “‘inflammatory response,” “immune response,” and “response to wounding,” highlighting the engagement of immune-related pathways. Furthermore, cellular component (CC) enrichment highlighted “plasma membrane” and “integral component of membrane,” suggesting a correlation between MG1 marker genes and membrane-associated structures. Molecular function (MF) enrichment emphasized “cytokine activity” and “receptor binding,” underscoring the critical role of cytokine signaling in mediating immune responses. KEGG pathway enrichment analysis further confirmed that pathways associated with immune responses and inflammation, particularly “cytokine–cytokine receptor interaction” and the “TNF signaling pathway,” were significantly enriched. This enrichment pattern suggests that microglial cells within MG1 are primarily involved in immune signaling and inflammatory processes. The chord diagram (Figure 6c–e) revealed the functional enrichment of the most highly expressed genes within MG1. The results revealed that Spp1 was significantly enriched across various functional processes, particularly in “immune cell migration,” “inflammation,” “acute-phase response,” and “cellular signal transduction.” GSEA of KEGG signaling pathways for the LRGs indicated that Spp1 is predominantly associated with the complement and coagulation cascades, IL-17 signaling pathway, and TNF signaling pathway. C1qbp was significantly enriched in endometrial cancer, glyoxylate and dicarboxylate metabolism, and proteasome pathways. Myc exhibited significant enrichment in glycosaminoglycan degradation, non-alcoholic fatty liver disease, and proteasome pathways (Figure 6f–h).

Figure 6 Functional enrichment analysis of the microglia MG1. (a) GO enrichment analysis of microglia MG1. (b) KEGG pathway enrichment analysis. (c)–(e). The chord diagram displays the connectivity between key genes and enriched GO terms. (f)–(h) GSEA of LRGs. KEGG signaling pathways involved in Spp1, C1qbp, and Myc.
Figure 6

Functional enrichment analysis of the microglia MG1. (a) GO enrichment analysis of microglia MG1. (b) KEGG pathway enrichment analysis. (c)–(e). The chord diagram displays the connectivity between key genes and enriched GO terms. (f)–(h) GSEA of LRGs. KEGG signaling pathways involved in Spp1, C1qbp, and Myc.

3.6 Pseudotime analysis of microglial subpopulations

Pseudotime analysis revealed that microglia subcluster 0 (MG0) and microglia subcluster 1 (MG1) exhibit distinct trajectories along two principal components, suggesting that these subpopulations may represent divergent cellular states. This observation is consistent with prior research, which documented the upregulation of MG1 following IS concurrent with the downregulation of MG0 (Figure 7a). The pseudotime values presented in Figure 7b illustrate a temporal progression from MG0 to MG1, where darker blue shades represent earlier stages, and lighter shades represent later stages. The U-shaped trajectory suggests a dynamic transition between cellular states. The microglial transition can be characterized by three distinct states (Figure 7c): red cells (state 1) occupy the early pseudotime, green cells (state 2) form a compact cluster at the midpoint of the trajectory, and blue cells (state 3) predominate in the later pseudotime stages. The expression levels of Spp1, assessed in the late stage (Figure 7d) and MG1 (Figure 7e), highlight its critical role in microglial responses to IS, particularly in later stages of inflammation and tissue repair. Furthermore, the heatmap (Figure 7f) shows that Spp1 expression is maximized at cell fate of 2.

Figure 7 Single-cell trajectory analysis of microglia subclusters. (a)–(c). The three different differentiation states of microglia MG0 and MG1. (d) and (e) Dynamic expression of three key LR-DEGs across microglia states and subclusters. (f) Heatmap of LR-DEGs across different states.
Figure 7

Single-cell trajectory analysis of microglia subclusters. (a)–(c). The three different differentiation states of microglia MG0 and MG1. (d) and (e) Dynamic expression of three key LR-DEGs across microglia states and subclusters. (f) Heatmap of LR-DEGs across different states.

3.7 Immune cell infiltration

To quantitatively evaluate the immune cell landscape after IS, we employed CIBERSORT on the bulk RNA sequencing data. The analysis revealed that macrophages were the predominant immune cells in the MCAO group (Figure 8a). In contrast, the sham group displayed a more uniform distribution of immune cells with lower activation markers, suggesting a baseline or quiescent state. Further analysis using a box plot revealed significant differences in the abundance of various immune cell types between the MCAO and sham groups (Figure 8b). Notably, T cells (CD8 memory), M2 macrophages, and plasma cells significantly increased in the MCAO group, whereas immature dendritic cells (DCs) and activated NK cells markedly decreased. The correlation heatmap (Figure 8c) underscored the strong association between LRGs and immune cell infiltration (Figure S4). Spp1 showed a strong positive correlation with activated DCs (activated), M2 macrophages, and mast cells and a negative correlation with Th17. Cells, T cells, CD4 memory, and gamma delta T cells (Figure 8d). C1qbp was positively correlated with T cells CD8, naive and activated natural killer cells (inactivated) and negatively correlated with monocytes, T cells, CD4 memory, and resting natural killer cells (NK, resting) (Figure 8e). Myc expression was positively associated with activated DC cells, activated NK cells, and a negative association with immature DC cells, naive CD4 T cells, and resting NK cells (Figure 8f). In summary, the observed changes in immune cell proportions following IS, in conjunction with gene correlation analysis, suggest that lactate metabolism plays a crucial role in modulating immune response. These observations offer valuable insights into the complex molecular mechanisms governing neuroinflammation and the subsequent post-stroke recovery.

Figure 8 Immune cell infiltration. (a) Bar plot showing the composition of 21 types of immune cells across samples. (b) Correlation heatmap of 21 types of immune cells and LR-DEGs. Red indicates a positive correlation, and green indicates a negative correlation. *p-value <0.05, **p-value <0.01, and ***p-value <0.001. (c) Box plot of 21 types of immune cells across different samples. (d)–(f). Correlation between expression levels of the Spp1, C1qbp, and Myc. The larger the circle, the stronger the correlations.
Figure 8

Immune cell infiltration. (a) Bar plot showing the composition of 21 types of immune cells across samples. (b) Correlation heatmap of 21 types of immune cells and LR-DEGs. Red indicates a positive correlation, and green indicates a negative correlation. *p-value <0.05, **p-value <0.01, and ***p-value <0.001. (c) Box plot of 21 types of immune cells across different samples. (d)–(f). Correlation between expression levels of the Spp1, C1qbp, and Myc. The larger the circle, the stronger the correlations.

4 Discussion

This study provides novel insights into the intricate relationship between lactate metabolism and microglial dynamics following IS. By integrating bulk sequencing and scRNA-seq, we systematically characterized the temporal expression trajectories of lactate-associated genes within microglia, highlighting dynamic changes in the expression levels of Spp1, C1qbp, and Myc during the late stages of post-ischemic recovery. These findings underscore the pivotal function of microglial lactate metabolism in the pathophysiology of stroke and offer promising therapeutic targets for modulating microglial responses in IS.

Lactate metabolism plays a crucial role in brain energy dynamics during and after an IS [28]. When oxygen levels are reduced due to ischemia, the brain shifts from oxidative phosphorylation to anaerobic glycolysis, leading to lactate accumulation [29]. Lactate, once considered merely a byproduct of anaerobic metabolism, has gained recognition as a significant metabolic substrate and signaling molecule that plays a crucial role in modulating immune and inflammatory responses in the brain [30]. Through our analysis, we identified nine LRGs that play a crucial role in modulating the reparative functions of various cell types in response to ischemic lesions. Specifically, Per2 and Cav1 have been shown to promote microglial polarization and inflammatory responses, which are essential for mitigating brain injury following ischemia [31,32]. Additionally, Col4a1, Apln, and Cav1 are significantly associated with the normal functioning of vascular endothelial cells and are critical for preserving the integrity of the blood–brain barrier (BBB). The aberrant expression of these genes may impair the recovery process after stroke [33,34]. Sfxn4 and Cdo1 are implicated in mitochondrial energy metabolism. We hypothesized that these genes may contribute to neuroprotection by enhancing mitochondrial biogenesis following IS [35,36]. Our findings revealed that these LRGs are predominantly expressed in monocyte-derived cells, astrocytes, microglia, and vascular endothelial cells, which are crucial for both health and disease. This distribution suggests that following IS, these genes contribute to maintaining cerebral homeostasis by preserving energy metabolism, antioxidant defenses, and anti-inflammatory processes.

Polarization of microglia is a critical process in the neuroinflammatory response following IS, and LRGs play a significant role in this process. Our results indicated that Spp1, C1qbp, and Myc are prominently enriched in microglial cells, which serve as the principal immune components of the CNS and are crucially involved in the pathogenesis of IS. The dynamic changes in the expression of these genes suggest their involvement in the transition of microglia from the MG0 microglia subcluster to the MG1 subcluster, underscoring the pivotal role of MG1 in the context of IS. Functional analysis of the MG1 subcluster revealed its crucial involvement in the immune response and anti-inflammatory activities. These cells play a significant role in modulating immune and inflammatory responses in the CNS post-ischemia. Therefore, we speculate that this transition is consistent with the polarization of microglia from a pro-inflammatory state to an anti-inflammatory state and that these three LRGs perform their neuroprotection at the late stage of IS.

Spp1, also known as osteopontin (OPN), is a multifunctional glycoprotein that is widely expressed in various tissues, including the CNS. Spp1 is significantly involved in modulating immune responses, inflammation, and tissue repair processes [37]. Post-IS, Spp1 expression is markedly upregulated in multiple cell types of the neurovascular unit, including microglia, endothelial cells, and astrocytes [38]. Elevated Spp1 expression plays a dual role: initially promoting a pro-inflammatory response aimed at clearing cellular debris and subsequently fostering a reparative environment that supports tissue healing [39]. Prior scRNA-seq studies have shown that certain subsets of microglia and macrophages exhibit increased Spp1 expression following ischemic events, which persist throughout the later stages of stroke recovery and play a critical role in mediating the transition from a pro-inflammatory to an anti-inflammatory microglial phenotype [40]. C1qbp is a multifunctional protein that plays a crucial role in diverse cellular processes and is primarily recognized for its regulatory function in the immune system. In addition to its immune functions, C1qbp is involved in energy metabolism by maintaining mitochondrial function, particularly in monocyte-derived cells and microglia. Within ischemic lesions, C1qbp upregulation enables microglia to meet increased metabolic demands and oxidative stress associated with neuroinflammation [41]. Myc is a key transcription factor that is essential for the regulation of cell cycle progression, apoptosis, and cellular transformation. In IS, Myc upregulation can mediate the transformation of microglia into dendritic-like cells, driven by the ERK/Myc signaling pathway. This pathway is crucial for microglial responses to ischemic injury, highlighting its role in post-stroke neuroinflammation [42]. Furthermore, it redirects metabolism towards oxidative phosphorylation under conditions of low glucose and high lactate levels, thereby promoting cell survival and function under hypoxic conditions by inhibiting glycolysis and increasing energy production [43]. Myc upregulation also promotes glycolysis and excessive lactic acid production by regulating the expression of GLUT1 and key glycolytic enzymes, including HK and PFK1 [44].

As the innate immune cell in the brain, microglia can exert its regulatory function in various diseases by interacting with other brain cells. In neurodegenerative disorders, microglia and neurons are interconnected primarily through the SPP1–ITGAV receptor–ligand pair, and this association is bidirectional. Additionally, microglia and astrocytes interact through the GAS6–MERTK and RELN–ITGB1 receptor–ligand pairs [45]. In an acute demyelination mouse model, activated astrocytes express multiple ligands, including Cx3cl1, Csf1, Il34, and Gas6, which act on both homeostatic and activated microglia, thereby potentially mediating microglial activation, recruitment, and enhancing their phagocytic activity [46,47]. It has also been reported that the Spp1 intercellular interaction pathway is significantly increased in mice with temporal lobe epilepsy. This interaction can be observed in all glial cells, with microglia and astrocytes displaying the strongest communication strength among others [48]. Gu et al. have also identified a microglial subcluster in rats with hemorrhagic stroke, characterized by the highest expression of Lcn2, Msr1, and Spp1 at 24 h post-stroke. These cells exhibit significant interactions with endothelial cells and participate in the inflammatory response. Furthermore, these microglia can also interact with neurons via the Lcn2-SLC22A17 signaling pathway to induce neuronal death [49]. In Alzheimer’s disease, specific transcription factors, such as MYC and CTNNB1, are altered in inhibitory neurons, leading to altered communication patterns between microglia and neurons. This microglia–neuron interaction may be mediated through the APOE–LRP8 ligand–receptor pair [50]. Although the analysis of cell–cell communication helps us understand the pattern of cellular interactions, it is, to some extent, unable to fully simulate the interconnections between cells in physiological and pathological conditions. First, when analyzing cell–cell communication, we use the point-to-point model of ligand–receptor to simulate cellular connections. However, under biological conditions, cellular connections are multi-dimensional, and relying solely on the ligand–receptor scale may not comprehensively reflect the strength of cellular interactions. Additionally, scRNA-seq analysis lacks spatial location information, and biological connections between cells often rely on spatial location. Moreover, scRNA-seq analysis lacks spatial location information, whereas biological connections between cells often rely on physical proximity. Although spatial transcriptomics can provide cell location information, it is difficult to accurately identify inter-cell interactions due to its low resolution. Cellular communication is a dynamic process, whereas scRNA-seq analysis primarily focuses on cellular changes at a specific point in time, failing to reflect changes in inter-cellular connections over time. Therefore, to achieve a comprehensive understanding of cellular interactions, we propose that the application of scRNA-seq analysis, combined with the verification of biological experiments, can provide solid evidence for understanding cellular communication.

Our findings indicate that C1qbp and Myc are predominantly expressed in the MG1 microglial subset post-ischemia and exhibit particularly high levels in the early stages of the disease, while in the chronic phase of IS, the expression level of these two genes is downregulated to the normal state. This observation suggests that C1qbp and Myc may serve as initiator genes in response to ischemic lesions, potentially inducing microglial polarization to counteract neuroinflammation. In contrast, Spp1 expression is upregulated at later stages of IS and will last for 2 weeks, suggesting a role for Spp1 in neurorestorative functions during the subsequent recovery phase following stroke.

Neuroinflammation following IS is triggered not only by resident immune cells but also by infiltrating immune cells from the peripheral immune system [51]. In our study, we characterized the post-IS infiltration patterns of immune cells and observed an increase in activated DCs and a concurrent decrease in memory B cells, plasma cells, memory CD4 T cells, and immature DCs. These findings highlight the dynamic and complex nature of the immune response after cerebral ischemia and demonstrate contrasting infiltration behaviors among specific immune cell populations. The elevated presence of activated DCs suggests a potential role in antigen presentation and the initiation of immune responses, while the diminished presence of other cell types, such as memory B cells and plasma cells, may indicate the resolution of initial inflammatory responses or a transition in the immune landscape towards a regulatory phenotype. These observations enhance our understanding of immune cell dynamics in the context of stroke and may inform the development of targeted immunomodulatory therapies.

In the present study, we employed an integrative approach of bulk-sequencing and scRNA-seq analyses to identify nine LRGs that exhibit dynamic expression patterns in microglia post-ischemia. By analyzing the distribution of these genes, we focused on temporal expression changes within microglia, potentially revealing the cellular response to IS. Additionally, by conducting a sub-clustering analysis of microglial populations, we delineated the phenotypic transitions of microglia at the onset of IS, thereby elucidating their biological functions throughout the disease process. However, our study has some limitations. The specific mechanisms underlying the action of these signature genes require further elucidation using both in vitro and in vivo experimental models.

5 Conclusion

In conclusion, through a series of bioinformatics analyses, we successfully identified nine signature genes (Spp1, Per2, Col4a1, Sfxn4, C1qbp, Myc, Apln, Cdo1, and Cav1) associated with IS and LRGs. Furthermore, three major LRGs are predominantly expressed in microglia and contribute to the polarization of these cells. Notably, the expression level of Spp1 increases significantly at the late stage of IS, suggesting that this gene may serve a neuroprotective function during later phases of the disease. Consequently, our findings provide novel insights for investigating dynamic alterations in LRGs within microglia. This discovery could facilitate the targeting of these LRGs at appropriate time points to modulate lactate metabolism, thereby potentially enhancing the therapeutic efficacy against IS.

  1. Funding information: No funding was received for this study.

  2. Author contributions: Jinzhong Yao: conceptualization, data curation, formal analysis, investigation, methodology, project administration, resources, software, validation, visualization, writing – original draft, and writing – review and editing. Huan Deng: conceptualization, data curation, formal analysis, investigation, methodology, and writing – original draft. Peng Wang: methodology. Zaisheng Qin and Bo Li: supervision, writing – review and editing.

  3. Conflict of interest: The authors declare no conflict of interest.

  4. Data availability statement: The dataset used in this study can be downloaded from GEO database.

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Received: 2025-01-03
Revised: 2025-03-13
Accepted: 2025-03-13
Published Online: 2025-04-15

© 2025 the author(s), published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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  74. Comprehensive biomedicine assessment of Apteranthes tuberculata extracts: Phytochemical analysis and multifaceted pharmacological evaluation in animal models
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  77. Correlation between albumin levels during the third trimester and the risk of postpartum levator ani muscle rupture
  78. Factors associated with maternal attention and distraction during breastfeeding and childcare: A cross-sectional study in the west of Iran
  79. Mechanisms of hesperetin in treating metabolic dysfunction-associated steatosis liver disease via network pharmacology and in vitro experiments
  80. The law on oncological oblivion in the Italian and European context: How to best uphold the cancer patients’ rights to privacy and self-determination?
  81. The prognostic value of the neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, and prognostic nutritional index for survival in patients with colorectal cancer
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  84. The effects of different types of RAGT on balance function in stroke patients with low levels of independent walking in a convalescent rehabilitation hospital
  85. Causal relationship between asthma and ankylosing spondylitis: A bidirectional two-sample univariable and multivariable Mendelian randomization study
  86. Correlations of health literacy with individuals’ understanding and use of medications in Southern Taiwan
  87. Correlation of serum calprotectin with outcome of acute cerebral infarction
  88. Comparison of computed tomography and guided bronchoscopy in the diagnosis of pulmonary nodules: A systematic review and meta-analysis
  89. Curdione protects vascular endothelial cells and atherosclerosis via the regulation of DNMT1-mediated ERBB4 promoter methylation
  90. The identification of novel missense variant in ChAT gene in a patient with gestational diabetes denotes plausible genetic association
  91. Molecular genotyping of multi-system rare blood types in foreign blood donors based on DNA sequencing and its clinical significance
  92. Exploring the role of succinyl carnitine in the association between CD39⁺ CD4⁺ T cell and ulcerative colitis: A Mendelian randomization study
  93. Dexmedetomidine suppresses microglial activation in postoperative cognitive dysfunction via the mmu-miRNA-125/TRAF6 signaling axis
  94. Analysis of serum metabolomics in patients with different types of chronic heart failure
  95. Diagnostic value of hematological parameters in the early diagnosis of acute cholecystitis
  96. Pachymaran alleviates fat accumulation, hepatocyte degeneration, and injury in mice with nonalcoholic fatty liver disease
  97. Decrease in CD4 and CD8 lymphocytes are predictors of severe clinical picture and unfavorable outcome of the disease in patients with COVID-19
  98. METTL3 blocked the progression of diabetic retinopathy through m6A-modified SOX2
  99. The predictive significance of anti-RO-52 antibody in patients with interstitial pneumonia after treatment of malignant tumors
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  102. Widespread venous thrombosis: Unveiling a complex case of Behçet’s disease with a literature perspective
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  107. Peptide CCAT1-70aa promotes hepatocellular carcinoma proliferation and invasion via the MAPK/ERK pathway
  108. Electroacupuncture-induced reduction of myocardial ischemia–reperfusion injury via FTO-dependent m6A methylation modulation
  109. Hemorrhoids and cardiovascular disease: A bidirectional Mendelian randomization study
  110. Cell-free adipose extract inhibits hypertrophic scar formation through collagen remodeling and antiangiogenesis
  111. HALP score in Demodex blepharitis: A case–control study
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  113. Risk and prognosis for brain metastasis in primary metastatic cervical cancer patients: A population-based study
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  115. Factors influencing hematological toxicity and adverse effects of perioperative hyperthermic intraperitoneal vs intraperitoneal chemotherapy in gastrointestinal cancer
  116. Endotoxin tolerance inhibits NLRP3 inflammasome activation in macrophages of septic mice by restoring autophagic flux through TRIM26
  117. Lateral transperitoneal laparoscopic adrenalectomy: A single-centre experience of 21 procedures
  118. Petunidin attenuates lipopolysaccharide-induced retinal microglia inflammatory response in diabetic retinopathy by targeting OGT/NF-κB/LCN2 axis
  119. Procalcitonin and C-reactive protein as biomarkers for diagnosing and assessing the severity of acute cholecystitis
  120. Factors determining the number of sessions in successful extracorporeal shock wave lithotripsy patients
  121. Development of a nomogram for predicting cancer-specific survival in patients with renal pelvic cancer following surgery
  122. Inhibition of ATG7 promotes orthodontic tooth movement by regulating the RANKL/OPG ratio under compression force
  123. A machine learning-based prognostic model integrating mRNA stemness index, hypoxia, and glycolysis‑related biomarkers for colorectal cancer
  124. Glutathione attenuates sepsis-associated encephalopathy via dual modulation of NF-κB and PKA/CREB pathways
  125. FAHD1 prevents neuronal ferroptosis by modulating R-loop and the cGAS–STING pathway
  126. Association of placenta weight and morphology with term low birth weight: A case–control study
  127. Investigation of the pathogenic variants induced Sjogren’s syndrome in Turkish population
  128. Nucleotide metabolic abnormalities in post-COVID-19 condition and type 2 diabetes mellitus patients and their association with endocrine dysfunction
  129. TGF-β–Smad2/3 signaling in high-altitude pulmonary hypertension in rats: Role and mechanisms via macrophage M2 polarization
  130. Ultrasound-guided unilateral versus bilateral erector spinae plane block for postoperative analgesia of patients undergoing laparoscopic cholecystectomy
  131. Profiling gut microbiome dynamics in subacute thyroiditis: Implications for pathogenesis, diagnosis, and treatment
  132. Delta neutrophil index, CRP/albumin ratio, procalcitonin, immature granulocytes, and HALP score in acute appendicitis: Best performing biomarker?
  133. Anticancer activity mechanism of novelly synthesized and characterized benzofuran ring-linked 3-nitrophenyl chalcone derivative on colon cancer cells
  134. H2valdien3 arrests the cell cycle and induces apoptosis of gastric cancer
  135. Prognostic relevance of PRSS2 and its immune correlates in papillary thyroid carcinoma
  136. Association of SGLT2 inhibition with psychiatric disorders: A Mendelian randomization study
  137. Motivational interviewing for alcohol use reduction in Thai patients
  138. Luteolin alleviates oxygen-glucose deprivation/reoxygenation-induced neuron injury by regulating NLRP3/IL-1β signaling
  139. Polyphyllin II inhibits thyroid cancer cell growth by simultaneously inhibiting glycolysis and oxidative phosphorylation
  140. Relationship between the expression of copper death promoting factor SLC31A1 in papillary thyroid carcinoma and clinicopathological indicators and prognosis
  141. CSF2 polarized neutrophils and invaded renal cancer cells in vitro influence
  142. Proton pump inhibitors-induced thrombocytopenia: A systematic literature analysis of case reports
  143. The current status and influence factors of research ability among community nurses: A sequential qualitative–quantitative study
  144. OKAIN: A comprehensive oncology knowledge base for the interpretation of clinically actionable alterations
  145. The relationship between serum CA50, CA242, and SAA levels and clinical pathological characteristics and prognosis in patients with pancreatic cancer
  146. Identification and external validation of a prognostic signature based on hypoxia–glycolysis-related genes for kidney renal clear cell carcinoma
  147. Engineered RBC-derived nanovesicles functionalized with tumor-targeting ligands: A comparative study on breast cancer targeting efficiency and biocompatibility
  148. Relationship of resting echocardiography combined with serum micronutrients to the severity of low-gradient severe aortic stenosis
  149. Effect of vibration on pain during subcutaneous heparin injection: A randomized, single-blind, placebo-controlled trial
  150. The diagnostic performance of machine learning-based FFRCT for coronary artery disease: A meta-analysis
  151. Comparing biofeedback device vs diaphragmatic breathing for bloating relief: A randomized controlled trial
  152. Serum uric acid to albumin ratio and C-reactive protein as predictive biomarkers for chronic total occlusion and coronary collateral circulation quality
  153. Multiple organ scoring systems for predicting in-hospital mortality of sepsis patients in the intensive care unit
  154. Single-cell RNA sequencing data analysis of the inner ear in gentamicin-treated mice via intraperitoneal injection
  155. Review Articles
  156. The effects of enhanced external counter-pulsation on post-acute sequelae of COVID-19: A narrative review
  157. Diabetes-related cognitive impairment: Mechanisms, symptoms, and treatments
  158. Microscopic changes and gross morphology of placenta in women affected by gestational diabetes mellitus in dietary treatment: A systematic review
  159. Review of mechanisms and frontier applications in IL-17A-induced hypertension
  160. Research progress on the correlation between islet amyloid peptides and type 2 diabetes mellitus
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  165. Plant polyphenols, terpenes, and terpenoids in oral health
  166. Laboratory medicine between technological innovation, rights safeguarding, and patient safety: A bioethical perspective
  167. End-of-life in cancer patients: Medicolegal implications and ethical challenges in Europe
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  179. Protective role of selenium in sepsis: Mechanisms and potential therapeutic strategies
  180. Strategies for hyperkalemia management in dialysis patients: A systematic review
  181. C-reactive protein-to-albumin ratio in peripheral artery disease
  182. Case Reports
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  190. Coronary artery anomalies: A case of the “malignant” left coronary artery and its surgical management
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  192. Biological properties of valve materials using RGD and EC
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  194. Letter to the Editor
  195. Role of enhanced external counterpulsation in long COVID
  196. Expression of Concern
  197. Expression of concern “A ceRNA network mediated by LINC00475 in papillary thyroid carcinoma”
  198. Expression of concern “Notoginsenoside R1 alleviates spinal cord injury through the miR-301a/KLF7 axis to activate Wnt/β-catenin pathway”
  199. Expression of concern “circ_0020123 promotes cell proliferation and migration in lung adenocarcinoma via PDZD8”
  200. Corrigendum
  201. Corrigendum to “Empagliflozin improves aortic injury in obese mice by regulating fatty acid metabolism”
  202. Corrigendum to “Comparing the therapeutic efficacy of endoscopic minimally invasive surgery and traditional surgery for early-stage breast cancer: A meta-analysis”
  203. Corrigendum to “The progress of autoimmune hepatitis research and future challenges”
  204. Retraction
  205. Retraction of “miR-654-5p promotes gastric cancer progression via the GPRIN1/NF-κB pathway”
  206. Retraction of: “LncRNA CASC15 inhibition relieves renal fibrosis in diabetic nephropathy through downregulating SP-A by sponging to miR-424”
  207. Retraction of: “SCARA5 inhibits oral squamous cell carcinoma via inactivating the STAT3 and PI3K/AKT signaling pathways”
  208. Special Issue Advancements in oncology: bridging clinical and experimental research - Part II
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  213. Meta-analysis of clinical efficacy and safety of immunotherapy combined with chemotherapy in non-small cell lung cancer
  214. Special Issue Computational Intelligence Methodologies Meets Recurrent Cancers - Part IV
  215. Exploration of mRNA-modifying METTL3 oncogene as momentous prognostic biomarker responsible for colorectal cancer development
  216. Special Issue The evolving saga of RNAs from bench to bedside - Part III
  217. Interaction and verification of ferroptosis-related RNAs Rela and Stat3 in promoting sepsis-associated acute kidney injury
  218. The mRNA MOXD1: Link to oxidative stress and prognostic significance in gastric cancer
  219. Special Issue Exploring the biological mechanism of human diseases based on MultiOmics Technology - Part II
  220. Dynamic changes in lactate-related genes in microglia and their role in immune cell interactions after ischemic stroke
  221. A prognostic model correlated with fatty acid metabolism in Ewing’s sarcoma based on bioinformatics analysis
  222. Red cell distribution width predicts early kidney injury: A NHANES cross-sectional study
  223. Special Issue Diabetes mellitus: pathophysiology, complications & treatment
  224. Nutritional risk assessment and nutritional support in children with congenital diabetes during surgery
  225. Correlation of the differential expressions of RANK, RANKL, and OPG with obesity in the elderly population in Xinjiang
  226. A discussion on the application of fluorescence micro-optical sectioning tomography in the research of cognitive dysfunction in diabetes
  227. A review of brain research on T2DM-related cognitive dysfunction
  228. Metformin and estrogen modulation in LABC with T2DM: A 36-month randomized trial
  229. Special Issue Innovative Biomarker Discovery and Precision Medicine in Cancer Diagnostics
  230. CircASH1L-mediated tumor progression in triple-negative breast cancer: PI3K/AKT pathway mechanisms
https://doi.org/10.1515/med-2025-1178
Keywords for this article
ischemic stroke; microglia; single-cell RNA sequencing; lactate metabolism
Creative Commons
BY 4.0

Articles in the same Issue

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  4. Knockdown of CCNB1 alleviates high glucose-triggered trophoblast dysfunction during gestational diabetes via Wnt/β-catenin signaling pathway
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  25. Mechanism research on inhibition of gastric cancer in vitro by the extract of Pinellia ternata based on network pharmacology and cellular metabolomics
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  77. Correlation between albumin levels during the third trimester and the risk of postpartum levator ani muscle rupture
  78. Factors associated with maternal attention and distraction during breastfeeding and childcare: A cross-sectional study in the west of Iran
  79. Mechanisms of hesperetin in treating metabolic dysfunction-associated steatosis liver disease via network pharmacology and in vitro experiments
  80. The law on oncological oblivion in the Italian and European context: How to best uphold the cancer patients’ rights to privacy and self-determination?
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  82. Factors affecting the measurements of peripheral oxygen saturation values in healthy young adults
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  84. The effects of different types of RAGT on balance function in stroke patients with low levels of independent walking in a convalescent rehabilitation hospital
  85. Causal relationship between asthma and ankylosing spondylitis: A bidirectional two-sample univariable and multivariable Mendelian randomization study
  86. Correlations of health literacy with individuals’ understanding and use of medications in Southern Taiwan
  87. Correlation of serum calprotectin with outcome of acute cerebral infarction
  88. Comparison of computed tomography and guided bronchoscopy in the diagnosis of pulmonary nodules: A systematic review and meta-analysis
  89. Curdione protects vascular endothelial cells and atherosclerosis via the regulation of DNMT1-mediated ERBB4 promoter methylation
  90. The identification of novel missense variant in ChAT gene in a patient with gestational diabetes denotes plausible genetic association
  91. Molecular genotyping of multi-system rare blood types in foreign blood donors based on DNA sequencing and its clinical significance
  92. Exploring the role of succinyl carnitine in the association between CD39⁺ CD4⁺ T cell and ulcerative colitis: A Mendelian randomization study
  93. Dexmedetomidine suppresses microglial activation in postoperative cognitive dysfunction via the mmu-miRNA-125/TRAF6 signaling axis
  94. Analysis of serum metabolomics in patients with different types of chronic heart failure
  95. Diagnostic value of hematological parameters in the early diagnosis of acute cholecystitis
  96. Pachymaran alleviates fat accumulation, hepatocyte degeneration, and injury in mice with nonalcoholic fatty liver disease
  97. Decrease in CD4 and CD8 lymphocytes are predictors of severe clinical picture and unfavorable outcome of the disease in patients with COVID-19
  98. METTL3 blocked the progression of diabetic retinopathy through m6A-modified SOX2
  99. The predictive significance of anti-RO-52 antibody in patients with interstitial pneumonia after treatment of malignant tumors
  100. Exploring cerebrospinal fluid metabolites, cognitive function, and brain atrophy: Insights from Mendelian randomization
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  103. Uterine fibroid embolization: An analysis of clinical outcomes and impact on patients’ quality of life
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  105. Serum-derived exomiR-188-3p is a promising novel biomarker for early-stage ovarian cancer
  106. Enhancing chronic back pain management: A comparative study of ultrasound–MRI fusion guidance for paravertebral nerve block
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  108. Electroacupuncture-induced reduction of myocardial ischemia–reperfusion injury via FTO-dependent m6A methylation modulation
  109. Hemorrhoids and cardiovascular disease: A bidirectional Mendelian randomization study
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  112. Assessment of SOX2 performance as a marker for circulating cancer stem-like cells (CCSCs) identification in advanced breast cancer patients using CytoTrack system
  113. Risk and prognosis for brain metastasis in primary metastatic cervical cancer patients: A population-based study
  114. Comparison of the two intestinal anastomosis methods in pediatric patients
  115. Factors influencing hematological toxicity and adverse effects of perioperative hyperthermic intraperitoneal vs intraperitoneal chemotherapy in gastrointestinal cancer
  116. Endotoxin tolerance inhibits NLRP3 inflammasome activation in macrophages of septic mice by restoring autophagic flux through TRIM26
  117. Lateral transperitoneal laparoscopic adrenalectomy: A single-centre experience of 21 procedures
  118. Petunidin attenuates lipopolysaccharide-induced retinal microglia inflammatory response in diabetic retinopathy by targeting OGT/NF-κB/LCN2 axis
  119. Procalcitonin and C-reactive protein as biomarkers for diagnosing and assessing the severity of acute cholecystitis
  120. Factors determining the number of sessions in successful extracorporeal shock wave lithotripsy patients
  121. Development of a nomogram for predicting cancer-specific survival in patients with renal pelvic cancer following surgery
  122. Inhibition of ATG7 promotes orthodontic tooth movement by regulating the RANKL/OPG ratio under compression force
  123. A machine learning-based prognostic model integrating mRNA stemness index, hypoxia, and glycolysis‑related biomarkers for colorectal cancer
  124. Glutathione attenuates sepsis-associated encephalopathy via dual modulation of NF-κB and PKA/CREB pathways
  125. FAHD1 prevents neuronal ferroptosis by modulating R-loop and the cGAS–STING pathway
  126. Association of placenta weight and morphology with term low birth weight: A case–control study
  127. Investigation of the pathogenic variants induced Sjogren’s syndrome in Turkish population
  128. Nucleotide metabolic abnormalities in post-COVID-19 condition and type 2 diabetes mellitus patients and their association with endocrine dysfunction
  129. TGF-β–Smad2/3 signaling in high-altitude pulmonary hypertension in rats: Role and mechanisms via macrophage M2 polarization
  130. Ultrasound-guided unilateral versus bilateral erector spinae plane block for postoperative analgesia of patients undergoing laparoscopic cholecystectomy
  131. Profiling gut microbiome dynamics in subacute thyroiditis: Implications for pathogenesis, diagnosis, and treatment
  132. Delta neutrophil index, CRP/albumin ratio, procalcitonin, immature granulocytes, and HALP score in acute appendicitis: Best performing biomarker?
  133. Anticancer activity mechanism of novelly synthesized and characterized benzofuran ring-linked 3-nitrophenyl chalcone derivative on colon cancer cells
  134. H2valdien3 arrests the cell cycle and induces apoptosis of gastric cancer
  135. Prognostic relevance of PRSS2 and its immune correlates in papillary thyroid carcinoma
  136. Association of SGLT2 inhibition with psychiatric disorders: A Mendelian randomization study
  137. Motivational interviewing for alcohol use reduction in Thai patients
  138. Luteolin alleviates oxygen-glucose deprivation/reoxygenation-induced neuron injury by regulating NLRP3/IL-1β signaling
  139. Polyphyllin II inhibits thyroid cancer cell growth by simultaneously inhibiting glycolysis and oxidative phosphorylation
  140. Relationship between the expression of copper death promoting factor SLC31A1 in papillary thyroid carcinoma and clinicopathological indicators and prognosis
  141. CSF2 polarized neutrophils and invaded renal cancer cells in vitro influence
  142. Proton pump inhibitors-induced thrombocytopenia: A systematic literature analysis of case reports
  143. The current status and influence factors of research ability among community nurses: A sequential qualitative–quantitative study
  144. OKAIN: A comprehensive oncology knowledge base for the interpretation of clinically actionable alterations
  145. The relationship between serum CA50, CA242, and SAA levels and clinical pathological characteristics and prognosis in patients with pancreatic cancer
  146. Identification and external validation of a prognostic signature based on hypoxia–glycolysis-related genes for kidney renal clear cell carcinoma
  147. Engineered RBC-derived nanovesicles functionalized with tumor-targeting ligands: A comparative study on breast cancer targeting efficiency and biocompatibility
  148. Relationship of resting echocardiography combined with serum micronutrients to the severity of low-gradient severe aortic stenosis
  149. Effect of vibration on pain during subcutaneous heparin injection: A randomized, single-blind, placebo-controlled trial
  150. The diagnostic performance of machine learning-based FFRCT for coronary artery disease: A meta-analysis
  151. Comparing biofeedback device vs diaphragmatic breathing for bloating relief: A randomized controlled trial
  152. Serum uric acid to albumin ratio and C-reactive protein as predictive biomarkers for chronic total occlusion and coronary collateral circulation quality
  153. Multiple organ scoring systems for predicting in-hospital mortality of sepsis patients in the intensive care unit
  154. Single-cell RNA sequencing data analysis of the inner ear in gentamicin-treated mice via intraperitoneal injection
  155. Review Articles
  156. The effects of enhanced external counter-pulsation on post-acute sequelae of COVID-19: A narrative review
  157. Diabetes-related cognitive impairment: Mechanisms, symptoms, and treatments
  158. Microscopic changes and gross morphology of placenta in women affected by gestational diabetes mellitus in dietary treatment: A systematic review
  159. Review of mechanisms and frontier applications in IL-17A-induced hypertension
  160. Research progress on the correlation between islet amyloid peptides and type 2 diabetes mellitus
  161. The safety and efficacy of BCG combined with mitomycin C compared with BCG monotherapy in patients with non-muscle-invasive bladder cancer: A systematic review and meta-analysis
  162. The application of augmented reality in robotic general surgery: A mini-review
  163. The effect of Greek mountain tea extract and wheat germ extract on peripheral blood flow and eicosanoid metabolism in mammals
  164. Neurogasobiology of migraine: Carbon monoxide, hydrogen sulfide, and nitric oxide as emerging pathophysiological trinacrium relevant to nociception regulation
  165. Plant polyphenols, terpenes, and terpenoids in oral health
  166. Laboratory medicine between technological innovation, rights safeguarding, and patient safety: A bioethical perspective
  167. End-of-life in cancer patients: Medicolegal implications and ethical challenges in Europe
  168. The maternal factors during pregnancy for intrauterine growth retardation: An umbrella review
  169. Intra-abdominal hypertension/abdominal compartment syndrome of pediatric patients in critical care settings
  170. PI3K/Akt pathway and neuroinflammation in sepsis-associated encephalopathy
  171. Screening of Group B Streptococcus in pregnancy: A systematic review for the laboratory detection
  172. Giant borderline ovarian tumours – review of the literature
  173. Leveraging artificial intelligence for collaborative care planning: Innovations and impacts in shared decision-making – A systematic review
  174. Cholera epidemiology analysis through the experience of the 1973 Naples epidemic
  175. Risk factors of frailty/sarcopenia in community older adults: Meta-analysis
  176. Supplement strategies for infertility in overweight women: Evidence and legal insights
  177. Scurvy, a not obsolete disorder: Clinical report in eight young children and literature review
  178. A meta-analysis of the effects of DBS on cognitive function in patients with advanced PD
  179. Protective role of selenium in sepsis: Mechanisms and potential therapeutic strategies
  180. Strategies for hyperkalemia management in dialysis patients: A systematic review
  181. C-reactive protein-to-albumin ratio in peripheral artery disease
  182. Case Reports
  183. Delayed graft function after renal transplantation
  184. Semaglutide treatment for type 2 diabetes in a patient with chronic myeloid leukemia: A case report and review of the literature
  185. Diverse electrophysiological demyelinating features in a late-onset glycogen storage disease type IIIa case
  186. Giant right atrial hemangioma presenting with ascites: A case report
  187. Laser excision of a large granular cell tumor of the vocal cord with subglottic extension: A case report
  188. EsoFLIP-assisted dilation for dysphagia in systemic sclerosis: Highlighting the role of multimodal esophageal evaluation
  189. Molecular hydrogen-rhodiola as an adjuvant therapy for ischemic stroke in internal carotid artery occlusion: A case report
  190. Coronary artery anomalies: A case of the “malignant” left coronary artery and its surgical management
  191. Rapid Communication
  192. Biological properties of valve materials using RGD and EC
  193. A single oral administration of flavanols enhances short-term memory in mice along with increased brain-derived neurotrophic factor
  194. Letter to the Editor
  195. Role of enhanced external counterpulsation in long COVID
  196. Expression of Concern
  197. Expression of concern “A ceRNA network mediated by LINC00475 in papillary thyroid carcinoma”
  198. Expression of concern “Notoginsenoside R1 alleviates spinal cord injury through the miR-301a/KLF7 axis to activate Wnt/β-catenin pathway”
  199. Expression of concern “circ_0020123 promotes cell proliferation and migration in lung adenocarcinoma via PDZD8”
  200. Corrigendum
  201. Corrigendum to “Empagliflozin improves aortic injury in obese mice by regulating fatty acid metabolism”
  202. Corrigendum to “Comparing the therapeutic efficacy of endoscopic minimally invasive surgery and traditional surgery for early-stage breast cancer: A meta-analysis”
  203. Corrigendum to “The progress of autoimmune hepatitis research and future challenges”
  204. Retraction
  205. Retraction of “miR-654-5p promotes gastric cancer progression via the GPRIN1/NF-κB pathway”
  206. Retraction of: “LncRNA CASC15 inhibition relieves renal fibrosis in diabetic nephropathy through downregulating SP-A by sponging to miR-424”
  207. Retraction of: “SCARA5 inhibits oral squamous cell carcinoma via inactivating the STAT3 and PI3K/AKT signaling pathways”
  208. Special Issue Advancements in oncology: bridging clinical and experimental research - Part II
  209. Unveiling novel biomarkers for platinum chemoresistance in ovarian cancer
  210. Lathyrol affects the expression of AR and PSA and inhibits the malignant behavior of RCC cells
  211. The era of increasing cancer survivorship: Trends in fertility preservation, medico-legal implications, and ethical challenges
  212. Bone scintigraphy and positron emission tomography in the early diagnosis of MRONJ
  213. Meta-analysis of clinical efficacy and safety of immunotherapy combined with chemotherapy in non-small cell lung cancer
  214. Special Issue Computational Intelligence Methodologies Meets Recurrent Cancers - Part IV
  215. Exploration of mRNA-modifying METTL3 oncogene as momentous prognostic biomarker responsible for colorectal cancer development
  216. Special Issue The evolving saga of RNAs from bench to bedside - Part III
  217. Interaction and verification of ferroptosis-related RNAs Rela and Stat3 in promoting sepsis-associated acute kidney injury
  218. The mRNA MOXD1: Link to oxidative stress and prognostic significance in gastric cancer
  219. Special Issue Exploring the biological mechanism of human diseases based on MultiOmics Technology - Part II
  220. Dynamic changes in lactate-related genes in microglia and their role in immune cell interactions after ischemic stroke
  221. A prognostic model correlated with fatty acid metabolism in Ewing’s sarcoma based on bioinformatics analysis
  222. Red cell distribution width predicts early kidney injury: A NHANES cross-sectional study
  223. Special Issue Diabetes mellitus: pathophysiology, complications & treatment
  224. Nutritional risk assessment and nutritional support in children with congenital diabetes during surgery
  225. Correlation of the differential expressions of RANK, RANKL, and OPG with obesity in the elderly population in Xinjiang
  226. A discussion on the application of fluorescence micro-optical sectioning tomography in the research of cognitive dysfunction in diabetes
  227. A review of brain research on T2DM-related cognitive dysfunction
  228. Metformin and estrogen modulation in LABC with T2DM: A 36-month randomized trial
  229. Special Issue Innovative Biomarker Discovery and Precision Medicine in Cancer Diagnostics
  230. CircASH1L-mediated tumor progression in triple-negative breast cancer: PI3K/AKT pathway mechanisms
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