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. 2024 Nov 9:17:8569-8587.
doi: 10.2147/JIR.S493828. eCollection 2024.

Uncovering SPP1+ Macrophage, Neutrophils and Their Related Diagnostic Biomarkers in Intracranial Aneurysm and Subarachnoid Hemorrhage

Haipeng Jie  1   2 , Boyang Wang  1   2 , Jingjing Zhang  1   2 , Xinzhao Wang  3   4 , Xiang Song  3 , Fan Yang  5 , Changning Fu  6 , Bo Dong  1   2 , Feng Yan  7
Affiliations

Uncovering SPP1+ Macrophage, Neutrophils and Their Related Diagnostic Biomarkers in Intracranial Aneurysm and Subarachnoid Hemorrhage

Haipeng Jie et al. J Inflamm Res. .

Abstract

Background: Intracranial aneurysms (IA) frequently cause subarachnoid hemorrhage (SAH) and have poor prognosis. However, the molecular mechanisms and diagnostic biomarkers associated with IA and ruptured IA (rIA) remain poorly understood.

Methods: In this study, single-cell and transcriptome datasets were obtained from the GEO database. The cell populations were annotated to identify potential pathogenic subpopulations, followed by intercellular communication, pseudotime, and SCENIC analyses. Proteome-wide and transcriptome-wide Mendelian randomization (MR) analyses were conducted to identify risk factors for IA and SAH. The major pathological changes and diagnostic biomarkers of IA and SAH were identified based on the transcriptome datasets. A clinical cohort was established to identify the diagnostic biomarkers and validate the results.

Results: Macrophages and neutrophils were predominantly increased in IA and rIA tissues, and neutrophils were markedly upregulated in the blood of SAH patients. SPP1+ Macrophage was progressively elevated in aneurysms, promoting vascular smooth muscle cell (VSMC) phenotypic transformation and collagen matrix remodeling through the SPP1 and TGF-β pathways. Furthermore, HIF1α regulon was enriched in SPP1+ Macrophage, mediating inflammation and metabolic reprogramming, which contributed to IA progression. Integrated MR analysis identified CD36 as a risk factor for both IA and SAH, and it has been recognized as an effective blood biomarker for SAH. Neutrophils and their related indicators have emerged as excellent biomarkers of SAH in clinical cohorts.

Conclusion: This study highlighted the detrimental role of SPP1+ Macrophage in IA and SAH using single-cell sequencing and MR analyses. CD36 was identified as a risk factor for IA and SAH and was also an efficient blood biomarker for SAH. In a clinical cohort, neutrophils and related indicators were valuable for the early diagnosis of SAH.

Keywords: Mendelian randomization; SPP1+ Macrophage; intracranial aneurysm; neutrophils; single-cell sequencing.

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Conflict of interest statement

The authors declare that this research is conducted in the absence of any commercial or financial relationships that could be construed as potential conflicts of interest.

Figures

Figure 1
Figure 1
The flow diagram of the study.
Figure 2
Figure 2
Overview of cell populations in sham, IA, and rIA groups. (A) UMAP plots of 10 cell populations in 3 groups. (B) UMAP plots of 10 cell populations in 3 groups. (C–E) UMAP plots and box plots of Chemokine score, Collagen matrix score, and Contractile score in 3 groups. (F) Volcano plots of DEGs in macrophages, VSMCs, fibroblasts, and endothelia in IA. (G) Volcano plots of DEGs in macrophages, VSMCs, fibroblasts, and endothelia in rIA. ****p < 0.0001.
Figure 3
Figure 3
Overall summary of modulatory mechanisms in sham, IA, and rIA groups. (A) Heatmap of intercellular interactions between sham and IA groups. (B) Heatmap of intercellular interactions between IA and rIA groups. (C) Circular plot of SPP1 pathway in 3 groups. (D) Heatmap of SPP1 pathway in 10 cell populations. (E) Circular plot of TGF-β pathway in 3 groups. (F) Bubble plot of L-R pairs in SPP1 pathway originating from macrophages and targeting VSMCs, fibroblasts, endothelial cells between sham and IA groups. (G) Bubble plot of increased L-R pairs in the SPP1 pathway originating from neutrophils and targeting VSMCs, fibroblasts, and endothelial cells between the IA and rIA groups. (H) Heatmap of top 10 TF regulons in macrophages, VSMCs, fibroblasts, and endothelia. (I) t-SNE plot of 10 cell populations. (J–M) t-SNE plot of MAFB, HMGN3, PRRX2, SOX17 regulons in macrophages, VSMCs, fibroblasts, and endothelia, respectively.
Figure 4
Figure 4
The roles of SPP1+ Macrophage in sham, IA, and rIA groups. (A) UMAP plots of 5 macrophage subpopulations. (B) Proportions of 5 macrophage subpopulations in three groups. (C) Gene set scores of NF-κB pathway, TNF pathway, and TLR pathway. (D) Intercellular interactions between sham and IA groups. (E) Intercellular interactions between IA and rIA groups. (F) UMAP plot of SPP1+ Macrophage in IA. (G) Circular plot of SPP1 pathway in 3 groups. (H) The composition of L-R pairs in SPP1 pathway. (I) Circular plot of TGF-β pathway in 3 groups. (J) Differentiation state of IL1b+ Macrophage, GAS6+ Macrophage, SPP1+ Macrophage, and proliferation+ Macrophage based on Pseudotime analysis. (K–M) Molecular docking of CD44-Progesterone, TGF-β1-Silybin, and TGF-β1-Ramipril, respectively. (N) Heatmap of top 5 TF regulons in IL1b+ Macrophage, GAS6+ Macrophage, SPP1+ Macrophage, and proliferation+ Macrophage.
Figure 5
Figure 5
The causality of risk factors in IA and SAH based on Mendelian randomization. (A and B) The causality of collagen families in patients with IA and SAH. (C and D) The causality of 12 cardiovascular risk factors in patients with IA and SAH. (E and F) The causality of Neutrophil number in patients with IA and SAH.
Figure 6
Figure 6
The roles of cis-pQTLs related to SPP1+ Macrophage in IA and SAH. (A and B) UMAP plot of SPP1+ Macrophage in CaCl2-induced AAA. (C) Volcano plot of cis-pQTLs related to IA, labeled as specifically expressed genes in SPP1+ Macrophage. (D) Enrichment analysis of cis-pQTLs related to IA. (E) PPI network of cis-pQTLs related to IA. (F) The causality of cis-pQTLs corresponding to the specifically expressed genes of SPP1+ Macrophage in IA. (G) Volcano plot of cis-pQTLs related to SAH, labeled as specifically expressed genes in SPP1+ Macrophage. (H) Enrichment analysis of cis-pQTLs related to SAH. (I) PPI network of cis-pQTLs related to SAH. (J) The causality of cis-pQTLs corresponding to the specifically expressed genes of SPP1+ Macrophage in SAH.
Figure 7
Figure 7
The roles of cis-eQTLs related to SPP1+ Macrophage in IA and SAH. (A) The causality of cis-eQTLs corresponding to the specifically expressed genes of SPP1+ Macrophage in IA. (B) The causality of cis-eQTLs corresponding to the specifically expressed genes of SPP1+ Macrophage in SAH.
Figure 8
Figure 8
Transcriptome sequencing data among control, IA, and rIA groups. (A) Volcano plot of DEGs between the control and IA groups. (B) Volcano plot of DEGs between the IA and rIA groups. (C and D) KEGG and GSEA enrichment analyses between the control and IA groups. (E) Immune infiltration analysis between the control and IA groups using CIBERSORT. (F) Composition of 10 cell populations based on single-cell and transcriptome sequencing data among the control, IA, and rIA groups. (G and H) Two PPI modules based on DEGs between the control and IA groups. (I) Violin plot of SPP1 using transcriptome sequencing data among the control, IA, and rIA groups. *p < 0.05, ***p < 0.001, ****p < 0.0001.
Figure 9
Figure 9
Diagnostic biomarkers of IA and rIA derived from SPP1+ Macrophage and transcriptome sequencing data. (A) Heatmap of associations between gene modules and IA, rIA, respectively. (B) Venn plot of common genes in WGCNA modules, DEG, and SPP1+ Macrophage in IA and rIA. (C) ROC curves for diagnostic biomarkers of IA and rIA in tissues. (D) Expression of EDEM2 among control, IA, and rIA in tissues. (E) ROC curves for diagnostic biomarkers of IA and rIA in blood. (F) Expression of CD36 among control, IA, and rIA in blood.
Figure 10
Figure 10
Clinical variables and diagnostic biomarkers among control, IA, and SAH participants. (A) Multivariate logistic regression analysis of clinical variables in IA participants. (B) Ranking of the top 11 variables in SAH participants based on RF algorithm. (C) ROC curves of risk score and neutrophil-related indicators in the blood of SAH patients.
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