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Multi-targeted olink proteomics analyses of cerebrospinal fluid from patients with aneurysmal subarachnoid hemorrhage

Proteome Science volume 22, Article number: 11 (2024) Cite this article

Abstract

Background

The complexity of delayed cerebral ischemia (DCI) after aneurysmal subarachnoid hemorrhage (aSAH) may require the simultaneous analysis of variant types of protein biomarkers to describe it more accurately. In this study, we analyzed for the first time the alterations of cerebrospinal fluid (CSF) proteins in patients with aSAH by multi-targeted Olink proteomics, aiming to reveal the pathophysiology of DCI and provide insights into the diagnosis and treatment of aSAH.

Methods

Six aSAH patients and six control patients were selected, and CSF samples were analyzed by Olink Proteomics (including 96-neurology panel and 96-inflammation panel) based on Proximity Extension Assay (PEA). Differentially expressed proteins (DEPs) were acquired and bioinformatics analysis was performed.

Results

PCA analysis revealed better intra- and inter-group reproducibility of CSF samples in the control and aSAH groups. 23 neurology-related and 31 inflammation-relevant differential proteins were identified. In the neurology panel, compared to controls, the up-regulated proteins in the CSF of SAH patients predominantly included macrophage scavenger receptor 1 (MSR1), siglec-1, siglec-9, cathepsin C (CTSC), cathepsin S (CTSS), etc. Meanwhile, in the inflammation group, the incremental proteins mainly contained interleukin-6 (IL-6), MCP-1, CXCL10, CXCL-9, TRAIL, etc. Cluster analysis exhibited significant differences in differential proteins between the two groups. GO function enrichment analysis hinted that the differential proteins pertinent to neurology in the CSF of SAH patients were mainly involved in the regulation of defense response, vesicle-mediated transport and regulation of immune response; while the differential proteins related to inflammation were largely connected with the cellular response to chemokine, response to chemokine and chemokine-mediated signaling pathway. Additionally, in the neurology panel, KEGG enrichment analysis indicated that the differential proteins were significantly enriched in the phagosome, apoptosis and microRNAs in cancer pathway. And in the inflammation panel, the differential proteins were mainly enriched in the chemokine signaling pathway, viral protein interaction with cytokine and cytokine receptor and toll-like receptor signaling pathway.

Conclusions

These identified differential proteins reveal unique pathophysiological characteristics secondary to aSAH. Further characterization of these proteins and aberrant pathways in future research could enable their application as potential therapeutic targets and biomarkers for DCI after aSAH.

Introduction

Aneurysmal subarachnoid hemorrhage (aSAH) is a catastrophic cerebrovascular event and the third most common subtype of stroke, with a case-fatality rate that can reach 40–50% [1,2,3]. Despite substantial advances in treatment techniques for aneurysms and critical care management of patients with SAH, many survivors remain dependent on others for activities of daily living due to residual neurological deficits [1, 4, 5]. This also highlights the current lack of effective therapeutic strategies targeting the pathophysiological mechanisms associated with secondary neurological injury in aSAH. Hence, in-depth exploration of its molecular pathological mechanisms and homologous drug targets is a pressing need for aSAH patients.

Delayed cerebral ischemia (DCI) is a common and serious complication of aSAH. It has been recognized as a major predictor of poor prognosis in patients with SAH, with an incidence of 20–40%, most often occurring 4–10 days after bleeding, which, when it occurs, increases the mortality and disability rate, leading to serious consequences [1, 2, 4]. Currently, it is understood as a distinct, multifactorial process that evolves over time. The main theories reported regarding the underlying pathology of DCI included cerebral vasospasm, microcirculatory dysfunction, glymphatic impairment, inflammation, cortical spreading depolarization, etc [1, 6, 7]. It is well known that proteins are the direct and immediate executors of life activities and physiological functions, yet the potential precise protein changes that occur in response to DCI post-aSAH are elusive [8]. Given the complexity of brain injury after aSAH, and DCI in particular, there is a necessity to simultaneously analyze different types of protein biomarkers to describe them more comprehensively and accurately [8, 9]. Cerebrospinal fluid (CSF) is a clear fluid produced by the choroid plexus and brain parenchyma with the ability to maintain cerebral homeostasis, and abnormalities in its protein composition can be a direct response to the pathogenesis and severity of various neurological disorders (including SAH) [10,11,12]. Multi-targeted Olink proteomics based on Proximity Extension Assay (PEA) technology is an emerging method for protein marker studies, which requires only 1 µL CSF or other biological samples (e.g. Serum or plasma) to synchronously measure multiple protein levels in the same sample with high specificity and sensitivity [13, 14]. Utilization of patient CSF and plasma samples, Whelan, C. D et al. identified novel therapeutic targets and biomarkers associated with early Alzheimer’s disease pathology via Olink™ ProSeek immunoassay [15]. Besides, Zhao, T et al. recently discovered critical protein markers and aberrant pathways associated with immune response based on the serum Olink proteomics in ischemic stroke patients, revealing the molecular mechanisms underlying the immune response after ischemic stroke [16].

This exploratory study aimed to simultaneously measure the levels of 96 neurology- and 96 inflammation-related proteins (Olink Proteomics) in the CSF of patients 5–7 d post-aSAH and utilize bioinformatics analysis to comprehensively understand the pathology and the underlying alterations of proteins and related pathways, to provide molecular theoretical basis for the treatment of DCI and the prognosis of patients after aSAH.

Materials and methods

Ethics statement

All human procedures of the study were approved by the Research Ethics Committee of the Renmin Hospital of Wuhan University and performed in accordance with the principles of Good Clinical Practice and the Declaration of Helsinki (Approval No. WDRY2023-K048).

Clinical data and samples

Patients with aSAH were diagnosed by computed tomography (CT) combined with CT angiography (CTA) and digital subtraction angiography (DSA). Six aSAH patients and six control patients were selected. The sex distribution did not differ between the two groups but females were more common. The mean age of the patients in the aSAH and control groups was (63.3 ± 14.8) years and (61.0 ± 11.4) years, respectively. The history of smoking, hypertension, hyperlipidemia, and diabetes were not significantly distinct between the two groups. CSF specimens of aSAH patients were obtained by lumbar puncture within 5–7 days after bleeding. Analogously, CSF samples from the control group (e.g. patients with hydrocephalus undergoing CSF release tests or conducting diagnostic lumbar puncture for headache, etc.) were collected in the same way. Patients and control individuals were excluded if they had a history of central nervous system (CNS) disease (e.g., stroke, brain or spinal cord injury, CNS infection) or other organ or system dysfunctions within 6 months. Once collected, the specimens were immediately centrifuged at 1500 ×g for 5 min at 4 °C and then preserved at -80 °C until assayed. Clinical characteristics of aSAH patients and controls have been shown in Table 1.

Table 1 Patient demographics
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Olink proteomics based on proximity extension assay (PEA) technology

The CSF samples were then analyzed utilizing Olink Proteomics Target 96-Neurology and 96-Inflammation, multiplex assay panels provided by Shanghai Applied Protein Technology (China) where 92 neurology and 92 inflammation-related protein biomarkers are simultaneously measured using Proximity Extension Assay (PEA) technology. The analytical method can be found online at https://www.olink.com/. In brief, this method is based on a matched pair of antibodies linked to unique oligonucleotides that bind to the respective protein target, and DNA amplicon can be subsequently quantified by quantitative real-time PCR. In the incubation phase, the 92 antibody pairs, labeled with DNA oligonucleotides, bind to their respective protein in the samples, and this will take 16–22 h. In the extension and amplification phase, oligonucleotides that are brought into proximity hybridize and are extended employing a DNA polymerase. This newly created piece of DNA barcode is amplified by PCR. Finally, the amount of each DNA barcode is quantified by microfluidic qPCR. The microfluidic qPCR was quantified by Olink® Signature Q100 and the data was read out by Olink® NPX Signature software. Values are provided in the output unit Normalized Protein Expression (NPX) on the log2 scale. NPX values express relative quantification between samples but are not an absolute quantification. Proteins with a missing value greater than 50% within the group were excluded. The two-sample equal variance t-test was utilized. The criteria for defining significantly differential proteins were based on P < 0.05 and fold change (FC) > 1.5 or < 0.667. The final NPX results including all of the quantified proteins were shown in the Supplementary Excel Tables 1 and 2.

Bioinformatic analysis

Principal components analysis (PCA)

After normalized to NPX, the processed data were uploaded before importing into SIMCA-P (version 14.1, Umetrics, Umea, Sweden), where it was subjected to multivariate data analysis, including Pareto-scaled principal component analysis (PCA).

Cluster analysis

Cluster analysis was done with all the differentially expressed proteins. Cluster 3.0 (http://bonsai.hgc.jp/mdehoon/software/cluster/software.htm) and Java Treeview software (http://jtreeview.sourceforge.net) were used to perform hierarchical clustering analysis. Euclidean distance algorithm for similarity measure and average linkage clustering algorithm (clustering uses the centroids of the observations) for clustering were selected when performing hierarchical clustering.

GO annotation

Gene ontology (GO) terms were mapped and sequences were annotated employing the software program Blast2GO. The GO annotation results were plotted by R scripts.

KEGG annotation

Following annotation steps, the studied proteins were blasted against the online Kyoto Encyclopedia of Genes and Genomes (KEGG) database (http://geneontology.org/) to retrieve their KEGG orthology identifications and were subsequently mapped to pathways in KEGG.

Enrichment analysis

Enrichment analysis was applied based on the Fisher exact test, considering the whole quantified proteins as the background dataset. Benjamini- Hochberg correction for multiple testing was further applied to adjust derived P-values. And only functional categories and pathways with P-values under a threshold of 0.05 were considered as significant.

Results

Analysis of CSF samples and protein identification

The principal components analysis (PCA) was employed to evaluate the variability of CSF samples. The intra- and inter-group reproducibility of CSF samples from both the control and SAH groups in this experiment was relatively good, as exhibited in Fig. 1A (neurology panel) and 1B (inflammation panel). The bar charts displayed the number of proteins identified in different CSF samples in the diverse Olink-target panels (Fig. 1C and D).

Fig. 1
figure 1

Analysis of CSF samples and protein identification. Principal component analysis (PCA) showed the variability of CSF samples between and within groups in the neurology panel (A) and inflammation panel (B), respectively. Each point represents a sample, blue represents the control group, and green represents the SAH group. The closer the samples are, the more similar the expression patterns of the samples are; the farther the samples are, the more different the expression patterns of the samples are. The bar charts displayed the number of proteins identified in different CSF samples in different panels (C and D). Blue represents the control group and red represents the SAH group, n = 6

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Differential analysis of protein expression in different groups

In the neurology panel, if the fold change (FC) was set at > 1.5 and P < 0.05, there were 23 proteins including 18 up-regulated proteins and 5 down-regulated proteins (Fig. 2A, See Supplementary Excel 3). In the inflammation panel, under the same conditions, there were 31 up-regulated proteins and no down-regulated proteins (Fig. 2B, See Supplementary Excel 4). To show the significant difference in proteins among the comparison groups, the volcano plots of proteins in the neurology panel (Fig. 2C) and inflammation panel (Fig. 2D) were drawn according to the two factors of FC value and P value. The down-regulated proteins were marked in blue (FC < 0.67), the up-regulated proteins were marked in red (FC > 1.5), and the proteins with no difference were gray.

Additionally, to analyze and compare the protein expression differences of the samples in the comparison group, the proteins of the comparison group were grouped and classified by the hierarchical clustering algorithm, and displayed in the form of heat maps (Fig. 3A, neurology panel; Fig. 3B, inflammation panel).

Fig. 2
figure 2

The Differentially expressed proteins analyzed by Olink-target panels. Quantity distribution of differentially expressed proteins in neurology panel (A) and inflammation panel (B), respectively. Volcano plots of differentially expressed proteins between the control and SAH groups in the neurology panel (C) and inflammation panel (D), separately. up, upregulated proteins. down, down-regulated genes. unchange, no significant difference proteins

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Fig. 3
figure 3

Clustering analysis of differentially expressed proteins in CSF samples. Heatmap of differentially expressed proteins between control and SAH groups derived from olink-neurology assay (A) and olink-inflammation assay (B). The abscissa is the sample information, and the ordinate is the protein with significant differences

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GO function analysis of differentially expressed proteins in CSF samples

GO function annotations are divided into 3 main categories: Biological Process (BP), Molecular Function (MF) and Cellular Component (CC). In the neurology panel, the BP of differential proteins in the CSF of SAH patients was closely linked with cellular process, biological regulation, response to stimulus, regulation of biological process, localization, etc. The MF of the differentially expressed proteins was tightly related to binding, molecular transducer activity, catalytic activity, etc. The CC of the proteins mainly included cell part, cell, membrane, membrane part, extracellular region, etc. (Fig. 4A). Likewise, in the inflammation panel, the BP of differential molecules in CSF of SAH patients was mainly related to cellular process, biological regulation, response to stimulus, regulation of biological process, signaling, etc. The MF of the differentially expressed proteins was tightly related to binding, molecular function regulator, molecular transducer activity, catalytic activity, etc. The CC of the proteins mainly included cell, extracellular region, extracellular region part, cell part, membrane, etc. (Fig. 4B).

Fig. 4
figure 4

Gene Ontology (GO) function analysis of differentially expressed proteins in CSF samples. (A) GO annotated statistical map of differentially expressed proteins in neurology panel. (B) GO annotated statistical map of differentially expressed proteins in neurology panel. The vertical coordinate of the graph indicates the GO secondary function annotation information, including Biological Process, Molecular Function and Cellular Component, which are distinguished by blue, red and orange. The horizontal coordinate indicates the number of differentially expressed proteins under each functional category

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GO functional enrichment analysis of differentially expressed proteins in CSF samples

GO function enrichment was exhibited by bubble plots. The circles (namely the rich factor) in the figures showed the enrichment of differentially expressed proteins. The size of the circle represents the number of differential proteins, and the larger the circle, the greater the number. The color of the circle (from green to red) represents the P-value of Fisher’s exact test. The redder the color, the smaller the P-value, hinting that the higher the significance level of the relevant GO functional category.

In the neurology panel, GO function enrichment hinted that the BP of the differential proteins in the CSF of SAH patients was mainly involved in the regulation of defense response, vesicle-mediated transport, regulation of immune response, defense response, transport, etc. The MF of the differential proteins was tightly related to the cysteine-type endopeptidase activity, cysteine-type peptidase activity, axon guidance receptor activity, glycosylated region protein binding, transmembrane signaling receptor activity, etc. The CC of the differential proteins mainly included the cell periphery, cytoplasmic vesicle part, extracellular matrix, plasma membrane part, vesicle, etc. (Figure 5A, C and E). Similarly, in the inflammation panel, the BP of the differential proteins was mainly in the cellular response to chemokine, response to chemokine, chemokine-mediated signaling pathway, neutrophil migration, neutrophil chemotaxis, etc. The MF of the differential proteins was tightly related to the chemokine activity, chemokine receptor binding, G protein-coupled receptor binding, CXCR chemokine receptor binding, heparin binding, etc. The CC of the differential proteins mainly included the plasma membrane receptor complex, plasma membrane protein complex, membrane protein complex, integral component of plasma membrane, etc. (Fig 5B, D and F).

Fig. 5
figure 5

GO functional enrichment analysis of differentially expressed proteins in CSF samples. GO functional enrichment analysis of the differentially expressed proteins in neurology and inflammation panels presented by Bubble Charts. (A and B) biological process, (C and D) molecular function, (E and F) cellular component

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KEGG pathway analysis of differentially expressed proteins in CSF samples

KEGG pathway was shown by bar plot. Uniting the number and P-values of the differential proteins in each pathway, KEGG analysis revealed that in the neurology panel, the differential proteins were mainly enriched in the MAPK signaling pathway, ras signaling pathway, cytokine-cytokine receptor interaction, lysosome, phagosome, etc. (Fig. 6A). The metabolic pathway annotation corresponding to the differential protein KEGG pathway is shown in Fig. 6C. Analogously, in the inflammation panel, the KEGG pathway of differential proteins predominantly included cytokine-cytokine receptor interaction, viral protein interaction with cytokine and cytokine receptor, chemokine signaling pathway, NF-kappa B signaling pathway, Toll-like receptor signaling pathway, etc. (Fig. 6B). The metabolic pathway annotation pertinent to the differential protein KEGG pathway is shown in Fig. 6D.

Fig. 6
figure 6

Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of differentially expressed proteins in CSF samples. KEGG pathways analysis of the differentially expressed proteins in the CSF of SAH patients in the neurology panel (A) and inflammation panel (B) exhibited by bar plots. The ordinate is the name of the pathway involving differentially expressed proteins, and the abscissa represents the number of differentially expressed proteins involved in the pathway. Annotation and attribution of metabolic pathways corresponding to the KEGG pathway of differential proteins in CSF from patients with SAH (C, neurology panel; D, inflammation panel). The abscissa represents the number of pathway protein annotations, and the ordinate represents the KEGG annotation name. Different colors represent the metabolic pathway of KEGG

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KEGG enrichment analysis of differentially expressed proteins in CSF samples

KEGG pathway enrichment was shown by bar plot and bubble plot. In the neurology panel, our data indicated that pathways related to the phagosome, apoptosis, microRNAs in cancer, and lysosome were significantly enriched in the CSF of SAH patients (Fig. 7A). And in the inflammation panel, the differential proteins were mainly enriched in the chemokine signaling pathway, viral protein interaction with cytokine and cytokine receptor, toll-like receptor signaling pathway, and NF-kappa B signaling pathway (Fig. 7B). To better examine the significance of the differential pathways of the differential proteins, KEGG pathway and pathway enrichment analyses were performed on the differential proteins separately from up- and down-regulation. In the neurology panel, the up-regulated pathways chiefly related to phagosome, apoptosis, and microRNAs in cancer, while the down-regulated pathways primarily involved in alcoholism, neurotrophin signaling pathway, and calcium signaling pathway (Fig. 7C). And in the inflammation panel, the up-regulated pathway is mainly implicated in chemokine signaling pathway, toll-like receptor signaling pathway, and NF-kappa B signaling pathway, while there is no down-regulated pathway (Fig. 7D).

Fig. 7
figure 7

KEGG enrichment analysis of the differentially expressed proteins in CSF samples. KEGG pathways enrichment analysis of the differentially expressed proteins in the CSF of SAH patients in the neurology panel (A) and inflammation panel (B) exhibited by bubble diagrams. Up-and down-regulated differential protein pathways enrichment analysis in the CSF of SAH patients in the neurology panel (C) and inflammation panel (D) exhibited by butterfly maps

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Discussion

aSAH is a multifaceted and complex hemorrhagic cerebrovascular disease that carries high morbidity and mortality. Though we have been digging into this disorder for decades, treatment options remain limited. DCI occurs 4–10 days after hemorrhage and is the most predominant complication, severely affecting the patient’s prognosis. Recently, our understanding of the pathophysiology of DCI has undergone a paradigm shift away from vasospasm and toward a multifactorial process. Considering the complexity of the molecular pathological mechanism of DCI after aSAH, simultaneous analysis of different types of protein biomarkers is needed to describe it more precisely. This study is the first multi-targeted olink proteomic analysis of CSF from patients 5–7 d post-aSAH, and the results indicate the presence of multiple abnormal levels of proteins associated with neurology and inflammation in the CSF of aSAH patients, providing new evidence for the molecular pathological mechanisms and treatment of DCI after aSAH.

Based on cluster analysis and diverse functional enrichment analyses, we found that, compared with the control group, 18 of the differential proteins in the neurology panel were up-regulated in the CSF of the aSAH group, mainly related to phagosome, apoptosis, microRNAs in cancer pathway, etc., and 5 were down-regulated, primarily associated with neurotrophin signaling pathway and so on. Meantime, in the inflammation panel, all 31 differential proteins were up-regulated, predominantly involving the chemokine signaling pathway, viral protein interaction with cytokine and cytokine receptor, and toll-like receptor signaling pathway, etc. These differentially expressed proteins and pertinent pathways may be involved in diversified pathophysiological processes, which in turn mediate the development of DCI after aSAH. Next, we will elaborate on the specific context of the identified neuro- and inflammation-related proteins and their potential mechanisms in the pathological progression of DCI after aSAH, respectively.

Neurology-panel

Siglecs (sialic acid-binding immunoglobulin-type lectins) are a type I transmembrane receptor that typically binds to sialic acid. Siglecs are predominantly expressed in immune cells and generate activating or inhibitory signals [17, 18]. Recently, they have also been shown to be expressed on the surface of nervous system cells and play pivotal roles in neuroinflammation [19]. The human siglecs family consists of 15 members, which are basically divided into two groups: evolutionarily conserved siglecs and rapidly evolving siglecs [17]. Our results reveal remarkably elevated levels of Siglec-1 and Siglec-9 proteins in the CSF of patients with aSAH. Specifically, siglec-1 (sialoadherin or CD169) belongs to the conserved siglecs and is essentially a macrophage-restricted glycoprotein with a molecular weight of 200 kDa. Siglec-1 is the largest member of the siglecs family, with 16 C2-set domains, one V-set domain, a transmembrane domain, and a cytoplasmic tail [17, 20]. It has been reported that injury to the CNS, which destroys the blood-brain barrier, induces siglec-1 expression on a portion of macrophages and microglia within the parenchyma. The expression of siglec-1 matches the temporal and spatial distribution of the plasma extravasation into the brain parenchyma [21]. The latest study discovered that siglec-1 works synergistically with other macrophage receptors to promote phagocytosis [20]. Besides, CD169+ macrophages may control the inflammatory response through promoting interleukin-10 (IL-10) production [22]. However, the role and mechanism of Siglec-1 in aSAH have been rarely reported.

Siglec-9 is an evolutionary siglecs with only two C2-set domains, one V-set domain, one transmembrane domain, and a cytoplasmic tail. In humans, siglec-9 is functionally equivalent to siglec-E [17]. Of note, siglec-9 is uniquely expressed by human neutrophils and monocytes, as well as a minor population of natural killer cells [23]. The research found that siglec-E may be a crucial negative regulator of neutrophil recruitment and activation at the site of pneumonia [24, 25]. A plethora of studies have confirmed that neutrophils and the neutrophil extracellular traps (NETs) released by their activation are closely related to vascular injury and microthrombosis [26] and can mediate the occurrence of DCI after aSAH, while targeting neutrophils and NETs may ameliorate the DCI and the prognosis of patients with aSAH [27]. Therefore, we speculate that upregulating neutrophil siglec-9 may reverse the progression of DCI after aSAH by reducing the formation of NETs. Additionally, a recent study found that siglec-E is also expressed in microglia, and confirmed through in vivo and in vitro experiments that ablation of siglec-E can contribute to the activation of microglia and increase brain inflammation and ischemic injury [19], hinting that endogenously induced siglec-E exerts a key anti-inflammatory and neuroprotective role after ischemic stroke. Collectively, the elevated levels of siglec-1 and siglec-9 in CSF of aSAH patients may be the internal mechanism of cerebral inflammation regression and self-repair. Early activation of them may be an effective strategy to salvage the inflammatory microenvironment in the brain post-aSAH, prevent DCI, and improve the prognosis of patients.

Macrophage scavenger receptor 1 (MSR1), also known as CD204 or SR-A, is a homo-trimeric transmembrane glycoprotein consisting of six distinct domains [28]. It is mainly expressed in brain microglia and macrophages of other tissues and is responsible for inflammation regulation in various pathophysiological processes [29, 30]. Studies have confirmed that MSR1 has been shown to promote post-ischemic damage-associated molecular patterns (DAMPs, such as HMGB1 and Prxs) clearance, leading to the resolution of neuroinflammation and attenuation of ischemic brain injury in various animal models [31, 32]. Contrarily, in a spinal cord injury model, Kong et al. reported that macrophage MSR1 promotes phagocytosis of myelin debris and the formation of foamy macrophage, contributing to the pro-inflammatory polarization of macrophages and neuronal apoptosis, and exacerbating spinal cord injury [33]. The above contradictory results may be related to the distinct microenvironment and activation status of MSR1+ macrophages. Besides, in a rat model of subarachnoid hemorrhage, Tian et al. first covered that Msr1 expression was elevated in injured brain tissues, and knockdown of Msr1 could exacerbate neuroinflammation by promoting activation of the PI3K-Akt/NF-kb pathway and production of the inflammatory factors [34], dropping us a hint that Msr1 may conduce to the resolution of neuroinflammation after aSAH. Additionally, a notable increase of Msr1 level in the CSF of aSAH patients was discovered in the present study, whereas whether Msr1 reverses the progression of DCI after aSAH by promoting dissipation of neuroinflammation is unclear and deserves to be explored in depth.

Cathepsins are a group of proteases predominantly discovered in the endosomal-lysosomal system of mammalian tissue cells, and their main function is protein degradation in lysosomes [35]. There are 15 human cathepsins, including 11 cysteine proteases, 2 serine proteases and 2 aspartic proteases. Studies have confirmed that cathepsins not only support ongoing inflammatory and immune responses, and mediate neuropathological processes through their enzymatic activities, but are also involved in the maintenance of brain homeostasis [36]. Our results showed that the levels of cathepsin C (CTSC) and cathepsin S (CTSS) were both markedly elevated in the CSF of aSAH patients. Specifically, cathepsin C, also known as dipeptidyl peptidase I, is a cysteine exopeptidase that is expressed in immune and inflammatory cells (e.g. microglia, neutrophils, and cytotoxic T lymphocytes, etc.) [36, 37]. Plenty of in vivo and ex vivo experiments have confirmed that CTSC exacerbates neuroinflammation and mediates neurological injury as well as neurological dysfunction by contributing to the neurotoxic polarization of microglia [38,39,40], whereas the targeted inhibition of CTSC may be an effective strategy to control inflammation. However, the precise role of CTSC in aSAH has rarely been explored. Recent studies have found that CTSC regulates neutrophil infiltration and the formation of NETs [41], and NETs were reported to progressively increase over time, reaching a peak at day 7, in the CSF from patients with aSAH [42]. Taken together, we hypothesize that the production of CTSC in microglia and neutrophils may be responsible for the sustained inflammatory cycle accompanying pathogenesis in the brain. Cathepsin S, a member of cysteine cathepsins, is preferentially expressed in cells of mononuclear phagocytic origin, including macrophages, microglia and dendritic cells [36]. Recently, Xie et al. revealed by peripheral blood monocyte sequencing that monocytes with high expression of CTSS aggravated cerebral ischemia-reperfusion injury, whereas CTSS knockdown significantly ameliorated BBB disruption, vascular leakage, and reduced cerebral infarct area and neurological function scores [43]. Moreover, in a mouse model of traumatic brain injury (TBI), Xu et al. discovered that inhibition of CTSS markedly decreased the level of TBI-induced inflammatory factors in brain tissue and attenuated cerebral edema [44]. Of interest, although it has been reported that Cathepsin S is expressed in cerebral aneurysms and promotes the progression of cerebral aneurysms [45], its role and mechanism in secondary brain injury after aSAH are not clear. In summary, based on the aforementioned destructive role of CTSC and CTSS in multiple neurological disorders, we conjecture that these biomarkers are promising for the exploration of mechanisms of secondary pathological injury and therapeutic strategies post-aSAH and deserve further validation.

Inflammation panel

Interleukin-6 (IL-6) is a pleiotropic cytokine with roles in inflammation, immunity, neurovascular regeneration, and metabolism [46,47,48]. IL-6 is a small polypeptide (molecular weight of 19–28 kDa), comprised of four alpha helices and produced by lymphocytes, macrophages, including microglia, as well as fibroblasts, vascular endothelial cells, mast cells, and dendritic cells [49]. In most situations, it is considered to be a pro-inflammatory culprit affecting the pathogenesis of multiple CNS diseases [50, 51]. Current data show a more than 2,000-fold burst of increased IL-6 levels in CSF after SAH compared to controls. A growing body of evidence demonstrated that interleukin 6 in CSF is a biomarker for cerebral vasospasm, delayed cerebral ischemia (DCI) and related infarctions after aSAH [52,53,54], and designing effective anti-IL-6 strategies may be a new hope to salvage the above complications and ameliorate the prognosis of patients with aSAH.

Chemokines (or chemotactic cytokines) are a large family of small, secreted proteins (molecular weight of 8–10 kDa) that signal through cell surface G protein-coupled heptahelical chemokine receptors. Since their primary function is to mediate the migration of cells, especially leukocytes, they play central roles in all protective or destructive immune and inflammatory responses. The chemokine system comprises approximately 50 chemokine ligands and 20 chemokine receptors in humans. Based on the number and spacing of chemokine N-terminal cysteines, they are classified into four distinct subfamilies: CXCL, CCL, XCL, and CX3CL [55,56,57]. Several pieces of evidence revealed that chemokine signaling in the CNS exerts critical homeostatic and neuroprotective roles, and is expressed in neurons, glia and endothelial cells [57,58,59]. However, in some neuropathological situations such as ischemic stroke, chemokines also display significant neurotoxic or neurodestructive effects [60, 61]. Data from the current study indicate that chemokines (including CXCL1, CXCL6, CXCL8 (or IL-8), CXCL9, CXCL10, CXCL11, CCL2 (MCP-1), CCL3, CCL4, CCL8 (or MCP-2), CCL13 (or MCP-4), CCL19 and CCL23) are increased to varying degrees in the CSF of patients with aSAH compared to controls. Several of these chemokines have been reported to correlate with prognosis and other clinical parameters in patients with aSAH, while others have not been previously studied in the context of aSAH.

CXCL10, also known as interferon γ-induced protein 10 kDa (IP-10), stimulates the migration of monocytes and T cells to inflammatory tissues and exerts its biological effects by binding to CXCR3 [62]. In the present study, CXCL10 showed an intense activation, with a 37-fold increase in CSF in aSAH patients compared to controls. Consistent with our findings, Niwa et al. showed that IP-10 levels in the CSF of aSAH patients peaked on day 5, and that the dynamic alterations may be closely associated with the development of delayed ischemic neurological deficits after SAH [63]. Additionally, Spantler et al. recently revealed that serum IP-10 levels present marked associations with poor functional outcomes but not DCI after aSAH [64].

MCP-1(or CCL2), MCP-2 (CCL8), and MCP-4 (or CCL13) monocyte chemoattractant proteins [65], all of which were markedly elevated in the current study, hint that monocytes may play crucial roles in the intrathecal inflammatory response after aSAH. Mohme et al. once clarified that monocyte accumulation and activation in the CSF secondary to the early peak of CCL2 (day 3 after aSAH) correlated with DCI but also poor functional outcome [66]. Similar results were also reported by Niwa et al. [63]. and pointed to that elevated levels of IL-6 may induce the expression of MCP-1 in the CSF after SAH, followed by increases in the expression of IP-10 and MIG (namely CXCL-9, mainly responsible for T cells recruitment [66]). In conformity with our findings, higher serum levels of CCL2 were described to correlate with worse clinical outcomes in studies by Kim et al. in 2008 [67] and Ahn et al. in 2019 [68]. Notably, enhanced CSF levels of MCP-2 and MCP-4 on day 10 after aSAH also were connected with unfavorable outcomes (based on the Glasgow Outcome Scale) [12].

CXCL8 (or IL-8) and CXCL1 are principally responsible for chemotaxis of neutrophils [65, 66]. They all showed strong activation in CSF of aSAH patients in the present study. In line with our results, Mohme et al. reported that elevated concentrations of IL-8 and CXCL1 in the CSF were tightly relevant to the occurrence of DCI after aSAH [66]. Besides, Vlachogiannis et al. recently reported that enhanced CSF CXCL8 levels on day 10 after aSAH were associated with poor clinical outcomes at 1 year after SAH [12]. Although they also indicated persistently elevated levels of CXCL-1 in the CSF, it did not appear to be significantly correlated with the occurrence of DCI and clinical prognosis of patients with aSAH.

Furthermore, it is of interest that our data show that several tumor necrosis factor superfamily (TNFSF) members (known as potential drug targets in ischaemic vascular disease [69]) have elevated levels in CSF after aSAH, such as the ligands TNF beta (Tumor necrosis factor ligand superfamily member 1, TNFSF10) and TRAIL (Tumor necrosis factor ligand superfamily member 10, TNFSF10), receptors TNFRSF9 (Tumor necrosis factor receptor superfamily member 9), TNFRSF11B (Tumor necrosis factor receptor superfamily member 11B, also known as Osteoprotegerin, OPG) and CD40 (Tumor necrosis factor receptor superfamily member 5). Chen et al. once reported that higher plasma soluble CD40 ligand but not receptor levels correlate with clinical severity and may be a good prognostic biomarker for aSAH [70]. Moreover, a recent study also found that the above-mentioned members of the TNFSF have distinct expression patterns in the CSF after aSAH [14], but their relationship with the development of DCI and functional prognosis after aSAH has not yet been confirmed and deserves to be further explored in depth.

Limitations and conclusions

However, some limitations must be recognized in our study. Firstly, the sample size of this study was small, which can lead to the omission of several of the proteins that are key for the pathological process of aSAH. Secondly, we did not detect the dynamic trends of the aberrant proteins in the CSF of aSAH patients and analyze the relationship between the protein indicators and clinical parameters of the patients (e.g., DCI occurrence). Thirdly, the protein changes do not necessarily represent the development of DCI after aSAH, and the specific mechanisms of their role in secondary brain injury in patients with aSAH have not been thoroughly explored. In summary, our study, in the cerebrospinal fluid of aSAH patients, revealed plenty of abnormal proteins and related pathways pertaining to neurology and inflammation, which provide a theoretical basis for clarifying the potential pathomechanism for the occurrence of DCI after aSAH. Meanwhile, further large-sample validation and in-depth mechanism exploration of these molecules identified in this study may bring new breakthroughs in the diagnosis and treatment of DCI after aSAH.

Data availability

No datasets were generated or analysed during the current study.

Abbreviations

DCI:

Delayed cerebral ischemia

aSAH:

Aneurysmal subarachnoid hemorrhage

CSF:

Cerebrospinal fluid

PEA:

ZPROXIMITY extension assay

DEPs:

Differentially expressed proteins

FC:

Fold change

CT:

Computed tomography

CTA:

CT angiography

DSA:

Digital subtraction angiography

CNS:

Central nervous system

NPX:

Normalized protein expression

PCA:

principal components analysis

GO:

Gene ontology

KEGG:

Kyoto encyclopedia of genes and genomes

BP:

Biological process

MF:

Molecular function

CC:

Cellular component

NETs:

Neutrophil extracellular traps

MSR1:

Macrophage scavenger receptor 1

DAMPs:

Damage associated molecular patterns

CTSC:

Cathepsin C

CTSS:

Cathepsin S

TBI:

Traumatic brain injury

IL-6:

Interleukin-6

TNFSF:

Tumor necrosis factor superfamily

OPG:

Osteoprotegerin

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Funding

This work was supported by the Fundamental Research Funds for the Central Universities (Grant number: 2042023kf0007), the Hubei Provincial Key Research and. Development Program, Hubei Provincial Department of Science and Technology, China (Grant number: 2022BCE020), and the Youth Foundation of the National Natural Science Foundation of China (Grant number: 82301536).

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Author notes

  1. Rui Ding, Liquan Wu and Shanshan Wei contributed equally to this work.

Authors and Affiliations

  1. Department of Neurosurgery, Renmin Hospital of Wuhan University, 99 Zhang Zhidong Street, Wuhan, 430060, China

    Rui Ding, Liquan Wu, Haoran Lu, Xiaohong Qin, Xizhi Liu & Zhibiao Chen

  2. Department of Oncology, Wuchang Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, 430063, China

    Shanshan Wei

  3. Department of Neurosurgery, Hanchuan Renmin Hospital, Hanchuan, Hubei, 431600, China

    Yanhua Wang, Wen Liu, Huibing Li, Baochang Luo, Teng Xie & Zhibiao Chen

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  1. Rui Ding
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Contributions

Zhibiao Chen, Teng Xie and Rui Ding conceived and designed the article. Haoran Lu and Xiaohong Qin were mainly responsible for collecting samples. Shanshan Wei and Rui Ding were mainly responsible for analyzing the data. Rui Ding and Xizhi Liu wrote the manuscript and drew the figures. Yanhua Wang, Wen Liu, Huibing Li and Baochang Luo revised the manuscript. All authors reviewed and approved the manuscript.

Corresponding authors

Correspondence to Teng Xie or Zhibiao Chen.

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All human procedures of the study were approved by the Research Ethics Committee of the Renmin Hospital of Wuhan University and performed in accordance with the principles of Good Clinical Practice and the Declaration of Helsinki (Approval No. WDRY2023-K048).

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Ding, R., Wu, L., Wei, S. et al. Multi-targeted olink proteomics analyses of cerebrospinal fluid from patients with aneurysmal subarachnoid hemorrhage. Proteome Sci 22, 11 (2024). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12953-024-00236-x

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