Protein profiling of extracellular vesicles from the cerebrospinal fluid of patients with multiple sclerosis

Protein profiling of extracellular vesicles from the cerebrospinal fluid of patients with multiple sclerosis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Protein profiling of extracellular vesicles from the cerebrospinal fluid of patients with multiple sclerosis Naotoshi Iwahara, Satoshi Muraoka, Taro Saito, Masayo Hirano, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5954841/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Extracellular vesicles (EVs) are membrane-bound particles that are released into the extracellular space and are thought to play a role in the pathogenesis of neuroinflammation and neurodegeneration. Nevertheless, the precise role of these vesicles in the context of multiple sclerosis (MS) remains uncertain. The objective of this study was to identify the distinctive characteristics of EVs associated with MS Methods EVs were isolated from CSF using phosphatidylserine affinity methods. Mass spectrometry was used to analyze cerebrospinal fluid (CSF) samples and EVs isolated from those CSF samples collected from a discovery cohort of 10 patients with other neurological diseases (OND) and 10 patients with MS. In addition, mass spectrometry was used to analyze EVs isolated from CSF samples in a validation cohort of 24 patients with OND, 38 patients with MS, and 14 patients with neuromyelitis optica spectrum disorders (NMOSD). Resultes The results revealed notable increases in the levels of 33 proteins in the CSF samples and 100 proteins in the CSF-derived EVs from patients with MS in the validation cohort. Increases in the levels of ITGA4, ITGAX, MS4A1 (CD20), CD3E, CD4, and CD8A, which are marker proteins of lymphocytes and myeloid cells, including activated microglia and dendritic cells, were observed in the CSF-derived EVs in discovery cohort. The results of the validation cohort revealed that the levels of four proteins, ITGA4, ITGAX, MS4A1, and CD3E, were significantly greater in MS patients than in OND patients. Furthermore, the level of ITGAX was greater in the patients with confirmed disability worsening (CDW) than that of without CDW. The results of the receiver operating characteristic (ROC) and Kaplan‒Meier analyses indicated that ITGAX levels in CSF-derived EVs may prove useful in predicting disease prognosis. Conclusion Our findings suggest that CSF-derived EVs reflect immunologic changes in MS and other neuroimmune diseases. In addition, these results raise the possibility that changing in myeloid cells as well as lymphocytes may also play a role in the pathogenesis of MS. CSF-derived EVs may serve as indicators of MS disease severity and could be utilized as biomarkers in the future. multiple sclerosis neuromyelitis optica spectrum disorder extracellular vesicles microglia Figures Figure 1 Figure 2 Figure 3 Figure 4 Background Multiple sclerosis (MS) is a chronic disease of the CNS that disporportionally affects young women and is characterized by inflammatory demyelination and neurodegeneration [ 1 ]. Neuromyelitis optica spectrum disorder (NMOSD), another demyelinating disease of the CNS, is caused by autoantibodies against aquaporin 4 (AQP4) in astrocytes; however, the cause of MS has not yet been determined [ 1 ]. The pathogenesis of MS involves a combination of genetic and environmental factors, and its clinical presentation varies significantly among and within patients. Recently, progression independent of relapse activity (PIRA) has been reported to be strongly correlated with MS symptom progression and brain atrophy [ 2 ]. In secondary progressive MS (SPMS), the primary cause of disease worsening is PIRA, although a small amount of symptom worsening caused by occasional relapses is still observed [ 3 ]. However, the mechanisms underlying neurodegeneration and brain atrophy in SPMS patients with PIRA are not completely understood. Extracellular vesicles (EVs) are small cell-derived membranous vesicles that carry a wide variety of molecules, including lipids, nucleic acids, and proteins [ 4 ]. They are formed by the direct outward budding from the plasma membranes of prokaryotic and eukaryotic cells. In eukaryotic cells, EVs can initially form intraluminal vesicles of internal multivesicular bodies (MVBs) via the endocytic pathway and are then secreted upon the fusion of these compartments with the plasma membrane. To clarify the nomenclature, the use of the term "exosomes", specifically for MVB-derived EVs, has recently been recommended. On the other hand, plasma membrane-derived EVs are referred to by various names, such as microvesicles, microparticles, or ectosomes [ 4 ]. EV cargo can be dynamically altered under pathophysiological conditions and reflects a disease-specific signature. Therefore, EVs are promising sources of information for understanding the state of the brain in neurological disorders. We previously reported the possible involvement of microglial EVs in tau propagation in the context of Alzheimer's disease (AD) through proteomic analysis of EVs from model animals [ 5 , 6 ]. Targeting microglial EVs is a potential treatment option for AD. An ongoing clinical trial (NCT04121208, https://clinicaltrials.gov/study/NCT04121208 ) is investigating the efficacy of a colony-stimulating factor 1 receptor inhibitor that essentially eliminates microglia. Therefore, we hypothesized that analysis of EVs harvested from patients with MS would enable us to investigate the neurodegenerative factors involved in MS. Despite the considerable number of microarray analyses performed on EVs in MS, proteomic analyses are rare [ 7 , 8 ]. As EVs are believed to mirror the pathophysiology of neurodegenerative diseases, in this study, we conducted a proteomic analysis of EVs isolated from the CSF of MS patients to clarify disease mechanisms and discover new therapeutic targets. Materials and methods Patient population and inclusion criteria Twenty patients in the discovery cohort and 76 patients in the validation cohort who were subjected to cerebrospinal fluid (CSF) sampling at the Department of Neurology, Sapporo Medical University Hospital, between April 2010 and November 2023 were included in this study. In the discovery cohort, CSF samples were collected from 10 patients with MS and 10 with other neurological diseases (OND) via a standard protocol. In the validation cohort, CSF samples were collected from 38 patients with MS, 14 with NMOSD, and 24 with OND. All samples were collected, coded, and stored in the hospital at -80°C. Patients with OND were age- and sex-matched with patients with MS and NMOSD. MS was diagnosed according to the 2005 or 2010 McDonald criteria [ 9 , 10 ], and NMOSD was diagnosed according to the 2006 or 2015 Wingerchuk criteria [ 11 , 12 ]. All patients in the NMOSD group tested positive for anti-AQP4 antibodies. The OND group in the discovery cohort included patients with neuropathy with liability to pressure palsies (n = 1), mitochondrial diseases (n = 1), hereditary spastic paraplegia (n = 1), amyotrophic lateral sclerosis (n = 1), epilepsy (n = 1), Charcot-Marie-Tooth disease (n = 1), cervical spondylosis (n = 2), Bell's palsy (n = 1) and psychology spectrum disorders (n = 1). in the OND group in the validation cohort included patients with acute disseminated encephalomyelitis (n = 2), chronic progressive external ophthalmoplegia (n = 1), dystonia (n = 2), eosinophilic granulomatosis with polyangiitis (n = 1), epilepsy (n = 1), Fisher’s syndrome (n = 3), headache (n = 2), hereditary spastic paraplegia (n = 2), Hirayama disease (n = 1), hydrocephalus (n = 1), myelitis (n = 1), myoclonus (n = 1), Parkinson’s disease (n = 1), psychology spectrum disorder (n = 4), and systemic lupus erythematosus (n = 1). Confirmed disability worsening (CDW) was defined as an increase in the Expanded Disability Status Scale (EDSS) score ≥ 0.5 from a baseline of ≥ 6.0, ≥ 1.0 from a baseline of 1.0–5.5, or ≥ 1.5 from a baseline of 0.0. CDW was confirmed if there were multiple hospital visits at least 3 months apart and records of increased EDSS scores, and patients with less than 1 year of hospitalization were excluded [ 13 ]. Kaplan‒Meier curves were used to estimate the cumulative probability of CDW. Standard protocol approvals, registrations, and patient consent All study protocols were approved by the Clinical Research Ethics Commission of Sapporo Medical University Hospital (No. 322 − 237) and the National Institute of Biomedical Innovation, Health and Nutrition (No. 266-02). Informed consent was obtained by providing an opt-out form on the website ( https://web.sapmed.ac.jp/neurol/introduction/qi5fku000000007e.html ). Those who decided to opt-out were excluded from the study. Isolation of EVs from CSF EV preparation platform for clinical proteomics (EP3) were employed in this study [ 14 ]. EVs were isolated from CSF using the MagCapture Exosome Isolation Kit PS version 2 (Fujifilm WAKO Pure Chemical Corporation) with the KingFisher Flex System (Thermo Fisher Scientific) in a 96-well plate-based format [ 14 ]. Briefly, 1,000 µL of CSF was centrifuged at 1,200 × g for 20 min at 4°C. The supernatant was filtered through FastRemover for protein (0.45 µm) (GL Sciences) using a positive pressure Resolvex M10 system 96-XT (TECAN). EVs were isolated from filtered CSF and eluted with 100 µL of elution buffer for proteomics analysis. The isolated EV fraction was lysed with lysis buffer (12 mM sodium deoxycholate, 12 mM sodium lauroyl sarcosinate, and 50 mM ammonium bicarbonate) [ 15 ], and then the samples were vortexed for 5 min at room temperature, followed by spin down and boiling for 10 min at 60°C. The samples were reduced with 10 mM tris(2-carboxyethyl)phosphine (Fujifilm WAKO Pure Chemical Corporation) and alkylated with 20 mM iodoacetamide (Nacalai Tesque) for 60 min at 37°C in the dark. Automated SP3 technology was carried out following the program of KingFisher Flex. Then, 100% acetonitrile (ACN) was added to the reduced and alkylated sample tube, which was subsequently vortexed. The samples were bound to SP3 beads for 30 min with medium mixing. The SP3 beads were collected and put into 100% ACN. The mixing beads were placed in 70% ethanol twice, and then, after which trypsin (Thermo Fisher) and LysC (Fujifilm WAKO Pure Chemical Corporation) were added. After 16 h, the digested EV peptides were collected using a Flex system to remove the magnetic beads, and the protease activity was quenched by acidification with trifluoroacetic acid (TFA). All the digested peptides were loaded on EvoTips according to the manufacturer’s protocol for the discovery cohort CSF-derived EV samples. For the discovery cohort CSF samples and validation cohort CSF-derived EV, all tryptic peptides were desalted via the stop-and-go-extraction tip (StageTip) protocol [ 16 , 17 ], dried via vacuum centrifugation, and resuspended in 2% ACN and 1% TFA. Mass spectrometry The discovery cohort CSF-derived EVs were analyzed on the Evosep One system (Evosep) using an in-house packed 15 cm, 75 µm i.d. capillary column with 1.9 µm Reprosil-Pur C18-AQ beads (Dr. Maisch) using a preprogrammed gradient (20 samples per day). The column temperature was maintained at 60°C using an integrated column oven and an Inspion system and interfaced online with the Orbitrap Lumos mass spectrometer. Data were acquired using data-independent acquisition (DIA). The Orbitrap Fusion Lumos mass spectrometer was used for gas-phase fractionation (GPF)-DIA of a pooled sample for the library, and full mass spectra were acquired with the following parameters: a resolution of 120,000, an automatic gain control (AGC) target of 1 × 10 6 , and an injection time of 250 ms. The five GPF-DIA runs collectively covered 418–782 m/z (i.e., 418–494, 490–566, 562–638, 634–710, and 706–782 m/z ). MS2 spectra were collected with the following parameters: a 2- m/z isolation window at 50,000 resolution, an AGC target of 2 × 10 5 ions, a maximum injection time of 86 ms, and a normalized collision energy of 30. For the individual samples used for proteome profiling, full mass spectra were acquired in the range of 410–780 m/z with the following parameters: a resolution of 120000, an AGC target of 4 × 10 5 , and an injection time of 100 ms. MS2 spectra were collected with the following parameters: a 10- m/z isolation window at 30000 resolution, an AGC target of 2 × 10 5 ions, a maximum injection time of 54 ms, overlapping window patterns, and a normalized collision energy of 30. For discovery cohort CSF-derived EVs and validation cohort CSF-derived EVs, nano-LC–MS/MS analysis was conducted with an LTQ-Orbitrap Fusion Lumos mass spectrometer (Thermo Fisher Scientific) equipped with an UltiMate 3000 Nano LC system (Thermo Fisher Scientific) and an HTC-PAL autosampler (CTC Analytics). Peptides were separated on an analytical column, and separation was achieved using a 45-min gradient of 5–30% ACN in 0.1% formic acid at a flow rate of 280 nL/min. Data were acquired using DIA. Mass spectrometry data analysis Mass spectrometry data (raw files) were processed with DIA-NN software (Ver. 1.8.1) [ 18 ]. The database search included all entries from the Homo sapiens UniProt database (downloaded in April 2020, taxonomy ID: 9606) and contaminant database. The search parameters were as follows: up to two missed cleavage sites, 7–30 peptide lengths, carbamidomethylation of cysteine residues (+ 57.021 Dalton) as static modifications, protein names from FASTA for implicit protein grouping, robust LC (high precision) for the quantification strategy, and global for cross-run normalization. The precursor ions were adjusted to a 1% false discovery rate (FDR). The mass spectrometry proteomics data have been deposited into the ProteomeXchange Consortium via the jPOST repository with the dataset identifier PDX 047351 [ 19 ]. Missing values were imputed with a provided constant value using perseus (Ver. 1.6.14.0). Western blotting The EV samples were incubated with mammalian cell lysis buffer (Sigma‒Aldrich) supplemented with a protease inhibitor cocktail (Nacalai Tesque) for 4 h. A bicinchoninic acid assay was performed to determine the protein concentration in each sample using a BCA protein assay kit (TaKaRa Bio). Western blotting was performed as previously described [ 6 ]. We added 5 µg of protein to each well, and an anti-CD9 antibody (1:1,000 dilution; #98734; Cell Signaling Technology) was used. Transmission electron microscopy (TEM) TEM was performed at Hanaichi Ultra Structure Research Institute (Aichi, Japan). The sample droplets were placed on a carbon film grid for 10 s. After the grid was partially dried, a drop of staining solution and 2% uranyl acetate were added to the grid and allowed to stand for 10 s. After excess uranyl acetate was removed via filter paper, the grids were examined, and the fields were photographed using a JEOL JEM1400Flash electron microscope at 100 kV. Nanoparticle tracking analysis All samples were diluted in phosphate-buffered saline at a ratio of at least 1:10 to obtain particles within the target reading range for a NanoSight 300 machine (Malvern Panalytical Inc.), with 10–100 particles per frame. Using a manual injection system, four 60 s videos were taken for each sample at 21°C. The analysis of the particle counts was performed using Nanosight NTA 3.3 software (Malvern Panalytical, Inc.) with a detection threshold of 5. Statistical analysis Statistical analyses were conducted using GraphPad Prism 10.3.1 (GraphPad Software) and JMP Pro17 (JMP). Between-group comparisons were analyzed using one-way analysis of variance, followed by Tukey’s honest significant difference test for multiple comparisons. Student’s t test and the chi-square test were used for comparisons between two groups. Pearson correlation, Kaplan‒Meier survival, and receiver operating characteristic (ROC) curve analyses were also conducted using JMP Pro17. Gene Ontology (GO) analysis of the identified proteins were elucidated using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) Bioinformatics Resources version 6.8 ( https://david.ncifcrf.gov ). Results Proteomic analysis of the CSF samples and EVs derived from CSF The experimental workflow is shown in Fig. 1 a. Proteomics analysis of CSF and EVs derived from CSF (CSF-derived EVs) from 10 patients diagnosed with MS and 10 with OND in the discovery cohort was conducted via DIA mass spectrometry (Table 1, Supplementary table and 2). Figure 1 b-g show the characteristics of the CSF-derived EVs. Nanoparticle tracking analysis revealed the presence of both microvesicles and exosomes in the EV population. The sizes of the EVs ranged from 50 to 300 nm, with a peak at 108 nm (Fig. 1 b). In addition, we observed isolated EVs via TEM, which revealed a typical exosomal morphology (Fig. 1 c). Moreover, the CSF-derived EVs clearly expressed CD9, a marker of EVs (Fig. 1 d), and mass spectrometry also confirmed high EV purity (Supplementary table 3). Table 1. Characteristics of patients in the discovery cohort Variables OND (n = 10) MS (n = 10) P value Female, n (%) 4 (40) 4 (40) 1.0 Age at onset, median (IQR) 32 (28–35.5) Age at sampling, median (IQR) 33.5 (26–44) 36.5 (31.5–40.75) 0.8596 EDSS score at sampling, median (IQR) 2.0 (1.0–3.125) Abbreviations: EDSS: expanded disability status scale; MS: multiple sclerosis; OND: other neurological diseases. Notes: Group comparisons were performed via student’s t test. DIA label-free quantitative proteomics analysis of the CSF samples and CSF-derived EVs revealed 694 and 3981 proteins, respectively. A clear correlation was observed between the protein levels of CD9 and CD81, which are marker proteins on the membrane of EVs, and PDCD6IP (ALIX) and TSG101, which are marker proteins within EVs (Supplementary Figure 1). Among these proteins, 584 were present in both the CSF samples and CSF-derived EVs (Figure 1e). A plot of the relative intensity of each of the 584 common proteins in CSF-EVs and CSF demonstrated that the proteins in the Exo top 100 [20] were expressed at higher levels in the CSF-EVs than in the CSF samples (Figure 1f). Moreover, the "immunoglobulin complex" cellular component term was found to be significantly enriched in the identified proteins in the CSF samples exclusively (CSF Ex), as determined by GO enrichment analysis via DAVID (Figure 1g) [21]. In contrast, the "extracellular exosome" cellular component term was found to be significantly enriched in the commonly identified proteins in the CSF samples and CSF-EVs (Co) and the identified proteins in CSF-EVs exclusively (CSF-EV Ex) (Figure 1g). In addition, the “cytosol”, “plasma membrane” and “membrane” terms were found to be significantly enriched in the proteins in CSF-EV Ex group [22]. Differential protein expression profiles between the OND and MS groups Figure 2a and b show volcano plots of the CSF and CSF-derived EV proteins, respectively, between the MS and OND groups. The CSF results revealed that 33 proteins were significantly upregulated ( p 1.5) in the MS group compared with the OND group (Figure 2a, c and Supplementary table 4), 25 of which were immunoglobulins (Igs). In contrast, two proteins were significantly downregulated ( p < 0.05, fold change < 0.67). The CSF-EV results demonstrated that 100 proteins were significantly upregulated in the MS group compared with the OND group (Figure 2b, c and Supplemental table 5), five of which were Igs. In contrast, 16 proteins were significantly downregulated. As most of the upregulated proteins in the CSF were Igs, the "immunoglobulin production" biological process term was found to be significantly enriched, as determined by GO enrichment analysis via DAVID (Figure 2d). Conversely, the upregulated proteins in CSF-EVs included surface markers for immune cells (T and B cells), and the "adaptive immune response" term was significantly enriched (Figure 2d). This study focused on surface marker proteins whose expression was significantly greater in the MS group than in the OND group. Among these markers, the lymphocyte marker ITGA4; the antigen-presenting cell (APC) and disease-associated microglia (DAM) marker ITGAX; the B-cell marker MS4A1 (CD20); and the T-cell markers CD3E, CD4, and CD8A were selected for further investigation (Figure 2e). Differential ITGA4, ITGAX, MS4A1, CD3E, CD4 and CD8 levels in CSF-derived EVs in patients with various inflammatory demyelinating diseases In the validation cohort, CSF samples were collected from 38 MS patients, 14 NMOSD patients, and 24 OND patients (Table 2 and Supplementary table 6). The CSF-derived EVs in the validation cohort were subjected to DIA label-free quantitative proteomic analysis, which identified 4,055 proteins. Among these proteins, 60 were significantly upregulated in the MS group compared with the OND group (Figure 3a, c and Supplementary table 7). Additionally, 147 proteins were identified as significantly upregulated in the NMOSD group compared with the OND group (Figure 3b, c and Supplementary table 8). The ITGAX levels were significantly increased in the MS compared to the OND group ( p = 0.008), but the fold change was only 1.47. A comparison of the levels of the six surface marker proteins in the three groups revealed that ITGA4, ITGAX, MS4A1 and CD3E levels were significantly greater in the MS group than in the OND group, whereas CD4 and CD8 levels were not significantly different (Figure 3d). The levels of these proteins were not significantly different between MS patients and NMOSD patients, with the exception of ITGA4 (Figure 3d). Table 2. Characteristics of patients in the validation cohort Characteristics OND, n (%) (n = 24) MS, n (%) (n = 38) NMOSD, n (%) (n = 14) P value OND vs MS OND vs NMOSD MS vs NMOSD Female 20 (83.3) 33 (86.8) 12 (85.7) 0.7043 0.8452 0.9163 Age at onset, median (IQR) 33.5 (24.75–48.5) 48 (43.75–60.5) < 0.001 Age at sampling, median (IQR) 46.5 (33.5–56.25) 38.5 (31–51.25) 55 (49.25–63.5) 0.0972 0.104 0.0013 EDSS score at sampling, median (IQR) 1.5 (1.0–3.0) 3.0 (1.75–5.75) 0.0027 EDSS score at last visit, median (IQR) 2 (1.125–4.0) 5.25 (2.625–6.0) 0.0227 Long cord lesion, n 37 8 3, n (%) 0 (0) 6 (75) Bilateral optic neuritis 2 (5.2) 6 (42.9) 0.0017 CSF protein, mean ± SD 39.8 ± 16.8 56.4 ± 29.2 0.0131 CSF cell count, mean ± SD 8.9 ± 11.0 12.9 ± 12.2 0.256 IgG index, mean ± SD 1.11 ± 0.75 0.62 ± 0.19 0.00169 MBP 7 (20) 7 (50) 0.0406 OCB 27 (77.1) 2 (15.4) < 0.001 Abbreviations: CSF: cerebrospinal fluid; EDSS: Expanded Disability Status Scale; MBP: myelin basic protein; MS: multiple sclerosis; NMOSD: neuromyelitis optica spectrum disorder; OCB: oligoclonal band; OND: other neurological diseases. Notes: Group comparisons were performed using Tukey’s honest significant difference test. P values written in bold are statistically significant. [insert Table 2 near here] Exploring predictors of prognosis in MS The objective of the subsequent phase of the study was to ascertain whether there was a correlation between the relative amount of these surface markers and the subsequent progression of the patient's symptoms. Among the 38 patients diagnosed with MS in the validation cohort, 30 were followed for over one year as outpatients at our hospital. The 30 patients were divided into two groups on the basis of the presence or absence of CDW after sample collection. Table 3 presents a summary of the demographic characteristics, clinical findings, and biomarker comparisons between the two groups. Among these, 19 (63.3%) were assigned to the Stable group, whereas 11 (36.7%) were classified into the CDW group. Compared with the Stable group, the CDW group had significantly higher EDSS scores at the time of sample collection and at the end of the study period. In addition, the CDW group tended to have a longer duration from the onset of illness to sample retrieval and more outpatient visits. A comparative analysis of the expression levels of six surface marker proteins between the CDW and Stable groups revealed that ITGAX was notably elevated in the CDW group relative to the Stable group. However, no significant differences were observed in the remaining five proteins (Figure 4a). Table 3. Comparison of MS patients with or without CDW Variables Stable, n (%) (n = 19) CDW, n (%) (n = 11) P-value Female 16 (84.21) 9 (81.81) 0.8654 Age at onset, median (IQR) 31 (23–48) 29 (21.5–43) 0.6589 Age at sampling, median (IQR) 35 (29.5–50.5) 37 (29.5–52.5) 0.7845 Duration between onset and sampling (months), median (IQR) 36 (14.5–86.5) 60 (19–120) 0.1267 EDSS score at sampling, median (IQR) 1.5 (1.0–2.0) 2.5 (2.0–3.5) 0.0193 EDSS score at last visit, median (IQR) 1.5 (1.375–2.375) 4.25 (2.625–6.0) 0.0103 Duration between sampling and last visit (months), median (IQR) 48 (20.5–103.5) 91 (71.5–120) 0.2315 Plaques in spine 15 (78.94) 11 (100) 0.1021 CSF protein, mean ± SD 37.26 ± 17.47 44.45 ± 17.88 0.3070 CSF cell count, mean ± SD 7.42 ± 9.06 14.18 ± 15.117 0.1510 IgG index, mean ± SD 1.11 ± 0.86 0.99 ± 0.26 0.7012 MBP 3 (16.67) 3 (27.27) 0.5089 OCB 12 (66.67) 9 (90) 0.1295 Abbreviations: CSF: cerebrospinal fluid; EDSS: expanded disability status scale; MBP: myelin basic protein; MS: multiple sclerosis; NMOSD: neuromyelitis optica spectrum disorder; OCB: oligoclonal band; OND: other neurological diseases. Notes: Group comparisons were performed via student’s t test. P values written in bold are statistically significant. P2RY12 and P2RX7, which are marker molecules for resting and active microglia, respectively, have been identified as potential positron emission tomography (PET) imaging targets for MS [23]. The correlation between the amount of ITGAX in CSF-derived EVs and the amount of P2RY12 was found to be weak (Supplementary Figure 2a). Nevertheless, a moderate correlation was identified between the amount of ITGAX and P2RX7, which are also involved in EV secretion (Supplementary Figure 2b) [24]. Although not statistically significant, the expression level of P2RX7 in CSF-derived EVs tended to be greater in the CDW group than in the Stable group ( p = 0.057; Supplementary Figure 2d). ROC curve analysis was conducted to further elucidate the potential diagnostic value of ITGAX. The analysis yielded an AUC of 0.8373 (95% CI 0.6968–0.9778, p = 0.0024) and a Youden index of 1.167 when the OND was 1. The Youden index of the ROC curve of ITGAX relative intensity was subsequently used for further Kaplan‒Meier analysis. Patients with ITGAX value ≥ 1.167 (ITGAX High) presented an elevated risk of CDW in time-to-event analysis (long-rank test, p = 0.0054; Figure 3c). Discussion This study aimed to highlight the characteristics of EVs that are specific to MS. Given that EVs contain cell-derived nucleic acids and proteins, they are being investigated as potential biomarkers for a range of diseases. In particular, microRNAs have attracted attention because of their ability to regulate gene expression in recipient cells [4]. Conversely, the proteins present within EVs are challenging to analyze, with the number of identified proteins in EVs in MS being as low as 600 in reports of EV proteome analysis [7, 8]. Previously, we reported highly accurate and advanced proteome analysis results for EVs isolated from patients with AD and chronic traumatic encephalopathy [25, 26]. In the present study, we were able to identify approximately 4,000 proteins from CSF-derived EVs (Figure 1e). The identification of a greater number of proteins than previously reported can be attributed to the use of phosphatidylserine affinity capture, which enabled the isolation of pure and stable EVs. This method removes high-concentration proteins such as albumin and immunoglobulins, allowing proteins present in low concentrations in a sample to be identified [27]. The use of DIA as an analysis method may aid the identification of proteins in CSF samples, which are believed to be derived from immune cells due to their large role in immunological diseases. We posit that the isolation of EVs is a valuable approach not only for the functional analysis of EVs but also for the identification of numerous proteins involved in various diseases. In addition to being enriched with EV-specific proteins, EVs also contain cytoplasm and numerous plasma membrane proteins (Figure 1g). This finding indicates that EVs are derived from intracellular proteins and membranes [22]. Consequently, we believe that the analysis of EVs is a useful method for identifying changes in cells that secrete EVs and for analyzing other neurological diseases in the future. In the present study, a difference in the relative intensities of immune cell surface antigens were observed between MS and OND patients (Figure 2), and we focused our analysis on the surface antigen components of lymphocyte markers, including CD20 (MS4A), CD3 (CD3E), CD4, and CD8 (CD8A), as well as ITGA4 and ITGAX (Figure 2e). The levels of ITGA4, ITGAX, MS4A, and CD3E were significantly greater in the MS group than in the OND group in the validation cohort (Figure 3d). ITGA4 is a target protein of natalizumab [28], and ITGAX is a marker molecule for immune cells of the myeloid lineage, including dendritic cells and DAM [6]. In our present study, it was not possible to determine whether these changes reflected changes in immune cells in the CSF or in the brain parenchyma. The available evidence suggests that CSF lymphocytes are similar to those found in the brain parenchyma [29-31]. These findings reflect the substantial presence of lymphocytes and dendritic cells within the brain parenchyma and CSF, as well as a type of microglia associated with neurodegenerative diseases [32]. Ultimately, our objective was to ascertain whether there was a discernible discrepancy in the levels of these marker proteins when the MS patients were stratified into two groups: the symptom progression (with CDW) group and the stable groups. No difference was observed in the level of lymphocyte marker proteins in the CSF-EVs between patients with and without CDW. However, the level of ITGAX was greater in the group with CDW (Figure 4a). A similar trend was observed for P2RX7, a marker of active microglia and a target for microglial PET imaging [33]. It has recently been reported that paramagnetic rim lesions, as identified on MR images, slowly expand, and their size is correlated with disease progression [34, 35]. In addition, it has been reported that active microglia that contain ferritin are present at their paramagnetic rim [36]. Elevated ITGAX levels may be implicated in these imaging and pathological findings. However, ITGAX has been demonstrated to be expressed not only in microglia but also in antigen-presenting cells, such as dendritic cells [37, 38], and age-associated B cells [39]. The present analysis did not permit the identification of the cellular origin of each of these proteins in EVs, which is a topic for future investigation. Moreover, BTK (Bruton tyrosine kinase) inhibitors, which were recently developed for the treatment of MS, have been shown to inhibit the survival and proliferation of microglia, dendritic cells and macrophages, in addition to B cells. However, evobrutinib, a BTK inhibitor, did not demonstrate superiority over teriflunomide in preventing relapses in relapsing-remitting MS [40], although it has been reported to slow lesion expansion [41]. The measurement of ITGAX levels in CSF-EVs and PET imaging of P2RX7 may be useful in patient selection for BTK inhibitor treatment. A limitation of this study is that we were unable to investigate the effects of disease-modifying drugs (DMDs). In Japan, the first DMD, IFN-β1b, was introduced in 2000, and numerous DMDs emerged over the subsequent two decades. Consequently, the DMDs used to treat patients over the course of their illness frequently change, which presents a significant challenge in analyzing the impact of DMDs. As the CSF samples utilized in this study were obtained from patients at the onset of the initial attack or relapse, they predominantly reflected alterations in active lesions, potentially obscuring those associated with chronic inactive lesions. Further studies are needed to evaluate pathological changes over time, including during periods of remission, in the same patient. Additionally, whether the outcomes of patients with varying levels of ITGAX differ was not able to be evaluated in a separate cohort in this study. Larger prospective studies are necessary to confirm that ITGAX in CSF-derived EVs is indeed a reliable predictor of progression. Abbreviations MS: multiple sclerosis NMOSD: neuromyelitis optica spectrum disorder AQP4: Aquaporin-4 PIRA: progression independent of relapse activity SPMS: secondary progressive ms EV: extracellular vesicle MVB: multivesicular body AD: Alzheimer’s disease OND: other neurological diseases CDW: confirmed disease worsening EDSS: Expanded Disability Status Scale DIA: data-independent acquisition CAN: acetonitrile TFA: trifluoroacetic acid AGC: automatic gain control GPF: gas-phase fractionation FDR: false discovery rate TEM: transmission electron microscopy GO: Gene Ontology DAVID: Database for Annotation, Visualization, and Integrated Discovery MBP: myelin basic protein OCB: oligoclonal band PET: positron emission tomography DMDs: disease-modifying drugs Declarations Ethics approval and consent to participate All study protocols were approved by the Clinical Research Ethics Commission of Sapporo Medical University Hospital (No. 322-237) and the National Institute of Biomedical Innovation, Health and Nutrition (No. 266-02). Informed consent was obtained by providing an opt-out form on the website (https://web.sapmed.ac.jp/neurol/introduction/qi5fku000000007e.html). Those who decided to opt-out were excluded from the study. Consent for publication All the authors have contributed to, reviewed and approved the final manuscript for publication. Availability of data and materials The mass spectrometry proteomics data were deposited in the ProteomeXchange Consortium via the jPOST repository with the dataset identifier PXD047351. Competing interests The authors declare that they have no competing interests. Funding This study was supported in part by research grants from the Japanese Society for the Promotion of Science KAKENHI (grant numbers 23K06967 and 23K14740), the Takeda Science Foundation and the Japan Multiple Sclerosis Society. Authors’ contributions Conceptualization: N.I., S.M., J.A. and S.H. Data curation: N.I. and S.M. Formal analysis: N.I., S.M. and M.H. Funding acquisition: N.I. and T.S. Investigation: N.I., S.M., T.S., K.Y., M.T., R.O. and T.N. Methodology: N.I., S.M. and J.A. Writing – original draft: N.I. Writing – review & editing: N.I., S.M., S.S., J.A. and S.H. Acknowledgements Not applicable Authors' information Not applicable References Stadelmann C, Wegner C, Brück W. Inflammation, demyelination, and degeneration - recent insights from MS pathology. Biochim Biophys Acta. 2011;1812:275-82. Fambiatos A, Jokubaitis V, Horakova D, Havrdova EK, Trojano M, Prat A, et al. Risk of secondary progressive multiple sclerosis: a longitudinal study. Mult Scler. 2020;26:79-90. Lublin FD, Häring DA, Ganjgahi H, Ocampo A, Hatami F, Čuklina J, et al. How patients with multiple sclerosis acquire disability. Brain. 2022;145:3147-61. You Y, Ikezu T. Emerging roles of extracellular vesicles in neurodegenerative disorders. Neurobiol Dis. 2019;130:104512. Clayton K, Delpech JC, Herron S, Iwahara N, Ericsson M, Saito T, et al. Correction to: plaque associated microglia hyper-secrete extracellular vesicles and accelerate tau propagation in a humanized APP mouse model. Mol Neurodegener. 2021;16:24. Muraoka S, Jedrychowski MP, Iwahara N, Abdullah M, Onos KD, Keezer KJ, et al. Enrichment of neurodegenerative microglia signature in brain-derived extracellular vesicles isolated from Alzheimer's disease mouse models. J Proteome Res. 2021;20:1733-43. Lee J, McKinney KQ, Pavlopoulos AJ, Han MH, Kim SH, Kim HJ, et al. Exosomal proteome analysis of cerebrospinal fluid detects biosignatures of neuromyelitis optica and multiple sclerosis. Clin Chim Acta. 2016;462:118-26. Welton JL, Loveless S, Stone T, Von Ruhland C, Robertson NP, Clayton A. Cerebrospinal fluid extracellular vesicle enrichment for protein biomarker discovery in neurological disease; multiple sclerosis. J Extracell Vesicles. 2017;6:1369805. Polman CH, Reingold SC, Edan G, Filippi M, Hartung HP, Kappos L, et al. Diagnostic criteria for multiple sclerosis: 2005 revisions to the "McDonald criteria". Ann Neurol. 2005;58:840-6. Polman CH, Reingold SC, Banwell B, Clanet M, Cohen JA, Filippi M, et al. Diagnostic criteria for multiple sclerosis: 2010 revisions to the McDonald criteria. Ann Neurol. 2011;69:292-302. Wingerchuk DM, Lennon VA, Pittock SJ, Lucchinetti CF, Weinshenker BG. Revised diagnostic criteria for neuromyelitis optica. Neurology. 2006;66:1485-9. Wingerchuk DM, Banwell B, Bennett JL, Cabre P, Carroll W, Chitnis T, et al. International consensus diagnostic criteria for neuromyelitis optica spectrum disorders. Neurology. 2015;85:177-89. Kappos L, Butzkueven H, Wiendl H, Spelman T, Pellegrini F, Chen Y, et al. Greater sensitivity to multiple sclerosis disability worsening and progression events using a roving versus a fixed reference value in a prospective cohort study. Mult Scler. 2018;24:963-73. Muraoka S, Hirano M, Isoyama J, Ishida M, Tomonaga T, Adachi J. Automated proteomics sample preparation of phosphatidylserine-positive extracellular vesicles from human body fluids. ACS Omega. 2022;7:41472-9. Masuda T, Tomita M, Ishihama Y. Phase transfer surfactant-aided trypsin digestion for membrane proteome analysis. J Proteome Res. 2008;7:731-40. Rappsilber J, Mann M, Ishihama Y. Protocol for micro-purification, enrichment, pre-fractionation and storage of peptides for proteomics using stagetips. Nat Protoc. 2007;2:1896-906. Adachi J, Hashiguchi K, Nagano M, Sato M, Sato A, Fukamizu K, et al. Improved proteome and phosphoproteome analysis on a cation exchanger by a combined acid and salt gradient. Anal Chem. 2016;88:7899-903. Demichev V, Messner CB, Vernardis SI, Lilley KS, Ralser M. DIA-NN: neural networks and interference correction enable deep proteome coverage in high throughput. Nat Methods. 2020;17:41-4. Okuda S, Watanabe Y, Moriya Y, Kawano S, Yamamoto T, Matsumoto M, et al. jPOSTrepo: an international standard data repository for proteomes. Nucleic Acids Res. 2017;45:D1107-11. Keerthikumar S, Chisanga D, Ariyaratne D, Al Saffar H, Anand S, Zhao K, et al. ExoCarta: a web-based compendium of exosomal cargo. J Mol Biol. 2016;428:688-92. Delker DA, Geter DR, Roop BC, Ward WO, Ahlborn GJ, Allen JW, et al. Oncogene expression profiles in K6/ODC mouse skin and papillomas following a chronic exposure to monomethylarsonous acid. J Biochem Mol Toxicol. 2009;23:406-18. Tian W, Shi D, Zhang Y, Wang H, Tang H, Han Z, et al. Deep proteomic analysis of obstetric antiphospholipid syndrome by DIA-MS of extracellular vesicle enriched fractions. Commun Biol. 2024;7:99. Beaino W, Janssen B, Kooij G, Van Der Pol SMA, Van Het Hof B, Van Horssen J, et al. Purinergic receptors P2Y12R and P2X7R: potential targets for PET imaging of microglia phenotypes in multiple sclerosis. J Neuroinflammation. 2017;14:259. Ruan Z, Delpech JC, Kalavai SV, Van Enoo AA, Hu J, Ikezu S, et al. P2RX7 inhibitor suppresses exosome secretion and disease phenotype in P301S tau transgenic mice. Mol Neurodegener. 2020;15:47. Muraoka S, Jedrychowski MP, Yanamandra K, Ikezu S, Gygi SP, Ikezu T. Proteomic profiling of extracellular vesicles derived from cerebrospinal fluid of Alzheimer's disease patients: a pilot study. Cells. 2020;9:1959. Muraoka S, Jedrychowski MP, Tatebe H, DeLeo AM, Ikezu S, Tokuda T, et al. Proteomic profiling of extracellular vesicles isolated from cerebrospinal fluid of former national football league players at risk for chronic traumatic encephalopathy. Front Neurosci. 2019;13:1059. Lozano IMD, Sork H, Stone VM, Eldh M, Cao X, Pernemalm M, et al. Proteome profiling of whole plasma and plasma-derived extracellular vesicles facilitates the detection of tissue biomarkers in the non-obese diabetic mouse. Front Endocrinol (Lausanne). 2022;13:971313. Polman CH, O'Connor PW, Havrdova E, Hutchinson M, Kappos L, Miller DH, et al. A randomized, placebo-controlled trial of natalizumab for relapsing multiple sclerosis. N Engl J Med. 2006;354:899-910. Planas R, Metz I, Ortiz Y, Vilarrasa N, Jelčić I, Salinas-Riester G, et al. Central role of Th2/Tc2 lymphocytes in pattern II multiple sclerosis lesions. Ann Clin Transl Neurol. 2015;2:875-93. Obermeier B, Lovato L, Mentele R, Brück W, Forne I, Imhof A, et al. Related B cell clones that populate the CSF and CNS of patients with multiple sclerosis produce CSF immunoglobulin. J Neuroimmunol. 2011;233:245-8. Salou M, Garcia A, Michel L, Gainche-Salmon A, Loussouarn D, Nicol B, et al. Expanded CD8 T-cell sharing between periphery and CNS in multiple sclerosis. Ann Clin Transl Neurol. 2015;2:609-22. Krasemann S, Madore C, Cialic R, Baufeld C, Calcagno N, El Fatimy R, et al. The TREM2-APOE pathway drives the transcriptional phenotype of dysfunctional microglia in neurodegenerative diseases. Immunity. 2017;47:566-81.e9. Janssen B, Vugts DJ, Windhorst AD, Mach RH. PET imaging of microglial activation-beyond targeting TSPO. Molecules. 2018;23:607. Reeves JA, Bartnik A, Jakimovski D, Mohebbi M, Bergsland N, Salman F, et al. Associations between paramagnetic rim lesion evolution and clinical and radiologic disease progression in persons with multiple sclerosis. Neurology. 2024;103:e210004. Reeves JA, Mohebbi M, Wicks T, Salman F, Bartnik A, Jakimovski D, et al. Paramagnetic rim lesions predict greater long-term relapse rates and clinical progression over 10 years. Mult Scler. 2024;30:535-45. Absinta M, Maric D, Gharagozloo M, Garton T, Smith MD, Jin J, et al. A lymphocyte-microglia-astrocyte axis in chronic active multiple sclerosis. Nature. 2021;597:709-14. Hou L, Sin YC, Chen Y, Yuki K. Integrin CD11c regulates B cell homeostasis. Front Immunol. 2024;15:1359608. Immig K, Gericke M, Menzel F, Merz F, Krueger M, Schiefenhövel F, et al. CD11c-positive cells from brain, spleen, lung, and liver exhibit site-specific immune phenotypes and plastically adapt to new environments. Glia. 2015;63:611-25. El Mahdaoui S, Hansen MM, Von Essen MR, Hvalkof VH, Hansen RH, Mahler MR, et al. CD11c + B cells in relapsing-remitting multiple sclerosis and effects of anti-CD20 therapy. Ann Clin Transl Neurol. 2024;11:926-37. Montalban X, Vermersch P, Arnold DL, Bar-Or A, Cree BAC, Cross AH, et al. Safety and efficacy of evobrutinib in relapsing multiple sclerosis (evolutionRMS1 and evolutionRMS2): two multicentre, randomised, double-blind, active-controlled, phase 3 trials. Lancet Neurol. 2024;23:1119-32. Arnold DL, Elliott C, Martin EC, Hyvert Y, Tomic D, Montalban X. Effect of evobrutinib on slowly expanding lesion volume in relapsing multiple sclerosis: a post hoc analysis of a phase 2 trial. Neurology. 2024;102:e208058. Additional Declarations No competing interests reported. Supplementary Files SupplementaryTableVer.2.0.xlsx Additionalinformation.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5954841","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":411066317,"identity":"b44d009f-fcda-4579-899c-60f49354fd5e","order_by":0,"name":"Naotoshi Iwahara","email":"","orcid":"","institution":"Sapporo Medical University","correspondingAuthor":false,"prefix":"","firstName":"Naotoshi","middleName":"","lastName":"Iwahara","suffix":""},{"id":411066319,"identity":"2c48394f-0039-4a74-8296-0f3b9c60fbc1","order_by":1,"name":"Satoshi Muraoka","email":"","orcid":"","institution":"Center for Drug Design Research, National Institute of Biomedical Innovation, Health and Nutrition","correspondingAuthor":false,"prefix":"","firstName":"Satoshi","middleName":"","lastName":"Muraoka","suffix":""},{"id":411066321,"identity":"f4fc2b06-e4e6-46ee-9152-6deb709b282c","order_by":2,"name":"Taro Saito","email":"","orcid":"","institution":"Sapporo Medical University","correspondingAuthor":false,"prefix":"","firstName":"Taro","middleName":"","lastName":"Saito","suffix":""},{"id":411066322,"identity":"cf3fb3b8-3fba-4d74-a958-8d7df6c99032","order_by":3,"name":"Masayo Hirano","email":"","orcid":"","institution":"Center for Drug Design Research, National Institute of Biomedical Innovation, Health and Nutrition","correspondingAuthor":false,"prefix":"","firstName":"Masayo","middleName":"","lastName":"Hirano","suffix":""},{"id":411066324,"identity":"f3991664-feb7-48cd-8c33-ed6b52e885fb","order_by":4,"name":"Kazuki Yokokawa","email":"","orcid":"","institution":"Sapporo Medical University","correspondingAuthor":false,"prefix":"","firstName":"Kazuki","middleName":"","lastName":"Yokokawa","suffix":""},{"id":411066326,"identity":"842d79fb-0698-4dce-a33c-cc81822180ce","order_by":5,"name":"Masanobu Tanemoto","email":"","orcid":"","institution":"Sapporo Medical University","correspondingAuthor":false,"prefix":"","firstName":"Masanobu","middleName":"","lastName":"Tanemoto","suffix":""},{"id":411066327,"identity":"6fc66715-86db-4833-80e0-6daa17785a35","order_by":6,"name":"Ryosuke Oda","email":"","orcid":"","institution":"Sapporo Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ryosuke","middleName":"","lastName":"Oda","suffix":""},{"id":411066329,"identity":"4848913c-66ae-4ec7-bda7-41504e271ef7","order_by":7,"name":"Takayuki Nonaka","email":"","orcid":"","institution":"Sapporo Medical University","correspondingAuthor":false,"prefix":"","firstName":"Takayuki","middleName":"","lastName":"Nonaka","suffix":""},{"id":411066332,"identity":"360f71bc-3a2f-423b-9b1a-b77c25f9bd6a","order_by":8,"name":"Shuuichirou Suzuki","email":"","orcid":"","institution":"Sapporo Medical University","correspondingAuthor":false,"prefix":"","firstName":"Shuuichirou","middleName":"","lastName":"Suzuki","suffix":""},{"id":411066334,"identity":"d55e7752-9953-4f63-899d-b43a6f847c64","order_by":9,"name":"Jun Adachi","email":"","orcid":"","institution":"Center for Drug Design Research, National Institute of Biomedical Innovation, Health and Nutrition","correspondingAuthor":false,"prefix":"","firstName":"Jun","middleName":"","lastName":"Adachi","suffix":""},{"id":411066336,"identity":"dd57b906-f68f-40da-b968-574d7279599b","order_by":10,"name":"Shin Hisahara","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA70lEQVRIiWNgGAWjYBACA2YGBmYQgx/CZ4aIEqVFsoFoLTBlBgeQtOAF5uzsDz8X1NyTNz5/+NmjGxXWDPztBxiKC/BosWzmMZaecazYcNuNNHPjnDPpDBJnEhiMZ+Bz2GEeBmketgTGbTcYzKRz2w4zMNxgYDDmwauF/fFvnn8J9pv7j3+Tzv13mEGesBag4bxtCYkbGHKAtjQcZjAgrIXHzJq3LyF5xo2ccuOcY+k8hmcSG/D75fzxx7d5viXY9vcf3/Y4p8ZaTu744WPG+EIMGbCBCKCTGNuMidQB0QICzI+J1TIKRsEoGAUjAgAAL0ZHmQ2u6mcAAAAASUVORK5CYII=","orcid":"","institution":"Sapporo Medical University","correspondingAuthor":true,"prefix":"","firstName":"Shin","middleName":"","lastName":"Hisahara","suffix":""}],"badges":[],"createdAt":"2025-02-04 04:23:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5954841/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5954841/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":75705796,"identity":"c1acee58-e9c2-4871-baf0-d7254c6aab35","added_by":"auto","created_at":"2025-02-07 10:18:42","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":143552,"visible":true,"origin":"","legend":"\u003cp\u003eCharacterization of EVs isolated from CSF using phosphatidylserine affinity capture.\u003c/p\u003e\n\u003cp\u003e(a) Schematic of the experimental workflow. CSF samples and CSF-derived EVs from a discovery cohort consisting of 10 patients with OND and 10 patients with MS were subjected to mass spectrometry analysis ; additionally, CSF samples and CSF-derived EVs from a validation cohort consisting of 26 patients with OND, 38 patients with MS, and 14 patients with NMOSD were subjected to mass spectrometry analysis. (b) EVs isolated from CSF were analyzed for size and number using nanoparticle tracking analysis. The black line shows the curve fit, and the red line represents the error of the mean of quadruplicate measurements. Y-axis: EV particle number (/mL); X-axis: EV particle size (nm). (c) TEM image of EVs separated from CSF. Scale bar = 100 nm. (d) Western blotting images of CD9 expression in total CSF and CSF-derived EVs. (e) Venn diagram of proteins identified in CSF and CSF-derived EVs via label-free proteomics analysis. (f) Identification of common proteins in CSF and CSF-derived EVs. EV-specific proteins listed in the Exocart Top 100 are indicated by orange dots. (g) GO analysis was performed via DAVID Bioinformatics Resources 6.8. Top 4 GO terms in the “Cellular component” category.\u003c/p\u003e","description":"","filename":"Picture1.png","url":"https://assets-eu.researchsquare.com/files/rs-5954841/v1/fd778403d9dd73c7594058a2.png"},{"id":75705448,"identity":"50d82afc-fd23-463d-8d30-743c04bced74","added_by":"auto","created_at":"2025-02-07 10:10:41","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":105279,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential protein expression profiles of CSF and CSF-derived EVs between OND patients and MS patients in the discovery cohort.\u003c/p\u003e\n\u003cp\u003e(a) Volcano plots comparing the MS and OND groups in CSF. The red and blue dots indicate significantly upregulated proteins (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, fold change \u0026gt; 1.5) and downregulated proteins (\u003cem\u003ep \u003c/em\u003e\u0026lt; 0.05, fold change \u0026lt; 0.67), respectively, in MS compared with OND. The green dots indicate immunoglobulins (Igs). (b) Volcano plots comparing the MS and OND groups in CSF-derived EVs. The red and blue dots indicate proteins significantly upregulated or downregulated in MS compared with OND, respectively. The green dots indicate Igs. (c) Venn diagram of proteins significantly upregulated in CSF and in CSF-derived EVs in the MS group. (d) GO analysis using DAVID Bioinformatics Resources 6.8. Top 4 GO terms in the “Biological process” category with -log\u003csub\u003e10\u003c/sub\u003e (FDR \u003cem\u003ep\u003c/em\u003e value). (d) Bar graph of relative intensity as measured by mass spectrometry for ITGA4, ITGAX, MS4A1, CD3E, CD4 and CD8. Student’s t test was used to statistically confirm significance. (ns; not significant, * \u003cem\u003ep \u0026lt; 0.05\u003c/em\u003e, ** \u003cem\u003ep \u0026gt; 0.01\u003c/em\u003e).\u003c/p\u003e","description":"","filename":"Picture2.png","url":"https://assets-eu.researchsquare.com/files/rs-5954841/v1/41f718dd6ab9175e0ccee2a7.png"},{"id":75705451,"identity":"0e9b1a88-cb4a-43ea-b797-12042a04c11c","added_by":"auto","created_at":"2025-02-07 10:10:42","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":103115,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential protein expression profile of CSF-derived EVs between OND, MS and NMOSD patients in the validation cohort.\u003c/p\u003e\n\u003cp\u003e(a) Volcano plots comparing the MS and OND groups in the validation cohort. The red and blue dots indicate significantly upregulated proteins (\u003cem\u003ep \u003c/em\u003e\u0026lt; 0.05, fold change \u0026gt; 1.5) and downregulated proteins (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, fold change \u0026lt; 0.67), respectively, in MS compared with OND. (b) Volcano plots comparing the NMOSD and OND groups in the validation cohort. The red and blue dots indicate proteins significantly upregulated or downregulated in NMOSD patients compared with OND patients, respectively. (c) Venn diagram of proteins significantly upregulated in the MS group in the discovery cohort (discovery), in the validation cohort (validation MS) and significantly upregulated in the NMOSD group in the validation cohort (validation NMOSD). (d) Bar graph of relative intensity as measured by mass spectrometry for ITGA4, ITGAX, MS4A1, CD3E, CD4 and CD8. One-way ANOVA followed by Tukey’s HSD multiple test (ns; not significant, * \u003cem\u003ep \u0026lt; 0.05, \u003c/em\u003e*** \u003cem\u003ep \u0026gt; 0.001,\u003c/em\u003e **** \u003cem\u003ep \u0026gt; 0.0001\u003c/em\u003e).\u003c/p\u003e","description":"","filename":"Picture3.png","url":"https://assets-eu.researchsquare.com/files/rs-5954841/v1/0784ce6d2a7007ef075574b6.png"},{"id":75705449,"identity":"cbca6a9f-8e78-4560-b701-b00762a0d8e4","added_by":"auto","created_at":"2025-02-07 10:10:42","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":64007,"visible":true,"origin":"","legend":"\u003cp\u003eChanges in the levels of several marker proteins in patients with and without CDW.\u003c/p\u003e\n\u003cp\u003e(a) Bar graph of relative intensity as measured by mass spectrometry for ITGA4, ITGAX, MS4A1, CD3E, CD4 and CD8. Student’s t test was used to statistically confirm significance. (ns; not significant, ***\u003cem\u003ep \u0026gt; 0.001\u003c/em\u003e). (b) ROC analysis of ITGAX levels at sampling to discriminate between patients with and without CDW. (c) Kaplan‒Meier survival analysis of the occurrence of CDW in patients with relative ITGAX values greater than 1.167 (ITGAX high, n = 18) and less than 1.167 (ITGAX low, n = 12).\u003c/p\u003e","description":"","filename":"Picture4.png","url":"https://assets-eu.researchsquare.com/files/rs-5954841/v1/98808e15e4d8b3f3d67415ef.png"},{"id":75708778,"identity":"99ace3c3-c22e-41f1-be08-8bea2dc22ac2","added_by":"auto","created_at":"2025-02-07 10:42:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1367354,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5954841/v1/22ee3751-f337-45a5-be79-e03c3520d1a6.pdf"},{"id":75705446,"identity":"130e9003-a960-4a80-99f1-efe950f54d5f","added_by":"auto","created_at":"2025-02-07 10:10:41","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":55452,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableVer.2.0.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5954841/v1/ee0b9ad2eef2ab37b378d39c.xlsx"},{"id":75705454,"identity":"9aadeebc-97b8-4f0d-90d9-0600ec598e3c","added_by":"auto","created_at":"2025-02-07 10:10:42","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":247266,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalinformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-5954841/v1/17f847b5e5015b668dd6bcc0.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Protein profiling of extracellular vesicles from the cerebrospinal fluid of patients with multiple sclerosis","fulltext":[{"header":"Background","content":"\u003cp\u003eMultiple sclerosis (MS) is a chronic disease of the CNS that disporportionally affects young women and is characterized by inflammatory demyelination and neurodegeneration [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Neuromyelitis optica spectrum disorder (NMOSD), another demyelinating disease of the CNS, is caused by autoantibodies against aquaporin 4 (AQP4) in astrocytes; however, the cause of MS has not yet been determined [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The pathogenesis of MS involves a combination of genetic and environmental factors, and its clinical presentation varies significantly among and within patients. Recently, progression independent of relapse activity (PIRA) has been reported to be strongly correlated with MS symptom progression and brain atrophy [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In secondary progressive MS (SPMS), the primary cause of disease worsening is PIRA, although a small amount of symptom worsening caused by occasional relapses is still observed [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. However, the mechanisms underlying neurodegeneration and brain atrophy in SPMS patients with PIRA are not completely understood.\u003c/p\u003e \u003cp\u003eExtracellular vesicles (EVs) are small cell-derived membranous vesicles that carry a wide variety of molecules, including lipids, nucleic acids, and proteins [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. They are formed by the direct outward budding from the plasma membranes of prokaryotic and eukaryotic cells. In eukaryotic cells, EVs can initially form intraluminal vesicles of internal multivesicular bodies (MVBs) via the endocytic pathway and are then secreted upon the fusion of these compartments with the plasma membrane. To clarify the nomenclature, the use of the term \"exosomes\", specifically for MVB-derived EVs, has recently been recommended. On the other hand, plasma membrane-derived EVs are referred to by various names, such as microvesicles, microparticles, or ectosomes [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. EV cargo can be dynamically altered under pathophysiological conditions and reflects a disease-specific signature. Therefore, EVs are promising sources of information for understanding the state of the brain in neurological disorders.\u003c/p\u003e \u003cp\u003eWe previously reported the possible involvement of microglial EVs in tau propagation in the context of Alzheimer's disease (AD) through proteomic analysis of EVs from model animals [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Targeting microglial EVs is a potential treatment option for AD. An ongoing clinical trial (NCT04121208, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://clinicaltrials.gov/study/NCT04121208\u003c/span\u003e\u003cspan address=\"https://clinicaltrials.gov/study/NCT04121208\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) is investigating the efficacy of a colony-stimulating factor 1 receptor inhibitor that essentially eliminates microglia. Therefore, we hypothesized that analysis of EVs harvested from patients with MS would enable us to investigate the neurodegenerative factors involved in MS. Despite the considerable number of microarray analyses performed on EVs in MS, proteomic analyses are rare [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. As EVs are believed to mirror the pathophysiology of neurodegenerative diseases, in this study, we conducted a proteomic analysis of EVs isolated from the CSF of MS patients to clarify disease mechanisms and discover new therapeutic targets.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatient population and inclusion criteria\u003c/h2\u003e \u003cp\u003eTwenty patients in the discovery cohort and 76 patients in the validation cohort who were subjected to cerebrospinal fluid (CSF) sampling at the Department of Neurology, Sapporo Medical University Hospital, between April 2010 and November 2023 were included in this study. In the discovery cohort, CSF samples were collected from 10 patients with MS and 10 with other neurological diseases (OND) via a standard protocol. In the validation cohort, CSF samples were collected from 38 patients with MS, 14 with NMOSD, and 24 with OND. All samples were collected, coded, and stored in the hospital at -80\u0026deg;C. Patients with OND were age- and sex-matched with patients with MS and NMOSD. MS was diagnosed according to the 2005 or 2010 McDonald criteria [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], and NMOSD was diagnosed according to the 2006 or 2015 Wingerchuk criteria [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. All patients in the NMOSD group tested positive for anti-AQP4 antibodies. The OND group in the discovery cohort included patients with neuropathy with liability to pressure palsies (n\u0026thinsp;=\u0026thinsp;1), mitochondrial diseases (n\u0026thinsp;=\u0026thinsp;1), hereditary spastic paraplegia (n\u0026thinsp;=\u0026thinsp;1), amyotrophic lateral sclerosis (n\u0026thinsp;=\u0026thinsp;1), epilepsy (n\u0026thinsp;=\u0026thinsp;1), Charcot-Marie-Tooth disease (n\u0026thinsp;=\u0026thinsp;1), cervical spondylosis (n\u0026thinsp;=\u0026thinsp;2), Bell's palsy (n\u0026thinsp;=\u0026thinsp;1) and psychology spectrum disorders (n\u0026thinsp;=\u0026thinsp;1). in the OND group in the validation cohort included patients with acute disseminated encephalomyelitis (n\u0026thinsp;=\u0026thinsp;2), chronic progressive external ophthalmoplegia (n\u0026thinsp;=\u0026thinsp;1), dystonia (n\u0026thinsp;=\u0026thinsp;2), eosinophilic granulomatosis with polyangiitis (n\u0026thinsp;=\u0026thinsp;1), epilepsy (n\u0026thinsp;=\u0026thinsp;1), Fisher\u0026rsquo;s syndrome (n\u0026thinsp;=\u0026thinsp;3), headache (n\u0026thinsp;=\u0026thinsp;2), \u003cem\u003ehereditary spastic\u003c/em\u003e paraplegia (n\u0026thinsp;=\u0026thinsp;2), Hirayama disease (n\u0026thinsp;=\u0026thinsp;1), hydrocephalus (n\u0026thinsp;=\u0026thinsp;1), myelitis (n\u0026thinsp;=\u0026thinsp;1), myoclonus (n\u0026thinsp;=\u0026thinsp;1), Parkinson\u0026rsquo;s disease (n\u0026thinsp;=\u0026thinsp;1), psychology spectrum disorder (n\u0026thinsp;=\u0026thinsp;4), and systemic lupus erythematosus (n\u0026thinsp;=\u0026thinsp;1).\u003c/p\u003e \u003cp\u003eConfirmed disability worsening (CDW) was defined as an increase in the Expanded Disability Status Scale (EDSS) score\u0026thinsp;\u0026ge;\u0026thinsp;0.5 from a baseline of \u0026ge;\u0026thinsp;6.0, \u0026ge; 1.0 from a baseline of 1.0\u0026ndash;5.5, or \u0026ge;\u0026thinsp;1.5 from a baseline of 0.0. CDW was confirmed if there were multiple hospital visits at least 3 months apart and records of increased EDSS scores, and patients with less than 1 year of hospitalization were excluded [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Kaplan‒Meier curves were used to estimate the cumulative probability of CDW.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStandard protocol approvals, registrations, and patient consent\u003c/h3\u003e\n\u003cp\u003eAll study protocols were approved by the Clinical Research Ethics Commission of Sapporo Medical University Hospital (No. 322\u0026thinsp;\u0026minus;\u0026thinsp;237) and the National Institute of Biomedical Innovation, Health and Nutrition (No. 266-02). Informed consent was obtained by providing an opt-out form on the website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://web.sapmed.ac.jp/neurol/introduction/qi5fku000000007e.html\u003c/span\u003e\u003cspan address=\"https://web.sapmed.ac.jp/neurol/introduction/qi5fku000000007e.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Those who decided to opt-out were excluded from the study.\u003c/p\u003e\n\u003ch3\u003eIsolation of EVs from CSF\u003c/h3\u003e\n\u003cp\u003eEV preparation platform for clinical proteomics (EP3) were employed in this study [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. EVs were isolated from CSF using the MagCapture Exosome Isolation Kit PS version 2 (Fujifilm WAKO Pure Chemical Corporation) with the KingFisher Flex System (Thermo Fisher Scientific) in a 96-well plate-based format [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Briefly, 1,000 \u0026micro;L of CSF was centrifuged at 1,200 \u0026times; \u003cem\u003eg\u003c/em\u003e for 20 min at 4\u0026deg;C. The supernatant was filtered through FastRemover for protein (0.45 \u0026micro;m) (GL Sciences) using a positive pressure Resolvex M10 system 96-XT (TECAN). EVs were isolated from filtered CSF and eluted with 100 \u0026micro;L of elution buffer for proteomics analysis. The isolated EV fraction was lysed with lysis buffer (12 mM sodium deoxycholate, 12 mM sodium lauroyl sarcosinate, and 50 mM ammonium bicarbonate) [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], and then the samples were vortexed for 5 min at room temperature, followed by spin down and boiling for 10 min at 60\u0026deg;C. The samples were reduced with 10 mM tris(2-carboxyethyl)phosphine (Fujifilm WAKO Pure Chemical Corporation) and alkylated with 20 mM iodoacetamide (Nacalai Tesque) for 60 min at 37\u0026deg;C in the dark. Automated SP3 technology was carried out following the program of KingFisher Flex. Then, 100% acetonitrile (ACN) was added to the reduced and alkylated sample tube, which was subsequently vortexed. The samples were bound to SP3 beads for 30 min with medium mixing. The SP3 beads were collected and put into 100% ACN. The mixing beads were placed in 70% ethanol twice, and then, after which trypsin (Thermo Fisher) and LysC (Fujifilm WAKO Pure Chemical Corporation) were added. After 16 h, the digested EV peptides were collected using a Flex system to remove the magnetic beads, and the protease activity was quenched by acidification with trifluoroacetic acid (TFA). All the digested peptides were loaded on EvoTips according to the manufacturer\u0026rsquo;s protocol for the discovery cohort CSF-derived EV samples. For the discovery cohort CSF samples and validation cohort CSF-derived EV, all tryptic peptides were desalted via the stop-and-go-extraction tip (StageTip) protocol [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], dried via vacuum centrifugation, and resuspended in 2% ACN and 1% TFA.\u003c/p\u003e\n\u003ch3\u003eMass spectrometry\u003c/h3\u003e\n\u003cp\u003eThe discovery cohort CSF-derived EVs were analyzed on the Evosep One system (Evosep) using an in-house packed 15 cm, 75 \u0026micro;m i.d. capillary column with 1.9 \u0026micro;m Reprosil-Pur C18-AQ beads (Dr. Maisch) using a preprogrammed gradient (20 samples per day). The column temperature was maintained at 60\u0026deg;C using an integrated column oven and an Inspion system and interfaced online with the Orbitrap Lumos mass spectrometer. Data were acquired using data-independent acquisition (DIA). The Orbitrap Fusion Lumos mass spectrometer was used for gas-phase fractionation (GPF)-DIA of a pooled sample for the library, and full mass spectra were acquired with the following parameters: a resolution of 120,000, an automatic gain control (AGC) target of 1 \u0026times; 10\u003csup\u003e6\u003c/sup\u003e, and an injection time of 250 ms. The five GPF-DIA runs collectively covered 418\u0026ndash;782 \u003cem\u003em/z\u003c/em\u003e (i.e., 418\u0026ndash;494, 490\u0026ndash;566, 562\u0026ndash;638, 634\u0026ndash;710, and 706\u0026ndash;782 \u003cem\u003em/z\u003c/em\u003e). MS2 spectra were collected with the following parameters: a 2-\u003cem\u003em/z\u003c/em\u003e isolation window at 50,000 resolution, an AGC target of 2 \u0026times; 10\u003csup\u003e5\u003c/sup\u003e ions, a maximum injection time of 86 ms, and a normalized collision energy of 30. For the individual samples used for proteome profiling, full mass spectra were acquired in the range of 410\u0026ndash;780 \u003cem\u003em/z\u003c/em\u003e with the following parameters: a resolution of 120000, an AGC target of 4 \u0026times; 10\u003csup\u003e5\u003c/sup\u003e, and an injection time of 100 ms. MS2 spectra were collected with the following parameters: a 10-\u003cem\u003em/z\u003c/em\u003e isolation window at 30000 resolution, an AGC target of 2 \u0026times; 10\u003csup\u003e5\u003c/sup\u003e ions, a maximum injection time of 54 ms, overlapping window patterns, and a normalized collision energy of 30.\u003c/p\u003e \u003cp\u003eFor discovery cohort CSF-derived EVs and validation cohort CSF-derived EVs, nano-LC\u0026ndash;MS/MS analysis was conducted with an LTQ-Orbitrap Fusion Lumos mass spectrometer (Thermo Fisher Scientific) equipped with an UltiMate 3000 Nano LC system (Thermo Fisher Scientific) and an HTC-PAL autosampler (CTC Analytics). Peptides were separated on an analytical column, and separation was achieved using a 45-min gradient of 5\u0026ndash;30% ACN in 0.1% formic acid at a flow rate of 280 nL/min. Data were acquired using DIA.\u003c/p\u003e\n\u003ch3\u003eMass spectrometry data analysis\u003c/h3\u003e\n\u003cp\u003eMass spectrometry data (raw files) were processed with DIA-NN software (Ver. 1.8.1) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The database search included all entries from the \u003cem\u003eHomo sapiens\u003c/em\u003e UniProt database (downloaded in April 2020, taxonomy ID: 9606) and contaminant database. The search parameters were as follows: up to two missed cleavage sites, 7\u0026ndash;30 peptide lengths, carbamidomethylation of cysteine residues (+\u0026thinsp;57.021 Dalton) as static modifications, protein names from FASTA for implicit protein grouping, robust LC (high precision) for the quantification strategy, and global for cross-run normalization. The precursor ions were adjusted to a 1% false discovery rate (FDR). The mass spectrometry proteomics data have been deposited into the ProteomeXchange Consortium via the jPOST repository with the dataset identifier PDX 047351 [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Missing values were imputed with a provided constant value using perseus (Ver. 1.6.14.0).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eWestern blotting\u003c/h2\u003e \u003cp\u003eThe EV samples were incubated with mammalian cell lysis buffer (Sigma‒Aldrich) supplemented with a protease inhibitor cocktail (Nacalai Tesque) for 4 h. A bicinchoninic acid assay was performed to determine the protein concentration in each sample using a BCA protein assay kit (TaKaRa Bio). Western blotting was performed as previously described [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. We added 5 \u0026micro;g of protein to each well, and an anti-CD9 antibody (1:1,000 dilution; #98734; Cell Signaling Technology) was used.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eTransmission electron microscopy (TEM)\u003c/h3\u003e\n\u003cp\u003eTEM was performed at Hanaichi Ultra Structure Research Institute (Aichi, Japan). The sample droplets were placed on a carbon film grid for 10 s. After the grid was partially dried, a drop of staining solution and 2% uranyl acetate were added to the grid and allowed to stand for 10 s. After excess uranyl acetate was removed via filter paper, the grids were examined, and the fields were photographed using a JEOL JEM1400Flash electron microscope at 100 kV.\u003c/p\u003e\n\u003ch3\u003eNanoparticle tracking analysis\u003c/h3\u003e\n\u003cp\u003eAll samples were diluted in phosphate-buffered saline at a ratio of at least 1:10 to obtain particles within the target reading range for a NanoSight 300 machine (Malvern Panalytical Inc.), with 10\u0026ndash;100 particles per frame. Using a manual injection system, four 60 s videos were taken for each sample at 21\u0026deg;C. The analysis of the particle counts was performed using Nanosight NTA 3.3 software (Malvern Panalytical, Inc.) with a detection threshold of 5.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were conducted using GraphPad Prism 10.3.1 (GraphPad Software) and JMP Pro17 (JMP). Between-group comparisons were analyzed using one-way analysis of variance, followed by Tukey\u0026rsquo;s honest significant difference test for multiple comparisons. Student\u0026rsquo;s t test and the chi-square test were used for comparisons between two groups. Pearson correlation, Kaplan‒Meier survival, and receiver operating characteristic (ROC) curve analyses were also conducted using JMP Pro17. Gene Ontology (GO) analysis of the identified proteins were elucidated using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) Bioinformatics Resources version 6.8 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://david.ncifcrf.gov\u003c/span\u003e\u003cspan address=\"https://david.ncifcrf.gov\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eProteomic analysis of the CSF samples and EVs derived from CSF\u003c/h2\u003e \u003cp\u003eThe experimental workflow is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003ea. Proteomics analysis of CSF and EVs derived from CSF (CSF-derived EVs) from 10 patients diagnosed with MS and 10 with OND in the discovery cohort was conducted via DIA mass spectrometry (Table\u0026nbsp;1, Supplementary table and 2). Figure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003eb-g show the characteristics of the CSF-derived EVs. Nanoparticle tracking analysis revealed the presence of both microvesicles and exosomes in the EV population. The sizes of the EVs ranged from 50 to 300 nm, with a peak at 108 nm (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). In addition, we observed isolated EVs via TEM, which revealed a typical exosomal morphology (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003ec). Moreover, the CSF-derived EVs clearly expressed CD9, a marker of EVs (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003ed), and mass spectrometry also confirmed high EV purity (Supplementary table 3).\u003c/p\u003e \u003cp\u003e \u003cb\u003eTable\u0026nbsp;1.\u003c/b\u003e Characteristics of patients in the discovery cohort\u003c/p\u003e \u003c/div\u003e\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 48.9796%;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3469%;\"\u003e\n \u003cp\u003eOND\u003cbr\u003e\u0026nbsp;(n = 10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.449%;\"\u003e\n \u003cp\u003eMS\u003cbr\u003e\u0026nbsp;(n = 10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2245%;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48.9796%;\"\u003e\n \u003cp\u003eFemale, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3469%;\"\u003e\n \u003cp\u003e4 (40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.449%;\"\u003e\n \u003cp\u003e4 (40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48.9796%;\"\u003e\n \u003cp\u003eAge at onset, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3469%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.449%;\"\u003e\n \u003cp\u003e32 (28\u0026ndash;35.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48.9796%;\"\u003e\n \u003cp\u003eAge at sampling, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3469%;\"\u003e\n \u003cp\u003e33.5 (26\u0026ndash;44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.449%;\"\u003e\n \u003cp\u003e36.5 (31.5\u0026ndash;40.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e0.8596\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48.9796%;\"\u003e\n \u003cp\u003eEDSS score at sampling, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3469%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.449%;\"\u003e\n \u003cp\u003e2.0 (1.0\u0026ndash;3.125)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: EDSS: expanded disability status scale; MS: multiple sclerosis; OND: other neurological diseases.\u003c/p\u003e\n\u003cp\u003eNotes:\u003c/p\u003e\n\u003cp\u003eGroup comparisons were performed via student\u0026rsquo;s t test.\u003c/p\u003e\n\u003cp\u003eDIA label-free quantitative proteomics analysis of the CSF samples and CSF-derived EVs revealed 694 and 3981 proteins, respectively. A clear correlation was observed between the protein levels of CD9 and CD81, which are marker proteins on the membrane of EVs, and PDCD6IP (ALIX) and TSG101, which are marker proteins within EVs (Supplementary Figure 1). Among these proteins, 584 were present in both the CSF samples and CSF-derived EVs (Figure 1e). A plot of the relative intensity of each of the 584 common proteins in CSF-EVs and CSF demonstrated that the proteins in the Exo top 100 [20] were expressed at higher levels in the CSF-EVs than in the CSF samples (Figure 1f). Moreover, the \u0026quot;immunoglobulin complex\u0026quot; cellular component term was found to be significantly enriched in the identified proteins in the CSF samples exclusively (CSF Ex), as determined by GO enrichment analysis via DAVID (Figure 1g) [21]. In contrast, the \u0026quot;extracellular exosome\u0026quot; cellular component term was found to be significantly enriched in the commonly identified proteins in the CSF samples and CSF-EVs (Co) and the identified proteins in CSF-EVs exclusively (CSF-EV Ex) (Figure 1g). In addition, the \u0026ldquo;cytosol\u0026rdquo;, \u0026ldquo;plasma membrane\u0026rdquo; and \u0026ldquo;membrane\u0026rdquo; terms were found to be significantly enriched in the proteins in CSF-EV Ex group [22].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eDifferential protein expression\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003eprofiles\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;between\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003ethe\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003eOND and MS\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;groups\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure 2a and b\u0026nbsp;show\u0026nbsp;volcano plots of the CSF and CSF-derived EV proteins, respectively, between\u0026nbsp;the\u0026nbsp;MS and OND groups. The CSF\u0026nbsp;results revealed\u0026nbsp;that 33 proteins were significantly upregulated (\u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.05,\u0026nbsp;fold\u0026nbsp;change \u0026gt; 1.5) in the MS group compared\u0026nbsp;with\u0026nbsp;the OND group (Figure 2a, c and Supplementary table 4), 25 of\u0026nbsp;which were\u0026nbsp;immunoglobulins (Igs). In contrast, two proteins\u0026nbsp;were significantly downregulated\u0026nbsp;(\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, fold change \u0026lt; 0.67). The CSF-EV results demonstrated that 100 proteins were significantly upregulated in the MS group compared with the OND group (Figure 2b, c and Supplemental table 5), five of which were Igs. In contrast, 16 proteins were significantly downregulated.\u003c/p\u003e\n\u003cp\u003eAs most of the upregulated proteins in the CSF were Igs, the \u0026quot;immunoglobulin production\u0026quot; biological process term was found to be significantly enriched, as determined by GO enrichment analysis via DAVID (Figure 2d). Conversely, the upregulated proteins in CSF-EVs included surface markers for immune cells (T and B cells), and the \u0026quot;adaptive immune response\u0026quot; term was significantly enriched (Figure 2d). This study focused on surface marker proteins whose expression was significantly greater in the MS group than in the OND group. Among these markers, the lymphocyte marker ITGA4; the antigen-presenting cell (APC) and disease-associated microglia (DAM) marker ITGAX; the B-cell marker MS4A1 (CD20); and the T-cell markers CD3E, CD4, and CD8A were selected for further investigation (Figure 2e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eDifferential ITGA4, ITGAX, MS4A1, CD3E, CD4 and CD8 levels in CSF-derived\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003eEVs\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;in patients with various inflammatory demyelinating diseases\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the validation cohort, CSF samples were collected from 38 MS patients, 14 NMOSD patients, and 24 OND patients (Table 2 and Supplementary table 6). The CSF-derived\u0026nbsp;EVs\u0026nbsp;in the validation cohort\u0026nbsp;were subjected to\u0026nbsp;DIA label-free quantitative proteomic analysis, which identified 4,055 proteins.\u0026nbsp;Among\u0026nbsp;these\u0026nbsp;proteins, 60 were significantly upregulated in the MS group compared\u0026nbsp;with\u0026nbsp;the OND group (Figure 3a, c and Supplementary table 7). Additionally, 147 proteins were identified as significantly\u0026nbsp;upregulated\u0026nbsp;in the NMOSD group compared\u0026nbsp;with\u0026nbsp;the OND group (Figure 3b, c and Supplementary table 8). The ITGAX levels were significantly increased in the MS compared to the OND group (\u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.008), but the fold change was only 1.47. A comparison of the levels of the six surface marker proteins in the three groups\u0026nbsp;revealed that\u0026nbsp;ITGA4, ITGAX, MS4A1 and CD3E levels\u0026nbsp;were significantly greater in the MS group than in the OND group, whereas CD4 and CD8 levels were not significantly different\u0026nbsp;(Figure 3d).\u0026nbsp;The levels of these proteins were not significantly different between MS patients\u0026nbsp;and NMOSD\u0026nbsp;patients, with the exception of ITGA4\u0026nbsp;(Figure 3d).\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e Characteristics of patients in the validation cohort\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eOND, n (%)\u003cbr\u003e\u0026nbsp;(n = 24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eMS, n (%)\u003cbr\u003e\u0026nbsp;(n = 38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eNMOSD, n (%)\u003cbr\u003e\u0026nbsp;(n = 14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003eOND vs MS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eOND vs NMOSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eMS vs NMOSD\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e20 (83.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e33 (86.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e12 (85.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.7043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.8452\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.9163\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003eAge at onset, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e33.5 (24.75\u0026ndash;48.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e48 (43.75\u0026ndash;60.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003eAge at sampling, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e46.5 (33.5\u0026ndash;56.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e38.5 (31\u0026ndash;51.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e55 (49.25\u0026ndash;63.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.0972\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0013\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003eEDSS score at sampling, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e1.5 (1.0\u0026ndash;3.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e3.0 (1.75\u0026ndash;5.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0027\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003eEDSS score at last visit, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e2 (1.125\u0026ndash;4.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e5.25 (2.625\u0026ndash;6.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0227\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003eLong cord lesion, n\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003e\u0026gt; 3, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e6 (75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003eBilateral optic neuritis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e2 (5.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e6 (42.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0017\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003eCSF protein, mean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e39.8 \u0026plusmn; 16.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e56.4 \u0026plusmn; 29.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0131\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003eCSF cell count, mean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e8.9 \u0026plusmn; 11.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e12.9 \u0026plusmn; 12.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.256\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003eIgG index, mean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e1.11 \u0026plusmn; 0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.62 \u0026plusmn; 0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.00169\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003eMBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e7 (20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e7 (50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0406\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003eOCB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e27 (77.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e2 (15.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: CSF: cerebrospinal fluid; EDSS: Expanded Disability Status Scale; MBP: myelin basic protein; MS: multiple sclerosis; NMOSD: neuromyelitis optica spectrum disorder; OCB: oligoclonal band; OND: other neurological diseases.\u003c/p\u003e\n\u003cp\u003eNotes:\u003c/p\u003e\n\u003cp\u003eGroup comparisons were performed using Tukey\u0026rsquo;s honest significant difference test.\u003c/p\u003e\n\u003cp\u003eP values written in bold are statistically significant.\u003c/p\u003e\n\u003cp\u003e[insert Table 2 near here]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eExploring predictors of prognosis in MS\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe objective of the subsequent phase of the study was to ascertain whether there was a correlation between the relative amount of these surface markers and the subsequent progression of the patient\u0026apos;s symptoms. Among the 38 patients diagnosed with MS in the validation cohort, 30 were followed for over one year as outpatients at our hospital. The 30 patients were divided into two groups on the basis of the presence or absence of CDW after sample collection. Table 3 presents a summary of the demographic characteristics, clinical findings, and biomarker comparisons between the two groups. Among these, 19 (63.3%) were assigned to the Stable group, whereas 11 (36.7%) were classified into the CDW group. Compared with the Stable group, the CDW group had significantly higher EDSS scores at the time of sample collection and at the end of the study period. In addition, the CDW group tended to have a longer duration from the onset of illness to sample retrieval and more outpatient visits. A comparative analysis of the expression levels of six surface marker proteins between the CDW and Stable groups\u0026nbsp;revealed that ITGAX\u0026nbsp;was notably elevated\u0026nbsp;in the CDW group relative to the Stable group. However, no significant\u0026nbsp;differences\u0026nbsp;were observed in the remaining five proteins (Figure 4a).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u003c/strong\u003e Comparison of MS patients with or without CDW\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 54.0816%;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3878%;\"\u003e\n \u003cp\u003eStable, n (%)\u003cbr\u003e\u0026nbsp;(n = 19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3673%;\"\u003e\n \u003cp\u003eCDW, n (%)\u003cbr\u003e\u0026nbsp;(n = 11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.16327%;\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 54.0816%;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e16 (84.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3673%;\"\u003e\n \u003cp\u003e9 (81.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.16327%;\"\u003e\n \u003cp\u003e0.8654\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 54.0816%;\"\u003e\n \u003cp\u003eAge at onset, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e31 (23\u0026ndash;48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3673%;\"\u003e\n \u003cp\u003e29 (21.5\u0026ndash;43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.16327%;\"\u003e\n \u003cp\u003e0.6589\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 54.0816%;\"\u003e\n \u003cp\u003eAge at sampling, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e35 (29.5\u0026ndash;50.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3673%;\"\u003e\n \u003cp\u003e37 (29.5\u0026ndash;52.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.16327%;\"\u003e\n \u003cp\u003e0.7845\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 54.0816%;\"\u003e\n \u003cp\u003eDuration between onset and sampling (months), median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e36 (14.5\u0026ndash;86.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3673%;\"\u003e\n \u003cp\u003e60 (19\u0026ndash;120)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.16327%;\"\u003e\n \u003cp\u003e0.1267\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 54.0816%;\"\u003e\n \u003cp\u003eEDSS score at sampling, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e1.5 (1.0\u0026ndash;2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3673%;\"\u003e\n \u003cp\u003e2.5 (2.0\u0026ndash;3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.16327%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0193\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 54.0816%;\"\u003e\n \u003cp\u003eEDSS score at last visit, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e1.5 (1.375\u0026ndash;2.375)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3673%;\"\u003e\n \u003cp\u003e4.25 (2.625\u0026ndash;6.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.16327%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0103\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 54.0816%;\"\u003e\n \u003cp\u003eDuration between sampling and last visit (months), median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e48 (20.5\u0026ndash;103.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3673%;\"\u003e\n \u003cp\u003e91 (71.5\u0026ndash;120)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.16327%;\"\u003e\n \u003cp\u003e0.2315\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 54.0816%;\"\u003e\n \u003cp\u003ePlaques in spine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e15 (78.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3673%;\"\u003e\n \u003cp\u003e11 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.16327%;\"\u003e\n \u003cp\u003e0.1021\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 54.0816%;\"\u003e\n \u003cp\u003eCSF protein, mean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e37.26 \u0026plusmn; 17.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3673%;\"\u003e\n \u003cp\u003e44.45 \u0026plusmn; 17.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.16327%;\"\u003e\n \u003cp\u003e0.3070\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 54.0816%;\"\u003e\n \u003cp\u003eCSF cell count, mean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e7.42 \u0026plusmn; 9.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3673%;\"\u003e\n \u003cp\u003e14.18 \u0026plusmn; 15.117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.16327%;\"\u003e\n \u003cp\u003e0.1510\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 54.0816%;\"\u003e\n \u003cp\u003eIgG index, mean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e1.11 \u0026plusmn; 0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3673%;\"\u003e\n \u003cp\u003e0.99 \u0026plusmn; 0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.16327%;\"\u003e\n \u003cp\u003e0.7012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 54.0816%;\"\u003e\n \u003cp\u003eMBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e3 (16.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3673%;\"\u003e\n \u003cp\u003e3 (27.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.16327%;\"\u003e\n \u003cp\u003e0.5089\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 54.0816%;\"\u003e\n \u003cp\u003eOCB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e12 (66.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3673%;\"\u003e\n \u003cp\u003e9 (90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.16327%;\"\u003e\n \u003cp\u003e0.1295\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: CSF: cerebrospinal fluid; EDSS: expanded disability status scale; MBP: myelin basic protein; MS: multiple sclerosis; NMOSD: neuromyelitis optica spectrum disorder; OCB: oligoclonal band; OND: other neurological diseases.\u003c/p\u003e\n\u003cp\u003eNotes:\u003c/p\u003e\n\u003cp\u003eGroup comparisons were performed via student\u0026rsquo;s t test.\u003c/p\u003e\n\u003cp\u003eP values written in bold are statistically significant.\u003c/p\u003e\n\u003cp\u003eP2RY12 and P2RX7,\u0026nbsp;which are\u0026nbsp;marker molecules for resting and active microglia, respectively, have been identified as potential positron emission tomography (PET) imaging targets for MS [23]. The correlation between the amount of ITGAX in CSF-derived\u0026nbsp;EVs\u0026nbsp;and the amount of P2RY12 was found to be weak (Supplementary\u0026nbsp;Figure\u0026nbsp;2a). Nevertheless, a moderate correlation was identified between the amount of ITGAX and P2RX7, which are also involved in EV secretion (Supplementary\u0026nbsp;Figure\u0026nbsp;2b) [24]. Although not statistically significant, the expression level of P2RX7 in CSF-derived\u0026nbsp;EVs tended\u0026nbsp;to be\u0026nbsp;greater\u0026nbsp;in the CDW group than in the Stable group (\u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.057; Supplementary\u0026nbsp;Figure\u0026nbsp;2d).\u003c/p\u003e\n\u003cp\u003eROC curve analysis was conducted to further elucidate the potential diagnostic value of ITGAX. The analysis yielded an AUC of 0.8373 (95% CI 0.6968\u0026ndash;0.9778, \u003cem\u003ep\u003c/em\u003e = 0.0024) and a Youden\u0026nbsp;index of 1.167 when the OND was 1.\u0026nbsp;The\u0026nbsp;Youden\u0026nbsp;index of the ROC\u0026nbsp;curve\u0026nbsp;of ITGAX relative intensity was\u0026nbsp;subsequently used\u0026nbsp;for further\u0026nbsp;Kaplan‒Meier\u0026nbsp;analysis. Patients with ITGAX value \u0026ge; 1.167 (ITGAX High) presented\u0026nbsp;an elevated risk of CDW in time-to-event analysis (long-rank\u0026nbsp;test, \u003cem\u003ep\u003c/em\u003e = 0.0054; Figure 3c).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study aimed to highlight the characteristics of EVs that are specific to MS.\u0026nbsp;Given that EVs contain cell-derived nucleic acids and proteins, they are being investigated as potential biomarkers for a range of diseases. In particular, microRNAs have attracted attention\u0026nbsp;because of\u0026nbsp;their ability to regulate gene expression in recipient cells [4].\u003c/p\u003e\n\u003cp\u003eConversely, the proteins present within\u0026nbsp;EVs\u0026nbsp;are challenging to analyze, with the number of identified proteins in\u0026nbsp;EVs\u0026nbsp;in MS\u0026nbsp;being\u0026nbsp;as low as 600 in reports of EV proteome analysis [7, 8]. Previously, we reported highly accurate and advanced proteome analysis results for EVs isolated from patients with AD and chronic traumatic encephalopathy [25, 26]. In the present study, we were able to identify approximately 4,000 proteins from CSF-derived EVs (Figure 1e). The identification of a greater number of proteins than previously reported can be attributed to the use of phosphatidylserine affinity capture, which enabled the isolation of pure and stable EVs. This method removes high-concentration proteins such as albumin and immunoglobulins, allowing proteins present in low concentrations in a sample to be identified [27]. The\u0026nbsp;use\u0026nbsp;of DIA as\u0026nbsp;an\u0026nbsp;analysis method may aid the identification of proteins in CSF samples, which are believed to be derived from immune cells due to their large role in immunological diseases. We posit that the isolation of\u0026nbsp;EVs\u0026nbsp;is a valuable approach not only for the functional analysis of\u0026nbsp;EVs\u0026nbsp;but also\u0026nbsp;for\u0026nbsp;the identification of numerous proteins involved in various diseases. In addition to\u0026nbsp;being enriched with\u0026nbsp;EV-specific proteins,\u0026nbsp;EVs\u0026nbsp;also\u0026nbsp;contain\u0026nbsp;cytoplasm and numerous plasma membrane proteins (Figure 1g). This finding indicates that EVs are derived from intracellular proteins and membranes [22]. Consequently, we believe that the analysis of EVs is a useful method for identifying changes in cells that secrete EVs and for analyzing other neurological diseases in the future.\u003c/p\u003e\n\u003cp\u003eIn the present study, a difference in the relative intensities of immune cell surface antigens were observed between MS and OND patients (Figure 2), and we focused our analysis on the surface antigen components of lymphocyte markers, including CD20 (MS4A), CD3 (CD3E), CD4, and CD8 (CD8A), as well as ITGA4 and ITGAX (Figure 2e). The levels of ITGA4, ITGAX, MS4A, and CD3E\u0026nbsp;were significantly greater\u0026nbsp;in the MS group\u0026nbsp;than in\u0026nbsp;the OND group in the validation cohort (Figure 3d). ITGA4 is a target protein of natalizumab [28], and ITGAX is a marker molecule for immune cells of the myeloid lineage, including dendritic cells and DAM [6]. In our present study, it\u0026nbsp;was\u0026nbsp;not possible to determine whether these changes\u0026nbsp;reflected\u0026nbsp;changes in immune cells in the CSF or in the brain parenchyma. The available evidence suggests that CSF lymphocytes are similar to those found in the brain parenchyma [29-31].\u0026nbsp;These\u0026nbsp;findings reflect the substantial presence of lymphocytes and dendritic cells within the brain parenchyma and CSF, as well as a type of microglia associated with neurodegenerative diseases [32].\u003c/p\u003e\n\u003cp\u003eUltimately, our objective was to ascertain whether there was a discernible discrepancy in the levels of these marker proteins when the MS patients were stratified into two groups: the symptom progression (with CDW) group and the stable groups. No difference was observed in the level of lymphocyte marker proteins in the CSF-EVs\u0026nbsp;between patients with and without CDW. However, the level of ITGAX was\u0026nbsp;greater\u0026nbsp;in the group with CDW (Figure 4a).\u0026nbsp;A similar trend was observed for P2RX7, a marker of active microglia and a target for microglial PET imaging [33]. It has recently been reported that paramagnetic rim lesions, as identified on MR\u0026nbsp;images, slowly\u0026nbsp;expand,\u0026nbsp;and their size is correlated with disease progression [34, 35].\u0026nbsp;In addition, it has been\u0026nbsp;reported\u0026nbsp;that active microglia\u0026nbsp;that\u0026nbsp;contain ferritin are present at their paramagnetic rim [36]. Elevated ITGAX levels may be implicated in these imaging and pathological findings. However, ITGAX has been demonstrated to be expressed not only in microglia but also in antigen-presenting cells, such as dendritic cells [37, 38], and age-associated B\u0026nbsp;cells [39].\u0026nbsp;The present analysis did not permit the identification of the cellular origin of each of these proteins in EVs, which is a topic for future investigation.\u0026nbsp;Moreover, BTK (Bruton tyrosine kinase) inhibitors, which were\u0026nbsp;recently developed for the treatment of MS, have been\u0026nbsp;shown\u0026nbsp;to inhibit the survival and proliferation of microglia, dendritic cells and\u0026nbsp;macrophages, in addition to B cells. However, evobrutinib,\u0026nbsp;a\u0026nbsp;BTK inhibitor, did not demonstrate superiority over teriflunomide in preventing relapses in relapsing-remitting MS [40], although it has been reported to slow lesion expansion [41]. The measurement of ITGAX levels in CSF-EVs\u0026nbsp;and PET imaging of P2RX7 may be useful in patient\u0026nbsp;selection for BTK inhibitor treatment.\u003c/p\u003e\n\u003cp\u003eA limitation of this study is that we were unable to investigate the effects of disease-modifying drugs (DMDs). In Japan, the first DMD, IFN-β1b, was introduced in 2000, and numerous DMDs emerged over the subsequent two decades. Consequently, the DMDs used to treat patients over the course of their illness frequently change, which presents a significant challenge in analyzing the impact of DMDs. As the CSF samples utilized in this study were obtained from patients at the onset of the initial attack or relapse, they predominantly reflected alterations in active lesions, potentially obscuring those associated with chronic inactive lesions. Further studies are needed to evaluate pathological changes over time, including during periods of remission, in the same patient. Additionally, whether the outcomes of patients with varying levels of ITGAX differ was not able to be evaluated in a separate cohort in this study. Larger prospective studies are necessary to confirm that ITGAX in CSF-derived EVs is indeed a reliable predictor of progression.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eMS: multiple sclerosis\u003c/p\u003e\n\u003cp\u003eNMOSD: neuromyelitis optica spectrum disorder\u003c/p\u003e\n\u003cp\u003eAQP4: Aquaporin-4\u003c/p\u003e\n\u003cp\u003ePIRA: progression independent of relapse activity\u003c/p\u003e\n\u003cp\u003eSPMS: secondary progressive ms\u003c/p\u003e\n\u003cp\u003eEV: extracellular vesicle\u003c/p\u003e\n\u003cp\u003eMVB: multivesicular body\u003c/p\u003e\n\u003cp\u003eAD: Alzheimer\u0026rsquo;s disease\u003c/p\u003e\n\u003cp\u003eOND: other neurological diseases\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCDW: confirmed disease worsening\u003c/p\u003e\n\u003cp\u003eEDSS: Expanded Disability Status Scale\u003c/p\u003e\n\u003cp\u003eDIA: data-independent acquisition\u003c/p\u003e\n\u003cp\u003eCAN: acetonitrile\u003c/p\u003e\n\u003cp\u003eTFA: trifluoroacetic acid\u003c/p\u003e\n\u003cp\u003eAGC:\u0026nbsp;automatic gain control\u003c/p\u003e\n\u003cp\u003eGPF:\u0026nbsp;gas-phase fractionation\u003c/p\u003e\n\u003cp\u003eFDR:\u0026nbsp;false discovery rate\u003c/p\u003e\n\u003cp\u003eTEM:\u0026nbsp;transmission\u0026nbsp;electron microscopy\u003c/p\u003e\n\u003cp\u003eGO: Gene Ontology\u003c/p\u003e\n\u003cp\u003eDAVID: Database for Annotation, Visualization, and Integrated Discovery\u003c/p\u003e\n\u003cp\u003eMBP: myelin basic protein\u003c/p\u003e\n\u003cp\u003eOCB: oligoclonal band\u003c/p\u003e\n\u003cp\u003ePET: positron emission tomography\u003c/p\u003e\n\u003cp\u003eDMDs: disease-modifying drugs\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthics approval and consent\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003eto\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;participate\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll\u0026nbsp;study protocols were approved by the Clinical Research Ethics Commission of Sapporo Medical University Hospital (No. 322-237) and the National Institute of Biomedical Innovation, Health and Nutrition (No. 266-02). Informed consent was obtained by providing an opt-out form on the website (https://web.sapmed.ac.jp/neurol/introduction/qi5fku000000007e.html). Those who decided to opt-out were excluded from the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConsent for publication\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the authors have contributed to, reviewed and approved the final manuscript for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAvailability of data and materials\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe mass spectrometry proteomics data were deposited in the ProteomeXchange Consortium via the jPOST repository with the dataset identifier PXD047351.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCompeting interests\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported in part by research grants from the Japanese Society for the Promotion of Science KAKENHI (grant numbers 23K06967 and 23K14740), the Takeda Science Foundation and the Japan Multiple Sclerosis Society.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAuthors’ contributions\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: N.I., S.M., J.A. and S.H.\u003c/p\u003e\n\u003cp\u003eData curation: N.I. and S.M.\u003c/p\u003e\n\u003cp\u003eFormal analysis: N.I., S.M. and M.H.\u003c/p\u003e\n\u003cp\u003eFunding acquisition: N.I. and T.S.\u003c/p\u003e\n\u003cp\u003eInvestigation: N.I., S.M., T.S., K.Y., M.T., R.O. and T.N.\u003c/p\u003e\n\u003cp\u003eMethodology: N.I., S.M. and J.A.\u003c/p\u003e\n\u003cp\u003eWriting – original draft: N.I.\u003c/p\u003e\n\u003cp\u003eWriting – review \u0026amp; editing: N.I., S.M., S.S., J.A. and S.H.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAcknowledgements\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAuthors' information\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eStadelmann C, Wegner C, Br\u0026uuml;ck W. Inflammation, demyelination, and degeneration - recent insights from MS pathology. Biochim Biophys Acta. 2011;1812:275-82.\u003c/li\u003e\n\u003cli\u003eFambiatos A, Jokubaitis V, Horakova D, Havrdova EK, Trojano M, Prat A, et al. Risk of secondary progressive multiple sclerosis: a longitudinal study. Mult Scler. 2020;26:79-90.\u003c/li\u003e\n\u003cli\u003eLublin FD, H\u0026auml;ring DA, Ganjgahi H, Ocampo A, Hatami F, Čuklina J, et al. How patients with multiple sclerosis acquire disability. Brain. 2022;145:3147-61.\u003c/li\u003e\n\u003cli\u003eYou Y, Ikezu T. Emerging roles of extracellular vesicles in neurodegenerative disorders. Neurobiol Dis. 2019;130:104512.\u003c/li\u003e\n\u003cli\u003eClayton K, Delpech JC, Herron S, Iwahara N, Ericsson M, Saito T, et al. Correction to: plaque associated microglia hyper-secrete extracellular vesicles and accelerate tau propagation in a humanized APP mouse model. Mol Neurodegener. 2021;16:24.\u003c/li\u003e\n\u003cli\u003eMuraoka S, Jedrychowski MP, Iwahara N, Abdullah M, Onos KD, Keezer KJ, et al. Enrichment of neurodegenerative microglia signature in brain-derived extracellular vesicles isolated from Alzheimer\u0026apos;s disease mouse models. J Proteome Res. 2021;20:1733-43.\u003c/li\u003e\n\u003cli\u003eLee J, McKinney KQ, Pavlopoulos AJ, Han MH, Kim SH, Kim HJ, et al. Exosomal proteome analysis of cerebrospinal fluid detects biosignatures of neuromyelitis optica and multiple sclerosis. Clin Chim Acta. 2016;462:118-26.\u003c/li\u003e\n\u003cli\u003eWelton JL, Loveless S, Stone T, Von Ruhland C, Robertson NP, Clayton A. Cerebrospinal fluid extracellular vesicle enrichment for protein biomarker discovery in neurological disease; multiple sclerosis. J Extracell Vesicles. 2017;6:1369805.\u003c/li\u003e\n\u003cli\u003ePolman CH, Reingold SC, Edan G, Filippi M, Hartung HP, Kappos L, et al. Diagnostic criteria for multiple sclerosis: 2005 revisions to the \u0026quot;McDonald criteria\u0026quot;. Ann Neurol. 2005;58:840-6.\u003c/li\u003e\n\u003cli\u003ePolman CH, Reingold SC, Banwell B, Clanet M, Cohen JA, Filippi M, et al. Diagnostic criteria for multiple sclerosis: 2010 revisions to the McDonald criteria. Ann Neurol. 2011;69:292-302.\u003c/li\u003e\n\u003cli\u003eWingerchuk DM, Lennon VA, Pittock SJ, Lucchinetti CF, Weinshenker BG. Revised diagnostic criteria for neuromyelitis optica. Neurology. 2006;66:1485-9.\u003c/li\u003e\n\u003cli\u003eWingerchuk DM, Banwell B, Bennett JL, Cabre P, Carroll W, Chitnis T, et al. International consensus diagnostic criteria for neuromyelitis optica spectrum disorders. Neurology. 2015;85:177-89.\u003c/li\u003e\n\u003cli\u003eKappos L, Butzkueven H, Wiendl H, Spelman T, Pellegrini F, Chen Y, et al. Greater sensitivity to multiple sclerosis disability worsening and progression events using a roving versus a fixed reference value in a prospective cohort study. Mult Scler. 2018;24:963-73.\u003c/li\u003e\n\u003cli\u003eMuraoka S, Hirano M, Isoyama J, Ishida M, Tomonaga T, Adachi J. Automated proteomics sample preparation of phosphatidylserine-positive extracellular vesicles from human body fluids. ACS Omega. 2022;7:41472-9.\u003c/li\u003e\n\u003cli\u003eMasuda T, Tomita M, Ishihama Y. Phase transfer surfactant-aided trypsin digestion for membrane proteome analysis. J Proteome Res. 2008;7:731-40.\u003c/li\u003e\n\u003cli\u003eRappsilber J, Mann M, Ishihama Y. Protocol for micro-purification, enrichment, pre-fractionation and storage of peptides for proteomics using stagetips. Nat Protoc. 2007;2:1896-906.\u003c/li\u003e\n\u003cli\u003eAdachi J, Hashiguchi K, Nagano M, Sato M, Sato A, Fukamizu K, et al. Improved proteome and phosphoproteome analysis on a cation exchanger by a combined acid and salt gradient. Anal Chem. 2016;88:7899-903.\u003c/li\u003e\n\u003cli\u003eDemichev V, Messner CB, Vernardis SI, Lilley KS, Ralser M. DIA-NN: neural networks and interference correction enable deep proteome coverage in high throughput. Nat Methods. 2020;17:41-4.\u003c/li\u003e\n\u003cli\u003eOkuda S, Watanabe Y, Moriya Y, Kawano S, Yamamoto T, Matsumoto M, et al. jPOSTrepo: an international standard data repository for proteomes. Nucleic Acids Res. 2017;45:D1107-11.\u003c/li\u003e\n\u003cli\u003eKeerthikumar S, Chisanga D, Ariyaratne D, Al Saffar H, Anand S, Zhao K, et al. ExoCarta: a web-based compendium of exosomal cargo. J Mol Biol. 2016;428:688-92.\u003c/li\u003e\n\u003cli\u003eDelker DA, Geter DR, Roop BC, Ward WO, Ahlborn GJ, Allen JW, et al. Oncogene expression profiles in K6/ODC mouse skin and papillomas following a chronic exposure to monomethylarsonous acid. J Biochem Mol Toxicol. 2009;23:406-18.\u003c/li\u003e\n\u003cli\u003eTian W, Shi D, Zhang Y, Wang H, Tang H, Han Z, et al. Deep proteomic analysis of obstetric antiphospholipid syndrome by DIA-MS of extracellular vesicle enriched fractions. Commun Biol. 2024;7:99.\u003c/li\u003e\n\u003cli\u003eBeaino W, Janssen B, Kooij G, Van Der Pol SMA, Van Het Hof B, Van Horssen J, et al. Purinergic receptors P2Y12R and P2X7R: potential targets for PET imaging of microglia phenotypes in multiple sclerosis. J Neuroinflammation. 2017;14:259.\u003c/li\u003e\n\u003cli\u003eRuan Z, Delpech JC, Kalavai SV, Van Enoo AA, Hu J, Ikezu S, et al. P2RX7 inhibitor suppresses exosome secretion and disease phenotype in P301S tau transgenic mice. Mol Neurodegener. 2020;15:47.\u003c/li\u003e\n\u003cli\u003eMuraoka S, Jedrychowski MP, Yanamandra K, Ikezu S, Gygi SP, Ikezu T. Proteomic profiling of extracellular vesicles derived from cerebrospinal fluid of Alzheimer\u0026apos;s disease patients: a pilot study. Cells. 2020;9:1959.\u003c/li\u003e\n\u003cli\u003eMuraoka S, Jedrychowski MP, Tatebe H, DeLeo AM, Ikezu S, Tokuda T, et al. Proteomic profiling of extracellular vesicles isolated from cerebrospinal fluid of former national football league players at risk for chronic traumatic encephalopathy. Front Neurosci. 2019;13:1059.\u003c/li\u003e\n\u003cli\u003eLozano IMD, Sork H, Stone VM, Eldh M, Cao X, Pernemalm M, et al. Proteome profiling of whole plasma and plasma-derived extracellular vesicles facilitates the detection of tissue biomarkers in the non-obese diabetic mouse. Front Endocrinol (Lausanne). 2022;13:971313.\u003c/li\u003e\n\u003cli\u003ePolman CH, O\u0026apos;Connor PW, Havrdova E, Hutchinson M, Kappos L, Miller DH, et al. A randomized, placebo-controlled trial of natalizumab for relapsing multiple sclerosis. N Engl J Med. 2006;354:899-910.\u003c/li\u003e\n\u003cli\u003ePlanas R, Metz I, Ortiz Y, Vilarrasa N, Jelčić I, Salinas-Riester G, et al. Central role of Th2/Tc2 lymphocytes in pattern II multiple sclerosis lesions. Ann Clin Transl Neurol. 2015;2:875-93.\u003c/li\u003e\n\u003cli\u003eObermeier B, Lovato L, Mentele R, Br\u0026uuml;ck W, Forne I, Imhof A, et al. Related B cell clones that populate the CSF and CNS of patients with multiple sclerosis produce CSF immunoglobulin. J Neuroimmunol. 2011;233:245-8.\u003c/li\u003e\n\u003cli\u003eSalou M, Garcia A, Michel L, Gainche-Salmon A, Loussouarn D, Nicol B, et al. Expanded CD8 T-cell sharing between periphery and CNS in multiple sclerosis. Ann Clin Transl Neurol. 2015;2:609-22.\u003c/li\u003e\n\u003cli\u003eKrasemann S, Madore C, Cialic R, Baufeld C, Calcagno N, El Fatimy R, et al. The TREM2-APOE pathway drives the transcriptional phenotype of dysfunctional microglia in neurodegenerative diseases. Immunity. 2017;47:566-81.e9.\u003c/li\u003e\n\u003cli\u003eJanssen B, Vugts DJ, Windhorst AD, Mach RH. PET imaging of microglial activation-beyond targeting TSPO. Molecules. 2018;23:607.\u003c/li\u003e\n\u003cli\u003eReeves JA, Bartnik A, Jakimovski D, Mohebbi M, Bergsland N, Salman F, et al. Associations between paramagnetic rim lesion evolution and clinical and radiologic disease progression in persons with multiple sclerosis. Neurology. 2024;103:e210004.\u003c/li\u003e\n\u003cli\u003eReeves JA, Mohebbi M, Wicks T, Salman F, Bartnik A, Jakimovski D, et al. Paramagnetic rim lesions predict greater long-term relapse rates and clinical progression over 10 years. Mult Scler. 2024;30:535-45.\u003c/li\u003e\n\u003cli\u003eAbsinta M, Maric D, Gharagozloo M, Garton T, Smith MD, Jin J, et al. A lymphocyte-microglia-astrocyte axis in chronic active multiple sclerosis. Nature. 2021;597:709-14.\u003c/li\u003e\n\u003cli\u003eHou L, Sin YC, Chen Y, Yuki K. Integrin CD11c regulates B cell homeostasis. Front Immunol. 2024;15:1359608.\u003c/li\u003e\n\u003cli\u003eImmig K, Gericke M, Menzel F, Merz F, Krueger M, Schiefenh\u0026ouml;vel F, et al. CD11c-positive cells from brain, spleen, lung, and liver exhibit site-specific immune phenotypes and plastically adapt to new environments. Glia. 2015;63:611-25.\u003c/li\u003e\n\u003cli\u003eEl Mahdaoui S, Hansen MM, Von Essen MR, Hvalkof VH, Hansen RH, Mahler MR, et al. CD11c\u003csup\u003e+\u003c/sup\u003e B cells in relapsing-remitting multiple sclerosis and effects of anti-CD20 therapy. Ann Clin Transl Neurol. 2024;11:926-37.\u003c/li\u003e\n\u003cli\u003eMontalban X, Vermersch P, Arnold DL, Bar-Or A, Cree BAC, Cross AH, et al. Safety and efficacy of evobrutinib in relapsing multiple sclerosis (evolutionRMS1 and evolutionRMS2): two multicentre, randomised, double-blind, active-controlled, phase 3 trials. Lancet Neurol. 2024;23:1119-32.\u003c/li\u003e\n\u003cli\u003eArnold DL, Elliott C, Martin EC, Hyvert Y, Tomic D, Montalban X. Effect of evobrutinib on slowly expanding lesion volume in relapsing multiple sclerosis: a post hoc analysis of a phase 2 trial. Neurology. 2024;102:e208058.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"multiple sclerosis, neuromyelitis optica spectrum disorder, extracellular vesicles, microglia","lastPublishedDoi":"10.21203/rs.3.rs-5954841/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5954841/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eExtracellular vesicles (EVs) are membrane-bound particles that are released into the extracellular space and are thought to play a role in the pathogenesis of neuroinflammation and neurodegeneration. Nevertheless, the precise role of these vesicles in the context of multiple sclerosis (MS) remains uncertain. The objective of this study was to identify the distinctive characteristics of EVs associated with MS\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eEVs were isolated from CSF using phosphatidylserine affinity methods. Mass spectrometry was used to analyze cerebrospinal fluid (CSF) samples and EVs isolated from those CSF samples collected from a discovery cohort of 10 patients with other neurological diseases (OND) and 10 patients with MS. In addition, mass spectrometry was used to analyze EVs isolated from CSF samples in a validation cohort of 24 patients with OND, 38 patients with MS, and 14 patients with neuromyelitis optica spectrum disorders (NMOSD).\u003c/p\u003e\u003ch2\u003eResultes\u003c/h2\u003e \u003cp\u003eThe results revealed notable increases in the levels of 33 proteins in the CSF samples and 100 proteins in the CSF-derived EVs from patients with MS in the validation cohort. Increases in the levels of ITGA4, ITGAX, MS4A1 (CD20), CD3E, CD4, and CD8A, which are marker proteins of lymphocytes and myeloid cells, including activated microglia and dendritic cells, were observed in the CSF-derived EVs in discovery cohort. The results of the validation cohort revealed that the levels of four proteins, ITGA4, ITGAX, MS4A1, and CD3E, were significantly greater in MS patients than in OND patients. Furthermore, the level of ITGAX was greater in the patients with confirmed disability worsening (CDW) than that of without CDW. The results of the receiver operating characteristic (ROC) and Kaplan‒Meier analyses indicated that ITGAX levels in CSF-derived EVs may prove useful in predicting disease prognosis.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eOur findings suggest that CSF-derived EVs reflect immunologic changes in MS and other neuroimmune diseases. In addition, these results raise the possibility that changing in myeloid cells as well as lymphocytes may also play a role in the pathogenesis of MS. CSF-derived EVs may serve as indicators of MS disease severity and could be utilized as biomarkers in the future.\u003c/p\u003e","manuscriptTitle":"Protein profiling of extracellular vesicles from the cerebrospinal fluid of patients with multiple sclerosis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-07 10:10:37","doi":"10.21203/rs.3.rs-5954841/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b70bf752-f24e-4294-9cf6-bb704c776a33","owner":[],"postedDate":"February 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-02-07T10:10:39+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-07 10:10:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5954841","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5954841","identity":"rs-5954841","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}