Brain-Derived Extracellular Vesicles as Nanobiotechnology Biomarkers of Small Vessel Disease (CADASIL)

Brain-Derived Extracellular Vesicles as Nanobiotechnology Biomarkers of Small Vessel Disease (CADASIL) | 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 Brain-Derived Extracellular Vesicles as Nanobiotechnology Biomarkers of Small Vessel Disease (CADASIL) Ana Bugallo-Casal, Elena Muino, Paula Villatoro-González, Laura Camacho-Meño, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7511858/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 Cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) is the most common hereditary small vessel disease (SVD) and currently lacks reliable biomarkers to monitor disease progression. Extracellular vesicles (EVs) are nanoscale carriers that cross the blood–brain barrier and provide a minimally invasive liquid biopsy of brain pathology. This study aimed to characterize brain cell–derived EVs in CADASIL and explore their potential as biomarkers of disease stage using advanced proteomic profiling. Results Plasma EVs were isolated from CADASIL patients stratified according to the NOTCH3-SVD staging system and further enriched into neuronal (nEVs), astrocytic (aEVs), and oligodendrocytic (oEVs) subpopulations by immunoaffinity capture. The analysis of canonical biomarkers showed that glial fibrillary acidic protein (GFAP), a marker of astrocytic activation, was significantly increased in aEVs from patients at intermediate/advanced stages. Similarly, myelin basic protein (MBP), reflecting oligodendrocyte integrity and myelin disruption, was elevated in oEVs in the same group. By contrast, neurofilament light chain (NfL), a marker of axonal injury, did not show significant stage-dependent changes in nEVs. Importantly, these differences were not detectable in plasma or in total EV fractions, highlighting the superior sensitivity of cell type–specific EV analysis. Complementary proteomic profiling identified stage-related molecular signatures in both plasma and EVs, including downregulation of proteins related to metabolism and cytoskeletal organization, and upregulation of immune and stress-response pathways. These molecular patterns suggest a shift toward a pro-inflammatory and neurodegenerative environment in patients with more advanced disease stages. Conclusions Brain cell–derived EVs constitute a nanobiotechnology platform for minimally invasive biomarker discovery in CADASIL. Cell type–specific EV profiling allows the detection of subtle glial alterations and proteomic shifts associated with disease progression, which are not evident in plasma or bulk EVs. These findings support the development of EV-based biomarkers as sensitive tools for monitoring disease course in CADASIL and potentially other small vessel diseases. Extracellular vesicles Brain-derived exosomes Nanobiotechnology biomarkers Proteomic profiling Cerebral small vessel disease CADASIL Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 BACKGROUND Cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) is a rare disease of genetic origin that represents the most common form of inherited small vessel disease (SVD). It is caused by pathogenic genetic variants in the NOTCH3 gene, which encodes a transmembrane receptor expressed predominantly in vascular smooth muscle cells and pericytes [ 1 , 2 ]. Pathogenic variants leading to CADASIL cause misfolding and aggregation of the Notch3 receptor. The primary clinical manifestations include headache, psychiatric disturbances, recurrent small subcortical infarcts, and early-onset dementia, highlighting the early appearance of both strokes and cognitive deterioration [ 3 ]. Recently, a new disease severity staging system has been published that captures the broad clinic-radiological spectrum of NOTCH3 -associated SVD including CADASIL. This NOTCH3 -SVD staging system encompasses five disease stages ranging from 0 to 4, with stages 1 to 4 each subdivided into two substages, forming a total of nine substages. These stages represent asymptomatic (Stage 0), early (Stages 1a and 1b), intermediate (Stages 2a and 2b), advanced (Stages 3a and 3b), and end-stage disease (Stages 4a and 4b) [ 4 ]. The staging is based on easily assessable clinical and imaging criteria, including white matter hyperintensities (WMHs), severity assessed by Fazekas scale, the number of lacunes detected by magnetic resonance imaging (MRI), and functional disability evaluated by the modified Rankin Scale (mRS). Besides this clinical classification, biomarkers have become essential tools in the context of CADASIL, both for diagnostic and prognostic purposes. Genetically, the identification of pathogenic genetic variants in the NOTCH3 gene remains the gold standard for diagnosis. However, the study of circulating and neuroimaging biomarkers has significantly improved early detection and allowed assessment of disease progression. For instance, WMHs on MRI are considered highly specific for CADASIL and represent critical diagnostic markers [ 3 , 5 – 7 ]. An important limitation in the use of biomarkers for CADASIL is related to the typically slow and heterogeneous progression of the disease. CADASIL often evolves over decades, with significant variability in the onset and severity of clinical manifestations even among individuals carrying the same NOTCH3 pathogenic variant [ 3 ]. This slow progression makes it challenging to correlate biomarker levels with immediate changes in disease activity or clinical status. In recent years, extracellular vesicles (EVs) have gained increasing attention in nanobiotechnology as natural nanoscale carriers with broad potential for biomedical applications. These vesicles are secreted by virtually all cell types and can cross the blood–brain barrier, transporting a diverse molecular cargo that includes proteins, lipids, and nucleic acids [ 8 , 9 ]. By reflecting the physiological and pathological state of their cells of origin, EVs act as dynamic messengers of intercellular communication and represent a valuable source of minimally invasive biomarkers. In the context of cerebrovascular and neurological diseases, the study of EVs provides a value opportunity to explore disease mechanisms at the molecular level, monitor clinical progression through liquid biopsy approaches, and identify novel targets for therapeutic intervention [ 10 , 11 ]. Despite the growing interest in EVs, studies directly addressing their role in CADASIL remain extremely limited. This gap represents a unique scientific and translational opportunity, as EVs offer a direct nanoscale window into the diseased brain and may anticipate clinical outcomes such as cognitive decline, infarct recurrence, or white matter lesion progression. In this context, the proteomic characterization of EV cargo in CADASIL patients can provide critical insights into the molecular mechanisms driving neurovascular pathology and reveal novel biomarker candidates with high sensitivity for disease staging and progression. Taken together, these considerations highlight the need for innovative biomarker strategies beyond conventional imaging or fluid assays. By integrating cell type–specific EV profiling (neuronal, astrocytic, and oligodendrocytic) with proteomic analysis, this study aims to define molecular patterns associated with CADASIL progression. Such an approach not only holds promise for improving diagnostic and prognostic accuracy in this rare monogenic SVD but may also establish a framework for EV-based biomarker discovery in other neurovascular and neurodegenerative disorders. METHODS Study Design and Participants Subjects were selected from CADAGENIA [12], a registry in which patients with genetic variants in NOTCH3 were consecutively recorded since 2017 from different parts of Spain. Epidemiological data, blood analyses, cognitive and neuroimaging profiles, and skin biopsies were registered. Sample collection from recruited patients was carried out at the Institut de Recerca Sant Pau (Barcelona, Spain) on January of 2025. All procedures were conducted in accordance with the Declaration of Helsinki and Good Clinical Practice guidelines. Written informed consent was obtained from all participants before their inclusion in the study. Inclusion criteria for this study were; 1) age >17 years, 2) having a cysteine-affecting NOTCH3 missense variant, and 3) having MRI and clinical available for NOTCH3 -SVD staging. CADASIL patients were classified according to the NOTCH3 -SVD staging system [4], recently proposed by the Leiden University Medical Center, and grouped into early/asymptomatic stages (1a and 1b), referred below as Leiden stage 1 (L1), and intermediate/advanced stages (≥2a), referred as Leiden stage 2 (L2). This grouping was based on clinical and imaging differences that may facilitate interpretation in a research context. Patient blood sample collection Whole blood was collected into Vacutainer™ Plasma Preparation Tubes (PPT™, 8.5 mL; Ref. 12977696) and processed within less than 2 hours of collection. Samples were first centrifuged at 2,000 × g for 15 minutes at 10 ºC temperature to separate plasma from cellular components. The upper plasma layer was carefully transferred to new sterile polypropylene tubes without disturbing the buffy coat. A second centrifugation was then performed at 2,500 × g for 15 minutes at 10°C to remove residual platelets and cellular debris. The resulting platelet-poor plasma was aliquoted into sterile cryovials and stored at −80°C until further use. Isolation of Total Extracellular Vesicles by Size Exclusion Chromatography To remove cellular debris, plasma samples were first centrifuged at 10,000 × g for 10 minutes at 4°C. Following similar procedures as reported previously by our team [13], total extracellular vesicles (EV T ) were subsequently isolated using qEV Original 70 nm Gen2 columns (IZON SCIENCE LTD), which were pre-equilibrated with 17 mL of filtered phosphate-buffered saline (PBS, 0.22 µm). A 500 µL aliquot of sample was loaded, and the initial 2.9 mL of void volume was discarded. Six consecutive 0.4 mL fractions were collected using an Automatic Fraction Collector (AFC). These EV-enriched fractions were pooled and concentrated using 100 kDa centrifugal filter devices (Amicon Ultra, Millipore) at 12,000 × g, 4°C, until a final volume of approximately 200 µL was reached. Concentrated EVs were transferred to sterile tubes and stored at −80°C. For each participant, two independent 500 µL aliquots were processed and subsequently pooled. Nanoparticle Tracking Analysis (NTA) Particle concentration and size distribution of EV T were assessed using the NanoSight PRO instrument (Malvern Instruments) equipped with a 488 nm laser and a high-sensitivity camera. Samples were diluted 1:10,000 in filtered PBS to fall within the optimal detection range (1 × 10⁷ – 1 × 10⁹ particles/mL). Five 60-second videos per sample were acquired with predefined settings (exposure: 13, contrast: 3), and automatic parameters were applied for tracking. Data were analyzed using NanoSight NTA 3.4 software, yielding values for mean, mode, and median particle diameters, as well as particle concentration. Transmission Electron Microscopy To visualize EV T morphology and integrity, samples were diluted 1:10 in distilled water. Ten microliters were placed onto carbon-coated copper grids and allowed to adsorb for 1 minute. Grids were negatively stained with 1% phosphotungstic acid (prepared in Milli-Q® water) for 1 minute, then blotted and air-dried. Imaging was performed using a JEOL JEM 2010 transmission electron microscope (TEM) operating at 200 kV. Extracellular Vesicle Characterization and Detection of Canonical EV Markers EV T preparations and matched depleted EV samples were analyzed by ELISA to detect the EV-enriched tetraspanins CD81 (Ref. 18095935; ThermoFisher Scientific), CD63 (Ref. 16853020; ThermoFisher Scientific), and the endosomal protein ALIX (Ref. 30240446; ThermoFisher Scientific). Calnexin (Ref. 18234959; ThermoFisher Scientific), an endoplasmic reticulum protein absents from EVs, served as a negative control to assess potential contamination. Fifty microliters of EV T were lysed in 50 µL of RIPA buffer, followed by four 10-second sonication cycles at 10% amplitude on ice. After centrifugation at 10,000 × g for 10 minutes at 4°C, 6 µL of the supernatant was used for protein quantification (Pierce BCA Protein Assay), and 90 µL were applied to ELISA plates. All steps were carried out following the manufacturer's instructions. Absorbance was measured at 450/620 nm using an Infinite M200 plate reader (TECAN, Männedorf, Switzerland), and analyte concentrations were calculated using a four-parameter logistic (4PL) curve. Enrichment of Brain-Derived Extracellular Vesicle Subtypes A sequential immunoaffinity-based approach was used to enrich EVs from specific brain cell types (neurons, astrocytes, and oligodendrocytes) starting from 100 µL of EV T (derived from 1 mL of plasma). To isolate neuronal EVs (nEVs), the EV T preparation was incubated overnight at 4°C under gentle agitation with 4 µg of biotinylated anti-L1CAM/CD171 antibody (Ref. 13-1919-82, ThermoFisher Scientific) in 300 µL PBS containing 1.3% bovine serum albumin (BSA). On the following day, 200 µL of streptavidin-coated magnetic beads (Ref. 10608D, ThermoFisher Scientific) were added and incubated for 3 hours at room temperature with continuous mixing. Bead-bound complexes containing nEVs were captured using a magnetic separator (Imán DynaMag™-2; Ref. 12321D from ThermoFisher Scientific), and the supernatant, depleted of nEVs, was retained for subsequent enrichments. The immunocomplexes were washed three times with 1 mL of PBS containing 0.1% BSA, resuspended in 100 µL of filtered PBS, and stored at −80°C for downstream applications. The nEV-depleted supernatant was then sequentially incubated with biotinylated antibodies targeting markers specific for astrocytes (aEV) (Glast, Ref. NB100-1869B, Novus Biologicals) and oligodendrocytes (oEV) (MOG, Ref. BAM2439, R&D Systems). Each isolation step involved incubation with 4 µg of biotinylated antibody, streptavidin bead capture, magnetic separation, and recovery of the supernatant for the next extraction step. Characterization of Brain Cell-Specific EV S Populations Enrichment of neuronal EVs was assessed via ELISA for neuron-specific enolase (NSE; Ref. ab217778, Abcam). Astrocytic EVs were validated using a GFAP ELISA kit (Ref. EEL079, Invitrogen). Oligodendrocyte-derived EVs were evaluated using myelin basic protein (MBP; Ref. MBS2502574, MyBioSource). For each assay, 50 µL of each EV subpopulation were lysed with an equal volume of RIPA buffer, subjected to four sonication cycles (10 seconds, 10% amplitude) on ice, and centrifuged at 10,000 × g for 10 minutes at 4°C. A 6 µL aliquot was used for protein quantification, and 90 µL were applied to ELISA plates. Absorbance readings at 450/620 nm were obtained using the Infinite M200 reader, and concentrations were calculated based on 4PL standard curves. Quantification of Biomarkers by Immunoassay Quantification of biomarkers was conducted across five biological matrices: plasma, EV T , nEVs, aEVs and oEVs. NfL concentrations were measured using the ultra-sensitive Single Molecule Array (Simoa) technology. The NF-light® Advantage Kit (Ref. 104364, Quanterix) was employed according to the manufacturer’s instructions for plasma samples. For total EVs (EV T ) and nEVs, 50 µL of each EV sample were resuspended in 50 µL of RIPA buffer and subjected to four cycles of sonication (10 seconds each at 10% amplitude) on ice to ensure effective vesicle disruption and protein release. The lysates were then centrifuged at 10,000 × g for 10 minutes at 4°C. The resulting supernatants were collected and used for NfL quantification on a Quanterix SR-X® Biomarker Detection System. GFAP and MBP concentrations were quantified in plasma, EV T , aEVs (GFAP) and oEVs (MBP) using high-sensitivity ELISA kits (GFAP: Ref. EEL079, Invitrogen; MBP: Ref. MBS2502574 from MyBioSource). Plasma samples were processed strictly following the manufacturer’s instructions. For EV T , aEV, and oEV samples, 50 µL of EV lysate (prepared as previously described using RIPA buffer, sonication, and centrifugation at 10,000 × g for 10 minutes at 4°C) were used per well. Subsequently, ELISA assays were conducted according to the manufacturer’s protocols, including all specified incubation times, reagent volumes, and washing steps. Optical density was measured at 450/620 nm using a calibrated microplate reader. Total protein concentration in all samples was determined using the Pierce™ BCA Protein Assay Kit (ThermoFisher Scientific), following the manufacturer’s microplate procedure. Final concentrations of NfL, GFAP, and MBP were normalized to total protein content and reported as picograms per milliliter per microgram of total protein (pg·mL⁻¹·µg⁻¹). Proteomic analysis of EV cargo Proteomic analysis procedure was developed as described elsewhere [14]. Briefly, for protein extraction and digestion, EV proteins were lysed in RIPA buffer supplemented with protease and phosphatase inhibitors. Protein concentrations were measured using a BCA assay. Equal amounts (10–20 μg) were reduced with dithiothreitol, alkylated with iodoacetamide, and digested overnight at 37 °C using trypsin in a 1:50 enzyme-to-protein ratio. The methodological details related to the quantitative proteomic analysis are described in the Supplementary Data. Mass spectrometry Analysis by LC-MS/MS using a triple TOF 6600 SCIEX was performed. The digested peptides were resuspended in 20 µL of mobile phase A (H 2 O with 0.1% formic acid LC-MS quality), by sonication for 10 min. For SWATH library creation 4 µL of each sample pools (pools of each condition) were injected in a loop (10 µL/min) where the peptide mixture was correctly resuspended in mobile phase A (H 2 O with 0.1% formic acid LC-MS quality). The peptides were transferred to a YMC TRIART C18 silica-based reversed phase guard column, 5 ×0.5 mm, 3 µm in particle size and 120 Å pore size (YMC Technologies) where possible contaminants that could damage the column were removed. This guard column is connected online to a YMC-TRIART C18 capture column, 150 ×0.30 mm, with a particle size of 3 µm and a pore size of 120 Å (YMC Technologies) where the peptides are separated by polarity at a speed of 5 µL/min. The elution gradient of the peptides ranges from 2% to 90% mobile phase B (acetonitrile (ACN) with 0.1% formic acid LC-MS quality). For DDA analysis a 40 min gradient was used, for SWATH. After library creation each individual sample were analyzed using a Data-independent acquisition (SWATH-MS) method, followed by quantitative data comparing each sample with the library. The methodological details related to the quantitative proteomic analysis are described in the Supplementary Data. Data analysis For the library creation, the LC-MS/MS files were processed with the ProteinPilot TM 5.0.2 (SCIEX) software using the Paragon TM (5.0.2) algorithm for the database search and Progroup TM for the grouping of the data. Searches were performed using a human-specific UniProt database (UniProt, https://www.uniprot.org/uniprot [15]) SWATH analysis was performed using Peak view 2.2 for retention time adjustment and areas extraction, and Marker view 1.3.1 for normalization and statistical analysis using a T-test. The methodological details related to the quantitative proteomic analysis are described in the Supplementary Data. Bioinformatics and functional enrichment analysis Protein–protein interaction analysis was performed using the significantly dysregulated proteins were analyzed using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING, https://string-db.org/) database to explore functional enrichment, biological pathways, and interaction networks. The biological processes associated with significantly dysregulated proteins were analyzed using Functional Enrichment Analysis Tool software (FunRich, https://www.funrich.org/). Gene Ontology Cellular Component (GO:CC) terms were used to evaluate the subcellular distribution and EV-related functions. Statistical analysis Data related to EV characterization are presented as mean ± standard error of the mean (SEM). Normality and homogeneity of variances were assessed using the Shapiro–Wilk and equal variance tests, respectively. Based on data distribution, either a parametric t -test or a non-parametric Mann–Whitney U test was used for comparisons between two groups. For analyses involving more than two groups, the Kruskal–Wallis test was applied, followed by the most appropriate post hoc test according to the data characteristics. Statistical significance was defined as p < 0.05. All statistical analyses were performed using SigmaPlot 11.0 (Systat Software, Inc., CA, U.S.A.) and all graphs were generated using GraphPad Prism 8 software (GraphPad; Inc., San Diego, CA, USA). Data processing and generation of volcano plots were performed using Prism 9 software (GraphPad Software, version 9.0.2, La Jolla, CA, USA), based on the proteomic analysis results. In these plots, values are represented as −Log₁₀ of the p-value on the Y-axis and Log₂ of fold change (FC) on the X-axis. The fold change was calculated as the ratio between protein expression levels (expressed as normalized area) when comparing the analyzed groups. An FC > 1 indicates upregulation, whereas an FC < 1 indicates downregulation. Proteins with a p-value < 0.05 were considered significantly regulated. Proteins meeting these criteria are represented by red (downregulated) and green (upregulated) dots. Data availability statement The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE [16] partner repository with the dataset identifier PXD066420. RESULTS Clinical description of patients A total of 13 patients (8 males and 5 females) with a confirmed diagnosis of CADASIL were included in the study. Key demographic and clinical characteristics of the cohort are summarized in Table 1. Seven patients were classified within L1 (subtypes 1a and 1b), and six in the group L2 (subtypes 2b and 3a). The L1 group included younger patients (mean age ~44 years), with limited structural brain damage on MRI and mostly preserved cognitive function, with very mild impairment. In contrast, the L2 group was older on average (~55 years) and comprised patients in stages 2b–3a, showing a greater burden of WMT and lacunar infarcts, and more frequent cognitive deficits ranging from very mild to moderate impairment. Table 1. Demographic, Genetic, and Clinical Characteristics of CADASIL Patients Included in the Study, Categorized by Dichotomized NOTCH3 -SVD Staging (Early/Asymptomatic; defined as L1 vs Intermediate/Advanced maned as L2) Group Identifier Sex Age Exon EGF Scale Cognitive impairment L1 CAD9 female 54 4 3 1b normal CAD18 male 49 3 2 1b mild impairment CAD41 female 53 5 4 1b * CAD42 male 35 2 1 1a normal CAD43 male 45 2 1 1b very mild impairment CAD53 male 29 3 2 1b normal L2 CAD8 male 53 4 3 2b mild impairment CAD15 female 61 6 7 3a mild impairment CAD23 male 49 4 4 2b mild impairment CAD29 male 63 11 14 3b moderate impairment CAD38 female 50 4 4 2b very mild impairment CAD55 female 55 4 4 2b mild impairment CAD57 male 57 3 2 2b mild impairment *Data not available Characterization of EVs isolated from CADASIL patients As first step of the study was to characterize EVs isolated from CADASIL patients. This step is essential to confirm their purity, structural integrity, and the presence of specific molecular markers before conducting further analyses. Nanoparticle Tracking Analysis (NTA) demonstrated a similar EV size distribution in patients stratified according to L1 (Figure 1A) and L2 (Figure 1B). The mean particle size did not differ significantly between L1 and L2, with values of 114.3nm ± 2.6 (SEM) and 109.6nm ± 3.3, respectively. Likewise, no significant differences were found between both groups with values of 1,693 × 10¹² ± 4,797 × 10¹¹ particles/mL for L1 patients and 1,432 × 10¹² ± 2,728 × 10¹¹ particles/mL in case of L2. TEM analysis confirmed the presence of vesicular structures displaying the characteristic cup-shaped morphology and a diameter below 150 nm, consistent with the size and ultrastructural features of small extracellular vesicles, classically referred to as exosomes (Figure 1C). The enrichment of EVs was validated by quantifying the levels of canonical EV markers, such as CD63, CD81 and ALIX. CD63 and CD81 are two membrane-associated tetraspanins commonly enriched in small EVs [17] and ALIX, a cytosolic protein involved in the endosomal sorting complex required for transport (ESCRT) machinery [18]. Significantly higher concentrations of CD63 (Figure 1D), CD81 (Figure 1E), and ALIX (Figure 1F) were detected in isolated EV T compared to the corresponding EV-depleted fractions ( p < 0.05 for all comparisons), thereby confirming the effective enrichment of EVs achieved through the isolation protocol previously reported [13]. Finally, to evaluate potential contamination with cellular components, the presence of calnexin, a marker of the endoplasmic reticulum, was assessed by immunodetection. Negligible levels of calnexin were detected in both the EV-depleted and EV ᴛ fractions, while significantly higher levels were observed exclusively in the positive control (lysates from the HMC3 human microglial cell line) (Figure 1G). These findings indicate that contamination with cellular debris was minimal in the EV preparations. Selective Enrichment of Brain Cell Type–Specific Extracellular Vesicle Subpopulations Once the EV T were characterized, the cellular origin of isolated and purified EV subpopulations was determined by quantifying cell type–specific protein markers in EVs enriched from nEVs, aEVs, and oEVs origin. Protein concentrations were normalized to the total protein content of each EV fraction and expressed as pg·mL⁻¹·µg⁻¹. Neuronal marker enolase levels (Figure 2A) were markedly elevated in nEVs (mean ~1,500 pg·mL⁻¹·µg⁻¹), compared to plasma (<0.09 pg·mL⁻¹·µg⁻¹) and EV T fractions (<12 pg·mL⁻¹·µg⁻¹), indicating a high degree of neuronal specificity. GFAP, an intermediate filament protein selectively expressed in astrocytes, was markedly enriched aEV fraction (Figure 2B). GFAP concentrations in plasma (<0.14 pg·mL⁻¹·µg⁻¹) and EVᴛ fractions (<0.4 pg·mL⁻¹·µg⁻¹) were significantly lower than those detected in aEVs, where significantly elevated levels were quantified (≈60 pg·mL⁻¹·µg⁻¹). These findings are consistent with the astrocytic origin of this EV subpopulation and support the successful immunoaffinity-based enrichment of aEVs. MBP (Figure 2C), a canonical myelin-associated protein predominantly expressed by oligodendrocytes, was detected at markedly elevated concentrations in oEVs (≈200 pg·mL⁻¹·µg⁻¹), in contrast to plasma (<5 × 10⁻³ pg·mL⁻¹·µg⁻¹) and EV T fractions (<0.14 pg·mL⁻¹·µg⁻¹). These results confirm the specificity and efficiency of the immunoaffinity-based method employed for the selective isolation of oEVs. Cell Type–Specific EVs Reveal Stage-Dependent Changes in GFAP and MBP NfL, GFAP, and MBP are important biomarkers reflecting neuroaxonal damage, astrocytic activation, and demyelination, respectively [19-23]. In CADASIL, elevated NfL levels correlate with axonal injury and clinical severity [7, 24]. Increased GFAP indicates reactive astrogliosis linked to microvascular pathology [24], while MBP reflects myelin breakdown and white matter damage, characteristic of CADASIL [25] and other neuronal pathologies [26]. To evaluate potential differences in the abundance of brain cell–derived EV markers in both clinical scenarios of CADASIL, the concentrations of NfL, GFAP, and MBP were quantified in plasma, EV T , nEV, aEV and oEV subpopulations isolated from individuals classified as L1 or L2. NfL levels analyzed in the five subpopulations are showed in the Figure 3A. No statistically significant differences were observed between patients classified as L1 and L2. NfL levels within nEVs fractions were comparable across both clinical groups, suggesting that the degree of neuronal injury, as reflected by this axonal biomarker, does not substantially vary between these two clinical groups in this cohort. In contrast, GFAP levels (Figure 3B) were significantly elevated in aEVs from patients classified as L2, compared to L1 patients ( p < 0.05). This finding indicates increased astrocytic activation in more advanced stages of the disease and aligns with the proposed involvement of progressive gliosis in CADASIL pathophysiology. The data further support the utility of aEVs as a non-invasive source of molecular information to monitor glial reactivity and neuroinflammatory dynamics in vivo . Similarly, a marked increase in MBP concentrations (Figure 3C) was detected in oEVs from L2 patients relative to L1 ( p < 0.05), suggesting enhanced oligodendroglial stress or myelin-related pathology in later stages. These observations reinforce the potential of cell type–specific EV profiling as a sensitive platform for assessing CNS cellular alterations associated with disease progression. No significant differences in NfL, GFAP, or MBP concentrations were observed in plasma or EVᴛ fractions between patients classified as L1 and 2. For all three biomarkers, levels remained similar across both clinical groups, indicating that neither plasma nor EVᴛ compartments are sensitive to stage-related CNS alterations in this population. Proteomic analysis of plasma samples and EV T from CADASIL patients A total of 1,141 proteins were identified with a false discovery rate (FDR) below 1%. Of these, we were able to quantify 746 proteins (Fig. S1A), the majority of which are predominantly represented in brain and nervous system tissue expression profiles, suggesting a strong relevance of the detected proteome to neurovascular physiology and pathology (Fig. S1B). Following the detailed analysis of neuronal, astrocytic, and oligodendrocyte-derived EVs (including quantification of NfL, GFAP and MBP) we performed a comprehensive proteomic analysis to explore additional molecular signatures that could serve as potential biomarkers. As an initial approach, a global proteomic analysis of plasma from CADASIL patients (L2 vs L1) revealed 44 differentially expressed proteins (summarized in the Table S1). Twelve significantly downregulated proteins (RPL23A, ATP1A1, ABCG2, CYTA, F10A1, IGF2BP1, RAB14, PYGM, FKBP1A, MAPT, MSN, SYNJ1) and six upregulated proteins (C4B, AK1, IGHG4, CAPS, YWHAB, CRP) were enriched among the most significant hits (p < 0.05) in the volcano plot (Fig. 4A). From the proteins significantly upregulated and downregulated with p < 0.05, the top 5 from each group were selected and represented in a heatmap. This visualization allows for an intuitive comparison of expression patterns across samples, highlighting relative protein abundance and clustering based on similarity (Fig.4B). Functional enrichment analysis performed on the complete set of dysregulated proteins (GO:CC, Fig. 4C) pointed to modest contributions from blood microparticles and secretory granules. Disease gene associations suggest their potential involvement in diverse pathophysiological mechanisms, including amyloidosis, coagulation, and genetic vascular disorders (Fig.S2A). In summary, plasma proteomics revealed a molecular signature characterized by downregulation of neuronal and immune regulatory proteins and upregulation of coagulation and inflammation-related factors, consistent with progressive neurovascular dysfunction in CADASIL. Functional enrichment showed dominant pathways in innate immunity (27.6%), complement activation (23.1%), adaptive immune response (22.7%), and B-cell signaling (13.8%). Additional contributions included coagulation (7–8%), proteolysis (6.7%), and lipid-handling processes (3–4%). Overall, these findings indicate a systemic pro-inflammatory and pro-thrombotic environment, in line with the vascular pathophysiology of small vessel disease [27] (Fig.S4A). Proteomic analysis of EV T identified 38 EV-derived proteins in CADASIL patients (described in detail in Table S2). Proteins with p < 0.05 were concentrated at the extremes of the volcano plot (Fig. 4D), with a total of 21 downregulated (THBS1, PCP4, PAPL, PGLYRP2, PRDX5, ALDH1L1, IGF2BP3, SEPTIN7, PAICS, CFL1, SLC2A1, C8A, DYNC1I1, CCT6A, ENPP6, TRIM28, HNRNPA0, ITIH1, PHB1, SH3BGRL3, YWHAE) and 5 upregulated (QDPR, RPL29, PCYT2, FTL, STXBP1). Among the proteins showing significant up- and downregulation with p < 0.05, the top five from each category were chosen and displayed in a heatmap (Fig.4E). GO:CC enrichment analysis (Fig. 4F) confirmed a predominant association with organelle- and vesicle-related components, consistent with the EVs origin of the proteome. Disease gene association category in STRING network, linking them to pathological conditions such as amyloidosis, blood coagulation disorders, autosomal dominant diseases, genetic syndromes, vascular pathologies, and cerebrovascular disease (Fig. S2B); supporting its potential utility as a biomarker of clinical progression. The functional analysis of proteins present in EV T (Fig.S4B) recapitulated the immune signature observed in plasma (innate [27.3%], adaptive [22.5%] and classical complement activation [24.1%]), but a distinctive enrichment for phagocytosis/engulfment (17.1%) emerged, alongside hemostatic functions (fibrinolysis [5.3%], blood coagulation [5.9%], plasminogen activation [2.1%]) and lipid transport/remodeling (reverse cholesterol transport [4.3%], lipoprotein metabolism [4.3%], positive regulation of cholesterol esterification [3.2%], HDL particle remodeling [3.7%]). These findings support the role of EVs as a dynamic reflection of cellular immunity and tissue homeostasis in vascular diseases such as CADASIL [28]. Proteomic analysis of neuronal, astrocytic, and oligodendrocyte-derived EVs A total of 26 proteins were differentially expressed in nEVs from CADASIL patients, with 15 downregulated and 11 upregulated (described in the Table S3). A global overview of the data showed 14 proteins with p < 0.05 concentrated at the extremes of the volcano plot. The most prominent downregulated proteins included HV226, PGRMC1, LUM, RTN4, RPL23, NPM1, and FCN2, while SPP24, PHB2, CTSD, RPS30, RPL28, and MOG were among the most upregulated. (Fig. 5A). The five most significantly upregulated and downregulated proteins (with p < 0.05) from each group were selected and visualized using a heatmap (Fig.5B). GO:CC analysis (Fig. 5C) revealed significant enrichment in proteins associated with organelles and extracellular vesicles. Network analysis using STRING (Fig. S3A) showed several proteins were significantly annotated in the Disease gene association category, linking them to vascular, genetic, and neurodegenerative conditions such as amyloidosis, coagulation disorders, coronary and cerebrovascular disease. These associations suggest that the altered nEVs proteome may reflect molecular mechanisms involved in CADASIL pathology. The upregulation of proteins such as MOG and CTSD, linked to demyelination and lysosomal degradation, points to the activation of neurodegenerative pathways. GO enrichment and network analyses further associate these nEV alterations with vascular, genetic, and neurodegenerative conditions, suggesting that nEVs may capture processes contributing to neuronal damage, endothelial dysfunction, and blood–brain barrier disruption. Overall, the proteomic profile of nEVs indicates a pathological environment characterized by inflammation, neurodegeneration, and vascular impairment, supporting their potential role as peripheral sensors of brain immune–metabolic state and as minimally invasive biomarkers of neuronal injury in CADASIL [29, 30] (Fig.S5A). In aEVs from CADASIL patients, a total of 31 proteins were differentially expressed (Table S4). From the subset of proteins significantly altered (up or down) with p < 0.05 (Fig. 6A), the top five in each group were represented in a heatmap (Fig.6B). GO:CC analysis (Fig. 6C) confirmed the enrichment in vesicle- and organelle-related components. Network analysis using STRING revealed a low PPI enrichment score (PPI = 0.07), suggesting direct protein–protein interactions. Nevertheless, several differentially expressed proteins were significantly associated with the Disease Gene Association category, including annotations for amyloidosis, autosomal dominant disorders, coagulation abnormalities, CNS and vascular diseases, and cerebrovascular pathology (Fig. S3B). These results suggest that proteomic alterations in aEVs may contribute to impaired astrocytic metabolic support, disrupted neuroimmune regulation, and loss of vascular integrity—processes critical for CNS homeostasis. The downregulation of stress-adaptive and structural proteins, combined with the upregulation of inflammatory and complement-related factors, suggests a shift toward a neuroinflammatory and vasculopathic profile. These alterations are consistent with severe astrocytic damage and reactive transformation previously described in CADASIL, supporting a role for aEVs as both sensitive biomarkers and potential mediators of gliovascular dysfunction and disease progression [31] (Fig.S5B). Finally, proteomic profiling of oEVs revealed 23 differentially expressed proteins in CADASIL patients (Table S5). These expression changes were visualized in the volcano plot (Fig. 7A), with significantly dysregulated proteins located at the extremes, and the top five up- and downregulated ( p < 0.05) shown in a heatmap (Fig. 7B) to illustrate expression patterns and clustering. GO:CC analysis (Fig. 7C) confirmed enrichment in proteins associated with organelle membranes and extracellular vesicles, consistent with their cellular origin. STRING network proteins from L1 and L2 patient groups revealed several proteins were annotated in the Disease gene association category, with links to amyloidosis, coagulation disorders, autosomal genetic diseases, and cerebrovascular conditions (Fig. S3C). oEVs from CADASIL patients show enrichment in complement pathways, inflammation, B-cell activation, and demyelinating processes, reflecting oligodendroglial degeneration and myelin loss characteristic of the disease (Fig.S5C). Similar to other pathologies [32], this secondary loss of oligodendrocytes in the white matter appears to result from chronic hypoperfusion rather than being a primary event. These findings might indicate that the protein cargo of oligodendrocytic EVs captures demyelination, oxidative stress, and impaired tissue repair processes, aligning with the progressive white matter deterioration described in CADASIL DISCUSSION This study provides a comprehensive characterization of total and brain cell–type–specific EVs isolated from plasma of CADASIL patients stratified by NOTCH3 -SVD staging system [4]. Following the 2024 MISEV guidelines [33], EVs were classified and characterized using complementary biophysical, molecular, and imaging approaches. Nanoparticle tracking analysis and transmission electron microscopy confirmed the expected size distribution (<150 nm) and cup-shaped morphology consistent with small EVs, without significant differences in particle size or concentration across clinical stages. The presence of canonical EVs markers (CD63, CD81, and ALIX) validated the successful enrichment of EVs populations, while the absence of calnexin, an endoplasmic reticulum marker, confirmed minimal contamination with cellular debris. Cell type–specific EVs subpopulations were selectively enriched via immunoaffinity capture, as demonstrated by the robust detection of enolase, GFAP, and MBP in neuronal, astrocytic, and oligodendroglial EVs, respectively. These results highlight the specificity and efficiency of the isolation workflow and underscore the potential of brain-derived EVs to serve as peripheral reporters of CNS cellular identity and pathology, particularly, in the context of CADASIL [13]. To date, studies specifically focused on EVs in CADASIL are extremely limited. The only published work systematically analyzing plasma exosomes — a specific subpopulation of small EVs rather than the entire EV spectrum — is that by Gao et al .[34], which reported alterations in exosome morphology and concentration, along with decreased Notch3 and increased NfL levels. However, no studies have yet investigated brain cell–derived EVs subpopulations in CADASIL. Importantly, stratification by clinical stage revealed disease-associated changes in specific glial EV markers. GFAP levels were significantly elevated in aEVs from L2 compared to L1, suggesting enhanced astrocyte activation in a more advanced condition. This observation is consistent with previous reports of reactive gliosis and astrocyte-mediated neuroinflammation in CADASIL pathogenesis [31]. Similarly, MBP levels were significantly increased in oEVs from L2 patients, indicating potential oligodendrocyte stress or progressive myelin disruption, which has also been described as a pathological hallmark of CADASIL [35, 36]. These findings provide further support for the hypothesis that glial dysfunction plays a pivotal role in disease progression and that glia-derived EVs may offer sensitive, non-invasive readouts of CNS pathology. Conversely, NfL levels in nEVs did not significantly differ between groups. Although plasma NfL is a well-established biomarker of axonal injury in various neurological disorders [19], and also in CADASIL [7, 34], its lack of stage-related variation in this cohort may reflect relative preservation of axonal integrity across early clinical stages, differential release dynamics from neurons, or limitations in detection sensitivity within this specific EVs compartment. Notably, no differences were observed in the levels of any of the three biomarkers when measured in plasma or EV T fractions, emphasizing that cell-type–specific EVs isolation significantly enhances detection sensitivity for CNS-related pathological changes. Taken together, these data demonstrate that selectively enriched brain-derived EVs represent a highly informative and pathophysiological relevant source of biomarkers in CADASIL. The differential expression of GFAP and MBP in cell-specific EVs, but not in EV T or plasma, highlights the necessity of targeted EV profiling to uncover subtle yet biologically meaningful alterations in CNS cell populations. EVs from neurons, astrocytes, and oligodendrocytes were analyzed, excluding contributions from vascular smooth muscle cells (VSMCs), which are primary targets in CADASIL. As vascular smooth muscle cell degeneration and Notch3 aggregation in these cells are central to CADASIL pathophysiology, the absence of their EVs limits our ability to fully capture the disease's cellular spectrum. This exclusion was due to the current lack of sufficiently specific and validated enrichment methods for isolating VSMC-derived EVs from peripheral blood, which hampers their reliable identification and analysis [37]. In addition to the SIMOA/ ELISA biomarker analysis, the proteomic profiling of plasma, EV T , and cell-type–specific EV subpopulations provided valuable complementary insights into CADASIL pathophysiology. Our proteomic analysis across plasma and cell type–enriched EVs reveals a consistent molecular signature associated with progressive neurovascular dysfunction in CADASIL patients with increasing clinical severity. A pattern of downregulated neuronal and immune regulatory proteins, coupled with the upregulation of coagulation and inflammation-related markers, supports the role of EVs as dynamic indicators of disease progression. In total plasma EVs, we observed broad dysregulation of proteins related to oxidative stress, immune function, cytoskeletal organization, and endothelial integrity, reflecting a systemic shift toward a pro-inflammatory and pro-thrombotic state. This altered proteome highlights the potential of EVs as minimally invasive biomarkers of clinical worsening. Similar approaches in other neurological and cerebrovascular diseases have demonstrated that EV proteomics can uncover subtle, disease-specific changes not detectable in bulk plasma proteomics [11] , [33]. nEVs exhibited loss of proteins involved in immune homeostasis, axonal plasticity, and vascular regulation, along with increased expression of proteins associated with demyelination and lysosomal degradation, suggesting activation of neurodegenerative processes and neuronal–vascular disruption. In aEVs, altered protein profiles indicated deficits in metabolic support, immune modulation, and blood–brain barrier maintenance. The combined downregulation of stress-adaptive proteins and upregulation of complement and inflammatory mediators suggest a transition toward a neuroinflammatory, vasculopathic phenotype, which may exacerbate disease progression. oEVs showed reduced expression of proteins linked to mitochondrial function, RNA metabolism, and proteostasis, alongside increases in stress- and apoptosis-related proteins, suggesting oligodendroglial dysfunction and potential contribution to white matter degeneration—a core feature of CADASIL. Altogether, these findings highlight converging yet cell-type–specific alterations across EV populations that reflect key pathological processes in CADASIL. The disease associations identified through STRING network analysis further support the relevance of these EV proteins to neurovascular and genetic pathologies. Nonetheless, this study has several limitations. A key limitation of this study is that the clinical stages included (stage 1a to 3b) represent relatively early and similar degrees of disease severity, which may reduce the sensitivity for detecting stage-dependent biomarker differences, mainly in the proteomic profiling analysis. Comparing these findings with patients in more advanced stages (e.g. ≥ 4) or including cases with more pronounced cognitive or structural deterioration would likely provide greater contrast and enhance the ability to identify robust biomarkers. Additionally, the absence of a healthy control group limits the interpretation of whether the observed molecular changes are specific to CADASIL or reflect more general vascular or neurodegenerative processes. Finally, the modest sample size in this exploratory study emphasizes the need for larger cohorts to confirm these preliminary findings, improve statistical power, and validate potential biomarkers for clinical use. CONCLUSIONS This study demonstrates that brain cell–derived EVs represent a powerful nanobiotechnology platform for minimally invasive biomarker discovery in CADASIL. By integrating cell type–specific EV profiling with proteomic analysis, we identified molecular alterations reflecting glial activation, oligodendrocyte dysfunction, and systemic immune–vascular dysregulation that were not detectable in plasma or bulk EVs. These findings highlight the sensitivity of EV-based approaches to capture subtle yet biologically relevant changes associated with disease stage and progression. Beyond their potential as biomarkers, neuronal, astrocytic, and oligodendrocytic EVs may also provide mechanistic insights into CADASIL pathophysiology, linking neuroinflammation, demyelination, and vascular dysfunction. Altogether, our results support the use of brain-derived EVs as a liquid biopsy tool for monitoring CADASIL and underscore their translational potential for biomarker development in small vessel disease and other neurovascular disorders. Abbreviations aEVs: Astrocyte-derived extracellular vesicles; BBB: Blood–brain barrier; BSA: Bovine serum albumin; CADASIL: Cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy; CFI: Complement factor I; CRP: C-reactive protein; CNS: Central nervous system; CSVD/SVD: Cerebral small vessel disease / Small vessel disease; CTSD: Cathepsin D; DDA: Data-dependent acquisition; DIA: Data-independent acquisition; EVs: Extracellular vesicles; EV T : Total extracellular vesicles; FDR: False discovery rate; FGA/FGB/FGG: Fibrinogen alpha, beta, and gamma chains; GFAP: Glial fibrillary acidic protein; GO: Gene Ontology; HDL: High-density lipoprotein; LC–MS/MS: Liquid chromatography–tandem mass spectrometry; L1/L2: Leiden stage 1 (early/asymptomatic) / Leiden stage 2 (intermediate/advanced); LUM: Lumican; MBP: Myelin basic protein; MISEV: Minimal Information for Studies of Extracellular Vesicles; MOG: Myelin oligodendrocyte glycoprotein; MRI: Magnetic resonance imaging; MSN: Moesin; nEVs: Neuron-derived extracellular vesicles; NfL: Neurofilament light chain; NOTCH3: Neurogenic locus notch homolog protein 3; NTA: Nanoparticle tracking analysis; oEVs: Oligodendrocyte-derived extracellular vesicles; PBS: Phosphate-buffered saline; PPI: Protein–protein interaction; Simoa: Single molecule array; STRING: Search Tool for the Retrieval of Interacting Genes/Proteins; SWATH-MS: Sequential window acquisition of all theoretical fragment-ion spectra mass spectrometry; TEM: Transmission electron microscopy; WMH: White matter hyperintensities. Declarations Ethics approval and consent to participate This study was conducted in accordance with the principles outlined in the Declaration of Helsinki and Good Clinical Practice guidelines. Ethical approval was obtained from the Clinical Research Ethics Committee of the Hospital de la Santa Creu i Sant Pau (Barcelona, Spain) and from the Ethics Committee of the Health Research Institute of Santiago de Compostela (IDIS, Santiago de Compostela, Spain). All procedures involving human participants complied with national and European regulations for biomedical research and data protection. Written informed consent was obtained from all participants prior to their inclusion in the CADAGENIA registry and before the collection of blood samples for extracellular vesicle and proteomic analyses. Patients were informed about the purpose of the study, the voluntary nature of their participation, and their right to withdraw consent at any time without affecting their medical care. Consent for publication All authors had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the analyses. All authors have read and approved the final version of the manuscript. Availability of data and materials Data supporting the figures and tables of this manuscript (analysis data) are available from the corresponding author upon reasonable request. Competing interests The authors declare no competing interests. Funding The experiments and analysis in the study were supported by Spanish Ministry of Science, Innovation and Universities (PID2021-126848NB-I00; PID2023-150743OB-I00), the Galician Government (XUGA, ED431C 2022/41), FEDER (Regional European Development Fund), Instituto de Salud Carlos III (ISCIII) through the projects, PI20/01014, RICORS-ICITUS RD24/0009/0017 and AC23-2/00029. AC23-2/00029 (named as CADANHIS) project has been supported by the EJP RD – European Joint Programme on Rare Diseases – Joint Transnational Call 2023 for Rare Diseases Research Project (JTC 2023). The EJP RD initiative has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement N°825575. E. Muiño is supported by the Juan Rodés contract (JR23/00045) from Instituto de Salud Carlos III. P. Villatoro-González is supported by a Joan Oró contract from the predoctoral program AGAUR FI ajuts (2023 FI-3 00065) Joan Oró of the Secretariat of Universities and Research of the Department of Research and Universities of the Generalitat of Catalonia and the European Social Plus Fund. Authors' contributions Conceptualization: FC, AIRP. Material preparation, data collection and analysis: ABC, EM, PVG, LCM, PAP, FA, IS, SBB. Patient recruitment and blood collection: EM, FA, IS, SAR. Methodology, Software, and Formal Analysis: ABC, EM, PVG, SBB, AIRP. Data Validation: ABC, JLG, FC, AIRP. Writing – Review & Editing: ABc, EM, SBB, SAR, IFC, JLG, AIRP, FC. Supervision: FC, AIRP. Funding Acquisition: FC, JLG, AIRP Acknowledgements The authors acknowledge the use of ChatGPT (GPT-5, OpenAI) for assistance in language editing and text revision. The authors reviewed and verified all generated content for accuracy. References Joutel A, Corpechot C, Ducros A, Vahedi K, Chabriat H, Mouton P, Alamowitch S, Domenga V, Cecillion M, Marechal E, et al: Notch3 mutations in CADASIL, a hereditary adult-onset condition causing stroke and dementia. Nature 1996, 383: 707-710. Kalimo H, Ruchoux MM, Viitanen M, Kalaria RN: CADASIL: a common form of hereditary arteriopathy causing brain infarcts and dementia. Brain Pathol 2002, 12: 371-384. Chabriat H, Joutel A, Dichgans M, Tournier-Lasserve E, Bousser MG: Cadasil. Lancet Neurol 2009, 8: 643-653. Gravesteijn G, Rutten JW, Cerfontaine MN, Hack RJ, Liao YC, Jolly AA, Guey S, Hsu SL, Park JY, Yuan Y, et al: Disease Severity Staging System for NOTCH3-Associated Small Vessel Disease, Including CADASIL. JAMA Neurol 2025, 82: 49-60. Markus HS, Martin RJ, Simpson MA, Dong YB, Ali N, Crosby AH, Powell JF: Diagnostic strategies in CADASIL. Neurology 2002, 59: 1134-1138. Gravesteijn G, Hack RJ, Mulder AA, Cerfontaine MN, van Doorn R, Hegeman IM, Jost CR, Rutten JW, Lesnik Oberstein SAJ: NOTCH3 variant position is associated with NOTCH3 aggregation load in CADASIL vasculature. Neuropathol Appl Neurobiol 2022, 48: e12751. Duering M, Konieczny MJ, Tiedt S, Baykara E, Tuladhar AM, Leijsen EV, Lyrer P, Engelter ST, Gesierich B, Achmuller M, et al: Serum Neurofilament Light Chain Levels Are Related to Small Vessel Disease Burden. J Stroke 2018, 20: 228-238. Thery C, Witwer KW, Aikawa E, Alcaraz MJ, Anderson JD, Andriantsitohaina R, Antoniou A, Arab T, Archer F, Atkin-Smith GK, et al: Minimal information for studies of extracellular vesicles 2018 (MISEV2018): a position statement of the International Society for Extracellular Vesicles and update of the MISEV2014 guidelines. J Extracell Vesicles 2018, 7: 1535750. Yanez-Mo M, Siljander PR, Andreu Z, Zavec AB, Borras FE, Buzas EI, Buzas K, Casal E, Cappello F, Carvalho J, et al: Biological properties of extracellular vesicles and their physiological functions. J Extracell Vesicles 2015, 4: 27066. Simons M, Raposo G: Exosomes--vesicular carriers for intercellular communication. Curr Opin Cell Biol 2009, 21: 575-581. Thompson AG, Gray E, Heman-Ackah SM, Mager I, Talbot K, Andaloussi SE, Wood MJ, Turner MR: Extracellular vesicles in neurodegenerative disease - pathogenesis to biomarkers. Nat Rev Neurol 2016, 12: 346-357. Muino E, Maisterra O, Jimenez-Balado J, Cullell N, Carrera C, Torres-Aguila NP, Carcel-Marquez J, Gallego-Fabrega C, Lledos M, Gonzalez-Sanchez J, et al: Genome-wide transcriptome study in skin biopsies reveals an association of E2F4 with cadasil and cognitive impairment. Sci Rep 2021, 11: 6846. Pedrosa MA, Labandeira CM, Lago-Baameiro N, Valenzuela R, Pardo M, Labandeira-Garcia JL, Rodriguez-Perez AI: Extracellular Vesicles and Their Renin-Angiotensin Cargo as a Link between Metabolic Syndrome and Parkinson's Disease. Antioxidants (Basel) 2023, 12 . Bugallo-Casal A, Muino E, Bravo SB, Hervella P, Arias-Rivas S, Rodriguez-Yanez M, Vara-Leon E, Quintas-Rey R, Perez-Gayol L, Maisterra-Santos O, et al: NOTCH3 Variant Position Affects the Phenotype at the Pluripotent Stem Cell Level in CADASIL. Neuromolecular Med 2025, 27: 18. UniProt C: UniProt: a worldwide hub of protein knowledge. Nucleic Acids Res 2019, 47: D506-D515. Perez-Riverol Y, Bandla C, Kundu DJ, Kamatchinathan S, Bai J, Hewapathirana S, John NS, Prakash A, Walzer M, Wang S, Vizcaino JA: The PRIDE database at 20 years: 2025 update. Nucleic Acids Res 2025, 53: D543-D553. Rubinstein E, Thery C, Zimmermann P: Tetraspanins affect membrane structures and the trafficking of molecular partners: what impact on extracellular vesicles? Biochem Soc Trans 2025, 0: 371-382. Yang Y, Wang M, Zhang YY, Zhao SZ, Gu S: The endosomal sorting complex required for transport repairs the membrane to delay cell death. Front Oncol 2022, 12: 1007446. Gaetani L, Blennow K, Calabresi P, Di Filippo M, Parnetti L, Zetterberg H: Neurofilament light chain as a biomarker in neurological disorders. J Neurol Neurosurg Psychiatry 2019, 90: 870-881. Abdelhak A, Foschi M, Abu-Rumeileh S, Yue JK, D'Anna L, Huss A, Oeckl P, Ludolph AC, Kuhle J, Petzold A, et al: Blood GFAP as an emerging biomarker in brain and spinal cord disorders. Nat Rev Neurol 2022, 18: 158-172. Khan A, Abedi V, Li J, Malik MT, Esch M, Zand R: CADASIL vs. Multiple Sclerosis: Is It Misdiagnosis or Concomitant? A Case Series. Front Neurol 2020, 11: 860. Mahajan KR, Herman D, Zheng Y, Androjna C, Thoomukuntla B, Ontaneda D, Nakamura K, Trapp BD: Neurodegeneration and Demyelination in the Multiple Sclerosis Spinal Cord: Clinical, Pathological, and 7T MRI Perspectives. Neurology 2025, 104: e210259. Zjukovskaja C, Larsson A, Cherif H, Kultima K, Burman J: Biomarkers of demyelination and axonal damage are decreased after autologous hematopoietic stem cell transplantation for multiple sclerosis. Mult Scler Relat Disord 2022, 68: 104210. Chen G, Li Z, Liu Y, Chen D, Beers J, Cudrici C, Ferrante EA, Schwartzbeck R, Dmitrieva N, Yang D, et al: Generation of human induced pluripotent stem cells (NIHTVBi004-A, NIHTVBi005-A, NIHTVBi006-A, NIHTVBi007-A, NIHTVBi008-A) from 5 CADASIL patients with NOTCH3 mutation. Stem Cell Res 2020, 45: 101821. Madjidov A, Abbott MA, Mullen KM, Hicks R, Schlaug G: Case Report: Inflammatory CADASIL phenotype associated with a rare cysteine-sparing NOTCH3 variant. Front Hum Neurosci 2025, 19: 1568937. Whitaker JN: Myelin basic protein in cerebrospinal fluid and other body fluids. Mult Scler 1998, 4: 16-21. Cuadrado-Godia E, Dwivedi P, Sharma S, Ois Santiago A, Roquer Gonzalez J, Balcells M, Laird J, Turk M, Suri HS, Nicolaides A, et al: Cerebral Small Vessel Disease: A Review Focusing on Pathophysiology, Biomarkers, and Machine Learning Strategies. J Stroke 2018, 20: 302-320. Raghav A, Singh M, Jeong GB, Giri R, Agarwal S, Kala S, Gautam KA: Extracellular vesicles in neurodegenerative diseases: A systematic review. Front Mol Neurosci 2022, 15: 1061076. Menendez-Valladares P, Acevedo Aguilera R, Nunez-Jurado D, Lopez Azcarate C, Dominguez Mayoral AM, Fernandez-Vega A, Perez-Sanchez S, Lamana Vallverdu M, Garcia-Sanchez MI, Morales Bravo M, et al: A Search for New Biological Pathways in Cerebral Autosomal Dominant Arteriopathy with Subcortical Infarcts and Leukoencephalopathy by Proteomic Research. J Clin Med 2024, 13 . Locatelli M, Padovani A, Pezzini A: Pathophysiological Mechanisms and Potential Therapeutic Targets in Cerebral Autosomal Dominant Arteriopathy With Subcortical Infarcts and Leukoencephalopathy (CADASIL). Front Pharmacol 2020, 11: 321. Hase Y, Chen A, Bates LL, Craggs LJL, Yamamoto Y, Gemmell E, Oakley AE, Korolchuk VI, Kalaria RN: Severe white matter astrocytopathy in CADASIL. Brain Pathol 2018, 28: 832-843. Yamanouchi H: Loss of white matter oligodendrocytes and astrocytes in progressive subcortical vascular encephalopathy of Binswanger type. Acta Neurol Scand 1991, 83: 301-305. Welsh JA, Goberdhan DCI, O'Driscoll L, Buzas EI, Blenkiron C, Bussolati B, Cai H, Di Vizio D, Driedonks TAP, Erdbrugger U, et al: Minimal information for studies of extracellular vesicles (MISEV2023): From basic to advanced approaches. J Extracell Vesicles 2024, 13: e12404. Gao D, Shang J, Sun R, Shi Y, Jiang H, Ma M, Zhang J: Changes in the Morphology, Number, and Protein Levels of Plasma Exosomes in CADASIL Patients. J Alzheimers Dis 2021, 81: 221-229. Denecke J, Dewenter A, Lee J, Franzmeier N, Valentim C, Kopczak A, Dichgans M, Pirpamer L, Gesierich B, Duering M, Ewers M: Reduced myelin contributes to cognitive impairment in patients with monogenic small vessel disease. Alzheimers Dement 2025, 21: e70127. Okeda R, Arima K, Kawai M: Arterial changes in cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) in relation to pathogenesis of diffuse myelin loss of cerebral white matter: examination of cerebral medullary arteries by reconstruction of serial sections of an autopsy case. Stroke 2002, 33: 2565-2569. Newman L, Rowland A: Detection and Isolation of Tissue-Specific Extracellular Vesicles From the Blood. J Extracell Biol 2025, 4: e70059. Additional Declarations No competing interests reported. Supplementary Files SUPPLEMENTALMATERIALJNBT.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-7511858","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":513328845,"identity":"81c1cbd5-ca79-4b39-970a-12c4223af3f5","order_by":0,"name":"Ana Bugallo-Casal","email":"","orcid":"","institution":"Health Research Institute of Santiago de Compostela (IDIS)","correspondingAuthor":false,"prefix":"","firstName":"Ana","middleName":"","lastName":"Bugallo-Casal","suffix":""},{"id":513328847,"identity":"8ac510ab-8d46-4528-ae7b-cc689c50d0bf","order_by":1,"name":"Elena Muino","email":"","orcid":"","institution":"Institut de Recerca Sant Pau (IR SANT PAU)","correspondingAuthor":false,"prefix":"","firstName":"Elena","middleName":"","lastName":"Muino","suffix":""},{"id":513328851,"identity":"e537734e-adf3-4c1f-a89e-9a33b766b8ea","order_by":2,"name":"Paula Villatoro-González","email":"","orcid":"","institution":"Institut de Recerca Sant Pau (IR SANT PAU)","correspondingAuthor":false,"prefix":"","firstName":"Paula","middleName":"","lastName":"Villatoro-González","suffix":""},{"id":513328857,"identity":"fbd9d9cc-e92c-4086-aabe-4aa680e79623","order_by":3,"name":"Laura Camacho-Meño","email":"","orcid":"","institution":"University of Santiago de Compostela","correspondingAuthor":false,"prefix":"","firstName":"Laura","middleName":"","lastName":"Camacho-Meño","suffix":""},{"id":513328858,"identity":"305be874-968c-412f-97c8-0084a11c47ef","order_by":4,"name":"Paula Aracil-Pastor","email":"","orcid":"","institution":"University of Santiago de Compostela","correspondingAuthor":false,"prefix":"","firstName":"Paula","middleName":"","lastName":"Aracil-Pastor","suffix":""},{"id":513328859,"identity":"c4323619-3f6e-446f-9ace-054bd4cd11d5","order_by":5,"name":"Franco Appiani","email":"","orcid":"","institution":"Institut de Recerca Sant Pau (IR SANT PAU)","correspondingAuthor":false,"prefix":"","firstName":"Franco","middleName":"","lastName":"Appiani","suffix":""},{"id":513328860,"identity":"ee809b00-b388-4cb8-b7be-254716fdcf6b","order_by":6,"name":"Isabel Sala","email":"","orcid":"","institution":"Hospital de la Santa Creu i Sant Pau","correspondingAuthor":false,"prefix":"","firstName":"Isabel","middleName":"","lastName":"Sala","suffix":""},{"id":513328861,"identity":"d1b5e552-22c0-4c84-bccf-b777a947b66c","order_by":7,"name":"Israel Fernández-Cadenas","email":"","orcid":"","institution":"Institut de Recerca Sant Pau (IR SANT PAU)","correspondingAuthor":false,"prefix":"","firstName":"Israel","middleName":"","lastName":"Fernández-Cadenas","suffix":""},{"id":513328862,"identity":"df76ca57-38fe-43b2-9cf5-a1c4ce2a92fe","order_by":8,"name":"Susana B. Bravo","email":"","orcid":"","institution":"University Clinical Hospital of Santiago de Compostela","correspondingAuthor":false,"prefix":"","firstName":"Susana","middleName":"B.","lastName":"Bravo","suffix":""},{"id":513328863,"identity":"d0615382-2f08-40af-826a-66d250565835","order_by":9,"name":"Susana Arias-Rivas","email":"","orcid":"","institution":"Health Research Institute of Santiago de Compostela (IDIS)","correspondingAuthor":false,"prefix":"","firstName":"Susana","middleName":"","lastName":"Arias-Rivas","suffix":""},{"id":513328864,"identity":"59d32897-7b69-443a-bd7e-71540f6af2e5","order_by":10,"name":"José Luis Labandeira-García","email":"","orcid":"","institution":"University of Santiago de Compostela","correspondingAuthor":false,"prefix":"","firstName":"José","middleName":"Luis","lastName":"Labandeira-García","suffix":""},{"id":513328865,"identity":"e126d0cc-13bb-4f56-b7e1-13a339d41129","order_by":11,"name":"Francisco Campos","email":"data:image/png;base64,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","orcid":"","institution":"Health Research Institute of Santiago de Compostela (IDIS)","correspondingAuthor":true,"prefix":"","firstName":"Francisco","middleName":"","lastName":"Campos","suffix":""},{"id":513328866,"identity":"e5cb11e8-49ac-4565-b9be-d592f6eac40a","order_by":12,"name":"Ana I Rodríguez-Pérez","email":"","orcid":"","institution":"University of Santiago de Compostela","correspondingAuthor":false,"prefix":"","firstName":"Ana","middleName":"I","lastName":"Rodríguez-Pérez","suffix":""}],"badges":[],"createdAt":"2025-09-01 23:23:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7511858/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7511858/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91506815,"identity":"137a729c-e666-498b-b057-54cafbc5e826","added_by":"auto","created_at":"2025-09-17 08:25:08","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":3358627,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCharacterization and validation of total extracellular vesicle (EVᴛ) fractions.\u003c/strong\u003e \u003cstrong\u003e(A–B) \u003c/strong\u003eNanoparticle tracking analysis (NTA) revealed a similar size distribution of EVᴛ isolated from L1 patients \u003cstrong\u003e(A)\u003c/strong\u003e and L2 \u003cstrong\u003e(B)\u003c/strong\u003e, with no significant differences in particle size between groups. Particle concentrations were on the order of 10¹² particles/mL in both groups. \u003cstrong\u003e(C)\u003c/strong\u003e Transmission electron microscopy (TEM) confirmed the presence of vesicular structures displaying the characteristic cup-shaped morphology and sizes \u0026lt;150 nm, consistent with small EVs. Scale bar = 100 nm. \u003cstrong\u003e(D–F)\u003c/strong\u003e Quantification of canonical EV markers by ultrasensitive ELISA demonstrated significant enrichment of CD63 \u003cstrong\u003e(D)\u003c/strong\u003e, CD81 \u003cstrong\u003e(E)\u003c/strong\u003e, and ALIX \u003cstrong\u003e(F)\u003c/strong\u003e in EVᴛ compared to EV-depleted fractions. \u003cstrong\u003e(G)\u003c/strong\u003e Calnexin, an endoplasmic reticulum marker, was undetectable in depleted and EVᴛ fractions, and only detected at high levels in lysates from the HMC3 microglial cell line (positive control), confirming minimal contamination with cellular debris. Data are expressed as pg·mL⁻¹·µg⁻¹ of total protein. *\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05\u003cem\u003e vs\u003c/em\u003e depleted fraction; #\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 \u003cem\u003evs\u003c/em\u003e EV\u003csub\u003eT\u003c/sub\u003e. Statistical analysis in panels D–F was performed using the Mann–Whitney rank sum test. For panel G, Kruskal–Wallis one-way analysis of variance on ranks followed by Dunn’s post hoc test was applied.\u003c/p\u003e","description":"","filename":"Figure140MP.png","url":"https://assets-eu.researchsquare.com/files/rs-7511858/v1/d68714a5ced1eb2207a759e9.png"},{"id":91505370,"identity":"23c9b27c-d0ff-4829-a130-6394a16a53d5","added_by":"auto","created_at":"2025-09-17 08:17:08","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2869351,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEnrichment of brain cell–specific protein markers in cell type–derived EV subpopulations.\u003c/strong\u003e \u003cstrong\u003e(A)\u003c/strong\u003e Enolase levels were markedly elevated in neuronal EVs (nEVs) compared to both plasma and EVᴛ fractions. \u003cstrong\u003e(B)\u003c/strong\u003eGFAP was significantly enriched in astrocyte-derived EVs (aEVs). \u003cstrong\u003e(C) \u003c/strong\u003eMBP concentrations were substantially higher in oligodendrocyte-derived EVs (oEVs), in contrast to minimal levels in plasma. Data are expressed as pg·mL⁻¹·µg⁻¹ of total protein. *\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05\u003cem\u003e vs\u003c/em\u003e plasma; #\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 \u003cem\u003evs\u003c/em\u003e EVᴛ. Statistical analysis was performed using Kruskal–Wallis one-way analysis of variance on ranks followed by Student–Newman–Keuls post hoc test.\u003c/p\u003e","description":"","filename":"Figure240MP.png","url":"https://assets-eu.researchsquare.com/files/rs-7511858/v1/9157dedf26122c7a891a98e7.png"},{"id":91505372,"identity":"5acae151-76c2-40fd-ae4d-809807f87b76","added_by":"auto","created_at":"2025-09-17 08:17:08","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2529809,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferential abundance of CNS-derived biomarkers in brain cell–specific EVs according to clinical stage. \u003c/strong\u003eQuantification of NfL \u003cstrong\u003e(A),\u003c/strong\u003e GFAP \u003cstrong\u003e(B),\u003c/strong\u003e and MBP \u003cstrong\u003e(C)\u003c/strong\u003e was performed in plasma, EVᴛ, and cell–type–specific EV subpopulations (nEVs, aEVs, oEVs) isolated from individuals classified as L1 and L2. NfL levels in nEVs did not differ significantly between stages \u003cstrong\u003e(A)\u003c/strong\u003e. In contrast, GFAP concentrations \u003cstrong\u003e(B)\u003c/strong\u003ewere significantly elevated in aEVs from L2 compared to L1 individuals. Similarly, MBP levels \u003cstrong\u003e(C)\u003c/strong\u003e were significantly increased in oEVs from L2. No differences were observed in plasma or EVᴛ fractions for any of the markers. Data are presented as pg·mL⁻¹·µg⁻¹ of total protein. *\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05\u003cem\u003e vs\u003c/em\u003e aEVs-L1 \u003cstrong\u003e(B) \u003c/strong\u003eor oEVs-L1 \u003cstrong\u003e(C)\u003c/strong\u003e. Depending on the distribution of the data, comparisons between groups were conducted using either a t-test or the non-parametric Mann–Whitney U test.\u003c/p\u003e","description":"","filename":"Figure340MP.png","url":"https://assets-eu.researchsquare.com/files/rs-7511858/v1/edbd8c36b4440b930864b497.png"},{"id":91505371,"identity":"a441cf73-850e-4b60-b633-37bcc0cd48a9","added_by":"auto","created_at":"2025-09-17 08:17:08","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":3725107,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProteomic analysis of EVs derived from plasma and EV\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003eT\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e. \u003c/strong\u003eVolcano plots showing Log₂ fold changes (X-axis) versus –Log₁₀ p-values (Y-axis), highlighting differentially expressed proteins between both clinical conditions (L2 vs L1) in the plasma \u003cstrong\u003e(A)\u003c/strong\u003e and EV\u003csub\u003eT\u003c/sub\u003e \u003cstrong\u003e(B-E)\u003c/strong\u003e. The heatmaps depict the top five upregulated proteins in L2-L1 and the top five downregulated proteins in L2-L1, respectively. Lollipop plots depicting GO:CC enrichment analysis (L2 vs L1), revealing significant overrepresentation of proteins associated with extracellular vesicles, exosomes, and extracellular space. Moderate enrichment was also observed for blood microparticles and secretory granules. Both in the case of plasma and EV\u003csub\u003eT \u003c/sub\u003e(\u003cstrong\u003eC\u003c/strong\u003e-\u003cstrong\u003eF\u003c/strong\u003e). FDR values for enriched terms ranged from 1.0 × 10⁻\u003csup\u003e15\u003c/sup\u003e to 1.0 × 10⁻².\u003c/p\u003e","description":"","filename":"Figure440MP.png","url":"https://assets-eu.researchsquare.com/files/rs-7511858/v1/d0d6eacbc6cb112e523981d5.png"},{"id":91505373,"identity":"76fad59e-154b-4821-aaf5-aff6163f6122","added_by":"auto","created_at":"2025-09-17 08:17:08","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":3264382,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProteomic analysis of nEVs. (A)\u003c/strong\u003e Volcano plot showing Log₂ fold changes (X-axis) vs –Log₁₀ p-values (Y-axis), highlighting differentially expressed proteins between L2 vs L1 in the nEVs fraction. \u003cstrong\u003e(B)\u003c/strong\u003e The heatmaps represent the five highest upregulated proteins in L2 and L1 and the five most downregulated proteins in L2 and L1, respectively. \u003cstrong\u003e(C) \u003c/strong\u003eLollipop plot depicting GO:CC enrichment analysis (L2 vs L1), revealing significant overrepresentation of proteins associated with extracellular vesicles, exosomes, and extracellular space. Moderate enrichment was also observed for blood microparticles and secretory granules. FDR values for enriched terms ranged from 3.0 × 10⁻⁸ to 4.0 × 10⁻².\u003c/p\u003e","description":"","filename":"Figure540MP.png","url":"https://assets-eu.researchsquare.com/files/rs-7511858/v1/35a28df7dff62d4176654c3b.png"},{"id":91507400,"identity":"d8c2f957-5387-4e0e-a957-c17eab9637db","added_by":"auto","created_at":"2025-09-17 08:33:08","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":3593090,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProteomic analysis of aEVs. (A)\u003c/strong\u003e Volcano plot illustrating Log₂ fold changes (X-axis) and –Log₁₀ p-values (Y-axis) for proteins differentially expressed in L2 vs L1 aEVs. \u003cstrong\u003e(B)\u003c/strong\u003e The heatmaps illustrate the five most upregulated and downregulated proteins for L2 and L1, respectively. \u003cstrong\u003e(C)\u003c/strong\u003e GO:CC enrichment lollipop plot showing a strong overrepresentation (L2 vs L1) of extracellular vesicle-, exosome-, and extracellular space-related proteins. Enrichment of blood microparticles and secretory granules was also detected, with FDR values between 4.0 × 10\u003csup\u003e⁻7\u003c/sup\u003e and 4.0 × 10⁻².\u003c/p\u003e","description":"","filename":"Figure640MP.png","url":"https://assets-eu.researchsquare.com/files/rs-7511858/v1/81eab34676acc6f46cd14b86.png"},{"id":91506817,"identity":"96024dc4-1820-4e7c-b76b-01dc54e0cf84","added_by":"auto","created_at":"2025-09-17 08:25:08","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":3060864,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProteomic analysis of oEVs. (A)\u003c/strong\u003e Volcano plot displaying Log₂ fold changes versus –Log₁₀ p-values for proteins in L2 vs L1 oEVs, identifying significant differential expression patterns. \u003cstrong\u003e(B)\u003c/strong\u003e Heatmaps display the top five upregulated and downregulated proteins in L2 and L1, respectively. \u003cstrong\u003e(C) \u003c/strong\u003eLollipop plot showing GO:CC enrichment analysis, with prominent terms related to extracellular vesicles, exosomes, and extracellular space. Additional enrichment in blood microparticles and secretory granules was observed (FDR range: 4.0 × 10\u003csup\u003e⁻7\u003c/sup\u003e and 4.0 × 10⁻²).\u003c/p\u003e","description":"","filename":"Figure740MP.png","url":"https://assets-eu.researchsquare.com/files/rs-7511858/v1/5baa8ad2fd3472e82e7e45c9.png"},{"id":93539377,"identity":"aca294ab-3d01-421e-9c4a-0ab14823dc18","added_by":"auto","created_at":"2025-10-15 02:17:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":24442867,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7511858/v1/a90e3134-3e1b-47de-8538-bde53d7ab4d4.pdf"},{"id":91506816,"identity":"ae7f9822-b3b8-4208-9f03-6032d02c4318","added_by":"auto","created_at":"2025-09-17 08:25:08","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":5103091,"visible":true,"origin":"","legend":"","description":"","filename":"SUPPLEMENTALMATERIALJNBT.docx","url":"https://assets-eu.researchsquare.com/files/rs-7511858/v1/061bf4f2928f430ec16784aa.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Brain-Derived Extracellular Vesicles as Nanobiotechnology Biomarkers of Small Vessel Disease (CADASIL)","fulltext":[{"header":"BACKGROUND","content":"\u003cp\u003eCerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) is a rare disease of genetic origin that represents the most common form of inherited small vessel disease (SVD). It is caused by pathogenic genetic variants in the \u003cem\u003eNOTCH3\u003c/em\u003e gene, which encodes a transmembrane receptor expressed predominantly in vascular smooth muscle cells and pericytes [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Pathogenic variants leading to CADASIL cause misfolding and aggregation of the Notch3 receptor. The primary clinical manifestations include headache, psychiatric disturbances, recurrent small subcortical infarcts, and early-onset dementia, highlighting the early appearance of both strokes and cognitive deterioration [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eRecently, a new disease severity staging system has been published that captures the broad clinic-radiological spectrum of \u003cem\u003eNOTCH3\u003c/em\u003e-associated SVD including CADASIL. This \u003cem\u003eNOTCH3\u003c/em\u003e-SVD staging system encompasses five disease stages ranging from 0 to 4, with stages 1 to 4 each subdivided into two substages, forming a total of nine substages. These stages represent asymptomatic (Stage 0), early (Stages 1a and 1b), intermediate (Stages 2a and 2b), advanced (Stages 3a and 3b), and end-stage disease (Stages 4a and 4b) [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The staging is based on easily assessable clinical and imaging criteria, including white matter hyperintensities (WMHs), severity assessed by Fazekas scale, the number of lacunes detected by magnetic resonance imaging (MRI), and functional disability evaluated by the modified Rankin Scale (mRS).\u003c/p\u003e\u003cp\u003eBesides this clinical classification, biomarkers have become essential tools in the context of CADASIL, both for diagnostic and prognostic purposes. Genetically, the identification of pathogenic genetic variants in the \u003cem\u003eNOTCH3\u003c/em\u003e gene remains the gold standard for diagnosis. However, the study of circulating and neuroimaging biomarkers has significantly improved early detection and allowed assessment of disease progression. For instance, WMHs on MRI are considered highly specific for CADASIL and represent critical diagnostic markers [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAn important limitation in the use of biomarkers for CADASIL is related to the typically slow and heterogeneous progression of the disease. CADASIL often evolves over decades, with significant variability in the onset and severity of clinical manifestations even among individuals carrying the same \u003cem\u003eNOTCH3\u003c/em\u003e pathogenic variant [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. This slow progression makes it challenging to correlate biomarker levels with immediate changes in disease activity or clinical status.\u003c/p\u003e\u003cp\u003eIn recent years, extracellular vesicles (EVs) have gained increasing attention in nanobiotechnology as natural nanoscale carriers with broad potential for biomedical applications. These vesicles are secreted by virtually all cell types and can cross the blood\u0026ndash;brain barrier, transporting a diverse molecular cargo that includes proteins, lipids, and nucleic acids [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. By reflecting the physiological and pathological state of their cells of origin, EVs act as dynamic messengers of intercellular communication and represent a valuable source of minimally invasive biomarkers. In the context of cerebrovascular and neurological diseases, the study of EVs provides a value opportunity to explore disease mechanisms at the molecular level, monitor clinical progression through liquid biopsy approaches, and identify novel targets for therapeutic intervention [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDespite the growing interest in EVs, studies directly addressing their role in CADASIL remain extremely limited. This gap represents a unique scientific and translational opportunity, as EVs offer a direct nanoscale window into the diseased brain and may anticipate clinical outcomes such as cognitive decline, infarct recurrence, or white matter lesion progression. In this context, the proteomic characterization of EV cargo in CADASIL patients can provide critical insights into the molecular mechanisms driving neurovascular pathology and reveal novel biomarker candidates with high sensitivity for disease staging and progression.\u003c/p\u003e\u003cp\u003eTaken together, these considerations highlight the need for innovative biomarker strategies beyond conventional imaging or fluid assays. By integrating cell type\u0026ndash;specific EV profiling (neuronal, astrocytic, and oligodendrocytic) with proteomic analysis, this study aims to define molecular patterns associated with CADASIL progression. Such an approach not only holds promise for improving diagnostic and prognostic accuracy in this rare monogenic SVD but may also establish a framework for EV-based biomarker discovery in other neurovascular and neurodegenerative disorders.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003e\u003cstrong\u003eStudy Design and Participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSubjects were selected from CADAGENIA [12], a registry in which patients with \u0026nbsp;genetic variants in \u003cem\u003eNOTCH3\u003c/em\u003e were consecutively recorded since 2017 from different parts of Spain. Epidemiological data, blood analyses, cognitive and neuroimaging profiles, and skin biopsies were registered. Sample collection from recruited patients was carried out at the Institut de Recerca Sant Pau (Barcelona, Spain) on January of 2025. All procedures were conducted in accordance with the Declaration of Helsinki and Good Clinical Practice guidelines. Written informed consent was obtained from all participants before their inclusion in the study.\u003c/p\u003e\n\u003cp\u003eInclusion criteria for this study were; 1) age \u0026gt;17 years, 2) having a cysteine-affecting \u003cem\u003eNOTCH3\u003c/em\u003e missense variant, and 3) having MRI and clinical available for \u003cem\u003eNOTCH3\u003c/em\u003e-SVD staging.\u003c/p\u003e\n\u003cp\u003eCADASIL patients were classified according to the \u003cem\u003eNOTCH3\u003c/em\u003e-SVD staging system [4], recently proposed by the Leiden University Medical Center, and grouped into early/asymptomatic stages (1a and 1b), referred below as Leiden stage 1 (L1), and \u0026nbsp;intermediate/advanced stages (\u0026ge;2a), referred as Leiden stage 2 (L2). This grouping was based on clinical and imaging differences that may facilitate interpretation in a research context.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatient blood sample collection \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhole blood was collected into Vacutainer\u0026trade; Plasma Preparation Tubes (PPT\u0026trade;, 8.5 mL; Ref. 12977696) and processed within less than 2 hours of collection. Samples were first centrifuged at 2,000 \u0026times; g for 15 minutes at 10 \u0026ordm;C temperature to separate plasma from cellular components. The upper plasma layer was carefully transferred to new sterile polypropylene tubes without disturbing the buffy coat. A second centrifugation was then performed at 2,500 \u0026times; g for 15 minutes at 10\u0026deg;C to remove residual platelets and cellular debris. The resulting platelet-poor plasma was aliquoted into sterile cryovials and stored at \u0026minus;80\u0026deg;C until further use.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIsolation of Total Extracellular Vesicles by Size Exclusion Chromatography\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo remove cellular debris, plasma samples were first centrifuged at 10,000 \u0026times; g for 10 minutes at 4\u0026deg;C. Following similar procedures as reported previously by our team [13], \u0026nbsp;total extracellular vesicles (EV\u003csub\u003eT\u003c/sub\u003e) were subsequently isolated using qEV Original 70 nm Gen2 columns (IZON SCIENCE LTD), which were pre-equilibrated with 17 mL of filtered phosphate-buffered saline (PBS, 0.22 \u0026micro;m). A 500 \u0026micro;L aliquot of sample was loaded, and the initial 2.9 mL of void volume was discarded. Six consecutive 0.4 mL fractions were collected using an Automatic Fraction Collector (AFC). These EV-enriched fractions were pooled and concentrated using 100 kDa centrifugal filter devices (Amicon Ultra, Millipore) at 12,000 \u0026times; g, 4\u0026deg;C, until a final volume of approximately 200 \u0026micro;L was reached. Concentrated EVs were transferred to sterile tubes and stored at \u0026minus;80\u0026deg;C. For each participant, two independent 500 \u0026micro;L aliquots were processed and subsequently pooled.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNanoparticle Tracking Analysis (NTA)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParticle concentration and size distribution of EV\u003csub\u003eT\u003c/sub\u003e were assessed using the NanoSight PRO instrument (Malvern Instruments) equipped with a 488 nm laser and a high-sensitivity camera. Samples were diluted 1:10,000 in filtered PBS to fall within the optimal detection range (1 \u0026times; 10⁷\u0026nbsp;\u0026ndash;\u0026nbsp;1\u0026nbsp;\u0026times;\u0026nbsp;10⁹\u0026nbsp;particles/mL). Five 60-second videos per sample were acquired with predefined settings (exposure: 13, contrast: 3), and automatic parameters were applied for tracking. Data were analyzed using NanoSight NTA 3.4 software, yielding values for mean, mode, and median particle diameters, as well as particle concentration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTransmission Electron Microscopy\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo visualize EV\u003csub\u003eT\u003c/sub\u003e morphology and integrity, samples were diluted 1:10 in distilled water. Ten microliters were placed onto carbon-coated copper grids and allowed to adsorb for 1 minute. Grids were negatively stained with 1% phosphotungstic acid (prepared in Milli-Q\u0026reg; water) for 1 minute, then blotted and air-dried. Imaging was performed using a JEOL JEM 2010 transmission electron microscope (TEM) operating at 200 kV.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExtracellular Vesicle Characterization and Detection of Canonical EV Markers\u003c/strong\u003e\u003cbr\u003eEV\u003csub\u003eT\u0026nbsp;\u003c/sub\u003epreparations and matched depleted EV samples were analyzed by ELISA to detect the EV-enriched tetraspanins CD81 (Ref. 18095935; ThermoFisher Scientific), CD63 (Ref. 16853020;\u0026nbsp;ThermoFisher Scientific), and the endosomal protein ALIX (Ref. 30240446; ThermoFisher Scientific). \u0026nbsp;Calnexin (Ref. 18234959; ThermoFisher Scientific), an endoplasmic reticulum protein absents from EVs, served as a negative control to assess potential contamination.\u003c/p\u003e\n\u003cp\u003eFifty microliters of EV\u003csub\u003eT\u003c/sub\u003e were lysed in 50 \u0026micro;L of RIPA buffer, followed by four 10-second sonication cycles at 10% amplitude on ice. After centrifugation at 10,000 \u0026times; g for 10 minutes at 4\u0026deg;C, 6 \u0026micro;L of the supernatant was used for protein quantification (Pierce BCA Protein Assay), and 90 \u0026micro;L were applied to ELISA plates. All steps were carried out following the manufacturer\u0026apos;s instructions. Absorbance was measured at 450/620 nm using an Infinite M200 plate reader (TECAN, M\u0026auml;nnedorf, Switzerland), and analyte concentrations were calculated using a four-parameter logistic (4PL) curve.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEnrichment of Brain-Derived Extracellular Vesicle Subtypes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA sequential immunoaffinity-based approach was used to enrich EVs from specific brain cell types (neurons, astrocytes, and oligodendrocytes) starting from 100 \u0026micro;L of EV\u003csub\u003eT\u003c/sub\u003e (derived from 1 mL of plasma).\u003c/p\u003e\n\u003cp\u003eTo isolate neuronal EVs (nEVs), the EV\u003csub\u003eT\u003c/sub\u003e preparation was incubated overnight at 4\u0026deg;C under gentle agitation with 4 \u0026micro;g of biotinylated anti-L1CAM/CD171 antibody (Ref. 13-1919-82, ThermoFisher Scientific) in 300 \u0026micro;L PBS containing 1.3% bovine serum albumin (BSA). On the following day, 200 \u0026micro;L of streptavidin-coated magnetic beads (Ref. 10608D, ThermoFisher Scientific) were added and incubated for 3 hours at room temperature with continuous mixing. Bead-bound complexes containing nEVs were captured using a magnetic separator (Im\u0026aacute;n DynaMag\u0026trade;-2; Ref. 12321D from ThermoFisher Scientific), and the supernatant, depleted of nEVs, was retained for subsequent enrichments. The immunocomplexes were washed three times with 1 mL of PBS containing 0.1% BSA, resuspended in 100 \u0026micro;L of filtered PBS, and stored at \u0026minus;80\u0026deg;C for downstream applications. The nEV-depleted supernatant was then sequentially incubated with biotinylated antibodies targeting markers specific for astrocytes (aEV) (Glast, Ref. NB100-1869B, Novus Biologicals) and oligodendrocytes (oEV) (MOG, Ref. BAM2439, R\u0026amp;D Systems). Each isolation step involved incubation with 4 \u0026micro;g of biotinylated antibody, streptavidin bead capture, magnetic separation, and recovery of the supernatant for the next extraction step.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCharacterization of Brain Cell-Specific EV\u003csub\u003eS\u003c/sub\u003e Populations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEnrichment of neuronal EVs was assessed via ELISA for neuron-specific enolase (NSE; Ref. ab217778, Abcam). Astrocytic EVs were validated using a GFAP ELISA kit (Ref. EEL079, Invitrogen). Oligodendrocyte-derived EVs were evaluated using myelin basic protein (MBP; Ref. MBS2502574, MyBioSource).\u003c/p\u003e\n\u003cp\u003eFor each assay, 50 \u0026micro;L of each EV subpopulation were lysed with an equal volume of RIPA buffer, subjected to four sonication cycles (10 seconds, 10% amplitude) on ice, and centrifuged at 10,000 \u0026times; g for 10 minutes at 4\u0026deg;C. A 6 \u0026micro;L aliquot was used for protein quantification, and 90 \u0026micro;L were applied to ELISA plates. Absorbance readings at 450/620 nm were obtained using the Infinite M200 reader, and concentrations were calculated based on 4PL standard curves.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQuantification of Biomarkers by Immunoassay\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eQuantification of biomarkers was conducted across five biological matrices: plasma, EV\u003csub\u003eT\u003c/sub\u003e, nEVs, aEVs and oEVs. NfL concentrations were measured using the ultra-sensitive Single Molecule Array (Simoa) technology. The NF-light\u0026reg; Advantage Kit (Ref. 104364, Quanterix) was employed according to the manufacturer\u0026rsquo;s instructions for plasma samples. For total EVs (EV\u003csub\u003eT\u003c/sub\u003e) and nEVs, 50 \u0026micro;L of each EV sample were resuspended in 50 \u0026micro;L of RIPA buffer and subjected to four cycles of sonication (10 seconds each at 10% amplitude) on ice to ensure effective vesicle disruption and protein release. The lysates were then centrifuged at 10,000\u0026nbsp;\u0026times;\u0026nbsp;g for 10 minutes at 4\u0026deg;C. The resulting supernatants were collected and used for NfL quantification on a Quanterix SR-X\u0026reg;\u0026nbsp;Biomarker Detection System.\u003c/p\u003e\n\u003cp\u003eGFAP and MBP concentrations were quantified in plasma, EV\u003csub\u003eT\u003c/sub\u003e, aEVs (GFAP) and oEVs (MBP) using high-sensitivity ELISA kits (GFAP: Ref. EEL079, Invitrogen; MBP: Ref. MBS2502574 from MyBioSource). Plasma samples were processed strictly following the manufacturer\u0026rsquo;s instructions. For EV\u003csub\u003eT\u003c/sub\u003e, aEV, and oEV samples, 50 \u0026micro;L of EV lysate (prepared as previously described using RIPA buffer, sonication, and centrifugation at 10,000\u0026nbsp;\u0026times;\u0026nbsp;g for 10 minutes at 4\u0026deg;C) were used per well. Subsequently, ELISA assays were conducted according to the manufacturer\u0026rsquo;s protocols, including all specified incubation times, reagent volumes, and washing steps. Optical density was measured at 450/620 nm using a calibrated microplate reader.\u003c/p\u003e\n\u003cp\u003eTotal protein concentration in all samples was determined using the Pierce\u0026trade; BCA Protein Assay Kit (ThermoFisher Scientific), following the manufacturer\u0026rsquo;s microplate procedure. Final concentrations of NfL, GFAP, and MBP were normalized to total protein content and reported as picograms per milliliter per microgram of total protein (pg\u0026middot;mL⁻\u0026sup1;\u0026middot;\u0026micro;g⁻\u0026sup1;).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProteomic analysis of EV cargo\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eProteomic analysis procedure was developed as described elsewhere [14]. Briefly, for protein extraction and digestion, EV proteins were lysed in RIPA buffer supplemented with protease and phosphatase inhibitors. Protein concentrations were measured using a BCA assay. Equal amounts (10\u0026ndash;20 \u0026mu;g) were reduced with dithiothreitol, alkylated with iodoacetamide, and digested overnight at 37 \u0026deg;C using trypsin in a 1:50 enzyme-to-protein ratio. The methodological details related to the quantitative proteomic analysis are described in the Supplementary Data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMass spectrometry\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnalysis by LC-MS/MS using a triple TOF 6600 SCIEX was performed. The digested peptides were resuspended in 20\u0026thinsp;\u0026micro;L of mobile phase A (H\u003csub\u003e2\u003c/sub\u003eO with 0.1% formic acid LC-MS quality), by sonication for 10\u0026thinsp;min. For SWATH library creation 4\u0026thinsp;\u0026micro;L of each sample pools (pools of each condition) were injected in a loop (10\u0026thinsp;\u0026micro;L/min) where the peptide mixture was correctly resuspended in mobile phase A (H\u003csub\u003e2\u003c/sub\u003eO with 0.1% formic acid LC-MS quality). The peptides were transferred to a YMC TRIART C18 silica-based reversed phase guard column, 5 \u0026times;0.5\u0026thinsp;mm, 3\u0026thinsp;\u0026micro;m in particle size and 120\u0026thinsp;\u0026Aring;\u0026nbsp;pore size (YMC Technologies) where possible contaminants that could damage the column were removed. This guard column is connected online to a YMC-TRIART C18 capture column, 150\u0026nbsp;\u0026times;0.30\u0026thinsp;mm, with a particle size of 3\u0026thinsp;\u0026micro;m and a pore size of 120\u0026thinsp;\u0026Aring;\u0026nbsp;(YMC Technologies) where the peptides are separated by polarity at a speed of 5\u0026thinsp;\u0026micro;L/min. The elution gradient of the peptides ranges from 2% to 90% mobile phase B (acetonitrile (ACN) with 0.1% formic acid LC-MS quality). For DDA analysis a 40\u0026thinsp;min gradient was used, for SWATH.\u003c/p\u003e\n\u003cp\u003eAfter library creation each individual sample were analyzed using a Data-independent acquisition (SWATH-MS) method, followed by quantitative data comparing each sample with the library. The methodological details related to the quantitative proteomic analysis are described in the Supplementary Data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor the library creation, the LC-MS/MS files were processed with the ProteinPilot\u003csup\u003eTM\u003c/sup\u003e 5.0.2 (SCIEX) software using the Paragon\u003csup\u003eTM\u003c/sup\u003e (5.0.2) algorithm for the database search and Progroup\u003csup\u003eTM\u0026nbsp;\u003c/sup\u003efor the grouping of the data. Searches were performed using a human-specific UniProt database (UniProt, https://www.uniprot.org/uniprot [15])\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSWATH analysis was performed using Peak view 2.2 for retention time adjustment and areas extraction, and Marker view 1.3.1 for normalization and statistical analysis using a T-test. The methodological details related to the quantitative proteomic analysis are described in the Supplementary Data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBioinformatics and functional enrichment analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eProtein\u0026ndash;protein interaction analysis was performed using the significantly dysregulated proteins were analyzed using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING, https://string-db.org/) database to explore functional enrichment, biological pathways, and interaction networks. The biological processes associated with significantly dysregulated proteins were analyzed using Functional Enrichment Analysis Tool software (FunRich, https://www.funrich.org/). Gene Ontology Cellular Component (GO:CC) terms were used to evaluate the subcellular distribution and EV-related functions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData related to EV characterization are presented as mean \u0026plusmn; standard error of the mean (SEM). Normality and homogeneity of variances were assessed using the Shapiro\u0026ndash;Wilk and equal variance tests, respectively. Based on data distribution, either a parametric \u003cem\u003et\u003c/em\u003e-test or a non-parametric Mann\u0026ndash;Whitney \u003cem\u003eU\u003c/em\u003e test was used for comparisons between two groups. For analyses involving more than two groups, the Kruskal\u0026ndash;Wallis test was applied, followed by the most appropriate post hoc test according to the data characteristics. Statistical significance was defined as \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. All statistical analyses were performed using SigmaPlot 11.0 (Systat Software, Inc., CA, U.S.A.) and all graphs were generated using GraphPad Prism 8 software (GraphPad; Inc., San Diego, CA, USA). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eData processing and generation of volcano plots were performed using Prism 9 software (GraphPad Software, version 9.0.2, La Jolla, CA, USA), based on the proteomic analysis results. In these plots, values are represented as \u0026minus;Log₁₀ of the p-value on the Y-axis and Log₂ of fold change (FC) on the X-axis. The fold change was calculated as the ratio between protein expression levels (expressed as normalized area) when comparing the analyzed groups. An FC \u0026gt; 1 indicates upregulation, whereas an FC \u0026lt; 1 indicates downregulation. Proteins with a p-value \u0026lt; 0.05 were considered significantly regulated. Proteins meeting these criteria are represented by red (downregulated) and green (upregulated) dots.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE [16] partner repository with the dataset identifier PXD066420.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003e\u003cstrong\u003eClinical description of patients\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 13 patients (8 males and 5 females) with a confirmed diagnosis of CADASIL were included in the study. Key demographic and clinical characteristics of the cohort are summarized in Table 1. Seven patients were classified within L1 (subtypes 1a and 1b), and six in the group L2 (subtypes 2b and 3a). The L1 group included younger patients (mean age ~44 years), with limited structural brain damage on MRI and mostly preserved cognitive function, with very mild impairment. In contrast, the L2 group was older on average (~55 years) and comprised patients in stages 2b\u0026ndash;3a, showing a greater burden of WMT and lacunar infarcts, and more frequent cognitive deficits ranging from very mild to moderate impairment.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Demographic, Genetic, and Clinical Characteristics of CADASIL Patients Included in the Study, Categorized by Dichotomized \u003cem\u003eNOTCH3\u003c/em\u003e-SVD Staging (Early/Asymptomatic; defined as L1\u003cem\u003e\u0026nbsp;vs\u003c/em\u003e Intermediate/Advanced maned as L2)\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGroup\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIdentifier\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eExon\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEGF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eScale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCognitive impairment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"6\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eL1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eCAD9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003efemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003enormal\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eCAD18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003emale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003emild impairment\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eCAD41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003efemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eCAD42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003emale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003enormal\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eCAD43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003emale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003every mild impairment\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eCAD53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003emale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003enormal\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"7\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eL2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eCAD8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003emale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e2b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003emild impairment\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eCAD15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003efemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e3a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003emild impairment\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eCAD23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003emale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e2b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003emild impairment\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eCAD29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003emale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e3b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003emoderate impairment\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eCAD38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003efemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e2b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003every mild impairment\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eCAD55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003efemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e2b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003emild impairment\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eCAD57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003emale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e2b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003emild impairment\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*Data not available\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCharacterization of EVs isolated from CADASIL patients\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs first step of the study was to characterize EVs isolated from CADASIL patients. This step is essential to confirm their purity, structural integrity, and the presence of specific molecular markers before conducting further analyses. Nanoparticle Tracking Analysis (NTA) demonstrated a similar EV size distribution in patients stratified according to L1 (Figure 1A) and L2 (Figure 1B). The mean particle size did not differ significantly between L1 and L2, with values of 114.3nm \u0026plusmn; 2.6 (SEM) and 109.6nm \u0026plusmn; 3.3, respectively. Likewise, no significant differences were found between both groups with values of 1,693 \u0026times; 10\u0026sup1;\u0026sup2; \u0026plusmn; 4,797 \u0026times; 10\u0026sup1;\u0026sup1; particles/mL for L1 patients and 1,432 \u0026times; 10\u0026sup1;\u0026sup2; \u0026plusmn; 2,728 \u0026times; 10\u0026sup1;\u0026sup1; particles/mL in case of L2. TEM analysis confirmed the presence of vesicular structures displaying the characteristic cup-shaped morphology and a diameter below 150 nm, consistent with the size and ultrastructural features of small extracellular vesicles, classically referred to as exosomes (Figure 1C). The enrichment of EVs was validated by quantifying the levels of canonical EV markers, such as CD63, CD81 and ALIX. CD63 and CD81 are two membrane-associated tetraspanins commonly enriched in small EVs [17] and \u0026nbsp;ALIX, a cytosolic protein involved in the endosomal sorting complex required for transport (ESCRT) machinery [18]. Significantly higher concentrations of CD63 (Figure 1D), CD81 (Figure 1E), and ALIX (Figure 1F) were detected in isolated EV\u003csub\u003eT\u003c/sub\u003e compared to the corresponding EV-depleted fractions (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 for all comparisons), thereby confirming the effective enrichment of EVs achieved through the isolation protocol previously reported\u0026nbsp;[13]. Finally, to evaluate potential contamination with cellular components, the presence of calnexin, a marker of the endoplasmic reticulum, was assessed by immunodetection. Negligible levels of calnexin were detected in both the EV-depleted and EV\u003csub\u003eᴛ\u003c/sub\u003e fractions, while significantly higher levels were observed exclusively in the positive control (lysates from the HMC3 human microglial cell line) (Figure 1G). These findings indicate that contamination with cellular debris was minimal in the EV preparations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSelective Enrichment of Brain Cell Type\u0026ndash;Specific Extracellular Vesicle Subpopulations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOnce the EV\u003csub\u003eT\u003c/sub\u003e were characterized, the cellular origin of isolated and purified EV subpopulations was determined by quantifying cell type\u0026ndash;specific protein markers in EVs enriched from nEVs, aEVs, and oEVs origin. Protein concentrations were normalized to the total protein content of each EV fraction and expressed as pg\u0026middot;mL⁻\u0026sup1;\u0026middot;\u0026micro;g⁻\u0026sup1;. Neuronal marker enolase levels (Figure 2A) were markedly elevated in nEVs (mean ~1,500 pg\u0026middot;mL⁻\u0026sup1;\u0026middot;\u0026micro;g⁻\u0026sup1;), compared to plasma (\u0026lt;0.09 pg\u0026middot;mL⁻\u0026sup1;\u0026middot;\u0026micro;g⁻\u0026sup1;) and EV\u003csub\u003eT\u003c/sub\u003e fractions (\u0026lt;12 pg\u0026middot;mL⁻\u0026sup1;\u0026middot;\u0026micro;g⁻\u0026sup1;), indicating a high degree of neuronal specificity. GFAP, an intermediate filament protein selectively expressed in astrocytes, was markedly enriched aEV fraction (Figure 2B). GFAP concentrations in plasma (\u0026lt;0.14 pg\u0026middot;mL⁻\u0026sup1;\u0026middot;\u0026micro;g⁻\u0026sup1;) and EVᴛ\u0026nbsp;fractions (\u0026lt;0.4 pg\u0026middot;mL⁻\u0026sup1;\u0026middot;\u0026micro;g⁻\u0026sup1;) were significantly lower than those detected in aEVs, where significantly elevated levels were quantified (\u0026asymp;60 pg\u0026middot;mL⁻\u0026sup1;\u0026middot;\u0026micro;g⁻\u0026sup1;). These findings are consistent with the astrocytic origin of this EV subpopulation and support the successful immunoaffinity-based enrichment of aEVs. MBP (Figure 2C), a canonical myelin-associated protein predominantly expressed by oligodendrocytes, was detected at markedly elevated concentrations in oEVs (\u0026asymp;200 pg\u0026middot;mL⁻\u0026sup1;\u0026middot;\u0026micro;g⁻\u0026sup1;), in contrast to plasma (\u0026lt;5 \u0026times; 10⁻\u0026sup3;\u0026nbsp;pg\u0026middot;mL⁻\u0026sup1;\u0026middot;\u0026micro;g⁻\u0026sup1;) and EV\u003csub\u003eT\u003c/sub\u003e fractions (\u0026lt;0.14 pg\u0026middot;mL⁻\u0026sup1;\u0026middot;\u0026micro;g⁻\u0026sup1;). These results confirm the specificity and efficiency of the immunoaffinity-based method employed for the selective isolation of oEVs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCell Type\u0026ndash;Specific EVs Reveal Stage-Dependent Changes in GFAP and MBP\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNfL, GFAP, and MBP are important biomarkers reflecting neuroaxonal damage, astrocytic activation, and demyelination, respectively [19-23]. In CADASIL, elevated NfL levels correlate with axonal injury and clinical severity [7, 24]. Increased GFAP indicates reactive astrogliosis linked to microvascular pathology [24], while MBP reflects myelin breakdown and white matter damage, characteristic of CADASIL [25] and other neuronal pathologies [26].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo evaluate potential differences in the abundance of brain cell\u0026ndash;derived EV markers in both clinical scenarios of CADASIL, the concentrations of NfL, GFAP, and MBP were quantified in plasma, EV\u003csub\u003eT\u003c/sub\u003e, nEV, aEV and oEV subpopulations isolated from individuals classified as L1 or L2.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNfL levels analyzed in the five subpopulations \u0026nbsp;are showed in the Figure 3A. No statistically significant differences were observed between patients classified as L1 and L2. \u0026nbsp;NfL levels within nEVs fractions were comparable across both clinical groups, suggesting that the degree of neuronal injury, as reflected by this axonal biomarker, does not substantially vary between these two clinical groups in this cohort. In contrast, GFAP levels (Figure 3B) were significantly elevated in aEVs from patients classified as L2, compared to L1 patients (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05). This finding indicates increased astrocytic activation in more advanced stages of the disease and aligns with the proposed involvement of progressive gliosis in CADASIL pathophysiology. The data further support the utility of aEVs as a non-invasive source of molecular information to monitor glial reactivity and neuroinflammatory dynamics \u003cem\u003ein vivo\u003c/em\u003e. Similarly, a marked increase in MBP concentrations (Figure 3C) was detected in oEVs from L2 patients relative to L1 (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05), suggesting enhanced oligodendroglial stress or myelin-related pathology in later stages. These observations reinforce the potential of cell type\u0026ndash;specific EV profiling as a sensitive platform for assessing CNS cellular alterations associated with disease progression. No significant differences in NfL, GFAP, or MBP concentrations were observed in plasma or EVᴛ\u0026nbsp;fractions between patients classified as L1 and 2. For all three biomarkers, levels remained similar across both clinical groups, indicating that neither plasma nor EVᴛ\u0026nbsp;compartments are sensitive to stage-related CNS alterations in this population.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProteomic analysis of plasma samples and EV\u003csub\u003eT\u003c/sub\u003e from CADASIL patients\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 1,141 proteins were identified with a false discovery rate (FDR) below 1%. Of these, we were able to quantify 746 proteins (Fig. S1A), the majority of which are predominantly represented in brain and nervous system tissue expression profiles, suggesting a strong relevance of the detected proteome to neurovascular physiology and pathology (Fig. S1B).\u003c/p\u003e\n\u003cp\u003eFollowing the detailed analysis of neuronal, astrocytic, and oligodendrocyte-derived EVs (including quantification of NfL, GFAP and MBP) we performed a comprehensive proteomic analysis to explore additional molecular signatures that could serve as potential biomarkers.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAs an initial approach, a global proteomic analysis of plasma from CADASIL patients (L2\u003cem\u003e\u0026nbsp;vs\u003c/em\u003e L1) revealed 44 differentially expressed proteins (summarized in the Table S1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTwelve significantly downregulated proteins (RPL23A, ATP1A1, ABCG2, CYTA, F10A1, IGF2BP1, RAB14, PYGM, FKBP1A, MAPT, MSN, SYNJ1) and six upregulated proteins (C4B, AK1, IGHG4, CAPS, YWHAB, CRP) were enriched among the most significant hits (p \u0026lt; 0.05) \u0026nbsp; in the volcano plot (Fig. 4A). From the proteins significantly upregulated and downregulated with p \u0026lt; 0.05, the top 5 from each group were selected and represented in a heatmap. This visualization allows for an intuitive comparison of expression patterns across samples, highlighting relative protein abundance and clustering based on similarity (Fig.4B). Functional enrichment analysis performed on the complete set of dysregulated proteins (GO:CC, Fig. 4C) pointed to modest contributions from blood microparticles and secretory granules. \u0026nbsp;Disease gene associations suggest their potential involvement in diverse pathophysiological mechanisms, including amyloidosis, coagulation, and genetic vascular disorders (Fig.S2A).\u003c/p\u003e\n\u003cp\u003eIn summary, plasma proteomics revealed a molecular signature characterized by downregulation of neuronal and immune regulatory proteins and upregulation of coagulation and inflammation-related factors, consistent with progressive neurovascular dysfunction in CADASIL. Functional enrichment showed dominant pathways in innate immunity (27.6%), complement activation (23.1%), adaptive immune response (22.7%), and B-cell signaling (13.8%). Additional contributions included coagulation (7\u0026ndash;8%), proteolysis (6.7%), and lipid-handling processes (3\u0026ndash;4%). Overall, these findings indicate a systemic pro-inflammatory and pro-thrombotic environment, in line with the vascular pathophysiology of small vessel disease [27] (Fig.S4A).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eProteomic analysis of EV\u003csub\u003eT\u003c/sub\u003e identified 38 EV-derived proteins in CADASIL patients (described in detail in Table S2). Proteins with p \u0026lt; 0.05 were concentrated at the extremes of the volcano plot (Fig. 4D), with a total of 21 downregulated (THBS1, PCP4, PAPL, PGLYRP2, PRDX5, ALDH1L1, IGF2BP3, SEPTIN7, PAICS, CFL1, SLC2A1, C8A, DYNC1I1, CCT6A, ENPP6, TRIM28, HNRNPA0, ITIH1, PHB1, SH3BGRL3, YWHAE) and 5 upregulated (QDPR, RPL29, PCYT2, FTL, STXBP1). Among the proteins showing significant up- and downregulation with p \u0026lt; 0.05, the top five from each category were chosen and displayed in a heatmap (Fig.4E). GO:CC enrichment analysis (Fig. 4F) confirmed a predominant association with organelle- and vesicle-related components, consistent with the EVs origin of the proteome. Disease gene association category in STRING network, linking them to pathological conditions such as amyloidosis, blood coagulation disorders, autosomal dominant diseases, genetic syndromes, vascular pathologies, and cerebrovascular disease (Fig. S2B); supporting its potential utility as a biomarker of clinical progression. The functional analysis of proteins present in EV\u003csub\u003eT\u003c/sub\u003e (Fig.S4B) recapitulated the immune signature observed in plasma (innate [27.3%], adaptive [22.5%] and classical complement activation [24.1%]), but a distinctive enrichment for phagocytosis/engulfment (17.1%) emerged, alongside hemostatic functions (fibrinolysis [5.3%], blood coagulation [5.9%], plasminogen activation [2.1%]) and lipid transport/remodeling (reverse cholesterol transport [4.3%], lipoprotein metabolism [4.3%], positive regulation of cholesterol esterification [3.2%], HDL particle remodeling [3.7%]). These findings support the role of EVs as a dynamic reflection of cellular immunity and tissue homeostasis in vascular diseases such as CADASIL [28].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProteomic analysis of neuronal, astrocytic, and oligodendrocyte-derived EVs\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 26 proteins were differentially expressed in nEVs from CADASIL patients, with 15 downregulated and 11 upregulated (described in the Table S3).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA global overview of the data showed 14 proteins with \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 concentrated at the extremes of the volcano plot. The most prominent downregulated proteins included HV226, PGRMC1, LUM, RTN4, RPL23, NPM1, and FCN2, while SPP24, PHB2, CTSD, RPS30, RPL28, and MOG were among the most upregulated. (Fig. 5A). The five most significantly upregulated and downregulated proteins (with p \u0026lt; 0.05) from each group were selected and visualized using a heatmap (Fig.5B). GO:CC analysis (Fig. 5C) revealed significant enrichment in proteins associated with organelles and extracellular vesicles. Network analysis using STRING (Fig. S3A) showed several proteins were significantly annotated in the Disease gene association category, linking them to vascular, genetic, and neurodegenerative conditions such as amyloidosis, coagulation disorders, coronary and cerebrovascular disease. These associations suggest that the altered nEVs proteome may reflect molecular mechanisms involved in CADASIL pathology. The upregulation of proteins such as MOG and CTSD, linked to demyelination and lysosomal degradation, points to the activation of neurodegenerative pathways. GO enrichment and network analyses further associate these nEV alterations with vascular, genetic, and neurodegenerative conditions, suggesting that nEVs may capture processes contributing to neuronal damage, endothelial dysfunction, and blood\u0026ndash;brain barrier disruption. Overall, the proteomic profile of nEVs indicates a pathological environment characterized by inflammation, neurodegeneration, and vascular impairment, supporting their potential role as peripheral sensors of brain immune\u0026ndash;metabolic state and as minimally invasive biomarkers of neuronal injury in CADASIL [29, 30] (Fig.S5A).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn aEVs from CADASIL patients, a total of 31 proteins were differentially expressed (Table S4). From the subset of proteins significantly altered (up or down) with p \u0026lt; 0.05 (Fig. 6A), the top five in each group were represented in a heatmap (Fig.6B). GO:CC analysis (Fig. 6C) confirmed the enrichment in vesicle- and organelle-related components. Network analysis using STRING revealed a low PPI enrichment score (PPI = 0.07), suggesting direct protein\u0026ndash;protein interactions. Nevertheless, several differentially expressed proteins were significantly associated with the Disease Gene Association category, including annotations for amyloidosis, autosomal dominant disorders, coagulation abnormalities, CNS and vascular diseases, and cerebrovascular pathology (Fig. S3B). These results suggest that proteomic alterations in aEVs may contribute to impaired astrocytic metabolic support, disrupted neuroimmune regulation, and loss of vascular integrity\u0026mdash;processes critical for CNS homeostasis. The downregulation of stress-adaptive and structural proteins, combined with the upregulation of inflammatory and complement-related factors, suggests a shift toward a neuroinflammatory and vasculopathic profile. These alterations are consistent with severe astrocytic damage and reactive transformation previously described in CADASIL, \u0026nbsp;supporting a role for aEVs as both sensitive biomarkers and potential mediators of gliovascular dysfunction and disease progression [31] (Fig.S5B).\u003c/p\u003e\n\u003cp\u003eFinally, proteomic profiling of oEVs revealed 23 differentially expressed proteins in CADASIL patients (Table S5). These expression changes were visualized in the volcano plot (Fig. 7A), with significantly dysregulated proteins located at the extremes, and the top five up- and downregulated (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05) shown in a heatmap (Fig. 7B) to illustrate expression patterns and clustering. GO:CC analysis (Fig. 7C) confirmed enrichment in proteins associated with organelle membranes and extracellular vesicles, consistent with their cellular origin. STRING network proteins from L1 and L2 patient groups revealed several proteins were annotated in the Disease gene association category, with links to amyloidosis, coagulation disorders, autosomal genetic diseases, and cerebrovascular conditions (Fig. S3C).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eoEVs from CADASIL patients show enrichment in complement pathways, inflammation, B-cell activation, and demyelinating processes, reflecting oligodendroglial degeneration and myelin loss characteristic of the disease (Fig.S5C). Similar to other pathologies [32], this secondary loss of oligodendrocytes in the white matter appears to result from chronic hypoperfusion rather than being a primary event. These findings might indicate that the protein cargo of oligodendrocytic EVs captures demyelination, oxidative stress, and impaired tissue repair processes, aligning with the progressive white matter deterioration described in CADASIL\u0026nbsp;\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis study provides a comprehensive characterization of total and brain cell\u0026ndash;type\u0026ndash;specific EVs isolated from plasma of CADASIL patients stratified by \u003cem\u003eNOTCH3\u003c/em\u003e-SVD staging system [4].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFollowing the 2024 MISEV guidelines [33], EVs were classified and characterized using complementary biophysical, molecular, and imaging approaches. Nanoparticle tracking analysis and transmission electron microscopy confirmed the expected size distribution (\u0026lt;150 nm) and cup-shaped morphology consistent with small EVs, without significant differences in particle size or concentration across clinical stages. The presence of canonical EVs markers (CD63, CD81, and ALIX) validated the successful enrichment of EVs populations, while the absence of calnexin, an endoplasmic reticulum marker, confirmed minimal contamination with cellular debris. Cell type\u0026ndash;specific EVs subpopulations were selectively enriched via immunoaffinity capture, as demonstrated by the robust detection of enolase, GFAP, and MBP in neuronal, astrocytic, and oligodendroglial EVs, respectively. These results highlight the specificity and efficiency of the isolation workflow and underscore the potential of brain-derived EVs to serve as peripheral reporters of CNS cellular identity and pathology, particularly, in the context of CADASIL\u0026nbsp;[13].\u003c/p\u003e\n\u003cp\u003eTo date, studies specifically focused on EVs in CADASIL are extremely limited. The only published work systematically analyzing plasma exosomes \u0026mdash; a specific subpopulation of small EVs rather than the entire EV spectrum \u0026mdash; is that by Gao \u003cem\u003eet al\u003c/em\u003e.[34], which reported alterations in exosome morphology and concentration, along with decreased Notch3 and increased NfL levels. However, no studies have yet investigated brain cell\u0026ndash;derived EVs subpopulations in CADASIL. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eImportantly, stratification by clinical stage revealed disease-associated changes in specific glial EV markers. GFAP levels were significantly elevated in aEVs from L2 compared to L1, suggesting enhanced astrocyte activation in a more advanced condition. This observation is consistent with previous reports of reactive gliosis and astrocyte-mediated neuroinflammation in CADASIL pathogenesis [31]. Similarly, MBP levels were significantly increased in oEVs from L2 patients, indicating potential oligodendrocyte stress or progressive myelin disruption, which has also been described as a pathological hallmark of CADASIL [35, 36]. These findings provide further support for the hypothesis that glial dysfunction plays a pivotal role in disease progression and that glia-derived EVs may offer sensitive, non-invasive readouts of CNS pathology.\u003c/p\u003e\n\u003cp\u003eConversely, NfL levels in nEVs did not significantly differ between groups. Although plasma NfL is a well-established biomarker of axonal injury in various neurological disorders [19], and also in CADASIL [7, 34], \u0026nbsp;its lack of stage-related variation in this cohort may reflect relative preservation of axonal integrity across early clinical stages, differential release dynamics from neurons, or limitations in detection sensitivity within this specific EVs compartment.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNotably, no differences were observed in the levels of any of the three biomarkers when measured in plasma or EV\u003csub\u003eT\u003c/sub\u003e fractions, emphasizing that cell-type\u0026ndash;specific EVs isolation significantly enhances detection sensitivity for CNS-related pathological changes.\u003c/p\u003e\n\u003cp\u003eTaken together, these data demonstrate that selectively enriched brain-derived EVs represent a highly informative and pathophysiological relevant source of biomarkers in CADASIL. The differential expression of GFAP and MBP in cell-specific EVs, but not in EV\u003csub\u003eT\u003c/sub\u003e or plasma, highlights the necessity of targeted EV profiling to uncover subtle yet biologically meaningful alterations in CNS cell populations.\u003c/p\u003e\n\u003cp\u003eEVs from neurons, astrocytes, and oligodendrocytes were analyzed, excluding contributions from vascular smooth muscle cells (VSMCs), which are primary targets in CADASIL. As vascular smooth muscle cell degeneration and Notch3 aggregation in these cells are central to CADASIL pathophysiology, the absence of their EVs limits our ability to fully capture the disease\u0026apos;s cellular spectrum. This exclusion was due to the current lack of sufficiently specific and validated enrichment methods for isolating VSMC-derived EVs from peripheral blood, which hampers their reliable identification and analysis [37].\u003c/p\u003e\n\u003cp\u003eIn addition to the SIMOA/\u003cs\u003e\u0026nbsp;\u003c/s\u003eELISA biomarker analysis, the proteomic profiling of plasma, EV\u003csub\u003eT\u003c/sub\u003e, and cell-type\u0026ndash;specific EV subpopulations provided valuable complementary insights into CADASIL pathophysiology. Our proteomic analysis across plasma and cell type\u0026ndash;enriched EVs reveals a consistent molecular signature associated with progressive neurovascular dysfunction in CADASIL patients with increasing clinical severity. A pattern of downregulated neuronal and immune regulatory proteins, coupled with the upregulation of coagulation and inflammation-related markers, supports the role of EVs as dynamic indicators of disease progression. In total plasma EVs, we observed broad dysregulation of proteins related to oxidative stress, immune function, cytoskeletal organization, and endothelial integrity, reflecting a systemic shift toward a pro-inflammatory and pro-thrombotic state. This altered proteome highlights the potential of EVs as minimally invasive biomarkers of clinical worsening. Similar approaches in other neurological and cerebrovascular diseases have demonstrated that EV proteomics can uncover subtle, disease-specific changes not detectable in bulk plasma proteomics [11]\u003csup\u003e,\u003c/sup\u003e[33]. nEVs exhibited loss of proteins involved in immune homeostasis, axonal plasticity, and vascular regulation, along with increased expression of proteins associated with demyelination and lysosomal degradation, suggesting activation of neurodegenerative processes and neuronal\u0026ndash;vascular disruption. In aEVs, altered protein profiles indicated deficits in metabolic support, immune modulation, and blood\u0026ndash;brain barrier maintenance. The combined downregulation of stress-adaptive proteins and upregulation of complement and inflammatory mediators suggest a transition toward a neuroinflammatory, vasculopathic phenotype, which may exacerbate disease progression. oEVs showed reduced expression of proteins linked to mitochondrial function, RNA metabolism, and proteostasis, alongside increases in stress- and apoptosis-related proteins, suggesting oligodendroglial dysfunction and potential contribution to white matter degeneration\u0026mdash;a core feature of CADASIL. Altogether, these findings highlight converging yet cell-type\u0026ndash;specific alterations across EV populations that reflect key pathological processes in CADASIL. The disease associations identified through STRING network analysis further support the relevance of these EV proteins to neurovascular and genetic pathologies.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNonetheless, this study has several limitations. A key limitation of this study is that the clinical stages included (stage 1a to 3b) represent relatively early and similar degrees of disease severity, which may reduce the sensitivity for detecting stage-dependent biomarker differences, mainly in the proteomic profiling analysis. Comparing these findings with patients in more advanced stages (e.g. \u0026ge; 4) or including cases with more pronounced cognitive or structural deterioration would likely provide greater contrast and enhance the ability to identify robust biomarkers. Additionally, the absence of a healthy control group limits the interpretation of whether the observed molecular changes are specific to CADASIL or reflect more general vascular or neurodegenerative processes. Finally, the modest sample size in this exploratory study emphasizes the need for larger cohorts to confirm these preliminary findings, improve statistical power, and validate potential biomarkers for clinical use.\u003c/p\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eThis study demonstrates that brain cell\u0026ndash;derived EVs represent a powerful nanobiotechnology platform for minimally invasive biomarker discovery in CADASIL. By integrating cell type\u0026ndash;specific EV profiling with proteomic analysis, we identified molecular alterations reflecting glial activation, oligodendrocyte dysfunction, and systemic immune\u0026ndash;vascular dysregulation that were not detectable in plasma or bulk EVs. These findings highlight the sensitivity of EV-based approaches to capture subtle yet biologically relevant changes associated with disease stage and progression. Beyond their potential as biomarkers, neuronal, astrocytic, and oligodendrocytic EVs may also provide mechanistic insights into CADASIL pathophysiology, linking neuroinflammation, demyelination, and vascular dysfunction. Altogether, our results support the use of brain-derived EVs as a liquid biopsy tool for monitoring CADASIL and underscore their translational potential for biomarker development in small vessel disease and other neurovascular disorders.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eaEVs: Astrocyte-derived extracellular vesicles; BBB: Blood\u0026ndash;brain barrier; BSA: Bovine serum albumin; CADASIL: Cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy; CFI: Complement factor I; CRP: C-reactive protein; CNS: Central nervous system; CSVD/SVD: Cerebral small vessel disease / Small vessel disease; CTSD: Cathepsin D; DDA: Data-dependent acquisition; DIA: Data-independent acquisition; EVs: Extracellular vesicles; EV\u003csub\u003eT\u003c/sub\u003e: Total extracellular vesicles; FDR: False discovery rate; FGA/FGB/FGG: Fibrinogen alpha, beta, and gamma chains; GFAP: Glial fibrillary acidic protein; GO: Gene Ontology; HDL: High-density lipoprotein; LC\u0026ndash;MS/MS: Liquid chromatography\u0026ndash;tandem mass spectrometry; L1/L2: Leiden stage 1 (early/asymptomatic) / Leiden stage 2 (intermediate/advanced); LUM: Lumican; MBP: Myelin basic protein; MISEV: Minimal Information for Studies of Extracellular Vesicles; MOG: Myelin oligodendrocyte glycoprotein; MRI: Magnetic resonance imaging; MSN: Moesin; nEVs: Neuron-derived extracellular vesicles; NfL: Neurofilament light chain; NOTCH3: Neurogenic locus notch homolog protein 3; NTA: Nanoparticle tracking analysis; oEVs: Oligodendrocyte-derived extracellular vesicles; PBS: Phosphate-buffered saline; PPI: Protein\u0026ndash;protein interaction; Simoa: Single molecule array; STRING: Search Tool for the Retrieval of Interacting Genes/Proteins; SWATH-MS: Sequential window acquisition of all theoretical fragment-ion spectra mass spectrometry; TEM: Transmission electron microscopy; WMH: White matter hyperintensities.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the principles outlined in the Declaration of Helsinki and Good Clinical Practice guidelines. Ethical approval was obtained from the Clinical Research Ethics Committee of the Hospital de la Santa Creu i Sant Pau (Barcelona, Spain) and from the Ethics Committee of the Health Research Institute of Santiago de Compostela (IDIS, Santiago de Compostela, Spain). All procedures involving human participants complied with national and European regulations for biomedical research and data protection. Written informed consent was obtained from all participants prior to their inclusion in the CADAGENIA registry and before the collection of blood samples for extracellular vesicle and proteomic analyses. Patients were informed about the purpose of the study, the voluntary nature of their participation, and their right to withdraw consent at any time without affecting their medical care.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the analyses. All authors have read and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData supporting the figures and tables of this manuscript (analysis data) are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe experiments and analysis in the study were supported by Spanish Ministry of Science, Innovation and Universities (PID2021-126848NB-I00; PID2023-150743OB-I00), the Galician Government (XUGA, ED431C 2022/41), FEDER (Regional European Development Fund), Instituto de Salud Carlos III (ISCIII) through the projects, PI20/01014, RICORS-ICITUS RD24/0009/0017 and\u0026nbsp;AC23-2/00029. \u0026nbsp;AC23-2/00029 (named as CADANHIS) project has been supported by the EJP RD \u0026ndash; European Joint Programme on Rare Diseases \u0026ndash; Joint Transnational Call 2023 for Rare Diseases Research Project (JTC 2023). The EJP RD initiative has received funding from the European Union\u0026apos;s Horizon 2020 research and innovation program under grant agreement N\u0026deg;825575.\u0026nbsp;E. Mui\u0026ntilde;o is supported by the Juan Rod\u0026eacute;s contract (JR23/00045) from Instituto de Salud Carlos III. P. Villatoro-Gonz\u0026aacute;lez is supported by a Joan Or\u0026oacute; contract from the predoctoral program AGAUR FI ajuts (2023 FI-3 00065) Joan Or\u0026oacute; of the Secretariat of Universities and Research of the Department of Research and Universities of the Generalitat of Catalonia and the European Social Plus Fund.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: FC, AIRP. Material preparation, data collection and analysis: ABC, EM, PVG, LCM, PAP, FA, IS, SBB. Patient recruitment and blood collection: EM, FA, IS, SAR. Methodology, Software, and Formal Analysis: ABC, EM, PVG, SBB, AIRP. Data Validation: ABC, JLG, FC, AIRP. Writing \u0026ndash; Review \u0026amp; Editing: ABc, EM, SBB, SAR, IFC, JLG, AIRP, FC. Supervision: FC, AIRP. Funding Acquisition: FC, JLG, AIRP\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors acknowledge the use of ChatGPT (GPT-5, OpenAI) for assistance in language editing and text revision. The authors reviewed and verified all generated content for accuracy.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eJoutel A, Corpechot C, Ducros A, Vahedi K, Chabriat H, Mouton P, Alamowitch S, Domenga V, Cecillion M, Marechal E, et al: \u003cstrong\u003eNotch3 mutations in CADASIL, a hereditary adult-onset condition causing stroke and dementia.\u003c/strong\u003e \u003cem\u003eNature \u003c/em\u003e1996, \u003cstrong\u003e383:\u003c/strong\u003e707-710.\u003c/li\u003e\n\u003cli\u003eKalimo H, Ruchoux MM, Viitanen M, Kalaria RN: \u003cstrong\u003eCADASIL: a common form of hereditary arteriopathy causing brain infarcts and dementia.\u003c/strong\u003e \u003cem\u003eBrain Pathol \u003c/em\u003e2002, \u003cstrong\u003e12:\u003c/strong\u003e371-384.\u003c/li\u003e\n\u003cli\u003eChabriat H, Joutel A, Dichgans M, Tournier-Lasserve E, Bousser MG: \u003cstrong\u003eCadasil.\u003c/strong\u003e \u003cem\u003eLancet Neurol \u003c/em\u003e2009, \u003cstrong\u003e8:\u003c/strong\u003e643-653.\u003c/li\u003e\n\u003cli\u003eGravesteijn G, Rutten JW, Cerfontaine MN, Hack RJ, Liao YC, Jolly AA, Guey S, Hsu SL, Park JY, Yuan Y, et al: \u003cstrong\u003eDisease Severity Staging System for NOTCH3-Associated Small Vessel Disease, Including CADASIL.\u003c/strong\u003e \u003cem\u003eJAMA Neurol \u003c/em\u003e2025, \u003cstrong\u003e82:\u003c/strong\u003e49-60.\u003c/li\u003e\n\u003cli\u003eMarkus HS, Martin RJ, Simpson MA, Dong YB, Ali N, Crosby AH, Powell JF: \u003cstrong\u003eDiagnostic strategies in CADASIL.\u003c/strong\u003e \u003cem\u003eNeurology \u003c/em\u003e2002, \u003cstrong\u003e59:\u003c/strong\u003e1134-1138.\u003c/li\u003e\n\u003cli\u003eGravesteijn G, Hack RJ, Mulder AA, Cerfontaine MN, van Doorn R, Hegeman IM, Jost CR, Rutten JW, Lesnik Oberstein SAJ: \u003cstrong\u003eNOTCH3 variant position is associated with NOTCH3 aggregation load in CADASIL vasculature.\u003c/strong\u003e \u003cem\u003eNeuropathol Appl Neurobiol \u003c/em\u003e2022, \u003cstrong\u003e48:\u003c/strong\u003ee12751.\u003c/li\u003e\n\u003cli\u003eDuering M, Konieczny MJ, Tiedt S, Baykara E, Tuladhar AM, Leijsen EV, Lyrer P, Engelter ST, Gesierich B, Achmuller M, et al: \u003cstrong\u003eSerum Neurofilament Light Chain Levels Are Related to Small Vessel Disease Burden.\u003c/strong\u003e \u003cem\u003eJ Stroke \u003c/em\u003e2018, \u003cstrong\u003e20:\u003c/strong\u003e228-238.\u003c/li\u003e\n\u003cli\u003eThery C, Witwer KW, Aikawa E, Alcaraz MJ, Anderson JD, Andriantsitohaina R, Antoniou A, Arab T, Archer F, Atkin-Smith GK, et al: \u003cstrong\u003eMinimal information for studies of extracellular vesicles 2018 (MISEV2018): a position statement of the International Society for Extracellular Vesicles and update of the MISEV2014 guidelines.\u003c/strong\u003e \u003cem\u003eJ Extracell Vesicles \u003c/em\u003e2018, \u003cstrong\u003e7:\u003c/strong\u003e1535750.\u003c/li\u003e\n\u003cli\u003eYanez-Mo M, Siljander PR, Andreu Z, Zavec AB, Borras FE, Buzas EI, Buzas K, Casal E, Cappello F, Carvalho J, et al: \u003cstrong\u003eBiological properties of extracellular vesicles and their physiological functions.\u003c/strong\u003e \u003cem\u003eJ Extracell Vesicles \u003c/em\u003e2015, \u003cstrong\u003e4:\u003c/strong\u003e27066.\u003c/li\u003e\n\u003cli\u003eSimons M, Raposo G: \u003cstrong\u003eExosomes--vesicular carriers for intercellular communication.\u003c/strong\u003e \u003cem\u003eCurr Opin Cell Biol \u003c/em\u003e2009, \u003cstrong\u003e21:\u003c/strong\u003e575-581.\u003c/li\u003e\n\u003cli\u003eThompson AG, Gray E, Heman-Ackah SM, Mager I, Talbot K, Andaloussi SE, Wood MJ, Turner MR: \u003cstrong\u003eExtracellular vesicles in neurodegenerative disease - pathogenesis to biomarkers.\u003c/strong\u003e \u003cem\u003eNat Rev Neurol \u003c/em\u003e2016, \u003cstrong\u003e12:\u003c/strong\u003e346-357.\u003c/li\u003e\n\u003cli\u003eMuino E, Maisterra O, Jimenez-Balado J, Cullell N, Carrera C, Torres-Aguila NP, Carcel-Marquez J, Gallego-Fabrega C, Lledos M, Gonzalez-Sanchez J, et al: \u003cstrong\u003eGenome-wide transcriptome study in skin biopsies reveals an association of E2F4 with cadasil and cognitive impairment.\u003c/strong\u003e \u003cem\u003eSci Rep \u003c/em\u003e2021, \u003cstrong\u003e11:\u003c/strong\u003e6846.\u003c/li\u003e\n\u003cli\u003ePedrosa MA, Labandeira CM, Lago-Baameiro N, Valenzuela R, Pardo M, Labandeira-Garcia JL, Rodriguez-Perez AI: \u003cstrong\u003eExtracellular Vesicles and Their Renin-Angiotensin Cargo as a Link between Metabolic Syndrome and Parkinson\u0026apos;s Disease.\u003c/strong\u003e \u003cem\u003eAntioxidants (Basel) \u003c/em\u003e2023, \u003cstrong\u003e12\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003eBugallo-Casal A, Muino E, Bravo SB, Hervella P, Arias-Rivas S, Rodriguez-Yanez M, Vara-Leon E, Quintas-Rey R, Perez-Gayol L, Maisterra-Santos O, et al: \u003cstrong\u003eNOTCH3 Variant Position Affects the Phenotype at the Pluripotent Stem Cell Level in CADASIL.\u003c/strong\u003e \u003cem\u003eNeuromolecular Med \u003c/em\u003e2025, \u003cstrong\u003e27:\u003c/strong\u003e18.\u003c/li\u003e\n\u003cli\u003eUniProt C: \u003cstrong\u003eUniProt: a worldwide hub of protein knowledge.\u003c/strong\u003e \u003cem\u003eNucleic Acids Res \u003c/em\u003e2019, \u003cstrong\u003e47:\u003c/strong\u003eD506-D515.\u003c/li\u003e\n\u003cli\u003ePerez-Riverol Y, Bandla C, Kundu DJ, Kamatchinathan S, Bai J, Hewapathirana S, John NS, Prakash A, Walzer M, Wang S, Vizcaino JA: \u003cstrong\u003eThe PRIDE database at 20 years: 2025 update.\u003c/strong\u003e \u003cem\u003eNucleic Acids Res \u003c/em\u003e2025, \u003cstrong\u003e53:\u003c/strong\u003eD543-D553.\u003c/li\u003e\n\u003cli\u003eRubinstein E, Thery C, Zimmermann P: \u003cstrong\u003eTetraspanins affect membrane structures and the trafficking of molecular partners: what impact on extracellular vesicles?\u003c/strong\u003e \u003cem\u003eBiochem Soc Trans \u003c/em\u003e2025, \u003cstrong\u003e0:\u003c/strong\u003e371-382.\u003c/li\u003e\n\u003cli\u003eYang Y, Wang M, Zhang YY, Zhao SZ, Gu S: \u003cstrong\u003eThe endosomal sorting complex required for transport repairs the membrane to delay cell death.\u003c/strong\u003e \u003cem\u003eFront Oncol \u003c/em\u003e2022, \u003cstrong\u003e12:\u003c/strong\u003e1007446.\u003c/li\u003e\n\u003cli\u003eGaetani L, Blennow K, Calabresi P, Di Filippo M, Parnetti L, Zetterberg H: \u003cstrong\u003eNeurofilament light chain as a biomarker in neurological disorders.\u003c/strong\u003e \u003cem\u003eJ Neurol Neurosurg Psychiatry \u003c/em\u003e2019, \u003cstrong\u003e90:\u003c/strong\u003e870-881.\u003c/li\u003e\n\u003cli\u003eAbdelhak A, Foschi M, Abu-Rumeileh S, Yue JK, D\u0026apos;Anna L, Huss A, Oeckl P, Ludolph AC, Kuhle J, Petzold A, et al: \u003cstrong\u003eBlood GFAP as an emerging biomarker in brain and spinal cord disorders.\u003c/strong\u003e \u003cem\u003eNat Rev Neurol \u003c/em\u003e2022, \u003cstrong\u003e18:\u003c/strong\u003e158-172.\u003c/li\u003e\n\u003cli\u003eKhan A, Abedi V, Li J, Malik MT, Esch M, Zand R: \u003cstrong\u003eCADASIL vs. Multiple Sclerosis: Is It Misdiagnosis or Concomitant? A Case Series.\u003c/strong\u003e \u003cem\u003eFront Neurol \u003c/em\u003e2020, \u003cstrong\u003e11:\u003c/strong\u003e860.\u003c/li\u003e\n\u003cli\u003eMahajan KR, Herman D, Zheng Y, Androjna C, Thoomukuntla B, Ontaneda D, Nakamura K, Trapp BD: \u003cstrong\u003eNeurodegeneration and Demyelination in the Multiple Sclerosis Spinal Cord: Clinical, Pathological, and 7T MRI Perspectives.\u003c/strong\u003e \u003cem\u003eNeurology \u003c/em\u003e2025, \u003cstrong\u003e104:\u003c/strong\u003ee210259.\u003c/li\u003e\n\u003cli\u003eZjukovskaja C, Larsson A, Cherif H, Kultima K, Burman J: \u003cstrong\u003eBiomarkers of demyelination and axonal damage are decreased after autologous hematopoietic stem cell transplantation for multiple sclerosis.\u003c/strong\u003e \u003cem\u003eMult Scler Relat Disord \u003c/em\u003e2022, \u003cstrong\u003e68:\u003c/strong\u003e104210.\u003c/li\u003e\n\u003cli\u003eChen G, Li Z, Liu Y, Chen D, Beers J, Cudrici C, Ferrante EA, Schwartzbeck R, Dmitrieva N, Yang D, et al: \u003cstrong\u003eGeneration of human induced pluripotent stem cells (NIHTVBi004-A, NIHTVBi005-A, NIHTVBi006-A, NIHTVBi007-A, NIHTVBi008-A) from 5 CADASIL patients with NOTCH3 mutation.\u003c/strong\u003e \u003cem\u003eStem Cell Res \u003c/em\u003e2020, \u003cstrong\u003e45:\u003c/strong\u003e101821.\u003c/li\u003e\n\u003cli\u003eMadjidov A, Abbott MA, Mullen KM, Hicks R, Schlaug G: \u003cstrong\u003eCase Report: Inflammatory CADASIL phenotype associated with a rare cysteine-sparing NOTCH3 variant.\u003c/strong\u003e \u003cem\u003eFront Hum Neurosci \u003c/em\u003e2025, \u003cstrong\u003e19:\u003c/strong\u003e1568937.\u003c/li\u003e\n\u003cli\u003eWhitaker JN: \u003cstrong\u003eMyelin basic protein in cerebrospinal fluid and other body fluids.\u003c/strong\u003e \u003cem\u003eMult Scler \u003c/em\u003e1998, \u003cstrong\u003e4:\u003c/strong\u003e16-21.\u003c/li\u003e\n\u003cli\u003eCuadrado-Godia E, Dwivedi P, Sharma S, Ois Santiago A, Roquer Gonzalez J, Balcells M, Laird J, Turk M, Suri HS, Nicolaides A, et al: \u003cstrong\u003eCerebral Small Vessel Disease: A Review Focusing on Pathophysiology, Biomarkers, and Machine Learning Strategies.\u003c/strong\u003e \u003cem\u003eJ Stroke \u003c/em\u003e2018, \u003cstrong\u003e20:\u003c/strong\u003e302-320.\u003c/li\u003e\n\u003cli\u003eRaghav A, Singh M, Jeong GB, Giri R, Agarwal S, Kala S, Gautam KA: \u003cstrong\u003eExtracellular vesicles in neurodegenerative diseases: A systematic review.\u003c/strong\u003e \u003cem\u003eFront Mol Neurosci \u003c/em\u003e2022, \u003cstrong\u003e15:\u003c/strong\u003e1061076.\u003c/li\u003e\n\u003cli\u003eMenendez-Valladares P, Acevedo Aguilera R, Nunez-Jurado D, Lopez Azcarate C, Dominguez Mayoral AM, Fernandez-Vega A, Perez-Sanchez S, Lamana Vallverdu M, Garcia-Sanchez MI, Morales Bravo M, et al: \u003cstrong\u003eA Search for New Biological Pathways in Cerebral Autosomal Dominant Arteriopathy with Subcortical Infarcts and Leukoencephalopathy by Proteomic Research.\u003c/strong\u003e \u003cem\u003eJ Clin Med \u003c/em\u003e2024, \u003cstrong\u003e13\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003eLocatelli M, Padovani A, Pezzini A: \u003cstrong\u003ePathophysiological Mechanisms and Potential Therapeutic Targets in Cerebral Autosomal Dominant Arteriopathy With Subcortical Infarcts and Leukoencephalopathy (CADASIL).\u003c/strong\u003e \u003cem\u003eFront Pharmacol \u003c/em\u003e2020, \u003cstrong\u003e11:\u003c/strong\u003e321.\u003c/li\u003e\n\u003cli\u003eHase Y, Chen A, Bates LL, Craggs LJL, Yamamoto Y, Gemmell E, Oakley AE, Korolchuk VI, Kalaria RN: \u003cstrong\u003eSevere white matter astrocytopathy in CADASIL.\u003c/strong\u003e \u003cem\u003eBrain Pathol \u003c/em\u003e2018, \u003cstrong\u003e28:\u003c/strong\u003e832-843.\u003c/li\u003e\n\u003cli\u003eYamanouchi H: \u003cstrong\u003eLoss of white matter oligodendrocytes and astrocytes in progressive subcortical vascular encephalopathy of Binswanger type.\u003c/strong\u003e \u003cem\u003eActa Neurol Scand \u003c/em\u003e1991, \u003cstrong\u003e83:\u003c/strong\u003e301-305.\u003c/li\u003e\n\u003cli\u003eWelsh JA, Goberdhan DCI, O\u0026apos;Driscoll L, Buzas EI, Blenkiron C, Bussolati B, Cai H, Di Vizio D, Driedonks TAP, Erdbrugger U, et al: \u003cstrong\u003eMinimal information for studies of extracellular vesicles (MISEV2023): From basic to advanced approaches.\u003c/strong\u003e \u003cem\u003eJ Extracell Vesicles \u003c/em\u003e2024, \u003cstrong\u003e13:\u003c/strong\u003ee12404.\u003c/li\u003e\n\u003cli\u003eGao D, Shang J, Sun R, Shi Y, Jiang H, Ma M, Zhang J: \u003cstrong\u003eChanges in the Morphology, Number, and Protein Levels of Plasma Exosomes in CADASIL Patients.\u003c/strong\u003e \u003cem\u003eJ Alzheimers Dis \u003c/em\u003e2021, \u003cstrong\u003e81:\u003c/strong\u003e221-229.\u003c/li\u003e\n\u003cli\u003eDenecke J, Dewenter A, Lee J, Franzmeier N, Valentim C, Kopczak A, Dichgans M, Pirpamer L, Gesierich B, Duering M, Ewers M: \u003cstrong\u003eReduced myelin contributes to cognitive impairment in patients with monogenic small vessel disease.\u003c/strong\u003e \u003cem\u003eAlzheimers Dement \u003c/em\u003e2025, \u003cstrong\u003e21:\u003c/strong\u003ee70127.\u003c/li\u003e\n\u003cli\u003eOkeda R, Arima K, Kawai M: \u003cstrong\u003eArterial changes in cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) in relation to pathogenesis of diffuse myelin loss of cerebral white matter: examination of cerebral medullary arteries by reconstruction of serial sections of an autopsy case.\u003c/strong\u003e \u003cem\u003eStroke \u003c/em\u003e2002, \u003cstrong\u003e33:\u003c/strong\u003e2565-2569.\u003c/li\u003e\n\u003cli\u003eNewman L, Rowland A: \u003cstrong\u003eDetection and Isolation of Tissue-Specific Extracellular Vesicles From the Blood.\u003c/strong\u003e \u003cem\u003eJ Extracell Biol \u003c/em\u003e2025, \u003cstrong\u003e4:\u003c/strong\u003ee70059.\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":"Extracellular vesicles, Brain-derived exosomes, Nanobiotechnology biomarkers, Proteomic profiling, Cerebral small vessel disease, CADASIL","lastPublishedDoi":"10.21203/rs.3.rs-7511858/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7511858/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eCerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) is the most common hereditary small vessel disease (SVD) and currently lacks reliable biomarkers to monitor disease progression. Extracellular vesicles (EVs) are nanoscale carriers that cross the blood\u0026ndash;brain barrier and provide a minimally invasive liquid biopsy of brain pathology. This study aimed to characterize brain cell\u0026ndash;derived EVs in CADASIL and explore their potential as biomarkers of disease stage using advanced proteomic profiling.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003ePlasma EVs were isolated from CADASIL patients stratified according to the NOTCH3-SVD staging system and further enriched into neuronal (nEVs), astrocytic (aEVs), and oligodendrocytic (oEVs) subpopulations by immunoaffinity capture. The analysis of canonical biomarkers showed that glial fibrillary acidic protein (GFAP), a marker of astrocytic activation, was significantly increased in aEVs from patients at intermediate/advanced stages. Similarly, myelin basic protein (MBP), reflecting oligodendrocyte integrity and myelin disruption, was elevated in oEVs in the same group. By contrast, neurofilament light chain (NfL), a marker of axonal injury, did not show significant stage-dependent changes in nEVs. Importantly, these differences were not detectable in plasma or in total EV fractions, highlighting the superior sensitivity of cell type\u0026ndash;specific EV analysis. Complementary proteomic profiling identified stage-related molecular signatures in both plasma and EVs, including downregulation of proteins related to metabolism and cytoskeletal organization, and upregulation of immune and stress-response pathways. These molecular patterns suggest a shift toward a pro-inflammatory and neurodegenerative environment in patients with more advanced disease stages.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eBrain cell\u0026ndash;derived EVs constitute a nanobiotechnology platform for minimally invasive biomarker discovery in CADASIL. Cell type\u0026ndash;specific EV profiling allows the detection of subtle glial alterations and proteomic shifts associated with disease progression, which are not evident in plasma or bulk EVs. These findings support the development of EV-based biomarkers as sensitive tools for monitoring disease course in CADASIL and potentially other small vessel diseases.\u003c/p\u003e","manuscriptTitle":"Brain-Derived Extracellular Vesicles as Nanobiotechnology Biomarkers of Small Vessel Disease (CADASIL)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-17 08:17:03","doi":"10.21203/rs.3.rs-7511858/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":"bef11fd5-d4bc-43f9-9eec-f191a4e9db0c","owner":[],"postedDate":"September 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-15T02:08:44+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-17 08:17:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7511858","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7511858","identity":"rs-7511858","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}