CNS Endothelial Derived Extracellular Vesicles are Biomarkers of Active Disease in Multiple Sclerosis

doi:10.21203/rs.3.rs-915375/v1

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Research Article

CNS Endothelial Derived Extracellular Vesicles are Biomarkers of Active Disease in Multiple Sclerosis

https://doi.org/10.21203/rs.3.rs-915375/v1

This work is licensed under a CC BY 4.0 License

Journal Publication

published 08 Feb, 2022

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Background: Multiple sclerosis (MS) is a complex, heterogenous disease characterized by inflammation, demyelination, and blood-brain barrier (BBB) permeability. Currently, active disease is determined by physician confirmed relapse or detection of contrast enhancing lesions via MRI indicative of BBB permeability. However, clinical confirmation of active disease can be cumbersome. As such, active disease monitoring in MS could benefit from identification of an easily accessible biomarker of active disease. We believe extracellular vesicles (EV) isolated from plasma are excellent candidates to fulfill this need. Because of the critical role BBB permeability plays in MS pathogenesis and identification of active disease, we sought to identify EV originating from CNS endothelial as biomarkers of active MS. Because endothelial cells secrete more EV when stimulated or injured, we hypothesized that circulating concentrations of CNS endothelial derived EV will be increased in MS patients with active disease. Methods: To test this, we developed a novel method to identify EV originating from CNS endothelial cells isolated from patient plasma using flow cytometry. Endothelial derived EV were identified by the absence of lymphocyte or platelet markers CD3 and CD41, respectively, and positive expression of pan-endothelial markers CD31, CD105, and CD144. To determine if endothelial derived EV originated from CNS endothelial cells, EV expressing CD31, CD105, or CD144 were evaluated for MAL expression, a protein specifically expressed by CNS endothelial cells. Results: Analysis of EV isolated from 20 healthy controls, 16 relapsing-remitting MS (RRMS) patients with active disease not receiving disease modifying therapy, 14 RRMS patients with stable disease not receiving disease modifying therapy, 17 relapsing-RRMS patients with stable disease receiving natalizumab, and 14 RRMS patients with stable disease receiving ocrelizumab revealed a significant increase in the plasma concentration of CNS endothelial derived EV in patients with active disease compared to all other groups ( p = 0.001). Conclusions: For the first time, we have identified a method to identify CSN endothelial derived EV in circulation from human blood samples. Results from our pilot study indicate that increased levels of CNS endothelial derived EV may be a biomarker of BBB permeability and active disease in MS.

Cellular & Molecular Neuroscience

Multiple sclerosis

extracellular vesicles

blood-brain barrier

endothelial cells

biomarker

Extracellular vesicles (EV) are a heterogenous group of nanosized vesicles released from all cell types. EV come in a wide range of sizes and compositions but generally refer to three main types of vesicles including exosomes (~ 30-150nm), microvesicles (~ 100-1000nm), and apoptotic bodies (~ 1-5um)(1–4). Per the recommendation of the International Society of Extracellular Vesicles, EV classification as exosomes, microvesicles, or apoptotic bodies strictly depends on their type of biogenesis and not by an EV’s size or protein composition(5). Exosomes are secreted via the endocytic pathway and exocytosis of multivesicular bodies. In comparison, microvesicles are shed via budding from the plasma membrane while apoptotic bodies are released by blebbing from the plasma membrane of apoptotic cells. All EV are composed of lipid bilayer and contain a wide array of biologically active components including proteins, lipids, and various nucleic acid species. EV secretion increases when cells are activated or injured and represent the biological state of their parental cell. In addition, EV influence the biological activity of recipient cells and are key regulators of intercellular communication(6–9). As a result, EV have been implicated as important mediators of both homeostasis and pathogenesis. Because of these properties, EV have gained recent interest as possible biomarkers for numerous diseases including neurological disorders like multiple sclerosis (MS)(10–13).

MS is a chronic, inflammatory disease of the CNS characterized by demyelination, blood-brain barrier (BBB) permeability, and immune infiltration (14–21). MS is a complex and heterogenous disease with various clinical and histopathological classifications(14, 22, 23). Clinically, MS is classified as relapsing remitting multiple sclerosis (RRMS), secondary progressive multiple sclerosis (SPMS), or primary progressive multiple sclerosis (PPMS). RRMS, the most common type, is defined by acute episodes of active disease (relapses) followed by periods of complete or partial recovery (remission)(24–26). Within ten to twenty years after disease onset, most RRMS patients transition to SPMS characterized by worsening neurologic decline without recovery. In comparison, PPMS is less common and is characterized by progressive neurological decline without recovery from disease onset. Active disease is characterized by acute neurological decline and/or detection of new lesion formation via MRI and can occur in all clinical states but is most common in RRMS.

Currently, disease activity in MS is typically monitored via detection of contrast enhancing lesions (CEL) on MRI(25, 27). On histopathological examination, CEL exhibit overt BBB permeability, active demyelination, and pronounced inflammation(28–30). Enhancement usually resolves within two to eight weeks, highlighting the transient nature of these lesions. However, due to costs, logistics, and possible safety concerns over repetitive use of contrast reagents, MRI are typically only performed annually after disease diagnosis. Consequently, detection of ongoing disease activity is often missed, emphasized by the detection of new, but non-enhancing lesions on annual, patient MRI. Delayed detection of active disease may have negative consequences for patients including delayed treatment and accumulation of lesions that may influence worse clinical outcomes(31–33). As a result, disease monitoring would greatly benefit from identification of an easily accessible biomarker of disease activity. We believe that EV, especially CNS derived EV, are excellent candidates to fulfill this need.

The use of EV as MS biomarkers has gained recent interest(10–13). Most of the studies focusing on EV as possible biomarkers have concentrated on EV isolated from peripheral blood (serum or plasma) or cerebral spinal fluid (CSF). Of these studies, EV isolated from CSF have demonstrated good correlation with disease activity(34–39). However, collection of CSF requires an invasive lumbar puncture with rare, but serious complications and significant patient anxiety(40–43). In comparison, EV isolated from peripheral blood use minimally invasive techniques that can be performed regularly. Although, previous studies examining EV isolated from peripheral blood have revealed differences between healthy controls and MS patients, these studies are limited as most do not focus on CNS derived EV and therefore may not be specific markers of neurological pathogenesis(44–55). Analysis of CNS derived EV, in comparison, may identify specific neurological maladies and elucidate the molecular mechanisms involved in their pathogenies. Under normal circumstance, the human CNS is inaccessible to experimental investigation. However, because EV represent the biological state of the cell they originate from, molecular examination of CNS derived EV isolated from peripheral blood may allow a unique opportunity to study the molecular state of CNS cells in real-time, in living patients.

In this study, we propose to evaluate the circulating concentrations of EV derived from CNS endothelial cells as a biomarker of active disease and BBB dysfunction in MS patients. CNS endothelial cells are a key component of the BBB and play a central role in MS pathogenesis(56–60). Because BBB permeability is a key step in MS pathogenesis, assessment of CNS endothelial cell activation or injury via EV analysis may help elucidate key processes in MS lesion development. To identify EV specifically originating from CNS endothelial cells, we will examine EV isolated from patient plasma for co-expression of pan-endothelial markers including CD31 (PECAM-1), CD105 (Endoglin), or CD144 (VE-Cadherin)(61). To determine if EV positive for CD31, CD105, or CD144 originate from CNS endothelial cells, we will examine EV for expression of the myelin and lymphocyte protein MAL, which has been demonstrated to be specifically expressed by CNS endothelial cells in mice(62) and whose expression in human brain endothelial cells has been confirmed by transcriptional analysis(63–65). We will compare circulating EV levels between healthy controls (HC), RRMS patients with active disease not receiving disease modifying therapy (DMT), RRMS patients with stable disease not receiving DMT, RRMS patients with stable disease receiving natalizumab, and RRMS patients with stable disease receiving ocrelizumab. We hypothesize that circulating concentrations of CNS endothelial derived EV (CNS-EEV) will be higher in RRMS patients with active disease compared to healthy controls or stable RRMS patients.

Subject recruitment

Study subjects were recruited and consented according to Weill Cornell Medicine Institutional Review Board–approved protocol 1003010940. Subjects were recruited at The Judith Jaffe Multiple Sclerosis Clinical Care and Research Center between 2017–2019. Study subjects included individuals with clinically definite MS according to the 2010 McDonald criteria (25, 66) and HC without MS. Active RRMS patients were identified by recent physician-confirmed clinical relapse and/or detection of a CEL via MRI within two months of specimen collection. RRMS patients were considered DMT negative if they were DMT naïve or had not received DMT for at least one year prior to specimen collection. For inclusion into the natalizumab or ocrelizumab groups, patients were required to be on DMT for at least one week prior to specimen collection. Disease duration was determined from estimated date of first symptoms to specimen collection. Expanded Disability Status Scale (EDSS) scores were calculated by physicians within three months of specimen collection.

Blood collection and plasma separation

Detailed methodology can be found in the supplemental material and methods section. Briefly, peripheral blood was harvested via venipuncture into K2 EDTA vacutainers and stored at room temperature (RT) up to six hours until processing. Platelet free plasma (PFP) was prepared by centrifuging vacutainer tubes at 400g for ten minutes at RT. Plasma was transferred to 15ml conical tubes and centrifuged twice at 2000g for ten minutes at RT to pellet platelets. PFP was separated into 1ml aliquots and stored in 2.0ml cryogenic vials at -80°C. Prior to use, 1ml aliquots were thawed in 37°C water bath for two minutes.

EV Experimentation

Detailed methodology for EV experimentation can be found in the supplemental material and method section. Below are brief explanations of the methodology used. Only 0.2um filtered phosphate buffered saline (PBS) and flow cytometry sheath fluid (BD FACSflow) were used for experiments.

EV isolation from PFP via size exclusion chromatography (SEC)

Izon qEV 70nm size exclusion columns (SEC) were equilibrated to RT and washed with 10mls of PBS. To collect SEC fractions, 1ml of PFP was applied, allowed to fully absorb, and then eluted with 6mls of PBS. Fractions were collected in twelve, 0.5ml, sequential aliquots. For patient analysis, fractions seven, eight, and nine were pooled together for a total of 1.5ml SEC isolated EV in PBS.

Transmission electron microscopy (TEM) analysis

SEC isolated EV in PBS were applied to Glow-discharge Formvar and carbon-coated 400 mesh copper grids (Electron Microscopy Sciences) and stained with 1.5% aqueous uranyl acetate. Samples were viewed on a JEM-1400 transmission electron microscope (JEOL, USA, Inc., Peabody, MA) operated at 100 kV and images were captured on a Veleta 2K x 2K CCD camera (EM-SIS, Germany).

Nanoparticle tracking analysis (NTA)

SEC isolated EV in PBS were characterized by NTA using a Malvren NanoSight NS500 instrument equipped with an Andor EM-CCD camera. Video acquisitions were performed with NTA software v3.1.

Western blot Analysis

SEC isolated EV were centrifuged at 100,000g for one hour at 4°C. The EV pellet was lysed in radioimmunoprecipitation assay buffer (Thermo Scientific) containing Halt Protease and Phosphatase Inhibitor (Thermo Scientific) for ten minutes on ice and stored a -20°C until use. Protein concentration was determined by Pierce BCA Protein Assay Kit (Thermo Scientific). Human umbilical cord vein endothelial cell (HUVEC) lysate (Novus Biologicals) was used as controls. 2ug of lysates were diluted into 4x Laemmli Sample Buffer (Bio-Rad) containing 5% 2-Mercaptoethanol and heated at 95°C for five minutes and loaded onto 4–20% Mini-PROTEAN TGX Stain-Free gels (Bio-Rad) then transferred to nitrocellulose membranes using the Bio-Rad Trans-Blot SD Semi-Dry Electrophoretic Transfer Cell system (Bio-Rad). Membranes were blocked with 5% Blotting-Grade Blocker nonfat milk (Bio-Rad) in Tris Buffered Saline with Tween 20 (TBS-T) (Bio-Rad) blocking solution for one hour at RT and then incubated with anti-flotillin, CD9, CD63, CD86, CD31, CD105, and CD144 antibodies in blocking solution overnight at 4°C as indicated in Supplementary Table 2. Blots were washed with TBS-T and then incubated with secondary antibody peroxidase-conjugated Affinipure Goat Anti-Rabbit IgG H + L (Jackson ImmunoResearch) at 0.024 µg/mL in blocking solution for two hours at RT, washed in TBS-T, and developed for five min at RT in Clarity Max Western ECL Substrate (Bio-Rad). The developed blots were visualized on 5 × 7 CL-XPosure Films (Thermo Fisher Scientific) at various exposure times using a Konica Minolta SRX-101A film processor.

Centrifugation Experiments

250ul aliquots of SEC isolated EV in PBS were centrifuged at 18,000g or 100,000g for one hour at 4°C. Supernatants were carefully removed and analyzed via flow cytometry. EV pellets were vigorously resuspended in 250ul PBS and analyzed via flow cytometry and TEM.

Preparation of fluorescently labeled MAL ligand

Epsilon protoxin (pETX), from Clostridium perfringens, Strain 34 Type B, was provided by Biodefence and Emerging Diseases Resources at a minimum > 95% purity at 0.5mg/ml. pETX was labeled with Life Technologies Alexa Fluor 647 Protein Labeling Kit per the manufacturer's instructions (pETX-647). Labeled toxin (~ 11uM) was stored at 4°C until use.

Flow cytometry settings for analysis of submicron particles

All samples were analyzed using a BD FACSVerse flow cytometer with a three-laser configuration (488 nm, 640 nm, and 405 nm) equipped with a BD Flow sensor using the manufacturer settings. BD FACSuite CS&T research beads were used daily to calibrate and perform quality control checks per the manufacturer’s instructions. All samples were analyzed in 5ml polystyrene round bottom tubes using a medium sample flow rate (60ul/min) with a normal sheath core stream fluid velocity (5.5 m/s). Data was collected using BD FACSuite software and exported to BD Flowjo software for analysis. Event counts and volume metrics were exported to Microsoft Excel for final calculations. For submicron analysis, Forward scatter (FSC) and side scatter (SSC) voltage was adjusted to 765 and 465, respectively, with a FSC threshold of 10,000. Settings were evaluated using Invitrogen’s Flow Cytometry Size Kit Calibration Kit containing nonfluorescent polystyrene microspheres ranging in size from 1-15um and Invitrogen’s Flow Cytometry Submicron Particle Size Reference Kit containing green-fluorescent polystyrene microspheres ranging in size from 0.02-2um. Quality control checks for the size gate was selected using Invitrogen’s 1um nonfluorescent polystyrene microspheres (Fig. 1).

Staining protocol for flow cytometry analysis

The concentration of SEC isolated EV in PBS was determined via flow cytometry using the size gate determined with 1um beads as previously described. Isolated EV were adjusted to 500 events/ul and stained with pETX-647, anti-CD3, CD9, CD31, CD41, CD63, CD81, CD105, and CD144 antibodies, and isotype controls as indicated in Supplementary Table 1 for two hours at RT. As a negative control, pETX-647 was pretreated with an anti-ETX neutralizing antibody, JL004(67), at 1mg/ml for 20 minutes at 37°C to prevent binding. Prior to flow cytometry analysis, stained EV were diluted with 500ul of PBS for a final concentration of 50 events/ul to prevent swarming artifacts during analysis. To determine gates positive for expression, EV stained with isotype controls were used as negative controls for each individual donor (Supplemental Fig. 1A-D). To determine gates positive for MAL expression, EV stained with pETX-647 pretreated with JL004 (Fig. 4A) were used as negative controls for each individual donor.

Statistics

When comparing two data points, unpaired student’s t-tests were used to determine significance. When comparing three or more data points, one-way ANOVA with post-hoc Tukey HSD test was used to determine significance. These instances are indicated in figure legends. D'Agostino's K-squared test was used to test for normality in the different patient groups using the total CNS-EEV concentrations as described in Fig. 6D. All patient groups including HC, active RRMS patients not receiving DMT (Active), stable RRMS patients not receiving DMT (Stable), and stable RRMS patients receiving natalizumab (NTZ) passed the normality test (alpha = 0.05). However, RRMS patients receiving ocrelizumab (OCZ) did not (p = 0.006).

Study Cohort

We enrolled a total of 20 HC, 16 RRMS patients with active disease not receiving DMT, 14 RRMS patients with stable disease not receiving DMT, 17 RRMS patients with stable disease receiving natalizumab, and 14 RRMS patients with stable disease receiving ocrelizumab (Table 1). Active disease was determined by recent physician-confirmed relapse or detection of a CEL via MRI. Of the 16 RRMS patients with active disease, 14 had confirmed CEL (87.5%). Donors with significant comorbidities known to alter circulating EV concentrations including cancer, other autoimmune diseases, morbid obesity (body mass index of 40 or more), diabetes mellitus type 2, or recent cardiovascular or cerebrovascular events were excluded(68–73). Subject groups were comparable in age however the sex ratio varied. For the RRMS groups, EDSS scores were comparable between groups however disease duration (DD) was varied.

Table 1

Demographics and clinical characteristics of study participants
	HC	Active	Stable	NTZ	OCZ
	n = 20	n = 16	n = 14	n = 17	n = 14
Age, mean, SD, y	37(13)^a	36(10)	43(12)	39(10)	41(11)
% Female (n)	75%(15)*	69%(11)	93%(13)*	94%(16)*	64%(9)
Clinical State		RRMS	RRMS	RRMS	RRMS
Disease State		Active^b	Stable	Stable	Stable
DD, median (IQR), mo		22.5(2.5–56)^#	142.5(62.5–183)	87(47.5-157.5)	159.5(33-239.3)^#
EDSS, median, (IQR)		1.5(0.3–2.4)	1.5(0.0-4.1)	1(0.0–3.0)	3.3(1.5–4.5)
^a one undetermined value, not included in correlation analysis
^b 14 out of 16 (87.5%) patients have confirmed contrast enhancing lesion
*p < 0.002 HC vs. stable, HC vs. NTX, determined by Fishers exact test between pairs
^#p = 0.007 Active vs. OCZ, determined by ANOVA with Tukey's multiple comparisons test
Abbreviations: DD = disease duration, EDSS = Expanded Disability Status Scale, IQR = interquartile range, m = month, SD = standard deviation, y = year

Determination of flow cytometry size resolution

To determine the optimal collection procedure for isolation of EV via SEC for flow cytometry analysis, twelve, 0.5ml fractions were collected via SEC after addition of 1ml PFP (Fig. 1A). To detect submicron events via flow cytometry, we used 1um beads (polystyrene microspheres) diluted in PBS to select appropriate flow cytometry settings (Fig. 1B). A size gate was designated based on 1um beads (polystyrene microspheres) and excluded mechanical noise observed in PBS alone. Analysis revealed that SEC fractions seven, eight and nine contained the highest number of events within our designated size gate. Based on these results, fractions seven, eight and nine were pooled together for all subsequent EV analysis. TEM analysis of the pooled aliquots from HC and RRMS patients revealed morphologies indicative of EV (Fig. 1D). Although there was a wide range of EV diameters observed via TEM, there was no significant difference in the mean size between EV isolated from HC or RRMS patients (Fig. 1E). These results were confirmed via NTA (Fig. 1F). Finally, western blot analysis of pelleted EV revealed the presence of EV related proteins including flotillin, CD9, CD63, CD86 (Fig. 1G).

To determine the size resolution of our flow cytometry size gate, we completed a series of experiments using high speed centrifugation (Fig. 2A-D). It is well accepted that centrifugation between 10–20,000g causes sedimentation of larger EV (~ 150nm and larger) while centrifugation at 100,000g causes sedimentation of smaller EV (~ 30-150nm and larger)(74–76). To determine if our flow cytometry settings detected small or large EV, isolated EV were centrifuged at 18,000g for one hour to pellet large EV and 100,000g for one hour to pellet small EV (Fig. 2B and C). Supernatants were harvested and analyzed via flow cytometry and concentrations compared to no-spin controls. EV pellets were resuspended in the same volume of PBS as the starting material prior to analysis. Results revealed that the 18,000g and 100,000g supernatants contained 5% and 3% of events compared to no-spin controls, respectively (Fig. 2C), indicating that flow cytometry analysis is detecting EV larger than ~ 150nm. These results were confirmed when examining the number of events detected in the resuspended pellets. The 18,000g and 100,000g resuspended pellets contained 73% or 44% of events compared to no-spin controls, respectively. The reduction in events seen in the resuspended pellets is most likely due to EV aggregation, as observed via TEM (Fig. 2D). This aggregation was not observed in the no-spin controls. Taken together, this data indicates that our size gate is detecting EV larger than 150nm.

To further define our size resolution via flow cytometry, we performed an experiment using an array of differently sized fluorescent-green submicron beads (polystyrene microspheres) (Fig. 2E and F). 1um, 0.5 and 0.2um green-fluorescent beads were analyzed via flow cytometry using the settings described above. Unlabeled 1um beads were used as a negative control. By selecting for events with positive green fluorescence compared to unlabeled 1um beads, we determined that our size gate detects 99% of 1um green-fluorescent beads, 63% of 0.5um green-fluorescent beads, and 15% green-fluorescent beads (Fig. 2F). This data indicates that our size gate detects events ranging in size from 0.2 to 1um, with the majority of events ranging in size from 0.5 to 1um. This size gate was used for all subsequent analysis of SEC isolated EV.

Characterization of EV isolated from patient plasma

To further characterize the events detected within our size gate, SEC isolated EV were analyzed for EV specific markers CD9, CD63, and CD81 via flow cytometry (Fig. 3A-F). SEC isolated EV were stained with fluorescently conjugated anti-CD antibodies and appropriate isotype controls (Fig. 3A and B). When staining for CD9, a significantly higher percent of EV stained positive for CD9 versus the isotype control, 60.7% versus 1.5%, respectively. When staining for CD63, a significantly higher percent of EV stained positive for CD63 versus the isotype control, 9.7% versus 1.3%, respectively. In comparison, when staining for CD81, a significantly lower percent of EV stained positive for CD81 versus the isotype control, 0.1% versus 1.23%, respectively. Taken together this data indicates EV within our size gate are positive for CD9 and CD63, but not CD81. Analysis of EV isolated from three HC and three RRMS patients revealed that the majority of events within the size gate expressed CD9 (14.8–78.7%) while a smaller portion expressed CD63 (1.4–9.7%) (Fig. 3C). When comparing the percentage of size events positive for CD9, CD63, or CD81 between HC and RRMS patients, no difference was observed. When examining size events for dual expression of CD9 and CD63, the majority of events either expressed CD9 only (CD9 + CD63-, 14.0-71.7%) or none (CD9- CD63-, 21.2–84.9%) (Fig. 3D and E). A small percentage of events were positive for both CD9 and CD63 (CD9 + CD63+, 0.8–7.6%) and very few events were positive for CD63 only (CD9- CD63+, 0.2–0.4%). No significant difference was observed between HC or RRMS patients. Taken together, this data indicates that our flow cytometry methods are successfully detecting events consistent with EV.

EV isolated from patient plasma express the Myelin and Lymphocyte protein MAL

We have previously demonstrated that CNS endothelial cells specifically express MAL compared to endothelial cells from other organ systems(62). To identify EV originating from CNS endothelial cells, we have proposed to detect a combination of pan-endothelial markers (CD31, CD105, or CD144) and MAL on individual EV. However, we first wanted to determine if MAL expression was detectable on EV by binding of the MAL-specific ligand, Clostridium perfringens epsilon protoxin (pETX)(77, 78) via flow cytometry. To detect MAL expression, SEC isolated EV were probed with Alexaflour-647 conjugated pETX (pETX-647). As a negative control, EV were also probed with pETX-647 pretreated with an antibody that prevents binding pETX binding to MAL(67) (Fig. 4A). Unstained EV were used as an additional control. Staining with pETX-647 demonstrated a significant increase in fluorescence compared to EV stained with the antibody treated pETX-647 (Fig. 4B). This data indicates that MAL expression on EV can be detected by pETX-647 binding.

The concentration of total EV (Fig. 4C) and the concentration of pETX/MAL + EVs (Fig. 4D) were then determined for our different patient groups. Enumeration of total EV concentrations revealed a significant difference in concentrations between RRMS patients with active disease and RRMS patients receiving natalizumab, but no other significant differences were observed (Fig. 4C). In comparison, when examining concentrations of pETX/MAL + EV, active RRMS patients had significantly higher concentrations than all other patient groups (Fig. 4D). This data indicates pETX/MAL + EV concentrations are increased in RRMS patients with active disease.

CNS-EEV concentrations are increased in active MS

To identify EV originating from CNS endothelial cells (CNS-EEV), we developed a phenotyping strategy to identify EV that express both pan-endothelial markers CD31, CD105, or CD144 and MAL (Fig. 5A and 5B). Representative scatter plots of anti-CD-stained EV and corresponding isotype controls are depicted in Supplemental Fig. 1A-D. Because platelets also express CD31, we excluded platelet derived EV by eliminating events positive for CD41. Because lymphocytes also express MAL, we also excluded any lymphocyte derived EV by eliminating events positive for CD3. EV negative for CD3 and CD41 (CD3/CD41-) were further analyzed for the presence of CD31, CD105, and CD144 to determine if they were endothelial derived EV (EEV). CD3/CD41- EV positive for CD31, CD105, or CD144 were referred to as EEV31, EEV105, and EEV144 respectively. To determine if EEV originated from CNS endothelial cells, MAL expression on EEV31, EEV105, and EEV144 was evaluated by pETX-647 binding. EEV31, EEV105, and EEV144 positive for pETX/MAL are referred to as CNS-EEV31, CNS-EEV105, and CNS-EEV144, respectively. Enumeration of CNS-EEV31, CNS-EEV105, and CNS-EEV144 from our different patient groups revealed significant increases in the concentration of CNS-EEV in active RRMS patients compared to all other patient groups (Fig. 5C). When examining CNS-EEV31 concentrations, active RRMS patients had significantly higher concentrations than stable RRMS patients not receiving DMT, stable RRMS patients receiving natalizumab, and a trend towards significance compared to HC. When examining CNS-EEV105, active RRMS patients had significantly higher concentrations than all other patient groups. When examining CNS-EV144, active RRMS patients had significantly higher concentrations compared to HC and RRMS patients receiving natalizumab or ocrelizumab. Expression of CD31, CD105, and CD144 on EV was confirmed via western blot analysis of EV lysate from three separate donors (Fig. 5D). This indicates that active RRMS patients have increased levels of CNS-EEV31, CNS-EEV105, and CNS-EEV compared to HC and stable RRMS patients.

Finally, we wished to determine if individual CNS-EEV populations or individual EEV populations were more sensitive markers of active disease in RRMS patients. When examining the separate EEV populations, we observed an increase in EEV31, EEV105, and EEV144 concentrations in active RRMS patients compared to some of the other patient groups (Supplementary Fig. 2F-G). However, the statistical difference observed between levels of CNS-EEV31, CNS-EEV105, and CNS-EEV144 were more common and more significant than their EEV counterparts. In addition, we saw no significant difference in CD3/CD41 + EV concentrations (Supplementary Fig. 2E). Taken together, this data indicates that measurement of individual CNS-EEV concentrations is a more specific marker of disease activity in RRMS patients compared to total EV concentrations, individual EEV populations, or CD3/CD41 + EV populations.

We next wanted to determine if CNS-EEV31, CNS-EEV105, and CNS-EEV144 are unique EV populations or express multiple pan-endothelial markers. To achieve this, CD3/CD41- EV were analyzed for the presence of multiple endothelial markers by comparing expression of CD31 versus CD105, CD31 versus CD144, and CD105 versus CD144 (Fig. 6A-B). Examination of these events from seven HC and seven active RRMS patients revealed that the majority of EV only expressed a single endothelial marker (Fig. 4C). When comparing CD31 versus CD105 expression, only 0.3–0.6% percent of CD3/CD41- events were positive for both CD31 and CD105. When comparing CD31 versus CD144 expression, only 0.3–0.5% percent of CD3/CD41- events were positive for both CD31 and CD144. Finally, when comparing CD105 versus CD144, only 0.5–0.8% of CD3/CD41- events express both CD105 and CD144. This suggests that EEV31, EEV105, and EEV144 are unique populations and therefor CNS-EEV31, CNS-EEV105, and CNS-EEV144 are unique populations as well. We therefore reasoned that the total concentration of CNS-EEV in a single patient sample could be calculated by taking the sum of the individual CNS-EEV31, CNSEEV105, and CNS-EEV144 concentrations (Fig. 4D). This analysis revealed a significantly higher level of total CNS-EEV in active RRMS patients compared to all other patient groups. The same methodology was applied to determine the total EEV concentration (Supplemental Fig. 3). Active RRMS patients had significantly higher concentrations of total EEV compared to all other patient groups. However, the statistical difference observed between total CNS-EEV concentration was more significant than the differences seen between total EEV concentrations. For example, when examining total CNS-EEV concentrations, active RRMS were significantly higher versus HC (p = 0.001), stable RRMS receiving no DMT (p = 0.001), stable RRMS receiving natalizumab (p = 0.001), and stable RRMS patients receiving ocrelizumab (p = 0.001). In comparison, when examining total EEV concentrations, active RRMS were significantly higher versus HC (p = 0.02), stable RRMS receiving no DMT (p = 0.003), stable RRMS receiving natalizumab (p = 0.003), and stable RRMS patients receiving ocrelizumab (p = 0.001). Taken together, this data indicates that CNS-EEV concentrations are a more sensitive measurement of disease activity in MS patients compared to total EEV concentrations.

Finally, we wished to determine if total CNS-EEV concentrations were a more sensitive measurement of disease activity compared to the percentage of EV determined to be CNS-EEV (Supplemental Fig. 3). First, we examined the percent of total EV determined to be CNS-EEV (Supplemental Fig. 3A). To calculate this, we divided the total CNS-EEV concentration by the total EV concentration for each individual donor. No significant difference was observed between patient groups. Next, we examined the percent of total EEV that were determined to be CNS-EEV (Supplemental Fig. 3B). To calculate this, we divided the total CNS-EEV concentration by the total EEV concentration for each individual donor. No significant difference was observed between the different patient groups. Taken together, this data indicates that measurements of total CNS-EEV concentrations is a better measurement of disease activity in MS patients versus percentage calculations.

Analysis of Active RRMS Group

Interestingly, when examining the total CNS-EEV concentrations in the active RRMS patient group, we observed what appeared to be a bimodal distribution (Fig. 6D) with 11 of the 16 (68.75%) patients having EV concentrations higher than 100 EV per ul of plasma, and 5 out of the 16 patients (31.25%) below 100 EV per ul of plasma. Although these active RRMS patients were not receiving DMT at the time of blood draw, a portion of them had received steroids within 35 days to help manage symptoms (range one to 34 days). To determine if steroid use may influence the total CNS-EEV concentrations in these patients, we compared total CNS-EEV concentrations between active RRMS patients who had received steroid treatment within 35 days prior to their blood draw (+ steroids) and those who had not (- steroids). Steroid use information was available for 14 out of our 16 active RRMS patients. Active RRMS patients who had received steroid treatment (n = 6) demonstrated a trend towards significantly lower total CNS-EEV concentrations than those without (n = 8) (p = 0.16) (Fig. 7A). In addition, no correlation was observed between total CNS-EEV concentrations and individual patient EDSS (Fig. 7B) nor was a correlation observed between total CNS-EEV concentration and the time elapsed from blood draw to MRI (Fig. 7C). MRI dates were available for 11 patients. Taken together this indicates that steroid use may influence total CNS-EEV concentrations, however our small sample size lacks sufficient power for a conclusive analysis and more testing is required.

Next, we wanted to determine if there was a consistent relationship between total CNS-EEV concentrations as well as individual CNS-EEV31, CNS-EEV105, CNS-EEV144 concentration in active RRMS patients. In other words, we wanted to know if CNS-EEV31 concentration were low for an individual patient, would CNS-EEV105 and CNS-EEV105 concentrations also be low? To test for this, we performed a correlation analysis and paired t tests. We observed significantly strong, positive correlations between total CNS-EEV concentrations and CNS-EEV31 concentrations (r = 0.75, r2 = 0.56, and p = 0.0008), CNS-EEV105 concentrations (r = 0.92, r2 = 0.85, and p < 0.0001), ad CNS-EEV1144 concentrations (r = 0.82, r2 = 0.67, and p = 0.0001) (Fig. 7D). Paired T test analysis also revealed a significant or close to significant relationship between individual CNS-EEV population concentrations including CNS-EEV31 and CNS-EEV105, CNS-EEV31 (p = 0.09) and CNS EEV144(p < 0.05), and CNS-EEV105 and CNS-EEV144 (p < 0.003) (Fig. 7E-G). Taken together, this indicates that CNS-EEV populations have a consistent relationship with one another in a single patient. In other words, if CNS-EEV31 concentrations are low for a single active RRMS patient, CNS-EEV105 and CNS-EV144 concentrations are typically low too and vice.

Total CNS-EEV concentrations correlate with total EEV concentrations in RRMS patients

To determine if the total CNS-EEV concentrations correlated with the total EV concentrations, we performed a correlation analysis examining all donors as a single group (Fig. 8A). When examining all donors as a single group, total CNS-EEV concentrations demonstrated a moderate, but significant positive correlation with total EV concentrations (r = 0.40, r² = 0.16, and p = 0.0002). However, when donors were separated into their individual patient groups, only active RRMS patients demonstrated a positive correlation between total CNS-EEV and total EV concentrations (r = 0.55, r² = 0.30, and p = 0.03) (Fig. 8B). This indicates that an increase in total CNS-EEV concentrations correlates with an increase in total EV concentrations in active RRMS.

To determine if the total CNS-EEV concentrations correlated with total EEV concentrations, we performed a correlation analysis examining all donors as a single group (Fig. 8C). When examining all donors as a single group, total CNS-EEV concentrations demonstrated a strong and significant positive correlation with total EEV concentrations (r = 0.74, r² = 0.54, and p < 0.0001). When donors were separated into their individual patient groups, a similar trend was observed for all RRMS groups, but not HC (Fig. 8B). Active RRMS demonstrated a significantly strong correlation (r = 0.74, r² = 0.55, and p = 0.001) while stable RRMS patients on natalizumab had a moderate but significant correlation (r = 0.62, r² = 0.39, and p = 0.007). Stable RRMS patients not receiving DMT (r = 0.46, r² = 0.21, and p = 0.09) and stable RRMS patients receiving ocrelizumab (r = 0.50, r² = 0.25, p = 0.07) both demonstrated a moderate correlation approaching significance. Taken together, this data indicates that CNS-EEV concentrations positively correlate with total EEV concentrations in RRMS patients, but not HC.

Total CNS-EEV concentrations do not depend on patient age but may be influenced by gender

To determine if patient age influences total CNS-EEV concentrations, a correlation analysis was performed examining at all donors as a single group or when donors were separated into their individual patient groups (Fig. 9A and B). No significant correlation was observed. This indicates that age does not influence total CNS-EEV concentrations.

To determine if gender influences total CNS-EEV concentrations, total CNS-EEV concentrations were compared between female and male donors in the individual patient groups (Fig. 9C). This analysis revealed no significant difference between males and females. However, this may be due to the low number of men enrolled in this study. To try and overcome this, we compared total CNS-EEV concentrations when all RRMS patients were grouped together or when all donors including HC were grouped together. This analysis revealed that male donors had significantly higher levels of total CNS-EEV compared to female donors when all RRMS donors were grouped together and when all donors including HC were grouped together. This indicates that men with RRMS may have higher levels of total CNS-EEV compared to females with RRMS. However, these results are only preliminary because of the small sample size.

To the best of our knowledge, this is the first time CNS-EEV or EV derived from the BBB have been identified in circulation from humans. To achieve this, we developed a novel method to identify EV specifically originating from CNS endothelial cells by detection of multiple, cell-specific markers on EV isolated from plasma via flow cytometry. First, lymphocyte or platelet derived EV are excluded from analysis via detection of CD3 or CD41, respectively. EEV are then detected via expression of pan-endothelial markers CD31, CD105, or CD144. Finally, EEV positive for CD31, CD105, or CD144 are determined to be of CNS-endothelial origin via expression of MAL, a cell surface protein recently identified to be specifically expressed on CNS endothelial cells(62). Using this method, we identified three distinct CNS-EEV populations including CNS-EEV31, CNS-EEV105, and CNS-EEV144. In our pilot study examining the circulating levels of CNS-EEV from 81 blood donors, we determined that CNS-EEV concentrations are higher in RRMS patients with active disease compared to HC, stable RRMS patients not receiving DMT, stable RRMS patients not receiving natalizumab, and stable RRMS patients receiving ocrelizumab. This includes an increase in total CNS-EEV as well as individual CNS-EEV31, CNS-EEV105, and CNS-EV144 populations. Interestingly, natalizumab and ocrelizumab treatment did not appear to significantly alter CNS-EEV levels between stable RRMS patients. In comparison, active RRMS patients not receiving DMT but who had received steroid treatment within 35 days of blood draw may have lowered CNS-EEV concentrations compared to active patients who had not, indicating that CNS-EEV levels may be useful biomarkers of treatment efficacy, however we recognize this conclusion is limited based on our small sample size and requires more investigation. Taken together, this data indicates that plasma concentrations of CNS-EEV may be biomarkers of active disease in MS patients. Because CNS-EEV are derived from the endothelial cells of the CNS, we hypothesize that increased CNS-EEV concentrations are indicators of CNS endothelial dysfunction including BBB permeability. This is supported by 1) a handful of previous publication that have reported an increase in BBB derived EV by detection of tight junction proteins occludin and claudin-5 in mouse models with known BBB permeability(79–81) and 2) the observation that 14 out of our 16 E-RRMS patients have confirmed BBB permeability by detection of CEL via MRI, indicative of overt BBB permeability.

Although previous publications have indicated an increase level of circulating EEV MS patients compared to HC (44, 50, 51, 53, 55), our data indicates that examination of CNS-EEV rather than EEV populations may be a more sensitive method for detecting disease activity in MS patients. In previous studies, EEV were identified by expression of a single pan-endothelial marker including CD31(50), CD146 (MCAM)(44), CD62E (E-Selectin)(51, 55), and CD105(53); or expression of CD31 in the absence of CD42(51, 54). Using our techniques, we confirmed an increase in total EEV concentrations between active RRMS patients versus all other patient groups. However, significant differences were more common and more significant when comparing CNS-EEV populations versus EEV populations. Although previous authors have surmised that increased EEV levels in MS patients may be indicative of BBB permeability, increased levels of EEV are not specific to MS and may confound results. Other common comorbidities like diabetes and heart disease have known endothelial pathologies and increased levels of circulating EEV (70–72, 82, 83). Therefore, increased levels of EEV are not a specific marker of BBB dysfunction and are not necessarily disease specific. By concentrating on CNS-EEV levels however, we can focus more specifically on CNS endothelial injury/activation including BBB permeability, a critical component of MS lesion pathophysiology (28–30, 59, 60).

To identify EV originating from CNS endothelial cells, we relied on detection of the myelin and lymphocyte protein MAL on EV expressing pan-endothelial markers CD31, CD105, and CD144. Importantly, MAL expression on endothelial cells has been shown to be limited to CNS endothelial cells in mice (62) and its expression in human brain endothelial cells has been confirmed in transcriptional studies(63–65). To detect MAL expression, we utilized binding of the MAL ligand, pETX(78, 84), as there is no currently available anti-MAL antibody suitable for flow cytometry. While CD31, CD105, and CD144 have been demonstrated to be expressed on EEV(61), expression of MAL on circulating EV has not been evaluated. MAL is a highly hydrophobic proteolipid, and its expression is limited to T-lymphocytes, myelin producing oligodendrocytes, polarized epithelial cells, and endothelial cells of the CNS(62, 77, 85–94). Although MAL expression in endothelial cells appears to be limited to the CNS, its function in these cells is unknown. In other MAL expressing cells, MAL is known to play an important role in apical protein sorting and lipid raft formation and maintenance(94–103). Interestingly, MAL may play an important role in EV biogenesis, as knockout of MAL in T-cells significantly impairs exosome secretion(100). Considering the importance lipid rafts play in EV biogenesis(104, 105), it seems probable that MAL’s role in EV biogenesis may be related to its role in lipid raft organization and apical protein sorting.

Interestingly, CNS-EEV population appear to be unique, as the majority of CNS-EEV appear to express a single pan-endothelial marker (CD31 CD105, or CD144). The reason for this is unclear and may be due to steric hindrance or limited signal when using antibodies to detect proteins on particles with such small a surface area. However, it is tempting to speculate that increases in specific CNS-EEV populations may be indicative of specific, pathogenic mechanisms occurring at the BBB. For example, in vitro experiments have demonstrated that stimulation of endothelial cells with different agents results in differential protein and miRNA packaging into secreted EEV which are representative of the parental cell activation(55, 106–108). Therefore, an increase in a specific CNS-EEV population with a specific protein content may reflect specific changes occurring at the BBB. For example, histopathological examination of MS lesions has revealed an increase in vasculature CD31 and CD105 expression. Hence, an increase in circulating CNS-EEV31 or CNS-EEV105 concentrations may be reflective of the cellular changes occurring in the endothelial of MS lesions. Because EV reflect the biological state of their mother cells cell(55, 106–112), molecular analysis including miRNA analysis of purified CNS-EEV may also provide cell-specific mechanisms of pathogenesis, as bulk EV analysis have indicated significant differences in miRNA expression in MS derived EV versus HC(113–118). These studies are ongoing.

Our SEC isolation of EV from patient plasma method was optimized for detection of EV via flow cytometry. Based on analysis of submicron polystyrene beads, we are reliably detecting EV ranging in size from 0.5 to 1um, with some limited detection of 0.2um EV. Although we have determined that our SEC isolation contains the highest amounts of EV detectable by flow cytometry, TEM and NTA analyses reveal that there are a significant amount of small EV. Therefore, flow cytometry analysis may be underestimating the total number of EV within our fractions. However, flow cytometry is the most acceptable method for analysis of multiple surface markers. Current studies are underway to improve detection of multiple markers on all EV within our SEC fractions of interest.

In summary, this work builds on the small but growing amount of evidence that CNS derived EV can be detected in peripheral circulation of MS patients and may be useful biomarkers of specific disease activity(46, 47). Taken together, this indicates that analysis of circulating CNS derived EV including CNS-EEV may be helpful biomarkers of specific disease activity in MS patients.

BBB

Blood-brain barrier

CEL

contrast enhancing lesions

CSF

cerebral spinal fluid

CNS-EEV

CNS endothelial derived extracellular vesicles

CNS-EEV31

CNS endothelial derived extracellular vesicles expressing CD31 and MAL

CNS-EEV105

CNS endothelial derived extracellular vesicles expressing CD105 and MAL

CNS-EEV144

CNS endothelial derived extracellular vesicles expressing CD144 and MAL

extracellular vesicles

EEV

endothelial derived extracellular vesicles

EDSS

Expanded Disability Status Scale

MAL

myelin and lymphocyte protein

FSC

forward scatter

DMT

disease modifying therapy

healthy controls

HUVEC

human umbilical cord vein endothelial cell

NTA

nanoparticle tracking analysis

PBS

phosphate buffered saline

PFP

platelet free plasma

PPMS

primary progressive multiple sclerosis

RRMS

relapsing remitting multiple sclerosis

room temperature

SPMS

secondary progressive multiple sclerosis

SEC

size exclusion column/chromatography

SSC

side scatter

TBS-T

Tris Buffered Saline with Tween 20

TEM

transmission electron microscopy

Ethics approval and consent to participate:

Study subjects were recruited and consented according to Weill Cornell Medicine Institutional Review Board–approved protocol 1003010940.

Consent for publication:

Not applicable.

Declarations

Competing interests:

Antibody JL004 used in this publication is the subject of granted and pending patent applications.

Declarations

Competing interests:

Antibody JL004 used in this publication is the subject of granted and pending patent applications.

Funding

This work, in part, was funded by National Multiple Sclerosis Society, the Myelin Repair Foundation, and EMD Serono.

Funding:

This work, in part, was funded by National Multiple Sclerosis Society, the Myelin Repair Foundation, and EMD Serono.

Authors' contributions:

Study conception and design: JRL; Data collection: MZ, WM, SVS, and JRL; Analysis and Interpretation of results: MZ and JRL; Draft manuscript preparation: MZ, WM, and JRL. All authors reviewed the results and approved the final version of the manuscript.

Acknowledgements:

We wish to thank all the blood donors who participated in this research. We are also grateful to the staff of the Multiple Sclerosis Center that helped make this study possible. We are also grateful to Research Specialist Juan Jimenez and Core Director Leona Cohen from the EM and Histology services lab of the Microscopy & Image Analysis Core at Weill Cornell Medicine for their processing and expertise concerning TEM analysis and to Dr. Timothy Vartanian for his guidance during this project

Supplementary material

Supplementary material can be found in a single PDF and include the following: Supplemental Fig. 1 Gating strategy and enumeration of individual EEV populations, Supplemental Fig. 2 Enumeration of total EEV, Supplemental Fig. 3 Percentages of Total CSN-EEV do not differ between patient groups, Supplemental Table 1 Flow cytometry antibodies, Supplemental Table 2 Western blot antibodies, and Supplemental Materials and Methods consisting of detailed experimentation.

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CNS Endothelial Derived Extracellular Vesicles are Biomarkers of Active Disease in Multiple Sclerosis

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