(2024) 21:154
Jank et al. Journal of Neuroinflammation
https://doi.org/10.1186/s12974-024-03147-y

Journal of Neuroinflammation

Open Access

RESEARCH

Characterization of spinal cord
tissue‑derived extracellular vesicles
in neuroinflammation
Larissa Jank1, Ajay Kesharwani1, Taekyung Ryu2, Deepika Joshi1, Dimitrios C. Ladakis1, Matthew D. Smith1,
Saumitra Singh1, Tanina Arab3, Kenneth W. Witwer1,3, Peter A. Calabresi1,4, Chan‑Hyun Na1,2 andPavan Bhargava1*

Abstract
Extracellular vesicles (EVs) are released by all cells, can cross the blood–brain barrier, and have been shown to play
an important role in cellular communication, substance shuttling, and immune modulation. In recent years EVs haveshifted into focus in multiple sclerosis (MS) research as potential plasma biomarkers and therapeutic vehicles. Yet little
is known about the disease-associated changes in EVs in the central nervous system (CNS). To address this gap, wecharacterized the physical and proteomic changes of mouse spinal cord-derived EVs before and at 16 and 25 days
after the induction of experimental autoimmune encephalomyelitis (EAE), a neuroinflammatory model of MS. Using
various bioinformatic tools, we found changes in inflammatory, glial, and synaptic proteins and pathways, as well
as a shift in the predicted contribution of immune and glial cell types over time. These results show that EVs provide
snapshots of crucial disease processes such as CNS-compartmentalized inflammation, re/de-myelination, and synap‑
tic pathology, and might also mediate these processes. Additionally, inflammatory plasma EV biomarkers previously
identified in people with MS were also altered in EAE spinal cord EVs, suggesting commonalities of EV-related patho‑
logical processes during EAE and MS and overlap of EV proteomic changes between CNS and circulating EVs.
Keywords Extracellular vesicles, EAE, Multiple sclerosis, Neuroinflammation, Synaptic pathology, Myelin, Tissuederived extracellular vesicles*Correspondence:
Pavan Bhargava
pbharga2@jhmi.edu
1
Department of Neurology, Johns Hopkins University School of Medicine,
Baltimore, MD, USA
2
Institute for Cell Engineering, Johns Hopkins University Schoolof Medicine, Baltimore, MD, USA
3
Department of Molecular and Comparative Pathobiology, Johns
Hopkins University School of Medicine, Baltimore, MD, USA
4
Solomon H. Snyder Department of Neuroscience, Johns Hopkins
University School of Medicine, Baltimore, MD, USA

Introduction
Multiple sclerosis (MS) is a chronic demyelinating disease of the central nervous system (CNS). Despite being
the most frequent non-traumatic disabling neurologicaldisease among young adults, the cause of MS and methods to predict its progression are still under investigation[1]. The widely-used experimental autoimmune encephalomyelitis (EAE) model of MS is a neuroinflammatorymodel where myelin-reactive T-cells, primed by injectionof myelin peptide with an adjuvant, target oligodendrocytes, leading to neuroinflammation, particularly the spinal cord and anterior visual pathway [2].
Extracellular vesicles (EVs) are a heterogeneousgroup of membrane-delimited particles, with the most

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Jank et al. Journal of Neuroinflammation(2024) 21:154

abundant “small EVs” often defined as 30–150 nm indiameter [3]. Released by all cells in the body, EVs arethought to cross the blood–brain barrier (BBB) bidirectionally [4]. Since EVs have many functions in healthand disease, including communication, substance shuttling, and immune modulation [5, 6], they have provokedinterest in neuroimmunology and MS research, where
they are now being studied to better understand disease
pathology, as a source of biomarkers, and as therapeutic
vehicles [7–10]. In EAE and other demyelination models, EVs reportedly mediate antigen presentation, propagate inflammatory signals, resolve inflammation, induce
tolerance, and promote remyelination [7, 10–14]. The
relevance of EVs in EAE pathology, and hence also theimportance of efforts to characterize them, is emphasized
by the fact that A-SMase mice, which have impaired EV
release through membrane budding, are highly resistant
to EAE induction [15].
Currently, there is a lack of studies characterizing
tissue-derived EVs in EAE, or other MS models. This isneeded to understand what EV-mediated cellular communication is taking place during the disease process, inorder to identify what changes to look for in biomarker
studies. Ultimately understanding EV contents in disease might also help develop better EV-based therapeutic
approaches.
Given this knowledge gap, we characterized EVs
derived from the whole spinal cords of EAE mice. After
first assessing the purity and physical characteristics ofseparated tissue EVs, we profiled EV proteins of naïve
mice and mice at 16 and 25 days of EAE induction using
18-plex Tandem Mass Tags (TMT) proteomics. Data
were used to identify molecular pathways and cell types
contributing to the EV pool. We also compared our
results with previously published peripheral EV biomarkers in MS [8, 9, 16–19]. We further compared the
EV-enriched fractions with an intermediate separation
product, the 10k pellet. In summary, our novel resource
identifies 7000 proteins in CNS-derived EVs and catalogs
changes during the course of EAE.

Material and methods
Mice

C57BL/6J mice were purchased from The Jackson Laboratory. All mice were kept in a pathogen-free animal
facility at Johns Hopkins University School of Medicine,given standard food and water ad libitum, and subjected
to a 12-h light/dark cycle. Use of experimental animals
adhered strictly to National Institutes of Health guidelines, and all experimental protocols were approved by
the Johns Hopkins Institutional Animal Care and Use
Committee.

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Induction of EAE and clinical scoring

EAE was induced in 10–13-week-old female C57BL/6J
mice as previously described [20]. In brief, to induce
EAE, we subcutaneously injected 150 µg of ­MOG33–55(obtained from Johns Hopkins Peptide Synthesis Core)
along with complete Freund’s adjuvant (CFA) (ThermoScientific, Rockford, IL, USA) containing 600 μg of
mycobacterium tuberculosis (BD, Franklin Lakes, NJ) in
the lateral abdomen, followed by intraperitoneal injection with 300 ng of pertussis toxin (List Biological Labs,
Campbell, California, USA) on the same day of immunization and after 2 days. EAE clinical scores were obtained
daily in a blinded manner using the established standard
clinical scoring system from 1 to 5: 0 = no signs of disease; 1 = loss of tail tonicity; 2 = loss of tail tonicity andmild paralysis of hindlimbs; 3 = paralysis of hindlimbs;
4 = hindlimbs paralysis and mild paralysis of forelimbs;
5 = complete paralysis or death.
Tissue collection and processing

Mice were sacrificed either on day 16 post-immunization,corresponding to the peak stage of EAE, or on day 25,
representing the chronic stage of EAE. After anesthesia
with isoflurane and trans-cardiac perfusion with ice-cold
PBS, whole spinal cords were collected and snap-frozen
on dry ice before storage at − 80 °C. EVs were separated
from frozen spinal cords.
EV separation by size exclusion chromatography
and ultracentrifugation

We separated EVs from mice spinal cords by size exclusion chromatography (SEC) and ultracentrifugation
as described previously [21] with some modifications.
Briefly, five frozen spinal cords (approximately 400 mg)
from EAE mice (peak or chronic) or naïve mice were
gently chopped into 2–4 mm pieces on dry ice and transferred to a 15 mL tube containing 75 U/mL Collagenase
type III (Worthington #CLS-3) in Hibernate-E medium(Thermo #A12476-01) at a ratio of 800ul buffer (75 U/
mL collagenase in Hibernate-E medium) per 100 mg oftissue, and incubated for 15 min at 37 °C. During thisincubation, the tube was mixed every 5 min by invertingand pipetting up and down gently using a 10 mL pipette.
After incubation, the tube was immediately transferred onto ice. Complete protease inhibitor (PI, Sigma#11697498001) and PhosSTOP (PS, Sigma #4906837001)
solution were then added to stop the digestion. The dissociated tissue was centrifuged at 300×g for 10 min at 4 °C.
The supernatant was collected and centrifuged at 2000×g
for 15 min at 4 °C to remove remaining cell debris. The
cell-free supernatant was then gently filtered (0.22 µm
syringe filter, Millipore Sigma, SLGS033SS) to further

Jank et al. Journal of Neuroinflammation(2024) 21:154

deplete cell debris and centrifuged at 10,000×g for 30 min
at 4 °C (S52-ST rotor, Beckman Ultra-Clear Tube with
5 mL capacity #344057). The pellet (“10k pellet”) was
then resuspended in PBS. The supernatant containing
small EVs was concentrated from 5 to 0.5 mL using a 100
kilodalton (kDa) molecular weight cut-off protein concentrator. (Millipore UFC901024). The resulting concentrate was applied onto a qEV Original 70 nm SEC column(IZON Science SP1-USD, Christchurch, New Zealand)and processed per manufacturer guidelines. After loading the sample, 0.5 mL fractions were collected by elution
with PBS. The initial 3 mL (F1–6) eluate was designatedas the void volume. Following this, a total of 2 mL eluate (Fractions 7–10) was collected and combined as EVenriched fractions. To further purify and concentrateEVs, the combined fractions 7–10 were ultracentrifuged
for 70 min at 110,000×g (average) at 4 °C. The resulting
supernatant was discarded, and the pellet—containing
the small tissue EVs—was resuspended in PBS containing
PI/PS. Downstream experiments included nanoparticle
tracking analysis (NTA), transmission electron microscopy (TEM), protein quantification by bicinchoninic acid
(BCA) assay (Thermo Scientific #PI23227), Western blot
analysis, and proteomics, for which the final EV pellet
was resuspended in filtered PBS.
NTA measurement

The size and number of particles in EV and 10k fractions were determined by ZetaView Nanoparticle Tracker(Particle Metrix GmBH, Meerbusch, Germany), and thecorresponding ZetaView software (8.05.14 SP7). The
instrument was calibrated with 100 nm diameter beads(Thermo Scientific, catalog #4310A). The samples werediluted in filtered PBS and injected into the viewing
chamber with a syringe. Light scattering was recorded
at 488 nm wavelength with the following settings: Temperature: 23 °C; camera sensitivity: 80.0; Shutter speed:100; Focus: autofocus; frame rate: 30 frames per second.
The analysis was conducted with the following parameters: minimum brightness 20, maximum particle size
1000 nM, minimum particle size 10 nM.
Transmission electron microscopy

Freshly thawed preparation of EVs and 10k pellet wereadsorbed onto glow-discharged ultrathin carbon coatedcopper grids of 400 mesh (EMS CF400-CU-UL) for
2 min. The grids were quickly rinsed in 1X Tris-bufferedsaline (TBS) three times. After rinsing, the grids were
negatively stained with 1% Uranyl Acetate (UA) with 1%
tylose in deionized water double-filtered with a 0.22 µm
syringe filter. The grids were dried by blotting andimaged on a Hitachi 7600 TEM operating at 80 kV withan AMT XR80 CCD (8 megapixel). Ten images of three

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independent spinal cord EV samples were taken for eachtime point.
Protein quantification and Western blot

Spinal cord tissue pellets obtained after the 300×g centrifugation step were resuspended in 1× RIPA lysis buffer(Cell Signaling Technology #9806) containing 1× PI/PS
and incubated on ice for 30 min. Tissue was homogenized and sonicated on ice for 20 s. The homogenate wascentrifuged at 10,000×g for 5 min, and the supernatant
was collected. All other samples were lysed using RIPA
buffer, sonicated for 20 s on ice, and spun to eliminate
debris.
Protein concentration was determined by BCA protein assay kit according to the manufacturer’s protocol.
Briefly, lysed samples were diluted, mixed with BCA
working reagent in a 96-well plate, and incubated for
30 min at 37 °C. Absorbance was measured at 562 nm. A
bovine serum albumin (BSA) standard curve was used todetermine protein concentrations.
For SDS-PAGE, 6 µg samples diluted in Laemmli buffer(non-reducing) was loaded onto 4–20% Mini-PROTEAN® TGX™ precast protein gel (Cat# 4561096, BioRad). After separation, proteins were transferred onto amethanol-activated 0.45 µm polyvinylidene difluoride
membrane (88585, ThermoFisher). Membranes were
blocked with freshly prepared intercept blocking buffer(927-66003, LI-COR) and probed with specific primary
antibodies in intercept (TBS) blocking buffer overnight at
4 °C. Primary antibodies were anti-CD81 (1:1000, Novus
Biologicals, NBP1-77039; 1:500, Santa Cruz, Sc-166028),
anti-Flotillin1 (1:1000, BD Biosciences, 610820), andanti-tumor susceptibility gene 101 (TSG101, 1:1000, BD
Biosciences, 612692). After incubation, membranes were
washed with 0.01% TBST and further incubated with
1:20,000 of IRDye® fluorophore-labelled secondary antibodies (LICOR-926) for 30 min at RT. Membranes wereimaged using the LI-COR Odyssey® M imaging system(LI-COR biosciences).
Sample preparation and LC–MS/MS analysis

The sample preparation and liquid chromatographytandem mass spectrometry (LC–MS/MS) analysis wereconducted as described previously with minor modifications [22]. EVs were dried under vacuum and reconstituted in lysis buffer containing 8 M urea (Sigma Aldrich)and 50 mM triethylammonium bicarbonate (TEAB,
Sigma Aldrich). Samples were reduced and alkylated in
the presence of 10 mM tris(2-carboxyethyl) phosphine(TCEP, Sigma Aldrich) and 40 mM 2-chloroacetamide(CAA, Sigma Aldrich) at RT for 1 h. EV proteins weredigested with Lys-C (Lysyl endopeptidase MS grade; Fujifilm Wako Pure Chemical Industries Co, Ltd) in a ratio

Jank et al. Journal of Neuroinflammation(2024) 21:154

of 1:100 (final concentration of 2 ng/µL) at 37 °C for 3 h.
Further digestion was conducted by diluting the urea
concentration to 2 M by adding three volumes of 50 mM
TEAB followed by trypsin (sequencing grade modified
trypsin; Promega) in a ratio of 1:50 (final concentration of
10 ng/µL) and incubating at 37 °C overnight. The resulting peptides were desalted using C18 StageTips (3 M
Empore; 3 M) and labeled with 18-plex TMTpro reagents(Thermo Fisher Scientific) after sample randomizationaccording to the manufacturer’s instruction. The labeled
peptides were pre-fractionated by basic pH reversedphase liquid chromatography into 96 fractions and concatenated into 24 fractions, which were vacuum-dried
and reconstituted in 0.5% formic acid (FA, Thermo Fisher
Scientific) for mass spectrometry. The Orbitrap Fusion
Lumos Tribrid Mass Spectrometer (Thermo Fisher Scientific) coupled with an Ultimate 3000 RSLCnano nanoflow
liquid chromatography system (Thermo Fisher Scientific)
was used to analyze fractionated peptides. Peptides fromeach fraction were loaded onto a trap column (Acclaim
PepMap 100, LC C18, 5 μm, 100 μm × 2 cm, nanoViper)at a flow rate of 8 μL/min and resolved at a flow rate of
0.3 μL/min by increasing the gradient of solvent B (0.1%
FA in 95% ACN) to 28% on an analytical column (EasySpray PepMap RSLC C18, 2 μm, 75 μm × 50 cm) fitted
onto an EASY-Spray ion source operated at a voltage ofabout 2.5 kV. The overall run time was 120 min. MS analysis was performed in data-dependent acquisition and“Top Speed” modes with 3 s per cycle. MS1 scan range
for precursor ions was set to m/z 300 to 1800. MS1 andMS2 mass resolution was set to 120,000 and 50,000 at
an m/z of 200. MS2 scans were acquired by fragmenting precursor ions using the higher-energy collisional
dissociation (HCD) mode, which was set to 35% of normalized collision energy. The automatic gain controls forMS1 and MS2 were set to one million and 0.05 million
ions, respectively. The maximum ion injection times forMS1 and MS2 were set to 50 and 100 ms, respectively.
The precursor isolation window was set to 1.6 with 0.4
of offset. Dynamic exclusion was set to 30 s, and singly
charged ions were rejected. Internal calibration was carried out using the lock mass option (m/z 445.12002) fromambient air.
Database searching and peptide quantification

Database searches and statistical analysis were conducted
as described previously with minor modifications [22].
Proteome Discoverer (version 2.4; Thermo Fisher Scientific) was used for quantitation and identification. The
top ten peaks in each window of 100 Da were selected
for database search during MS2 preprocessing. The MS/
MS data were then searched using SEQUEST HT algorithms against a Mus musculus UniProt database that

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includes both Swiss-Prot and TrEMBL (released in January 2019 with 55,435 entries) with common contaminant proteins (115 entries). The precursor mass (MS1)
tolerances were set to 10 ppm and 0.02 Da for precursor and fragment ions, respectively. Trypsin was usedas the protease, allowing for a maximum of two missedcleavages. Carbamidomethylation (+ 57.02146 Da) of
cysteine and TMTpro tags (+ 304.20715 Da) on lysine
and peptide N termini were set as fixed modifications.
Oxidation (+ 15.99492 Da) of methionine and acetylation (+ 42.01057 Da) on protein N termini was selected
as a variable modification. Peptides and proteins were
filtered at a 1% false discovery rate (FDR) at the peptidespectrum match level using a percolator node and at
the protein level using the protein FDR validator node,
respectively. Both unique and razor peptides were used
for peptide quantification. Protein groups were considered for peptide uniqueness. The co-isolation threshold
was set to 50%, and the reporter ion abundance was computed based on signal-to-noise ratios. The missing intensity values were replaced with the minimum value, andthe average reporter signal-to-noise threshold was set to
50. Protein grouping was performed with a strict parsimony principle to generate the final protein groups [23].
Further bioinformatic and statistical analysis

In the following steps, data were reformatted in R (version 4.3.1, R Core Team). Uniprot IDs were matched toEntrezIDs and gene symbols using the AnnotationDbi
package (version 1.62.2, [85]) and org.Mm.eg.db annotation (version 3.17.0, [86]). Principal component analysis
was carried out using the stat package (version 3.6.2, R
core team) and plotted using the autoplot function of
ggplot2 (version 3.4.4). Proteins missing data points inany sample were excluded from analysis. Data were normalized using median centering and then log2 transformed. To compare overall protein content in EVs and
10k pellet, samples were normalized together (i.e., forFig. 1). To compare changes at different time points, EV
and 10k samples were normalized separately (all remaining analyses). We used LIMMA (version 3.56.2) [24, 25]
for a three-group differential expression/abundance
analysis comparing naïve, peak and chronic samples. We
fitted a linear model [analysis of variance (ANOVA)] to
the data and moderated our test statistics with empirical Bayesian methods using LIMMA. Volcano plots andheatmap were generated using ggplot2, ggrepel (version 0.9.3) and the heatmap.2 function of gplots (version 3.1.3.1). Enrichment analyses were carried out using
clusterProfiler (version 4.8.2) [26]. The gene sets tested
included the integrated biological pathway GO terms(GO.db version 3.17.0) [27], KEGG pathways [28] andReactome (ReactomePA 1.44.0) [29] gene sets. Cut-offs

Jank et al. Journal of Neuroinflammation(2024) 21:154

Page 5 of 17

Fig. 1 Workflow for spinal cord-derived EV separation and characterization. a The diagram depicting the EV separation method using size exclusion
chromatography and ultracentrifugation (created using BioRender.com). b Clinical scores of mice at day 16 and day 25 showed no significant
difference (n = 15 per time point). c Representative electron microscopy images of negatively stained EVs from spinal cords before EAE induction
and at days 16 and 25 (ten images taken for three samples from each time point). d Particle concentration and size at the three time points
obtained using NTA (bar graphs show the mean ± SD of n = 3 samples (pooled from 5 spinal cords) at each time point. Statistical significancewas determined using one-way ANOVA and Turkey’s post-test. ns = not significant. e Number of the top 100 EV-enriched proteins, according
to Vesiclepedia and Exocarta, present in the spinal cord EV proteome. f Heatmap showing the relative abundance of commonly used EV markers
in spinal cord EV samples and 10k pellet samples (EV depleted particles) obtained as a byproduct of the EV isolation. g Heatmap showing
the relative abundance of frequently described possible co-isolated proteins recovered from tissue during EV separation. (n = 3 for each time pointand each sample type, i.e., EV and 10k)

were defined as < − 1.5/> 1.5-fold change and adjusted
p-value < 0.05. Results were plotted using enrichplot (version 1.20.1) and ggplot2. For immune cell deconvolution
analyses, mouse gene IDs were converted to human genes
by orthology using Orthology.eg.db (version 3.17.0, Marc
Carlson, 2023) and org.Hs.eg.db annotation (version

3.17.0, Marc Carlson, 2023). Deconvolution analyses werecarried out with the online CIBERSORTx [30, 31] tool
using the absolute mode, the integrated batch correction
tool, and the integrated LM22 deconvolution matrix and
Immunodeconv (version 2.1.0) [32] using the integrated
Microenvironment Cell Populations-counter method.

Jank et al. Journal of Neuroinflammation(2024) 21:154

The LM22 deconvolution matrix uses the M1/M2/M0
macrophage polarization terminology to annotate cell
types. We re-named these populations as homeostatic(for M0), pro-inflammatory (for M1) and alternatively
activated (for M2). Statistical analysis of fold changes of
immune populations was done in LIMMA as describedabove. For glia and neuron deconvolution analysis using
BRETIGEA (version 1.0.3) [33], a singular value decomposition estimation method was used, and the numberof markers for each cell type was set to 400. To further
study the changes in synaptic protein subtypes, we queried the associated cellular compartment GO terms forterms associated with our subsets of interest. To comparechanges in pre- and postsynaptic proteins, we queried theterms to include either “presynaptic” and not “postsynaptic” or “postsynaptic” but not “presynaptic”. To compare
inhibitory and excitatory proteins in EVs, we queried
either (1) both “GABA-ergic” and “inhibitory” or (2) both“glutamatergic” and “excitatory”. To further confirm theglial cell deconvolution, we compiled a list of unique glial
EV markers using the overlap of three data sets: proteomics of cultured murine CNS cells [34], transcriptomics ofmurine CNS tissue [71], and proteomics of EVs from thesupernatant of human iPSC-derived CNS cells [35]. Venn
diagrams showing the dataset overlaps were generatedusing VennDiagramm (version 1.7.3) in R. For microglia,
astrocytes, and neurons, our marker list was the overlapof all three datasets. For oligodendrocyte-lineage cells,
the list was the overlap of the datasets from [34] and [36].
Statistical analyses of changes in cellular markers in supplementary Fig. S4 (in supplementary figures file) werecarried out in GraphPad Prism 10 using a Friedman testwith Dunn’s multiple comparisons. The graphs were also
generated in Prism. The diagrams in Fig. 1 and Fig. S1 (insupplementary figures file) were created using Biorender.
com, and figures were compiled using Adobe Illustrator
2023.

Results
This study aimed to profile the changes in CNS tissuederived EVs in EAE, a mouse model of multiple sclerosis,
at the peak and chronic phase of disease, i.e. day 16 and
25 after induction, respectively.
Spinal cord EV concentration and morphology are similar
to healthy mice throughout EAE

EVs were separated from the whole spinal cords of naïve
mice and mice with EAE using a previously publishedprotocol [21] (Fig. 1A). EAE mice all developed hind limb
paralysis with no significant difference in clinical scores
recorded for mice taken down at day 16 or 25 postimmunization (Fig. 1B).

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Nanoparticle tracking analysis (NTA) and transmission
electron microscopy (TEM) were used to study physicalcharacteristics of the EVs. There was no difference in the
EV particle yield per tissue mass in naive mice comparedto the EAE mice at the peak and chronic phase (Fig. 1D),
nor was there in the size profile (Fig. 1D and S1A, in supplementary figures file). Further, in TEM EVs had a typical “cup-shaped” morphology (Fig. 1C), an artifact of the
TEM fixation process.
Proteomics showed enrichment of EV markers including CD81, CD9, CD63, flotillin 1/2, annexin A1/A2 andtumor susceptibility gene 101 (TSG101) in the spinal
cord EVs compared with the 10k pellet from the same
mice (Fig. 1F). Western blots also confirmed enrichment of EV markers CD81, flotillin 1, and TSG101 (Fig.
S1B-C, in supplementary figures file). Furthermore, EVs
were depleted of nuclear proteins, cytoplasmic proteins,and most mitochondrial proteins: translocase of outermitochondrial membrane 20 (TOMM20), TNF receptor
associated protein 1 (TRAP1), and inner membrane mitochondrial protein (IMMT) (Fig. 1G), while cytochromec1 (CYC1) was enriched in EVs. Blood plasma proteins
albumin, apolipoprotein A1, and A2 were elevated atpeak, when the BBB is most severely compromised, andassociated with disease state (i.e., naïve, day 16, or day 25)
rather than sample type (i.e., EV or 10k). 90% and 94% of
the Vesiclepedia [37] and Exocarta [38] “top 100” EV proteins, respectively, were detected in EVs (Fig. 1E).
EAE spinal cord EVs show disease‑stage‑dependent
proteomic changes

Proteomics detected 7010 proteins, of which 2823 weredifferentially abundant (adjusted p ≤ 0.02) in EAE at any
time point in EVs, while only 1824 were differentiallyabundant in the 10k pellet. Principal Component Analysis (PCA) distinguished EV samples into three clusters
representing naïve spinal cord EVs and the various EAEtime points (Fig. 2A), indicating a dynamic alteration intissue-derived EVs throughout the disease progression.
(Fig. 2A).
Most of the top differentially abundant proteins(ranked by adjusted p-value) and pathways identified
have known relevance in MS and EAE pathology. The
most significantly dysregulated proteins at the peakof disease (vs. naïve) were all upregulated and were all
inflammation-associated (Fig. 2Bi). Many of these werealso among the top upregulated proteins (filtered for
upregulated and ranked by p-value) at the chronic stage,
although they were significantly less upregulated thanat day 16 (Fig. 2Bii, 2Biii). The top 10 most significantly
downregulated proteins (filtered for downregulated andranked by p-value) at peak included myelin proteins, proteins associated with actin bundle formation, and glial

Jank et al. Journal of Neuroinflammation(2024) 21:154

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Fig. 2 Proteomic changes in spinal cord EVs during EAE. a Principal component analysis using normalized abundances of proteins before EAE
induction (green) and 16 (blue) and 25 (red) days after. b Volcano plots showing significantly up- (red) and downregulated (blue) proteins.
Significance is defined by an adjusted p-value of < 0.05 and a < − 1.5 or > 1.5-fold change. Proteins are shown as gene symbols. c Dot plot of gene
ontology pathway enrichment analysis showing the top upregulated and downregulated biological processes. The dot size represents the generatio, i.e., the identified proteins per gene set and the dot color represents the significance of the enrichment. d Heatmap showing the relative
abundance of the proteins from the “proton motive force-driven ATP synthesis” GO term gene set. Proteins are shown as gene symbols [n = 3 (from5 score-matched spinal cords) for each time point and each sample type, i.e., EV and 10k]

markers—P2RY12 for homeostatic microglia and Btbd17
for astrocytes (Fig. 2Bi). Again, these changes were also
observed in the chronic samples vs. naive, but to a lesser
extent, except for Btbd17, which further decreased by day
25 (Fig. 2Bii, Biii).
Upon comparing the peak and chronic samples, thetop 10 most significantly differentially abundant proteins
were mostly downregulated at day 25 compared to day
16 (Fig. 2Biii). These included: plasma proteins including lipoproteins (ApoH, ApoA1), acute-phase proteins(haptoglobin), immunoglobins (Ighv4-1 immunoglobulin
heavy variable 4-1), and coagulation proteins (prothrombin and α-2-antiplasmin); and inflammatory proteinspromoting TH17 polarization (SAA3, Arg1). The only
upregulated protein among the top 10 dysregulated

proteins at day 25 compared with day 16 was the myelin
protein MPZ. Other proteins, upregulated at the chronic
time point compared with peak (i.e., the top 10 downregulated proteins) were: ceruloplasmin (Crb), which is
involved in iron metabolism, and Schwann cell-associated regulators of peripheral nerve ensheathment, periaxin (Prx), Mpp6, and Naalad2.
Changes in individual proteins were reflected in
hypergeometric gene set analysis performed with ClusterProfiler (Fig. 2C). On both days 16 and 25, immune
response-related pathways were enriched. Comparedwith day 16, these inflammatory pathways were lesssignificantly enriched at day 25 (among the top-down
regulated pathways at the chronic stage). Proteins associated with the synaptic vesicle cycle, neurotransmitter

Jank et al. Journal of Neuroinflammation(2024) 21:154

regulation, and ATP synthesis were enriched in naïve
samples. Interestingly, downregulation of ATP synthesis pathways was more prominent in EVs than in the 10kpellet. KEGG and Reactome pathway analysis confirmed
the downregulation of synaptic vesicle and ATP synthesis-associated pathways and the upregulation of inflammatory pathways including the phagosome, complement,
and coagulation cascade-associated pathways in bothpeak and chronic EAE (Fig. S2A-B in supplementary figures file). The proteins driving the changes in the “proton motive force driven ATP synthesis” pathway were
NADH: ubiquinone oxidoreductase subunits (NDUFs)
and ATPase subunits primarily associated with mitochondrial complexes I and V, respectively (Fig. 2D).
Immune response‑related proteins are strongly
upregulated in EVs during EAE, especially at day 16,
and the relative abundance of immune cell EVs changes
throughout the course of EAE

Since immune response-related proteins were among
the most significantly differentially abundant proteins in
EVs at both day 16 and 25 compared with naïve mice, we
further investigated the changes in these proteins. We
assessed the proteins driving the enrichment of immune
response-related pathways in the above-mentioned
pathway analysis; investigated changes in the predicted
immune cell type contribution using a bioinformatic celldeconvolution tool; and looked at changes in proteinsthat have been suggested as EV-associated biomarkers in
people with MS (pwMS).
The top 10 most significantly dysregulated proteins in
EVs at peak of disease (vs naïve) were all immune-related
proteins: antigen presentation-associated proteins(H2-DMa/b1, H2-Aa/b1, CD74) and immunity-related
GTPases (Iigp1, Igtp, Irgm1)—all upregulated (Fig. 3A).
Besides these, we found other immune-related proteins
to be strongly upregulated by investigating the proteins
driving the upregulation of the immune-related pathway.
These included: other antigen presenting proteins (Tap1
and 2, B2m), pattern-recognition receptors (such as toll
like receptors 2, 7, and 9), complement cascade proteins(complement C3, factor b, and factor properdin), andFc gamma receptor (Fcgr2b and 3). All of these proteins
were also upregulated at day 25 compared with naïve
mice but were lower at this time point compared to day
16.
To assess possible changes in the cellular contribution to the tissue EV pool, we performed immune celldeconvolution analysis with CIBERSORTx (Fig. 3B and
C). This tool uses single-cell expression data of 547 markers, of which we were able to detect 128, to deconvolutethe abundance of immune cell types in a bulk sample. We
excluded eosinophils and mast cells from the analysis

Page 8 of 17

because fewer than 4 out of the top 25 markers identifying these cells were expressed in our dataset. We
found an increase in the relative abundance of immune
cell-derived EVs after EAE induction: an increase ofcontributing T cells, NK cells, DCs, monocytes/macrophages, and neutrophils. An increase in T cell, B cell,and monocyte/macrophage sources was confirmed inanother immune cell deconvolution analysis—Immunedeconv—(Fig. 3D) and when assessing selected markers(Fig. S3A in supplementary figures file). When assessing
immune cell subsets, CIBERSORTx assigned a significantly reduced contribution of Tregs during EAE and asignificantly increased contribution of activated CD4
memory T cell at day 25 compared with both naïve andday 16 (Fig. 3C). Interestingly, we also found a significant
shift in the polarization of macrophages contributing
to the EV pool. EV-associated pro-inflammatory macrophage markers significantly increased at day 16 but
fell by day 25, while alternatively activated macrophage
markers were increased at day 25 versus both naïve andday 16. There was a trend towards consistent reduction of
homeostatic contribution, with a significant reduction at
day 25 compared with naïve. The 10k pellet showed fewer
significant changes in putative EV cell sources.
Inflammatory EV biomarkers identified in people with MS
are altered in EAE spinal cord EVs

Recent work on plasma-derived CNS- and immune cellenriched EV biomarkers for MS has reported inflammation-associated proteins including T cell proteins (CD4[16], gelsolin, and alpha1-microglobulin [17]), toll-likereceptors (TLR3, TLR4 [8] and the TLR co-receptor
CD14 [16]), coagulation cascade proteins (fibrinogen[18] and fibronectin [19]), and complement components(C1qa, C3, C4b, C5, Factor H and B [9]). To investigate
the relevance of our findings in a preclinical model tohuman disease and determine how our findings in tissue
might be reflected in plasma EVs, we examined the levels of these proteins in our samples (Fig. 3E). Most of thecomplement proteins, with the exception of C5, were significantly upregulated at days 16 and 25. C3, C4, Factor
H, and B were significantly less abundant on day 25 thanon day 16, while C1q remained unchanged on days 16
and 25. The T-cell associated proteins also followed thetrend reported in plasma EVs of MS patients: CD4 andalpha1-microglobulin were upregulated at day 16 and toa lesser extent at day 25, and gelsolin was downregulated
similarly at both days 16 and 25. CD14 and TLR3 wereupregulated (significantly more on day 16 than on day25), while TLR4 was not significantly altered in our study.
Fibrinogen was upregulated as reported in plasma EVs,
and fibronectin was downregulated at day 25 compared
to day 16.

Jank et al. Journal of Neuroinflammation(2024) 21:154

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Fig. 3 Immune response-related proteins and pathways in EVs during EAE. a Heatmap showing the relative abundance of the top proteins(adjusted p-value < 0.0001 and fold change of > 4 at day 16 or 25 compared to naïve samples) from the “immune effector processes” GO term.
Proteins are shown as gene symbols. b Relative immune cell contribution to the spinal cord EV pool at different time points estimated using
CIBERSORT cell deconvolution analysis. The graph shows mean predicted absolute contributions (SD and statistical analysis shown in C). Eachbar represents a time point and each color a cell type. c Mean predicted cellular contribution (± SD; n = 3 samples) of distinct cell populations(colors) over time in the 10k pellet and EVs. Statistical analysis was performed using a linear model [analysis of variance (ANOVA)] in LIMMA. Stars (*)
indicate comparisons of naïve to peak or chronic and hashes (#) represent comparisons of peak to chronic. P-values: * and #p < 0.05, ** and ##p < 0.01,***p < 0.001. d Mean relative immune cell contribution (± SD; n = 3 samples) to the spinal cord EV pool at the different time points using
Immunedeconv cell deconvolution analysis. e Mean relative abundance (± SD; n = 3 samples) of selected published MS plasma EV biomarkers in our
murine tissue EV samples. Significant differences are indicated as adjusted p-values per LIMMA fold change analysis. P-values: *p < 0.05, **p < 0.01,***p < 0.001, ****p < 0.0001

Besides plasma EVs, CSF-derived EVs have beenstudied in pwMS. We assessed the levels of proteinsthat have been shown to be unique to CSF EVs in
pwMS compared to healthy controls [39] in our data

set. Many of these proteins—in particular, inflammatory response-associated proteins (C3, S100a9, Ighg1,
Apoe) and coagulation cascade proteins (fibrinogenand fibronectin)—were also upregulated with EAEJank et al. Journal of Neuroinflammation(2024) 21:154

compared to baseline in our study (Fig. S3B in the supplementary figures file).
Glial and neuronal cell type contributions to tissue‑derived
EVs changes throughout EAE

Relative glia and neuron cell type contribution to the EVpool was investigated using the Brain Cell Type Specific
Gene Expression Analysis (BRETIGEA) [33], and confirmed with selected cell-specific markers. The glia andneuron deconvolution analysis was based on 400 markers
per cell type: microglia, astrocyte, neuron, mature oligodendrocyte, and oligodendrocyte precursor cell (Fig. 4A).
Decreased astrocytic, sharply upregulated microglial,
continuously declining neuronal, transiently decreasing oligodendrocyte, and increased OL precursor cells(OPC) proteins were observed during EAE (Fig. 4A). Thiswas further confirmed when assessing the change in glial
cell and neuronal markers selected manually (Fig. 4B,
C–F) and by the changes of cell-specific markers identified by selecting the overlapping proteins from proteomic analyses of EVs from human iPSC-derived CNS
cell supernatants [35], proteomics of murine CNS cell
cultures [34] and single-cell RNASeq of the murine CNS[13] (Fig. 4A–D).
We noted a difference in the dynamics of proteins associated with OPC differentiation versus the dynamics ofmyelin proteins (Fig. 4B). While myelin proteins continuously decreased, differentiation-associated proteins wereless abundant at peak but partially recovered at day 25.
Similarly, the dynamics of immature and mature neuronal markers in the EV pool markedly differed (Fig. 4E,
F). The abundance of immature neuronal markers
dropped at day 16 and recovered at day 25, while the relative contribution of mature neurons to the EV pool continuously decreased.
Although the predicted microglia contribution to thetissue EV pool in EAE was increased across all analyses,
this was primarily driven by an increased abundance of
inflammatory microglial markers (Fig. 4D), while homeostatic markers such as TMEM119 and P2RY12 were
unchanged or significantly downregulated during EAE.
EVs from EAE tissue contain lower levels of synaptic
proteins with a particularly pronounced loss of presynaptic
and inhibitory proteins

Next, we identified the proteins contributing to the
reduced neuronal contribution observed in the celldeconvolution analysis and the perturbation of synaptic processes in the pathway analysis (Fig. 4G). Interestingly, we found numerous presynaptic proteins, including
Stxbp1, Syt1, Syt7, Pick1, and Complexin I/II (cplx1/2),
among the top dysregulated proteins in this pathway.
Therefore, we next investigated if the loss in synaptic

Page 10 of 17

proteins shows distinct patterns, i.e., if there is a different
dynamic in the change of pre- vs post-synaptic or inhibitory vs excitatory synaptic proteins. For this, we queried
the cellular compartment gene ontology terms to include
either “presynaptic” and not “postsynaptic” (n = 110) or“postsynaptic” but not “presynaptic” (n = 295) (Fig. 4H
and S4F in supplementary figures file). We found a larger
percentage of significantly downregulated presynaptic
proteins (23.6% downregulated of all identified presynaptic proteins at day 16 and 39.1% at day 25) comparedwith postsynaptic proteins (22.7% at day 16 and 18.6%
at day 25), with the largest difference at the day 25 time
point. To compare the dysregulation of inhibitory andexcitatory proteins in EVs we performed a gene ontology query for either both “GABA-ergic” and “inhibitory”(n = 17) or both “glutamatergic” and “excitatory” (n = 20)(Fig. 4I and S4F in supplementary figures file). We founda larger number of significantly downregulated inhibitory(23.5% downregulated of all identified inhibitory synaptic
proteins at day 16 and 47.1% at day 25) than excitatory(25.0% at day 16 and 20.0% at day 25) synaptic proteins at
day 25. We confirmed this by comparing inhibitory andexcitatory proteins categorized according to a previously
published inhibitory and excitatory synapse proteomics
dataset [40], identifying 34 inhibitory and 141 excitatory
proteins. 17.6% of inhibitory and 12.1% of excitatory proteins were reduced at day 16, while 35.3% of inhibitoryand 18.4% of excitatory proteins were reduced at day 25(Fig. S4E and S4G in supplementary figures file).

Discussion
This study characterizes the dynamics of spinal cordderived EVs during EAE, giving novel insights into possible roles of EVs in the pathophysiology of MS. We foundmarked changes in inflammatory, glial, and synaptic
proteins and pathways, as well as a shift in the predicted
contribution of various immune and glial cells types,
corroborating that EVs can provide a snapshot of crucial disease processes such as CNS compartmentalized
inflammation, re/de-myelination and synaptic pathology
and might mediate them. We further found publishedinflammatory EV biomarkers identified in people with
MS [8, 9, 16–19] to be altered in our EAE spinal cordEVs, suggesting an overlap of the pathological processes
in EVs during EAE and MS and an overlap of EV proteomic changes in the CNS and periphery.
We chose spinal cord-derived EVs as the focus of thisstudy because the spinal cord is an early and severely
affected region in the EAE model and is clinically relevant
to MS pathology. The major limitation of this choice was
the small amount of spinal cord tissue compared withthe less severely affected brain tissue per mouse. Despite
pooling five spinal cords for three EAE score-matchedJank et al. Journal of Neuroinflammation(2024) 21:154

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Fig. 4 Glial and neuronal proteins and pathways in EVs during EAE. a Heatmap of the relative neuron and glial cell contribution to the spinal
cord EV pool before and 16 and 15 days after EAE induction. The deconvolution analysis was carried out using BRETIGEA. Graphs showing
mean relative abundance of selected b oligodendrocyte lineage, c astrocyte, d microglial, e immature neuronal, and f mature neuronal markers
in EVs at the different time points (± SD; n = 3 samples). g Heatmap showing the relative abundance of the top proteins (filtered for adjusted
p-value < 0.01 and fold change kept at < − 1.5/> 1.5) from the “vesicle-mediated transport in synapse” GO term enriched in our data. Proteins areshown as gene symbols. h, i Pie charts showing the change of pre- vs postsynaptic protein and inhibitory vs excitatory synapse protein abundance.
The cellular compartment gene ontology terms associated with the proteins detected were filtered: for pre- vs postsynaptic analysis to include
either “presynaptic” and not “postsynaptic” (110 identified proteins) or “postsynaptic” but not “presynaptic” (295 identified proteins); and for inhibitory
vs excitatory to include either both “GABA-ergic” and “inhibitory” (17 identified proteins) or both “glutamatergic” and “excitatory” (20 identified
proteins). Numbers on the pie chart represent the percentage significantly up- (red) or downregulated (blue) proteins of all identified proteinsof the particular synaptic compartment or type (significance defined by p < 0.05 and fold change > 1.2/< − 1.2)

Jank et al. Journal of Neuroinflammation(2024) 21:154

biological batches for each time point, the protein output was low, limiting the number of possible downstream
experiments. The selection of two time points, reflecting
acute inflammation at day 16 and the initiation of resolution of neuroinflammation at day 25, offers a comprehensive view of EV changes across different phases of thedisease.
Despite advances over the last decade, the separation,accurate characterization, and uniform classification of
EVs remains challenging but constantly evolving. We
followed a well-established, frequently used EV separation method involving differential centrifugation followed by SEC [21] and characterized the EVs according
to the MISEV criteria [41]. We found a strong enrichment of known EV markers and a reduction of plasma,
nuclear, cytosolic, and most mitochondrial proteins. A
recent study comparing three CNS tissue EV separation
protocols—SEC, sucrose gradient ultracentrifugation,and phosphatidylserine affinity capture—highlighted
the speed and robustness of SEC, but also reported contamination with soluble proteins using this method [42].
However, our protocol differs from this publication inseveral steps: we carried out filtration with a smaller cutoff (0.22 µM vs 0.45 µM) and used higher centrifugation
speeds to eliminate larger EVs—which reduces contamination and refines the studied EV subset. Indeed, in a
direct comparison of 0.22 µM filtered to non-filtered EVs,
it has been shown that 0.22 µM filtration resulted in amore homogenous population of EVs including exosomes
and small microvesicles [43]. This aligns with the small
EV size we detected with our method, and which others have obtained using this method [44]. To investigate
if our observations were specific to or enriched in EVs,
we compared the changes in the EV fraction to changes
in the 10k pellet. In several analyses, we detected more
pronounced changes in EVs, supporting interest in theseparticles providing a snapshot of ongoing pathologicalprocesses during EAE.
We characterized changes in the EV proteome using
18-plex TMT proteomics since this method allows fora small sample input and results in better detection of
low abundance peptides and more robust quantification
accuracy than traditional mass spectrometry methods. A
limitation of this approach however is the lack of resolution of proteomic changes on a singular EV level or EV
subpopulation level. New developments in cell-origin
specific proteomics might help address this in future [45].
To nonetheless estimate how the relative contribution ofdifferent EV subpopulations changes throughout EAE wecarried out deconvolution analysis using CIBERSORTx
for immune cells and BRETIGEA for glial cells. One limitation of the use of these bioinformatic tools however isthat they are based on cellular marker matrixes which

Page 12 of 17

might differ from markers expressed on EVs released
from this cell type. Finally, when interpreting the changes
in the predicted relative contribution of EV subpopulations, it also has to be noted that in the case of EVs (compared to cells) such changes can have manifold reasons
including: a change in the number of cells that produce
this EV subpopulation, a change in the secretory capacity of the cell type or change in the uptake or clearance of
the EV subpopulation.
Our proteomics data identified several dysregulated
proteins that play a role in the propagation and regulation of immune responses in EAE. At day 16 and, to alesser extent, also at day 25, many of the top dysregulated
proteins in EAE EVs compared to naïve EVs are related
to MS-associated immune pathways. We noted a substantial increase in the abundance of antigen-presenting
proteins in the spinal cord EVs from EAE mice. Whether
this is due to an increased loading of antigen-presenting
proteins onto EVs or an increase in infiltrating immune
cells that produce antigen-presenting EVs is unclear. It
has previously been shown that EVs can present antigen
and activate leukocytes independent of their cell of origin either directly [46], with the help of cross-dressing
bystander antigen-presenting cells [47], or through thetransfer of MHC-mounted antigens onto acceptor cells[48]. It is tempting to speculate that the increase in antigen-presenting EVs might play a part in activating lymphocytes locally and in the periphery or bordering tissue
since EVs are thought to cross the BBB [4]. Since weobserve an increase in MHC class II, class I proteins andconstituents of the immunoproteasome in EVs, this antigen presentation might occur through both direct- andcross-presentation mechanisms.
Another group of immune-related proteins upregulated
in the EAE tissue-derived EVs were complement proteins.
There are several reports of complement proteins in or
possibly bound to EVs [9, 49]. Complement-containing
EVs from adventitial fibroblasts induce proinflammatory
responses in myeloid cells [49] and complement-containing circulating astrocyte-enriched EVs are neurotoxic
and reduce neurite complexity [50]. In a previous study,
our group found that plasma-derived astrocyte-enriched
EVs from pwMS contained higher levels of complement
components than healthy controls [9]. This suggests parallels between the changes we see in CNS-derived EVs in
the EAE model and subsets of circulating EVs in pwMS.
ATP synthesis and aerobic respiration-related proteinsand pathways were among the most strongly downregulated in EAE EVs. EVs are known to shuttle glycolytic
enzymes and produce ATP [51, 52]. In the CNS, Neuronalstem cell (NSC) EVs have been shown to contain several
ATPases we found to be reduced in EAE in our study(Atp5f1d and a). The transfer of intact mitochondria toJank et al. Journal of Neuroinflammation(2024) 21:154

phagocytes through these NSC-EVs prevented the proinflammatory metabolic activation of phagocytes in vitro
and in EAE [53]. Further, astrocyte-derived EVs, containing mitochondria, are reportedly shuttled to neurons andpromote their survival [54]. In EAE, axonal mitochondria
are reduced in number, less mobile, and show reduced
complex 1 activity [55]. These signs of mitochondrial dysfunction are detected as early as day 15 [56]. This aligns
with our observation of reduced complex 1 associatedproteins in the EAE EVs, supporting the hypothesis thatEVs may mirror changes occurring in cellular mitochondria and perhaps the disruption of homeostatic metaboliccues transferred by EVs may promote neurodegenerationand inflammation in EAE. In future, targeting these metabolic changes in EVs or restoring metabolic enzymes in
EVs might be interesting therapeutic approaches in EAE
and MS.
Besides inflammatory responses, we also detectedmarked changes in glial cell-derived EV proteins. Gliaspecific cell deconvolution analysis showed that astrocyte
and mature oligodendrocyte proteins are decreased in
EAE EVs, while microglial and oligodendrocyte precursor proteins are upregulated. Demyelinating lesions withextensive loss of myelin and mature oligodendrocytes arehallmarks of both MS and EAE. In vitro, both murine
and human mature oligodendrocytes have been shown torelease myelin-containing EVs [7, 57]. In EAE spinal cord
EVs the abundance of many myelin proteins was reduced
at both day 16 and 25 post-immunization, with lower
levels at the chronic time point, likely reflecting the lossof oligodendrocytes. Interestingly, two peripheral myelin proteins—MPZ and PMP2—and regulators of nerve
ensheathment—Prx, Mpp6, and Naalad2—[58–60],
were among the top upregulated proteins at day 25 compared to day 16. These proteins are predominantly foundin Schwann cells from the peripheral nervous system(PNS). In mouse models of demyelination [61] and more
recently also in spinal cord and cerebellar MS lesions[62] it has been shown that Schwann cells can migrate
to demyelinated areas and in rare contexts contribute toremyelination. The PNS myelin protein and ensheathment regulator containing EVs in EAE may be an indicator of the role of Schwann cell remyelination in the CNS
at day 25. Alongside the changes in myelin protein, we
found an increase in proteins associated with OPC differentiation, likely reflecting the induction of this reparative process. The functional relevance of the changes in
OL-derived proteins in the EAE tissue EV pool remains
unclear. In the literature OL-EVs from culture supernatants have been shown to promote axonal health andfunction [63, 64]. Further OL-EVs injected into EAE mice
suppress disease by inducing immunosuppressive monocytes and reducing autoreactive T cells [7]. However

Page 13 of 17

accumulating OL-EVs containing PLP, MAG, and MOG
might also exacerbate disease, as they have been shown
to block OPC differentiation in vitro [65].
Microglial and astrocytic proteins were also alteredthroughout EAE. Homeostatic microglial markers wereamong the top downregulated markers at both day 16
and 25, while the relative predicted microglial contribution using BRETIGEA [33] showed a sharp increase in
microglial contribution. This aligns with previous studies that have reported a higher number of microglia(and macrophage-) derived EVs in the CSF during EAE
and in pwMS [10]. The increase could be explained by ahigher EV secretory capacity of microglia in response toinflammatory stimuli, such as ATP or pro-inflammatory
cytokines in EAE. In vitro ATP treatment of microglia
induces their release of EVs, that contain inflammatory
proteins and promote the induction of neurotoxic astrocytes [66]. Similarly, treatment of microglia with IL-1β,
TNF-α, and IFN-ɣ induces their release of pro-inflammatory EVs that block OPC differentiation in vitro andremyelination in vivo through indirect effects on astrocytes [67]. Complementing these findings, homeostatic
and regenerative microglia, induced by IL-4 or co-culturing with immunosuppressive mesenchymal stem cells,
produce EVs that dampen inflammation and promoteremyelination and synaptic function [15, 67]. Our finding of reduced homeostatic microglial markers, such as
TMEM119 and P2YR12, in tissue EVs could therefore
also contribute to a disruption of these functions at day
16 and 25 of EAE. A reduction of these homeostatic
markers in EVs has also been reported in humans, in
CNS tissue-derived EVs from Alzheimer’s patients [68].
With respect to astrocytic EVs, we noted a reductionin their relative contribution to the tissue EV pool, using
cell deconvolution analysis. Possible explanations for this
could be reduced production of astrocyte-derived EVs
or faster CNS clearance or uptake. The former seems
less likely considering previous studies that have shown
increased astrocytic EV production in vitro in response
to inflammatory stimuli [69, 70]. Interestingly, supporting the latter, Dickens et al. have shown that neuroinflammation-induced astrocytic EVs rapidly cross the BBB andregulate the peripheral immune response to CNS inflammation [70]. Besides clearance across the BBB, astrocytederived EVs might also be depleted from the tissue EVpool by cellular uptake during neuroinflammation. Supporting this, EVs produced by reactive human astrocytes
in vitro show significantly higher uptake into neurons
than resting astrocyte EVs [71]. Synaptic pathology andneurodegeneration are hallmarks of MS and EAE, andwere detectable in the proteome of the EVs in our study.
Synaptic vesicle and neurotransmitter regulation-related
pathways were among the top dysregulated GO terms

Jank et al. Journal of Neuroinflammation(2024) 21:154

downregulated at both day 16 and 25 of EAE and theneuronal contribution to the EV pool was reduced in the
BRETIGEA deconvolution analysis. Our results suggestthat in EAE neuronal EV release is reduced (or neuronal
EVs are more rapidly cleared) and they contain lower
levels of synaptic proteins. Based on the physiological
function of neuronal EVs [73–79], this might contribute to the synaptic pathology and neurodegeneration in
the model. The more pronounced reduction of inhibitory synapse-associated proteins and post-synaptic proteins compared to excitatory and presynaptic proteins,is particularly noteworthy, since these imbalances havebeen described as a pattern of synaptic pathology in EAE
and MS: Inhibitory networks are disrupted earlier, andmore pronounced in EAE [80], MS and focal demyelination models [81, 82], compared to excitatory networks.
Interestingly, our findings also align with EV biomarker
studies in pwMS. Previously, we detected lower levelsof the synaptic proteins in circulating neuronal EVs ofpwMS compared to healthy controls [9]. Further, a recent
study by another group showed reduced circulating levels of neuronal EVs in pwMS compared to healthy controls [17], though in our earlier study with a comparable
cohort size, we did not see any differences [9]. Alongside
the loss in mature neuronal markers, we observed a loss
in immature neuronal markers in the EAE EVs, the latter
however normalized at day 25, indicating an increased
number of neuronal progenitors or an increase in theirsecretory activity.
Besides providing insight into the pathologicalchanges in EVs during EAE, our study is also a resource
for future studies investigating biomarkers or developing EV-based or -targeted therapies in EAE and ultimately MS. One possible approach for EV-targeted
therapies is to inhibit EV release, with the aim to prevent the spread of pathological signals contained inthem. This has shown some success in preclinical models of acute brain injury and Alzheimer’s disease [83,84]. Further knockout of A-SMase, which is required
for the shedding of microvesicles, has also been shown
to be protective in EAE. Yet these are preventative
studies and their protective effect might primarily be
explained through the dampening of EVs’ contribution
to neuroinflammation. Our data shows a shift of the EV
proteome through the course of EAE from predominantly inflammatory to attempted remyelinating andregenerating proteins, including an increase of OPC
differentiation-associated, neuronal progenitor marker
proteins and alternatively activated macrophage markers at day 25 compared to day 16. This suggests that
continuous blocking of EV release, though beneficial
in early inflammatory disease stages, might worsen

Page 14 of 17

the outcome later by inhibiting beneficial EV signaling
during the resolution of the neuroinflammation andrepair. Indeed, there is evolving literature on the protective effects of EVs subsets in neuroinflammation [7].
In future studies, it will be interesting to investigate thefunctional relevance and factors regulating the releaseof the OPC, newly-formed OLs or neuronal progenitor
protein containing EVs we characterized in our study.
Our study also provides insights into how plasma
and CSF EV biomarkers might reflect changes in CNS
tissue EVs and whether changes noted in EVs during
EAE reflect changes in EVs in pwMS. Overall, we identified several changes in the EAE tissue EV proteomealigned with changes in EV biomarkers in MS includingchanges in complement proteins, T-cell and B-cell associated proteins, and TLRs. We and others have foundincreased complement component levels in plasma
astrocyte-enriched EVs [9] and CSF EVs [39] in pwMS,
which coincides with what we now detected in EAE tissue EVs. Which cell types drive the changes in complement we detect in our spinal cord EVs however remains
unclear but could be investigated in future in light of
the new developments in cell-origin specific proteomics [45]. T- and B-cell-derived plasma EV biomarkers
include CD4, alpha1-microglobulin (Ambp), fibrinogen(Fgb), and gelsolin (Gsn) content [17]. In both plasma
EVs from pwMS (compared to healthy controls) and in
our EAE spinal cord EVs (compared to naïve mice) Gsn
was reduced, and Ambp and fibrinogen were increased.
Fibrinogen and fibronectin have also been detected
in MS CSF EVs (but not in healthy control CSF EVs)[39] and, interestingly, fibrinogen-containing EVs havebeen shown to induce CNS-reactive T cells in EAE[18]. CD4+ plasma EV numbers are increased in active
compared to stable MS [17] and in RRMS compared tohealthy controls [16]. In EAE tissue EV CD4 levels were
elevated at peak of disease compared to naïve mice andshowed some normalization at the chronic time pointcompared to peak. The changes in plasma EV TLR3 andCD14 levels in pwMS [8, 16] were also noted in EAE
EVs in our study [8, 16]. Taken together, the abovedescribed overlaps in changes in tissue EVs in EAE
and plasma/CSF EVs in MS suggest that there might
be commonalities in the role of EVs in EAE and MS
and that some changes observed in the CNS tissue EVs
might also be observed in the periphery. In the future,obtaining side-by-side data sets of plasma and tissue
EVs will give further insights into which EVs are crossing the BBB at different disease stages and whether the
BBB shows a changed selectivity to specific EV subpopulations throughout disease. This might further help
identify new circulating CNS-derived EV biomarkers.

Jank et al. Journal of Neuroinflammation(2024) 21:154

Conclusion
Spinal cord-derived EVs showed dynamic changes in
inflammatory, glial and synaptic proteins and pathways,
as well a shift in the predicted contribution of immune
and glial cell types through the course of EAE. Our findings provide insight into EV-mediated cellular communication during EAE disease processes, such as such as
CNS compartmentalized inflammation, re/de-myelination, and synaptic pathology. Further, our findings can beused as a resource to query proteins of interest in EV biomarkers or EV-targeted therapeutic studies.
Abbreviations
ANOVA	Analysis of varianceATP	Adenosine triphosphate
BBB	Blood brain barrier
BCA	Pierce bicinchoninic acid assay
BRETIGEA	BRain cEll Type specIfic Gene Expression Analysis
CFA	Complete Freund’s adjuvant
CIBERSORTx	Cell-type identification by estimating relative subsets of RNAtranscripts
CNS	Central nervous system
EAE	Experimental autoimmune encephalomyelitis
EVs	Extracellular vesicles
FDR	False discovery rate
GO	Gene ontology
iPSC	Induced pluripotent stem cells
kDa	Kilodalton
KEGG	Kyoto Encyclopedia of Genes and Genomes
LIMMA	Linear models for microarray data
MHC	Major histocompatibility complex
MS	Multiple sclerosis
NTA	Nano-tracking analysis
OL	Oligodendrocyte
OPC	Oligodendrocyte precursor cells
PBS	Phosphate buffered saline
PI/PS	Protease inhibitor and PhosphoSTOPPNS	Peripheral nervous system
PRR	Pattern recognition receptor
pwMS	People with MS
SEC	Size exclusion chromatography
TEM	Transmission electron microscopy
TLR	Toll-like receptor
TMT	Tandem mass tags

Supplementary Information
The online version contains supplementary material available at https://​doi.​
org/​10.​1186/​s12974-​024-​03147-y.
Supplementary Material 1. Supplementary figures S1–S4 with legends.
Supplementary Material 2. Proteomics dataset for EV and 10k samples.
Acknowledgements
We acknowledge the NIH High-End Instrumentation grant (S10OD021844) for
the support of the mass spectrometer.
Author contributions
PB contributed to the conception of the work. AK, TK, LJ, CN, DJ, MS, andSS conducted experiments and contributed to the acquisition of data. TA
and KWW played a pivotal role in pioneering the tissue-derived EV method
within PB’s laboratory. LJ, TK, and DCL contributed to the analyses. LJ and PB
contributed to the interpretation of the data and significantly contributed topreparing the manuscript and figures. All authors contributed to editing thefinal manuscript.

Page 15 of 17

Funding
NMSS postdoctoral fellowship to LJ. Harry Weaver Neuroscience Scholar award
from the National Multiple Sclerosis Society to PB (Grant Nos. FG-2207-40205,
JF-2007-36755).
Availability of data and materials
The datasets supporting the conclusions of this article are included within thearticle and its additional files.

Declarations
Ethics approval and consent to participate
Approved by IACUC of Johns Hopkins University.
Consent for publication
Not applicable.
Competing interests
The authors have nothing to declare.
Received: 15 April 2024 Accepted: 30 May 2024

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