Abstract

Background

   Mesenchymal stromal cell derived extracellular vesicles (MSC-EVs) are a
   promising therapeutic for neuroinflammation. MSC-EVs can interact with
   microglia, the resident immune cells of the brain, to exert their
   immunomodulatory effects. In response to inflammatory cues, such as
   cytokines, microglia undergo phenotypic changes indicative of their
   function e.g. morphology and secretion. However, these changes in
   response to MSC-EVs are not well understood. Additionally, no
   disease-relevant screening tools to assess MSC-EV bioactivity exist,
   which has further impeded clinical translation. Here, we developed a
   quantitative, high throughput morphological profiling approach to
   assess the response of microglia to neuroinflammation- relevant signals
   and whether this morphological response can be used to indicate the
   bioactivity of MSC-EVs.

Results

   Using an immortalized human microglia cell-line, we observed increased
   size (perimeter, major axis length) and complexity (form factor) upon
   stimulation with interferon-gamma (IFN-γ) and tumor necrosis
   factor-alpha (TNF-α). Upon treatment with MSC-EVs, the overall
   morphological score (determined using principal component analysis)
   shifted towards the unstimulated morphology, indicating that MSC-EVs
   are bioactive and modulate microglia. The morphological effects of
   MSC-EVs in TNF-α /IFN-γ stimulated cells were concomitant with reduced
   secretion of 14 chemokines/cytokines (e.g. CXCL6, CXCL9) and increased
   secretion of 12 chemokines/cytokines (e.g. CXCL8, CXCL10). Proteomic
   analysis of cell lysates revealed significant increases in 192 proteins
   (e.g. HIBADH, MEAK7, LAMC1) and decreases in 257 proteins (e.g. PTEN,
   TOM1, MFF) with MSC-EV treatment. Of note, many of these proteins are
   involved in regulation of cell morphology and migration. Gene Set
   Variation Analysis revealed upregulation of pathways associated with
   immune response, such as regulation of cytokine production, immune cell
   infiltration (e.g. T cells, NK cells) and morphological changes (e.g.
   Semaphorin, RHO/Rac signaling). Additionally, changes in microglia
   mitochondrial morphology were measured suggesting that MSC-EV modulate
   mitochondrial metabolism.

Conclusion

   This study comprehensively demonstrates the effects of MSC-EVs on human
   microglial morphology, cytokine secretion, cellular proteome, and
   mitochondrial content. Our high-throughput, rapid, low-cost
   morphometric approach enables screening of MSC-EV batches and
   manufacturing conditions to enhance EV function and mitigate EV
   functional heterogeneity in a disease relevant manner. This approach is
   highly generalizable and can be further adapted and refined based on
   selection of the disease-relevant signal, target cell, and therapeutic
   product.

Supplementary Information

   The online version contains supplementary material available at
   10.1186/s13036-024-00449-w.

   Keywords: Microglia, Mesenchymal stromal cell-derived extracellular
   vesicles (MSC-EVs), Morphology, Secretion, Proteomics, Lipidomics,
   Mitochondria, Neuroinflammation, Morphometry

Background

   Mesenchymal stromal cell derived-extracellular vesicles (MSC-EVs) have
   been investigated for their therapeutic potential across a wide range
   of diseases [[43]1–[44]3]. Their function is mediated through delivery
   of cargo, such as proteins, lipids and miRNAs [[45]4, [46]5]. Compared
   to the MSCs that produce them, MSC-EVs may be safer due to reduced
   risks of thrombosis and tumorigenicity [[47]6–[48]12]. Additionally,
   EVs derived from other cell types have been shown to readily cross
   physiological barriers (e.g. blood-brain-barrier) in the body due to
   their small size and targeting capability [[49]13–[50]18]. Multiple in
   vitro and in vivo studies have shown their safety and therapeutic
   efficacy in different neurodegenerative disease models [[51]4, [52]19].

   Despite these advantages, there are several challenges with using
   MSC-EVs as therapeutics. Like MSCs, MSC-EVs have well-recognized
   functional heterogeneity, which is similarly derived from differences
   in cell source (donor and tissue) and a lack of standardized
   manufacturing conditions [[53]20]. The International Society of
   Extracellular Vesicles (ISEV) criteria for EV characterization, like
   size, particle count, protein content, presence of tetraspanins (CD63,
   CD81 and CD9), and single vesicle analysis, are limited to particle
   identification [[54]21]; however, they do not reflect the bioactivity
   (i.e. functional characteristics) of EVs. Commonly used in vitro assays
   of EV bioactivity assess conditioned media (e.g. ELISA) or cell
   extracts (western blot, qPCR) from a target cell of interest in
   response to EV treatment, and essentially provide a population level
   assessment [[55]22, [56]23]. Moreover, some potency assays used for the
   assessment of MSC-EV bioactivity, like wound healing and endothelial
   tube formation assays, are helpful for understanding regeneration, but
   are not specific to a tissue or disease of interest [[57]22, [58]24].
   The performance of MSC-EVs in those assays may not be indicative of
   their function in a more tissue-specific disease context such as
   neuroinflammation. Thus, there is need for developing disease-specific
   tools to evaluate the bioactivity of MSC-EVs with a focus on treating
   neuroinflammation associated with chronic neurodegenerative diseases
   such as Alzheimer’s Disease (AD) or Parkinson’s Disease (PD).

   Microglia play a key role in the onset and progression of
   neuroinflammation and have been explored as a therapeutic target for
   small molecule drugs and, more recently, cell therapies such as MSCs
   and MSC-EVs [[59]25–[60]28]. As resident immune cells of the brain,
   microglia have been widely studied because of their role in homeostasis
   and disease pathology of the central nervous system (CNS) [[61]29].
   Synaptic pruning, phagocytosis, remyelination and maintenance of
   vasculature are some of their key functions [[62]26]. Furthermore, they
   are phenotypically plastic, which is reflected by their morphological
   response to different microenvironmental cues (e.g. inflammatory
   cytokines). For instance, in patients that have suffered a stroke or
   have AD, there is a higher density of microglia that possess reduced
   number of processes and an increased cell body size [[63]30, [64]31].
   Studies have shown MSC-EV can modulate microglial secretion of
   pro-inflammatory cytokines, like IL-6, IL-1β and TNF-α, which are
   involved with neuroinflammation [[65]32]. Nevertheless, microglia
   exhibit significant heterogeneity at the single cell level and thus
   changes in production of individual secreted factors (or a limited
   panel) may be insufficient to fully represent the complex mechanisms of
   action of MSC-EVs [[66]33–[67]35]. There have also been no studies
   demonstrating the effects of MSC-EVs on human microglia, as the
   entirety of the literature has used primarily non-human cell-lines,
   like primary rat/mouse cells, immortalized BV2 and N9 cell lines, among
   others [[68]19, [69]32, [70]36]. It is important to consider
   species-dependent differences in microglial response to MSC-EVs, as
   these differences could provide misleading interpretations of MSC-EV
   mechanism of action especially when in vivo animal models of
   neurodegenerative diseases do not fully capture key aspects of their
   analogous human disease [[71]37, [72]38]. Moreover, despite the strong
   evidence that their morphology is associated with function [[73]33,
   [74]34], morphological profiling has not been applied to elucidate the
   bioactivity of cell-based therapies in the context of
   neuroinflammation.

   Inspired from previous work [[75]39–[76]41], we developed a
   high-throughput, single-cell based microglia morphology assay as a
   readout of MSC-EV bioactivity that could inform their therapeutic
   potential. We comprehensively profiled the morphological response of an
   immortalized human microglia cell-line [[77]42] upon stimulation with
   pro-inflammatory cytokines that are involved in neuroinflammation:
   IFN-γ (interferon-gamma) and TNF-α (tumor necrosis factor-alpha)
   [[78]43–[79]45]. The activated microglia were then treated with MSC-EVs
   to assess the overall change in morphology. Phenotypic assessment of
   microglia was carried out using proteomic and lipidomic profiling
   following cytokine stimulation and upon MSC-EV treatment. This
   comprehensive approach of single cell morphological analysis combined
   with tools reflecting overall microglial phenotypic changes enabled us
   to demonstrate morphology as a robust, high-throughput outcome
   indicative of MSC-EV bioactivity. This assay can be further employed to
   screen different MSC cell-lines, EV manufacturing conditions and
   potentially elucidate their mechanism of action in the context of
   neuroinflammation.

Methods

MSC-EV manufacturing

   A vial of cryopreserved, previously expanded, bone marrow-derived MSCs
   (donor RB71, additional donor information in Table [80]S1) was thawed
   in RoosterBio growth medium (RGM) (RoosterNourish-MSC-XF [KT-016]
   containing RoosterBasal™-MSC (SU-005/SU-022) + RoosterBooster™-MSC-XF
   (SU-016)) on Day 0. On Day 3, ~ 90% confluency, cells were split into 6
   T-175 flasks (Greiner Bio-One, Cat#660175) at a seeding density of
   1 × 10^6 cells/flask in RGM. The media was supplemented with RB
   replenish (RoosterReplenish-MSC-XF [SU-023]) 24 h post cell seeding
   (Day4). At ~ 90% confluency (Day 6), spent media was aspirated and
   cells were washed once with sterile PBS-/- (Corning, Cat#21-040-CM)
   followed by addition of Rooster EV Collect media (RoosterCollect™-EV
   (M2001)) (Day7). The conditioned media was collected after 48 h (Day9)
   for MSC-EV isolation.

   MSC-EVs were isolated using a modified 2-step ultracentrifugation
   method [[81]46]. The media collected from 3 flasks (25mL/T-175flask)
   was filtered through a 0.2 μm filter (VWR, Cat#10040-464) followed by
   first round of ultracentrifugation (Thermo Scientific™ Sorvall™ WX
   ultraCentrifuges, Rotor = F37L-8 × 100–6202741) at RCFmax = 133,900 x
   g, for 1 h @ 4℃. Each ultracentrifugation tube contains a maximum of
   65mL spent media. The supernatant from this spin was discarded and the
   pellet was resuspended in cold PBS+/+ (Corning, Cat#MT21030CM)
   (4mL/tube) and collected in microultracentrifuge tubes and spun at
   RCFmax = 140,000 x g for 1 h @ 4℃. The supernatant was discarded, and
   the pellet was resuspended in a total volume of 2mL PBS+/+. To mitigate
   any potential loss of MSC-EV bioactivity due to long-term storage and
   ensure consistency across all experiments, we used freshly prepared
   (i.e. never frozen) MSC-EVs for all experiments [[82]47–[83]49].

MSC-EV characterization

Protein: Micro-BCA assay

   The micro-BCA assay (ThermoFisher, Ca#23235) was used to quantify the
   amount of protein in our MSC-EV batch. We quantified the protein using
   1X, 2X and 4X dilutions of the MSC-EV batch across different EV batches
   to ensure consistent manufacturing. 75µL of sample was combined with
   75µL of the reagent mixture, uniformly mixed, and incubated for 2 h in
   37℃ (higher dilutions are diluted in PBS + + to match the 1X sample
   volume). The 96-well plate was read in a plate reader @562nm to
   quantify absorbance.

Size, count and surface marker: NanoFCM

   MSC-EV size, concentration and tetraspanin surface marker expression
   (CD63) were assessed using the Flow Nanoanalyzer (NanoFCM, Inc.,
   Xiamen, China). 1:100 dilution of MSC-EVs was used for assessment of
   MSC-EV size and concentration. MSC-EVs were stained with 20µM CFSE
   (BioLegend, Cat#423801) for 30 min at 37 °C to indicate the presence of
   an intact lipid bilayer. FITC (10/50mW, 488) channel was used to assess
   %positive staining. CD63 (1:2000, BDBiosciences, Cat#561983) antibody
   was incubated with MSC-EV samples for 2 h at room temperature (RT)
   prior to assessment. Surface marker intensity was measured using the
   APC channel (20/100mW, 638). Representative flow gating strategy for EV
   size, count, CFSE, and CD63 assessment can be found in Figure [84]S1.

Microglia cell-line

   C20 cells, an immortalized human cell-line, were generously provided by
   the Kim lab from Kent State University. The growth media consists of
   DMEM-F12 1:1 (Gibco, Cat#11320033), 10% FBS (Neuromics, Cat#FBS008,
   Lot#218H19), 1% PenStrep (Gibco, Cat#15070-063) and 0.2% normocin
   (InvivoGen, Cat#ant-nr-1). C20s were thawed in growth media containing
   1% N2 supplement (Gibco, Cat#17502048) additionally for efficient
   recovery + 1 µM dexamethasone (Millipore sigma, Cat#D4902). The cells
   were recovered post thaw for 48 h, reaching ~ 90% confluence, followed
   by harvesting and seeding for experiments.

Microglia morphology assay

   For morphology assays, cells were seeded in a Poly-D lysine
   hydrobromide (PDL) (Sigma-Aldrich, Cat#P6407) coated 96-well plate
   (Corning, Cat#3603; Greiner Bio-One, Cat #655090) at a density of 480
   cells/well (1500cells/cm^2) with 100µL of media/well. PDL coating aids
   in better adhesion of the cells preventing cell loss due to washing
   during staining. Post 24 h of seeding, cells were stimulated (+ CTL)
   using half media change with a cytokine cocktail of 5 ng/mL IFN- ү
   (Gibco, Cat# PHC4033) + 5 ng/mL TNF-α (Sino Biological,
   Cat#10602-HNAE). Treatment groups received an additional 10 µL of
   MSC-EV preparation (+ CTL + EVs). After 24 h, cells were fixed, stained
   and imaged.

Cell staining and imaging

   The morphology plates were stained using a modified version of the Cell
   Painting protocol [[85]50]. The mitochondria were stained in live cells
   using Mitotracker Deep Red (Invitrogen, Cat#[86]M22426) @ 10 ng/mL
   concentration for 30 min at 37℃. Cells were then fixed using 4% PFA
   (Electron Microscopy Sciences, Cat#15714-S) for 30 min and washed 2X
   with PBS-/- (Corning, Cat#21-040-CM) or 1X HBSS (Gibco, Cat#14065056).
   Further staining was carried out using HBSS 1X + 1%(w/v) Bovine Serum
   Albumin (BSA) (Sigma-Aldrich, Cat#A9418) + 0.1% Triton filtered through
   a 0.22 μm filter. Wheat Germ Agglutinin, Alexa Fluor™ 555 Conjugate
   (WGA, 1.5 µg/mL) (Invitrogen, Cat#[87]W32464) Phalloidin/AlexaFluor 568
   conjugate (8.25nM) (Invitrogen, A12380) and Hoechst(10ug/mL)
   (Invitrogen, Cat#H3570) were added for 30 min protected from light. The
   wells were washed three times with 1X HBSS and imaged using a 10X
   objective with a BioTek Cytation5 (Agilent) automated microscope. A 6
   by 6 montage (36 total images) was captured for each replicate well
   with 6–8 technical replicate wells used for both control and EV
   treatment groups in each experiment.

Image analysis

   Single cell analysis morphological profiling was carried out with
   CellProfiler [[88]51] using a custom pipeline (Supplemental File
   [89]F1) The morphological data weas passed through a series of
   processing steps. First, we combined all the channel files for cell,
   cytoplasm and nucleus resulting in a larger merged dataset. The
   experimental conditions were then added to the merged file which was
   further processed to add features like aspect ratio (major: minor axis
   length) and perimeter: area ratio. Poorly segmented outliers and/or
   debris were eliminated using cut off parameters like solidity = 1 and
   nucleus: cytoplasm ratio > = 0.85. Following image preprocessing,
   medians were calculated per well from the single cell data consisting
   of thousands of cells to ensure any other outlier effects were
   minimized. This median data was further normalized using min-max
   normalization where the average of negative controls(-CTL) was used as
   the min value and the average of positive controls (+ CTL) represented
   the max value (Supplemental File [90]F2). We then plot different
   features as well as perform principal component analysis (PCA) [[91]52,
   [92]53] on 21 cellular and nuclear features to assess the overall
   changes in microglia morphology (Supplemental File [93]F3).

Microglia secretion profile assessment

   For secretion assays, C20 cells were seeded at a higher density of 6000
   cells/well (18,750 cells/cm^2) in a 96-well plate with 100µL of media.
   The stimulation and treatment strategy were identical to the morphology
   assay (concurrent cytokine and EV treatment) followed by collection of
   the spent media from each well after 24 h. The collected media was
   stored at -80℃ and then shipped to RayBiotech Inc. on dry ice for
   screening a panel of 200 targets (Human Cytokine Array Q4000,
   QAH-CAA-4000).

Microglia lipidomic profile assessment

   C20 cells were seeded at a density of 10^5 cells/well in a 6-well plate
   in 2mL of media. The timeline and treatment procedure was the same as
   morphology experiments with a half media change for stimulation and
   treatment carried out 24 h post seeding. 24 h post treatment, cells
   were washed 3X with 1mL cold sterile PBS-/- (Corning, Cat#21-040-CM)
   and harvested with 300µL of TrypLE (Gibco, 12604-013) for 5 mins in
   37℃, neutralized with 1.5mL of growth media and spun at 490 g for
   7 min. The supernatant was aspirated and the pellet was resuspended in
   200µL of extraction solvent (80% methanol solution (MeOH)). The
   suspension was then flash frozen in liquid nitrogen and stored in -80℃
   freezer for further processing.

   Lipids were obtained from cell samples using a biphasic extraction
   method and identified using LC-IM-MS/MS. Sample preparation for LC-MS
   was carried out using the methods from Hewelt-Belka W et al. [[94]54].
   In brief, pellets were resuspended in 0.5-1mL of water or 4:1 MeOH
   /H20, vortexed briefly and transferred to a 5mL glass centrifuge tube.
   Blank was prepared similarly. All samples were sonicated and placed in
   an ice bath for 30 min followed by addition of 2mL 1:2 chilled
   chloroform/methanol solution. The samples were then vortexed on and off
   for 5 min with storage on ice between vortex cycles. 0.5mL of chilled
   chloroform followed by 0.5mL of chilled water was added to the samples
   and vortexed for 60 s. The samples were centrifuged for 10 min at 4 °C
   and 3,500 g. The bottom organic layer was transferred to a new 5mL
   glass tube followed by drying in a vacuum concentrator (Savant,
   ThermoScientific). Dried extracts were reconstituted with 500µL of 1:1
   chloroform/methanol solution. Using pasteur pipettes, extracts were
   transferred to 2mL glass vials and stored at -80 °C. For mass
   spectrometry sample preparation, each extract was prepared as a 2.5X
   dilution in HILIC A (95:5 ACN/H[2]0, 10mM ammonium acetate), with 10µL
   of each sample combined for QC. Mass spectrometry was carried out using
   the Waters Synapt XS instrument with a 100 mm BEH-HILIC column with
   HILIC A (95:5 ACN/H2O, 10 mM Ammonium Acetate) and HILIC B (50:50
   ACN/H2O, 10 mM Ammonium Acetate) used as mobile phases [[95]55].
   Samples were collected for 7 min. Data were collected using MassLynx
   and processed with Progenesis QI, EZInfo, and Lipostar software. Lipid
   pathway enrichment analysis was carried out using LIPEA [[96]56].

Microglia cellular proteomics analysis

   C20 cells were seeded at a density of 10^5 cells/well in a 6-well plate
   in 2mL of media. The timeline and treatment procedure were the same as
   morphology experiments with a half media change for stimulation and
   treatment carried out 24 h post seeding. 24 h post treatment, cells
   were harvested with 300µL of TryplE (Gibco, 12604-013) for 5 mins in
   37℃, neutralized with 1.5mL of growth media and spun at 490 g for
   7 min. The pellets were resuspended in sterile PBS-/- (Corning,
   Cat#21-040-CM) for a wash followed by the spin settings mentioned
   earlier. The supernatant was aspirated and cell pellets were flash
   frozen in liquid N2 and stored in -80 C. The tubes were then
   transported to the core facility on dry ice.

   Approximately half a million cells were lysed in a lysis buffer
   containing 5% SDS, 50 mM TEAB (pH 8.5) and 1X protease and phosphatase
   inhibitor cocktail and MgCl2 (2mM) at 95 °C for 15 min. The lysate was
   incubated in 100 units of benzonase for 5 min at room temperature. The
   lysate was centrifuged at 21,000 g for 10 min and the supernatant was
   collected. Protein concentration was estimated by nanodrop and 50 µg of
   protein was used for tryptic digestion. Protein concentration was
   adjusted to contain 50 µg in 23µL of lysis buffer and 1µL of 100 mM
   TCEP was added to the lysate and incubated at 55 °C for 15 min. Sample
   was alkylated by adding 1µL of Iodoacetamide (200 mM) and incubating in
   dark at room temperature for 15 min. Sample was acidified by adding
   2.5µL of phosphoric acid (55%) to the tube. The sample was transferred
   to S-traps after mixing it with 165µL of S-trap binding/wash buffer
   (90% methanol in 100 mM TEAB). S-trap column was placed in a collection
   tube and centrifuged at 4000 g for 1 min to bind the protein to the
   resin. Protein was washed thrice with 150µL of S-trap binding/wash
   buffer. The proteins were digested by adding 2.5 µg (1:20) of trypsin
   in 20µL of 50mM TEAB to the trap and incubating it at 37 °C for
   overnight. Tryptic peptides were eluted by adding 40µL of elution
   buffer1 (50 mM TEAB) and spinning the trap at 4000 g. Elution was
   repeated with elution buffer 2 (0.1% Formic Acid) and elution buffer 3
   (50% Acetonitrile). The pooled eluate was dried in speedvac. Samples
   were then reconstituted in 20µL of 0.1% formic acid (FA). To facilitate
   peptide resuspension, tubes were incubated in a thermomixer at 2000 rpm
   and 37℃ for 10 min and centrifuged at 21,000 g for 10 min at room
   temperature. 10µL of digested peptides were carefully transferred from
   the top of the microfuge tube to the sample vial. A 1 µg equivalent of
   peptide was subjected to LCMS/MS analysis on Orbitrap Q-Exactive Plus
   mass spectrometer (Thermo Scientific) in conjunction with Dionex
   UltiMate3000 Ultra High-Performance Liquid Chromatography (Thermo
   Scientific) system. An externally calibrated Thermo Q Exactive Plus
   (high-resolution electrospray tandem mass spectrometer) was used in
   conjunction with Dionex UltiMate3000 RSLCnano System. A 2µL sample was
   aspirated into a 50µL sample loop and loaded onto the trap column
   (Thermo µ-Precolumn 5 mm, with nanoViper tubing 30 μm i.d. × 10 cm).
   The flow rate was set to 300nL/min for separation on the analytical
   column (Acclaim pepmap RSLC 75µM × 15 cm nanoviper). Mobile phase A was
   composed of 99.9% H2O (EMD Omni Solvent), and 0.1% formic acid and
   mobile phase B was composed of 99.9% ACN, and 0.1% formic acid. A
   120-minute step gradient from 3 to 45% B was performed. The LC eluent
   was directly nanosprayed into Q Exactive Plus mass spectrometer (Thermo
   Scientific).During the chromatographic separation, the Q Exactive plus
   was operated in a data-dependent mode and under direct control of the
   Thermo Excalibur 3.1.66 (Thermo Scientific). The MS data were acquired
   using the following parameters: 20 data-dependent
   collisional-induced-dissociation (CID) MS/MS scans per full scan (400
   to 1800 m/z) at 70,000 resolution. MS2 were acquired at 17,500
   resolution. Ions with single charge or charges more than 7 as well as
   unassigned charge were excluded. A 10 s dynamic exclusion window was
   used. All measurements were performed at room temperature. Resultant
   Raw files were searched with Proteome Discoverer 3.0 using SequestHT as
   the search engine using human proteome FASTA database downloaded from
   Uniprot. Percolator was used as peptide validator.

Gene set variation analysis

   To study functional impact of proteomic changes within each
   experimental group, gene set variation analysis (GSVA) was conducted.
   GSVA is an unsupervised enrichment algorithm which identifies
   variations of pathway activity by defining enrichment score for gene
   sets which each contain a set of genes that share same cellular
   function. To enable GSVA, we used the Molecular Signatures Database C2
   [v7] gene sets (MSigDB) [[97]57], which represents cell states and
   perturbations within the immune system. The gsva R package (available
   on Bioconductor) [[98]58] was used to compute enrichment of immune
   related signatures within each group. To evaluate statistical
   differences between groups for each pathway of interest, we employed
   ANOVA followed by Tukey post-hoc test, then Benjamini–Hochberg false
   discovery rate (FDR) adjustment for multiple comparisons were used. FDR
   adjusted p-values ≤ 0.05 were considered significant (Supplemental File
   [99]F4).

Hierarchical clustering for heatmap

   Data were z-scored for each protein across samples and clustered using
   the Euclidian distance metric and the Ward. D2 agglomeration method.

Differential abundance analysis

   Differential abundance analysis was conducted using the limma package
   in R [[100]59]. Treatment was used as the variable in the design
   matrix. Significance level (p-value, linear model) and fold changes
   were computed using the lmFit() function. |Fold Change|≥2 and p < 0.05
   were considered to be significant (Supplemental File [101]F5).
   Differentially Abundant Proteins (DAPs) were displayed as volcano plots
   using EnhancedVolcano R package [[102]60], available through
   Bioconductor.

Gene ontology enrichment analysis

   Gene Ontology (GO)_was conducted for differentially abundant proteins
   using the PANTHER overrepresentation test with PANTHER 18.0 through the
   Gene Ontology resource [[103]61]. The homo sapiens GO biological
   processes complete annotation set was used with Fisher’s exact test,
   followed by Benjamini–Hochberg false discovery rate (FDR) adjustment to
   compute significance of the biological processes linked with
   differential abundant proteins. The reference list consisted of all
   detectable proteins in our dataset after filtering.

Statistical analysis

   Graphpad Prism 9 was used to perform all univariate statistical tests.
   Each figure legend describes the tests used for the respective
   experiment.

Results

Microglia increase in size and complexity upon stimulation with cytokines

   Upon stimulation with IFN-ү and TNF-α (5 ng/mL each, +CTL), we observed
   significant changes in microglia morphology (Fig. [104]1), including
   perimeter, form factor and major axis length (Fig. [105]1B). Increase
   in perimeter and major axis length illustrate the characteristic
   increased cell size and elongation of + CTL cells compared to -CTL
   (unstimulated) cells. +CTL cells were more ‘complex’ as indicated by
   reduced form factor, a measure of the roundness of the object (form
   factor for circle = 1) [[106]51].

Fig. 1.

   [107]Fig. 1
   [108]Open in a new tab

   Microglia morphology changes upon activation with cytokines. (A)
   Representative images of C20 microglia in -CTL group (unstimulated) and
   +CTL group (stimulated with 5ng/mL each of IFN-γ and TNF-α). Actin,
   Golgi & plasma membrane (PM) (Red, Phalloidin/WGA), Nuclei (Blue,
   Hoechst): Scale bar=100μm (B) Individual cellular features (i)
   Perimeter, (ii) Form Factor and (iii) Aspect Ratio and (iv) Principal
   component 1 (PC1[morpho]) calculated using 21 features. Each point is a
   median of ~700-1000 cells/well with mean and standard deviation plotted
   for n=8 wells per group. *p<0.01 vs -CTL for all groups conducted using
   unpaired t-test with Welch’s correction. (C, D) Experimental and
   Operator variability: Microglia controls (-CTL vs +CTL) are
   significantly different across multiple experiments and operators.
   p<0.001 vs +CTL for all experiments using 2-way ANOVA with Sidak’s
   multiple comparisons test. (E) Z-factor for PC1[morpho] is > 0.5
   (dotted line) for 7/8 experiments

   To better account for the contribution of each feature to the overall
   morphology (and considering that a single feature does not fully
   encompass each microglia’s morphological response), we used PCA to
   effectively create a composite overall morphological score, as
   previously shown using MSCs (40,41). The first principal component
   (PC1[morpho]) accounted for 83% of the variance in the dataset and thus
   was used to represent an overall composite morphological response of
   microglia (Figure [109]1B(iv)). This trend across individual parameters
   as well as PC1[morpho] is consistent across multiple experiments (n=5)
   as shown in supplementary Figure [110]S2.

Demonstration of morphological assay reproducibility and robustness

   We quantified the reproducibility and robustness of our morphological
   assay based on experiment-experiment variability and different assay
   operators, respectively. In Fig. [111]1C, we showed a consistent change
   in overall microglia morphology between operators O1 and O2 for 4
   independent experiments. Despite some observed experimental variability
   within a given operator the differential morphological response
   (indicated by ∆PC1[morpho], Fig. [112]1D) is not significantly
   different between experiments or operators. Moreover, for high
   throughput screening assays, a common metric of assay quality is the
   Z-factor (Z’) [[113]62]. Used extensively in drug screening, it is
   calculated using the following formula:
   graphic file with name M1.gif

   Where,

   σ = standard deviation.

   c + = positive control (+ CTL).

   c- = negative control (-CTL).

   µ = mean.

   Across all but 1 of our experiments (Fig. [114]1E), the Z-factor
   calculated using PC1[morpho] values is > 0.5. Based on Z’ value
   classification, an assay with a Z’> 0.5 corresponds to an optimized
   assay with a desirable dynamic range [[115]62].

Microglial lipid content increases upon stimulation

   Nine different classes of lipids were identified (Fig. [116]2A),
   including phosphatidylcholines (PCs), phosphatidylethanolamine (PE),
   phosphatidylglycerols (PGs), plasmalogen phosphatidylethanolamines
   (PE-Ps), sphingomyelins (SMs), Ceramides (Cer), Hexosylceramides
   (HexCer), triglycerides (TGs) and lysophosphatidylcholines (LPCs).
   Additionally, a higher number of individual lipids were detected for
   PCs, PEs and PGs compared to other lipid classes. An increase in all
   lipid classes except LPCs was observed upon microglia stimulation,
   which may be attributed to the increased cell size. Increased lipid
   accumulation has been linked to pro-inflammatory microglia with
   compromised ability to perform critical functions like phagocytosis
   [[117]63, [118]64]. Lipid pathway enrichment analysis (LIPEA) [[119]56]
   was used to identify the various pathways associated with the increased
   lipid content. Ferroptosis and sphingolipid pathways, which have been
   associated with neurodegeneration [[120]65–[121]67], were enriched post
   microglia stimulation (Fig. [122]2B).

Fig. 2.

   [123]Fig. 2
   [124]Open in a new tab

   Microglia lipid content increases upon activation with cytokines. (A)
   Overall cellular lipid content across different lipid classes increases
   following stimulation (+CTL). Lipid classes identified using negative
   mode (A) and positive mode (B) ionization. Each lipid species is
   significantly different between –CTL and +CTL (p<0.05) except LPC
   (denoted by ‘ns’) using unpaired t-test with Welch’s correction. (C)
   Pathways ordered by p-value significance (1-log10(p-val)) with
   significantly enriched pathways above red horizontal line at p-value
   cutoff = 0.05

Activated microglia secrete factors that relate morphology with inflammation

   Across both unstimulated (-CTL) and stimulated (+ CTL) microglia, we
   detected quantifiable secretion of 94 cytokines, chemokines and growth
   factors from a 200-plex antibody array panel (Fig. [125]3A). Of the 94
   targets (Table [126]S2), 40 of them were significantly different
   between controls with increased levels of 38 proteins and decreased
   levels of only 2 proteins following cytokine treatment. We conducted
   pathway analysis using the 40 significantly different proteins as an
   input to the STRING database (Fig. [127]3B). The map shows the physical
   and functional interactions between all the proteins (nodes) that were
   increased (red) and decreased (green) upon stimulation. An overview of
   proteins differentially secreted between -/+CTL groups ordered in terms
   of the number of connected node degrees with other proteins in this
   network (Table [128]S3) is shown in Fig. [129]3C. Notably, the
   cytokines/chemokines IL-6, CXCL8 and CCL2 had the highest number of
   connections and were secreted at much higher levels in + CTL groups vs.
   -CTL microglia.

Fig. 3.

   [130]Fig. 3
   [131]Open in a new tab

   Effects of cytokine stimulation on microglia secretion. (A) Heatmap
   representing microglia proteins that change following IFN-γ/TNF-α
   stimulation (-CTL vs. + CTL). Two-way hierarchical clustering using
   Ward method. (B) STRING database generated network map of proteins
   increased (red) and decreased (green) after stimulation (+ CTL) and
   their predicted functional associations (edges). Thicker and darker
   edges (line connecting 2 proteins), implies higher level of confidence
   in the interactions and correlation between the proteins
   ([132]http://version10.string-db.org/help/faq/). (C) Proteins that are
   significantly different between controls and arranged in descending
   order of their number of functional associations (node degrees).
   p < 0.05 using unpaired t-test with Welch correction for all proteins.
   (D) Pathways associated with changes in microglia secretion post
   stimulation

   Using the entire proteome as a background, we performed pathway
   enrichment analysis (based on Gene Ontology (GO) Terms) using STRING to
   identify potential disease-relevant pathways of microglia associated
   with changes in secretion and morphology. The entire list of pathways
   is available in Table [133]S4 with a curated subset of pathways of
   interest shown in Fig. [134]3D. These pathways were selected based on
   categories relevant to observed morphological response (i.e. cell
   adhesion/motility), cell metabolism, regulation and activation of
   cells, as well as specific signaling pathways. Of note, many of these
   pathways involve microglia regulation of other cells involved in
   neuroinflammation such as T cells, macrophages, and astrocytes through
   processes such as chemotaxis and growth/survival [[135]68, [136]69]. We
   also performed enrichment analysis using our 200plex panel as a
   background, but no pathways were significantly enriched; however, this
   is not unexpected as this is a preselected, focused panel of
   chemokines/cytokines/growth factors that are commonly produced by
   microglia both in vitro and in vivo.

MSC-EV treatment modulates microglia morphological response to cytokines

   MSC-EV treatment prevents the morphological shift of stimulated
   microglia by essentially inhibiting this cytokine-induced change in
   morphology of increased size/complexity (Fig. [137]4A). Morphological
   features were normalized based on controls (using max-min approach) to
   reflect the relative morphological change after MSC-EV treatment. With
   concurrent MSC-EV treatment, the microglia are effectively smaller and
   less complex compared to + CTL microglia as reflected by differences in
   the normalized features like perimeter, aspect ratio and form factor,
   respectively (Fig. [138]4B). The overall change in morphology assessed
   using composite morphological score PC1[morpho] (72.2% variance)
   indicates a shift in the morphology of MSC-EV treated microglia towards
   the -CTL microglia. This trend was observed across multiple experiments
   (using independently manufactured MSC-EV batches from the same
   cell-line to ensure consistency), as shown in Figure [139]S3.
   Additionally, a dose-dependent response of MSC-EVs can be seen both in
   response to different concentrations of IFN-γ and TNF-α (Figure
   [140]S4) as well as different doses of MSC-EVs at fixed concentration
   of 5ng/mL IFN-γ and TNF-α (Figure [141]S5).

Fig. 4.

   [142]Fig. 4
   [143]Open in a new tab

   Stimulated microglia morphology changes upon treatment with MSC-EVs.
   (A) Representative images of C20 microglia cells in -CTL group
   (unstimulated), +CTL group (stimulated with 5ng/mL each of IFN-γ and
   TNF-α) and + CTL + EVs (stimulated with 5ng/mL each of IFN-γ and
   TNF-α + treated with MSC-EVs). Actin, Golgi & plasma membrane (PM)
   (Red, Phalloidin/WGA), Nuclei (Blue, Hoechst), Scale bars = 100 μm
   (columns 1–3 images) and 50 μm (column 4 images). (B) Individual
   min-max normalized cellular features (i) Perimeter, (ii) Major Axis
   Length and (iii) Form Factor. (iv)Normalized principal component
   1(PC1[morpho]) calculated using 21 min-max normalized features
   represents 72.2% of the variance. Each point is a median of ~ 700–1000
   cells/well. Bar indicates the mean of the 8 wells/group. *p < 0.001
   vs. + CTL for all groups conducted using Brown-Forsythe and Welch
   one-way ANOVA with multiple corrections using Dunnett T3 testing

Microglial lipid content does not change in response to MSC-EVs

   Despite the changes in morphology, no changes in lipid content were
   observed in response to MSC-EV treatment (Fig. [144]5). The detected
   amounts were unaltered between different classes of lipids across the
   stimulated (+ CTL) and the MSC-EV treated groups (+ EVs). Therefore, no
   pathway enrichment analysis was carried out. However, the PCA plot
   shows overall changes in the lipid profiles (using lipid feature
   intensities instead of annotated lipids as input to PCA) between
   activated and MSC-EV treated cells (Figure [145]S6) suggesting other
   unidentified/unannotated lipids may be different between the 2 groups.

Fig. 5.

   [146]Fig. 5
   [147]Open in a new tab

   Microglia lipid content remains unchanged with MSC-EV treatment.
   Similar amount of lipids observed between the stimulated (+ CTL) and
   MSC-EV treated (+ EVs) microglia. Lipid classes identified using
   negative mode (A) and positive mode (B) ionization. P > 0.05 vs. + CTL
   for all lipids calculated using unpaired t-test with Welch’s correction

Mitochondrial morphology changes in response to MSC-EV treatment

   Overall changes in mitochondrial morphology were assessed using PCA on
   225 mitochondrial features across different treatment conditions
   (Fig. [148]6A, D&E). PC1[mito] captures the maximum variance in data
   (58.1%) using features including the intensity, distribution and
   texture of the mitochondria (stained using Mitotracker Deep Red,
   features listed in Table [149]S5). In accordance with the cellular
   morphology, the mitochondrial morphology is also significantly
   different between unstimulated (-CTL) and stimulated (+ CTL) microglia
   (*p < 0.05, Fig. [150]6B, C) across 4 independent experiments using 2
   different cell culture plate-types. Additionally, MSC-EV treatment
   (+ EVs) results in a greater significant change in mitochondrial
   morphology in 2 out of 4 experiments where notably the type of tissue
   culture plates may impact the significance of the observed changes with
   more consistent shift (i.e. decrease in PC1[mito]) when using plates
   with µClear^® film bottom (Fig. [151]6C).

Fig. 6.

   [152]Fig. 6
   [153]Open in a new tab

   Microglia mitochondrial morphology altered by MSC-EV treatment. (A)
   Representative image of microglia mitochondria (Cyan) used to calculate
   single-cell features. Principal component analysis indicates maximum
   variance in PC1[mito] (58.1% variance) (B &C) PC1[mito] used as
   composite mitochondrial morphological score to assess effects with
   (+ CTL) and without (-CTL) cytokine stimulation and concurrent
   treatment with MSC-EVs (+ EVs). Assay conducted on Costar Cat#3603 (B)
   and Greiner Cat#655090 (C) 96-well plates, respectively. *p < 0.05 vs.
   -CTL and #p < 0.05 vs. + CTL conducted using Brown-Forsythe and Welch
   one-way ANOVA with multiple corrections using Dunnett T3 test. (D &E
   )Representative images of mitochondria from each experimental group.
   Actin, Golgi & plasma membrane (PM) (Red, Phalloidin/WGA), Nuclei
   (Blue, Hoechst) and Mitochondria (Cyan, Mitotracker Deep Red (D) &
   grayscale (E). Scale: 50 μm

MSC-EVs modulate the secretion profile of stimulated microglia

   Using the same 200plex antibody array, we profiled microglia secretome
   in response to MSC-EV treatment. PCA (Fig. [154]7A) was conducted using
   24 proteins (Fig. [155]7B) that were significantly different between
   + CTL and + EV groups. Distinct clusters were apparent for all 3
   experimental groups using a plot of principal components PC1[secretion]
   (51.8%) and PC2[secretion] (36.5%) that accounted for the majority of
   data variance. Changes in overall secretome reflected by PC1[secretion]
   suggest a different (‘alternate’) response of microglia to EVs as
   compared to the morphological, mitochondria, and lipidomic responses.
   Notably, PC1[secretion] scores for the + EV group were significantly
   different from both -CTL and + CTL, but these controls were not
   different from each other. PC2 [secretion] scores, on the other hand,
   exhibited the previously observed behavior of MSC-EVs inhibiting the
   microglia response (in this case secretion). Figure [156]7C shows the
   pathway analysis conducted using the STRING database on the subset of
   24 proteins differentially regulated by MSC-EVs. Secretion of 14
   proteins decreased (green nodes) and 10 proteins increased (red nodes)
   in response to MSC-EV treatment (Fig. [157]7C(i)). These proteins were
   found to be linked with some of the previously identified metabolic and
   cell migration pathways in control groups (Fig. [158]7C(ii)). Moreover,
   new functional pathways related to regulation of inflammation and
   EV-mediated responses (e.g. regulation of cytokine/chemokine
   production, regulation of angiogenesis) were identified. These
   differentially regulated proteins were also found to be involved in a
   number of non-microglia cell pathways such as T cell proliferation,
   neurogenesis, and immune cell migration (e.g. macrophages, natural
   killer cells and T cells). A complete list of all enriched pathways,
   including the differentially secreted proteins associated with each
   pathway, can be found in Table [159]S6.

Fig. 7.

   [160]Fig. 7
   [161]Open in a new tab

   Microglia secretion profile is altered post MSC-EV treatment. (A)
   Individual clusters of -CTL (blue), +CTL (red) and + EVs (green)
   observed with PCA conducted using 24 proteins that were different
   between + CTL and + EVs. PC1[secretion] (51.8% variance) and
   PC2[secretion] (36.5% variance) account for 88.3% of total variance.
   (B) Observed changes in secretion of individual proteins (14 decreased,
   10 increased) with concurrent MSC-EV treatment. *p < 0.05 conducted
   using unpaired t test with Welch correction. (C(i)) STRING database
   generated network map for group of proteins decreased (green) and
   increased (red) after MSC-EV treatment and their predicted functional
   associations (edges). Thicker and darker edges (line connecting 2
   proteins), implies higher level of confidence in the interactions and
   correlation between the proteins
   ([162]http://version10.string-db.org/help/faq/). (C(ii)) Pathways
   associated with changes in microglia secretion following MSC-EV
   treatment

Alterations in microglia proteome in response to MSC-EVs

   Using LC-MS/MS, 2662 proteins were identified across the 3 experimental
   groups -CTL, +CTL and + EVs. Figure [163]8A is a heatmap representation
   of z-scored expression of 2662 proteins across three different
   experimental groups suggesting distinct proteomic profile across
   groups, clustered based on their Euclidian distance. PCA revealed
   separation of experimental groups into distinct clusters based on their
   overall cellular proteomic profile (Fig. [164]8B). Like the overall
   secretion profile (PC1[secretion], Fig. [165]7A), PC1[proteome] (34.2%
   variance) suggested an alternate proteomic profile for the MSC-EV
   treated microglia compared to both controls. PC2[proteome] (26.9%
   variance) corresponded to shift in proteomic profile towards the -CTL
   in response to MSC-EVs similar to the overall morphological response.
   Of the 2662 proteins, we identified 687 differentially abundant
   proteins (DAP) where 258 proteins were upregulated and 429 proteins
   were downregulated in MSC-EV group (|Fold Change|≥2 vs. +CTL microglia,
   p < 0.05, linear mixed models) (Fig. [166]8C). The differential
   abundance analysis as represented using a volcano plot in Fig. [167]8C
   highlights certain proteins that were upregulated (e.g. RHOC, RHOA,
   PAC2) and downregulated (e.g. RAB23, PTEN, ACP1) in MSC-EV treated
   microglia as compared to + CTL microglia (significance p < 0.05 and
   |Fold Change|≥2 vs. +CTL microglia). All 2662 proteins were used to
   conduct gene set variation analysis (GSVA, Methods) to identify the
   pathways that were altered in response to MSC-EV treatment with a
   subset of 8 gene sets highlighted in Fig. [168]8D. Some pathways (e.g.
   TGF-β, Platelet homeostasis) were enriched in the + CTL vs. -CTL
   microglia while others (e.g. TNFR1, Semaphorin) were suppressed. In
   some cases, MSC-EV treatment modulated the pathway expression towards
   the -CTL; however, for certain pathways (e.g. Rac1, IL-4 and IL-13
   signaling) MSC-EVs had an alternate response compared to the control
   groups and were highly enriched. The complete list of pathways that
   were significantly altered can be found in Table [169]S7 (enriched gene
   sets) and Table [170]S8 (suppressed gene sets).

Fig. 8.

   [171]Fig. 8
   [172]Open in a new tab

   Microglia proteomic profile shifts after cytokine stimulation and with
   MSC-EV treatment. (A) Heatmap representing the 2662 proteins identified
   using LC-MS/MS and clustered based on Euclidian distance across all 3
   experimental groups. (B) PCA reveals overall change in protein profile
   with PC1[proteome] (34.2% variance) accounting for alternate protein
   expression and PC2[proteome] (26.9% variance) accounting for shift in
   protein expression towards -CTL after MSC-EV treatment. (C) Volcano
   plot depicting alterations in protein expression upon MSC-EV treatment
   vs. + CTL. (D) Subset of enriched pathways identified using GSVA
   analysis. *p < 0.05 conducted using ANOVA followed by Tukey post-hoc
   test, then Benjamini–Hochberg false discovery rate (FDR) adjustment for
   multiple comparisons were used. FDR adjusted p-values ≤ 0.05 were
   considered to be significant

Discussion

   In this study, we have shown that morphological profiling of microglia
   in vitro is a robust, reproducible approach to assess microglia
   phenotype relevant to neuroinflammation in response to a cell-derived
   therapeutic: MSC-EVs. We illustrated that significant changes in
   microglia morphology in response to IFN-ү and TNF-α correspond to
   increased production of other pro- inflammatory cytokines (e.g. IL6,
   CXCL8, CCL5, CCL2, CCL3), as well as increased lipid production (e.g.
   PCs, PEs), which are common indicators of microglia function [[173]45,
   [174]64, [175]70–[176]72]. Additionally, pathways linked to metabolic
   activity (e.g. sphingolipid metabolism), cell motility (e.g. migration,
   extravasation) and regulation of immune cells (e.g. macrophages, T
   cells) are enriched upon exposure to cytokines and have previously been
   linked to inflammatory microglia profiles [[177]73–[178]76]. With
   consistent controls and reproducible results across multiple
   experiments and operators, this assay enables high throughput,
   single-cell profiling of microglia in the context of neuroinflammation.

   The primary motivation for using C20 cell line was that they are an
   immortalized human microglia cell-line. This due in part due to
   inherent homogeneity afforded by clonal derivation (because of
   immortalization) along with potentially higher physiological relevance
   as compared to BV2s or N9s. Also, given that microglia are highly
   dynamic in their response, we have only used a single time-point (24 h
   post treatment) to measure these phenotypic changes. Designed to be
   used as an investigative bioactivity assay, our main goal was to
   optimize the platform for ease of implementation, enhanced consistency
   and reproducibility between experiments. Further optimizations for time
   points, dosing strategy and other parameters will be critical for
   extended applicability of the assay. Importantly, having a more
   consistent immortalized cell-line to assess MSC-EV preparations will be
   essential in standardization efforts as a control to compare to for
   future investigations of MSC-EV modulation of primary patient derived
   microglia, as well as iPSC-derived microglia.

   Using this assay, we were able to demonstrate that microglial
   morphological response to MSC-EV treatment is indicative of a desired,
   therapeutic effect in terms of microglia secretion. Other studies using
   non-human microglia have shown that changes in morphology in response
   to a therapeutic treatment (e.g. dexamethasone) can reduce secretion of
   inflammatory cytokines (e.g. TNF-α, IL-1β) [[179]77]. Similar behavior
   was observed in our studies as microglia had significantly reduced
   levels of inflammatory cytokines in response to MSC-EVs (e.g. CXCL6,
   CXCL9, CCL7). Pathway analysis carried out using differentially
   regulated proteins suggest MSC-EVs may exert their effects on microglia
   through modulation of inflammation, metabolism, regulation of
   vasculature and specific signaling pathways like MAPK and ERK.
   Chemokines like CXCL9 and CCL3 are involved with recruitment and
   proliferation of T cells which may aid in regulation of the immune
   response and thus inflammation [[180]78, [181]79]. However, it’s
   important to note that MSC-EV modulation of microglia does not only
   result in an overall reduction in secretion of chemokines/cytokines as
   we also observed an increase in several proteins such as CXCL8, CXCL10
   and CCL5. While commonly associated with inflammation, CXCL8 and CXCL5
   have also been shown to relay neuroprotective effects by regulating
   neuronal apoptosis [[182]80, [183]81]. Increased levels of these
   cytokines with MSC-EV treatment may relay a similar effect as pathways
   like ‘positive regulation of neurogenesis’ and ‘generation of neurons’
   were associated with the proteins differentially secreted in response
   to MSC-EVs (Fig. [184]7D). These differences may influence the
   recruitment and activation of different immune cell sub-types like
   T-cells, NK cells and macrophages to the site of inflammation.
   Regulation of blood brain barrier (BBB) permeability is critical to
   promote immune cell recruitment, and dysregulation/breakdown of the BBB
   is a hallmark of many neurodegenerative diseases. Increased secretion
   of vascular endothelial growth factors (VEGFs), which occurs in
   ischemic stroke and intracerebral hemorrhage has been linked with
   improved BBB permeability [[185]82–[186]84]. Moreover, treatment with
   VEGF and VEGF-C have been shown to improve microglia phagocytosis of
   amyloid beta oligomers [[187]85] and induce an anti-inflammatory
   profile in rat traumatic brain injury model [[188]86]. By producing
   these cytokines in the brain, microglia also promote angiogenesis,
   neurogenesis as well as facilitate indirect secretion of neurotrophic
   factors by neighboring endothelial cells [[189]87–[190]89]. Angiogenic
   factors like VEGF-A and VEGF-C were increased in response to MSC-EVs
   suggesting a neuroprotective phenotype of the treated microglia. This
   interplay of different cytokines and chemokines are largely dependent
   on the disease context and may influence the outcome of the immune
   response. Balancing effective response and minimal damage to
   surrounding cells are complex functions of the immune system and
   require deeper investigation.

   In addition to secretion of neuroinflammation-relevant proteins, we
   assessed the intracellular proteome of microglia and identified gene
   sets that were differentially enriched in response to MSC-EV treatment;
   thus, further demonstrating the significance of their morphological
   response. Some of the identified gene sets enriched in response to
   MSC-EVs were associated with amino acid metabolism (e.g.
   phenylalanine), inflammation (e.g. IL-4 and IL-13 signaling) and
   morphology (e.g. semaphorin and RHO/Rac signaling). Impaired
   phenylalanine metabolism was observed in our cytokine-stimulated
   microglia controls (+ CTL) and increased phenylalanine concentration
   has been shown to activate microglia and alter their cytoskeleton due
   to an increase in their Iba-1 expression and size [[191]90]. MSC-EV
   treatment shows enrichment for phenylalanine metabolism in stimulated
   cells, reflecting a potentially desired therapeutic mechanism for
   addressing aberrant microglia activation in disease [[192]91].
   Extracellular signaling proteins, such as semaphorins, are associated
   with changes in cell migration and motility for different cell-types in
   the nervous system [[193]92] and proteins involved in this gene set
   were enriched in response to MSC-EV treatment. These results agree well
   with our observation of secreted proteins involved in regulation of
   cell motility and adhesion based on our STRING analysis. High
   enrichment score was observed for the IL-4 and IL-13 signaling gene set
   in MSC-EV treated groups. Reduced expression of disease associated
   microglia markers (e.g. TREM2, ApoE) have been reported following IL-4
   and IL-13 treatment. In the same study, IL-4 also induced a metabolic
   shift in microglia towards oxidative phosphorylation (OXPHOS)
   [[194]93]. While commonly associated with anti-inflammatory response,
   these cytokines may augment neuroinflammation to promote influx of
   molecules that aid in repair and recovery [[195]94]. Increased levels
   of IL-4 along with CXCL8 in early AD patients was associated with a
   higher permeability of the BBB [[196]95]. Rho GTPase and Rac1 were
   among other pathways that were downregulated with stimulation and
   upregulated with MSC-EV treatment. These molecular switches are
   responsible for various cellular functions (e.g. proliferation,
   polarization) as well as morphology (e.g. reorganization of actin
   cytoskeleton, migration) [[197]96]. In microglia, RhoA balance is
   crucial to prevent neurodegeneration while simultaneously promoting
   microglial function [[198]96–[199]98]. Additionally, microglia with
   reduced RhoA have modified actin stress fibres inducing an inflammatory
   phenotype [[200]99].

   Microglia lipid metabolism is dysregulated in different
   neurodegenerative diseases leading to lipid accumulation in cells
   [[201]70]. We observed significantly increased lipid content post
   stimulation which reflects the increased cell size [[202]65] quantified
   using our morphological profiling approach. While PCA revealed
   separation of the EV treated group from + CTL group based on raw
   lipidomic feature data (Figure [203]S6), no significant changes in
   annotated lipids were observed in response to MSC-EV treatment
   (Fig. [204]5). This could be explained by our relatively short (24 h)
   experimental timeline and what is known about lipid metabolism
   dynamics. MSC-EV treatment effectively prevented the cytokine-induced
   morphological response of microglia, but 24 h may not be sufficient for
   metabolism of accumulated lipids from the cell. Previous studies using
   immortalized mouse microglia cell-line BV2s (relevant to our work), as
   well as liver tissue, range from 18 to 72 h to assess lipid degradation
   [[205]63, [206]100, [207]101]. Moreover, lipid metabolism is a
   multi-stage process and is dependent on lipid droplet size as observed
   in liver cells [[208]100, [209]102]. It is possible that the regulation
   of microglia lipid content occurs over a longer period of time and thus
   temporal monitoring of lipidome is worth further investigation.

   It is also important to consider MSC-EVs possess a lipid bilayer and
   [[210]97] internalization of MSC-EVs through endocytosis, phagocytosis
   or attachment to microglia surface could be potentially contributing to
   the observed results [[211]103]. In our own published work, we have
   shown that MSC-EVs contain many of the same lipid classes (e.g. PCs,
   PEs, sphingomyelins, and PEPs) that were detected in microglia in this
   study [[212]55]. Vanherle S et al. have effectively shown that
   cholesterol from macrophage-derived EVs support the remyelination and
   other regenerative functions of brain macrophages [[213]104]. Our
   results could be interpreted as differential lipid density/organization
   (i.e. lipids/area or lipids/volume) as we observed the same increased
   lipid accumulation in MSC-EV treated microglia as + CTL microglia, but
   that increase was not associated with increased cell size. This can be
   reconciled based on knowledge that lipids (e.g. cholesterol,
   sphingolipids, ceramides) are instrumental in regulation of membrane
   mechanics/fluidity and organization. Cell plasma membrane consists of
   differently saturated lipid classes which regulate the intercellular
   transport of signaling molecules as well as cellular morphology through
   actin re-organization [[214]105]. Like our observation, studies have
   shown microglia stimulation using other inflammatory signals (e.g.
   lipopolysaccharide, LPS) alters their lipid content [[215]106] and
   leads to increased lipid raft formation [[216]107] which induce
   morphological changes [[217]108, [218]109]. Meanwhile studies have also
   shown that microglia cells with lipid accumulation activate different
   pathways (e.g. LXR, PPAR) thus influencing cellular function
   [[219]110]. Further insight could be obtained through advanced
   single-cell based lipidomic approaches (e.g. MALDI based imaging
   [[220]106, [221]111, [222]112]) as limitations exist with population
   wide approaches when considering heterogeneous cell populations (like
   microglia). Additionally, it will be critical to parse out the observed
   changes in lipid content and whether they are associated with the
   microglia or from lipids derived from the MSC-EVs, which could be
   achieved using isotopic labeling [[223]112–[224]114].

   Although no significant overall changes in lipids were observed upon
   MSC-EV treatment, our imaging approach enabled assessment of another
   important regulator of microglia metabolism: mitochondria. An abundance
   of literature demonstrates the role and response of microglial
   mitochondria to neuroinflammation; for example, primary microglia
   stimulated with LPS show a significant increase in mitochondria
   fragmentation [[225]115, [226]116]. This change in mitochondrial
   morphology is accompanied by metabolic reprogramming from OXPHOS to
   glycolysis [[227]116, [228]117]. We observed that mitochondrial
   morphology in microglia is altered following cytokine stimulation and
   mitigated by MSC-EV treatment in a similar manner as overall cellular
   morphology. This further underscores the ability of morphological
   profiling to capture another important change in microglia phenotype
   relevant to their role in disease. Microglia mitochondrial
   dysregulation occurring due to either reduced expression of TREM2
   [[229]118], increased P2 × [7]R expression [[230]119], reduced
   TSPO-hexokinase2 activity [[231]120] or impaired mitophagy [[232]121]
   have been extensively reviewed in the context of Alzheimer’s disease
   [[233]122, [234]123]. Further refinement of this assay could help
   investigate these mechanisms by additional mitochondrial membrane dyes
   (JC-1 [[235]124], TMRM [[236]125, [237]126]) and/or more advanced high
   resolution microscopy approaches like confocal microscopy, transmission
   electronic microscopy (TEM) and STED super resolution microscopy that
   can profile mitochondria on a single-organelle level (albeit at
   significantly lower throughput) [[238]123–[239]128].

Conclusion

   We demonstrated that significant overall changes in microglia
   morphology can be confidently interpreted to implicate the ability of
   MSC-EVs to modulate a multitude of disease-relevant microglia behaviors
   through comprehensive profiling of microglia mitochondrial morphology,
   lipidomics, secretion, and proteomics. The response of microglia to
   MSC-EVs suggest that MSC-EVs can exert a profound impact on the overall
   brain immune environment with microglia serving as a critical mediator
   of their therapeutic function. This is captured by changes in cellular
   and secreted proteins involved in pathways associated with
   inflammation, regulation of vasculature and other immune cells (e.g.
   macrophage migration, T-cell chemotaxis), which in turn may be
   responsible for regulation of the BBB. To explore these interactions
   further, we need to employ more complex assays involving neurons,
   endothelial cells and other glial cells using co-culture models
   [[240]127–[241]129] and organ-on-chip models with MSC-EV treatment
   [[242]130–[243]132].

   This study established microglia morphology as an effective readout of
   MSC-EV bioactivity from a single donor and manufacturing protocol, and
   thus lays the foundation for using this assay to assess other key
   aspects of MSC manufacturing [[244]47–[245]49]. Moving forward, this
   assay can be employed to screen different MSC cell-lines, manufacturing
   conditions (e.g. priming, media-type, effects of scaling) and EV
   long-term storage conditions (e.g. temperature, buffer). Additionally,
   this approach is amenable to high throughput screening and could thus
   be paired with compound libraries and genetic screens to elucidate
   MSC-EV mechanisms of action. Finally, this approach is generalizable
   and could be adapted to other MSC-EV target cells (besides microglia)
   that exhibit morphological-dependent behavior, as well as using this
   assay as a phenotypic readout of other therapeutics e.g. small
   molecules and other (non-MSC) cell-derived therapies.

Electronic supplementary material

   Below is the link to the electronic supplementary material.
   [246]Supplementary Material 1^ (8.4KB, py)
   [247]Supplementary Material 2^ (1.5MB, cpproj)
   [248]Supplementary Material 3^ (17KB, xlsx)
   [249]Supplementary Material 4^ (232.9KB, xlsx)
   [250]Supplementary Material 5^ (364.8KB, xlsx)
   [251]Supplementary Material 6^ (831.6KB, docx)

Acknowledgements