In an elegant study, Kailashiya et al. documented that doxorubicin-loaded PEVs (doxo-PEVs) were taken by HL60, K562 cells (leukemia cell lines), and blast cells, in whole blood harvested from patients with newly diagnosed leukemia. Doxo-PEVs were uptaken by cells via P-selectin ligands and integrins. Moreover, doxo-PEVs transfer into leukemia cells was higher, compared to free doxorubicin, which could be used to increase the effectiveness of the therapy and minimize the side effects of drugs [56]. Gasperi et al. showed that PEVs with miR-126 and with miR-223 increased sensitivity of BT549 cells to the cisplatin chemotherapy [62].

PEVs drug-loaded could be a natural vectors-targeted medications. Engineering them from autologous platelets in large quantity and storing for several days, seems to be a new biocompatible and non-immunogenic new-generation medicine. However, to make PEVs applicable and efficacious in clinical treatments, some of their underlying functions still need to be better researched and understood.

#### **7. Summary**

PEVs biogenesis depends on different signals that control their formation from PLTs. The role of PEVs in various physiological conditions, like hemostasis, or pathological like inflammation or atherosclerosis was confirmed. This review focused on the PEVs participation in cancerogenesis. A better understanding of the biology of PEVs and the mechanisms that allow them to function as mediators in cell-to-cell communication in cancer growth, could become a contribution to the development of new therapeutic strategies, which could also be applicable in cancer. Moreover, determining the number of PEVs and their cargo becomes a useful diagnostic marker or prognostic factor for the different clinical stages in a variety of neoplasia. Knowledge about the formation of distinct PEVs types dependent on PLTs activators could lead to the development of specific techniques for PEVs-mediated drug delivery to cancer cells, or to TME, to modulate their immune response or angiogenesis.

**Author Contributions:** Conceptualization, M.Z. and O.W.-P., Writing-Original Draft Preparation, M. ˙ Z., O.W.-P.; ˙ Writing-Review & Editing, M.Z., A.W. and O.W.-P. Supervision, M. ˙ Z. All authors have read and agreed to the ˙ published version of the manuscript.

**Funding:** Support for this study was provided by NCN (National Science Centre, Poland) MINIATURA grant number 2017/01/X/NZ5/01481 for (M.Z.).

**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

#### **Abbreviations**

#### **References**

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## *Review* **Exploiting Manipulated Small Extracellular Vesicles to Subvert Immunosuppression at the Tumor Microenvironment through Mannose Receptor**/**CD206 Targeting**

### **Maria Luisa Fiani \*, Valeria Barreca, Massimo Sargiacomo, Flavia Ferrantelli, Francesco Manfredi and Maurizio Federico \***

National Center for Global Health, Istituto Superiore di Sanità, 00161 Rome, Italy<EMAIL_ADDRESS>(V.B<EMAIL_ADDRESS>(M.S<EMAIL_ADDRESS>(F.F<EMAIL_ADDRESS>(F.M.)

**\*** Correspondence<EMAIL_ADDRESS>(M.L.F<EMAIL_ADDRESS>(M.F.); Tel.: +39-06-4990-2518 (M.L.F.); +39-06-4990-6016 (M.F.)

Received: 6 July 2020; Accepted: 27 August 2020; Published: 31 August 2020

**Abstract:** Immunosuppression at tumor microenvironment (TME) is one of the major obstacles to be overcome for an effective therapeutic intervention against solid tumors. Tumor-associated macrophages (TAMs) comprise a sub-population that plays multiple pro-tumoral roles in tumor development including general immunosuppression, which can be identified in terms of high expression of mannose receptor (MR or CD206). Immunosuppressive TAMs, like other macrophage sub-populations, display functional plasticity that allows them to be re-programmed to inflammatory macrophages. In order to mitigate immunosuppression at the TME, several efforts are ongoing to effectively re-educate pro-tumoral TAMs. Extracellular vesicles (EVs), released by both normal and tumor cells types, are emerging as key mediators of the cell to cell communication and have been shown to have a role in the modulation of immune responses in the TME. Recent studies demonstrated the enrichment of high mannose glycans on the surface of small EVs (sEVs), a subtype of EVs of endosomal origin of 30–150 nm in diameter. This characteristic renders sEVs an ideal tool for the delivery of therapeutic molecules into MR/CD206-expressing TAMs. In this review, we report the most recent literature data highlighting the critical role of TAMs in tumor development, as well as the experimental evidences that has emerged from the biochemical characterization of sEV membranes. In addition, we propose an original way to target immunosuppressive TAMs at the TME by endogenously engineered sEVs for a new therapeutic approach against solid tumors.

**Keywords:** tumor-associated macrophages; tumor microenvironment; macrophage polarization; mannose receptor; exosomes; extracellular vesicles; HIV-1 Nef

#### **1. Introduction**

Both immunosuppression and genetic escape are formidable weapons through which tumors can elude host immune surveillance. Solid tumors develop in a quite complex context, referred to as tumor microenvironment (TME) [1,2], which is composed of both cellular and non-cellular elements, usually resulting in an immunosuppressive behavior. Counteracting such a general effect would favor both spontaneous and therapeutic anti-tumor immunity, hence critically contributing to control tumor cell growth. Therefore, subverting TME immunosuppression represents a major goal for anticancer immunotherapies.

Both normal and tumor cells constitutively release membrane-bilayered vesicles, commonly referred to as extracellular vesicles (EVs) [3,4]. They differ in the mechanisms of biogenesis and

secretion, giving rise to the generation of a heterogeneous population of vesicles with different sizes and contents [5,6], which include small EVs (sEVs) or exosomes and microvesicles or ectosomes. Exosomes are vesicles of 30–150 nm diameter generated by inward budding of endosomal membranes to form intraluminal vesicles that accumulate in intracellular organelles called multivesicular bodies (MVBs). MVBs ultimately fuse with the plasma membrane, thereby releasing intraluminal vesicles into the extracellular environment (Figure 1). On the contrary, ectosomes are 100–500 nm vesicles shed by direct budding from the plasma membrane [7,8]. Different types of EVs often show overlapping features that make difficult to obtain relatively pure preparations when purified from cell-conditioned media or biological fluids. In this review, we will use the term sEV to refer to EV types co-isolated by typical purification methods and exosomes to distinguish EV whose subcellular biogenesis strictly derives from multivesicular bodies/endosomes [8].

**Figure 1.** Electron microscopy of multivesicular bodies (MVB) and small extracellular vesicles (sEVs) (**a**) TEM micrograph of multivesicular bodies with intraluminal vesicles in Mel501, a melanoma cell line (**b**) SEM (Scanning Electron Microscope). Micrograph of sEVs purified from conditioned medium of Mel501 cells by differential centrifugations. Courtesy of Francesca Iosi and the Microscopy Area of the ISS Core Facilities.

SEVs carry a complex cargo of nucleic acids, proteins, and lipids that largely reflects the characteristics and the functional state of the cells they originate from, and that will be delivered to neighboring or distant cells [9,10]. As a result, the functions of those recipient cells will be modulated by sEVs in a manner that is strictly dependent on the nature of producer cells, making sEVs central players in intercellular communication and reprogramming of target cells [11]. Ectosomes generation is a much less known process that requires the accumulation of their cargo at the cytosolic surface of specific plasma membrane microdomains [7,12].

SEVs-mediated transfer of molecular and genetic material from one cell to another, either locally or at long distance, is a key contributor to the mechanisms of intercellular communication involved in various physiological and pathological conditions [13–15]. Moreover, for these reasons, sEVs are now considered powerful tools for clinical applications, including advanced diagnostics, therapeutics, and regenerative medicine [16–19].

The molecular composition of sEVs is determined by the cell type of origin as well as by the intracellular pathway followed en route to their release into the extracellular space [8,20,21]. This heterogeneity confers to sEVs distinct properties, such as tropism to certain organs, and uptake by specific cell types. In the case of tumor-derived sEVs, these events often lead to the impairment of immune responses at TME [22], also favoring pre-metastatic niche formation and metastasis [23,24].

In tumor cells, sEV biogenesis and ultimately sEV composition is a complex and regulated process, which involves many different molecules associated with the sEV biogenesis pathway [4,25]. Whatever the cell type of origin, sEVs can be characterized, although not exclusively, by the presence of different types of cell surface proteins, such as tetraspanins, (i.e., CD9, CD81, CD63), ESCRT (endosomal sorting complex required for transport) proteins (Alix and TSG101), integrins, RNA, DNA, lipids such as ceramide and the atypical phospholipid, lysobisphosphatidic acid (LBPA) [26], and oligosaccharides [27].

TME exerts a key influence on tumor cells, and the resulting sEVs, responsible for proteins and genetic material transfer from primary tumor cells, play a crucial role in metastatic colonization and in the formation of the pre-metastatic niche, driving recipient cells to acquire a pro-tumorigenic phenotype [23,28]. The selective conditions present in TME, such as the generation of a hypoxic [29] and acidic environment [30], strongly influence sEV secretion by tumor cells, thus contributing to the malignant tumor phenotype. Furthermore, sEV membrane composition reflects TME changes and conceivably influence and control the different mechanisms of entry or interaction of sEVs with target cells supporting tumor growth [31,32].

Interestingly, it has been described that major players of immunosuppression at the TME, i.e., immunosuppressive tumor-associated macrophages (TAMs), express on their surface high levels of mannose receptor (MR, CD206) [33]. The MR is an endocytic receptor with a high affinity for high mannose oligosaccharides, glycans highly enriched on the surface of sEVs [34]. In this review, literature data regarding both TAM functions and the molecular structure of sEVs are reviewed. In addition, we propose an original way to exploit typical molecular signatures of both TAMs and sEVs to counteract the immunosuppression at the TME.

#### **2. The Tumor Microenvironment**

In solid tumors, cancer cells are embedded within a milieu that favors their proliferation and comprises both cellular and non-cellular components. Fibroblasts, endothelial cells, and essentially all types of immune cells are part of the TME [35,36]. Among non-cellular components, tumor-derived sEVs play a key role in immune suppression. TME composition can vary among different tumors, and between primary and metastatic neo-formations in the same patient, and is tightly associated with the clinical outcome of cancer patients.

TMEs can be categorized based on different criteria. In terms of abundance of tumor-infiltrating cytotoxic CD8+ T lymphocytes (CTLs), TMEs can be distinguished in either hot/inflamed, with the highest content of CTLs, or cold/desert, with a virtual absence of infiltrated CTLs [37]. TME core infiltrated by CTLs represents a favorable condition for an effective anticancer immune response, both spontaneous and induced by immunotherapeutic interventions.

TME is populated by different kinds of immune cells having immune suppressive actions. Among these are myeloid-derived suppressor cells, neutrophils, CD4+ Treg lymphocytes, and immunosuppressive M2-like TAMs [38]. These latter cells can represent up to 50% of the tumor mass, and play a key role in the immune evasion at TME by secreting proteases, angiogenic factors, and pro-tumoral products. The functional plasticity of TAMs modifies their phenotype and activity in response to a great number of microenvironmental stimuli, although the mechanisms that determine the different polarization states are still to be elucidated [39]. These different functional states often coexist and can significantly vary between different tumors [40,41]. TAMs can also dispose at the tumor margin, where they can interact with CTLs, thus inhibiting their infiltration towards tumor cells [42]. For all these reasons, immunosuppressive TAMs have been identified as a major cell target for novel designs of cancer immunotherapies focused on improving the overall anti-tumor immune response. A schematic representation of cells populating TEM is illustrated in Figure 2.

**Figure 2.** Schematic representation of cells populating the tumor microenvironment (TME).

#### **3. The TAM-Mediated Immunosuppression at the TME**

A large number of macrophages infiltrate solid tumors, thereby influencing several aspects of tumor development [43]. The most relevant effects include suppression of anticancer immunity, angiogenesis promotion, and support for metastasis. Macrophages are recruited at TME, in response to the secretion by tumor cells and other TME cell types, of a number of chemoattractant soluble factors, including vascular-endothelial growth factor A (VEGF-A) [44], chemokine ligand 2 (CCL2) [45], and colony-stimulating factor 1 (CSF-1) [46].

TME-populating macrophages can be schematically distinguished in M1- and M2-like macrophages. M1-like macrophages show both pro-inflammatory and immune-stimulatory properties, thus exerting an anti-tumor function. On the other hand, M2-like macrophages favor tumor angiogenesis and immunosuppression. Such a distinction, although useful from both therapeutic and diagnostic points of view, is now outdated, due to the identification of a large number of intermediate subclasses, i.e., up to 19 [47]. They have been identified through most recent transcriptomic techniques, e.g., single-cell mass cytometry and single-cell RNA sequencing [48–50], and in vivo represent a continuum of functional phenotypes with intermediates showing overlapping features.

TAMs can be characterized by the expression of different surface markers [47,51], distinct metabolic changes [52,53], and a broad transcriptional repertoire with the involvement of key transcription factors, which can be activated by the environmental signals received. In particular, members of the signal transducer and activator of transcription (STAT), peroxisome proliferator-activated receptors (PPARs), interferon regulatory factor (IRF), and nuclear transcription factor-κB (NF-κB) families are essential for macrophage polarization toward the M1 profile [41,54,55].

M2-like TAMs contribute to tumor angiogenesis by secreting soluble factors inducing endothelial cell proliferation, including VEGF-A, interleukin (IL)-1β, IL-6, tumor necrosis factor (TNF)α, CXCL8, and fibroblast growth factor (FGF)-2 [56]. In particular, the secretion of VEGF-A by perivascular TAMs can increase vascular permeability and access of tumor cells to peripheral blood circulation [57]. On the other hand, the production of proteases, e.g., matrix-metalloproteases, induces degradation of extracellular matrix and the consequent liberation of embedded soluble factors released by both cancer and stromal cells having pro-tumoral effects and favoring metastasization.

TAM-mediated immunosuppression at TME is essentially mediated by three concurrent mechanisms: (i) Release of soluble immunosuppressive factors, e.g., IL-10, CCL22, and transforming growth factor (TGF)-α as well as factors recruiting regulatory T cells (Treg) [58]; (ii) expression of ligands for lymphocyte suppressor factors PD-1 and CTLA-4, i.e., PDL-1 and CD80, as well as other checkpoint inhibitors with similar functions, including B7-H4, V-domain Ig suppressor of T cell activation (VISTA) [59], and vascular endothelial receptor (CLEVER) [60], and (iii) starving the TME of l-arginine, i.e., an essential factor for T-cell activity, through the release of arginase-1 [61]. Figure 3 illustrates the principal mechanisms of TAM mediated immunosuppression at the TME.

**Figure 3.** Principal mechanisms of tumor-associated macrophage (TAM) mediated immunosuppression at the tumor microenvironment (TME).

The multiple immunosuppressive signals at play within the TME greatly reduce the efficacy of current immunotherapies. Therefore, new strategies to effectively reprogram the various immunosuppressive cell types at the TME are urgently needed.

#### **4. Re-Programming of TAMs**

Immunosuppressive TAMs represent a privileged therapeutic target for the treatment of solid tumors, especially in the case immune checkpoint blockers (ICBs) are used. Given the enormous therapeutic value of TAMs re-education towards M1-like macrophages to promote tumor regression, much attention has focused on effective strategies aimed at targeting TAMs, including the blockade of the M2 phenotype, enhanced activation of M1 macrophages and reprogramming of TAMs toward M1-like phenotype [58,62–65]. Many of these different approaches against immunosuppressive TAMs have been summarized in Table 1 and strategies directed at TAM reprogramming illustrated in Figure 4.

**Table 1.** Selected strategies to target tumor-associated macrophages (TAMs).

AMPKα1, AMP-activated protein kinase; CCL2, C–C chemokine ligand 2; CCR2, C–C chemokine receptor type 2; CSF-1, Colony-Stimulating Factor 1; CSF-1R, colony-stimulating factor 1 receptor; CXCL12, C–X–C motif chemokine 12; CXCR4, C-X-C chemokine receptor type 4; CpG-ODN, unmethylated cytosineguanine (CpG) oligodeoxynucleotides; IFN-γ, interferon gamma; MARCO, macrophage receptor with collagenous structure; MR, mannose receptor/CD206; NF-κB, nuclear factor kappa B; PI3Kγ, phosphoinositide 3-kinase; STAT, signal transducer and activator of transcription.

Promising results have been obtained with direct activation of M1-like macrophages by Th1 cytokines like IFN-γ [87], and by targeting toll-like receptors (TLR) and/or CD40 with agonists and monoclonal antibodies [97–99]. However, the onset of systemic inflammation limited the therapeutic efficacy of these approaches in vivo, and additional investigations are ongoing to circumvent this hurdle. In any case, considering the functional plasticity of macrophages, re-educating M2- versus an M1-like macrophage phenotype currently appears the most attractive therapeutic option.

**Figure 4.** Scheme of current strategies for tumor-associated macrophage (TAM) reprogramming.

A major hindrance to effective targeting of immunosuppressive TAMs is represented by the scarcity of specific protein markers expressed on M2 macrophages. Some potential targets, whose expression also correlates with poor prognosis, have been investigated. Among them, the selective targeting of MARCO (macrophage receptor with collagenous structure) with monoclonal antibodies has been recently used to promote a switch to an M1-activated phenotype [100].

Increasing evidence suggests that a valuable alternative is represented by targeting the MR, which is highly expressed on M2, but not M1 macrophages [92,103–105].

#### **5. The Mannose Receptor in M1 Polarization**

MR is expressed on TAMs where is a prototypical marker of M2-type activation. It is also expressed on the surface of immature dendritic cells (DCs), liver sinusoidal endothelial cells, and other tissue macrophages. Earlier studies have demonstrated that MR expression is strongly down-regulated by IFN-γ [106], and upregulated by interleukin-4 (IL-4) [107]. TheMR is a 175 kDa Type I integral membrane protein that belongs to the family of C-type lectin receptors and binds glycoconjugates terminated in mannose, fucose, or *N*-acetil-β-d-glucosamine (GlcNAc) in a calcium-dependent manner [108–110]. The receptor contains three distinct extracellular domains, i.e., an N-terminal cysteine-rich domain (CR) that binds sulfated carbohydrates, a fibronectin type II domain (FNII) that binds collagen, and eight tandem C-type lectin carbohydrate-recognition domains (CRDs) [111,112]. CRDs have only weak affinity affinities for single sugars, and several CRDs need to be clustered to achieve high-affinity binding to oligosaccharides. This clustering allows for the internalization of mannosylated proteins and other exogenous molecules, including allergens and microbial products.

MR is a highly effective clathrin-dependent endocytic receptor that constantly recycles between the plasma membrane and the early endosomal compartment [111]. Most part of MRs is intracellular, while only ∼15% of the cellular pool can be found on the cell surface. Like other members of the C-type lectin receptor family, the MR undergoes conformational changes upon ligand binding or as pH decreases in intracellular compartments [111,113]. Once acidification takes place in the endosomal compartment, the MR dissociates from its ligands, and the empty receptor recycles back to the plasma membrane.

Several approaches have been adopted to target the MR and selectively deliver therapeutic nanoparticles. Among these, drug-free mannosylated liposomes have been shown to induce effective anti-tumor activity by enhancing the expression ratio of CD86/MR [102]. In another study, mannosylated nanoparticles suitable for intracellular delivery of drug carriers have been shown to selectively target with high specificity MR expressing macrophages [104].

MR conformational changes that occur upon ligand binding have been recently exploited to target M2 macrophages and induce reprogramming toward M1 phenotypes. For instance, precision targeting with short peptides showed some potential for the intracellular delivery of therapeutically relevant molecules [114]. In addition, a very recent report showed that direct binding of MR with a synthetic peptide (RP-182), i.e., an analogue of naturally occurring antimicrobial peptides, activates phagocytosis and autophagy in M2-like macrophages, reverting these cells into an anti-tumor M1-like phenotype with increased M1 cytokine production and phagocytosis of cancer cells [103].

On the other hand, also cell-secreted sEVs can be considered attractive candidates to specifically target M2-like macrophages via the MR, since they expose high mannose and other classes of N-linked oligosaccharides on their surface [115,116].

#### **6. Extracellular Vesicles for Anti-Tumor Therapy**

Pioneering studies have shown that sEVs secreted by DCs pulsed with cancer peptides successfully eradicate established tumors in mice [117]. Furthermore, tumor-derived EVs are a source of neoantigens that, once internalized by DCs, could cross-prime CD8+ T cells and lead to tumor rejection [118]. Since these early studies, the field of sEVs-based cancer therapeutics has attracted many efforts, and sEVs have emerged as promising tools for targeted drug delivery. Despite a growing interest in these nanovesicles as natural carriers, there are still many open questions that need further investigation. For example, specific recognition by target cells is of fundamental importance for an effective delivery of bioactive molecules. EVs uptake may occur via receptor-mediated endocytosis or phagocytosis, or direct fusion with the plasma membrane. Some studies have pointed at integrins [24] and scavenging receptors [119] as mediators of EVs targeting, but current knowledge on this matter is rapidly evolving and has been recently comprehensively reviewed [5,7,120–122].

The different modes of sEV uptake may result in distinct localization and functional effects of the sEVs components, but it is still unknown whether a specific route of entry is to be preferred for a successful transfer of EVs cargo. Thus, understanding through which mechanisms sEVs deliver their content into target cells is a central point that needs to be further elucidated. To the best of current knowledge, while either non-selective uptake or direct fusion with the plasma membrane of target cells seem to be the preferential mechanisms of bulk sEV incorporation (Figure 5) [123], alternative and more specific routes of uptake may depend on the characteristics of surface components of both sEV and target cells.

**Figure 5.** Fluorescent sEVs uptake by different cell lines. Confocal fluorescence microscopy images of green fluorescent sEVs derived from melanoma Me665/1 cells transferred on (**a**) CHO cells and (**b**) Me665/1 cells in nonspecific conditions [66]. Blue-fluorescent nuclei are stained with DAPI.

Tumor-derived EVs can be efficiently taken up by DCs for antigen processing and cross-presentation to tumor-specific CTLs [124]. Immature DCs (iDCs) internalize EVs more efficiently than mature DCs, whereas mature DCs retain more EVs on the cell surface [125]. The surface of iDCs harbors sugar-binding C-type lectin receptors (CLRs) [126,127], which is a characteristic shared with M2-like macrophages.

Glycomic studies conducted to date demonstrate that surface glycoprofiles of sEVs contain high amounts of mannose and other classes of N-linked oligosaccharides [27,34,128,129] making them suitable ligands for the MR. Recently, it has also been shown that mannose-modified serum sEVs display elevated uptake by murine DCs [116]. This evidence, together with a number of studies showing that mannosylation of both liposomes [102] and synthetic nanocarriers [130,131] enhance cellular uptake by M2 macrophages, inducing stimulation and polarization of macrophages toward the M1 phenotype, point to sEVs as powerful ligands of M2 macrophages. Furthermore, the affinity of EVs membrane components (i.e., proteins, lipids, and glycans) for certain tissues greatly affects biodistribution of EVs in vivo, thus, encouraging studies aimed at altering the surface of EVs to improve targeting to selected organs. Many different strategies have been adopted, but there is now accumulating evidence that carbohydrates on the vesicle surface participate in the recognition and uptake of EVs by phagocytes. Interestingly, manipulation of surface glycans on EVs either by removal of sialic acid [132] or by treatment with *N*-glycosidases [133,134] alters the uptake capacity of different cells. In the first case, the change also affects the in vivo biodistribution of sEVs showing accumulation of desialylated sEVs in the lungs. Altogether, these results point to the importance of *N*-glycosylation in cellular uptake, but since different cell types respond differently to glycosylation changes appears evident that the cell to be targeted, with its endowment of specific protein receptors, represent the cornerstone of receptor ligand recognition.

#### **7. Molecular Basis of TAM Re-Programming by Engineered sEVs**

The subversion of TAM-mediated immunosuppression at TME through macrophage transcriptional reprogramming represents a quite attractive option for anticancer combined immunotherapy. Like normal cells, tumor cells also constitutively release sEVs, which interact primarily with TME cell constituents. SEVs, as naturally occurring vesicles, have a low intrinsic immunogenic profile, are able to avoid, at least in part, the degradative pathway and possess the ability to overcome the blood-brain barrier. Consequently, they have emerged as an important means to deliver therapeutic agents. In this context, engineering tumor-derived sEVs with molecules inducing an inflammatory-like macrophage phenotype would be instrumental in alleviating the immunosuppression at TME. Over the past few years, several engineering strategies have been devised to manipulate tumor-derived sEVs in order to induce cellular and innate immunity. SEV engineering can be carried out either at the level of producer cells or directly on purified sEVs. The different approaches used mostly depend on cargo properties, such as hydrophilicity, hydrophobicity, and molecular weight, as each method has a different loading capacity. For example, chemicophysical methods like electroporation are widely used for loading relatively large molecules. such as siRNA or miRNA into sEVs [19]. Particularly for cancer research, the observation that exosomal miRNAs effectively engage target mRNA and suppress gene expression in recipient cells has been heavily favored. Due to the availability of various cellular engineering methods, different types of RNAs that are released via sEVs have also been exploited and recently reviewed [11].

During the past decade, many efforts have been devoted to transfect sEV-producing cells with plasmids encoding protein sequences that, once uploaded in the nanovesicles, are able to alter the phenotype of target cells [135–138]. However, a method to target M2 macrophages with sEVs capable of inducing their reprogramming to M1 anti-tumor phenotype is not yet available. One major hurdle in EVs research for an effective therapeutic application is represented by the lack of optimized isolation, characterization and quantification procedures. Various purification techniques, such as differential ultracentrifugation, density gradients, precipitation, filtration, size exclusion chromatography, and immunoisolation are currently used to obtain less heterogeneous sEVs preparation; however, quantification often relies on the total protein content of EVs. Fluorescent

labeling techniques are more accurate and have the main advantage that track EVs in vivo [139,140]. The different approaches for TAMs reprogramming by engineered EVs are schematically illustrated in Figure 6.

**Figure 6.** TAMs reprogramming by engineered EVs.

#### **8. HIV-1 Nef Protein as E**ff**ector of TAM Reprogramming**

A plausible candidate for reprogramming M2 macrophages is the Human immunodeficiency virus (HIV)-1 Negative Regulatory Factor (Nef) protein [141], a 27 kilodalton (kDa) scaffold protein, which lacks enzymatic activities. After synthesis at free ribosomes, Nef reaches both intracellular and plasma membranes with which it tightly interacts through its N-terminal myristoylation. Nef acts as a scaffold/adaptor element in triggering activation of signal transducing molecules like p21 PAK-2, NF-κB, STATs, ERK1/2, Vav, and Src family kinases. In most cases, signal activation occurs upon Nef association with lipid raft microdomains at cell membranes [142–144]. The fact that also sEV membranes are enriched in lipid raft microdomains explains why Nef can be found in EVs [145–149].

Cumulate literature data demonstrate that the presence of Nef inside macrophages induces a strong pro-inflammatory response. In particular, Nef switches on the transcription of many inflammatory genes, as well as the release of inflammatory factors like CCL3, CCL4, IL-1β, IL-6, TNF-α [150,151], and interferon gamma (IFN)-γ [152]. This potent pro-inflammatory response is mediated by the activation of several signal transduction molecules, including STAT-1, 2, and 3, NF-κB, JNK, ERK1/2, and MAPK [153,154]. The inflammatory effects of Nef on macrophages depends on four glutamate-acidic cluster domain located at 62–65 amino acid position [155].

Data from many independent investigation groups strongly support the idea that Nef associates with EVs at low levels [156,157]. Conversely, we identified a Nef mutant incorporating in sEVs/EVs at quite high levels [158]. This Nef mutant (referred to as Nefmut) is defective for the most part of Nef functions, including down-regulation of cell membrane receptors, Nef-associated kinase (NAK) activation, an increase of HIV expression [159]. Nevertheless, it maintains an unaltered acidic cluster domain that correlates with the induction of cell activation in antigen-presenting cells when it is delivered by nanovesicles [160].

Considering this evidence, one may hypothesize that the delivery of Nefmut-engineered EVs inside M2-TAMs would be instrumental in re-educating macrophages at TME from an M2-like to an M1-like phenotype. In the case of solid tumors, this design could be applied through a quite simple strategy, i.e., tumor cell engineering for Nefmut expression through retroviral vector-mediated transduction. In this way, only actively replicating cells at the TME, hence preferably tumor cells, are expected to be transduced. Nef-engineered tumor-derived sEVs may diffuse within TME, thereby preferentially

entering MR positive TAMs in view of the mannose expressed on the sEV surface. Once internalized by macrophages, Nefmut might switch on intracellular signals—ultimately leading to the release of pro-inflammatory factors. These factors might act in an autocrine/paracrine loop to induce the reprogramming of macrophage transcriptional profile toward the M1-like phenotype (Figure 7).

**Figure 7.** Scheme of the proposed mechanism for TAM reprogramming. Tumor cells are transfected with retroviral vectors expressing Nefmut (Nef-RV). Nef-engineered sEVs (Nef-sEV) are then released into the TME infiltrated with M2 like macrophages expressing the mannose receptor (MR/CD206). MR mediated uptake of Nef-Sev might induce polarization of M2 into M1 like macrophages—ultimately leading to the release of pro-inflammatory factors.

Eventually, this mechanism, which essentially hijacks the sEV-mediated intercellular communication at the TME, is expected to alleviate the immune suppression at the TME, thereby favoring the action of anticancer adaptive immune responses.

Hopefully, once supported by experimental confirmation, this design would have a therapeutic utility in the battle against solid tumors.

#### **9. Conclusions**

Immunosuppression at TME protects cancer cells from both spontaneous and artificially generated host immune responses. Hence, subverting immunosuppression should be considered a priority for any anticancer immunotherapeutic strategy. Even if M2-like TAMs are major players, other cell types contribute to the general immunosuppression at TME, including CD4+ Tregs lymphocytes, myeloid-derived suppressor cells, and neutrophils [42]. Similar to macrophages, neutrophils can polarize in pro- and anti-tumor phenotypes, depending on the stimuli they receive at TME. Interestingly, it has been reported that IFN-γ can polarize neutrophils towards an anti-tumor phenotype [161]. Considering the quite high levels of IFN-γ transcripts induced by Nef in macrophages, the delivery of Nefmut-engineered, tumor-derived sEVs to M2-like TAMs is expected to have paracrine anti-tumor effects also on neutrophils that populate the TME.

The strategy we propose is certainly only one of many potential new anti-tumor therapeutic approaches that the manipulation of sEVs/EVs can offer. The increase in knowledge of the sEV/EVs biology, mainly regarding the mechanisms of cell entry, will favor the implementation of new and more efficient therapeutic approaches against tumors and infectious diseases.

**Funding:** This work was supported by the grant PGR00810 from Ministero degli Affari Esteri e della Cooperazione Internazionale, Italy.

**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

#### **References**

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## *Article* **Phenotypic and Functional Characteristics of Exosomes Derived from Irradiated Mouse Organs and Their Role in the Mechanisms Driving Non-Targeted E**ff**ects**

**Seda Tuncay Cagatay 1, Ammar Mayah 1, Mariateresa Mancuso 2, Paola Giardullo 2,3, Simonetta Pazzaglia 2, Anna Saran 2, Amuthachelvi Daniel 4, Damien Traynor 4, Aidan D. Meade 4, Fiona Lyng 4, Soile Tapio <sup>5</sup> and Munira Kadhim 1,\***

Received: 29 September 2020; Accepted: 5 November 2020; Published: 9 November 2020

**Abstract:** Molecular communication between irradiated and unirradiated neighbouring cells initiates radiation-induced bystander effects (RIBE) and out-of-field (abscopal) effects which are both an example of the non-targeted effects (NTE) of ionising radiation (IR). Exosomes are small membrane vesicles of endosomal origin and newly identified mediators of NTE. Although exosome-mediated changes are well documented in radiation therapy and oncology, there is a lack of knowledge regarding the role of exosomes derived from inside and outside the radiation field in the early and delayed induction of NTE following IR. Therefore, here we investigated the changes in exosome profile and the role of exosomes as possible molecular signalling mediators of radiation damage. Exosomes derived from organs of whole body irradiated (WBI) or partial body irradiated (PBI) mice after 24 h and 15 days post-irradiation were transferred to recipient mouse embryonic fibroblast (MEF) cells and changes in cellular viability, DNA damage and calcium, reactive oxygen species and nitric oxide signalling were evaluated compared to that of MEF cells treated with exosomes derived from unirradiated mice. Taken together, our results show that whole and partial-body irradiation increases the number of exosomes, instigating changes in exosome-treated MEF cells, depending on the source organ and time after exposure.

**Keywords:** exosomes; ionising radiation; non-targeted effects; signalling

#### **1. Introduction**

Radiation affects not only targeted cells but also non-irradiated neighbouring cells, a response described as radiation-induced bystander effects (RIBE). Molecular communication between irradiated and unirradiated neighbouring cells initiates RIBE and out-of-field (abscopal) effects [1]. Abscopal effects are not always categorised as completely separate from RIBE, and both are examples of non-targeted effects of ionising radiation (NTE) [2]. NTE, particularly RIBE, represents a plethora of other biological effects such as DNA damage, epigenetic changes, changes in proliferation, and apoptosis observed in non-targeted cells and tissues that have received molecular signals produced by irradiated cells via intercellular communication through cell gap junctions or through soluble secreted factors [3–8]. A relatively new mechanism that has been identified for mediating RIBE by soluble secreted factors is intercellular communication via exosomes.

Exosomes, which are one type of cell-derived vesicle, exist in different biological conditions and serve as an important additional pathway for signal exchange between cells. They are small (30–120 nm of diameter) membrane vesicles of endosomal origin that are secreted by normal or pathological cells into the microenvironment [9–12]. Exosomes carry a variety of bioactive molecules including proteins, mRNA, microRNA, DNA, and lipids [13–17]. Exosomes can serve as mediators of cell–cell communication. Upon internalisation, they can release their bioactive cargo molecules, which can change molecular profile, signalling pathways, and gene regulation in the recipient cells [18]. Although functions of exosomes have been extensively studied in many fields, including neurodegenerative diseases [19–21] and cancer [22–27], their roles in radiobiology have not been recognised until recently. Exosomes secreted by irradiated cells are likely to engage in various aspects of the systemic response to ionising radiation (IR), including RIBE, as well as abscopal effects. Recent studies have shown that exosomes derived from irradiated cells can cause ionising radiation-induced effects in unirradiated recipient cells [28–31]. Moreover, studies have also shown that exosomes play a role in RIBE in cancer cells and resistance to radiotherapy [32–36]. Mechanistically, IR causes changes in exosomal secretion patterns and content of exosomal cargo of the target cells, which can cause RIBE in the bystander cells as shown in several in vitro studies. [30,32,37–39].

In vivo partial body irradiation (PBI) constitutes a major problem in radiation protection, with contradictory evidence suggesting that PBI may contribute to and/or protect against detrimental health effects. PBI exposures are the norm in diagnostic radiology, radiation therapy and occupational exposures and may have significant implications for systemic consequences and human health effects at low and intermediate doses of ionising radiation [40–43]. However, to date, only limited mechanistic studies are available in understanding the consequences of PBI effects.

Diagnostic X-rays are the primary human-made source of radiation exposure to the general population, accounting for 14% of the total annual exposure worldwide from all sources. Estimates of risk of cancer from these exposures are ranged from 0.6% to 3.0% based on the annual number of diagnostic X-rays undertaken in developed countries [44]. For a better understanding of the underlying processes, epidemiological evidence mounting this risk should be augmented with radiobiological justifications. Moreover, radiation is the mainstay of cancer therapy, as radiotherapy is used in more than 50% of localised patients and is an indispensable component of comprehensive cancer treatment and care [45,46]. Therefore, it is vital to elucidate key mechanisms that could increase the risk of secondary malignancies, which could be developed as a result of non-targeted effects.

The risks posed by partial body as well as whole body exposures after both low, and therapeutic doses for cancer and non-cancer endpoints can be evaluated for radiation protection purposes if there is a plausible and consistent mechanism for detriment, a dose–response relationship that allows risk assessment, and biomarkers of response available for molecular epidemiological analysis. Exosomes can be ideal candidates as both prognostic and predictive biomarkers to monitor the radiation response and risk assessment given that radiation affects not only the production of exosomes but also their composition.

Growing evidence has suggested that radiation therapy can result in an increase in the release of exosomes and increase in oncogenic materials within the exosomes. Studies with glioblastoma multiforme cell lines have shown that radiation can elevate exosome release with a molecular profile containing an abundance of molecules essential for cell motility such as connective tissue growth factor (CTGF) mRNA and insulin-like growth factor binding protein 2 (IGFBP2) protein [32]. Evidence also showed that head and neck squamous cell carcinoma (HNSCC) FaDu cells release exosomes with a different proteome profile compared to the unirradiated control cells [47]. Other studies carried out with irradiated HNSCC cells demonstrated that exosomes carry pro-survival signals following ionising radiation [34]. Exosome transfer from stromal to breast cancer cells can regulate therapy resistance pathways including the STAT1 pathway. It has been recently reported that in glioblastoma, exosomes increase the cancer cell's ability to survive radiation by increasing oncogenic cargo and decreasing tumour-suppressive cargo [48].

Despite the extensive range of studies that have been carried out in exosome-mediated functions in radiation therapy and oncology, there is a lack of knowledge regarding the role of exosomes derived from inside and outside of the radiation field in the initial and delayed induction of non-targeted effects of ionising radiation. We have therefore investigated the role of exosomes as possible molecular signalling mediators of radiation induced out-of-target effects/RIBE. Exosomal characteristics were investigated by qNano analysis, TEM, Western blot and Raman spectroscopy. In vitro functional effects were investigated by transfering exosomes derived from whole body irradiated (WBI) or partial body irradiated (PBI) mice after 24 h and 15 days post-irradiation to recipient mouse embryonic fibroblast (MEF) cells and evaluating their effect on cellular viability, DNA damage and calcium, reactive oxygen species and nitric oxide signalling compared to that of exosomes derived from unirradiated mice. Taken together, our results show that whole and partial-body irradiation increases the number of exosomes, instigating changes in exosome-treated MEF cells, depending on the source organ and time after exposure.

#### **2. Results**

#### *2.1. Characterisation of Exosomes*

Exosomes were extracted from plasma at 24 h and from organ samples (brain, liver, and heart) at 24 h and 15 days post different IR exposure conditions by the ultracentrifugation method. Exosome concentration and their size distribution, presence of exosome markers and the biochemical profile of exosomes were evaluated by qNano analysis, TEM, Western blot and Raman spectroscopy. Significant differences were particularly observed in exosome number between samples, as fully described below.

#### 2.1.1. Characterisation of Exosomes by qNano

Exosome size and concentration of 2 Gy WBI and PBI mice organs (brain, liver, and heart) and plasma samples were assessed by using a tunable resistance pulse sensing (TRPS) system via qNano after 24 h and 15 days after irradiation (Figure 1).

Although exosome concentration levels varied for all organs and plasma, they were increased in the brain, liver, heart, and plasma of both 2 Gy X-ray irradiated WBI and PBI mice compared to the 0 Gy control groups after 24 h post-IR. Exosome concentrations in PBI mice organs showed a more dramatic increase compared to the WBI organs, while plasma obtained from WBI irradiated mice showed a higher level of exosomes compared to the plasma obtained from PBI mice.

For 15 days post-IR samples, exosome concentrations increased in the brain, liver, and heart of both 2 Gy WBI and PBI mice compared to the 0 Gy control groups. Taken together, the results show that ionising radiation can increase the yield of exosomes derived from 2 Gy mice organs after 24 h (Figure 1a–d) and 15 days (Figure 1e–g) following irradiation.

qNano analysis also showed that the exosome suspensions had a relatively narrow size distribution (70–130 nm) for all samples. Differences in the mean diameter of exosomes were significant for 2 Gy WBI and PBI brain samples compared to the unirradiated controls, whereas no significant difference in exosome size was observed between liver, heart, and plasma samples and their controls [30], indicating that ionising radiation can also alter the size distribution of exosomes in an organ-specific manner.

**Figure 1.** Concentration (exosome/mL) and size (nm) distribution of exosome suspensions obtained from (**a**–**d**) 24 h post ionising radiation (IR) and (**e**–**g**) 15 days post-IR 2 Gy whole body irradiated (WBI) and 2 Gy partial body irradiated (PBI) mouse compared to unirradiated mouse organs (brain, liver, heart) and plasma. Bars represent mean ± SD; significance was tested by Student's *t*-test (\* *p* < 0.05, \*\* *p* < 0.01, \*\*\* *p* < 0.001).

2.1.2. Characterisation of Exosomes by Transmission Electron Microscopy (TEM)

Exosome suspensions were investigated by transmission electron microscopy for size. Negative staining of exosome suspensions showed vesicular structures in the anticipated size range and classical exosome morphology, as shown in Figure 2a.

(**b**)

**Figure 2.** Confirmation of presence of exosomes. (**a**) TEM micrographs of exosomes. Representative images 1:10 or 1:100 diluted plasma exosome samples. (**b**) Western blot analysis of exosomes for CD63. Lane 1 protein ladder, lane 2: total cell lysate, lane 3: unirradiated (0 Gy) plasma sample, lane 4:2 Gy WBI plasma protein and lane 5:2 Gy PBI plasma protein sample.

#### 2.1.3. Characterisation of Exosomes by Western Blot

The presence of exosomes in extracted samples was confirmed by Western blot analysis against exosome marker CD63. A representative Western blot analysis for plasma exosome samples is shown in Figure 2b. CD63 bands of samples were observed in the predicted molecular weight (26 kDa), which further confirms the presence of exosomes together with findings from qNano analysis and electron microscopy.

#### 2.1.4. Characterisation of Exosomes by Raman Spectroscopy

Exosomes were also analysed by Raman spectroscopy, a label-free method based on light scattering which provides a biochemical profile of the sample [49,50]. Partial least squares discriminant analysis (PLSDA) showed good separation of the Raman spectral data from exosomes from brain, heart, and liver into control, PBI and WBI groups at 24 h and 15 days post-irradiation (Figure 3a,b). The spectral differences were based on changes in nucleic acid and protein features and these will be analysed in more detail in future work.

**Figure 3.** *Cont.*

**Figure 3.** Raman spectroscopy of exosomes (**a**) 24 h post-IR: (**A**) mean Raman spectra from exosomes from brain of control, PBI and WBI mice at 24 h post irradiation, (**B**) PLSDA scatterplot of Raman spectral data from exosomes from control (blue), PBI (pink) and WBI (red) brain, (**C**) mean Raman spectra from exosomes from heart of control and WBI mice at 24 h post irradiation, (**D**) PLSDA scatterplot of Raman spectral data from exosomes from control (blue) and WBI (red) heart, (**E**) mean Raman spectra from exosomes from liver of PBI and WBI mice at 24 h post irradiation, (**F**) PLSDA scatterplot of Raman spectral data from exosomes from PBI (pink) and WBI (red) liver. (**b**) Fifteen days post-IR: (**A**) mean Raman spectra from exosomes from brain of control, PBI and WBI mice at 15 days post irradiation, (**B**) PLSDA scatterplot of Raman spectral data from exosomes from control (blue), PBI (pink) and WBI (red) brain, (**C**) mean Raman spectra from exosomes from heart of control and WBI mice at 15 days post irradiation, (**D**) PLSDA scatterplot of Raman spectral data from exosomes from control (blue) and WBI (red), (**E**) mean Raman spectra from exosomes from liver of control, PBI and WBI mice at 15 days post irradiation, (**F**) PLSDA scatterplot of Raman spectral data from exosomes from control (blue), PBI (pink) and WBI (red) liver.

#### *2.2. E*ff*ects of Exosomes from WBI and PBI Mice on Bystander MEF Cells*

#### 2.2.1. Effects of Exosomes on Cell Viability

The effects of exosomes on cell viability were evaluated by using the MUSE Cell analyser. The results in Figure 4a show that 24 h post-IR, 2 Gy WBI brain, 2 Gy WBI liver, 2 Gy PBI liver and 2 Gy PBI heart exosomes significantly reduced the viability/survival of MEF cells. However, cells that received plasma exosomes did not show a significant change in the cell viability levels. High levels of cell death responses were observed in cells that received 15 days post-IR exosomes derived from organ samples, particularly the brain and liver, suggesting that exosomes can transfer long-lived signal-inducing radiation-induced genomic instability, as shown in Figure 4b. Moreover, a significant decrease in cell viability was also observed in cells treated with 15-day WBI and PBI exosomes compared to those that received 24-h WBI and PBI exosomes. Surprisingly, cells that received 0 Gy liver exosomes showed a high level of cell death compared to those that received WBI and PBI exosomes at the delayed time point (15-day exosomes), suggesting that delayed cell death can also be induced by exosomes derived from unirradiated liver cells. More investigations are needed to confirm this.

**Figure 4.** Viability of mouse embryonic fibroblast (MEF) cells treated with (**a**) 24 h post-IR and (**b**) 15 day post-IR exosomes obtained from organs and plasma of 2 Gy WBI or PBI mouse compared to unirradiated mouse organ and plasma exosomes. Data groups were obtained by triplícate measurements (\* *p* < 0.05, \*\* *p* < 0.01, \*\*\* *p* < 0.001).

#### 2.2.2. Effects of Exosomes on DNA Damage

#### DNA Damage in Comet Tail

Total DNA damage in the comet tail was measured in order to assess the role of exosomes obtained from 2 Gy WBI or PBI organs and plasma on the induction of DNA damage on the MEF recipient cells. As shown in Figure 5b–e, an increase in DNA damage was observed in 24 h post-IR brain, liver, heart, and plasma exosome-treated cells compared to unirradiated control groups. For brain and heart samples, significantly higher DNA damage was observed in MEF cells treated with 24 h 2 Gy WBI mice exosomes, while treatment with plasma exosomes from both 2 Gy WBI and PBI mice caused significantly higher DNA damage in MEF cells compared to unirradiated control treated MEF cells.

Similarly, as shown in Figure 5f–h, a significant increase in DNA damage was also observed in MEF cells treated with 15 days post-IR 2 Gy WBI and PBI brain, 2 Gy PBI liver, and 2 Gy WBI and PBI heart exosomes compared to their corresponding controls treated with unirradiated exosomes. In addition to the immediate bystander signal effects transferred by exosomes, exosome-transferred long-lived signals also have an ability to induce DNA damage responses in the treated cells.

#### γH2AX Immunostaining

In order to further evaluate DNA damage in terms of Double-strand breaks (DSBs)γH2AX immunostaining was carried out in MEF cells treated with 2 Gy PBI or WBI mice organ and plasma exosomes and unirradiated organ exosomes for both 24 h and 15 day time points. DSBs were significantly higher in MEF cells treated with all 24 h post-IR organ exosomes and plasma exosomes, as shown in Figure 6b–e, while the highest levels of DSBs were observed in MEF cells treated with 24 h post-IR brain exosomes, as shown in Figure 6a,b. The foci count was not significant for 15 days post-IR exosome-treated MEF cells (Figure 6f–h), suggesting that 15 day post-IR exosome-induced DNA DSBs could be faithfully or unfaithfully repaired. Alternatively, severely DSB damaged cells may have been removed from culture, as significantly delayed cell death was observed in the treated cells, so the γH2AX method was unable to detect the breaks in the 15 days post-IR exosome-treated samples.

(**a**)

**Figure 5.** *Cont.*

**Figure 5.** Comet assay showing the induction of DNA damage in MEF cells. (**a**) Representative fluorescent microscope images of untreated MEF cells and comet tails in treated MEF cells. (**b**–**e**) Induction of DNA damage in MEF cells treated with 24 h post-IR exosomes, (**f**–**h**) 15 day post-IR exosomes obtained from organs, and plasma of 2 Gy WBI or PBI mouse compared DNA damage in MEF cells treated with exosomes obtained from unirradiated mouse organ exosomes. Percentage of DNA in the comet tail was scored in 200 cells treated for each group. Statistical analysis was performed using the Mann–Whitney U Test (\* *p* < 0.05, \*\* *p* < 0.01, \*\*\* *p* < 0.001).

(**a**)

**Figure 6.** *Cont.*

**Figure 6.** γH2AX foci formation in MEF cells after 24 h treatment with 24 h and 15 days post-IR organ and plasma of 2 Gy WBI or PBI mouse compared to unirradiated mouse organ exosomes treated cells. (**a**) Representative 63X fluorescent microscope images of MEF cells. Arrows indicate the location of γH2AX foci (Alexa488), cells were counterstained with 4 ,6-Diamidine-2-phenylindole dihydrochloride (DAPI). (**b**–**e**) Bars represent the mean γH2AX foci formed per cell ± SEM after treatment with 24 h post-IR exosomes and (**f**–**h**) 15 day post-IR exosomes obtained from organs and plasma of 2 Gy WBI or PBI mouse compared to corresponding unirradiated mouse organ and plasma exosomes (\* *p* < 0.05, \*\* *p* < 0.01, \*\*\* *p* < 0.001).

#### Chromosomal Aberrations

Chromosomal damage was assessed in metaphase chromosomes of MEF cells treated with 24 h post-IR exosomes and 15 day 2 Gy WBI and PBI mice post-IR exosomes. As shown in Figure 7b–e, chromosomal aberrations were increased significantly in MEF cells treated with 24 h post-IR WBI brain, heart, and plasma and PBI liver exosomes compared to the MEF cells treated with control organs and plasma exosomes. However, chromosomal aberrations do not show significant differences between treatment groups for MEF cells treated with 15 days post-IR exosomes, as shown in Figure 7f–h. The findings suggest that exosomes derived from organs and plasma are able to induce chromosome aberrations in the MEF cells only at early time point, while the exosomes' role in inducing chromosomal damage was reduced to the control level at the late time point post-exposure. This could be due to the ability of the cells to repair the damage at this time point of analysis.

#### 2.2.3. Role of Exosomes as Signalling Mediators

To further investigate the role of exosomes as mediators of radiation induced damage, calcium, reactive oxygen species (ROS), and nitric oxide (NO) were monitored in real time in MEF cells exposed to media containing exosomes from organs and plasma as well as from unirradiated, WBI, and PBI mice.

#### Calcium Signalling

Increases in intracellular calcium were measured using the calcium-sensitive dyes Fluo 3 and Fura Red. Rapid calcium fluxes were induced in MEF recipient cells following addition of exosomes from brain, heart and liver from WBI and PBI mice but not from sham irradiated mice at 24 h (Figure 8a–c) and 15 days (Figure 8d–f) post-irradiation. Similarly, rapid calcium fluxes were induced in MEF recipient cells following addition of exosomes from plasma from WBI and PBI mice but not from sham irradiated mice at 24 h post irradiation (Figure 8g).

(**a**)

**Figure 7.** *Cont.*

**Figure 7.** Chromosome analysis of MEF cells treated with 24 h and 15 days post-IR organ and plasma of 2 Gy WBI or PBI mouse compared to unirradiated mouse organ exosomes treated cells. (**a**) Normal and aberrant metaphase of MEF cells treated with exosomes derived from 24-h post-IR brain exosomes isolated from control (0 Gy), 2 Gy WBI and PBI mouse. Mean chromosomal aberrations/cell in MEF cells treated with (**b**–**e**) 24-h and (**f**–**h**) 15-day post-IR exosomes obtained from organs and plasma of 2 Gy WBI or PBI mouse compared to corresponding unirradiated mouse organ exosomes. Chromosomal aberrations (total) were scored in 100 metaphase spreads in cells treated with corresponding exosomes for 24 h. Bars represent mean chromosomal aberrations per cell ± SEM, significance was tested by Fisher's exact test. (\* *p* < 0.05, \*\* *p* < 0.01, \*\*\* *p* < 0.001).

**Figure 8.** Intracellular calcium levels in MEF cells as indicated by the ratio of fluorescence emissions from the calcium sensitive dyes Fluo-4 and Fura Red after addition of media containing exosomes from (**a**–**c**) 24 h post-IR organs (**d**–**f**) 15 days post-IR organs and (**g**) 24 h post-IR plasma of 2 Gy WBI or PBI mice compared to corresponding unirradiated mouse organ and plasma exosomes.

#### ROS and NO Signaling

Production of ROS in MEF cells was measured using a fluorescent dye, CM-H2 DCFDA. The data were expressed as mean fluorescence intensity normalised to each respective control. Significant ROS production was observed within 5 min of addition of exosomes from brain, heart and liver from WBI and PBI mice at 24 h and 15 days post irradiation and within 5 min of addition of exosomes from plasma from WBI and PBI mice at 24 h post irradiation as shown in Figure 9a.

Similarly, NO levels were measured using a fluorescent dye, 4-amino-5-methylamino-2,7 ifluorofluorescein diacetate (DAF-FM). Again, the data were expressed as mean fluorescence intensity normalised to each respective control. Significant NO production was observed within 5 min of addition of exosomes from brain, heart and liver from WBI and PBI mice at 24 h and 15 days post-irradiation and within 5 min of addition of exosomes from plasma from WBI and PBI mice at 24 h post-irradiation (Figure 9b).

**Figure 9.** (**a**) Intracellular reactive oxygen species (ROS) levels in MEF cells as measured using CM-H2 DCFDA fluorescence after addition of media containing exosomes from brain, heart and liver from mice (**A**) 24 h and (**B**) 15 days after whole or partial body irradiation and (**C**) from plasma from mice 24 h after whole or partial body irradiation. Data are presented as mean ± SD after each sample was normalised to its respective control. (**b**) Intracellular NO levels in MEF cells as measured using DAF fluorescence after addition of media containing exosomes from brain, heart, and liver from mice (**A**) 24 h and (**B**) 15 days after whole or partial body irradiation and (**C**) from plasma from mice 24 h after whole or partial body irradiation. Data are presented as mean ± SD after each sample was normalised to its respective control.

#### **3. Discussion**

Exosome research is a new and growing field in radiobiology. To date, growing evidence supports the observation that ionising radiation can induce increased exosome release as well as changes in exosome content in in vitro models [32,34,51,52]. However, there is a lack of knowledge regarding the profile of exosomes released from directly irradiated versus abscopal organs and the systemic non-targeted effects of IR caused by those exosomes. Therefore, in the present study, we first investigated the organ responses to 2 Gy X-ray, a radiotherapeutic dose of ionising radiation, in terms of exosome profile including exosome concentration and size. Subsequently, we explored the role of these radiation-derived exosomes in the induction of cell death and DNA damaging effects in recipient MEF cells. Finally, the role of exosomes as mediators of radiation-induced damage was investigated by measuring calcium, ROS, and NO in exosome-exposed MEF cells.

Our data suggested that exosome yield can vary according to the organ type and whether exosomes were derived from directly irradiated organs (obtained from whole body irradiated animals) or abscopal organs (obtained from partial body irradiated animals). Differences in exosome concentrations between organs were also observed upon early (24 h post-IR) and delayed time (15 days post-IR) points.

Only significant change for size distribution of exosomes was observed in 24 h post-IR mice brain where WBI and PBI mice organs had an increased size distribution of exosomes compared to the unirradiated samples.

Although all three organs, the brain, liver, and heart, showed organ-specific exosome secretion levels, they exhibited a highly similar pattern of secretion when whole body and partial body irradiated sample organs were compared to their unirradiated counterparts at 24 h of post irradiation (Figure 1a–c). Both WBI and PBI organs showed a significantly increased level of exosome concentrations, particularly in the brain and liver, while exosome concentration levels were highest in PBI organs. One of the well-explained mechanisms of exosome release is linked to radiation-induced DNA damage and induction of the p53-related additional pathway of exosome biogenesis and secretion, which in turn leads to a significant increase in exosome release [52]. The same pattern of exosome secretion was also observed for all three organs at the delayed time point (Figure 1e–g).

Analysis of plasma samples from WBI and PBI mice, on the other hand, also showed significantly higher yield of exosomes for irradiated mice, albeit showing higher levels for WBI mice compared to PBI mice (Figure 1d). In addition, there was no alteration in size distribution between plasma samples.

Several studies support the involvement of exosomes in mediating RIBE in vivo and in vitro [29,30,37,53–55]. Bystander signals can be communicated through exosomes that can result in functional changes in target cells either by receptor-mediated interactions or by transfer of various bioactive molecules such as proteins, mRNA, miRNA or bioactive lipids carried by exosomes [56,57]. Therefore, in subsequent steps of our study, we interrogated whether WBI and PBI mice organ and plasma exosomes have an impact on the survival and DNA stability of recipient MEF cells.

It is evident from the literature that radiotherapeutic doses of X-ray can induce cell survival in cancer cell line models [34,48]. However, there has been a lack of information regarding impact of exosomes derived from both directly irradiated and abscopal organs on normal cells such as MEF cells, which was our model system in this study. Data showed that those exosomes from both early and delayed time points have an ability to reduce recipient MEF cell viability (Figure 4a,b). Albanese and co-authors showed that levels of TNFSF6 exfoliated on extracellular vesicles were increased following IR, suggesting a mechanism for abscopal and bystander effects after irradiation [58] which can also be one possible explanation for our findings.

Finally, we attempted to explore DNA damaging effects of exosomes in our model system as another aspect of bystander effects. Our findings show that both WBI and PBI organs and plasma induce DNA damage and chromosomal aberrations in the recipient MEF cells, where the extent of DNA damage was found to be organ and time specific. The amount of DNA in the comet tail showed significant results for both WBI and PBI organs when compared to MEF cells that received unirradiated organ and plasma exosomes as both early and delayed effects of IR, as depicted in Figure 5. Conversely, only early effects were significant when DSB foci (Figure 6b–e) or chromosomal aberrations (Figure 7b–e) were investigated. An absence of significant DNA DSBs or chromosomal aberrations in MEF cells that received delayed time point exosomes can be explained by the repair of DNA DSBs or removal of cells from the population (Figures 6f–h and 7f–h) as a delayed time point response to IR. It has been shown that the phosphorylation status of critical DNA damage repair proteins can be changed by exosomes released from breast cancer cells [59]. Moreover, exosomes from irradiated HNSCC cells were shown to enhance DNA repair in unirradiated recipient cells [34].

In addition, our study showed the involvement of exosomes in mediating radiation damage by increasing calcium levels (within 30 s) and inducing ROS and NO (within 5 min) following addition of exosomes from brain, heart and liver from WBI and PBI mice at 24 h and 15 days post-irradiation and from plasma from WBI and PBI mice at 24 h post irradiation. Calcium, ROS, and NO signalling has been shown previously in bystander cells exposed to media from irradiated cells [60–64], and to exosomes from irradiated cells [29].

#### **4. Materials and Methods**

#### *4.1. Animal Breeding, Irradiation and Sample Collection*

Animal studies were performed according to the European Community Council Directive 2010/63/EU, approved by the local Ethical Committee for Animal Experiments of the ENEA on 01/10/2017 with the project identification code No.004/2017- SEPARATE, and authorized by the Italian Ministry of Health (n◦ 539/2018-PR). Eighty days of age C57 BL/6 female mice were either whole body irradiated (WBI) or partial body irradiated (PBI) with 2 Gy X-rays or sham irradiated (0 Gy).

For partial body exposure, the upper two-thirds of the adult mouse body was shielded, whilst exposing the lower one-third. At two different time points (24 h and 15 days) post-irradiation, animals were sacrificed by perfusion, washing out the blood and running physiological saline through the vascular system. The brains, livers and hearts were collected, snap frozen in liquid nitrogen, and stored at −80 ◦C for later exosome collection. Furthermore, at 24 h post-irradiation, animals were sacrificed, blood was collected and blood plasma were separated and snap frozen for later exosome extraction. All the samples were shipped to Oxford Brookes University (OBU) for exosome isolations as described below.

#### *4.2. Exosome Isolation*

The procedure of exosomes isolation from mouse organs (brain, liver and heart) were adapted from a protocol that has previously been established by Polanco et al. [65]. Briefly, organs (brain, liver and heart) were slowly defrosted, dissected and gently homogenised before being incubated in 7 mL of 20 units/mL papain (LS003119, Worthington, Lakewood, NJ, USA) in RPMI-1640 (R7388, Sigma, St Louis, MO, USA) for 20 min at 37 ◦C. Similarly, the reaction was stopped with 14 mL of ice-cold RPMI. The homogenised samples were then gently disrupted by pipetting with a 10 mL pipette, which was followed by a series of differential 4 ◦C centrifugations at 300× *g* for 10 min, 2000× *g* for 10 min, and 10,000× *g* for 30 min. However, the plasma samples were slowly defrosted, collected in falcon tubes and then similarly subjected to serial centrifugations at 300× *g* for 10 min, 2000× *g* for 10 min, and 10,000× *g* for 30 min. The supernatants from the 10,000× *g* centrifugations were passed through 0.45 μm and then 0.22 μm syringe filters, and then centrifuged at 120,000× *g* for 90 min at 4 ◦C to pellet exosomes. Exosome pellets were resuspended either in PBS or in exosome resuspension buffer (4478545, Invitrogen, Carlsbad, CA, USA) for downstream experiments.

#### *4.3. Tunable Resistance Pulse Sensing (TRPS) via qNano*

Exosome size and concentration were measured using TRPS via qNano machine (Izon Science™, Lyon, France), as described by Al-Mayah et al. [30], in which sample particles are driven through the nanopore (NP100 mm) by applying a combination of pressure and voltage.

Each particle causes a resistive pulse or blockade signal, which is detected and measured by the application software. Blockade magnitude is directly proportional to the volume of each particle [66], while blockade frequency is used to determine particle concentration [67]. Finally, magnitude and frequency values are converted to respective particle properties as size and concentration by normalizing against a known particle standard such as carboxylated polystyrene calibration nanoparticles.

#### *4.4. Transmission Electron Microscopy (TEM): Morphological Analysis*

In addition to qNano analysis and Western blotting to confirm existence of exosomes derived from organs and plasma, samples were morphologically analysed by electron microscopy [68]. In brief, 1:10 or 1:100 diluted exosome suspensions in PBS were incubated on formvar-coated and charged nickel grids (200 mesh) for 2 min. They were then fixed in 2.5% glutaraldehyde for 10 min, and then washed three times in 0.1 M phosphate buffer by dipping onto the surface of a water droplet and then stained with 8 μL of 2% aqueous uranyl acetate for 2 min. The stain was drawn off with cartridge paper to leave a thin negative stain. The grids were then examined and photographed under Jeol JEM-1400 Flash transmission electron microscope, with a Gatan OneView 16 Megapixel camera.

#### *4.5. Western Blot*

Western blotting has frequently been used for identifying and determining proteins, as one of the most commonly used methods in laboratories. It has also been utilised as a semi-quantitative technique in order to compare the expression of proteins in the cells and tissues [69].

Exosome proteins were extracted by using Total Exosome RNA and Protein Isolation kit (4478545, Invitrogen, Carlsbad, CA, USA). The protein content was measured using the modified Bradford assay using a Coomassie solution and Pre-Diluted Protein Assay Standards (23208, Thermo Scientific, Waltham, MA, USA). Exosome proteins were mixed with LDS sample buffer (NP0007, NuPAGE, Invitrogen, Carlsbad, CA, USA), and 30 μg from each sample were separated in a 4–20% polyacrylamide Mini-PROTEAN®TGX Stain-Free™ gels (456-8095, Bio-Rad, Hercules, CA, USA) and transferred onto AmershamTM HybondTM PVDF membrane (10600090, GE Healthcare, Little Chalfont, UK). The membranes were blocked in 5% BSA and were incubated with the primary antibodies CD63 (ab 217345, Abcam, Cambridge, UK), in 5% BSA for 2 h at 1:1000 dilution. Membranes were washed three times with PBS-T which was followed by incubation with Goat Anti-Rabbit IgG H&L Alexa Fluor® 488 (ab150077, Abcam, Cambridge, UK) at 1:10,000 dilution for 1 h.

#### *4.6. Raman Spectroscopy*

A LabRam HR confocal Raman instrument (HORIBA, Northampton, UK) was used for spectral acquisition. Manual calibration of the grating was carried out using the 520.7 cm−<sup>1</sup> Raman line of crystalline silicon. Dark current measurement and recording of the substrate and optics signal was also performed, for data correction. As a source, a 532 nm laser of ~12 mW power was focused by a 100 X (MPlanN, Olympus, NA = 0.9) objective onto the sample; and the resultant Raman signals were detected using a spectrograph with a 1200 g/mm grating coupled with a CCD. Raman spectra were acquired in the 400 to 1800 cm−<sup>1</sup> region. Multiple calibration spectra of 1,4-bis(2-methylstyryl) benzene were recorded along with each sample acquisition. All spectra were subsequently wavenumber calibrated using in-house developed procedures in Matlab v.9.3 (Mathworks Inc., Natick, MA, USA). The instrument response correction was performed using the spectrum of NIST Standard Reference Material (SRM) no.2242.

Raman spectroscopic data were pre-processed (normalization, baseline subtraction, etc.) using in-house developed protocols within the Matlab (The Mathworks Inc.) environment and corrected spectra were subjected to Partial Least Squares Discriminant Analysis (PLSDA).

#### *4.7. Cell Culture and Exosome Treatments*

Mouse embryonic fibroblast (C57 BL/6) (ATCC®SCRC-1008™) cells (MEF-BL/6-1) were grown in ATCC-formulated Dulbecco's modified Eagle's medium (30-2002, ATCC, Manassas, VA, USA) in the presence of 15% Fetal Bovine Serum (FBS) and 1% penicillin (P4333, Sigma, St Louis, MO, USA) and 5% CO2. A total of 1.5 <sup>×</sup> 106 MEF cells were treated with exosomes obtained from tissue corresponding to the 1:5 of the organ mass in15 mL for 24 h prior to experiments.

#### *4.8. Cell Count and Viability*

Cell count and viability were measured by using Muse™ Count and Viability Cell Dispersal Reagent (MCH100107, Merck, Millipore, Kenilworth, NJ, USA) and Muse™ Cell Analyzer (0500-3115, Merck, Millipore, Kenilworth, NJ, USA) as described by Laka et al. [70]. Briefly, 380 μL Muse™ Count and Viability reagent was added to 20 μL cell suspensions in PBS, and incubated for 5 min. Then, cell viability was measured in Muse™ Cell Analyzer according to the dilution factor, 20. Viability percentages were evaluated. Each sample was analysed in triplicates.

#### *4.9. Alkaline Single Cell Gel Electrophoresis (Comet Assay)*

Single-cell gel electrophoresis, or the comet assay, is a sensitive method to quantify total DNA damage (double-strand breaks, single-strand breaks and base damage) in individual cells [71,72]. The comet assay was carried out as described by Al-Mayah et al. [28]. Briefly, microscope slides were coated with 1% normal melting point agarose (NMPA) (A9539, Sigma, St Louis, MO, USA), and were allowed to dry overnight. The coated slides were then placed on a metal tray on ice. Twenty thousand cells were resuspended with 200 μL of 0.6% low melting point agarose (LMPA)

(BP165-25, Fisher Scientific, Pittsburgh, PA, USA) and placed immediately onto chilled pre-coated slides. The slides were then transferred to a Coplin jar, which was filled with cold alkaline lysis buffer (2.5 M NaCl, 100 mM EDTA pH 8.0, 10 mM Tris-HCl pH 7.6, and 1% Triton X-100, pH 10), and the jar was kept at 4 ◦C overnight.

The slides were then moved to a horizontal electrophoresis tank filled with electrophoresis buffer (0.3 M NaOH and 1 mM EDTA, (pH 13) at 4 ◦C for 40 min. The electrophoresis was run for 30 min, at 19 V, 300 A. Slides were neutralized with neutralizing buffer (0.4 M Tris-HCl, pH 7.5), washed with distilled water, and immediately stained with a 1:10,000 dilution of Diamond Nucleic Acid Dye (H1181, Promega, Madison, MA, USA). The slides were analysed using fluorescent microscopy and Comet Assay IV Image Analysis Software (Perceptive Instruments, Bury St Edmunds, UK). Tail intensities were evaluated for comparisons.

#### *4.10.* γ*H2AX Immunostaining*

To investigate DNA damage in the exosome-treated cells, γH2AX Immunostaining assay was adapted from Zhang et al. [73]. Briefly, cells, fixed with 25% acetic acid in methanol, were dropped on slides and air-dried. Then cells were permeabilised with 0.2% Triton X-100 in PBS for 10 min. The cells were then blocked with 3% BSA in PBS for 1 h at room temperature (RT). Cells were incubated with γH2AX monoclonal antibody (ab26350, Abcam, Cambridge, UK) at 1:500 dilution overnight at 4 ◦C. Then, cells were washed three times with PBS-T and incubated with the secondary antibody conjugated with Alexa Fluor 488 (ab150113, Abcam, Cambridge, UK) for 1 h at RT at 1:1000 dilution. Cells were washed again three times with PBS-T and then mounted with anti-fade reagent containing DAPI.

#### *4.11. Chromosome Analysis*

Chromosomal preparation for Giemsa solid staining technique was carried out as described by [30]. Briefly, exosome-treated cells were incubated with 20 ng/mL demecolcine (D1925, Sigma, St Louis, MO, USA) for 1.5 h in a humidified 5% CO2 incubator at 37 ◦C. Cells were collected and centrifuged 300× *g* for 10 min at RT. Cell pellet was re-suspended with 0.075 M KCL as hypotonic solution for 20 min at 37 ◦C. The hypotonic cell suspensions were centrifuged at 200× *g* for 10 min at RT after addition of few drops of 25% acetic acid in methanol 3:1 fixative. Next, the cell pellet was fixed twice with 25% acetic acid in methanol. Fixed cells were dropped onto clean slides, and stained using the Giemsa solid staining technique. Slides were mounted and at least 100 metaphases were analysed per group.

#### *4.12. Live Cell Imaging*

Intracellular calcium levels were determined using Fluo 3 and Fura Red (Invitrogen/Molecular Probes, BioSciences, Dublin, Ireland) and ROS and NO were followed in real time using the fluorescent probes 5-(and 6-)chloromethyl 2,7 dichlorodihydrofluorescein diacetate, acetyl ester (CM-H2 DCFDA) (Invitrogen/Molecular Probes) and 4-amino-5-methylamino-2,7-ifluorofluorescein diacetate (DAF-FM) (Invitrogen/Molecular Probes) respectively, as previously described (Lyng et al., 2006).

Briefly, MEF recipient cells were grown on 35 mm glass bottom culture dishes (Mat Tek Corporation, Ashland, MA, USA; # P35 G-0-20-C). Twenty-four hours after plating, cells were washed twice with a buffer containing 130 mM NaCl, 5 mM KCl, 1 mM Na2 HPO4, 1 mM CaCl2 and 1 mM MgCl2 (pH 7.4) and incubated with 3 μM Fluo 3 and 3 μM Fura Red acetoxymethyl esters for 1 h and with 5 μM CM-H2 DCFDA or DAF-FM for 30 min in the buffer at 37 ◦C. Subsequently, the cultures were washed three times with buffer. All dyes were excited at 488 nm and fluorescence emissions at 525 (Fluo 3, CM-H2 DCFDA and DAF-FM) and 660 nm (Fura Red) were recorded using a Zeiss LSM 510 confocal microscope. Exosomes were added after 60 s when a stable baseline had been established. All measurements were performed at room temperature.

#### *4.13. Statistical Analysis*

For exosome size, diameter and viability data, significance was tested by Student's *t*-test using raw data. Each experiment was carried out in triplicate. Analysis showed no significant inter-experimental variation; therefore, data from these experiments were pooled. For analysis of comet assays, statistical analysis was performed using the Mann–Whitney test, utilising the median of the raw data. Meanwhile, chromosomal analysis and γH2AX immunostaining data were subjected to Fisher's exact test. Data were considered statistically significant if *p*-value was lower than 0.05 (\* *p* < 0.05, \*\* *p* < 0.01, \*\*\* *p* < 0.001).

#### **5. Conclusions**

In this study, we provide the first insights into the in vivo systemic effects of early and delayed effects of X-ray irradiation in terms of exosome profile and bystander effects. Taken together, the findings show that exosome yield is organ-specific and can be significantly increased in both directly irradiated and abscopal organs. On the other hand, changes in the levels of survival and DNA damage in MEF cells, receiving PBI or WBI exosomes, were not necessarily correlated with the increase in exosome yield in different radiation conditions or time points. Those manifested bystander effects in MEF cells also differed as early and delayed responses to ionizing radiation. The role of exosomes in mediating radiation damage was shown by rapid calcium fluxes and induction of ROS and NO in MEF recipient cells following addition of exosomes from WBI and PBI mice, but not from unirradiated mice. Altogether, these results draw attention to the content of those exosomes, which can be the key to understand the observed effects. Further studies such as miRNA expression profiles and proteomics will help to discover the molecules that are responsible for the observation of bystander effects in recipient cells.

**Author Contributions:** Conceptualization, M.K., F.L., S.T. and M.M.; methodology, S.T.C., A.M., S.P., M.M. and M.K.; software, A.M. and S.T.C.; validation, A.M., S.T.C. and M.K.; formal analysis, A.M., S.T.C., D.T., A.D., F.L. and M.K.; investigation, S.T.C., A.M., P.G., S.P., M.M., D.T., A.D., F.L. and M.K.; resources, A.S., M.M., F.L. and M.K.; data curation, S.T.C., A.M., F.L. and M.K.; writing—original draft preparation, S.T.C., A.M., D.T., A.D., A.D.M., F.L. and M.K.; writing—review and editing, S.T.C., A.M., D.T., A.D., A.D.M., F.L. and M.K.; visualization, S.T.C., A.M., F.L. and M.K.; supervision, M.K.; project administration, M.K., F.L., S.T., A.S. and M.M. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the European Union's Horizon 2020 research and innovation programme under grant agreement No 662287.

**Acknowledgments:** We confirm there was no any support given other than was covered by the author contribution and the funding sections.

**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

#### **Abbreviations**

#### **References**

1. Kadhim, M.; Salomaa, S.; Wright, E.; Hildebrandt, G.; Belyakov, O.V.; Prise, K.M.; Little, M.P. Non-targeted effects of ionising radiation—Implications for low dose risk. *Mutat. Res.* **2013**, *752*, 84–98. [CrossRef] [PubMed]

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© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## *Article* **Large Extracellular Vesicles Can be Characterised by Multiplex Labelling Using Imaging Flow Cytometry**

### **Suzanne M Johnson 1,\*, Antonia Banyard 2, Christopher Smith 1, Aleksandr Mironov <sup>3</sup> and Martin G. McCabe 1,4**

Received: 23 September 2020; Accepted: 16 November 2020; Published: 18 November 2020

**Abstract:** Extracellular vesicles (EVs) are heterogeneous in size (30 nm–10 μm), content (lipid, RNA, DNA, protein), and potential function(s). Many isolation techniques routinely discard the large EVs at the early stages of small EV or exosome isolation protocols. We describe here a standardised method to isolate large EVs from medulloblastoma cells and examine EV marker expression and diameter using imaging flow cytometry. Our approach permits the characterisation of each large EVs as an individual event, decorated with multiple fluorescently conjugated markers with the added advantage of visualising each event to ensure robust gating strategies are applied. Methods: We describe step-wise isolation and characterisation of a subset of large EVs from the medulloblastoma cell line UW228-2 assessed by fluorescent light microscopy, transmission electron microscopy (TEM) and tunable resistance pulse sensing (TRPS). Viability of parent cells was assessed by Annexin V exposure by flow cytometry. Imaging flow cytometry (Imagestream Mark II) identified EVs by direct fluorescent membrane labelling with Cell Mask Orange (CMO) in conjunction with EV markers. A stringent gating algorithm based on side scatter and fluorescence intensity was applied and expression of EV markers CD63, CD9 and LAMP 1 assessed. Results: UW228-2 cells prolifically release EVs of up to 6 μm. We show that the Imagestream Mark II imaging flow cytometer allows robust and reproducible analysis of large EVs, including assessment of diameter. We also demonstrate a correlation between increasing EV size and co-expression of markers screened. Conclusions: We have developed a labelling and stringent gating strategy which is able to explore EV marker expression (CD63, CD9, and LAMP1) on individual EVs within a widely heterogeneous population. Taken together, data presented here strongly support the value of exploring large EVs in clinical samples for potential biomarkers, useful in diagnostic screening and disease monitoring.

**Keywords:** extracellular vesicles; imaging flow cytometry; biomarker reservoirs; cancer diagnostics; disease monitoring; large EVs

#### **1. Introduction**

The term extracellular vesicle (EV) refers to particles released from cells which are delimited by a lipid bilayer, but do not contain a nucleus [1]. EVs are heterogeneous in biogenesis [2], size, and content. They range in size from 30 nm exosomes [3] to oncosomes up to 10 μM [4], and contain cargo of all biomolecule categories [5]. Attempts to categorise EVs primarily by surface marker expression have been confounded by the recognition that many of the markers previously considered to be subset- or derivation-specific, are actually present on multiple or even all classes of EVs [6]. Delineation and characterisation of specific EV subsets is an essential goal to achieve a better understanding of EV biology [7], yet there are no techniques that accurately quantify EVs across the full EV size range, or combine quantification with the ability to screen for EV marker expression or EV content.

Small EVs are characteristically isolated by high-speed centrifugation at 100,000× *g* [8], and include both EVs derived intracellularly from late endosomes and released by exocytosis (exosomes), and other small EVs not derived from endosomes (3). Multiple commercial solutions exist for the isolation of exosomes from a variety of biological fluids including tissue culture supernatant, plasma, and urine. In contrast, there are no commercially available solutions for the isolation of large EVs. As a result, isolation methods vary, and knowledge of large EV content and function in biological samples is relatively lacking. Large EVs, defined as >200 nm by recent guidelines set out by the International Society of Extracellular Vesicles [1], include cancer cell-derived oncosomes, dead cell-derived apoptotic bodies and platelets, and are visible by light microscopy [9]. In published literature, EVs larger than 1 μm have historically been assumed to be apoptotic bodies [10]. However, we and others [4] have demonstrated that viable cell cultures produce large EVs which do not have the ultrastructural features reminiscent of fragments of apoptotic cells.

EVs are released by all cells providing an efficient mechanism of cell to cell communication [11]. Increasing evidence points to key roles for EVs in cancer diagnosis, prognostication, and surveillance of cancer [12]. Large EVs from prostate cancer cells were shown to contain tumour-specific biomarkers [4,13] and mediate intercellular transfer of bioactive molecules including miRNA [14]. We previously reported a population of large EVs released by leukaemic cells which were actin-rich and contained intact organelles [15]. These large EVs could be internalised by normal stromal cells and induced a switch in the preferred metabolic pathway of the recipient cells [9]. Additionally, we found that leukaemia-derived EVs expressed a surface marker indicative of their parent cell (CD19) and could be detected in the peripheral blood of murine models and patient bone marrow plasma [9]. Taken together, our previous work and existing large EV literature suggest that large EVs, often discarded in techniques to isolate smaller EVs and exosomes, could be considered as extensive reservoirs of biomolecules useful to study EV biogenesis and function, and to identify clinically relevant biomarkers for disease detection and treatment monitoring [16,17].

The principle advantage of characterising large EVs as single events by imaging flow cytometry is the potential for simultaneously identifying parent cell, EV, and tumour markers. We report for the first time a characterisation of size distribution and EV marker expression in this heterogeneous EV population, undertaken in accordance with the most recent international consensus guidelines for EV research from the International Society of Extracellular Vesicles [1].

In this proof of concept study, we set out to: (1) highlight the abundance of large EVs produced by cells derived from the malignant brain tumour medulloblastoma in vitro; (2) describe variations in the expression of established EV markers in the large EV population; (3) describe how the Imagestream (ISX) can address sample heterogeneity by facilitating high throughput, single event EV analyses. We describe a protocol to isolate intact large EVs without cell contamination, from cells growing in serum-free medium, using gravity flow filtration combined with low-speed centrifugation. Our data show the breadth of heterogeneity in the size and marker expression of large EVs isolated from a single cell line and serves to highlight the importance of sample purity, isolation techniques and experimental controls as we seek to identify tumour-specific EV markers for use in the clinic.

#### **2. Results**

Large EVs from the medulloblastoma cell line UW228-2 are visible with light microscopy, can be isolated from viable, serum-free cell culture supernatant with intact membranes, and contain a polymerised actin cytoskeleton. A proportion of these large EVs express reported EV markers.

In this study the large EV isolation SOP was applied to the SHH-driven medulloblastoma cell line UW228-2. In accordance with the recently published international consensus MISEV2018 guidelines [1], we demonstrated the existence and membrane integrity of large EVs using both light and electron microscopy (Figure 1A,B). The pan-EV marker CD63 was expressed by parent medulloblastoma cells and a proportion of large EVs in viable cultures (Figure 1A). Large EVs also exhibited an active cytoskeleton indicated by the polymerised actin marker Phalloidin, but did not stain for DAPI, indicating that they did not contain nuclear double-stranded DNA. EV membrane integrity, size, and intra-vesicle content was examined by transmission electron microscopy (TEM) (Figure 1B). Isolated EVs were spiked with medulloblastoma parent cells for comparison and adhered to ACLAR film coated with CellTak prior to TEM. Serial sections demonstrated that the EVs had a limiting membrane, internal organelles but no nucleus, and were independent from cells. By contrast, cells had internal organelles including a nucleus and cytoplasmic protrusions indicative of filopodia.

Cells were grown in serum-free medium for 24 h prior to EV isolation, staining, and analysis. Experimental conditions were optimised to eliminate false positive membrane labelling or bovine EV contamination [18]. To assess whether growth in serum-free medium resulted in increased cell apoptosis, phosphatidyl serine exposure on the surface of cells was examined. Figure 1C indicates the quadrant gating strategy applied using positive controls and the comparison of cells grown in standard culture versus serum-free conditions. Imaging flow cytometry was also performed to visualise Annexin V/PI staining on parent cells (Figure 1D). In triplicate experiments, there was no significant difference between the viability of cells cultured in complete or serum-free media (Figure 1E). Figure 1F provides a workflow for the isolation and characterisation of large EVs used in this study. At the time of each EV isolation, total parent cell count and viability was assessed using trypan blue exclusion and found to be 7.9 <sup>×</sup> 106 cells (98% viable), 8.3 <sup>×</sup> 106 cells (99% viable), and 6.8 <sup>×</sup> <sup>10</sup><sup>6</sup> cells (98% viable).

**Figure 1.** *Cont*.

**Figure 1.** Immunofluorescence: UW228-2 cells and large extracellular vesicles (EVs) are CD63+. Medulloblastoma cell lines produce large extracellular vesicles (EVs) which can be harvested from serum-free conditioned media after 24 h. (**A**) Medulloblastoma cells produce EVs in vitro which contain polymerised actin and CD63, but no nucleus. The medulloblastoma cell line UW228-2 was cultured on glass bottom plates for 24 h; fixed and probed for the tetraspanin and EV marker CD63 (FITC—green). Polymerised actin was labelled using Alexa Fluor 555 phalloidin (f-ACTIN—red). Nuclei were counterstained with DAPI (DAPI—blue). Images of large EVs (yellow arrows) and cells were captured using the Perkin Elmer Operetta system at ×40 magnification. The individual images were captured in black and white using the appropriate emission filters and a coloured composite image created using Columbus software. Scale bar represents 20 μM. (**B**) Transmission electron microscopy: UW228-2 cell and adjacent large EV with limiting membrane. Large EVs have a limiting membrane, contain organelles but no nucleus and are < 6 μm in size. Cells and EVs were applied to Poly-D-lysine coated ACLAR film and fixed with glutaraldehyde before processing for Transmission Electron

Microscopy. Serial sections were taken to assess sample purity, EV membrane integrity, size, and content comparative to the parent cell. Images were captured using a Biotwin Philips TECNAI G2 microscope at ×1900 magnification. Scale bar represents 5 μM. Left panel shows a cell and a large EV. Right panel: serial sections taken through the entire EV (4 images shown). The wide-field image shows an EV of 5.5 μM diameter at its central plane of focus, alongside a parent medulloblastoma cell of 18 μM. (**C**) Flow cytometry: Cell viability Annexin V/PI assessment. Cell apoptosis was assessed by flow cytometry. Unstained cells (i) and cells treated with 200 μM Etoposide (ii; Annexin V positive gate) or heated (iii; PI positive gate) provided positive controls which were used to assign accurate quadrant gates to bivariate scatter plots showing PI versus Annexin V APC. Live cells appear in Q4, Annexin only in Q2 (Early apoptosis), dual labelled in Q3 (Late apoptosis) and PI only (Dead cells). UW228-2 cells were cultured for 24 h in complete media (iv; DMEM with 10% FBS) or serum-free media (v; DMEM only). Representative plots of triplicate experiments shown. (**D**) Imagestream (ISX) analysis: Annexin V/PI assessment in cells. Imaging flow cytometry allows visual distinction between early and late stages of apoptosis. Cells in standard culture conditions were labelled using an Annexin FITC/PI kit and examined using the Imagestream (ISX) Mark II. Images were captured at x60 magnification and representative gallery images demonstrate Annexin V FITC only membrane labelling, indicating early apoptosis (upper panels) and dual Annexin V FITC/PI labelling, indicating late apoptosis (lower panels). (**E**) Cell viability prior to EV isolation. UW228-2 cells can be cultured in serum-free conditions for 24 h with no loss in viability. No difference in viability could be seen between culture conditions: Percentage unlabelled cells (live), Annexin V positive only (Early apoptosis), Annexin V and PI positive (Late Apoptosis) or PI positive only (DEAD) after 24 h in complete (grey bars) or serum-free medium (open bars). (**F**) Large EV isolation and characterisation. EV isolation protocol from cultured cells was optimised to harvest large EVs with minimal processing. Cells were seeded into large flasks (225 cm3) and allowed to adhere. Media was changed after 24 h for 50 mL serum-free DMEM and cells cultured at 37 ◦C for a further 24 h. Trypsinised cells were collected by 2 successive centrifugation steps at 300× *g* 5 min keeping the supernatant each time. EV containing supernatant was filtered using a double layered 5 μm pore nylon membrane by gravity. Filtered, cell-free supernatant was centrifuged at 2000× *g* for 30 min using a bench top centrifuge with swing out buckets. The resulting cell and EV pellets were processed appropriately for the downstream technique: For flow cytometry cells were processed and analysed separately, whilst cells were spiked into wells for direct comparison by microscopy.

#### *2.1. EVs are Highly Heterogeneous and Di*ff*erentially Express EV Markers*

A reliable fluorescence marker was essential to demarcate EVs from background scatter events. Cell mask orange (CMO) is a fluorescent plasma membrane label composed of amphipathic molecules comprising a lipophilic moiety for membrane loading and a negatively charged hydrophilic dye for anchoring of the probe in the plasma membrane. We performed a titration using the parent cells with serial dilutions of the dye in serum-free media ranging from 1 in 1000 (5 μg/mL) to 1 in 100,000 (50 ng/mL) (Figure 2A) and found that 2.5 μg/mL (1 in 2000) was an optimal concentration providing high median fluorescence intensity without saturation. Using the Imagestream Mark II (ISX) we confirmed that the final concentration of CMO labelled EVs in a typical preparation did not result in coincidence events or swarm which could lead to false positive results when looking at multi-colour labelling. Serial dilutions of CMO labelled EVs showed a linear decrease in objects per mL (Figure 2B(i)) with an increasing dilution factor, whilst the fluorescence intensity remained stable across the dilutions (Figure 2B(ii)). To facilitate reproducible reporting across experiments and to offer a means to standardise EV measurements, we determined the minimum MESF value for CMO+ events which would distinguish between unstained and CMO labelled EVs using our staining protocol, as described elsewhere [19]. Figure 2C shows a representative bivariate plot (i) of the low, medium-low, medium-high, and high fluorescence bead populations. The histogram (ii) was used to determine the MFI of each peak and log fluorescence intensities were converted to MESF values using information supplied by the manufacturer (iii). Linear regression of log MFI versus log MESF

(iv) was used to calculate the MESF corresponding to the minimum fluorescence intensity of events within the designated CMO+ gate as follows. The maximum MESF values of the unstained EVs in each experiment were: Replicate 1: 271.42 = MESF 48.23; Replicate 2: 271.0 = MESF 49.27; Replicate 3: 272.88 = MESF 42.48. Therefore, to set a standardised lower threshold of detection which could be used across replicate experiments we assigned a lower MESF threshold for CMO<sup>+</sup> events of 50 (Example shown in Figure 2C(v)). We therefore report here CMO<sup>+</sup> events as number of events >MESF 50.

Previous reports have identified that lipid dye aggregates can mimic EVs when using fluorescence lipid markers [20]. We used Triton-X 100 treatment [21] of fully labeled EVs to disrupt EV signals demonstrated by a loss of CMO + events (Figure 2D(i)). Figure 2D(ii) shows an overlay of unstained EVs, fully stained EVs and fully stained EVs treated with Triton-X 100. The MFI of the post-Triton-X 100 treated sample is reduced to the level of the US-EVs.

**Figure 2.** *Cont*.

**Figure 2.** Cell Mask Orange can label EVs to enable standardisation across experiments. (**A**) Cell Mask Orange (CMO) labelling was optimised by titration using parent cells. Serial dilutions from 1 in 1000–100,000 were used to label parent UW228-2 cells and analysed by flow cytometry. A clear relationship between CMO concentration and fluorescence intensity is shown. (**B**) ISX: Objects/mL reduce with increased sample dilution (i) whilst CMO intensity remains constant (ii). Sample dilution was used to validate EV staining protocol. Using a CMO dilution of 1 in 2000 (2.5 μg/mL), the Imagestream acquired fewer CMO positive objects per ml with increasing dilution of CMO labelled EVs (i), whilst the CMO fluorescence intensity in channel 03 was maintained (ii). (**C**) Quantibrite PE Beads were run at the time of each experiment and used to assign molecules of equivalent soluble fluorochrome (MESF) values to the CMO intensity of labelled EVs. (Representative example from a single experiment shown) Quantibrite PE beads were acquired at the time of each experiment to assign MESF value. Quantibrite PE beads were separated on a bivariate plot of intensity against side scatter (i). The fluorescence intensity of each bead set was gated on the histogram (ii) and assigned a MESF value according to the number of PE molecules per bead as provided by the manufacturer (iii). Log MFI against Log MESF provided a standard curve for each acquisition (iv). Regression analysis was used to extrapolate the equivalent MESF value for CMO intensity for unstained and labelled samples (v) which enabled lower threshold for CMO intensity to be set to distinguish between unstained and CMO labelled EVs. Representative experiment shown. MESF 50 was subsequently used to set the lower CMO+ threshold to standardise across experiments. (**D**) CMO labelling was diminished following the addition of detergent. CMO positive (CMO+) EVs could be distinguished from unstained EVs by ISX (i). Treatment of the same sample with Triton-X 100 diminished the CMO labelling (ii). Exported .fcs files from the same acquisition were overlaid using FlowJo 10.6 (Ashland). The fully stained sample (orange) shows increased fluorescence intensity in channel 3. The same sample post Triton–X treatment (blue) showed a reduction in fluorescence intensity to a similar level of the unstained sample (grey).

#### *2.2. Large EVs Size Distribution and Quantity was Assessed Using Tunable Resistance Pulse Sensing (TRPS)*

At the time of each preparation, an aliquot of freshly isolated EVs was assessed using the qNano GOLD particle counter to ascertain EV size distribution (diameter) in terms of percentage population (%) and concentration (particles/mL) prior to labelling for ISX analysis. Using a series of 3 overlapping Nanopores (Figure 3A(i)), each with an optimal size range which spans a total size of approximately 275 nm to 5.7 μm, we assessed the size range of particles in the EV preparations. Representative profiles of diameter against percentage population are shown for a single sample using NP600, NP1000, and NP2000 Nanopores (Figure 3A(ii–iv)). By selecting overlapping Nanopore sizes, the same calibrator beads (1000 nm) could be used and therefore it was possible to overlay the resulting profiles to visualise the total large EV population within each sample (Figure 3A(v)). As expected, the EVs present in each biological replicate were heterogeneous in size. However, the size range of EVs across biological replicates was consistent (250 nm to 6 μm); with some variation in the median diameter per sample. Size is presented with a bin of 100 (nm) and in each case the most prevalent large EVs were detected using

the NP600. The median size across the replicates using the NP600 for comparison were 250–350 nm 5.15 <sup>×</sup> 107; 450–550 nm 1.7 <sup>×</sup> 107, and 250–350 nm 2.8 <sup>×</sup> 108. The starting volume of EV-containing media used per replicate was 100 mL. The resulting pellet was re-suspended in a total of 700 μL PBS (prior to labelling). As an example, applying this dilution factor (142.86) to the first replicate equates to 7.4 <sup>×</sup> 109 EVs specifically of the 250–350 nm size range that were released by 7.9 <sup>×</sup> 106 UW228-2 cells (NB cell count at harvest) in 24 h into 100 mL serum-free media. It should be noted that due to the number of washes and centrifugation steps during the isolation, this can only be considered as an illustration of the quantity of EVs produced. The total larger EVs as counted using the NP2000 (935 nm − 5.7 μm) were less common but are nevertheless abundantly present in concentrations of 2.3 <sup>×</sup> <sup>10</sup>8, 6.9 <sup>×</sup> 108 and 2.1 <sup>×</sup> <sup>10</sup><sup>9</sup> in the 100 mL harvested media from this particular cell line. The total particle count for each Nanopore size, across 3 biological replicates is shown in Table 1. It should be noted that the counts shown are not cumulative as they represent the same sample analysed across 3 Nanopore sizes.

**Figure 3.** EV size and quantity was assessed by Tunable Resistance Pulse Sensing (TRPS) using the qNANO instrument (iZON Science) and compared to diameter masks in the ISX IDEAS software. (**A**) Three Nanopores of over-lapping size ranges were used to determine the particle diameter and concentration in each EV preparation (i). Size profiles were established for each individual Nanopore (ii NP600; iii NP1000; iv NP2000). The overlaid profiles of diameter by concentration provided by the 3 Nanopores show a size range of 250 nm and 6μm against a common calibrator bead of 1000 nm (representative sample shown) (v). (**B**) Calibration beads of known size were used to determine the most accurate diameter mask. The masks available within the ISX IDEAS software for diameter analysis were compared. The 3 which assigned the most accurate sizes according to manufacturer specified beads sizes were the Object, Erode and Adaptive Erode masks. These were compared across bead size by applying the mask (ii) visualising a scatter plot (iii) and histogram for each mask type. For the size range within our EV preparations, we found the Erode mask to be the most accurate (iv).

**Table 1.** Total particle count using different Nanopore sizes (qNANO) in biological replicates.

Size calibration beads were used to determine the most appropriate diameter mask to use for ISX analysis of EV diameter. The bead sizes provided by the manufacturer were 1, 2, 4, 6, 10, and 15 μm. We used the feature tool in IDEAS to apply a range of different diameter masks to the bright field image of the beads (Figure 3B(i)). We selected bright field for this using non-labelled beads because the Quantibrite beads showed over exaggeration of EV size, possibly due to saturation and flare of fluorescence. The beads were visualised on bivariate plots of diameter versus side scatter (channel 06). Density plots (Figure 3B(ii)) allowed the bead populations to be gated individually and subsequent histograms (Figure 3B(iii)) to be viewed. We compared the diameters for each bead population assigned using the Object, Adaptive Erode and Erode Masks against the manufacturer specified size (Figure 3B(iv)) and found the Erode mask on the bright field image, using 3 pixel reduction to be the most comparable.

#### *2.3. Fluorescence Membrane Labelling Can Be Used to Distinguish EVs from Background and Speed Beads*

We devised an analysis template within IDEAS which comprised a hierarchical gating strategy aimed at characterising heterogeneous large EVs (Figure 4A). All buffers were filtered using a 0.2 μm filter and samples were acquired for 5 min to avoid recording different amounts of background per acquisition. Speed beads were included. The speed beads and CMO− events were defined using density plots of channel 03 (CMO) versus channel 06 (side scatter) intensity (Gate i). Running acquisition buffer only (left side) and unstained EVs (centre) showed the instrument detected a high level of background with low to medium side scatter. Labelling with the cell membrane dye (CMO) therefore helped to distinguish between the low CMO intensity/low side scatter EVs and background detected in both the acquisition buffer and unstained sample (right side). Applying a gate to capture low Raw Max Pixel events (channel 06—SSC: Gate ii) eliminated those events with saturated side scatter, including the speed beads. Outliers in the side scatter versus CMO intensity plots were individually inspected and found to be dual events comprising a speed bead (CMO−, high SSC) and an EV (CMO+, low SSC) which occupied the flow chamber at the same time. This resulted in aberrant events with both high CMO intensity and high SSC and was therefore excluded from further analysis.

Events classed as CMO+ at this point were included in the initial CMO+ gate (Gate iii). Within the same acquisition, we ran the Quantibrite PE beads which enabled a minimum threshold for CMO+ events to be calculated and converted to MESF units, as described. The lower MESF cut off for this experiment was defined at >50 and a further gate on the CMO intensity histogram (Gate iv) captured all CMO+ events with an MESF >50 for downstream analysis. The proportion of CMO+ events as a percentage of all events acquired over the 5 min period varied across 10 replicates (mean 18.73% ± 1.2 SEM) CMO+ MESF > 50; Figure 4B). A final gate was included to eliminate outliers which were either high CMO intensity but appeared on the bright field image to be membrane fragments (Gate v) or were not assigned a diameter due to lack of bright field image focus. This stringent gating strategy was designed to eliminate any events which could not be further analysed for EV marker expression or diameter.

**Figure 4.** Optimised acquisition by Imaging flow cytometry and florescence membrane labelling can distinguish large extracellular vesicles from background and enable multiplex labelling. (**A**) Hierarchical gating can confidently refine the EV population for characterisation. Samples were acquired for 5 min using the ISX INSPIRE software and all events visualised using bivariate plots for fluorescence intensity in channel 03 (CMO) and channel 06 (side scatter). Initial gates for speed beads and CMO− events were set using the Acquisition buffer only and unstained EV samples included in each run (Gate i). These gates were applied to the labelled samples. Saturated events were excluded from the analysis by gating on the histogram plot: Raw Max Pixel for Channel 06 (Gate ii) thus removing the speed beads and very high side scatter events. An initial CMO + gate was placed (Gate iii). A lower fluorescence threshold of MESF > 50 in channel 3 (CMO) was set in each experiment using the regression analysis of Quantibrite PE Beads as described. The CMO + gate was anchored using co-ordinates for the equivalent fluorescence intensity and a further CMO + MESF > 50 gate applied to the histogram (Gate iv). Finally, a refined gate was used to eliminate outliers (Gate v). The analysis template was set for each experiment

and applied to all samples. (**B**) Percentage positive CMO+ events (MESF > 50). The percentage of CMO+ events (MESF > 50) is represented by an orange dot for 10 replicate experiments. Black bar represents mean 18.73% (± 1.2 SEM). (**C**) A compensation matrix was applied to all samples. The compensation wizard was used to create a matrix which was applied to all samples. (**D**) Single, double, and triple labelling protocols were used for compensation and accurate gating. Dual labelled EVs provided Fluorescence minus one (FMO) controls for (i) AF647; EVs labelled with CMO and CD63 BV421) or (ii) BV421; (EVs labelled with CMO and LAMP1 AF647) and used to set positive gates. (**E**) UW228-2 cells were used to optimise multiplex labelling. Cell mask orange (CMO) provides a general membrane label (i) and could be used to co-label with EV markers CD63 BV421 and CD9 AF647 (ii) or CD63 BV421 and LAMP1 AF647 (iii). Representative gallery images from IDEAS software are shown.

We tested a number of approaches to apply compensation to our data in consultation with the manufacturer's specialists. Initially, we attempted to construct a matrix using single stained EVs; however, the single pixel fluorescence was insufficient for the in-built compensation Wizard to assign a matrix. Next, we tried single stained parent cells, but the compensation matrix formulated by the wizard resulted in over-compensation and negative fluorescence intensity events. Over-compensation was thought to be due to the imbalance between the strong fluorescence signal resulting from cell mask orange, a membrane marker which indiscriminately labels lipids, and relatively weak fluorescence signal from target specific antibodies. These experiments were originally performed using a FITC conjugated CD63 antibody, however we found the level of adjustment required between channel 02 (FITC) and channel 03 (CMO) contributed to the negative populations. We re-optimised using the same CD63 antibody clone (HSC6) conjugated to BV421 (channel 07) which is spectrally more distinct from CMO. Finally, we found that using commercially available compensation beads labelled with our antibodies and acquired in the channels used for our study, in conjunction with assisted manual adjustment, provided the most reproducible compensation matrix.

We combined CMO with BV421 and AF647 and Fluorescence Minus One (FMO) controls for each fluorophore were used to set positive gates (Figure 4D). The parent UW228-2 cells were screened for expression of the EV markers chosen for this study: CD63, CD9, and LAMP1 as defined in the latest MISEV guidelines [1]. Multiplex labelling was performed using the following combinations: CMO with CD63 BV421 and CD9 AF647; or CMO with CD63 BV421 and LAMP1 AF647.

#### *2.4. Multiplex Labelling Reveals Heterogeneity in EV Marker Expression*

The proportion of CMO+ only events varied across the 10 replicates (Figure 5A). The mean percentage positive CMO+ (MESF > 50) events (63.3% ± 4.6 SEM) which did not demonstrate expression of any EV markers included in this study were designated CMO only. Considering the single EV markers, the proportion of EVs positive for each EV marker varied CD63 19.0%, (± 2.2 SEM), CD9 mean 10.3% (± 0.5 SEM); LAMP1 47.6% (± 3.8 SEM). These data shown LAMP1 to be the most prevalent EV marker screened in this study.

**Figure 5.** *Cont*.

**Figure 5.** Isolated large EVs can be triple-labelled and individual events can be scrutinised post-acquisition. (**A**) The proportion of CMO+ events expressing a single EV marker varied across replicates. The percentage positive events within the CMO+ MESF > 50 gate which expressed either no EV marker (CMO only), CD63, CD9, or LAMP1 varied across replicates. Black bar represents the mean across 10 (CMO or CD63) or 5 (CD9 or LAMP1) replicates. CMO only 63.3% (± 4.6 SEM), CD63 only 19% (± 2.2 SEM), CD9 (± 0.5 SEM) only 10.3% and LAMP1 only 47.6% (± 3.8 SEM). (**B**) UW228-2 cell derived EVs differentially express EV markers CD63 and CD9, or both. EVs were labelled with CMO, CD63 BV421 and CD9 AF647. CMO <sup>+</sup> events (MESF > 50) were analysed for co-expression with CD63, CD9 or both. Quadrant gating of bivariate plots for fluorescence intensity in channel 07 (BV421) and channel 11 (AF647) demonstrated single labelled (CMO<sup>+</sup> only), dual and triple labelled EV populations (i). Percentage positive in each quadrant is shown. Representative experiment. Gallery images display examples of individual events from each of the 4 quadrants (ii). Bright Field (BF) and side scatter (SSC) channels are shown alongside CMO, CD63 BV421 and CD9 AF647. Of the CMO+ MESF >50: 75.1% (<sup>±</sup> 3.0 SEM) were CMO <sup>+</sup> only; 13.3% (<sup>±</sup> 2.4 SEM) expressed CD63 BV421 and 3.5% (<sup>±</sup> 0.8 SEM)

expressed CD9 AF647. 7.1% (<sup>±</sup> 1.1 SEM) of the CMO<sup>+</sup> events expressed both CD63 and CD9. (**C**) UW228-2 cell derived EVs differentially express EV markers CD63 and LAMP1, or both. EVs were labelled with CMO, CD63 BV421 and LAMP1 AF647. CMO<sup>+</sup> events were analysed for co-expression with CD63, LAMP1 or both. Quadrant gating of bivariate plots for fluorescence intensity in channel 07 (BV421) and channel 11 (AF647) demonstrated single labelled (CMO<sup>+</sup> only), dual and triple labelled EV populations (i). Percentage positive in each quadrant is shown. Representative experiment. Gallery images display examples of individual events from each of the 4 quadrants (ii). Bright Field (BF) and side scatter (SSC) channels are shown alongside CMO, CD63 BV421 and LAMP1 AF647. Of the CMO+ MESF <sup>&</sup>gt;50: 54.1% (<sup>±</sup> 4.3 SEM) were CMO <sup>+</sup> only; 2.8% (<sup>±</sup> 0.4 SEM) expressed CD63 BV421 and 25.6% (<sup>±</sup> 2.7 SEM) expressed LAMP1 AF647 whilst 18.0% (<sup>±</sup> 2.8 SEM) of the CMO <sup>+</sup> events expressed both CD63 and LAMP1. (\* *p* = 0.0159 or 0.0317; \*\* *p* = 0.0079). Mann Whitney U test.

Differential expression and co-expression of EV markers was examined between five replicate experiments. Representative bivariate plots of AF647 intensity against BV421 intensity (Figure 5B(i) and Figure 5C(i) for CD63/CD9 and CD63/LAMP1, respectively), and representative galleries (Figure 5B(ii) and Figure 5C(ii)) of EVs displaying each labelling combination taken from the quadrant plots are shown. The proportion of EVs which co-express EV markers within each gate for the five replicates is shown in Figure 5B(iii) and Figure 5C(iii). Where the EVs were labelled for CMO, CD63 BV421, and CD9 AF647 (Figure 5B(iii)): 75.1% (± 3.0 SEM; yellow) were CMO only, 13.3% (± 2.4 SEM; purple) were CMO <sup>+</sup> CD63 <sup>+</sup> CD9 - ; 3.5% (<sup>±</sup> 0.8 SEM; red) were CMO <sup>+</sup> CD63 – CD9 <sup>+</sup> and 7.1% (<sup>±</sup> 1.1 SEM; blue) were CMO <sup>+</sup> CD63 <sup>+</sup> CD9 <sup>+</sup>. There were significantly fewer CD63 <sup>−</sup> CD9 <sup>+</sup> EVs compared with CD63 <sup>+</sup> (*p* = 0.0079) only or CD63<sup>+</sup> CD9 <sup>+</sup> EVs (*p* = 0.0159). Similarly, there were fewer EV co-expressing CD63 and CD9 compared with CD63<sup>+</sup> alone (*p* = 0.0317). (Mann Whitney U test). Considering the EVs labelled with CMO, CD63 BV421 and LAMP1 AF647 (Figure 5C(iii)). 51.4% (+/− 4.3 SEM; yellow) were CMO<sup>+</sup> only, a comparatively low 2.8% (<sup>±</sup> 0.4 SEM; purple) were CMO <sup>+</sup> CD63 <sup>+</sup> LAMP1 –; whilst 25.6% (<sup>±</sup> 2.7 SEM; red) were CMO <sup>+</sup> CD63 <sup>−</sup> LAMP1 <sup>+</sup> and 18% (<sup>±</sup> 2.8 SEM; blue) co-expressed both CD63 and LAMP1. There were significantly more EVs which expressed LAMP1 compared with CD63 alone (*p* = 0.0079) and more expressing both CD63 and LAMP1 compared with CD63 alone (*p* = 0.0079).

#### *2.5. ISX Allow Correlative Analyses of Diameter with EV Marker Expression for Phenotyping*

We applied the Diameter Mask; Erode (M01, 3) in IDEAS software to all fully labelled replicates using the Batch analysis function and compared the range of diameters present within the CMO+ MESF> 50 gate. Figure 6A shows single event diameters within the 10 replicates. This median diameter (indicated with the black line) varied significantly across the replicates (922–1129 nm: Kruskal-Wallis; *p* > 0.0001). However, these data represent EVs isolated as 3 biological replicates and when each replicate is analysed using ANOVA there was no significant difference between the median diameters of each EV preparation. There was a clear correlation between EV diameter and CMO intensity as shown in Figure 6B. Regression analysis was performed on the individual exported feature values from each replicate (2 representative plots are shown from different biological replicates) (*p* < 0.0001).

**Figure 6.** *Cont*.

**Figure 6.** The ISX can accurately assign diameter to large EVs and facilitate individual event analyses to phenotype heterogeneous EV populations. (**A**) The median diameter of CMO <sup>+</sup> EVs from UW228-2 cells was comparable across 10 samples. The erode mask was applied to the bright field images of those EVs which were included in the CMO<sup>+</sup> >MESF 50 gate. The diameter of individual events within this gate was exported from IDEAS into PRISM for analysis. This figure shows a median diameter (represented by the black bar) of 922–1129 nm across the 10 samples and visualises the broad size range within each sample. (\*\*\*\* *p* > 0.0001 Kruskal-Wallis test). (**B**) The fluorescence intensity of the CMO was proportional to EV diameter. Bivariate plots showing CMO intensity against diameter consistently demonstrates a proportional relationship between EV size and intensity of the membrane label across samples run in triplicate experiments. 2 representative samples shown. (Regression analysis *p* < 0.0001). (**C**) Comparison of EV phenotypes across triplicate experiments. Individual histograms displaying EV diameter for each phenotype demonstrated differential size ranges according to EV marker(s) expression (i a and i b). In each case, those labelled with CMO only and not expressing either CD63, CD9 or LAMP1 have a smaller median diameter compared with EVs expressing CD63 and/or CD9 or LAMP1 (ii a and ii b). In both cases, those EVs co-expressing both CD63 with CD9 (iii a) or CD63 with LAMP1 (iii b) are significantly larger than CMO only. (\*\* *p* < 0.005, \*\*\* *p* < 0.0004, \*\*\*\* *p* < 0.0001).

We found that large EVs are heterogeneous in both diameter and EV marker expression. Histogram plots of individual events exported from the quadrant positive gates show the different diameter range and frequency within each phenotypic subgroup (Figure 6C(i)). Representative acquisitions from a single replicate are shown. ISX analysis facilitates the comparison of individual EV diameters from within each phenotype (Figure 6C(ii)). A representative plot for all CMO <sup>+</sup> (MESF > 50) in a single replicate of each phenotype is shown and the black bars represent the median diameter for each subgroup. Within this typical replicate, the median diameter and range for each subgroup was as follows: All CMO >MESF 50 1.1 μm (362 nm–6.2 μm), CMO only 1.0 μm (362 nm−5.4 μm), CMO+ CD63<sup>+</sup> CD9<sup>−</sup> 1.8 μm (362 nm–5.1 μm), CMO+ CD63<sup>−</sup> CD9<sup>+</sup> 1.8 μm (362 nm–4.7 μm), and CMO+ CD63<sup>+</sup> CD9<sup>+</sup> 2.5 μm (362 nm–6.2 μm). When considering the second phenotype: All CMO >MESF 50 1.4 μm (362 nm–6.2 μm), CMO only 1.1 μm (362 nm–4.6 μm), CMO+ CD63<sup>+</sup> CD9- 1.7 μm (362 nm–4.5 μm), CMO+ CD63<sup>−</sup> CD9<sup>+</sup> 1.6 μm (362 nm–5.6 μm), and CMO+ CD63<sup>+</sup> CD9<sup>+</sup> 2.2 μm (362 nm–6.1 μm). In each case the median diameter increases in size with accumulating EV marker expression.

When considering all 5 replicates; we compared the mean diameter for each subgroup (Figure 6C(iii)). For the first group (left panel): CMO only 0.89 <sup>μ</sup>m (±0.03 SEM), CMO+ CD63<sup>+</sup> CD9<sup>−</sup> 1.53 <sup>μ</sup>m (<sup>±</sup> 0.1 SEM), CMO<sup>+</sup> CD63<sup>−</sup> CD9<sup>+</sup> 1.76 <sup>μ</sup>m (±0.17 SEM) and CMO<sup>+</sup> CD63<sup>+</sup> CD9<sup>+</sup> 2.19 <sup>μ</sup><sup>m</sup> (±0.18 SEM). Those EVs expressing either CD63 (*p* < 0.0004) or CD9 (*p* < 0.0010) were significantly larger than EV which did not (CMO only). For the second subgroup (right panel) the mean diameters were as follows: CMO only 0.84 <sup>μ</sup>m (±0.04 SEM), CMO+ CD63<sup>+</sup> CD9<sup>−</sup> 1.06 <sup>μ</sup>m (±0.12 SEM), CMO+ CD63<sup>−</sup> CD9<sup>+</sup> 1.31 <sup>μ</sup>m (±0.12 SEM), and CMO+ CD63<sup>+</sup> CD9<sup>+</sup> 2.02 <sup>μ</sup>m (±0.07 SEM). In this group, those EVs expressing CD63 only were not significantly larger than those which did not. However, EVs expressing LAMP1 were larger (*p* < 0.005). In both cases, those CMO <sup>+</sup> EVs co-expressing 2 EVs markers: either CD63 and CD9, or CD63 and LAMP1 (blue) were significantly larger than those which do not carry the markers screened in this study. (\*\*\*\* *p* < 0.0001. Un-paired *t*-test).

#### **3. Materials and Methods**

#### *3.1. Cell Line*

UW228-2 cells were kindly provided by DTW Jones (DKFZ, Heidelberg, Germany). Cells were grown in DMEM with L-glutamine (Lonza, Manchester, UK; cat: R8758) supplemented with 10% FCS (SIGMA, Gillingham, UK; cat: F9665) in Corning 225 cm2 Angled Neck Cell Culture Flask with Vent Cap (Fisher Scientific, Leicestershire, UK; cat: 431082) at 37 ◦C with 5% CO2 in normoxia. Cell lines were passaged with 1 × Trypsin-EDTA (Lonza, UK; cat: T3924). All cell lines tested negative for Mycoplasma and all were authenticated in-house (CRUK-Manchester Institute) by examination of a total of 21 loci across the genome using the Powerplex 21 System (Promega, Southampton, UK).

#### *3.2. Cell Culture*

For each experiment, cells were seeded at 2.5 <sup>×</sup> 106 cells in 50 mL DMEM 10% FBS (complete media) per 225 cm3 tissue culture flask and allowed to adhere overnight. On day 2 media was switched to 50 mL serum-free DMEM for 24 h prior to EV isolation and cell preparation. Conditioned media (containing EVs) was removed, and the cells washed x2 with PBS before trypsinisation using 1 x Trypsin-EDTA (Lonza, Manchester, UK; cat: T3924). Cell counts and viability were checked at the time of EV harvest using the trypan blue exclusion assay (0.4% Trypan blue solution; SIGMA; Gillingham, UK; cat T8154).

#### *3.3. Vesicle Isolation*

Large EVs were harvested using a standard operating procedure (SOP) as previously reported (Figure 1F) [15]. For each experiment, EVs were isolated from the serum-free, conditioned media from 2 <sup>×</sup> 225 cm<sup>2</sup> flasks (pooled; total 100 mL). Cell culture supernatant (conditioned media: CM) was centrifuged in 2 tubes to remove cells (300× *g* 5 min, ×2) and filtered using a double layered 5 μm pore nylon Sieve (Fisher Scientific, Leicestershire, UK; cat 12994257). The supernatant was collected and centrifuged at 2000× *g* for 30 min and prepared for ISX analysis. Centrifugation steps were performed using an Eppendorf 5702 bench top centrifuge with an A-4-38 rotor. All EV preparations were performed on the day of analysis and not stored. Experimental procedures for EV isolation have been submitted to the EV-TRACK database (EV TRACK ID: EV190013) [22].

#### *3.4. Chemicals and Reagents Including Antibodies*

#### 3.4.1. Apoptosis Assay