Analyse exosomaler microrna-profile und der Herkunft von Exosomen im Plasma von Melanom-Patienten und gesunden Individuen

Transkript

1 Analyse exosomaler microrna-profile und der Herkunft von Exosomen im Plasma von Melanom-Patienten und gesunden Individuen Analysis of the microrna profile and origin of exosomes in plasma of melanoma patients and healthy individuals der Naturwissenschaftlichen Fakultät der Friedrich-Alexander-Universität Erlangen-Nürnberg zur Erlangung des Doktorgrades Dr. rer. nat. vorgelegt von Nina Koliha aus Hamburg

2 Als Dissertation genehmigt von der Naturwissenschaftlichen Fakultät der Friedrich-Alexander-Universität Erlangen-Nürnberg Tag der mündlichen Prüfung: 01. September 2016 Vorsitzender des Promotionsorgans: Gutachter: Prof. Dr. Jörn Wilms Prof. Dr. Falk Nimmerjahn Prof. Dr. Andreas Baur

3 Teile der vorliegenden Arbeit wurden bereits in den folgenden Originalartikeln veröffentlicht: Parts of the present doctoral thesis have been published as original research articles: Nina Koliha, Yvonne Wiencek, Ute Heider, Christian Jüngst, Astrid Schauss, Susanne Krauthäuser, Ian Johnston, Andreas Bosio, Stefan Wild A novel multiplex bead-based platform highlights the diversity of extracellular vesicles. Journal of Extracellular Vesicles (2016), Vol. 5: Nina Koliha, Ute Heider, Tobias Ozimkowski, Martin Wiemann, Andreas Bosio, Stefan Wild Melanoma affects the composition of blood cell derived extracellular vesicles. Frontiers in Immunology (2016), Vol. 7: 282 Jung-Hyun Lee, Stephan Schierer, Katja Blume, Jochen Dindorf, Sebastian Wittki, Wei Xiang, Christian Ostalecki, Nina Koliha, Stefan Wild, Gerold Schuler, Oliver Fackler, Kalle Saksela, Thomas Harrer, Andreas S. Baur HIV-Nef and ADAM17-containing plasma extracellular vesicles induce and correlate with immune pathogenesis in chronic HIV infection. EBioMedicine (2016), Vol. 6, page Diese Arbeit wurde bei der Miltenyi Biotec GmbH unter Betreuung durch Dr. Stefan Wild und in Zusammenarbeit mit Prof. Dr. Falk Nimmerjahn und Prof. Dr. Andreas Baur (Universität Erlangen- Nürnberg) durchgeführt. Die Rechte an den Arbeitsergebnissen einschließlich der Rechte an Erfindungen oder Verbesserungen sowie etwa bestehender Urheberrechte habe ich an die Miltenyi GmbH übergeben und räume damit der Miltenyi GmbH das ausschließliche Nutzungsrecht ein. Das Erfinderpersönlichkeitsrecht verbleibt bei mir.

4 Table of contents SUMMARY... 3 ZUSAMMENFASSUNG INTRODUCTION Biogenesis of exosomes Exosomal micrornas Exosomal proteins Exosomes in melanoma Common methods in exosome research AIM OF THE STUDY MATERIALS AND METHODS TotalRNA extraction for mirna analysis Agilent microrna microarray MiRNA Mimic transfection Cell isolation, culture and stimulation for exosome production Exosome isolation Mass spectrometry Multiplex bead-based platform Flow cytometry analysis of bead-bound exosomes Confocal microscopy and high-resolution microscopy using stimulated emission depletion (STED) Flow cytometry analysis of cells Blood samples Nanoparticle tracking analysis (NTA) Statistics

5 4 RESULTS MicroRNA profiling and transfection MicroRNA profiles of cell culture cells and their secreted exosomes MicroRNA transfection of melanoma cells MicroRNA profiles of healthy donors and different melanoma patient groups Protein profiling of cells and exosomes Mass spectrometry of melanoma exosomes Establishment of a multiplex bead-based platform Establishment of a protocol for high-resolution microscopy of exosomes Protein profiling of exosomes from PBMC Protein profiling of T cells and their exosomes Protein profiling of B cells and their exosomes Protein profiling of platelets and their exosomes Protein profiling of natural killer cells and their exosomes Protein profiling of monocytes, monocyte-derived dendritic cells, and their secreted exosomes Exosome production rate per cell and protein amount per exosome Protein profiling of plasma exosomes from healthy donors and melanoma patients Summary of the results DISCUSSION MicroRNA analysis Establishment of new tools for exosome characterization Analysis of cell culture derived exosome populations Analysis of plasma exosomes from healthy donors and melanoma patients Conclusion and outlook ABBREVIATIONS REFERENCES APPENDIX ERKLÄRUNG / DECLARATION OF AUTHORSHIP ACKNOWLEDGEMENTS

6 Summary Exosomes are extracellular vesicles released from living cells in an energy-dependent process. They differ from other extracellular vesicles such as apoptotic vesicles and membrane vesicles due to their endocytic origin. Exosomes from tumor cells and immune cells were shown to activate as well as inhibit immune responses. Therefore, we aimed to investigate whether plasma exosomes from melanoma patients and healthy donors differ in their microrna (mirna) content. However, we could not identify a subset of tumor-related micrornas for all patients. Instead, we observed variations between donors and cannot exclude that some micrornas might be an indicator of tumor burden in individual patients. On average, the exosomal microrna signal intensity increased by the factor of 6.9 for melanoma plasma exosomes as compared to healthy donors. To identify the cell types that are responsible for this signal increase of exosomal micrornas, we decided to continue with the protein characterization of plasma exosomes because there are hardly any cell type-specific mirnas. For this purpose, we established a multiplex bead-based platform that allows the detection of 39 surface proteins in parallel. Our analysis revealed that some surface proteins that are used to identify cell populations are transferred to the respective exosomes and can be used to relate exosomes to their originating cells. For example, CD14 was detected on monocytederived exosomes, CD19 on exosomes from B cells and CD41b, CD42a, CD61, and CD62P on platelet exosome preparations. B cell stimulation and the generation of monocyte-derived dendritic cells (modcs) further demonstrated that some markers for activation and differentiation are also transferred to exosomes. The combination of different capture and detection antibodies with the multiplex platform revealed unidentified exosome subpopulations such as CD19 low CD20 high exosomes in exosome preparations from stimulated B cells. Interestingly, two of the so called exosome markers were not detected on every exosome sample. CD9 was missing on exosomes from B cells and NK cells, while CD81 was hardly detectable on platelet exosomes. These observations were validated by high-resolution fluorescence microscopy (STED) that was established to visualize the protein distribution on single exosomes. CD63 was present on every exosome preparation and might be more reliable as an exosome marker. The comparison of surface protein profiles from various immune-cell derived exosomes with plasma exosomes revealed the contribution of platelets, modcs, B cells, T cells, and NK cells to the exosome pool found in plasma. On plasma exosomes from melanoma patients, we observed changed signals for the platelet markers CD42a and CD62P as compared to plasma exosomes from healthy donors, indicating an altered exosome secretion or protein loading of exosomes by platelets. A weaker CD8 signal on melanoma plasma exosomes as compared to healthy plasma exosomes might point at a diminished exosome secretion by cytotoxic cells such as T cells and NK cells. The melanoma markers MCSP, CD146, and CD49e were detected on exosomes from melanoma cell culture, but only the CD49e signal was elevated in two out of four plasma exosome samples from melanoma patients as compared to healthy donors. We conclude that melanoma exosomes are not a major component of the pool of plasma exosomes. Consistent with the similar microrna profiles for melanoma patients and healthy controls, the increase of exosomal micrornas in plasma is likely not caused by micrornas being secreted from the tumor. We suggest that melanoma alters the exosome secretion of blood cells such as platelets and cytotoxic cells. Revealing the contribution and the function of different immune cell-derived exosomes in plasma might advance our understanding of cell-cell communication via exosomes during tumor progression. 3

7 Zusammenfassung Exosomen sind extrazelluläre Vesikel, die von lebenden Zellen in einem Energie-abhängigen Prozess sekretiert werden. Studien haben gezeigt, dass Exosomen von Tumorzellen und Immunzellen Immunreaktionen stimulieren und unterdrücken können. Daher sollte in dieser Arbeit untersucht werden, ob sich Plasmaexosomen von Melanom-Patienten und gesunden Spendern in ihrem microrna-profil unterscheiden. Wir entdeckten kein microrna-set, das alle Melanom-Patienten als solche identifizieren konnte. Die Signale schwankten jedoch zwischen den Spendern und wir können nicht ausschließen, dass einige micrornas bei einzelnen Patienten auf den Tumor hinweisen können. Im Durchschnitt zeigten die Plasmaexosomen der Melanom-Patienten einen 6.9-fachen Signalanstieg aller detektierten micrornas im Vergleich zu gesunden Kontrollen. Um die Zelltypen zu identifizieren, die den Anstieg der exosomalen mirna-signale bewirken, haben wir uns dazu entschieden, die Proteinbeladung der Exosomen zu charakterisieren da es kaum Zelltypspezifische mirnas gibt. Dazu haben wir eine multiplex bead-based platform entwickelt, die die parallele Detektion von 39 Oberfächenproteinen ermöglicht. Manche Proteine, die zur Identifikation von Zelltypen verwendet werden, werden auf die entsprechenden Exosomen übertragen und lassen so auf die Herkunftszellen der Exosomen schließen. Zum Beispiel wurde CD14 auf Monozyten- Exosomen detektiert, CD19 auf B-Zell-Exosomen und CD41b, CD42a, CD61 und CD62P auf Thrombozyten-Exosomen. Die Aktivierung von B-Zellen und die Differenzierung von dendritischen Zellen aus Monozyten (modcs) zeigten, dass auch manche Reifungs- und Differenzierungsmarker auf Exosomen übertragen werden. Die Kombination verschiedener Fangantikörper-Partikel mit verschiedenen Detektionsantikörpern innerhalb der multiplex bead-based platform ermöglichte die Entdeckung unbekannter Exosomen-Subpopulationen wie CD19 low CD20 high Exosomen in der Exosomen-Präparation von stimulierten B-Zellen. Interessanterweise fehlte der so-genannte Exosomen-Marker CD9 auf Exosomen von B-Zellen und NK-Zellen und die CD81-Signale waren auf Thrombozyten-Exosomen kaum nachweisbar. Diese Erkenntnisse wurden mittels hochauflösender Fluoreszenz-Mikroskopie (STED) validiert. CD63 wurde in allen Exosomen-Präparationen nachgewiesen und ist daher möglicherweise ein verlässlicherer Exosomen-Marker. Die Protein-Profile verschiedener Immunzell-Exosomen wurden mit denen von Plasmaexosomen verglichen und zeigten, dass sich Exosomen von Thrombozyten, modcs, B-Zellen, T-Zellen und NK-Zellen im Blut befinden. Plasmaexosomen von Melanom-Patienten und gesunden Spendern zeigten unterschiedliche Signalintensitäten für die Thrombozyten-Marker CD42a und CD62P, was auf eine veränderte Exosomen-Sekretion oder eine geänderte Proteinbeladung der Thrombozyten-Exosomen hinweist. Das schwächere CD8-Signal auf den Melanom-Plasmaexosomen könnte auf eine verringerte Exosomen-Sekretion von zytotoxischen Zellen wie T-Zellen und NK-Zellen hinwiesen. Die Melanom- Marker MCSP, CD146 und CD49e wurden zwar auf Exosomen aus Melanom-Zellkulturen detektiert, aber nur das CD49e-Signal war auf den Plasmaexosomen von zwei von vier Melanom-Patienten im Vergleich zu gesunden Spendern erhöht. Wir schließen aus unseren Ergebnissen, dass Melanom-Exosomen nicht den Großteil der Plasmaexosomen ausmachen. Da sich die mirna-profile der Melanom-Patienten und der gesunden Spender stark ähneln, wurde der Signalanstieg der exosomalen mirnas im Plasma der Melanompatienten ebenfalls nicht durch mirnas vom Tumor verursacht. Wir denken, dass das Melanom die Exosomen-Sekretion von Blutzellen (wie Thrombozyten und zytotoxischen Zellen) verändert. Indem die Zusammensetzung und die Funktion verschiedener Blutzell-Exosomen aufgedeckt werden, könnten wichtige Erkenntnisse gewonnen werden, um die interzelluläre Kommunikation über Exosomen während der Tumorentwicklung besser zu verstehen. 4

8 Introduction 1 Introduction Communication between cells is essential in all multicellular organisms for adaption to the surrounding microenvironment. Cell to cell communication can be mediated via direct cell-cell contact or via soluble factors such as hormones, growth factors and cytokines. Additionally, molecules can be transported via vesicles. Endosome-derived vesicles were first discovered during reticulocyte maturation by Harding and Stahl in 1983 (Harding et al., 1983) and called exosomes by Johnstone and colleagues in 1987 (Johnstone et al., 1987). The secretion of extracellular vesicles is an evolutionary conserved process that was observed by eukaryotes cells including mammals and plants (Yanez-Mo et al., 2015) as well as prokaryotes (Lee et al., 2009). Extracellular vesicles play roles in physiological processes such as intercellular signaling, antigen presentation, inflammation, and coagulation (van der Pol et al., 2012a) as well as in pathological processes such as tumor progression and virus transmission (Yanez-Mo et al., 2015). Extracellular vesicles were first noticed independently in different physiological settings without noticing their universal relevance (reviewed in (Yanez-Mo et al., 2015). Meanwhile, various cell types such as B cells (Raposo et al., 1996), T cells (Blanchard et al., 2002), NK cells (Lugini et al., 2012), platelets (Heijnen et al., 1999), monocytes (Aharon et al., 2008), and monocyte-derived dendritic cells (Johansson et al., 2008) were described to release extracellular vesicles. Extracellular vesicles were also found in diverse body fluids such as plasma (Caby et al., 2005), urine (Pisitkun et al., 2004), saliva (Ogawa et al., 2011), and ascites (Andre et al., 2002). 1.1 Biogenesis of exosomes Commonly, three main types of extracellular vesicles are defined. Apoptotic vesicles are released during apoptosis by membrane blebbing. Microvesicles, microparticles or ectosomes pinch directly off the membrane. In contrast, exosomes stand out due to their endocytic origin and active release by live cells (Yanez-Mo et al., 2015). Exosome biogenesis starts with the inward budding of the endosomal limiting membrane that harbor molecules from the Golgi (e.g. MHC II molecules) or the cell surface (e.g. growth factor receptor) (Zhang et al., 2014). The invagination forms intraluminal vesicles (ILVs) that include cytosolic compounds. The endosome is now called multivesicular endosome (MVE) or multivesicular body (MVB). The origin of exosomes from MVEs was revealed by electron microscopic studies (Harding et al., 1984; Pan et al., 1985). MVEs can fuse with lysosomes to degrade their content or fuse with the plasma membrane releasing exosomes (Figure 1). As a consequence, exosomes exhibit the same membrane orientation as cells by displaying membrane bound proteins on their surface (Thery et al., 2009; Thery et al., 2002). As a consequence, exosomes are rich in cytosolic proteins and plasma membrane proteins that can be classified in two groups: common proteins that should be present in every type of exosome and cell type-specific proteins that reflect the type and status of the originating cell (Simpson et al., 2008; Thery et al., 2002). Some of those proteins that are often found in exosome preparations are commonly called and used as exosomes markers, in particular the tetraspanins CD9, CD63, and CD81 (Escola et al., 1998; Mulcahy et al., 2014; Yanez- Mo et al., 2015). 5

9 Introduction Figure 1: Exosome biogenesis. Schematic representation showing a Ligand receptor complex that is internalized and transported to an early endosome. The receptor can then be recycled to the plasma membrane or kept in the endosome. By inward budding of the endosomal membrane, multiple vesicles are formed giving the late endosome its new name multivesicular endosome (MVE) or multivesicular body (MVB). A MVE can follow two different ways: either it fuses with a lysosome resulting in the degradation of the ligand receptor complex. Or the late endosome fuses with the plasma membrane leading to the secretion of the ligand receptor complex via exosomes. Exosomes can then be taken up by a recipient cell where the same routes can be taken and the exosomal content influences the physiology of the recipient cell. ILV: Internal luminal vesicle, MVB: multivesicular body, also called multivesicular endosome (MVE). Adapted by permission from Taylor & Francis (Simpson et al., 2009), copyright Exosomal micrornas In addition to proteins, lipids, and DNA, exosomes also contain multiple types of ribonucleic acids (RNAs) (Yanez-Mo et al., 2015). In contrast to cellular RNAs that have a length of 2,100 nucleotides (nt) on average (Ravasi et al., 2006), most exosomal RNAs have a size of <700 nt. This indicates that exosomes are mainly enriched in shorter RNA sequences including messengerrnas (mrnas), premicrornas (70 nt), and mature micrornas (mirnas, 22 nt) (Batagov and Kurochkin, 2013). MicroRNAs are small non-coding RNAs that regulate gene expression post-transcriptionally by cleavage or inhibition of their target mrna (Jonas and Izaurralde, 2015). They are transcribed mainly from the introns of protein-coding or non-coding genes by polymerase II. The resulting pre-mirna consists of a stem, a terminal loop and two single stranded RNA segments (Figure 2). The RNase III-type endonucleases Drosha and Dicer cooperate with other proteins to cleave the pri-mirna into pre-mirna and into two mature mirnas: one from the 5 side and one from the 3 side of the precursor (Ha and Kim, 2014), Figure 2). The mature mirnas guide their associated argonaute proteins to their target mrna for cleavage or inhibition of translation. MiRNAs bind their target mrnas by base pairing, but simultaneously each mirna can bind to hundreds of different mrnas (Jonas and Izaurralde, 2015). Meanwhile 2,588 mirnas were discovered in the human genome and more than 60% of human protein-coding genes contain at least one conserved mirna-binding site. 6

10 Introduction Therefore, the biogenesis and function of mirnas are well controlled and alterations can lead to diseases including cancer (Ha and Kim, 2014). Figure 2: Biogenesis of mirna. The primary mirna (left) is cleaved by the RNase III protein Drosha into premirna (right) und further processed by another RNase III protein called Dicer into mature mirna of ~22 nt in length. Adapted by permission from Macmillan Publishers Ltd: Nature Review Molecular Cell Biology (Ha and Kim, 2014), copyright 2014 and Nature (Park et al., 2011), copyright MicroRNAs were found to be remarkably stable in plasma (Mitchell et al., 2008) and cell culture media (Turchinovich et al., 2011) while synthetic mirnas are rapidly degraded by RNases (Mitchell et al., 2008). MiRNAs might be passively released during tissue injury that can also occur in tumor progression (Brase et al., 2010). They are protected from RNases by association with argonaute proteins (Arroyo et al., 2011) or are transported by high-density lipoproteins (Vickers et al., 2011). In contrast, Gallo and colleagues found that the majority of mirnas in serum is concentrated in exosomes (Gallo et al., 2012) and therefore actively secreted. The packaging of circulating mirnas is still controversial and might be at last a combination of the proposed mechanisms as observed by Wang and colleagues (Wang et al., 2010). However, mirnas transported in exosomes can be taken up by recipient cells and can be functional evidenced by inhibited translation of their target mrnas. Interestingly, the mirna profile of exosomes differs from their originating cells (Valadi et al., 2007). Some mirnas (e.g. mir-150, mir-142-3p, mir-451a) seem to be preferentially incorporated into exosomes (Guduric-Fuchs et al., 2012). In different types of cancer, the level of exosomal mirnas differs between healthy donors and cancer patients. Therefore, exosomal mirna are considered as potential cancer biomarkers. For example, lower levels of mir-21 were found in exosomes from the serum of healthy donors than from glioblastoma patients (Skog et al., 2008). Conversely, exosomes from the plasma of non-small-cell lung carcinoma patients showed lower levels of three other mirnas (Silva et al., 2011). A set of eight exosomal mirnas is capable to distinguish benign tumors from ovarian cancers (Taylor and Gercel-Taylor, 2008). These findings indicate that cells possess an active mechanism to selectively load mirnas into exosomes. Currently, three potential sorting mechanisms are debated (reviewed in (Zhang et al., 2015). First, neural sphingomyelinase 2 (nsmase2) was reported to increase the number of exosomal mirnas 7

11 Introduction when it is overexpressed and to decrease their number when its expression was inhibited (Kosaka et al., 2013). But the ceramide producing enzyme was described as essential in general exosome secretion (Trajkovic et al., 2008; Villarroya-Beltri et al., 2014), therefore NSMase2 might regulate exosome secretion in general instead of mirna loading into exosomes. The second mirna loading process is based on sequence variations of mature mirnas that are called isomirs (Kuchenbauer et al., 2008). It was observed that a specific motif in the 3 portion of mirnas is recognized by the heterogeneous ribonucleoprotein A2B1 (hnrnpa2b1). HnrNPA2B1 probably interacts with cytoskeletal components to guide the mirna into exosomes. Two further members of the hnrnp family bind exosomal mirnas and might be further candidates for sorting, but no binding motifs have been discovered yet (Villarroya-Beltri et al., 2013). Interestingly, 3 -end adenylated mirnas were found to be relatively enriched in cells while 3 -end uridylated mirnas were overrepresented in exosomes (Koppers-Lalic et al., 2014). The 3 -end or the 3 portion of mirnas seems to be crucial for their sorting into exosomes. The third sorting pathway depends on the mirna inducing silencing complex (mirisc). The mirisc involves the argonaute2 (AGO2) protein that mediates translation repression or mrna degradation after binding of mirnas to their target mrna (Zhang et al., 2015). The AGO2 protein was detected in exosomes (Melo et al., 2014) and its knockout reduced the level of the mirnas preferentially sorted into exosomes (Guduric-Fuchs et al., 2012). The detection of other mirisc components in MVEs (Gibbings et al., 2009) further point to the engagement of mirisc in the sorting of mirnas into exosomes (Zhang et al., 2015). In summary, mirna sequence dependent sorting as well as sequence-independent but enzyme driven packaging exist that guide specific intracellular mirnas into exosomes. After arrival in the recipient cell, exosomal mirnas fulfill their known function in regulating mrna translation. In doing so, exosomal mirnas have been observed to promote metastasis (Rana et al., 2013; Zhou et al., 2014) and angiogenesis (van Balkom et al., 2013; Zhuang et al., 2012). The tumorpromoting effects of several exosomal mirs have been reviewed by Gajos-Michniewicz and colleagues (Gajos-Michniewicz et al., 2014). Beyond that, exosomal mirnas can act as ligands of tolllike receptors (TLRs) and induce an inflammatory response that might lead to tumor growth and metastasis (Fabbri et al., 2012). The selective loading of mirnas into exosomes and their impact on tumor progression bears the potential to use exosomal mirnas as biomarkers in cancer. Therefore, we isolated plasma exosomes from healthy donors and melanoma patients to compare their mirna profiles and detect melanomaspecific exosomal mirnas that might support the progression of this immunogenic tumor. 1.3 Exosomal proteins Exosomes do not just reflect the protein content of their parental cell, but rather carry a distinct and specific set of proteins. This is demonstrated by the findings that dendritic cell-derived exosomes lack Fc receptors (Thery et al., 2001) and T cell-derived exosomes present no CD45 (Blanchard et al., 2002). Furthermore, exosomes from mature and immature dendritic cells can be discriminated by their loading with co-stimulatory molecules that go along with their different potency to activate antigenspecific T cells (Segura et al., 2005b). 8

12 Introduction Currently, several models are discussed that might define the protein content of exosomes. Several post-translational modifications such as phosphorylation (Valapala and Vishwanatha, 2011), glycosylation (Batista et al., 2011; Escrevente et al., 2013) and ubiquitination (Smith et al., 2015) were found on proteins of extracellular vesicles and are therefore considered as potential markers that guide proteins into vesicles (Moreno-Gonzalo et al., 2014). Ubiquitinated proteins are recognized by the endosomal sorting complexes required for transport (ESCRT) resulting in the energy-dependent budding of the formed vesicle (Figure 1A). It is still controversial whether the ESCRT mechanism is responsible for recruiting ubiquitinated proteins to degradation or packing into exosomes (Buschow et al., 2005; Villarroya-Beltri et al., 2014). Even though ESCRT proteins and ubiquitinated proteins were detected in exosomes (Buschow et al., 2005; Thery et al., 2001), these proteins are soluble and not membrane bound indicating that another mechanism than the membrane bound ESCRT complex recruited these proteins (van der Pol et al., 2012a). Interestingly, major histocompatibility complex II (MHC II) is ubiquitinated in immature DCs for degradation in lysosomes while non-ubiquitinated MHC II is secreted by activated DCs via exosomes. Instead, the tetraspanin CD9 was required for the secretion of MHC II-containing exosomes (Buschow et al., 2009). This indicates that different types of sorting mechanisms exist that are ubiquitination dependent or not (Moreno-Gonzalo et al., 2014). An alternative sorting system involves tetraspanins. Tetraspanins are transmembrane proteins that contain four transmembrane domains (Levy and Shoham, 2005) and are selectively enriched in exosomes (Escola et al., 1998). They are known to associate mainly with integrins, immunoglobulin superfamily members of adhesion receptors, signaling receptors, and enzymes such as metalloproteinases (Andreu and Yanez-Mo, 2014). By interacting with each other, they build so-called tetraspanin-enriched microdomains (TEMs) that are described to be cell type-specific and related to distinct cellular functions (reviewed by (Andreu and Yanez-Mo, 2014; Levy and Shoham, 2005). Tetraspanins are considered to regulate exosome sorting and to determine the protein composition of exosomes. For example, the tetraspanin CD63 is required for normal sorting of a melanocyte protein to ILVs of MVEs during exosome biogenesis (van Niel et al., 2011) and dendritic cells from CD9 knockout mice showed a reduced exosome secretion (Chairoungdua et al., 2010). The interaction of tetraspanins with cell type-specific proteins was reported for CD81 that is required for normal expression of CD19 by B cells (Shoham et al., 2003) and CD9 that is associated with CD41/CD61 on platelets (Maecker et al., 1997). The transfer of these tetraspanin-associated proteins from the respective cells to their exosomes could be detected for B cell exosomes (Admyre et al., 2007) as well as for platelet exosomes (Janowska-Wieczorek et al., 2006). Consistent with this finding, Fang and colleagues demonstrated that plasma membrane binding and higher-order oligomerization as observed for tetraspanins (Min et al., 2006) is sufficient for protein sorting into exosomes (Fang et al., 2007). In summary, the different ways to sort proteins into exosomes might depend on the type and status of the originating cell. The specific loading of exosomes with proteins also impacts their function. It has been demonstrated that the biodistribution of exosomes depends on the secreting cell type indicating that exosomes may adopt the homing pattern of the parental cell of origin (Wiklander et al., 2015). Exosomal surface proteins also mediate the binding to proteins of the extracellular matrix or recipient cells (Yanez-Mo et al., 2015). In addition the nonspecific uptake of exosomes by phagocytosis, e.g. by 9

13 Introduction monocytes, exosomes can be internalized through receptor-ligand recognition. This specific uptake depends on the type and status of recipient cells and the protein exposed by the exosome (Thery et al., 2009), e.g. tetraspanins, integrins, immunoglobulins, proteoglycans, and lectins (reviewed by (Mulcahy et al., 2014). Taken together, exosomes carry proteins from their originating cell and their protein content can affect exosome function. Consequently, the proportion and type of immune cell-derived exosomes in patient s blood might be relevant to understand what happens during antitumor immune response and immune escape by the tumor. 1.4 Exosomes in melanoma Cutaneous malignant melanoma is a form of skin cancer that accounts for 65% of skin cancer-related deaths (Jemal et al., 2010). The incidence increases continuously, and while early detection leads to nearly 100% survival rates, the mortality raises to greater than 80% for patients with advanced disease (Howell et al., 2010). Interestingly, 5% of patients with metastatic melanoma lack a primary tumor. This is explained by eradication of the tumor by the immune system before diagnosis. Together with the finding of lymphocytes within the tumor microenvironment and the success of immunomodulatory therapies, these observations indicate that malignant melanoma is one of the most immunogenic solid tumors (Weiss et al., 2014). Genetic predisposition and environmental stressors such as ultraviolet radiation support the genesis of melanoma. Following the Clark model, melanocytes first develop benign nevi that are composed of neval melanocytes. 15% of melanomas show mutations in the N-RAS gene and 50% of metastatic melanoma harbor BRAF mutations (Miller and Mihm, 2006). Since BRAF mutations were also found in normal nevi with high frequencies (Pollock et al., 2003), additional gene mutations are requested for the development into melanoma cells. For example in combination with cell cycle regulating genes, N-RAS and BRAF mutations activate serine-threonine kinases that stimulate aberrant growth of melanocytes or neval melanocytes that results in the formations of dysplastic nevi (Miller and Mihm, 2006). Such nevi can be discovered by the ABCDE mnemonic: A for asymmetry, B for irregular border, C for varied color, D for diameter larger than 6 mm and E for evolving, i.e. changing of the nevi in size, shape or color over time (American Academy of Dermatology Ad Hoc Task Force for the et al., 2015). In about half of the patients, tumor-suppressor genes are inactivated by mutations and the cells become able to proliferate intraepidermally. Additional mutations initiate the vertical-growth phase and metastasis that is characterized by the spread of melanoma cells to other areas of the skin and other organs (Miller and Mihm, 2006). Melanoma can be staged based on the TNM categories. Briefly, T stands for thickness, e.g. melanomas thinner than 1 mm are staged as T1, melanomas that are thicker than 4 mm are staged as T4. N stands for nodal metastatic burden and specifies the number of sentinel lymph nodes next to the tumor that harbor metastatic melanoma cells. M indicates the number of distant metastases. Melanoma patients with stage IV, that is the highest stage, can have varying thicknesses of the primary tumor and affected number of sentinel lymph nodes, but do have distant metastasis (Piris et al., 2012). Furthermore, melanoma patients are grouped into low risk and high risk patients corresponding to the TNM categories by the American Joint Committee on Cancer. High risk 10

14 Introduction patients include patients whose stage IV melanoma was resected completely and patients with TNM stages II-III, i.e. patients with a tumor thickness >1 mm and metastatic sentinel lymph nodes potentially, but no distant metastases (Weiss et al., 2014) % of melanoma patients are low risk patients that had a primary tumor thickness of less than 1 mm before tumor removal or were categorized in stage I (Leiter et al., 2014), i.e. as having a melanoma thickness <1 mm, no affected lymph nodes and no metastases (Piris et al., 2012). The exosome secretion by melanoma cells seem to be increased in comparison to normal melanocytes (Xiao et al., 2012). Melanoma exosomes carry pro-angiogenic proteins to promote tumor progression (Ekstrom et al., 2014) and to facilitate lymphatic metastasis (Hood et al., 2011). Peinado and colleagues revealed in a mouse model that this is performed by the exosomal transfer of the oncoprotein MET from melanoma cells to bone marrow progenitor cells. These cells are educated by MET to develop a provasculogenic phenotype and move to lungs and lymph nodes to promote angiogenesis, invasion, and metastasis of primary tumor cells (Peinado et al., 2012; Somasundaram and Herlyn, 2012). Hao and colleagues reported that exosomes secreted by highly metastatic melanoma cell lines can transfer the metastatic activity to poorly metastatic melanoma cells in vitro (Hao et al., 2006). Hakulinen and colleagues observed that vesicles released by fibrosarcoma and melanoma cells spread active matrix-degrading enzymes to induce metastasis (Hakulinen et al., 2008). The impact of tumor exosomes on the immune system can be activating as well as inhibitory (reviewed by (Thery et al., 2009). For example, tumor-derived exosomes are able to activate cytotoxic T cells via tumor antigens after being loaded onto dendritic cells (Andre et al., 2002; Wolfers et al., 2001). In contrast, exosomes from melanoma cells and other tumor cells carry Fas ligand, TRAIL, or galectin-9 to induce T cell apoptosis (Andreola et al., 2002; Huber et al., 2005; Klibi et al., 2009). Furthermore, tumor cells can down-regulate the expression of surface NKG2D expression by NK cells and cytotoxic T cells resulting in an impaired lymphocyte effector function (Clayton et al., 2008; Liu et al., 2006). Exosomes from melanoma patients are able to redirect the differentiation of monocytes into DCs in favor of generating an immunosuppressive cell subset that is called myeloid-derived suppressor cells (Valenti et al., 2006). The resulting lack of sufficient co-stimulatory molecules (CD80/86) on DCs leads to increased levels of regulatory T cells that further repress immune responses against the tumor by suppressing T cell activation (Yu et al., 2007). Melanoma exosomes have unique gene expression signatures, mirna and proteomics profiles (Xiao et al., 2012) including the tumor-associated antigen Mart-1 (Mears et al., 2004) that might depend on the aggressiveness of the originating melanoma cell lines (Lazar et al., 2015). On the basis of these findings, tumor exosomes might serve as biomarkers that can help to characterize the originating tumor (reviewed in (Verma et al., 2015). In melanoma patients, the amount of plasma exosomes were found to be significantly increased in comparison to healthy donors (Logozzi et al., 2009). Moreover, the protein load of plasma exosomes from melanoma patients correlates with the survival rate (Peinado et al., 2012). But the question remains from which cells besides the tumor these exosomes derive. Because of the interaction between the tumor and the immune system, one can hypothesize that both exosome types also influence the secretion of each other. Thus, melanoma might be a suitable model to investigate the impact of the tumor on exosome secretion of immune cells in favor of tumor progression. 11

15 Introduction 1.5 Common methods in exosome research After release from their originating cell, different types of extracellular vesicles can hardly be discriminated (Villarroya-Beltri et al., 2014). Even though the tetraspanins CD9, CD63, and CD81 are enriched in exosomes (Escola et al., 1998) and commonly used as exosome markers (Mulcahy et al., 2014), there is no list of markers that distinguish subsets of extracellular vesicles from each other (Lotvall et al., 2014). The most practical method to distinguish exosomes from larger extracellular vesicles is centrifugation. Cell culture supernatant or body fluids are depleted of cells and cell debris by centrifugation at 300 xg and 2,000 xg. Larger extracellular vesicles (100 nm to 1 µm) are subsequently sedimented at 10,000 xg and are mostly called microvesicles. Finally, exosomes with a size ranging from 50 nm to 100 nm are pelleted from the microvesicle-depleted supernatant by ultracentrifugation at 100,000 xg (Figure 3, (Gould and Raposo, 2013; Gyorgy et al., 2011; Thery et al., 2006). To increase the purity, exosomes that have a density of 1.08 to 1.22 g/ml (Raposo et al., 1996) can be separated from protein aggregates and small apoptotic bodies by density gradient centrifugation (Van Deun et al., 2014; Webber and Clayton, 2013). But the density of high-density lipoproteins were shown to overlap with that of extracellular vesicles (Vickers et al., 2011). Moreover, exosome density might vary among different cells of origin depending on the content of the exosomes (Chen et al., 2010) and sucrose gradients are not resolutive enough to separate vesicles with small differences in densities (Bobrie et al., 2012). Figure 3: Size ranges of cells and extracellular vesicles. Exosomes are commonly defined as vesicles ranging from 30nm to 100 nm similar to viruses. Microvesicles are larger with 100 nm to 1 µm in diameter overlapping with protein aggregates and bacteria. Apoptotic bodies are very heterogeneous in size ranging from 50 nm to 5 µm (Thery et al., 2009). Adapted by permission for noncommercial use, distribution and reproduction (Gyorgy et al., 2011), open access article (2011). Exosomes can also be isolated by volume-excluding polymers that precipitate exosomes by interacting with water molecules and push the less soluble exosomes out of solution (Zeringer et al., 2015). This technique is also commercially available but performed worse compared to ultracentrifugation or density gradient centrifugation in terms of purity, exosome recovery, and yield (Lobb et al., 2015; Van Deun et al., 2014). At the start of this study, ultracentrifugation (optional in combination with filtration) was still the gold standard for exosome isolation in most laboratories. 12

16 Introduction Therefore, we kept the original exosome isolation protocol for cell culture supernatants and plasma (Thery et al., 2006) to minimize divergence. However, new isolation protocols have been introduced in the field recognizing the need for a standardized exosome isolation protocol to allow a better comparability of different studies. Well-established methods such as size-exclusion chromatography (Bauer et al.) and microfiltration have been optimized for exosome isolation. SEC allows the separation of exosomes from contaminating high density lipids (HDL) and protein aggregates (Boing et al., 2014). For high volumes of cell culture supernatant, ultrafiltration was improved to reach higher particle yield compared to ultracentrifugation ( ) in a shorter processing time (Lobb et al., 2015). In combination with SEC, it provides purities that are comparable to density gradient centrifugation (Lobb et al., 2015). As criterion to identify exosomes, one can make use of the protein profile of exosomes in addition to their size and density. Specific antibodies can be bound to chromatography matrices, plates, microfluidic devices or magnetic beads (Zeringer et al., 2015). Various studies have demonstrated that exosomes from different sources can be isolated by using suitable antibodies coated onto magnetic beads (Clayton et al., 2001; Coren et al., 2008; Gieseler et al., 2014; Koga et al., 2005; Tauro et al., 2012; Taylor and Gercel-Taylor, 2008). Mathivanan and colleagues isolated exosomes from a colon carcinoma cell line using anti-a33 magnetic beads and reached a higher purity compared to ultracentrifugation alone (Mathivanan et al., 2010). Inherent of this method is the question, which antibodies are the most appropriate ones to isolate the target exosomes with the highest recovery. To characterize exosome samples, several well-established methods have been optimized for exosome analysis. For example, a protocol for electron microscopy was developed that avoid ultrathin sectioning to prevent exosome loss. Nevertheless, dehydration and fixation of exosomes instead of freezing for cryo-em analysis lead to the characteristic but artificial cup-shaped morphology. However, it is suitable to analyze the size and morphology of exosomes as well as the presence of up to three proteins by immunolabeling (Thery et al., 2006). Western blotting can be used to detect proteins in an exosome sample, but it is not capable to quantify exosomes, exclude contaminants or distinguish exosome subpopulations. The same is true for mass spectrometry whose main advantage is the detection of proteins without bias by selection of distinct proteins. Its suitability for exosome analysis was already demonstrated after performing one- (Thery et al., 1999) and two-dimensional polyacrylamide gel electrophoresis (Mears et al., 2004). To size and quantify exosomes, nanoparticle tracking analysis (NTA) can be applied. NTA records the scattered laser beam by the particles to calculate their mean velocity based on their Brownian motion (Dragovic et al., 2011). Dynamic light scattering (DLS) works in a similar way to size particles (Sokolova et al., 2011). Alternatively, tunable resistive pulse sensing (RPS) is used that measures the decreased ionic current in a nanopore when a particle passes (Garza-Licudine et al., 2010). But NTA, DLS and TRPS are currently limited to measure particle size and concentration. For flow cytometric analysis of exosomes, the small size of the exosomes has been overcome by using latex beads that bind exosomes and increase their size to reach a detectable dimension (Thery et al., 2006). Meanwhile, flow cytometry has also been improved to reach high resolution for exosome quantitation and qualification and might be one of the most promising new methods to analyze single vesicles or exosomes when the technical requirements are given (van der Vlist et al., 2012b). However, the simultaneous analysis of multiple proteins is not feasible yet (van der Vlist et al., 2012b). 13

17 Aim of the study 2 Aim of the study Exosomes are specifically loaded with micrornas and proteins that might qualify them to serve as cancer biomarkers. We aimed to identify tumor-related micrornas that differ between plasma exosomes from healthy controls and melanoma patients. The exosomal micrornas identified should then be investigated in terms of their influence on tumor growth or on immune cells, to promote immune escape of the melanoma. We identified micrornas that are selectively transferred from melanoma cells into exosomes in cell culture. However, the comparison of averaged microrna signal intensities for plasma exosomes from melanoma patients and healthy donors revealed no micrornas that differ between the two groups. Although we cannot exclude that micrornas in individual patients or patient subgroups are related to the tumor, we could not identify a subset of tumor-related micrornas that would be indicative for all melanoma patients. Instead, we observed that the average microrna levels were strongly increased for plasma exosomes from melanoma patients as compared to healthy donors. The global increase of exosomal micrornas in the plasma of melanoma patients raised the question of which cells secrete exosomes into the plasma and might be stimulated in melanoma patients. As cell type-specific mirnas are rare, we decided to analyze the protein profiles of exosomes to assign the potential originating cells of plasma exosomes. For this purpose, we planned to investigate the protein composition of exosomes from primary blood cells as they were expected to contribute to the exosome pool in plasma. This study should also include immune cell-derived exosomes before and after cell stimulation because plasma exosomes might also derive from stimulated cells e.g. due to an enhanced inflammatory response during tumor progression. As a reference for potential tumor exosomes, we intended to also analyze the protein compositions of exosomes from primary melanoma cell lines. We aimed to compare the protein profiles of exosomes from defined cell populations with plasma exosomes from melanoma patients as well as from healthy donors. For the analysis of plasma exosomes, different anticoagulants and sample preparations should be tested. Established methods for protein analysis such as Western blot or mass spectrometry give information on the bulk population, i.e. the average protein composition of exosomes in a given sample, but the composition of individual exosomes remains undefinable. We intended to set up a new method that would also allow to identify potential exosome subpopulations and to use this method to compare protein surface profiles of the different exosome populations. The overall goal was to investigate the impact of melanoma on the mirna content and protein composition of plasma exosomes. Differences in these aspects between plasma exosomes from melanoma patients and healthy donors could give indications on the metastatic potential or immunosuppressive phenotype of the tumor. 14

18 Materials and methods 3 Materials and methods Note: parts of this section were copied from the publications listed on page TotalRNA extraction for mirna analysis RNA was isolated using the mirneasy Kit (Qiagen). The protocol for the Purification of totalrna, including small RNAs, from animal cells was applied following the manufacturer s instructions. TotalRNA were extracted from 1 to 3x10 6 melanoma cells, 100 µg platelet exosomes, or from a melanoma exosome pellet from 750 ml cell culture supernatant. The isolation of exosomes from modcs, liver cells and plasma exosomes from healthy donors and patients as well as the respective totalrna extraction were done by the group of Andreas Baur at the university dermatology clinic in Erlangen. For the totalrna extraction from whole plasma and exosome-depleted plasma, TRIzol LS (Ambion, Life Technologies) was used. 9 ml whole plasma, exosome depleted plasma or exosomes isolated from an equal plasma volume were mixed with a 3-fold volume of TRIzol, vortexed and incubated for 5 min at room temperature (RT). An equal sample volume of chloroform was added, the sample was shaken for 15 sec and incubated for 2 to 15 min at RT. Due to the high volume, the phase separation of the plasma samples were performed in MaXtract High Density tubes (Qiagen) at 1,500 xg for 5 min. The following steps were done according to the manufacturer s instructions. The optional drying of the column before sample elution was performed for all samples. TotalRNA was eluted twice with 30 µl RNase free water each. The yield of five-fold dilutions was measured by Nanodrop (Thermo Scientific). 3.2 Agilent microrna microarray 100 ng cellular or exosomal totalrna was concentrated to 50 ng/µl and Cy3-labelled using Agilent s mirna Complete Labeling and Hyb Kit (Agilent Technologies, Santa Clara, USA). After purification through Micro Bio Spin Columns (Bio Rad) the totalrna samples were hybridized for 20 hours at 55 C to human mirna microarrays (Agilent, Version V16, 8x60K). The microarrays were washed in Tritoncontaining washing buffer as recommended by the manufacturer and scanned with the Agilent s Microarray Scanner System (Agilent Technologies). The image files were analyzed and processed by Agilent Feature Extraction Software (Version ). 3.3 MiRNA Mimic transfection 5x10 4 primary melanoma cells per well were seeded in a 24-well plate and cultured in RPMI 1640 medium containing 10% FCS, 2 mm L-Glutamin and Pen/Strep for 24 h at 37 C with 95% humidity and 5% CO nm miridian Mimic hsa-mir-451, miridian mirna Mimic negative control (scramblerna, Dharmacon, GE Healthcare) or a FITC-labeled mirna (Qiagen) were prepared in serum free medium, mixed and incubated with 3 µl HiPerFect (Qiagen) per well for 5 to 10 min at RT before added to the cells. 120 hours after transfection, transfection efficiency was evaluated by the percentage of positive cells for the FITC-labeled mirna, viability and apoptosis were determined by AnnexinV and PI (Propidium iodide) staining using the MACSQuant Analyzer (Miltenyi Biotec). 15

19 Materials and methods 3.4 Cell isolation, culture and stimulation for exosome production Primary melanoma cells Primary melanoma cells were provided by the university dermatology clinic Erlangen (Prof. Dr. Baur). 4x10 6 primary melanoma cells per ml were cultured in RPMI1640 (Miltenyi Biotec), 10% FCS (Biochrom), 2mM L-glutamine (Biozym), 50 U/ml Penicillin and 50 µg/ml Streptomycin (PAA). For exosome production the cells were washed with phosphate buffered saline (PBS) and cultured for 72 h in FCS-free medium to avoid any contamination with bovine exosomes. At least 90% of the cells were viable. For establishing a protocol for exosome analysis by stimulated emission depletion (STED) microscopy, primary melanoma cells were genetically modified to express a CD63-GFP fusion protein (Miltenyi Biotec). The modified melanoma cells were cultured for exosome generation as described above for primary melanoma cells. Peripheral blood mononuclear cells (PBMCs) PMBCs from a CMV-positive donor were separated by Ficoll gradient and washed with PBS to reduce the amount of contaminating platelets. 3x10 6 PBMCs per ml were cultured for eight days in basis medium consisting of RPMI1640 (Miltenyi Biotec), 5% exosome-depleted human AB serum (Gem Cell), 50 U/ml Penicillin and 50 µg/ml Streptomycin (PAA) either without stimulation, with stimulation by 0.75 µg/ml CD40 ligand, cross linking antibody and 50 U/ml IL-4 or with stimulation with a peptide pool that covers the complete sequence of the pp65 protein of human cytomegalovirus (Miltenyi Biotec). Stimulations were performed according to the manufacturer s instructions. T cells T Cells were isolated from Buffy Coats by Pan T Cell Isolation Kit (Miltenyi Biotec, Germany) with purities of 96-99%. To generate as many exosomes as possible, the protocol by van der Vlist et al. were used with minor modifications. Briefly, cells were cultured in TexMACS medium (Miltenyi Biotec) without serum with 5 U/ml IL-2 and with 2.5 µg/ml CD28 (clone 15E8, Miltenyi Biotec) in CD3 (clone OKT3, Miltenyi Biotec) coated tissue culture flasks for 24 h with viability rates >90% % of T cells were positive for CD69 after activation. B cells B cells were isolated from Buffy coats after Ficoll gradient by immunomagnetic cell sorting using CD19 MicroBeads (Miltenyi Biotec) with purities of 97-99%. 2x10 6 B cells per ml were cultured in StemMACS HSC Expansion Media XF (Miltenyi Biotec) with 5% exosome-depleted human serum (Gem Cell). To stimulate the cells 1 µg/ml CD40-Ligand, cross-linking antibody and 20 IU/ml IL-4 (Miltenyi Biotec) were incubated for 30 min at RT before cells were added to the medium for four days following the manufacturer s instructions. On the day of exosome harvest, the viability rates were >90% % of activated B cells were positive for CD80 and 83-95% were positive for CD86. 16

20 Materials and methods Platelets Platelets were isolated from fresh whole blood that was diluted with an equal volume of Krebs Ringer buffer (100 mm NaCl, 4 mm KCl, 20 mm NaHCO 3, 2 mm Na 2 SO 4, 4,7 mm citric acid, 14,2 mm tri-sodium citrate) at ph 7.4 and centrifuged at 190 xg for 10 min (Heijnen et al., 1999). To deplete leukocytes and erythrocytes the platelet rich plasma was centrifuged at 100 xg for 20 min. Platelets were pelleted at 1,000 xg for 15 min and washed twice with Krebs Ringer buffer. 1 to 9 x10 7 platelets per ml whole blood were isolated and platelet purities ranged from 82-99%. After adjusting to 1x10 9 platelets per ml they were activated with 50 nm Calcium Ionophore (Sigma Aldrich) and 10 mm calcium chloride (Sigma Aldrich) for 30 min at RT (Leong et al., 2011). Natural killer (NK) cells Natural killer (NK) cells were isolated from buffy coats using the MACSxpress NK Cell Isolation Kit (Miltenyi Biotec) with purities of 80-90%. NK cells were cultured in TexMACS GMP medium (Miltenyi Biotec) with 5% human AB serum (Life Technologies) and 500 U/ml Proleukin S (Novartis) for 14 days. Monocytes and monocyte-derived dendritic cells (modcs) Monocytes were isolated from Buffy coats after Ficoll gradient by immunomagnetic cell sorting using CD14 MicroBeads (Miltenyi Biotec, Germany) with purities of 92-98% and cultured in RPMI1640 (Miltenyi Biotec) with 2mM L-glutamine (Biozym), 50 U/ml Penicillin and 50 µg/ml Streptomycin (PAA) for 24 h with viability rates >90%. To generate modcs, monocytes were isolated from leukapheresis by immunomagnetic cell sorting using CliniMACS CD14 Beads and the semi-automatic CliniMACS Prodigy system (Miltenyi Biotec). 2 to 6 x10 6 monocytes per ml were cultured in RPMI (Miltenyi Biotec) with 2 mm L-glutamine (Biozym), autologous serum, 250 IU/ml IL-4 (Miltenyi Biotec) and 800 IU/ml GM-CSF (Miltenyi Biotec). After two and four days, half of the medium was replaced by fresh medium adjusted to the same final cytokine concentrations. On day six half of the medium was replaced by fresh medium to reach final concentrations of 1 µg/ml PGE 2 (Merck), 1000 IU/ml TNFα (Miltenyi Biotec), 1000 IU/ml IL-6 and 200 IU/ml IL-1ß (Miltenyi Biotec). To isolate exosomes, supernatants from immature modcs were harvested on day two, four and six and supernatants from mature modcs on day seven and ten. 17

21 Materials and methods 3.5 Exosome isolation Cell culture supernatants were depleted of cells and large cell debris by centrifugation at 2,000 xg for 30 min. Dead cells and cell debris were depleted by centrifugation at 10,000 xg for 45 min and larger vesicles by filtration through 0.22 µm membranes. Exosomes were pelleted at 108,000 xg for 2 h in a Beckman Avanti J30i centrifuge with an appropriate rotor (JA Ti), washed with PBS and pelleted again at 108,000 xg. To isolate plasma exosomes whole blood was centrifuged at 1,000 xg for 10 min without brakes. Plasma was diluted with an equal volume PBS before performing the centrifugation steps described above. The supernatant after the first ultracentrifugation step at 108,000 xg was kept for analysis and called exosome-depleted plasma. Plasma exosomes were filtrated through a 0.22 µm membrane in between the two ultracentrifugation steps at 108,000 xg for 2h. Exosome pellets were resuspended in a minimal volume of PBS. The exosome concentration was measured indirectly by BCA assay (Thermo Fisher Scientific) using BSA as standard. 3.6 Mass spectrometry 10 µg or 20 µg melanoma exosomes were denatured in sample puffer containing SDS and DTT for 5 min at 95 C and ran on a 4-12% SDS gel at 200 V, 30 ma, and 5 W for 1 h. Gel were washed three times in double-distilled water and stained with coomassie for 30 min at RT. The thirteen most prominent bands were excised (as done by (Thery et al., 1999) and de-stained in 50% acetonitrile with 50 mm ammonium bicarbonate at 550 rpm and RT for 30 min. The supernatant was discarded and 100% acetonitrile was incubated again at 550 rpm and RT for 30 min. After removal of the supernatant, samples were dried in a vacuum concentrator. After incubation with 10 mm DTT for 5 min at 95 C, 50 mm Iodacetamid were incubated for 30 min at RT protected from light. The incubation steps with acetonitrile and the drying step were repeated. Proteins were trypsin digested in 50 mm ammonium bicarbonate for 1h on ice followed by an overnight incubation at 550 rpm and 37 C. Proteins were extracted by incubating the gel pieces twice with 0.1% trifluoroacetic acid and 50% acetonitrile for 30 min at RT and once with 100% acetonitrile for 60 min at 37 C. The extracts were combined and dried in a vacuum concentrator at 60 C for 2 h. The samples were resolved in 2% acetonitrile with 0.1% trifluoroacetic acid for 30 min. Peptides were analyzed by LC-MS/MS using an LTQ-Orbitrap mass spectrometer (Thermo Fisher scientific). Peptide mass spectra, coverage, and score were evaluated by the Peaks Viewer software 5.2. The coverage indicates the percentage of identified amino acids among the total length of the protein. The score describes the similarity between the de novo sequence and the database peptide by the number of common amino acids. 3.7 Multiplex bead-based platform Carboxylated polystyrene beads (Microparticles GmbH) were hard dyed by swelling them in an organic solution of pyrromethene / LD688. For the calculation of dye concentrations in the mixture beads were dyed with single dyes at varying concentrations beforehand. The emission into fluorescence channels was measured and linear equations correlating mean fluorescence intensities (MFIs) of the different emission channels with staining concentrations were established. The data were combined into a matrix equation to allow calculation of MFIs from the concentrations of a dye 18

22 Materials and methods mixture. By inverting the matrix the equation was used to calculate concentrations of dye mixtures for the desired MFIs of bead types. For the indirect binding of the capture antibodies, beads were coated with streptavidin and each bead type was incubated with a different biotin-conjugated capture antibody. For the direct binding of the capture antibodies, capture antibodies (Appendix table 2) were reduced at room temperature and incubated with dyed, imide covered beads. The capture antibody beads were washed with PBS % Pluronic F % sodium azide. Approximately 800 beads per bead type were incubated with isolated exosomes, cell culture supernatant, plasma or serum at 4 C overnight in 100 or 300 µl, respectively, or the indicated volume of plasma or serum. To adjust the volume of different samples, PAP buffer was used consisting of phosphate buffered saline (PBS) with 0.1% Pluronic and 0.09% azide, except for plasma or serum samples. The optimal amount of isolated exosomes (typically 4-32 µg) was determined for each exosome type by titrating the exosome sample. To remove unbound exosomes the beads were washed in PAP and pelleted at 3,000xg for 5 min. The beads were resuspended in 100 µl PAP and bound exosomes were stained with 0.5 µg of the indicated detection antibody for 1 h. After another washing step with PAP, the beads were resuspended in 100 µl PAP for flow cytometry analysis. The workflow of the assay is depicted in Figure 15. The capture antibodies used are listed in Appendix table 2. The same antibody clones from the same companies conjugated to APC were used as detection antibodies. In the blocking experiment, 8 µg NK cell exosomes from four donors each were incubated overnight with a 8-plex alone or in addition with 10-fold excess of the isotype control antibody mouse IgG1 (anti- KLH) or the anti-cd63 antibody in relation to the amount of capture antibodies on the beads. After the washing step, the bead bound exosomes were stained with anti-cd81-apc. 3.8 Flow cytometry analysis of bead-bound exosomes For flow cytometry analysis, the MACSQuant Analyzer 10 with the corresponding software (Miltenyi Biotec) was used. For the multiplex bead-based platform, a trigger for the side scatter (SSC) and the forward scatter (FSC) were selected to confine the measurement on the multiplex beads. Single beads were gated to exclude bead doublets and non-bead events. Voltages for the FITC and PE channels were adapted to ensure that each of the differentially labeled bead types were detectable. The 39 single bead types were gated to allow the determination of the APC signal intensity on the respective bead type (Figure 15). The signals depicted in the figures are either raw signals, background corrected signals or normalized signals. Background signals were determined by analyzing beads incubated only with the respective staining antibodies. The background signals were subtracted from the signals obtained for beads incubated with exosomes and stained with the respective antibody. For the NK cell-derived exosomes that were isolated from medium with human AB serum, an equivalent volume of medium including human AB serum was used for exosome isolation. The signals gained for the isolated AB serum exosomes were set as background and were subtracted from the NK cell exosome sample signals. For the modc exosomes, the same protein amount isolated from medium with 1% human serum without contact to cells by ultracentrifugation was analyzed and the 19

23 Materials and methods obtained signals were subtracted from the exosome samples. The normalization was performed with the background corrected signals in relation to the mean signal intensity obtained with the capture antibody beads against CD9, CD63, and CD81. The normalization should reveal surface proteins that are changed independently of a changed exosome amount or a changed general exosome loading that is indicated by changed signals of these exosome markers. 3.9 Confocal microscopy and high-resolution microscopy using stimulated emission depletion (STED) Standard Menzel glass slides were cleaned in a 1% (w/v) ammonium persulfate solution in sulfuric acid for 1.5 h at room temperature and modified with amino groups by incubation with 2% (w/v) (3-Aminopropyl)triethoxysilane (APTES) in ethanol for 1 h at room temperature. SMCC was dissolved in DMSO and further diluted with PBS containing 2 mm EDTA. After cleaning in ethanol, slides were incubated with 0.5 mg/ml SMCC for 3 h at room temperature to be activated with maleimide groups. The isotype control mouse IgG1, anti-cd9 antibody (clone SN4), anti-cd63 (H5C6) antibody or a cocktail of anti-cd9, anti-cd63 and anti-cd81 (clone 5A6) antibodies (Miltenyi Biotec) were reduced by TCEP-HCl for 1h at room temperature. The antibodies were incubated for 3h at room temperature to bind to the maleimide groups on the activated glass slides. Antibody coated slides were washed in PBS with 2 mm EDTA, 0.03% Pluronic F68 and 0.05% sodium azide and stored in the same buffer at 4 C until use. 1 µg CD63-GFP + exosomes, 1 µg melanoma exosomes, 13 µg B cell exosomes, 2 µg monocyte exosomes or a mixture of 2.4 µg platelet exosomes and 2 µg NK cell exosomes in 30 µl PBS/EDTA each were immobilized on one half of a slide each overnight at 4 C. Slides were washed with PBS and bound exosomes were stained anti-cd9-apc (Miltenyi Biotec) or with a final concentration of 5 µg/ml of each respective antibody conjugate for 3h at 4 C, i.e. anti-cd9-star488 (Miltenyi Biotec), anti-cd9-starred (Miltenyi Biotec), anti-cd81-star488 (Miltenyi Biotec), anti-cd81-starred (Miltenyi Biotec), anti-igg1-starred (Miltenyi Biotec), anti-cd42a-starred (Miltenyi Biotec), and anti-cd19-star488 (Miltenyi Biotec). During all incubation steps, cover slips were used to prevent the slides from drying and maximal humidity was established. Slides were washed twice in PBS and exosomes were preserved with Pro Long Gold antifade reagent (Life Technologies). For the confocal microscopy analysis, a LSM 710 microscope (Zeiss) was used with a 63x oilimmersion objective. The images were evaluated using the ZEN 2011 software (Zeiss). For STED analysis a Leica TCS SP8 gsted microscope with a 100x objective was used. Image processing and determination of the proportion of double positive spots were performed by Nikolay Kladt (CECAD Imaging Facility, Cologne) as follows. Fiji (ImageJ rc-38, was used to align the STED images and to perform a basic peak detection. After this initial step, R (3.3.0) was used to calculate the full width at half maximum (FWHM) for each spot identified at the respective peak positions (gaussian fitting on neighborhood surrounding each peak). Identified spots with intensities below an automatically determined threshold (histogram based) were ignored for further analysis. Using a nearest neighbor approach, colocalized spots were identified and their proportion is given in percentages. 20

24 Materials and methods 3.10 Flow cytometry analysis of cells 2x10 5 melanoma cells or PBMCs, 2x10 6 platelets or 5x10 5 T cells, B cells, monocytes or modcs were used for each staining. The cells were spun down at 300 xg (or 1,000 xg for platelets) for 5 to 10 min and resuspended in an appropriate amount of PEB (PBS supplemented with 2 mm EDTA and 0.5% BSA). Antibody conjugates were applied in a 1:11 dilution to stain the cells for 10 min at 4 C. After washing with PEB, cells were resuspended in 100 µl PEB and analyzed at the MACSQuant Analyzer 10 with the corresponding software (Miltenyi Biotec). Dead cells were identified with PI and excluded from the analysis. PI and all antibody conjugates were from Miltenyi Biotec Blood samples Blood was obtained from healthy volunteers after written informed consent on seven different days with the safety blood collection set 21G x 3/4 (Greiner) in vacutainers (BD) with silica particles to accelerate clotting of serum or with heparin or sodium citrate as anticoagulant. The used anticoagulant, age, sex, smoking habit and medication of the healthy donors and the melanoma patients are summarized in Table 1. Table 1: Overview of healthy donors and melanoma patients, n.a. = not available Healthy donor s blood with citrate Donor no Age in 2015 sex Smoking habit Medication D1 29 female non-smoker Hormonal contra-ception D2 42 female non-smoker Thyronaiod 50 D3 41 male non-smoker non D4 33 female non-smoker non D5 33 female non-smoker non D6 34 female non-smoker non D7 24 male smoker non D8 44 male smoker non D9 46 female non-smoker non D10 44 male non-smoker Berodual if needed D11 37 female non-smoker non D12 33 male smoker non D13 37 male non-smoker non Needle type, tube type Safety Blood Collection Set, Greiner, 21G x ¾, 0.105M sodium citrate Vacutainer, BD Healthy donor s blood with heparin Melanoma patient s blood with heparin D1 40 male non-smoker n. a. D2 33 male smoker n. a. D3 24 male smoker n. a. D4 37 male non-smoker n. a. D5 37 male non-smoker n. a. D1 70 male n. a. n. a. D2 75 female n. a. n. a. D3 46 male n. a. n. a. D4 48 male n. a. n. a. Safety Blood Collection Set, Greiner, 21G x ¾, 17 IU/ml heparin, BD For the isolation of platelets and plasma exosomes, blood was anticoagulated with sodium citrate and donors had not consumed alcohol or ibuprofen in the last 24 hours and did not use aspirin, heparin or anti-histamines in the preceding two weeks to exclude impacts of these drugs on platelet activation and exosome secretion. Following the recommendations of the International Society for Extracellular Vesicles (ISEV) (Witwer et al., 2013), donors were fasting to decrease lipoproteins that might 21

25 Materials and methods sediment with exosomes during ultracentrifugation and contaminate the sample. Blood was drawn during the same time of day (i.e. between 8:30 and 9:30 am) to minimize circadian rhythm effects that were observed to impact platelet activation (Scheer et al., 2011). The first 3 ml of blood were discarded to reduce contamination with activated platelets and fibroblasts. Donors were not pregnant. Plasma was isolated 1 h after blood draw at the latest. After declaration of informed consent as approved by the Ethics Committee of the Friedrich-Alexander Universität Erlangen Nürnberg (Ethik-Kommission/Re.-No. 4602) blood from melanoma patients was collected with heparin as anticoagulant. Plasma was isolated and stored at -80 C until use Nanoparticle tracking analysis (NTA) Size distribution and concentration of exosomes were measured with a Nano Sight LM10 instrument equipped with a 532nm laser and an EMCCD camera (Malvern Instruments Ltd, Malvern, UK). For data analysis, NTA software version 3.1 was used. All measurements were carried out with optimized settings from the same experienced operator to achieve comparable results. Exosomes from different sources were serially diluted in essentially particle-free PBS. Concentration was calculated from the particle concentration step ideally suited for tracking analysis (ca. 5x10 8 particles/ml) and the respective dilution factor. The value obtained this way together with the protein concentration of the diluted sample were used to calculate the protein amount per particle Statistics T tests were performed using Microsoft Excel Figures were generated by Microsoft Excel 2010 or GraphPad Prism 6. The Venn diagrams depicted in Figure 6, Figure 7B, and Figure 13D were calculated by the Venn diagram and data calculator (Miltenyi Biotec). P values were calculated using Microsoft Excel A p value <0.05 was considered as statistically significant. To determine the linear correlation between samples (page and Figure 51 on page 69), Pearson product-moment correlation coefficients were calculated. To create the heat maps depicted in Figure 10 and Figure 46, the public software Multi Experiment Viewer (MeV) was applied. For unsupervised hierarchical clustering, average linkage clustering was performed with the distance metric approach using Pearson correlation including optimization of sample leaf order. 22

26 Results 4 Results 4.1 MicroRNA profiling and transfection MiRNAs are selectively loaded into exosomes. To investigate the role of exosomal mirnas in melanoma progression, we first identified mirnas that are selectively secreted by melanoma cells via exosomes. Secondly, their impact on melanoma cells was studied. Finally, we were looking for tumorspecific mirnas by comparing plasma exosomes from melanoma patients and healthy donors MicroRNA profiles of cell culture cells and their secreted exosomes To identify mirnas that are secreted by melanoma cells, we compared the microrna expression profiles of four primary melanoma cell lines and the respective exosomes. The similarity in mirna signal intensities between the four melanoma cell lines and exosomes were compared by determining the linear correlation between two samples by calculating the Pearson product-moment correlation coefficients (R 2 ). The correlation coefficients were averaged each for the cells and the exosomes resulting in a strong correlation between the four primary cells lines and the respective exosomes, respectively (R 2 = 0.78 for cells and exosomes, respectively). To detect micrornas that are selectively exported via exosomes, we analyzed only micrornas with a reliable mean signal intensity of at least 50 light units. Ten micrornas with higher signal intensities in cells could be distinguished. We identified a set of five micrornas with significantly higher signal intensities in exosomes compared to the originating cells (Figure 4; p < 0.05). This indicates that melanoma cells preferentially export these micrornas (mir-142-5p, mir-144-3p, mir-150-5p, mir-223-3p, and mir-451a) via exosomes. A B Figure 4: MiRNAs that are selectively secreted by melanoma cells via exosomes. (A) Mean signal intensities of micrornas in melanoma cell lines vs. micrornas from the respective exosomes (four independent microarray experiments, non-normalized data). The gray vertical line indicates the threshold of a mean signal intensity of 50 light units for exosomal micrornas. (B) Signal intensities of five selected micrornas with significantly higher signal intensities for exosomes compared to the originating cells (p < 0.05). 23

27 Results MicroRNA transfection of melanoma cells One of the reasons of a preferred microrna export by melanoma cells might be a tumor-suppressive effect of the exported microrna. Among the preferentially exported mirnas via exosomes, mir-451a was detected with the highest signal intensity in each of the four investigated primary melanoma cell line-derived exosomes (Figure 4). To investigate the potential tumor-suppressive effect of mir-451a, a mir-451a-mimic was transfected into cells of one of the investigated primary melanoma cell lines. Five days after the transfection, the mir-451a-mimic induced only a slight decrease of living cells and few more apoptotic and dead cells as compared to the untreated control (UTC), the scramblerna control or the transfection control without RNA (Figure 5). Figure 5: Vitality and amount of apoptotic and dead cells after transfection of mir-451a-mimic into primary melanoma cells. UTC = untreated control, scramblerna = non-targeting RNA control, w/o RNA = cells were incubated with the transfection reagent without any microrna mimic. In an additional single experiment, melanoma cells were transfected three times with the doubled amount of the mirna mimic reaching transfection efficiencies of up to 90%. Still, the potentially deleterious effect of mir-451a could not be shown in this in vitro assay. This might be due to our experimental setting. The transfected amounts might be too low or the export of the mimic could diminish the effective amount of the mimic within the cells. 24

28 Results MicroRNA profiles of healthy donors and different melanoma patient groups MiRNAs can be transported in plasma by Ago2 proteins, high or low density lipids or within exosomes. To evaluate the proportion of plasma mirnas in exosomes and potential losses due to the exosome isolation, we compared the number of mirnas detected in exosomes, whole plasma, and exosomedepleted plasma from two melanoma patients and two healthy donors. Total RNA was extracted from equal amounts of whole plasma or exosome depleted plasma or from exosomes that were isolated from an equal original plasma volume. In all of the four samples, most micrornas were detectable in the isolated exosomes and not in the whole plasma. In whole plasma, four to 48 micrornas were exclusively detectable, while there were 27 to 131 exclusively detected in the isolated exosomes (Figure 6). As the whole plasma also contains plasma exosomes, all mirnas in plasma and exosomes should be detectable by using whole plasma. Possibly, 27 to 131 mirnas were exclusively detected in isolated exosomes because exosome lysis in whole plasma is less efficient than after exosome isolation. Additionally, RNA extraction might be improved after increasing the concentration of exosomal and protein-bound mirnas by ultracentrifugation. Consequently, RNA isolation seemed to be biased and we could not determine the proportions of exosome-associated and free circulating mirnas. However, we observed that clearly fewer mirnas were undetected when isolated exosomes were analyzed as compared to whole plasma. Therefore, exosomes are a suitable source to investigate plasma mirnas and are more advantageous as compared to whole plasma. Figure 6: Number of detected micrornas in equal amounts of whole plasma, exosome depleted plasma and exosomes isolated from an equal original plasma volume from two healthy donors and two melanoma patients. 25

29 Results To investigate whether the mirnas that were identified as selectively exported via exosomes in cell culture are also relevant in vivo, we compared the mirna profiles of exosomes from four primary melanoma cell lines and plasma exosomes from fifteen melanoma patients. 89% (162 out of 182) of the micrornas detected in at least three out of four melanoma cell culture-derived exosome samples were also detected in the plasma exosomes from at least ten out of fifteen melanoma patients (Figure 7). The common mirnas include the selectively exported micrornas by melanoma cells in cell culture (indicated by red dots in Figure 7A). 66 additional micrornas were detected in plasma exosomes compared to melanoma cell culture-derived exosomes (Figure 7B). This can be explained by a complex mixture of exosomes from distinct origins in plasma. A B Figure 7: Comparison of the mirna content of exosomes from melanoma patient s plasma and from melanoma cell culture. (A) Mean signal intensities of micrornas in exosomes from melanoma plasma samples (n = 15) vs. melanoma cell culture derived exosomes (n = 4, non-normalized data) (B) Venn diagram representing the number of detected exosomal micrornas from plasma of melanoma patients and melanoma cell culture, respectively. We compared the mirna profiles of plasma exosomes from sixteen healthy donors with exosomes from fifteen melanoma patients by plotting the averaged signal intensity of each mirna depicted in Figure 8A. We detected the selectively exported micrornas by melanoma cells also in healthy plasma samples (Figure 8A, highlighted in red). Surprisingly, the averaged mirna signals of plasma exosomes from healthy donors and melanoma patients correlated strongly (R 2 = 0.86) and no set of potential melanoma-specific mirnas could be identified. Instead, we observed that the signals of most detected mirnas were increased in exosomes from melanoma patients (Figure 8). The averaged ratio across all detected mirnas amounted to 6.9. The increased signal intensities were confirmed by qrt- PCR giving lower ct-values for selected micrornas in exosomes from melanoma patients (Florian Dreyer, Jochen Dindorf, unpublished data). To calculate whether the signal increase of mirnas is common among the donors, we assessed the mean ratio of all mirnas per donor. Despite a high donor variance, the signal increase across all micrornas was highly significant (p < 0.01) between the patient group and the healthy control group (Figure 8B). 26

30 Results A B Figure 8: Comparison of the mirna content of plasma exosomes from melanoma patients and healthy donors. (A) Mean signal intensities of micrornas in plasma exosomes from melanoma patients (n = 15) vs. healthy donors (n = 16; non-normalized data, micrornas selectively exported via exosomes in vitro are highlighted in red) (B) The averaged ratios across all detected exosomal mirnas to the mean of the healthy donors are indicated for the plasma exosomes of each melanoma patient (n = 15) and each donor (n = 16) as well as the mean ratio per group. However, the mean ratio of the signal intensities was not sufficient to assign individuals to one of the groups. For some of the melanoma patients, the mean ratios of signal intensities were not or only moderately increased, whereas a few healthy donors showed also a slight increase (Figure 8B). On the level of single micrornas, the signal intensities varied strongly among the patients and healthy controls (90% or 75% of micrornas with a coefficient of variation >1, respectively). The variance between individuals might be too high to detect potential tumor-specific micrornas. This is illustrated by an unsupervised clustering of the plasma exosomes of melanoma patients and healthy donors by their mirna profiles (Figure 9, Figure 10). Also statistical analysis such as ANOVA did not reveal micrornas distinguishing melanoma patients from healthy donors. Figure 9: Unsupervised hierarchical clustering dendrogram of exosomal micrornas from the plasma of melanoma patients (n = 15) and healthy donors (n = 16) with mirnas that showed a mean signal intensity for all samples of >10 light units. 27

31 Results Figure 10: Unsupervised hierarchical cluster analysis of exosomal micrornas from the plasma of melanoma patients (n = 15) and healthy donors (n = 16) with mirnas that showed a mean signal intensity for all samples of >10 light units. 28

32 Results To evaluate whether the level of mirnas in plasma exosomes might allow to distinguish different tumor stages, we included patients after surgery that were categorized in two groups: high risk patients had a tumor with > 2mm depth before tumor removal or metastasis or elevated tumor markers, low risk patients had a tumor of < 1mm in depth before tumor removal. For each donor, the ratio of each mirna signal was calculated to the mean mirna signal for the sixteen healthy donors. These ratios were averaged per donor and are depicted in Figure 11A. Depending on the tumor status and the tumor risk assessment, we observed higher mean ratios for melanoma patients as compared to healthy donors (6.9) than for high risk patients (2.7), while the mean ratio of low risk patients (1.9) was close to the mean ratio of the healthy donors (1.4) as compared to the mean of all healthy donors. Despite the high variation within each group, the groups of low risk patients and high risk patients differed significantly (p<0.05) from the melanoma patients. Additionally, the high risk patients also differed significantly from the healthy control group (p<0.05, Figure 11A). The significantly higher microrna level in the high risk group compared to the healthy control group could indicated remaining tumor cells in some of the patients. The lower mean ratios for the low risk patients would be consistent with the fact that most tumor cells were resected. Nevertheless, the results should be interpreted with care as they are based only on a limited period of time after surgery. To evaluate the potential changes of the mirna level before and after surgery in the same patient, we analyzed the mirna content of plasma exosomes that were isolated from one donor before and after treatment with a BRAF inhibitor and two donors before and after removal of dysplastic nevi. Interestingly, the mean ratio of all mirnas decreased clearly already two weeks after treatment or removal of the nevi, respectively (Figure 11B). After BRAF treatment, the mirna ratio decreased from 2.8 to 1.0, i.e. to the mean ratio of the healthy donors. Two weeks after the removal of dysplastic nevi, we also observed a decrease of the mirna ratio from 10.5 or 3.8. While the mean ratio even kept on decreasing after nevi removal for the plasma exosomes from patient 2, the ratio for patient 1 increased again to 2.0 five weeks after BRAF treatment. This might be a slight indication that malignant cells recover without treatment and induce an increased amount of mirnas in plasma exosomes. Figure 11: Averaged ratio across all detected mirnas of different donors to the healthy donors. (A) Melanoma patients (n = 15) in comparison to high risk patients (n = 16), low risks patients (n = 16), and healthy donors (n = 16). (B) Three patients before and after removal of dysplastic nevi or anti-tumor treatment. Pre = before treatment, post 1 = two weeks after the treatment with BRAF inhibitor (patient 1) or removal of dysplastic nevi (patients 2 and 3), post 2 = five weeks after the treatment with BRAF inhibitor (patient 1) or four weeks after removal of dysplastic nevi (patient 2). 29

33 Results We found that the mirna level was significantly increased in plasma exosomes from melanoma patients and high risk patients compared to healthy donors. Furthermore, the exosomal mirna level decreased in three patients within two weeks after BRAF treatment or removal of dysplastic nevi. On the one hand, one could therefore conclude that the tumor (or pre-malignant cells in the case of dysplastic nevi) affects the mirna level in plasma exosomes. On the other hand, the similarity between the mirna profiles of plasma exosomes from melanoma patients and healthy donors are remarkable. Therefore, we hypothesized that non-tumor cells might be the source of the circulating exosomes in plasma. To investigate whether the increased mirna signals are specific for melanoma, we analyzed the mirna profiles of plasma exosomes of three patients with multiple sclerosis (MS) and four patients that were positive for human immunodeficiency virus (HIV) antibodies. The averaged mirna profile of plasma exosomes from healthy donors strongly correlated with the exosomal mirna profiles of MS patients (R 2 = 0.9) and HIV patients (R 2 = 0.8, Figure 12). The coefficients of determination are comparable to the correlation between healthy donors and melanoma patients (R 2 =0.86, Figure 8). The averaged ratio between the patient groups and the healthy donors indicated no increase in mirna signals for MS patients (averaged ratio: 1.1), but a strong mirna signal increase for HIV patients (averaged ratio: 13.3, Figure 12B). Consequently, the increase in mirna signals is not limited to plasma exosomes from melanoma patients, but was also observed for plasma exosomes from HIV patients. A B Figure 12: Comparison of the mirna profiles of plasma exosomes from MS patients and HIV patients with healthy donors. Mean signal intensities of micrornas in plasma exosomes from healthy donors (n = 16; nonnormalized data (A) vs. plasma exosomes from patients with multiple sclerosis (n = 3) (B) vs. plasma exosomes from HIV-positive patients (n = 4). Therefore, we concluded that non-tumor cells induced the mirna increase in plasma exosomes. We analyzed the mirna profiles of exosomes that were isolated from macrophages, monocyte-derived dendritic cells (modcs), platelets, and three liver cell lines in comparison to the plasma exosomes from melanoma patients. The profiles of exosomes from macrophages, immature and mature modcs were very similar. But only 4% of the detected mirnas (35 out of 808 mirnas that were detected in at least one exosome preparation) were simultaneously identified in exosomes from macrophages, immature and mature modcs, and in the plasma of melanoma patients (Figure 13D). Consequently, the correlation between the mirna signals of the mature modc exosomes and the plasma exosomes 30

34 Results from melanoma patients was very weak (R 2 = 0.005, Figure 13A). The correlation between the exosomal mirna signals of platelets (R 2 = 0.22) or liver cells (R 2 = 0.35) and melanoma plasma was stronger but still weak in comparison to the remarkable similarity between plasma exosomes from melanoma patients and healthy donors. We concluded that the pool of plasma exosomes consists of exosomes from various sources that can hardly be distinguished by mirna analysis. Figure 13: Comparison of the mirna profiles of melanoma plasma exosomes with exosomes from different cell types. Mean signal intensities of micrornas in plasma exosomes from 15 melanoma patients (A) vs. exosomes from mature modcs, (B) vs. exosomes from platelets, or (C) exosomes from a mesenchymal liver cell line (non-normalized data). (D) Venn diagram of detected mirnas in exosomes from macrophages, immature modcs, mature modcs and plasma exosomes from melanoma patients. DCs = dendritic cells. In summary, we have shown that selectively exported mirnas via exosomes could also be detected in plasma exosomes from melanoma patients and healthy donors. No mirna could be identified that is specific for melanoma or tumor burden. However, we observed a link between the tumor and the mirna level in plasma exosomes by analyzing high risk patients and low risk patients as well as patients before and after tumor treatment or removal of dysplastic nevi. Nevertheless, we assume that the tumor itself is not the source for the increased levels of mirna in patient s plasma as compared to plasma of healthy donors because an elevated mirna level was also observed in plasma exosomes from HIV patients. We could not assign the plasma exosomes to potential originating cell types as only few tissue- or cell type-specific mirnas are known. Alternatively, we decided to investigate exosome proteins to determine the cell types that secrete vesicles into the plasma. 31

35 Results 4.2 Protein profiling of cells and exosomes We hypothesized from the mirna analysis that the majority of exosomes in the plasma of melanoma patients do not directly derive from the tumor. Nevertheless, we observed an increase in mirna signals that indicate an increased exosome secretion or an increased mirna loading of exosomes. It is known that exosomes adopt surface proteins from their originating cell. Therefore, we aimed to find surface proteins on exosomes that can be used to identify the parental cell. Furthermore, we investigated whether melanoma exosomes are present in patient s plasma and from which cell types the exosomes in plasma are derived Mass spectrometry of melanoma exosomes To identify melanoma-specific proteins, exosomes from a primary melanoma cell culture were lysed and the proteins were separated by SDS page. The main bands were trypsin-digested and analyzed by LC/MS-MS (Figure 14). We detected the tetraspanins CD9 and CD81 that are commonly used as exosome markers. In addition, annexin proteins, heat-shock proteins, Glycerinaldehyde-3-phosphate- Dehydrogenase (GAPDH), actin, and potential melanoma-associated proteins such as ADAM10, CD109, CD151, CD166, CD228 and Protein S100 were detected (Appendix table 1). Subsequently, we aimed to test whether potential melanoma-associated proteins would also be detectable in plasma exosomes from melanoma patients. But as high amounts of exosomes are required for mass spectrometry and plasma exosome preparations consist of a complex mixture of proteins, the required plasma volumes could not be obtained. Furthermore, the calculation of a relative quantity of the proteins would require a more advanced sample preparation and would be limited to few proteins. Therefore, we decided to establish new techniques to detect and compare proteins between different exosome samples. Figure 14: SDS PAGE analysis of exosomes isolated from a primary melanoma cell culture. 10 µg or 20 µg protein from exosomes were separated by SDS PAGE and stained with Coomassie blue staining. Arrows indicate the cut bands for trypsin digestion and LC/MS-MS. 32

36 Results Establishment of a multiplex bead-based platform We aimed to investigate which cell types in peripheral blood contribute to plasma exosomes and might be responsible for the increase of the mirna content observed in melanoma patients. It is known that exosomes incorporate cell surface proteins from their originating cell. Therefore, we wanted to use the protein profile of exosomes to identify surface proteins that allow the identification of the originating cell type. For this purpose, we were looking for an assay to detect more than one or two proteins on the exosomes and to compare the proportion of surface proteins between samples. Surface proteins on exosomes can be detected by standard flow cytometers when exosomes are bound to particles to enable the detection by the bead s scatter properties. Miltenyi has developed a multiplex bead-based platform comprising 39 polystyrene bead types that were color-coded with differing proportions of two fluorescent dyes that can be distinguished by flow cytometry (Figure 15C). We coupled each bead type with a different capture antibody that recognizes and binds the respective epitope on exosomes. Thereby we would reveal whether the target antigen of the capture antibody bead is present on exosomes of the given sample. After incubation of the beads with the exosome sample, the bound exosomes are detected by an APC-conjugated detection antibody (Figure 15). Figure 15: The multiplex bead-based platform. (A) Workflow of the multiplex platform. Isolated exosomes or cell culture supernatant were incubated overnight with up to 39 different bead types, each coupled to a different capture antibody. The different bead types are distinguishable by flow cytometry. Exosomes bound to the beads were detected with single antibodies, e.g. surface proteins, or exosome markers such as anti-cd9-apc, anti-cd63-apc, anti-cd81-apc antibodies, or with a cocktail of the latter three antibodies. Copyright Miltenyi Biotec. (B-D) Analysis example showing (B) exclusion of doublets and no bead events, (C) discrimination of differently labeled bead types, and (D) measurement of signal intensities of the single bead types. Positive bead types are highlighted in colors. Black events represent beads that did not bind exosomes or beads with exosomes that were not detected by the staining cocktail. 33

37 Results A typical example of the flow cytometric analysis using 39 different bead types is shown in Figure 15B-D. Bead aggregates are excluded in the first panel by using the forward/side scatter (Figure 15B). Single beads are selected for the second panel that distinguishes the bead types in the PE and FITC channel (Figure 15C). In the third panel, the shift of the single bead types in the APC channel can be visualized to identify beads that bound exosomes which were detected by the used APC-conjugated detection antibody (Figure 15D). Using this method, we were able to investigate up to 39 surface proteins in parallel and could easily compare a variety of different exosome samples. To establish the multiplex bead-based assay for exosome analysis, we started with testing candidate capture antibodies with one to three different bead types. To easily couple different candidate capture antibodies to the same bead types, we coated the beads with streptavidin and used biotin-conjugated capture antibodies. The coating of the beads with streptavidin was verified by staining with APCconjugated biotin. The streptavidin coated beads showed a clear shift of the APC signal (Figure 16A) confirming bound streptavidin on the beads. Next, the binding of a Biotin-conjugated anti-cd63 antibody was detected by an APC-conjugated anti-igg1 antibody. The clear shift also demonstrated the effective binding of anti-cd63-biotin to the streptavidin coated beads (Figure 16B). Finally, exosomes isolated from primary melanoma cell culture supernatant were incubated with the capture antibody bead and bound exosomes were detected by an anti-cd63-apc antibody. The beads showed a clear increase of the APC signal in comparison to the beads that were incubated with buffer (Figure 16C). Figure 16: Control stainings of functionalized beads. (A) Detection of streptavidin on the beads with streptavidin coating (red) by staining with Biotin-APC and control staining of beads without streptavidin coating (black). (B) Detection of the capture antibody anti-cd63 on beads with capture antibody (red) by anti-igg1-apc and control staining on beads without capture antibody (black), (C) Detection of exosomes on streptavidin coated beads with anti-cd63-biotin as capture antibody incubated with 4 µg melanoma exosomes or control with buffer (black) by staining with anti-cd63-apc. The amount of melanoma exosomes was titrated and based on the signals depicted in Figure 17A, we decided to use 4 µg exosomes for further experiments. Because of diverging signal intensities on individual beads recognizing the same epitope, we decided to use the median signal intensities for the following experiments. We next titrated the concentration of the detection antibody. In addition, we tested whether the exosomes should be stained before or after they were captured by the beads. 4 µg melanoma exosomes were stained with increasing amounts of the detection antibody anti-cd63-apc 34

38 Results before or after incubation with 5000 beads. Low signal intensities (maximum of 1.5 light units) for the exosomes stained before bead capture indicated blocking effects by the staining antibody. If exosomes were first incubated with the capture beads and then stained, signal intensities increased up to 15 light units. Therefore, exosomes should be incubated with the capture antibody beads before they are stained with the detection antibody (Figure 17B). The titration of the detection antibody revealed that comparable signal intensities could be achieved by using 0.5 µg instead of 3 µg anti- CD63-APC (as used for the exosome titration in Figure 17A). Therefore, 0.5 µg detection antibody was used in the following experiments. A B Figure 17: Titration of exosomes and the detection antibody. (A) Increasing amounts of melanoma exosomes were incubated with anti-cd63-beads and stained with 3 µg anti-cd63-apc. (B) Exosomes were stained using increasing amounts of anti-cd63-apc and then incubated with the capture antibody beads (staining first, red line) or exosomes were stained after the incubation with the capture antibody beads (beads first, black line). Per sample, 5000 beads, 4 µg melanoma exosomes and the indicated amounts of anti-cd63-apc were used. Asterisk: 3 µg acd63-apc as used for exosome titration in (A); arrow: 0.5 µg acd63-apc as used in further experiments. To detect all bound exosomes, we tested different lipid dyes that should stain exosomes independently of their protein composition. But despite optimization experiments, neither CellTrace violet (Invitrogen), nor CellVue Claret (Sigma-Aldrich) or Di-8-ANEPPS (Life Technologies) reliably distinguished negative controls without exosomes from samples with exosomes (data not shown). Therefore, we used a cocktail of APC-conjugated antibodies against the so-called exosome markers CD9, CD63, and CD81 to detect bound exosomes. The comparison of single stainings with the cocktail staining revealed that the cocktail staining was stronger than any of the single stainings but did not reach the sum of the signal intensities obtained by single stainings (Figure 18). 35

39 Results Figure 18: Comparison of signal intensities after single stainings and after staining with a cocktail of the three respective antibodies. 2 µg melanoma exosomes were incubated with capture antibody beads against CD9, CD63, CD81, and IgG1 (isotype control) and stained with only one of the APC-conjugated detection antibodies or with a cocktail of these three antibodies. To test for nonspecific exosome binding to the beads, we coupled the isotype control antibody mouse IgG1 to a bead type. Mouse IgG1 belongs to the same antibody class (IgG1) as anti-cd9, anti-cd63, and anti-cd81, but recognizes keyhole limpet hemocyanin that is not found in humans. In addition to the isotype control beads (Figure 18), we tested the specificity of the platform by an antibody blocking experiment. In contrast to the samples with exosomes incubated with the soluble mouse IgG1 isotype control, the signals of the exosome samples incubated with the soluble anti-cd63 antibody decreased clearly but exclusively for the anti-cd63-beads (Figure 19). These control experiments confirmed that the capture antibody beads specifically bind exosomes and different surface proteins can be detected independently. Figure 19: Antibody blocking experiment to test the specificity of exosome capture. Median signal intensities after overnight incubation of capture antibody beads with NK cell exosomes from four donors (8 µg each) alone or in combination with soluble mouse IgG1 isotype control or soluble acd63 antibody, followed by staining with an acd81-apc antibody. 36

40 Results We then used the streptavidin coated beads to test 72 capture antibodies that are listed in the Appendix table 2. The capture antibodies were chosen to detect suggested melanoma markers or well-established cell type-specific markers such as CD3 for T cells, CD61 for platelets or CD14 for monocytes. We compared the indirect system of Biotin-conjugated capture antibodies bound to streptavidin coated beads to a direct system using capture antibodies that were bound directly to polystyrene beads via maleimide-sulfhydryl group binding. The signal intensities obtained with directly bound capture antibodies were similar or even stronger as compared to the indirect binding approach (Figure 20A). To simplify our antibody screening, we aimed to analyze more proteins in parallel on multiple bead types. Therefore, we tested whether we obtain comparable signal intensities independently of the number of bead types. We incubated eight or 34 different bead types each coupled to a different capture antibody together with NK cell exosomes from four donors (Figure 20B). The results show that neither the number of beads nor the composition of the bead set affected the signal intensities. A B Figure 20: Comparison of signal intensities with different bead sets. (A) 4 µg melanoma exosomes were incubated with beads that were indirectly (Streptavidin-biotin coupling) or directly (direct coupling) bound to capture antibodies and stained with an anti-cd81-apc antibody. (B) 8 µg NK cell exosomes from four different donors (D1-D4) were incubated with eight bead types (8-plex) or 34 bead types (34-plex) that were directly bound to the capture antibodies. REA, migg1: isotype controls. The incubation of the exosomes can be shortened from overnight to 1 hour if the exosome concentration in the sample is sufficient. Exosomes can also be incubated together with the beads and the detection antibody during the 1 h incubation time. However, during longer incubation times we observed decreasing signals (data not shown). We suppose that this is due to the competition between capture antibody beads and the detection antibody for the antigens on the exosomes. Due to their smaller size and higher concentration in comparison to the beads, the detection antibodies can bind more efficiently to the exosomes and prevent the binding of the capture antibody beads. In order to obtain optimal exosome binding to the beads and a reliable detected of bead-bound exosomes, we stayed with the overnight exosome incubation and the subsequent staining of bead-bound exosomes for the further experiments. Commonly, exosomes are isolated from cell culture supernatants or body fluids by ultracentrifugation to enrich exosomes. Since ultracentrifugation is very time-consuming, we considered to use cell culture supernatant or plasma directly after removal of cell debris and filtration through a 0.22 µm 37

41 Results membrane. While supernatants from primary melanoma cells could be used directly for exosome analysis (data not shown), exosomes in the supernatant of primary immune cells such as T cells and monocytes needed to be concentrated to reach reliable signal intensities (Figure 21A). We also tried to enable the analysis of exosomes directly out of plasma. But reliable signals were obtained for only one out of four donors. As the successful exosome analysis seemed to be donor-dependent but anticoagulant-independent (Figure 21B, donor no. 11), we assume that the exosome concentration is the crucial point. Additionally, plasma might contain inhibitory molecules for exosome analysis because exosomes in the urine of three donors and in ascites from five breast cancer patients could be analyzed successfully without ultracentrifugation (data not shown). To allow the flow cytometric analysis of samples that are not tested for infectious diseases such as hepatotropic virus or HIV, we fixed the captured and stained exosomes with formaldehyde. As no loss of beads or signals was observed (data not shown), exosome samples can be fixed and analyzed by flow cytometry without compromising the data. A B Figure 21: Test of different sample types for the multiplex platform. (A) Signal intensities of selected surface proteins obtained with cell culture supernatant compared to isolated exosomes from the same cell culture. (B) Signal intensities of selected surface proteins obtained with 2 ml serum or plasma from four different donors. migg1: isotype control. Although the platform allows analyzing different surface proteins in parallel, the number of bead types limits the scope. To add flexibility, we intended to use different staining antibodies and thereby to test for additional surface proteins. The captured exosome sample can be split in multiple samples and each sample is stained with a different detection antibody. In addition to the additional flexibility, we imagined that using a pair of antibodies to capture and detect exosomes, the signal intensity might not be the same if the capture and detection antibody are swapped. If fewer protein molecules are present per exosome, exosome capture will not be affected. In contrast, if fewer exosomes carry a surface protein, fewer exosomes will be detected and stained by the respective antibody resulting in lower signals. Signal intensities will drop by applying a detection antibody for a less abundant protein per exosome or a protein that is only present on few exosomes. Consequently, the respective signal intensities will be affected by the proportion of exosomes in a sample comprising a given protein if capture and detection antibodies are swapped. 38

42 Results As an example, we incubated capture antibody beads against the common exosome markers CD9, CD63, and CD81 as well as an isotype control with exosomes from NK cells, platelets or monocytes and stained them with antibodies against CD9, CD63, or CD81 or a cocktail of these antibodies (Figure 22). After staining with the antibody cocktail, NK cell exosomes showed only slightly differing signals after capture with anti-cd63-beads and anti-cd81-beads (APC median signal 17.5 and 14.5, respectively). But independently of the detection antibody, very low signals were detected on anti- CD9-beads. In addition, poor signals were measured by anti-cd9 staining irrespectively of the capture antibody beads used. This indicates that a low frequency of NK cell exosomes is CD9-positive. The comparable signals on anti-cd63-beads and anti-cd81-beads after the cocktail staining indicated a comparable amount of CD63-positive and CD81-positive exosomes. Furthermore, we observed a stronger signal for anti-cd63-beads after anti-cd81 staining (APC median signal 18.8) than with the same antibodies but swapped (APC median signal intensity 14.7) implying more CD81 molecules per exosome than CD63 molecules. Capture and staining with the same antibody (anti-cd63-beads with anti-cd63-apc or anti-cd81-beads with anti-cd81-apc) resulted in low signals that might derive from blocking effects (Figure 22A). On platelet exosomes, we detected slightly stronger signals on anti-cd9-beads than on anti-cd63- beads after cocktail staining (APC median signal 28.4 and 21.9, respectively), but also after staining with anti-cd63 or anti-cd9 alone indicating a higher amount of CD9-positive platelet exosomes than CD63-positive platelet exosomes. The signals on anti-cd81-beads were remarkably low (APC median signal 0.7). Additionally, no signals were detected on any bead type after anti-cd81 staining (Figure 22B) implying a relatively low amount of CD81-positive platelet exosomes. Platelet exosomes showed higher signals on anti-cd63-beads after anti-cd9-apc staining (APC median signal 19.1) as compared to the swapped antibody pair (anti-cd9-beads and anti-cd63-apc staining; APC median signal 6.4; Figure 22B). This suggests that the lower amount of CD63-positive platelet exosomes carry more CD9 molecules as compared to the amount of CD63 molecules on the larger population of CD9- positive platelet exosomes. Technical issues with the assay could be excluded because monocyte exosomes gave signals for all combinations of anti-cd9, anti-cd63, and anti-cd81 beads and the corresponding stainings (Figure 22D). To test whether the use of different staining antibodies can actually be used to discriminate exosome subpopulations, we made use of the finding that NK cell exosomes carry no detectable CD9 molecules and platelet exosomes present hardly any CD81 molecules. We mixed the CD9 - NK cell exosomes and the CD81 low platelet exosomes. After staining with the detection antibody cocktail, we obtained signals on each of the three bead types that recognize the exosome markers CD9, CD63, or CD81 indicating the presence of CD9 +, CD63 +, and CD81 + exosomes. But using the staining cocktail antibodies separately, the signals of anti-cd81-beads after anti-cd9 staining and of anti-cd9-beads after anti-cd81 staining were lower than any other antibody combination (APC median signal 1.1 and 0.7, respectively, Figure 22C). Taken together, we found CD9 +, CD63 +, and CD81 + exosomes, but no CD9 + /CD81 + exosomes. Therefore, the data confirms that CD9 + /CD81 - and CD9 - /CD81 + exosome subpopulations can be distinguished. Accordingly, we conclude that the platelet-derived exosomes indeed comprise a CD63 + CD9 + and a CD63 - CD9 + subpopulation. In the following experiments, single stainings were used additionally to verify the existence of assumed exosome subpopulations. 39

43 Results Figure 22: Matrix profiles of different exosome samples. Background corrected APC median signal intensities of anti-cd9-, anti-cd63-, anti-cd81-beads and isotype control-beads after incubation with (A) 16 µg NK cell exosomes, (B) 16 µg platelet exosomes, (C) a mixture of NK cell exosomes and platelet exosomes in the same protein proportions (8 µg each) or (D) 8 µg monocyte exosomes, followed by staining with anti-cd9-apc, anti-cd63-apc, or anti-cd81-apc antibodies or with a cocktail of these antibodies. migg1: isotype control. 40

44 Results Establishment of a protocol for high-resolution microscopy of exosomes Note: this section contains passages that were transposed directly from the publication A novel multiplex bead-based platform highlights the diversity of extracellular vesicles. To validate the results that were obtained by the multiplex bead platform, we aimed to establish a second method for the protein detection on exosomes. Ideally, single exosomes could be made visible to verify that the same exosome carries both or only one out of two investigated proteins. The detection of proteins on single exosomes is challenging due to the small exosome size. High-resolution fluorescence microscopy is an attractive alternative to electron microscopy without sterical hindrance of gold particles. Stimulated Emission Depletion (STED) is one of the techniques that were recently established to reach high-resolution in fluorescence microscopy. STED bypasses the diffraction limit of light microscopy by controlling fluorescence emission. The excitation beam is supplemented by a STED beam that de-excites fluorophores by stimulated emission. The combination of these two beams limits fluorescence emission to predefined sample coordinates and let adjacent features emit sequentially in time to increase resolution (Vicidomini et al., 2013; Willig et al., 2006). Figure 23 shows an example of two single exosomes located close to each other. While a common fluorescent area appears by confocal microscopy (Figure 23A), two independent spots can be distinguished by STED demonstrating the higher resolution (Figure 23B). The risk of incorrectly identifying two neighboring exosomes as a single event is minimized by STED microscopy. Figure 23: Confocal microscopy versus high-resolution microscopy. NK cell exosomes bound to anti-cd63- coated glass slide, stained with anti-cd81-starred, and visualized by (A) confocal microscopy at 100-fold magnitude and (B) STED. (C) Intensity profiles of the image section marked with the yellow line after confocal microscopy (black curve) and STED microscopy (red curves). Scale bar represents 1 µm. To analyze isolated exosomes, a protocol was established to fix and stain the exosomes on glass slides. It was not feasible to adopt conventional sample preparation methods for cells to exosomes because they did not adhere to agarose, L-lysine, matrigel or gelatin. Therefore, we coupled antibodies against common exosome markers, i.e. CD9, CD63, and CD81, in excess on glass slides to bind the exosomes. To evaluate the required amount of exosomes for an equal distribution and the analysis of single exosomes, a titration of exosomes with a CD63-GFP fusion protein was performed and analyzed by conventional confocal microscopy. As a negative control, the highest protein amount was also incubated on glass slides coated with an isotype control antibody (migg1). The signals detected on the negative control slides were mainly aggregates or associated with clumps that were visible in bright field (Figure 24). 41

45 Results Coating with anti-cd9 antibody Coating with migg1 + 10µg GFP-Exosomes + 5µg GFP-Exosomes + 1µg GFP-Exosomes + 10µg GFP-Exosomes Figure 24: Titration of exosome amounts for microscopy. Confocal microscopy of different amounts of protein from exosomes isolated from melanoma cells that express a CD63-GFP fusion protein. The exosomes seemed to be singularized when only 1 µg CD63-GFP + exosomes were incubated on the glass slides. Nevertheless, the suitable amount of exosomes had to be determined for each exosome type. This is probably due to varying amounts of exosomal proteins in the sample. The lower the proportion of exosomal proteins, the lower the amount of exosomes that can be made visible by staining. Next, the amount of detection antibody was determined by titration of anti-cd9-apc on CD63-GFP + exosomes. The analysis by confocal microscopy showed that 2.5 µg/ml or 5 µg/ml anti-cd9-apc is sufficient to obtain bright signals (Figure 25). All following experiments were performed with 5 µg/ml detection antibody. Coating with anti-cd9 antibody, incubation with 1 µg GFP exosomes 1.25 µg/ml acd9-apc 2.5 µg/ml acd9-apc 5 µg/ml acd9-apc Figure 25: Titration of the detection antibody for microscopy. Confocal microscopy of exosomes from melanoma cells with a CD63-GFP fusion protein detected with different amounts of anti-cd9-apc. To investigate whether two proteins are present on the same exosome, we established the staining with two dyes with non-overlapping spectra. STAR488 has an absorption maximum at 501 nm, a fluorescence maximum at 524 nm and was depleted with the STED laser at 592 nm. STARRED has an absorption maximum at 637 nm, a fluorescence maximum at 660 nm and was depleted with the STED laser at 775 nm. It was verified by confocal microscopy that 5 µg/ml of these antibodies are also sufficient for exosome stainings (data not shown). Despite the distinct spectra of STAR488 and STARRED, the pictures were taken sequentially to avoid any cross signaling. Due to their small size and the unknown amount of antigens per exosome, it was challenging to analyze exosomes by fluorescence microscopy, even though we applied a technique for highresolution imaging and optimized fluorescent dyes. We observed that the STED laser power had to be limited to 50% or 30% to enable the highest resolution possible without depleting the signal. The image section had to be selected as fast as possible in each dimension to prevent signal depletion before image acquisition. 42

46 Results As positive control for the detection of two antibody-fluorophore conjugates on the same exosome, we stained monocyte exosomes with the humanized anti-cd81-star488 and anti-human IgG1- STARRED as a secondary antibody. The proportion of double positive spots was determined by Nikolay Kladt (CECAD Imaging Facility, Cologne). In two image sections, 73% and 89 % of all detected green fluorescent spots (anti-cd81-star488) were also positive for the secondary antibody staining in red (Figure 26A), respectively. We detected a higher number of red spots than green spots (118 and 139 vs. 177 and 216) resulting in a lower proportion (49 and 57%) of red spots that were double positive. In the control staining of exosomes with only the secondary antibody, hardly any signals were detected (data not shown). Next, we stained monocyte exosomes with a mixture of the anti-cd81 antibody coupled to either STAR488 or STARRED to investigate whether the frequency of antigens on one exosome is sufficient to enable the binding of two antibodies in parallel. We analyzed 599 (146 to 278 each on three different images) green spots and 983 (223 to 404) red spots resulting in 16 to 28% green spots that were also red and 7 to 18% double positive red spots (Figure 26D). The STARRED dye appeared brighter or more stable resulting in an improved detection limit for STARRED. To evaluate the effect of the two dyes, we stained monocyte exosomes with anti-cd9 and anti-cd81 antibodies each coupled to STAR488 or STARRED and vice versa. After staining with anti-cd9- STAR488 and anti-cd81-starred, we analyzed three images and detected a total of 263 green spots (63 to 113 per image) and 1942 (558 to 744) red spots. 17 to 27% of the green spots were also red and 1 to 5% of the red spots were also green (Figure 26C). After we swapped the dyes and stained with anti-cd81-star488 and anti-cd9-starred, we detected on three images 426 (97 to 175) green spots and 1038 (320 to 364) red spots. 9 to 14% of the green spots were also red and 3 to 7% of the red spots were also green (Figure 26F). Comparing the STARRED or STAR488 positive counts respectively, we saw more CD81 positive spots as compared to the respective CD9 staining. The percentage of double positive green and red spots were similar when the brighter STARRED dye was used for the anti-cd9 antibody (Figure 26G). The brighter dye somewhat compensated for the lower amounts of CD9 on the exosomes or the lower affinity of the antibody. Although we cannot rule out differences in antibody affinity, the results could indicate more CD81 positive exosomes or more CD81 epitopes giving brighter stainings as compared to CD9. The spatial resolution of STED microscopy enabled the detection of single exosomes and the data gave no indication on false positives due to exosome doublets or aggregates. However, the control staining with antibody-conjugates directed against the same epitope but labeled with different dyes resulted in a much lower proportion of double positive spots than the secondary antibody control. This raised the question why the majority of the exosomes appeared only single positive. We could rule out technical limitations of the STED microscope as well as potential repulsion of the dyes as demonstrated by the secondary antibody control staining. Instead, we suggest that the number of tetraspanin proteins per exosome is limited or tetraspanin proteins build clusters on the surface of exosomes. That means that the binding of a second antibody to the same cluster might be hindered by the first bound antibody resulting in a higher frequency of single stained exosomes. Maybe the reason is a combination of clustered molecules and the fact that indeed not every exosome carries both exosome markers CD9 and CD81. 43

47 Results Therefore, co-staining using different antibodies might not be representative for the proportion of exosomes that carry the two respective epitopes and the results should be interpreted with care. In conclusion, we probably underestimated the proportion of double positive exosomes by using antibody stainings to visualize proteins on single exosomes by STED microscopy. However, it is suitable to discriminate between single exosomes that carry different proteins and double positive exosomes that carry two different proteins on the same vesicle. STED microscopy provides fluorescence images of single exosomes that might help to investigate protein distribution on exosomes. In the following experiments, STED was applied to validate the results obtained by the multiplex bead platform. Figure 26: STED analysis of monocyte exosomes bound to anti-cd63-coated glass slides. Monocyte exosomes (A) stained with anti-cd81-star488 and anti-human IgG1-STARRED and (B) the corresponding intensity profile. (C) Monocyte exosomes stained with anti-cd81-star488 and anti-cd81-starred and (D) the corresponding intensity profile. (E) Monocyte exosomes stained with anti-cd9-star488 and anti-cd81- STARRED or (F) with anti-cd81-star488 and anti-cd9-starred and (G) the corresponding overview of the percentage of double-positive spots with the dye swap. Scale bars represent 500 nm. 44

48 Results Protein profiling of exosomes from PBMC For the analysis of exosomes that circulate in peripheral blood, analysis of plasma or serum would be preferred. However, plasma supposably comprises exosomes from various cell types in addition to exosomes from hematopoietic origin, e.g. epithelial cells, endothelial cells or tissue cells. To focus on exosomes from hematopoietic cells, we used the supernatant of peripheral blood mononuclear cells (PBMCs) instead of plasma. PBMCs are assumed to secrete exosomes into the blood stream. PBMCs were isolated by density gradient centrifugation and cultured for eleven days without stimulation. After day one, four, six, eight and eleven, an aliquot of the supernatant was taken, depleted from cell debris and larger particles by centrifugation and filtration. To compare the protein profiles of the cells and the exosomes in the supernatant, cells were stained for selected surface proteins on the day of separation and after culture on day eleven. We obtained signals with the cell culture supernatant already on day four. The strongest signals on cells were detected for CD3 and CD42a. While CD3 was not detected on exosomes, the CD42a signal was also the strongest on exosomes (Figure 27). This most likely referred to platelets even though additional washing steps were performed after density gradient centrifugation to minimize the amount of platelets. Probably, an efficient exosome secretion by platelets leads to such strong signals. Some detected surface proteins are not cell type-specific, e.g. the exosome markers CD9, CD63, CD81, and CD82. Furthermore, the hyaluronic acid receptor CD44, the integrin CD29, and the ubiquitous expressed HLA class I cannot be considered as cell typespecific markers. But the signals for HLA class II, CD11c, CD14, CD40, and CD105 (Figure 27) indicated exosomes that most likely derived from antigen presenting cells (APCs) such as B cells, monocytes, and dendritic cells (DCs). Exosomes that were positive for CD69 and CD8 might originate from T cells or NK cells. The PBMCs were not stained for all surface proteins the exosomes were analyzed for, but some of the proteins that were stained on the cells were not detectable on exosomes, i.e. CD3, CD4, CD13, CD20, CD25, CD86, and CD209. This might be due to a low exosome secretion of the respective cells expressing these proteins or an insufficient amount of proteins on exosomes for detection. To investigate whether the protein loading of exosomes can be affected by stimulation of the originating cells, two cell stimulations were performed. To stimulate antigen-specific T cells, T cells of a cytomegalovirus (CMV) positive donor were activated with a peptide pool that covers the complete sequence of the pp65 protein of human CMV. B cell stimulation was performed by addition of CD40 ligand and IL-4 to mimic cell activation by T cells. Potentially, exosome secretion was increased by stimulation because stronger signals were detected. However, as the signals for most of the proteins were very low, determining the originating cell types of PBMC-derived exosomes was not possible. We also detected very weak signals for activation markers and comparable protein signals on the exosomes after both stimulations and without stimulation (data not shown). Exosomes of stimulated cells might have little impact on the exosome pool or the stimulation does not affect the composition of the secreted exosomes. Additionally, it should be considered that PBMC supernatant was used and the signals could be increased by exosome isolation. In conclusion, after four days of culture, the detection of exosomes from the supernatant of PBMC culture was possible. The mixture of exosomes in the supernatant was used to mimic the contribution of different blood cells to the pool of exosomes that might also exist in vivo. However, it is difficult to 45

49 Results address surface proteins that were found on exosomes clearly to a respective cell type. We expect that concentrated exosome samples of separated cell type cultures are superior to PBMC supernatant when aiming to reveal surface proteins that point at the originating cell types of exosomes. Figure 27: Protein characterization of PBMCs and their secreted exosomes. (A) Percentage of positive cells (after dead cell exclusion) for selected surface proteins at the starting day of culture and after eleven days of culture without any stimulation. Arrows point at proteins that were also detected on exosomes. (B) Background corrected signal intensities of selected surface proteins on exosomes in PBMC supernatant that showed stronger signals than the isotype controls REA, migg1, migg2a, and migg2b. The REA isotype control is depicted representing all isotype controls. 46

50 Results Protein profiling of T cells and their exosomes T cells were isolated from four donors with purities between 96% and 99% (Figure 28A). T cells were stimulated with immobilized anti-cd3 antibody, soluble anti-cd28 antibody and IL-2. The stimulation was verified by the staining of the activation markers CD25 and CD69 (Figure 28B, C). Figure 28: Purity and stimulation of T cells. (A) Representative histogram of the T cell purity after immunomagnetic separation. Black curve: isotype control staining. (B) Representative density plots of unstimulated and (C) stimulated T cells after 20 hours and staining for the activation markers CD25 and CD69. To evaluate an effect of the cell stimulation on the protein profiles of exosomes, exosomes were isolated from the supernatant of unstimulated and stimulated T cells. Isolated exosome preparations were each incubated with two 39-plex bead sets covering in total 59 surface proteins. After subtraction of the background signals, we calculated the signal intensity ratio for each detected protein between the exosomes from stimulated T cells and the exosomes from unstimulated T cells. In Figure 29A all proteins are depicted that showed stronger background corrected signal intensities than the corresponding isotype control beads and a coefficient of variation smaller than 1 between the four donors. Three out of four donors showed ratios larger than 1 for these proteins, i.e. stronger signals for these proteins on exosomes from stimulated T cells as compared to exosomes from unstimulated T cells (Figure 29A). As the signal increase was also observed for the commonly used exosome markers CD9, CD63, and CD81, we concluded that the stronger signal intensities are most likely due to an increased exosome secretion by stimulated T cells as compared to unstimulated T cells. 47

51 Results A B Figure 29: Protein profiles of exosomes from stimulated and unstimulated T cells. (A) Ratio between background corrected signal intensities of exosomes from stimulated T cells and exosomes from unstimulated T cells of four donors for thirteen selected surface proteins. (B) Signals normalized to the mean signal of the exosome marker beads CD9, CD63, and CD81. Surface proteins that showed a coefficient of variation smaller than 1 between the four donors were selected. REA, migg1, migg2a, migg2b: isotype controls. To reveal surface proteins that are changed between the two conditions independently of the exosome amount or the loading of exosome markers, signals were normalized to the mean of the signals obtained with the exosome marker beads anti-cd9, anti-cd63, and anti-cd81 (Figure 29B). Only those surface proteins that showed a standard deviation smaller than the mean, i.e. a coefficient of variation smaller than 1 between donors under the same conditions, are depicted in Figure 29B. Even if the donor with decreased signal intensities after T cell stimulation was excluded and a paired t test was applied, only the CD2 signal would be significantly increased on exosomes from stimulated T cells as compared to exosomes from unstimulated T cells. Taken together, T cell-derived exosomes can be analyzed by the multiplex platform, but we could not observe a significant effect of the T cell stimulation on the protein profiles of T cell-derived exosomes. 48

52 Results Protein profiling of B cells and their exosomes Note: in this section passages that describe Figure 31 and Figure 32 were transposed directly from my publications (see publication list on page 3). B cells were isolated from four donors with purities of 97% to 99% (Figure 30A). B cells were stimulated with IL-4 and CD40 ligand mimicking cell activation by T cells. After four or five days, B cells were stained for the activation markers CD40, CD80, and CD86. The median signal intensity for CD40 increased 5.7-fold on average (from an averaged median signal intensity of 8.9 to 50.4), 85% to 90% of the stimulated B cells were CD80 + and 95% to 98% were CD86 + (Figure 30B-D). Figure 30: Purity and level of activation marker on B cells. (A) Representative histogram of the B cell purity after immunomagnetic separation. Black curve: isotype control. (B-D) Representative histograms of the activation marker staining after B cell stimulation for 4 to 5 days with IL-4 and CD40 ligand,. Black curves: control staining of unstimulated cells. After isolation of exosomes from the supernatant of B cells without and with stimulation, the protein profiles of B cell-derived exosomes were investigated by two 39-plex bead sets covering 59 different surface proteins. Background signals were subtracted from the raw signal intensities of each bead type and the ratios between the background corrected signal intensities obtained with the exosomes from stimulated B cells and exosomes from unstimulated B cells were calculated. The ratios of proteins with signal intensities above the isotype control beads and a coefficient of variation smaller than 1 for the four donors are depicted in Figure 31A. For most of the surface proteins, the ratios were larger than 1 for each donor reflecting an increase in protein signal intensities for exosomes due to the stimulation of the B cells. To reveal proteins that are changed between the two conditions independently of the exosome amount or the loading of exosome markers, signals were normalized to the mean signal obtained by the exosome marker beads. A paired t test was performed to identify significantly changed surface proteins and only those proteins that showed a coefficient of variation smaller than 1 for the four donors are depicted in Figure 31B. Eleven out of twelve significantly changed surface proteins showed an increased signal while the CD81 signal was decreased. As the protein load per exosome was comparable with and without B cell stimulation (Figure 42B, p. 61), the data indicate that the composition of the exosomes was changed as a consequence of the stimulation. The increased transfer of B cell activation markers such as CD19, CD20, CD40 and CD69 might be of particular interest in the context of antitumor immune responses. 49

53 Results A B Figure 31: Protein profiles of exosomes from stimulated and unstimulated B cells. (A) Ratio between background corrected signal intensities of exosomes from stimulated B cells and exosomes from unstimulated B cells of four donors for fourteen selected surface proteins. (B) Signals normalized to the mean signal of the exosome marker beads CD9, CD63, and CD81. Surface proteins that showed a coefficient of variation smaller than 1 between the four donors were selected. REA, migg1, migg2a, migg2b: isotype controls. Taken together, exosomes from unstimulated B cells in comparison to exosomes from stimulated B cells showed significantly changed signal intensities for twelve out of 59 investigated surface proteins. Among them, the signals of the B cell activation markers CD19, CD20, CD40, and CD69 were significantly increased on exosomes after B cell stimulation and might influence B cell-mediated immune responses. For a more detailed analysis of exosomes from stimulated B cells, we used B cell markers for additional stainings. After the antibody cocktail staining against the exosome markers CD9, CD63, and CD81, we detected signals for each of the capture antibody beads against exosome markers and 50

54 Results selected B cell markers. For the B cell marker detection antibodies (CD19, CD20,CD69, CD80, CD86, or HLA-DQ), we did not detect signals on anti-cd9- and anti-cd42a-beads (Figure 33). Because CD42a is a known platelet marker, this finding pointed to a contamination of CD42a + platelet exosomes. We suggest that the exosomes that were only detectable by the exosome marker cocktail on anti-cd9- and anti-cd42a-beads might derive from platelets. We used STED analysis to investigate whether CD9 is co-localized with the platelet marker CD42a or the B cell marker CD19 on exosomes from B cell culture supernatants. After staining with anti-cd19-star488 and anti-cd9- STARRED, we detected a total of 162 green spots on three image sections (39 to 84 spots each) and 1355 (353 to 619) red spots. In only one out of three image sections, 1% of the spots were positive for both antibodies (Figure 32A) indicating that the CD19-positive B cell exosomes do not carry CD9. In the control experiment, we detected a total of 255 anti-cd9-star488 positive spots (72 to 105 each on three different images) and 1660 spots that were positive for the platelet marker specific antibody conjugate anti-cd42a-starred (500 to 617). 8% to 12% of the green spots were also red and 1% to 2% of the red spots were also green (Figure 32B) confirming that CD42 + platelet exosomes give rise to the CD9 positive events detected by the multiplex platform in the B cell exosome sample. Figure 32: STED analysis of B cell exosomes bound to anti-cd9/63/81-coated slides and stained with (A) anti- CD19-STAR488 and anti-cd9-starred or (B) stained with anti-cd9-star488 and anti-cd42a-starred. Scale bars represent 500 nm. In the B cell preparation 10-20% CD42a + events were detected when no gates or triggers were set (data not shown). We concluded that in primary blood cell isolations, platelet contaminations are likely and they might be overseen because platelets stick to the target cells or because of exclusion of supposedly cell debris that includes platelets. Therefore, platelet markers on exosomes should be analyzed with caution as they probably originate from platelets in the primary culture. In the B cell exosome preparation, we expected exosome subpopulations of non-activated B cells because some exosomes might derive from unstimulated B cells. We used the B cell markers CD19 and CD20 as well as the B cell activation markers CD80 and CD86 as capture and detection antibodies to investigate the B cell exosomes. After staining with the acd9/acd63/acd81-apc cocktail, the signals of the anticd63-beads were higher (APC median signal 10.0) than the signals of the anti-cd80 and anti-cd86-beads (APC median signal 1.0 and 5.0, respectively, Figure 33). This 51

55 Results might indicate an exosome subpopulation from unstimulated B cells that did not adopt activation markers from the originating cells. After cocktail staining, signals for anti-cd19-beads were stronger than for anti-cd20-beads (APC median signal 11.3 and 5.4, respectively, Figure 33), i.e. fewer exosomes carry CD20 than CD19. Additionally, the single stainings showed stronger signals after anti- CD20 staining compared to the anti-cd19 staining on each capture antibody bead type (Figure 33). Therefore, we concluded that a subpopulation of B cell exosomes presented high levels of CD20 while most B cell exosomes carried lower levels of CD19. In summary, we found evidence for five exosome subpopulations in the exosome preparation from stimulated B cells: platelet exosomes, exosomes from unstimulated B cells, exosomes from stimulated B cells, CD19 low CD20 - exosomes and CD19 low CD20 high exosomes. Figure 33: Matrix profile of exosomes from stimulated B cells. Background corrected APC median signal intensities of different capture antibody beads after incubation with 32 µg B cell exosomes, followed by staining with different APC conjugated detection antibodies. REA and migg1: isotype controls. 52

56 Results Protein profiling of platelets and their exosomes In addition to erythrocytes, platelets are the most abundant component in peripheral blood. To investigate platelet exosomes, we first compared different protocols for platelet stimulation in order to increase exosome secretion. We isolated platelets from the blood of one donor via serial centrifugations that resulted in a purity of 98% (Figure 35). The isolated platelets were stimulated by TRAP (thrombin receptor activating peptide), Calcium Ionophore, or thrombin. The secreted exosomes were isolated and compared referring to size and protein profile. The size was determined using nanoparticle tracking analysis (NTA) and the protein profiles were analyzed by the multiplex platform. Exosomes from non-stimulated platelets and platelets stimulated with TRAP and thrombin resembled each other in size with 10 to 21% particles larger than 250 nm, 50 to 54% particles ranging from 110 to 250 nm and 25 to 37% particles smaller than 110 nm. But the exosomes from Calcium Ionophore stimulated platelets showed a higher percentage (60%) of particles smaller than 110 nm and fewer particles (23%) between 110 and 250 nm (Figure 34A). Hardly any signals could be detected with exosomes derived from platelets that were stimulated with TRAP on the multiplex beads. When the same amount of exosomal protein was applied, the signal intensities for the exosome markers CD9 and CD63 and the platelet marker CD61 clearly increased for platelet exosomes from stimulated platelets with Calcium Ionophore or thrombin as compared to exosomes from unstimulated platelets (Figure 34B). Thrombin was resuspended in a buffer containing 0.1 % FCS that included bovine exosomes which bias protein quantification by the BCA assay. Consequently, we used exosomes from platelets that were stimulated with Calcium Ionophore for the following experiments. A B Figure 34: Comparison of exosomes after different platelet stimulation protocols. (A) Size distribution and (B) detection of selected surface proteins on exosomes isolated from platelets that were not stimulated (control; ctrl) or stimulated with different reagents (TRAP = thrombin receptor activating peptide, Calcium Ionophore or thrombin). migg1: isotype control. Next, the surface protein profile of platelet-derived exosomes should be analyzed. For this purpose, platelets from whole blood of thirteen healthy volunteers were isolated by serial centrifugations resulting in purities of 82 to 99% (Figure 35B). Platelet preparations were adjusted to the same cell density and incubated for 30 min at room temperature alone or with Calcium Ionophore for stimulation. 53

57 Results The platelets showed a donor-dependent decrease in signal intensities for the platelet markers CD61 and CD42a. The increased signals for the activation markers CD62P and CD63 also varied among donors (Figure 35B). While the changes in signal intensities are significant across all donors, some donors showed only a minor increase. This might be due to an unintentional stimulation of platelets from these donors during blood draw, isolation, or staining. A B Figure 35: Isolation and stimulation of platelets. (A) Platelets were isolated from 100 ml whole blood with citrate as anticoagulant and stained with the platelet markers CD42a and CD61 (red) to evaluate purity. The black lines indicate the isotype control staining. (B) Platelets of thirteen healthy volunteers were stimulated or not with Calcium Ionophore, followed by staining for common platelet markers to determine efficiency of activation and the purity. To determine the required amount of exosomes for the analysis by the multiplex platform, platelet exosomes from one donor were titrated from 8 µg to 32 µg (Figure 36). The signals for the detected surface proteins increased with increasing amounts of exosomes and we decided to use 32 µg for the following exosome analyses. Two out of thirteen donors showed extraordinary strong signals for the isotype control migg2b and were therefore excluded from the analysis. Despite a broad range of signal intensities for the eleven donors, each donor showed strong signals for the platelet markers CD41b, CD42a, CD61, and CD62P, and the exosome markers CD9, CD63, and CD82. But the signal for CD81 was on the level of the isotype control beads and thus considered as not present on platelet exosomes (Figure 36). This finding was confirmed by single stainings with exosome markers as no signals could be detected after anti-cd81-apc staining on any of the capture antibody beads (Figure 22B, p. 40). Taken together, platelet exosomes were positive for the exosome markers CD9, CD63, and CD82, but negative for the suggested exosome marker CD81. Exosomes that were isolated from primary platelets adopted the platelet markers CD41b, CD42a, CD61, and CD62P from their parental cells and also carried HLA class I and II, CD24, the integrins CD29 and CD49e, CD184 (CXCR4) and the erythrocyte marker CD235a. 54

58 Results A B Figure 36: Analysis of platelet exosomes. (A) Signal intensities of selected surface proteins increased with increasing protein amounts of exosomes from Calcium Ionophore stimulated platelets. Platelet exosomes were incubated with a 39-plex bead set of capture antibody beads, followed by staining with anti-cd9-apc. (B) Signal intensities of capture beads against selected surface proteins after incubation of platelets from eleven healthy volunteers. Platelets were stimulated with Calcium Ionophore, 32 µg platelet exosomes were incubated with two 39-plex bead sets covering 59 different surface proteins, followed by staining with a cocktail of anti-cd9-apc, anti-cd63-apc, and anti-cd81-apc. REA, migg1, migg2a, and migg2b: isotype controls Protein profiling of natural killer cells and their exosomes For the analysis of exosomes from natural killer (NK) cells, NK cells were immuno magnetically isolated and expanded for 14 days to increase the exosome yield. In addition to NK cell-derived exosomes, NK cells themselves were also analyzed by the multiplex platform as a proof-of-principle to show that the platform can also be applied to analyze larger particles such as cells. NK cells that bound to the multiplex beads were detected by an APC-conjugated antibody against the NK cell marker CD56. The multiplex platform can be used as an alternative to conventional cell stainings to screen for proteins. However, in contrast to exosomes, cells are detectable without beads by conventional flow cytometers and multicolor stainings allow the identification of cell subsets by combining different antibody stainings. On NK cell exosomes, we detected HLA class I and II, CD29 and the NK cell markers CD2, CD8, and CD56. Interestingly, the exosome markers CD63, CD81, and CD82 were present, but CD9 was missing on NK cells (Figure 37A) as well as on each of the four NK cell exosome preparations (Figure 37B). In addition to platelet exosomes that seemed to be lacking CD81 (p. 53), NK cell exosomes are another type of exosomes that are missing one of the so-called exosome markers. Accordingly, no signals were obtained for all single stainings with anti-cd9-apc on different capture antibody beads (Figure 22A, p. 40). 55

59 Results A B Figure 37: Protein profiles of natural killer (NK) cells and their exosomes. (A) Background corrected signal intensities of selected surface proteins after incubation of 5x10 6 NK cells with a 39-plex bead set and staining with anti-cd56-apc. (B) Background corrected signal intensities of selected surface proteins after incubation of 8 µg NK cell exosomes from four donors with two 39-plex bead sets covering 56 surface proteins and staining with a cocktail of anti-cd9-, anti-cd63-, and anti-cd81-apc antibodies. REA, migg1, migg2a, migg2b: isotype controls. The tetraspanin proteins CD9 and CD81 are commonly thought as exosome markers, but were not present on each exosome type after exosome analyses by the multiplex bead assay. NK cell-derived exosomes showed hardly any signal for CD9 and platelet-derived exosomes were CD81 negative (p. 53). To validate this observation exosomes from NK cells and platelets were analyzed by STED microscopy. After staining with anti-cd9-star488 and anti-cd81-starred, NK cell exosomes were only positive for anti-cd81-starred and the platelets were exclusively green, i.e. CD9 + (Figure 38). Consistent with these results, in a mixture of NK cell exosomes and platelet exosomes stained with the same antibodies, exosomes were exclusively positive for either of the two dye-conjugated antibodies (Figure 38C). Taken together, NK cell-derived exosomes are negative for the suggested exosome marker CD9 as well as NK cells. NK cell-derived exosomes adopt most of the investigated surface proteins from their originating cells (e.g. CD9, CD63, CD2, CD29). In contrast, CD24 and CD56 were detected on NK cells but showed weak signals on NK cell-derived exosomes. Additionally, STED microscopy demonstrated to be a suitable tool to validate the presence of proteins on single exosomes. A B C Figure 38: STED analysis of exosomes bound to anti-cd9/63/81-coated slides stained with anti-cd9-star488 and anti-cd81-starred. (A) NK cell exosomes, (B) platelet exosomes, or (C) a mixture of NK cell exosomes and platelet exosomes. Scale bars represent 200 nm. 56

60 Results Protein profiling of monocytes, monocyte-derived dendritic cells, and their secreted exosomes For the isolation of monocyte exosomes, monocytes were immuno magnetically isolated with purities between 92% and 98% and three media were tested for culture. While most of the monocytes died in OptiMEM medium (protocol adopted from (Bhattacharjee et al., 2008), cell viability of 80% were reached with RPMI1640 alone (protocol adopted from (Ringenbach et al., 1998) or supplemented with IL-2 (protocol adopted from (Espinoza-Delgado et al., 1995). As we obtained stronger signals by the multiplex platform with exosomes isolated from the supernatant of monocytes cultured in RPMI without IL-2, we chose this medium for the following experiments. Monocytes were isolated from buffy coats of three donors and exosomes were isolated from the cell culture supernatant to be analyzed by the multiplex bead assay. Each of the four suggested exosome markers (CD9, CD63, CD81, and CD82) were detected on the monocyte-derived exosomes. Additionally, the exosomes were positive for the platelet markers CD41b, CD42a, CD61, and CD62P. This is most likely due to platelet contaminations of the monocyte culture. The five surface proteins with the next strongest signal intensities were HLA class I and II, CD29, CD44, and CD14 (Figure 39). Figure 39: Protein profile of exosomes from monocytes. Background corrected signal intensities of surface proteins after incubation of 8 µg monocyte exosomes from three donors with two 39-plex bead sets covering 57 surface proteins and staining with a cocktail of anti-cd9-, anti-cd63-, and anti-cd81-apc antibodies. REA, migg1, migg2a, migg2b: isotype controls. Because of the low frequency of DCs in peripheral blood (Hart, 1997; Jongbloed et al., 2010), we generated DCs artificially by differentiation and maturation of monocytes into monocyte-derived dendritic cells (modcs). As the manual differentiation and maturation of monocytes isolated from buffy coats into modcs did not lead to sufficient exosome protein amounts, modc generation was performed semi-automatically with higher numbers of monocytes from leukaphereses in the CliniMACS Prodigy system. Monocytes were stained before culture, thus the cells from the same donors could be analyzed during differentiation and maturation. Unlike the cells, exosomes had to be isolated from the cell culture supernatant after every medium change and monocytes were cultured 57

61 Results directly in differentiation medium. Thus, monocyte exosomes were isolated from other cells than the ones used for modc generation. But the modcs and their exosomes could be analyzed in parallel. Figure 40: Detection of selected markers on monocytes, modcs and their secreted exosomes. (A) Flow cytometric analysis of monocytes, immature DCs (idcs), and mature DCs (mdcs). Median signal intensities of CD14, CD40, CD86, and CD209 were background-corrected by subtracting the signal of the respective isotype controls. (B) Multiplex bead assay analysis of exosomes isolated from the cell culture supernatant of monocytes, idcs and mdcs. Background corrected signal intensities of CD14, CD40, CD86, and CD209 were normalized to the mean of the exosome marker proteins CD9, CD63, and CD81. The monocyte exosomes derived from other monocytes than the ones used for modc generation. The monocyte marker CD14 was detected on monocytes and their secreted exosomes. The CD14 signal decreased already highly significantly on modcs on day 6 compared to monocytes (p<0.001) and with delay on exosomes significantly on day 10 compared to the other days (p<0.05, Figure 40). We next focused on known markers for differentiation and maturation to investigate whether they are 58

62 Results transferred from the cells to the respective exosomes. The median signal intensities for the activation markers CD40 and CD86 increased significantly (p<0.05) on the modcs on day 10 as compared to day 6 (Figure 40A). Additionally, the CD40 and CD86 signals increased on the corresponding exosomes (Figure 40B) demonstrating a transfer of the activation markers from the cells to their exosomes. The increase of the CD86 signal was highly significant on exosomes on day 7 compared to day 4 and on cells compared to monocytes (p<0.01). In contrast, the signals for CD209 (DC-SIGN) decreased significantly on modcs on day 10 as compared to day 6 (Figure 40A) as well as on the respective exosomes (p<0.05, Figure 40B). The signal increased significantly (p<0.05) on immature modcs on day 6 compared to monocytes and decreased again significantly (p<0.5) from day 6 to day 10 and highly significantly (p<0.01) but time-shifted on exosomes from day 7 to day 10 (Figure 40). In summary, activation markers known from modcs were observed to be increased and decreased on exosomes with a delay in comparison to their originating cells. In the last sections, we showed that surface proteins of the originating cell type can be identified on exosomes from the supernatant of PBMC culture that might mimic the exosome mixture in plasma. However, the analysis of exosomes from separated cell types simplifies the classification of exosome markers. Therefore, exosomes from different primary blood cells, namely, B cells, T cells, NK cells, monocytes, modcs, and platelets were generated, isolated, and analyzed. The suggested exosome markers CD9, CD63, CD81, and CD82 were detected on each exosome type with the two mentioned exceptions: CD9 was hardly detected on NK cell-derived exosomes and CD81 was missing on platelet-derived exosomes. HLA class I and II, CD24, and CD29 were detected on almost all exosome types. The mean signal intensities of 39 surface proteins on the investigated exosome types are depicted in Figure 41. Among the detected proteins, we identified surface proteins that were probably adopted from the originating cells. B cell-derived exosomes were positive for the B cell markers CD19 and CD20 and T cell-derived exosomes were positive for CD2 and CD8. In contrast to the parental cells, the signals for CD3 and CD69 were very low on T cell-derived exosomes. Exosomes from NK cells were positive for CD2 and CD8, but the NK cell marker CD56 was hardly detectable. As T cellderived exosomes and NK cell-derived exosomes were both positive for CD2 and CD8 without presenting another cell type-specific marker in our analysis, exosomes from T cells and NK cells are not distinguishable in a exosome mixture such as plasma. Monocyte-derived exosomes were CD14 positive and modc-derived exosomes showed signals for CD40 and CD86 (Figure 41). Even though we cannot exclude that the assay was not sensitive enough to detect low abundant proteins in the samples, we assume that the protein loading of exosomes is specific and that some proteins (e.g. CD3 from T cells and CD56 from NK cells) are not efficiently transported from the cells to exosomes. After stimulation by an artificial immune stimulus, we observed that the signals for CD19, CD20, CD40, and CD69 among others were significantly stronger on exosomes from stimulated B cell as compared to exosomes from unstimulated B cells. The comparison of exosomes from monocytes, immature modcs and mature modcs revealed that the exosomes reflect the changing signals for the markers CD14, CD40, CD86, and CD209 on the originating cells. Hence, the presence or abundance of surface proteins on exosomes seems to be linked with the type and status of the originating cell. 59

63 Results Taken together, we identified surface proteins on exosomes that might help to assign plasma exosomes to the potential originating cell types (Figure 41). Figure 41: Overview of the background corrected signal intensities of 39 surface proteins on exosomes from different cell types. 32 µg B cell-derived exosomes, 3 µg T cell-derived exosomes, 8 µg NK cell-derived exosomes, 8 µg monocyte-derived exosomes, 4 µg modc-derived exosomes, or 32 µg platelet-derived exosomes were incubated with 39-plex bead sets and stained with an antibody cocktail of anti-cd9-apc, anti-cd63-apc, and anti-cd81-apc. (A) Common exosome proteins that were detected on most exosome preparations and the isotype controls REA, migg1, migg2a, and migg2b. (B) Proteins that might indicate the originating cell type of the analyzed exosomes. 60

64 Results Exosome production rate per cell and protein amount per exosome The size and particle concentration of exosomes from different cell types were measured by Nanoparticle Tracking Analysis (NTA). The peaks of the exosome size distribution determined by NTA ranged from 90 to 140 nm (Figure 42A). Based on the particle concentration determined by NTA and the protein concentration that was measured by BCA assay, we calculated the protein amount per particle/exosome. Exosomes isolated from melanoma cell culture supernatant showed a 10-fold lower protein amount per particle in comparison to the other exosome preparations (Figure 42B). This might be due to a lower protein content of melanoma exosomes or due to protein aggregates in the other exosome preparations that biased the calculated protein amount per particle. While exosomes from plasma, platelets, modcs and T cells had comparable protein amounts per particle, B cell-derived exosomes seem to have the second lowest protein content per particle after melanoma exosomes. However, the protein content per B cell-derived exosome did not change significantly (p=0.22, twotailed paired t test) when the parental cells were stimulated. Figure 42: Size and protein amount per particle of different exosome preparations. (A) Superimposed nanoparticle tracking analyses to determine particle sizes of exosomes from healthy plasma, unstimulated B cells, Calcium Ionophore-stimulated platelets, melanoma cell culture, and mature monocyte-derived dendritic cells. (B) Protein amount per particle: Particle concentration was defined using nanoparticle tracking analysis (NTA); protein concentration was measured by BCA assay. Melanoma exosomes were isolated from primary melanoma cell cultures of three patients. All exosome types were isolated from primary blood cells and while the abundance of cell types in peripheral blood is known, exosome yields were rarely published. However, we think it is important for further projects to know an approximate exosome yield per cell. Therefore, we calculated the exosome production rate per cell type and cell stimulation. To obtain the exosome production rate, the exosome yield was divided by the cell number at culture start. The exosome production rate for monocytes, T cells and platelets was between 0.04 and 0.61 pg protein per cell while the exosomal protein amounts per modc were ranging from 0.86 to 4.55 pg per cell. The highest exosome production rate was reached by the B cells with 4.41 to 13.9 pg protein per cell. Interestingly, while the exosome production of T cells did not increase after cell stimulation (p=0.15, two-tailed paired t test), the exosome production rate decreased significantly in response to B cell stimulation (p<0.01, two-tailed paired t test). Potentially, B cells load their exosomes with a comparable amount of proteins after cell 61

65 Results stimulation (Figure 42B) while decreasing their exosome secretion (Figure 43). The exosome yield per platelet could be slightly increased from 0.07 pg exosomal protein per platelet to pg per platelet (Figure 43) by stimulation with Calcium Ionophore (CI platelets) or thrombin, but not with TRAP. Apparently, the thrombin receptor activating peptide TRAP is not as efficient as thrombin itself to increase exosome secretion by platelets. Besides other reasons (discussed on p. 53), we therefore did not stimulate platelets with TRAP to obtain as many exosome as possible. However, as the exosome production rates are based on estimations about the cell number and the exosome yield (measured by BCA assay), variations should not be overvalued. Figure 43: Yield of isolated exosomes from different cell types calculated as protein amount after exosome isolation per cell number at start of culture. idc: immature monocyte-derived dendritic cells, mdcs: mature monocyte-derived dendritic cells, Unstim: unstimulated, Stim: stimulated, CI: Calcium Ionophore stimulation, TRAP: stimulation with thrombin receptor activating peptide, Thrombin: thrombin stimulation. 62

66 Results Protein profiling of plasma exosomes from healthy donors and melanoma patients For routine testing, a simplified workflow would detect exosomes in plasma without isolating exosomes by ultracentrifugation. To test whether this is feasible with the multiplex platform, plasma and isolated plasma exosomes were analyzed. Plasma exosomes were concentrated from plasma of a healthy volunteer by ultracentrifugation with a yield of 13 µg exosomes per ml plasma. In a 4-plex bead analysis with acd9, acd63, acd81 and migg1 antibody beads, 1 ml plasma was analyzed in comparison to 4 µg isolated plasma exosomes that correspond to 0.3 ml plasma. Whereas no signals were obtained with plasma, low but specific signals were detected with the isolated plasma exosomes (Figure 44A). Next, we increased the amount of isolated plasma exosomes in 2-fold steps resulting in a linear, but not directly proportional increase of the signal intensities (Figure 44B). We decided to use 64 µg of isolated plasma exosomes for further experiments as the reached signal intensities were sufficient. A B Figure 44: Isolation and titration of plasma exosomes. (A) 4-plex analysis with acd9, acd63, acd81 and migg1 antibody beads of 1 ml heparin plasma in comparison to 4 µg exosomes from the same plasma that is equivalent to 0.3 ml plasma. (B) Titration of plasma exosomes with a 39-plex bead set. migg1: isotype control. For a broader analysis, we isolated exosomes from whole blood of thirteen healthy donors that was anticoagulated with citrate as recommended by the Society for Extracellular Vesicles (Witwer et al., 2013). On average, we isolated 800 µg plasma exosomes per donor corresponding to 14 µg exosome protein per ml plasma (n = 13). We incubated plasma exosomes from thirteen donors each with two 39-plex bead sets covering 58 different surface proteins. To compare the exosome protein profiles of different donors independently of potentially differing amounts of exosome protein and differing loading of exosome markers, signals were normalized to the mean of the signals obtained with the exosome marker beads anti-cd9, anti-cd63, and anti-cd82. In Figure 45, the normalized signal intensities are depicted of capture antibody beads that showed stronger signals than the signals from the isotype control beads. In addition, the normalized signals of anti-hla-abc-beads and anti-cd45- beads are included in the figure to demonstrate that hardly any plasma exosomes bound to them. These two surface proteins are known as general markers for mononuclear blood cells and can be assumed as being expressed by most of the blood cells. We detected HLA-ABC on exosomes from B cells, monocytes, modcs, and platelets and CD45 on modc-derived exosomes (Figure 41, p. 60). 63

67 Results The fact that they were not detectable on plasma exosomes most likely indicate that exosomes carrying these proteins are underrepresented in plasma. Figure 45: Protein profile of selected surface proteins detected on plasma exosomes from thirteen healthy donors. 64 µg plasma exosomes per donor were incubated with two 39-plex bead sets each that covered in total 58 surface proteins, followed by the staining of bound exosomes with a cocktail of anti-cd9, anti- CD63, and anti-cd81-apc antibodies. REA, migg1, migg2a, migg2b: isotype controls. To identify the originating cell types of plasma exosomes, the protein profiles of plasma exosomes were compared with the protein profiles of exosomes derived from the six investigated primary blood cell types (T cells, B cells, platelets, NK cells, monocytes, and modcs). Hierarchical clustering grouped the exosome preparations according to their originating cells. Within the groups, we found cell type-specific markers such as CD14 on monocyte exosomes, CD19 on B cell exosomes or CD40, CD80, CD83, and CD86 on modc-derived exosomes and CD2 and CD8 on T cell exosomes. The platelet markers CD41b, CD42a, CD61, and CD62P were detected in all exosome preparations, except in those from NK cells (Figure 46, red arrows). This is most likely because platelets were present in the primary culture of the cells. During the culture of NK cells over 14 days with media changes, it is likely that most of the platelets died or were removed resulting in a pure NK cell population. T cell-derived exosomes showed only weak signals for the platelet markers. We could not completely exclude platelet exosomes in our exosome preparations from primary cells because we observed that platelets tend to stick to cells after isolation from buffy coats (data not shown). Actually, we detected exosomes that were positive for platelet markers in B cell exosome preparations. However, we preferred to use primary cells for exosome production to receive exosomes that are less artificial than exosomes from cell lines. Consequently, we decided to exclude platelet markers from the identification of contributing cell types to the pool of plasma exosomes in the following analyses. The tetraspanin proteins CD63 and CD82 were detected on every exosome preparation but for T cell exosomes that generally gave low signal intensities. As discussed before, the signals for the tetraspanin CD9 were remarkably low on NK cell-derived exosomes and the tetraspanin CD81 was absent on platelet exosomes (Figure 46, black arrows). CD24, CD29, HLA-ABC and HLA class II were 64

68 Results detected on every exosome sample (Figure 46). We would assign the CD8 + exosomes in plasma to T cells or NK cells. Plasma exosomes were also predominantly positive for CD40 and CD86 implying the existence of DC exosomes and B cell exosomes in plasma. The CD19 signals for two out of thirteen plasma exosome preparations could be another hint of B cell exosomes in plasma. In summary, surface proteins that were detected on platelet exosomes (CD41b, CD42a, CD61, and CD62P), modc exosomes, B cell exosomes (CD40, CD86), as well as T cell exosomes and NK cell exosomes (CD8), were detected on plasma exosomes indicating a contribution of these cells to the pool of plasma exosomes (Figure 46). Figure 46: Heat map depicting the protein profiles of exosomes from different primary blood cells and plasma. Raw signals were background corrected by subtracting the signals obtained with the multiplex beads without exosomes after cocktail staining. D = donor; idc = immature monocyte-derived dendritic cell, mdc = mature monocyte-derived dendritic cell. 65