Cancer cells copy migratory behavior and exchange signaling networks via extracellular vesicles
2018; Springer Nature; Volume: 37; Issue: 15 Linguagem: Inglês
10.15252/embj.201798357
ISSN1460-2075
AutoresSander Christiaan Steenbeek, Thang V. Pham, Joep de Ligt, Anoek Zomer, Jaco C. Knol, Sander R. Piersma, Tim Schelfhorst, Rick Huisjes, Raymond M. Schiffelers, Edwin Cuppen, Connie R. Jiménez, Jacco van Rheenen,
Tópico(s)MicroRNA in disease regulation
ResumoArticle15 June 2018Open Access Source DataTransparent process Cancer cells copy migratory behavior and exchange signaling networks via extracellular vesicles Sander C Steenbeek Sander C Steenbeek Molecular Pathology, Oncode Institute, The Netherlands Cancer Institute, Amsterdam, The Netherlands Oncode Institute, Hubrecht Institute-KNAW & University Medical Centre Utrecht, Utrecht, The Netherlands Search for more papers by this author Thang V Pham Thang V Pham OncoProteomics Laboratory, Department of Medical Oncology, Cancer Center Amsterdam, VU University Medical Center, Amsterdam, The Netherlands Search for more papers by this author Joep de Ligt Joep de Ligt Division Biomedical Genetics, Center for Molecular Medicine, Oncode Institute, University Medical Centre Utrecht, Utrecht, The Netherlands Search for more papers by this author Anoek Zomer Anoek Zomer Oncode Institute, Hubrecht Institute-KNAW & University Medical Centre Utrecht, Utrecht, The Netherlands Search for more papers by this author Jaco C Knol Jaco C Knol OncoProteomics Laboratory, Department of Medical Oncology, Cancer Center Amsterdam, VU University Medical Center, Amsterdam, The Netherlands Search for more papers by this author Sander R Piersma Sander R Piersma OncoProteomics Laboratory, Department of Medical Oncology, Cancer Center Amsterdam, VU University Medical Center, Amsterdam, The Netherlands Search for more papers by this author Tim Schelfhorst Tim Schelfhorst OncoProteomics Laboratory, Department of Medical Oncology, Cancer Center Amsterdam, VU University Medical Center, Amsterdam, The Netherlands Search for more papers by this author Rick Huisjes Rick Huisjes Department of Clinical Chemistry and Haematology, University Medical Center Utrecht, Utrecht, The Netherlands Search for more papers by this author Raymond M Schiffelers Raymond M Schiffelers Department of Clinical Chemistry and Haematology, University Medical Center Utrecht, Utrecht, The Netherlands Search for more papers by this author Edwin Cuppen Edwin Cuppen orcid.org/0000-0002-0400-9542 Division Biomedical Genetics, Center for Molecular Medicine, Oncode Institute, University Medical Centre Utrecht, Utrecht, The Netherlands Search for more papers by this author Connie R Jimenez Corresponding Author Connie R Jimenez [email protected] orcid.org/0000-0002-3103-4508 OncoProteomics Laboratory, Department of Medical Oncology, Cancer Center Amsterdam, VU University Medical Center, Amsterdam, The Netherlands Search for more papers by this author Jacco van Rheenen Corresponding Author Jacco van Rheenen [email protected] orcid.org/0000-0001-8175-1647 Molecular Pathology, Oncode Institute, The Netherlands Cancer Institute, Amsterdam, The Netherlands Oncode Institute, Hubrecht Institute-KNAW & University Medical Centre Utrecht, Utrecht, The Netherlands Search for more papers by this author Sander C Steenbeek Sander C Steenbeek Molecular Pathology, Oncode Institute, The Netherlands Cancer Institute, Amsterdam, The Netherlands Oncode Institute, Hubrecht Institute-KNAW & University Medical Centre Utrecht, Utrecht, The Netherlands Search for more papers by this author Thang V Pham Thang V Pham OncoProteomics Laboratory, Department of Medical Oncology, Cancer Center Amsterdam, VU University Medical Center, Amsterdam, The Netherlands Search for more papers by this author Joep de Ligt Joep de Ligt Division Biomedical Genetics, Center for Molecular Medicine, Oncode Institute, University Medical Centre Utrecht, Utrecht, The Netherlands Search for more papers by this author Anoek Zomer Anoek Zomer Oncode Institute, Hubrecht Institute-KNAW & University Medical Centre Utrecht, Utrecht, The Netherlands Search for more papers by this author Jaco C Knol Jaco C Knol OncoProteomics Laboratory, Department of Medical Oncology, Cancer Center Amsterdam, VU University Medical Center, Amsterdam, The Netherlands Search for more papers by this author Sander R Piersma Sander R Piersma OncoProteomics Laboratory, Department of Medical Oncology, Cancer Center Amsterdam, VU University Medical Center, Amsterdam, The Netherlands Search for more papers by this author Tim Schelfhorst Tim Schelfhorst OncoProteomics Laboratory, Department of Medical Oncology, Cancer Center Amsterdam, VU University Medical Center, Amsterdam, The Netherlands Search for more papers by this author Rick Huisjes Rick Huisjes Department of Clinical Chemistry and Haematology, University Medical Center Utrecht, Utrecht, The Netherlands Search for more papers by this author Raymond M Schiffelers Raymond M Schiffelers Department of Clinical Chemistry and Haematology, University Medical Center Utrecht, Utrecht, The Netherlands Search for more papers by this author Edwin Cuppen Edwin Cuppen orcid.org/0000-0002-0400-9542 Division Biomedical Genetics, Center for Molecular Medicine, Oncode Institute, University Medical Centre Utrecht, Utrecht, The Netherlands Search for more papers by this author Connie R Jimenez Corresponding Author Connie R Jimenez [email protected] orcid.org/0000-0002-3103-4508 OncoProteomics Laboratory, Department of Medical Oncology, Cancer Center Amsterdam, VU University Medical Center, Amsterdam, The Netherlands Search for more papers by this author Jacco van Rheenen Corresponding Author Jacco van Rheenen [email protected] orcid.org/0000-0001-8175-1647 Molecular Pathology, Oncode Institute, The Netherlands Cancer Institute, Amsterdam, The Netherlands Oncode Institute, Hubrecht Institute-KNAW & University Medical Centre Utrecht, Utrecht, The Netherlands Search for more papers by this author Author Information Sander C Steenbeek1,2, Thang V Pham3, Joep Ligt4, Anoek Zomer2, Jaco C Knol3, Sander R Piersma3, Tim Schelfhorst3, Rick Huisjes5, Raymond M Schiffelers5, Edwin Cuppen4, Connie R Jimenez *,3,‡ and Jacco Rheenen *,1,2,‡ 1Molecular Pathology, Oncode Institute, The Netherlands Cancer Institute, Amsterdam, The Netherlands 2Oncode Institute, Hubrecht Institute-KNAW & University Medical Centre Utrecht, Utrecht, The Netherlands 3OncoProteomics Laboratory, Department of Medical Oncology, Cancer Center Amsterdam, VU University Medical Center, Amsterdam, The Netherlands 4Division Biomedical Genetics, Center for Molecular Medicine, Oncode Institute, University Medical Centre Utrecht, Utrecht, The Netherlands 5Department of Clinical Chemistry and Haematology, University Medical Center Utrecht, Utrecht, The Netherlands ‡These authors contributed equally to this work *Corresponding author. Tel: +31 20 4442340; E-mail: [email protected] *Corresponding author. Tel: +31 20 5126906; E-mail: [email protected] The EMBO Journal (2018)37:e98357https://doi.org/10.15252/embj.201798357 PDFDownload PDF of article text and main figures. Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Abstract Recent data showed that cancer cells from different tumor subtypes with distinct metastatic potential influence each other's metastatic behavior by exchanging biomolecules through extracellular vesicles (EVs). However, it is debated how small amounts of cargo can mediate this effect, especially in tumors where all cells are from one subtype, and only subtle molecular differences drive metastatic heterogeneity. To study this, we have characterized the content of EVs shed in vivo by two clones of melanoma (B16) tumors with distinct metastatic potential. Using the Cre-LoxP system and intravital microscopy, we show that cells from these distinct clones phenocopy their migratory behavior through EV exchange. By tandem mass spectrometry and RNA sequencing, we show that EVs shed by these clones into the tumor microenvironment contain thousands of different proteins and RNAs, and many of these biomolecules are from interconnected signaling networks involved in cellular processes such as migration. Thus, EVs contain numerous proteins and RNAs and act on recipient cells by invoking a multi-faceted biological response including cell migration. Synopsis Imaging microscopy in live animals combined with transcriptome–proteome analyses are used to characterize the function and content of extracellular vesicles (EVs) shed in vivo by mouse melanoma tumors with distinct metastatic potential. These data suggest that EVs promote cell migration by transferring clusters of RNA and proteins acting in the same physiological signaling networks. B16F1 and B16F10 melanoma cell clones with differential metastatic potencies functionally exchange EVs in vivo. The less metastatic B16F1 cells display increased invasive potential after uptake of EVs from the more aggressive B16F10 cells. EVs purified from B16F10 microenvironments contain more complex and distinct cargo networks of RNAs and proteins involved in cell migration than seen for B16F1. Introduction Tumors are heterogeneous with respect to mutational pattern, gene expression, protein profiles, and microenvironment (e.g., oxygen and nutrient levels, extracellular matrix composition, and immune cell infiltration; Hamm et al, 2010; Swanton, 2012; Junttila & de Sauvage, 2013; Venkatesan & Swanton, 2016). This heterogeneity leads to large functional intratumoral differences in cancer cell behavior so that a tumor may contain or develop small numbers of cells that possess properties to migrate, metastasize, or evade therapy treatment (Scheele et al, 2016). Therefore, intratumoral heterogeneity has directly been linked to disease progression and metastasis (Inda et al, 2010; Calbo et al, 2011; Cleary et al, 2014) and has been suggested to be the main reason for cancer treatment failure in the clinic (Burrell & Swanton, 2014; Jamal-Hanjani et al, 2015). In recent years, extracellular vesicles (EVs) have attracted a lot of attention as microenvironmental intercellular messengers that may severely complicate tumor heterogeneity (Zomer & van Rheenen, 2016). EVs are small lipid membrane-enclosed vesicles that carry biologically active molecules including lipids, proteins, DNA, and various RNA species such as mRNA, miRNA, and lncRNA. A variety of EVs are described, including exosomes arising from fusion of multivesicular bodies (MVBs) with the limiting plasma membrane, shed microvesicles or large oncosomes budding from the limiting plasma membrane, and apoptotic bodies being released by apoptotic cells (Raposo & Stoorvogel, 2013). Because it remains to be determined what the contribution of these populations is in the in vivo tumor microenvironment, we use the collective term "extracellular vesicles" to commonly refer to all EV subtypes (Gould & Raposo, 2013). EV-associated biomolecules such as EV-RNA are stable in EVs and functional upon delivery into recipient cells. For example, upon EV uptake, vesicular mRNA is translated into functional proteins (Valadi et al, 2007), and vesicular miRNAs suppress target genes in recipient cells (Hergenreider et al, 2012; Fong et al, 2015; Zhang et al, 2015). Moreover, EV-RNA-based reporter systems have confirmed the transport of functional mRNA for Cre (Ridder et al, 2015; Zomer et al, 2015, 2016) or GlucB (Lai et al, 2015) into recipient cells. Thus, by exchanging EVs, cells can transfer biomolecules to recipient cells, thereby potentially influencing the recipient cell's behavior (Valadi et al, 2007) and tumor heterogeneity (Zomer & van Rheenen, 2016). For instance, EV-mediated crosstalk between cancer cells and non-cancer cells has been linked to promote tumor growth by inducing proliferation (Rajappa et al, 2016; Richards et al, 2017) and angiogenesis (Park et al, 2010; Umezu et al, 2014; Feng et al, 2017). In addition, cancer cell-derived EVs have been shown to promote metastasis by priming of the pre-metastatic niche (Costa-Silva et al, 2015; Hoshino et al, 2015; Liu et al, 2016) and inducing leakiness of the blood–brain barrier (Tominaga et al, 2015; Treps et al, 2016). However, in addition to the non-transformed cell types, cancer cells are also highly exposed to cancer cell-released EVs. Importantly, recent data suggest that cancer cells can also take up cancer EVs. EV exchange between cancer cell subsets with different phenotypic properties has been shown to transfer apoptosis resistance (Wojtuszkiewicz et al, 2016) and drug resistance (Sousa et al, 2015), and metastatic (Zomer et al, 2015) properties. Although accumulating evidence suggests that cancer cells can phenocopy behavior through exchange of EVs, it is widely debated how the transfer of small amounts of cargo can mediate this effect. For example, it is hard to imagine how transfer of single proteins or RNAs can induce a phenotypic change. In a proof of concept study, we have previously shown that the highly aggressive basal breast cancer cell line MDA-MB-231 can phenocopy its metastatic behavior to the more benign luminal A breast cancer line T47D when co-transplanted into immune-deficient mice (Zomer et al, 2015). Although these proof-of-principle experiments illustrate the potential of EV exchange to phenocopy differential behavior, they do not model EV exchange in tumors with cells from the same subtype. To model the more physiological and moderate subclonal heterogeneity, we here study the exchange of EV content between two syngenic melanoma cancer lines with distinct metastatic potential: the B16F1 and B16F10 model (Hart & Fidler, 1980; Poste et al, 1980; Cillo et al, 1987; Nakamura et al, 2002; Mathieu et al, 2007). Importantly, these cancer cell lines have a common origin, and therefore, differences in phenotype and EV cargo are linked to clonal disease progression and developed metastatic potential, instead of tumor origin or subtype. B16F10 cells have been shown to shed EVs that can educate bone marrow-derived cells (Peinado et al, 2012), but it is unknown to what extent these vesicles influence cancer subclones with a less metastatic phenotype. Here, we studied the mutual influence of cancer subclones and showed large discrepancy between the efficiency of EV transfer in vitro and in vivo, underlining the importance of studying EV exchange between cells in their in vivo setting. We isolated EVs from the in vivo setting and identified that cancer cell subclones with distinct metastatic potential transfer RNAs and proteins that are interconnected in networks involved in migration, leading to phenocopying of migratory behavior. Results and Discussion Modeling tumor heterogeneity using the B16F1 and B16F10 model To investigate the influence of EVs on heterogeneity of cancer cell behavior, we studied two clones that were derived from serial transplantations of a melanoma (B16) that developed spontaneously behind the ear of a C57BL/6 mouse (El, 1962). These clones, B16F1 and B16F10, have been shown to have differential metastatic potential, with the B16F10 model being more metastatic than the B16F1 model upon intravenous injection of cancer cells (Hart & Fidler, 1980; Poste et al, 1980; Cillo et al, 1987; Nakamura et al, 2002; Mathieu et al, 2007). Subcutaneous injection of fluorescently labeled B16F1 and B16F10 cells in 20 C57BL/6 mice led to tumors within 3–4 weeks, and co-injection of both lines led to tumors that contain both fluorescent B16 subclones. In nine mice, B16F10 cells formed the majority (> 50%) of the cancer cells whilst in 11 mice, the majority was formed by B16F1 cancer cells. Examination of lungs, lymph nodes, and livers showed the presence of micrometastases derived from B16F10 cells (six out of nine mice in which B16F10 cells are the major cancer type) but only occasionally from B16F1 cells (one out of 11 mice in which the B16F1 cells are the major cancer type) confirming their differential metastatic potential. Next, we tested whether we observe differential behavior in an early step of metastasis, namely migration. Using intravital microscopy (IVM), we tracked the migration of individual cells within one imaging field (Fig 1A and B), and averaged the migration speed of each cell type, connecting measurements from the same imaging field with a line (Fig 1C). Consistent with other studies, we observe that average cancer cell migration speed varies between imaging fields, as previously proposed most likely due to microenvironmental differences (Condeelis & Segall, 2003; Joyce & Pollard, 2009; Zomer et al, 2015). In heterogeneous tumors, B16F10 cancer cells have an average 1.5-fold higher migration speed than B16F1 cancer cells present in the same tumor area, confirming that cells demonstrated to be more metastatic have a higher migration speed (Fig 1D). From these results, we conclude that co-injection of B16F1 and B16F10 cells leads to tumors that consist of cancer cells with heterogeneous metastatic behavior. Figure 1. Melanoma subclones with differential phenotypes functionally exchange EVs Cartoon displaying intravital microscopy to study tumor cell migration in B16F1 and B16F10 mixed tumors. Representative rose plots of tumor cell migration tracks of B16F1 and B16F10 tumor cells within the same imaging field, track shown for 2h30 migration. Average migration speed of B16F1 and B16F10 tumor cells within the same imaging field is connected with a line. Black lines show faster migration in B16F10 cells, and gray lines show faster migration of B16F1 cells within one imaging field. B16F10 cells have an average faster migration speed in 22 of 26 positions. Relative migration speed of B16F10 cells to the average B16F1 cell migration speed. Data represented as mean ± SEM with the Wilcoxon signed rank test. n = 26 positions in eight mice. Single z plane optical sections confirm uptake of in vitro-derived 16.5K and 100K EVs across cell types 3 h after addition to culture medium. B16F10-derived EVs were added to B16F1 cells (left) and vice versa (right), arrows point to internalized EVs, scale bar 20 μm. Cells, 16.5K EVs, and 100K EVs were isolated from B16F10 Cre+ and B16F1 Cre− tumors. RT-PCR for Cre and ribosomal protein L38 (RPL38) mRNA was performed as indicated. Cartoon of the Cre-LoxP system to study functional Cre+ EV transfer. Reporter+ cells change fluorophore expression upon uptake of Cre+ EVs from the Cre+ donor cells followed by excision of the DsRed-stop. Cartoon and representative images of reporter+ only tumors and local in vivo Cre+ and reporter+ B16F1 and B16F10 tumor mixes, scale bar 50 μm. Cartoon and representative images of a 3-week co-culture of Cre+ and reporter+ B16F1 and B16F10 cell lines, scale bar 100 μm. Quantification of in vitro and in vivo Cre+ EV transfer, grand mean of three replicates of three wells (in vitro) or three replicate mice, 15 sections each (in vivo). T-test for in vitro co-culture to reporter only and Mann–Whitney for in vitro–in vivo Cre+ EV transfer, n = 3 independent experiments. Source data are available online for this figure. Source Data for Figure 1 [embj201798357-sup-0011-SDataFig1.eps] Download figure Download PowerPoint Cancer cells functionally exchange EVs in vivo Next, we studied whether the B16F1 and B16F10 cells release and exchange EVs. First, EVs were purified from in vitro cultures using ultracentrifugation and stained with the lipophilic dye PKH67. To test whether B16F1 cells can take up EVs released from B16F10 cells and vice versa, we added labeled EVs to recipient cells of the other cell type. We observed that the pool of EVs enriched at a lower centrifugation speed (16,500 g) and the pool of EVs enriched at a higher speed (100,000 g; Thery et al, 2006; Greening et al, 2015; Szatanek et al, 2015) are both taken up by recipient cells in vitro (Fig 1E). To test whether the mutual uptake of EVs also led to the functional release of the content in the recipient cells, we employed the Cre-LoxP system (Ridder et al, 2015; Zomer et al, 2015, 2016). In the EVs released by Cre-expressing B16 cells, the mRNA of Cre was present (Fig 1F). Reporter cells that take up these EVs, and get exposed to the luminal cargo, switch expression of DsRed to eGFP (Fig 1G). Indeed, we observed reporter+ cells that report Cre activity only in tumors that also contained Cre+ cells (Fig 1H and J). Importantly, GFP+ cells did not express CFP, excluding that the reporter+ cells fused with Cre+ cells (Fig EV1A–D). These data suggest that the color switch reports EV-mediated functional transfer of Cre activity. Nevertheless, formally we cannot exclude a small fraction of the Cre exchange may have occurred via EV-independent mechanisms, such as Cre transfer through cell–cell contacts. However, the latter one we excluded previously in other tumor models in which cells exchanged Cre activity when located in physically separated tumors (Zomer et al, 2015). Click here to expand this figure. Figure EV1. GFP+ reporter cells are negative for expression of CFP A, B. Representative images of eGFP+ reporter cells from B16F10 Cre+ B16F1 reporter+ tumor mixes and B16F1 Cre+ B16F10 reporter+ tumor mixes. Displayed are the overlay and single channels for DsRed, GFP, and CFP, and red outlines indicate borders of GFP-expressing cells. Numbered zooms for four regions of interest are displayed for the GFP and CFP channels below the overview image. Scale bars are 50 μm. C, D. A total of 638 eGFP cells were assessed for expression of CFP; B16F1 reporter+ GFP+ cells; n = 331 cells in four mice total, B16F10 reporter+ GFP+ cells, n = 307 cells in four mice total. No co-expression of GFP and CFP was observed. Download figure Download PowerPoint Interestingly, whilst EVs are taken up in vitro (Fig 1E), in a 3-week co-culture of B16F1-Cre+ cells and B16F10-reporter+ cells, and vice versa, we did not observe a substantial number of cells that report Cre activity (< 0.01%; Fig 1I and J). These data suggest that the Cre-Lox system reports the release of cargo into the cytoplasm rather than only the uptake of EVs and that the in vitro EV uptake (i.e., uptake of labeled EVs in Fig 1E) did not coincide with substantial functional release of the content (i.e., lack of Cre-mediated color switch in Fig 1I and J). Moreover, the large discrepancy between the efficiency of Cre+ EV transfer in vitro and in vivo suggests divergent mechanisms of EV exchange and underlines the importance of studying EV exchange between cells in their in vivo setting. B16F1 cancer cells have a higher migration speed after uptake of B16F10-derived EVs Since B16F10 cancer cells have a higher metastatic and migratory capacity than B16F1 cancer cells, we tested whether the migration of B16F1 recipient cells is affected upon the transfer and release of cargo of EVs produced by B16F10 cells. To test this, we considered to study whether inhibition of the release of EVs by B16F10 cells would affect the migratory behavior of B16F1 and B16F10 recipient cells. Unfortunately, good tools to only inhibit EV release without affecting the donor cells do currently not exist. However, as mentioned above, the Cre-LoxP system allows to address exactly this question using an alternative approach: The DsRed+ cells did not release the luminal EV cargo and will behave similarly to cells that did not receive luminal EV cargo upon inhibition of EV release in the donor cells and can therefore act as a control for cells that do take up EVs (i.e., GFP-expressing cells). To test the effect of EV transfer between the different models, we visualized the migratory behavior of recipient cells by IVM (Fig 2A and B). Tumors consisting of B16F10 Cre+ cancer cells and B16F1 reporter+ cancer cells, or B16F1 Cre+ cancer cells and B16F1 or B16F10 reporter+ cancer cells were intravitally imaged for 4–6 h. The positions of Cre+ CFP+ cells, and the cells that did not (DsRed+ reporter+) or did receive EV cargo (eGFP+ reporter+) were annotated in every image to determine the migration speed (Fig 2A and B). B16F1 cells that have taken up B16F10 EV cargo (eGFP+ reporter+ cells) have a higher migration speed than B16F1 cells that have not taken up this EV cargo (DsRed+ reporter+ cells; Fig 2C). By contrast, when the B16F1 or B16F10 cells take up EVs produced by B16F1 cells, the migration speed is not enhanced (Fig 2D and E). Figure 2. B16F1 cells have a higher migration speed after uptake of B16F10-derived EVs The effects of in vivo EV transfer are studied by local co-injection of Cre+ and reporter+ cells and intravital microscopy of established mixed tumors to study tumor cell migration. Representative rose plots of tumor cell migration tracks of B16F10 Cre+, B16F1 DsRed+ reporter+, and eGFP+ B16F1 reporter+ tumor cells within the same imaging field, tracks shown for 2h30 migration. Average migration speed of B16F10 Cre+ and DsRed+ and eGFP+ B16F1 reporter+ cells, n = 15 positions in four mice. Average migration speed of B16F1 Cre+ and DsRed+ and eGFP+ B16F10 reporter+ cells, n = 16 positions in four mice. Average migration speed of B16F1 Cre+ and DsRed+ and eGFP+ B16F1 reporter+ cells, n = 22 positions in six mice. Data information: (C–E) Average speed of cells within the same imaging field is connected with a line (left) and migration speed of cells relative to DsRed+ reporter+ migration speed is plotted (right). Data represented as mean ± SEM with the Wilcoxon signed rank test. Download figure Download PowerPoint Isolation of EVs released by cancer cells located in their in vivo setting To identify EV cargo that may explain the phenocopy of the migratory behavior, we isolated EVs from tumors consisting of either B16F1 or B16F10 cells (Fig 3A). Since B16 tumors consist predominately of cancer cells (on average > 70%, Appendix Fig S1), it is expected that the vast majority of EVs in tumors are produced by cancer cells, although we cannot exclude the co-isolation of some EVs derived from non-cancer cells. To isolate EVs, we used a procedure that previously was successfully used for brain tissues (Levy, 2017; Vella et al, 2017) based on gentle enzymatic dissociation to release cells and the population of EVs from tumors. Next, we isolated cells and EVs by differential ultracentrifugation (D-UC). We aim to study the total landscape of EVs, instead of focusing just on exosomes, since other EVs such as microvesicles, oncosomes, and apoptotic bodies may also have an important function. Based on the maximum centrifugation speed required to pellet vesicles, we identified two vesicle populations: 16.5K EVs that were pelleted at 16,500 g and 100K EVs that were pelleted at 100,000 g (Fig 3A). Electron microscopy (EM) shows that the 16.5K fraction is enriched for larger EVs (≥ 150 nm, black arrows), and the 100K fraction is enriched for smaller EVs (≤ 150 nm, red arrows; Fig 3B). Both fractions also contain characteristic melanosome structures, known to be released by B16 cells into the extracellular environment (Willms et al, 2016). Although melanosomes are generally not considered to be EVs, cancer-associated fibroblasts can get reprogrammed upon uptake of melanosomes released by transformed melanocytes (Dror et al, 2016; Garcia-Silva & Peinado, 2016). Figure 3. Successful isolation of EVs from the in vivo tumor setting Cartoon demonstrating the isolation of cells and EVs directly from tumors grown in syngeneic mice. Tumors are enzymatically dissociated, and cells and EVs are isolated from the single cell and EV mixture by D-UC. Representative electron microscopy images of 16.5K EVs and 100K EVs. Black arrows point to EVs ≥ 150 nm, red arrows point to EVs < 150 nm, and green asterisks mark melanosome-like structures. Scale bar is 500 nm. Cartoon of the sample comparisons used for EV enrichment over the donor cell. Normalized MS/MS count of the classical EV markers tetraspanins, flotillins, ESCRT machinery, and HSP proteins, and data represented as mean ± SD of three independent EV preparations. GO term enrichment for cellular compartment of proteins (FC ≥ 10 and P ≤ 0.01) enriched in EVs to the donor cell for 16.5K EVs and 100K EVs of the B16F1 model (top) and B16F10 model (bottom). Western blot of cells and 500 g, 2,000 g, 16.5K g, and 100K g fractions of osmotically lysed B16F10 cells for calnexin, cytochrome-C, and GM130. Representative Western blot of three experiments. Western blot (left) and fold change of normalized MS/MS count (right) of cells and 16.5K EVs and 100K EVs for calnexin, cytochrome-C, and GM130. MS/MS fold change represented as mean ± SD of three independent EV preparations. Data information: Full scans of Western blots from panels (F) and (G) are available as source data. Source data are available online for this figure. Source Data for Figure 3 [embj201798357-sup-0012-SDataFig3.eps] Download figure Download PowerPoint To profile the population of tumor microenvironmental EVs, we performed total RNA sequencing and proteome profiling using label-free mass spectrometry. In total, we detected 12,450 and 12,802 transcripts for B16F1 16.5K and 100K EVs, respectively, and 11,696 and 11,527 transcripts for B16F10 16.5K and 100K EVs, respectively, that were present in all three replicates (Fig EV2A, top and Fig EV3). At the proteome level, we detected 3,210 and 3,333 proteins for B16F1 16.5K and 100K EVs, respectively, and 3,213 and 3,276 proteins for B16F10 16.5K and 100K EVs, respectively, that were present in all three replicates (Fig EV2A bottom and B, and Fig EV4). To test whether the EVs contained truncated proteins, we analyzed the low molecular weight gel bands of the cell and EV samples that contain small proteins and potentially truncated proteins (gel bands 4 and 5 in Fig EV2B and Appendix Fig S2). This analysis showed that only 3.2% of the 16.5K EV protein car
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