Global extracellular vesicle proteomic signature defines U87-MG glioma cell hypoxic status with potential implications for non-invasive diagnostics

Purpose Glioblastoma multiforme (GBM) is the most common and lethal of primary malignant brain tumors. Hypoxia constitutes a major determining factor for the poor prognosis of high-grade glioma patients, and is known to contribute to the development of treatment resistance. Therefore, new strategies to comprehensively profile and monitor the hypoxic status of gliomas are of high clinical relevance. Here, we have explored how the proteome of secreted extracellular vesicles (EVs) at the global level may reflect hypoxic glioma cells. Methods We have employed shotgun proteomics and label free quantification to profile EVs isolated from human high-grade glioma U87-MG cells cultured at normoxia or hypoxia. Parallel reaction monitoring was used to quantify the identified, hypoxia-associated EV proteins. To determine the potential biological significance of hypoxia-associated proteins, the cumulative Z score of identified EV proteins was compared with GBM subtypes from HGCC and TCGA databases. Results In total, 2928 proteins were identified in EVs, out of which 1654 proteins overlapped with the ExoCarta EV-specific database. We found 1034 proteins in EVs that were unique to the hypoxic status of U87-MG cells. We subsequently identified an EV protein signature, “HYPSIGNATURE”, encompassing nine proteins that strongly represented the hypoxic situation and exhibited close proximity to the mesenchymal GBM subtype. Conclusions We propose, for the first time, an EV protein signature that could comprehensively reflect the hypoxic status of high-grade glioma cells. The presented data provide proof-of-concept for targeted proteomic profiling of glioma derived EVs, which should motivate future studies exploring its utility in non-invasive diagnosis and monitoring of brain tumor patients. Electronic supplementary material The online version of this article (10.1007/s11060-019-03262-4) contains supplementary material, which is available to authorized users.


Introduction
Glioblastoma multiforme (GBM) is the most common and malignant type of primary brain tumor in adults with a median survival of approximately 15 months [1][2][3]. GBM is identified from less malignant, low grade gliomas, by extensive regions of hypoxia [4] that directly correlate with the aggressive behaviour [5]. Hypoxia results from the high proliferative and metabolic activity of malignant cells [6] and is associated with pseudopalisading necrosis as well as vascular hyperproliferation [7]. Tumor hypoxia modulates stromal cell interactions in the microenvironment that further support the survival and dissemination of malignant cells [4,[8][9][10][11]. Numerous studies have previously shown that tumor progression is driven by hypoxic signaling [12], and the expression of hypoxia-related markers correlate 1 3 with poor patient outcome in several tumor types, including GBM [13]. However, the development of strategies for non-invasive monitoring of brain tumor hypoxic signalling remains a challenge of high clinical relevance, especially with regard to the relative inaccessibility and spatiotemporal heterogeneity of GBM tumors.
Extracellular vesicles (EVs) are excessively secreted by tumor cells into the circulation, and are emerging as a promising candidate for liquid biopsy-based approaches in cancer [14][15][16]. Exosomes and microvesicles are lipid-bilayer EVs [17] that have come to be recognized in intercellular communication, promoting the development and progression of various disease conditions [18]. Numerous studies have shown that exosome-like EVs may mediate hypoxiadependent intercellular signaling in GBM [19]. Moreover, pilot studies based on an antibody array targeted at angiogenesis-related proteins, suggested that the EV proteome may reflect the tumor oxygenation status in GBM [20]. To further develop EV-based strategies for non-invasive tumor diagnosis and monitoring of hypoxia, it is essential to comprehensively identify proteins that are efficiently sorted to EVs and that reflect the hypoxic status of the cell or tissue of origin.
In this study, we employed label free quantification (nontargeted method) and parallel reaction monitoring (targeted method) to globally characterize the proteome of EVs derived from U87-MG high-grade glioma cells with the aim to understand how EV profiling can be exploited to noninvasively define the hypoxic status of glioma tumors.

Global proteome identification in EVs derived from high-grade glioma cells
EVs from U87-MG, i.e. the most well-characterized human glioma cell-line [21,22], grown under normoxic (EV NORM ) or hypoxic (EV HYP ) conditions were isolated by standard sequential ultracentrifugation [20]. The size distribution and morphology of EVs was analyzed by transmission electron microscopy (TEM), where EV NORM and EV HYP predominantly were found in the size range of 50-150 nm in diameter with no apparent difference in their morphology (Fig. 1a, b). Nanoparticle tracking analysis (NTA) showed similar size distribution, where both EV NORM and EV HYP were found in the size range of 80-150 nm (Fig. 1c, d), which is consistent with the typical size distribution profile of exosomes [23]. We found significantly increased secretion of EVs by U87-MG cells when cultured under hypoxia as compared to normoxia (Fig. 1d), which is in accordance with previous findings [24,25]. Currently, in addition to the mechanism of biogenesis and size [26], EVs are generally referred to as exosomes also based on the expression of CD9, CD63, and CD81 proteins [27], which were all found to be present in U87-MG derived EVs, together with a strong enrichment of the membrane raft marker Flotillin 1 (Fig. 1e).
We then employed shotgun proteomics by data-dependent acquisition to comprehensively determine the proteome of EV NORM and EV HYP derived from U87-MG cells. We identified a total of 2089 EV HYP and 2035 EV NORM proteins ( Fig. 1f; Supplementary Tables 1, 2). There were 1034 protein groups unique to EV HYP ( Fig. 1f; Supplementary Table 3) and 1055 protein groups common to both EV NORM and EV HYP ( Fig. 1f; Supplementary Table 4). We next created a multiconsensus list combining EV NORM and EV HYP protein identities (Supplementary Table 5) and then compared the multiconsensus protein group to the ExoCarta EV public database [28]. The multiconsensus EV identities (2928 proteins) showed extensive overlapping of 1654 common identities with the ExoCarta database and also identified 1274 unique identities (Fig. 1g), which support the sensitivity of detection of the EV proteome with the current approach.

Processing of the EV proteome by label free quantification (LFQ)
Discovery MS analysis resulted in the identification of thousands of proteins, and it is not feasible to analyze the abundance signature of each individual protein by targeted MS/ MS. Therefore, to filter the proteins identified in EV NORM and EV HYP based on their significance in hypoxia, we subjected the discovery MS-identified proteins to nontargeted LFQ in Proteome Discoverer (PD) version 2.2 (Fig. 2a). We could then obtain the abundance value of each protein in EV HYP and EV NORM in terms of the LC/MS precursor peak quantification of the unique peptides for a particular protein.
Subsequently, a ratio of the abundance values of each protein in EV HYP over EV NORM was calculated, which identified a total of 580 hypoxia significant (H significant ) proteins (Log2 fold change, cut-off > 0.01), and other proteins that were above Log2 fold change cut-off > 0.01, were taken as hypoxia downregulated (H nonsignificant ) proteins (Supplementary Table 6).

Validation of H significant profile by parallel reaction monitoring (PRM)
To validate the H significant proteins identified above by LFQ, we next performed PRM (Fig. 3a). A set of selection criteria specific for targeted PRM analysis as described in Rauniyar was applied [32], including peptide length, uniqueness, miscleavage, modification, precursor charge, chromatographic peak, and signal intensity to further filter identified protein groups and select appropriate quantotypic peptides for proteins of interest using Skyline version 3.1. In addition, we added a few protein groups based on their relevance in glioma. Consequently, we selected a total of 135 protein groups with 5 unique quantotypic peptides per protein group for quantification by targeted PRM. Firstly, we performed an unscheduled PRM run on EV NORM and EV HYP samples to analyze the ionization of selected peptides and optimize their retention time and transition charge state. The chromatogram output was analyzed in Skyline and the 2 to 3 most quantotypic flyable peptides and appropriate transition states per protein were selected for the scheduled PRM run for all 135 protein groups (Supplementary Table 7 Table 8). On analysing the fold change, we found 17 proteins significantly differentially expressed in EV HYP as compared to EV NORM ( Fig. 3b; Supplementary Table 8). We further applied peptide significance and normalized peak area restrictions on the hypoxia response of the H significant EV proteins (N = 17) and filtered it down to a signature of 9 proteins that included Insulin-like Growth Factor-Binding Protein 3 (IGFBP3), Tissue Factor (F3), Carbonic Anhydrase 9 (CA9), Solute Carrier Family 2 Facilitated Glucose Transporter Member 1 (SLC2A1), Nucleolin (NCL), Osteopontin (SPP1), Monocarboxylate Transporter 1 (SLC16A1), Membrane-Associated Progesterone Receptor Component 1 (PGRMC1), and Annexin A5 (ANXA5) (Fig. 3c). These proteins defined a profile of unique proteins (N = 9) efficiently sorted from donor cells to EVs and enriched at hypoxic conditions, hereafter referred to as "HYP SIGNATURE " (the PAN of the replicates of the different peptides is given in Supplementary Fig. 1). We assayed the pathways enriched by the HYP SIGNATURE proteins using ConsensusPathDB-human interaction database [33]. This identified HYP SIGNATURE to be closely associated with the Hypoxia-Inducible Factor-1α (HIF-1α) transcription factor network (adjusted P value = 0.00012) and HIF-1 signalling pathway (adjusted P value = 0.0057) with high significance (Fig. 4a). Tissue factor (F3) was previously shown by our group to be enriched in hypoxia-derived EVs [20]. The hypoxic enrichment of other top candidates of the HYP SIGNATURE (Fig. 3c), was supported by immunoblotting, which showed increased levels of IGFBP3 (Fig. 4b) and CA9 (Fig. 4c). Immunoblotting analysis was unable to detect other candidate proteins (NCL, SLC16A1, SPP1, ANXA5) in EVs, either from normoxia or hypoxia ( Supplementary  Fig. 2b). A potential limitation of these results is the lack of EV housekeeping proteins, and equal protein loading rely on BCA total protein concentration. However, gene array analysis showed increased expression of IGFBP3 (P = 0.0012), F3  Fig. 2a).
Several studies have established the association of GBM mesenchymal subtype with hypoxia and an aggressive tumor phenotype [34][35][36]. To address how the HYP SIGNATURE may associate with the mesenchymal phenotype, we compared the cumulative Z score of HYP SIGNATURE with different subtypes of primary GBM cells obtained from Human Glioblastoma Cell Culture (HGCC) i.e. classical, proneural, neural and mesenchymal (Fig. 5a). The cumulative HYP SIGNATURE Z score (1.78) was in close proximity to the HGCC mesenchymal subtype (0.24), evident by their average positive Z score as compared with the classical (− 0.18), proneural (− 0.28), and neural (− 0.41) subtypes (Fig. 5b). Next, we compared the HYP SIGNATURE cumulative Z score with GBM subtypes obtained from Cancer Genome Atlas Program (TCGA) using the Gliovis portal, which again showed the proximity of HYP SIGNATURE Z score with the mesenchymal (1.26) as compared with classical (0.94), proneural (0.89), and neural (0.83) GBM subtypes (Fig. 5c).

Discussion
In this study, we used an optimized combination of nontargeted and targeted quantitative proteomics to comprehensively profile hypoxia-regulated proteins associated with high-grade glioma cell derived EVs. We have identified a protein signature, "HYP SIGNATURE ", in EVs secreted by U87-MG cells that is associated with the HIF hypoxic signaling response and exhibited close proximity to the mesenchymal GBM subtype. Importantly, out of the nine proteins encompassing the HYP SIGNATURE , seven proteins are known as plasma membrane integrated proteins with an extracellular domain available for specific recognition by antibodies and other targeting agents. Together, our findings thus propose that the hypoxic status of GBM tumors can be defined by the EV HYP SIGNATURE, which may be utilized not only to noninvasively immunephenotype glioma tumors but also as potential therapeutic targets.
The utility of EVs across diverse cellular functions, including recent investigations that support the application of EVs as non-invasive biomarker tools [14,16,37,38], strongly motivates improved efforts to comprehensively profile the proteome of EVs derived from cells grown at disease mimicking conditions. Using discovery proteomics, a previous study [39] identified a total of 844 proteins in EVs isolated from GBM cells. In comparison, we identified approximately 3000 proteins in EVs, out of which 1034 proteins were unique to hypoxic EVs. Importantly, the major aim of the present study was to specifically identify an EV signature that mimics the hypoxic situation, i.e. a pathognomonic feature of GBM tumors associated with disease aggressiveness and treatment resistance. Although the studies are limited to one glioma cell-line, it may be argued that the obtained results have general relevance given the substantial overlap between EV protein identities found here and the ExoCarta EV proteome database. Moreover, the hypoxic response is a universal phenomenon of high-grade gliomas as well as other highly malignant tumors. Clearly, future studies will have to further assess the generalizability of the present data, including validation in primary GBM cell models as well as in vivo.
LFQ has now become a widely accepted analytical approach for comparison of the relative abundance of proteins across multiple samples [40][41][42]. The possibility to analyse untreated proteins or peptides in a large number of samples makes LFQ a preferred protocol over other relative quantification approaches. However, previous studies have shown that sample preparation for the LFQ approach is highly susceptible to variability [43]. Therefore, to reduce this variability, we used 9 replicates of normoxia and 12 replicates of hypoxia samples for LFQ.
In addition, the conforming pattern of differential levels of most proteins analyzed in LFQ (Supplementary Table 6) and PRM (Supplementary Table 8), suggest a high degree of sample preparation consistency. In support of EV proteomics data, immunoblotting showed an enrichment of top candidates of the HYP SIGNATURE , and gene array analysis showed increased expression of IGFBP3, F3, CA9, SLC2A1 and PGRMC1 mRNA in hypoxic as compared with normoxic U87-MG cells. We were unable to detect other candidate proteins (NCL, SLC16A1, SPP1, ANXA5) in EVs by immunoblotting analysis, either from normoxia or hypoxia, and did not detect a hypoxic enrichment of these proteins in U87-MG cells. A potential explanation to the discrepancy between an induction of these proteins in EVs collected over a cumulative time period of 48 h of hypoxia, and cells analyzed at a fixed time-point, is the well-known temporal dynamics of the hypoxic response.
Several previous studies have associated tumor cell expression of HYP SIGNATURE proteins with increased GBM aggressiveness. For example, F3 expression was demonstrated to be hypoxia-dependent in highly aggressive P7 GBM cells, leading to increased F3 activity [44], and F3-positive EVs were shown to induce angiogenesis [20]. Hypoxia also induced increased SLC16A1 plasma membrane expression in glioma cells, both in in vitro and in vivo models [45]. Additionally, SLC16A1 plasma membrane expression was associated with HIF-1α and CA9 positivity in hypoxic regions. Further, SLC16A1 was found to be upregulated in GBM as compared with normal tissues [46]. NCL was also found to be overexpressed in patient-derived GBM tumors and cells as compared with normal brain [47]. ANXA5 has been found to promote invasion and chemoresistance to the alkylating drug temozolomide in GBM cells [48]. Since hypoxic cells and components in the hypoxic niche have been increasingly implicated in resistance to temozolomide [49], it is conceivable that ANXA5 is associated with the hypoxic component of drug resistance. SPP1 was shown to be induced by hypoxia both in vitro and in vivo [50] and is predominantly observed in the microvasculature of GBM [51]. Several studies have implicated SPP1 with crucial roles in invasion [52] and malignant gliomas [53]. In several glioma cell models, CA9 strongly co-localized with HIF-1α, indicating its induction in hypoxic regions of this tumor type. Clinically, CA9 is minimally expressed in normal brain tissue, whereas its high expression in brain tumors strongly correlated with the level of malignancy [54]. SLC2A1 is another well-established hypoxia-induced protein that has been associated with hypoxic regions of GBM [55]. These studies support a functional role of HYP SIGNATURE protein expression in tumor cells, and future studies that define the tumor promoting role of these proteins when associated with EVs, especially in the context of e.g. pH regulation (CA9), metabolite transport (SLC2A1, SLC16A1), and coagulation activation (F3), will be of high interest.
To conclude, our data strongly support that a specific subset of mostly membrane intercalated EV proteins could define the hypoxic status of high-grade glioma cells. The proteins identified as part of the HYP SIGNATURE warrant further clinical examination using a targeted approach to validate their capacity to differentiate the highly heterogeneous nature of high-grade glioma tumors from e.g. low grade gliomas and other brain lesions that are challenging to define by imaging alone. This proof-of-principle study to noninvasively define the glioma hypoxic status utilizing advanced proteomics is a significant step in this direction.

EV isolation
Normoxic or hypoxic EVs were isolated in parallel from U87-MG cells at a particular passage by standard procedures, using differential ultracentrifugation [20]. Routinely cultured U87-MG cells at sub-confluency were grown in DMEM supplemented with 1% BSA at normoxic or hypoxic conditions for 48 h. Conditioned media were collected after 48 h and centrifuged at 300×g twice to eliminate cell debris. Supernatant fractions were then centrifuged at 100,000×g for 2 h to pellet EVs, followed by washing twice with PBS at 100,000×g for 2 h. EVs were then resuspended in 6 M Urea for downstream proteomics experiments.
Nanoparticle Tracking Analysis, Transmission Electron Microscopy, Trypsin digestion and peptide preparation, Discovery LC-MS/MS, label free quantification, and quantitative LC-PRM-MS/MS were performed as described in Supplementary Materials and Methods.

Data analysis
The Gene Ontology functional classification of H significant proteins was performed using PANTHER (https ://www. panth erdb.org/). Enriched pathways of EV HYP signature proteins were determined using ConsensusPathDB-human interaction database (https ://cpdb.molge n.mpg.de/). Wilcoxon test was employed for pathway enrichment analysis with a P value cut-off of 0.01.
For HYP SIGNATURE comparison in U87-MG cell-derived EVs, the Z scores of 9 HYP SIGNATURE candidates were individually calculated for their protein levels with the respective normoxic values as reference as shown by the formula below: where "EV HYP " is the mean protein level measured in hypoxic EVs; "EV NORM " is the mean protein level measured in normoxic EVs; and "SD EV NORM " is the standard deviation value of the protein level measurements in normoxic EVs. Generation of a cumulative score was done by arithmetic mean of Z scores of all 9 HYP SIGNATURE proteins.
For Z score calculation on the TCGA dataset, subtype classification of GBM patients was performed with GlioVis portal, and gene expression values for all 9 HYP SIGNATURE candidates were downloaded. Low Grade Glioma (LGG) Z-score = EV HYP −EV NORM ∕ SD EV NORM expression data of the 9 HYP SIGNATURE protein genes was downloaded and used as reference value for Z score calculations, as indicated in the formula below: where "GBM subtype" is the mean gene expression value in subtypes such as Classical, Mesenchymal, or Proneural GBM; "TCGA-LGG" is the mean gene expression value for the corresponding gene in LGG patients; and "SD TCGA-LGG" is the standard deviation value of the analyzed gene among the LGG patients. Generation of cumulative score for each GBM subtype was done by arithmetic mean of Z scores of all 9 HYP SIGNATURE candidates.
For HGCC data analysis, the gene expression Z score for each HYP SIGNATURE candidate in subtypes (Classical, Mesenchymal, Proneural, or Neural) was directly extracted from the HGCC database. Cumulative Z score was generated as described for TCGA dataset.

Statistical analyses
Data are expressed as mean ± STDEV. Statistical analyses were done using unpaired Student t test. All values with P < 0.05 were considered to be statistically significant.