Multiparametric analysis in GBM plasma extracellular vesicles (Evs) and surface marker expression profile.

INTRODUCTION: Plasma extracellular vesicles (EVs) have been shown as a promising source for biomarker identification in glioblastoma (GBM) and could help differential diagnosis, treatment evaluation and tumor progression monitoring. These EVs are enriched in molecular signatures indicative of their cell origins, giving an indication of the key players in this pathology. In this project, we aimed to identify diagnostic biomarkers for GBM plasma EVs and their cells of origin. METHODS: Plasma EV samples were prepared following the MIFlowCyt-EV guideline of the International Society for Extracellular Vesicles, then stained for EV markers (CD9/CD63/CD81) and markers indicative of cell origins (CD31/CD45/CD41a/CD11b). Actin phalloidin was used as a negative marker. Stained plasma samples were analyzed using a Cytek Aurora flow cytometer. Percentages of different EV subpopulations were analyzed and compared between GBM and normal donor (ND) plasma EVs (reference group). Further clustering analysis was performed on EV events by t-distributed stochastic neighbor embedding (t-SNE) and self-organizing maps on flow cytometry data (FlowSOM) analysis. The predictive value of multiparametric qualities derived from the reference group was tested in blinded test group samples. RESULTS: Percentages of CD9, CD81, and CD11b positive EVs were higher in GBM patient plasma, while ND plasma had more CD41a positive EVs. GBM plasma EVs had unique multiparametric signatures compared to ND plasma EVs based on t-SNE and FlowSOM analysis. Our analysis also identified 15 distinct EV subpopulations which differed in size and various surface marker expression levels. Eight of these subpopulations were enriched for GBM EVs, while three were enriched for ND EVs. Our method of multiparametric analysis demonstrates high sensitivity and specificity in predicting disease status in human samples. CONCLUSIONS: GBM plasma EVs have a unique surface marker expression profile and distinct EV subpopulations compared to ND plasma EVs. Multiparametric signatures show promise as potential diagnostic markers of GBM.

Glioblastoma (GBM) is the most common malignant brain tumor and has a poor prognosis. Imaging findings at diagnosis and in response to treatment are nonspecific. Developing noninvasive assays to augment imaging would be helpful. Plasma extracellular vesicles (EVs) are a promising biomarker source for this. Here, we develop spectral flow cytometry techniques that demonstrate differences in bulk plasma EV phenotype between GBM patients and normal donors that could serve as the basis of a liquid biopsy. Plasma EVs were stained for EV-associated tetraspanins (CD9/CD63/CD81), markers indicating cell of origin (CD11b/CD31/CD41a/CD45), and actin/phalloidin (to exclude cell debris). EVs were analyzed using spectral flow cytometry. Multiparametric analysis using t-distributed stochastic neighbor embedding (t-SNE) and self-organizing maps on flow cytometry data (FlowSOM) was performed comparing GBM and normal donor (ND) plasma EVs. Size exclusion chromatography plus spectral-based flow cytometer threshold settings enriched plasma EVs while minimizing background noise. GBM patients had increased CD9+, CD63+, CD81+, and myeloid-derived (CD11b+) EVs. Multiparametric analysis demonstrated distinct surface marker expression profiles in GBM plasma EVs compared to ND EVs. Fifteen plasma EV sub-populations differing in size and surface marker expression were identified, six enriched in GBM patients and two in normal donors. Multiparametric analysis demonstrates that GBM patients have a distinct nonneoplastic plasma EV phenotype compared to ND. This simple rapid analysis can be performed without purifying tumor EVs and may serve as the basis of a liquid biopsy.

Author response: Improved isolation of extracellular vesicles by removal of both free proteins and lipoproteins

To improve the treatment outcome and survival of patients with advanced high-grade serous carcinoma (HGSC), prognostic biomarkers for assessing the feasibility of complete (R0) or optimal (R1) primary cytoreductive surgery are needed. Additionally, biomarkers for predicting the response to neoadjuvant chemotherapy (NACT) in patients with primary inoperable disease could help stratify patients for tailored therapy and improve personalised approach. Such promising biomarkers are extracellular vesicles (EVs), which are present in ascites and plasma and are available for minimally invasive liquid biopsy. EV concentration and EV molecular profile have been at the forefront of research in the field of biomarkers for many years now, but recent studies have highlighted the importance of EV size distribution. Our study aimed to evaluate the potential of the EV concentration and size distribution in pretreatment ascites and plasma samples from patients with advanced HGSC as prognostic biomarkers. In our prospective cohort study, nanoparticle tracking analysis (NTA) was used to determine EV characteristics in paired pretreatment ascites and plasma samples from 37 patients with advanced HGSC. Patients were treated with primary cytoreductive surgery followed by adjuvant chemotherapy (ACT) (N = 15) or NACT followed by interval debulking surgery (IDS) when optimal cytoreduction was not feasible (N = 22). The correlations of the EV concentration and size distribution in ascites and plasma with treatment outcome, progression-free survival (PFS) and overall survival (OS) were analysed. We found a significant correlation between the EV size distribution in ascites and residual disease after primary cytoreductive surgery. Larger EVs in ascites correlated with worse resection success after primary cytoreductive surgery. A significant correlation between the D10 value of EVs in plasma and the chemotherapy response score (CRS) after NACT was observed. A smaller D10 value of plasma EVs was correlated with a better chemotherapy response. Receiver operating characteristic (ROC) curve analysis revealed excellent performance for D10 value in ascites for the prediction of suboptimal (R2) resection at primary debulking surgery and excellent performance for D10 value in plasma for the prediction of complete or near-complete chemotherapy response score (CRS 3) at interval debulking surgery. There was a significant correlation between the mean diameter, D90 value and proportion of medium/large (> 200 nm) EVs in ascites and those in plasma. On the other hand, there was no correlation of the EV concentration or D10 and D50 values between the ascites fluid and plasma samples. Our results indicate that the EV size distribution in ascites has the potential to predict resection success after primary cytoreductive surgery and that the EV size distribution of the smallest EVs in plasma might help predict the chemotherapy response of patients treated with NACT. In the future, molecular analyses of size-dependent EV cargo could provide more insight into their biological functions and potential as predictive biomarkers.

A multi-platform assessment of extracellular vesicles from the plasma and urine of women with preeclampsia.

BACKGROUND Glioblastoma (GBM), the deadliest brain tumor in adults, is traditionally diagnosed and characterized using neuroimaging and brain biopsies. These methods, however, suffer from limited specificity and sensitivity. The surgical risks associated with tumor sampling further complicate the monitoring of GBM’s molecular progression. Consequently, the identification of circulating GBM biomarkers is crucial for non-invasive tracking of the disease from diagnosis to relapse. Extracellular vesicles (EVs) secreted by GBM cells, which cross the blood-brain barrier and carry tumor-derived molecules, present a promising avenue for the continuous evaluation of GBM presence and status. MATERIAL AND METHODS We isolated extracellular vesicles (EVs) using Size Exclusion Chromatography (SEC) from 2 mL of plasma and characterized them via immunoblotting, flow cytometry, and transmission electron microscopy. The concentration and size of EVs were measured using Tunable Resistive Pulse Sensing (TRPS). We compared EV levels in 50 glioblastoma (GBM) patients pre-surgery with those in non-GBM controls, which included 100 healthy individuals and 50 patients with brain malignancies that present similarly to GBM in neuroimaging studies. Longitudinal assessments of plasma EVs were performed in 44 GBM patients by comparing pre-operative and post-operative samples taken 72 hours after surgery. Additionally, we analyzed surface markers on GBM and non-GBM plasma EVs using MACSPlex and conducted proteomic analyses via mass spectrometry in 27 GBM patients and 38 healthy individuals. RESULTS We found higher concentrations and mean sizes of plasma EVs in GBM patients compared to controls, with a noticeable decrease post-operatively. Multiplex flow cytometry indicated universal expression of CD9, CD63, and CD81 across all samples, with significant enrichment of CD63 and CD81 in GBM plasma EVs. The expression profiles of the other 34 markers were similar across all samples, suggesting dilution of tumor-derived EVs among non-tumor EVs. However, T-cell markers CD8 and HLA-DRDPDQ were more prevalent on GBM-derived EVs. Proteomic analysis identified over 2,000 proteins in all samples, with 117 being upregulated in GBM samples, predominantly within the complement cascade pathways. CONCLUSION Our findings suggest that monitoring circulating EV levels provides a reliable, non-invasive method for the differential diagnosis and follow-up of GBM patients. The combined analysis of EV concentration, size, and proteome offers potential as promising biomarkers for longitudinal monitoring of GBM, supporting the implementation of liquid biopsy in GBM care.

Introduction: Extracellular vesicles (EVs) have emerged as a very attractive source of cancer- derived RNA biomarkers for the early detection, prognosis and monitoring of various cancers, including prostate cancer (PC). However, biofluids contain a mixture of EVs released from a variety of tissues and the fraction of total EVs that are derived from PC tissue is not known. Moreover, the optimal biofluid—plasma or urine—that is more suitable for the detection of EV- enclosed RNA biomarkers is not yet clear.Methodology: In the current study, we performed RNA sequencing analysis of plasma and urinary EVs collected before and after radical prostatectomy, and matched tumor and normal prostate tissues of 10 patients with prostate cancer.Results and Discussion: The most abundant RNA biotypes in EVs were miRNA, piRNA, tRNA, lncRNA, rRNA and mRNA. To identify putative cancer-derived RNA biomarkers, we searched for RNAs that were overexpressed in tumor as compared to normal tissues, present in the pre-operation EVs and decreased in the post-operation EVs in each RNA biotype. The levels of 63 mRNAs, 3 lncRNAs, 2 miRNAs and 1 piRNA were significantly increased in the tumors and decreased in the post-operation urinary EVs, thus suggesting that these RNAs mainly originate from PC tissue. No such RNA biomarkers were identified in plasma EVs. This suggests that the fraction of PC-derived EVs in urine is larger than in plasma and allows the detection and tracking of PC-derived RNAs.

BackgroundInfection and graft-versus-host disease (GvHD) are the major causes for mortality and morbidity of allogeneic hematopoietic stem cell transplantation (allo-HSCT). Plasma-derived extracellular vesicles (EVs) contain disease-related proteins, DNAs and RNAs, and have recently been suggested as potential biomarker candidates for transplantation complications. However, EV isolation from small plasma volumes in clinical biomarker studies using conventional methods is challenging. We therefore investigated if EVs isolated by novel automated acoustic trapping could be developed as potential biomarkers for allo-HSCT complications by performing a clinical proof-of-principle study.ResultsPlasma samples were collected from twenty consecutive patients with high-risk/relapsed hematologic malignancies undergoing allo-HSCT before transplantation and post-transplant up to 12 weeks. EVs were isolated from small plasma sample volumes (150 μl) by an automated, acoustofluidic-based particle trapping device, which utilizes a local λ/2 ultrasonic standing wave in a borosilicate glass capillary to capture plasma EVs among pre-seeded polystyrene microbeads through sound scatter interactions. We found that EVs could be reliably isolated from all plasma samples (n = 173) and that EV numbers increased more than 2-fold in the majority of patients after transplantation. Also, sufficient quantities of RNA for downstream microRNA (miRNA) analysis were obtained from all samples and EV miRNA profiles were found to differ from whole plasma profiles. As a proof of principle, expression of platelet-specific miR-142-3p in EVs was shown to correlate with platelet count kinetics after transplantation as expected. Importantly, we identified plasma EV miRNAs that were consistently positively correlated with infection and GvHD, respectively, as well as miRNAs that were consistently negatively correlated with these complications.ConclusionsThis study demonstrates that acoustic enrichment of EVs in a clinical biomarker study setting is feasible and that downstream analysis of acoustically-enriched EVs presents a promising tool for biomarker development in allo-HSCT. Certainly, these findings warrant further exploration in larger studies, which will have significant implications not only for biomarker studies in transplantation but also for the broad field of EV-based biomarker discovery.

Rationale: Glioblastoma multiforme (GBM) is the most aggressive primary malignant brain tumor in adults, characterized by high invasiveness and poor prognosis. Glioma stem cells (GSCs) drive GBM treatment resistance and recurrence, however, the molecular mechanisms activating intracranial GSCs remain unclear. Extracellular vesicles (EVs) are crucial signaling mediators in regulating cell metabolism and can cross the blood-brain barrier (BBB). This study aimed to elucidate how EV cargo contributes to the intracranial GSC state and validate a non-invasive diagnostic strategy for GBM relapse. Methods: We isolated plasma extracellular vesicles (pl-EVs) from three groups: recurrent GBM patients post-resection, non-recurrent GBM patients post-resection, and healthy individuals. Newly diagnosed GBM patients served as an additional control. EVs were characterized and co-cultured with primary GBM cell lines to assess their effect on tumor stemness. EV cargo was analyzed using proteomics to investigate specific EV subpopulations contributing to GBM relapse. Based on these findings, we generated engineered LDHA-enriched EVs (LDHA-EVs) and co-cultured them with patient-derived organoids (PDOs). Metabolomics was performed to elucidate the underlying signal transduction pathways. Results: Our study demonstrated that pl-EVs from recurrent GBM patients enhanced aerobic glycolysis and stemness in GBM cells. Proteomic analysis revealed that plasma EVs from recurrent GBMs encapsulated considerable amounts of the enzyme lactate dehydrogenase A (LDHA). Mechanistically, LDHA-loaded EVs promoted glycolysis, induced cAMP/ATP cycling, and accelerated lactate production, thereby maintained the GSC phenotype. Concurrently, post-surgical therapy-induced stress-modulated hypoxia in residual tumors, promoted LDHA-enriched EV release. Clinically, high levels of circulating LDHA-positive EVs correlated with increased glycolysis, poor therapeutic response, and shorter survival in recurrent GBM patients. Conclusion: Our study highlights LDHA-loaded EVs as key mediators promoting GSC properties and metabolic reprogramming in GBM. These findings provide insights into recurrence mechanisms and suggest potential liquid biopsy approaches for monitoring and preventing GBM relapse.

<div>AbstractPurpose:<p>Glioblastoma (GBM) is the most common primary brain tumor. The identification of blood biomarkers reflecting the tumor status represents a major unmet need for optimal clinical management of patients with GBM. Their high number in body fluids, their stability, and the presence of many tumor-associated proteins and RNAs make extracellular vesicles potentially optimal biomarkers. Here, we investigated the potential role of plasma extracellular vesicles from patients with GBM for diagnosis and follow-up after treatment and as a prognostic tool.</p>Experimental Design:<p>Plasma from healthy controls (<i>n</i> = 33), patients with GBM (<i>n</i> = 43), and patients with different central nervous system malignancies (<i>n</i> = 25) were collected. Extracellular vesicles were isolated by ultracentrifugation and characterized in terms of morphology by transmission electron microscopy, concentration, and size by nanoparticle tracking analysis, and protein composition by mass spectrometry. An orthotopic mouse model of human GBM confirmed human plasma extracellular vesicle quantifications. Associations between plasma extracellular vesicle concentration and clinicopathologic features of patients with GBM were analyzed. All statistical tests were two-sided.</p>Results:<p>GBM releases heterogeneous extracellular vesicles detectable in plasma. Plasma extracellular vesicle concentration was higher in GBM compared with healthy controls (<i>P</i> < 0.001), brain metastases (<i>P</i> < 0.001), and extra-axial brain tumors (<i>P</i> < 0.001). After surgery, a significant drop in plasma extracellular vesicle concentration was measured (<i>P</i> < 0.001). Plasma extracellular vesicle concentration was also increased in GBM-bearing mice (<i>P</i> < 0.001). Proteomic profiling revealed a GBM-distinctive signature.</p>Conclusions:<p>Higher extracellular vesicle plasma levels may assist in GBM clinical diagnosis: their reduction after GBM resection, their rise at recurrence, and their protein cargo might provide indications about tumor, therapy response, and monitoring.</p></div>

<div>AbstractPurpose:<p>Glioblastoma (GBM) is the most common primary brain tumor. The identification of blood biomarkers reflecting the tumor status represents a major unmet need for optimal clinical management of patients with GBM. Their high number in body fluids, their stability, and the presence of many tumor-associated proteins and RNAs make extracellular vesicles potentially optimal biomarkers. Here, we investigated the potential role of plasma extracellular vesicles from patients with GBM for diagnosis and follow-up after treatment and as a prognostic tool.</p>Experimental Design:<p>Plasma from healthy controls (<i>n</i> = 33), patients with GBM (<i>n</i> = 43), and patients with different central nervous system malignancies (<i>n</i> = 25) were collected. Extracellular vesicles were isolated by ultracentrifugation and characterized in terms of morphology by transmission electron microscopy, concentration, and size by nanoparticle tracking analysis, and protein composition by mass spectrometry. An orthotopic mouse model of human GBM confirmed human plasma extracellular vesicle quantifications. Associations between plasma extracellular vesicle concentration and clinicopathologic features of patients with GBM were analyzed. All statistical tests were two-sided.</p>Results:<p>GBM releases heterogeneous extracellular vesicles detectable in plasma. Plasma extracellular vesicle concentration was higher in GBM compared with healthy controls (<i>P</i> < 0.001), brain metastases (<i>P</i> < 0.001), and extra-axial brain tumors (<i>P</i> < 0.001). After surgery, a significant drop in plasma extracellular vesicle concentration was measured (<i>P</i> < 0.001). Plasma extracellular vesicle concentration was also increased in GBM-bearing mice (<i>P</i> < 0.001). Proteomic profiling revealed a GBM-distinctive signature.</p>Conclusions:<p>Higher extracellular vesicle plasma levels may assist in GBM clinical diagnosis: their reduction after GBM resection, their rise at recurrence, and their protein cargo might provide indications about tumor, therapy response, and monitoring.</p></div>

Glioblastoma (GBM) is the most common primary brain tumor. The identification of blood biomarkers reflecting the tumor status represents a major unmet need for optimal clinical management of patients with GBM. Their high number in body fluids, their stability, and the presence of many tumor-associated proteins and RNAs make extracellular vesicles potentially optimal biomarkers. Here, we investigated the potential role of plasma extracellular vesicles from patients with GBM for diagnosis and follow-up after treatment and as a prognostic tool. Plasma from healthy controls (n = 33), patients with GBM (n = 43), and patients with different central nervous system malignancies (n = 25) were collected. Extracellular vesicles were isolated by ultracentrifugation and characterized in terms of morphology by transmission electron microscopy, concentration, and size by nanoparticle tracking analysis, and protein composition by mass spectrometry. An orthotopic mouse model of human GBM confirmed human plasma extracellular vesicle quantifications. Associations between plasma extracellular vesicle concentration and clinicopathologic features of patients with GBM were analyzed. All statistical tests were two-sided. GBM releases heterogeneous extracellular vesicles detectable in plasma. Plasma extracellular vesicle concentration was higher in GBM compared with healthy controls (P < 0.001), brain metastases (P < 0.001), and extra-axial brain tumors (P < 0.001). After surgery, a significant drop in plasma extracellular vesicle concentration was measured (P < 0.001). Plasma extracellular vesicle concentration was also increased in GBM-bearing mice (P < 0.001). Proteomic profiling revealed a GBM-distinctive signature. Higher extracellular vesicle plasma levels may assist in GBM clinical diagnosis: their reduction after GBM resection, their rise at recurrence, and their protein cargo might provide indications about tumor, therapy response, and monitoring.

The expression of programmed death-ligand 1 (PD-L1) on extracellular vesicles (EVs) is an emerging biomarker for cancer, and has gained particular interest for its role mediating immunotherapy. However, precise quantification of PD-L1+ EVs in clinical samples remains challenging due to their sparse concentration and the enormity of the number of background EVs in human plasma, limiting applicability of conventional approaches. In this study, we develop a high-throughput droplet-based extracellular vesicle analysis (DEVA) assay for ultrasensitive quantification of EVs in plasma that are dual positive for both PD-L1 and tetraspanin (CD81) known to be expressed on EVs. We achieve a performance that significantly surpasses conventional approaches, demonstrating 360× enhancement in the limit of detection (LOD) and a 750× improvement in the limit of quantitation (LOQ) compared to conventional plate enzyme-linked immunoassay (ELISA). Underlying this performance is DEVA's high throughput analysis of individual EVs one at a time and the high specificity to targeted EVs versus background. We achieve a 0.006% false positive rate per droplet by leveraging avidity effects that arise from EVs having multiple copies of their target ligands on their surface. We use parallelized optofluidics to rapidly process 10 million droplets per minute, ∼100× greater than conventional approaches. A validation study on a cohort of 14 patients with melanoma confirms DEVA's ability to match conventional ELISA measurements with reduced plasma sample volume and without the need for prior EV purification. This proof-of-concept study demonstrates DEVA's potential for clinical utility to enhance prognosis as well as guide treatment for cancer.

Introduction. The high mortality rate in patients with lung cancer (LC) is due to the lack of highly sensitive diagnostic markers of this disease. Genetic and epigenetic alterations in tumor cells, for example, aberrant microRNA expression, can be proposed. It is known that extracellular/circulating microRNA of biological fluids, in complexes with proteins, or packaged in extracellular vesicles is of interest for the diagnosis of tumor diseases.Aim. To perform a comparative analysis of miRNA expression in plasma and plasma extracellular vesicles of LC patients and healthy donors. Based on the obtained results, to propose a diagnostic panel to identify patients with LC.Materials and methods. Blood plasma was obtained from blood samples of healthy donors and LC patients by sequential centrifugation. Then, a fraction of extracellular vesicles (40–150 nm in size) was isolated from a part of the obtained plasma supernatant by the method of aggregation-precipitation with polyethylene glycol/blue dextran. MicroRNAs were isolated from both blood plasma fractions of patients and healthy donors using guanidine isothiocyanate and octanoic acid. Expression of 17 miRNAs most characteristic for the development of LC according to our and literature data in the above-mentioned blood plasma fractions was analyzed by stem-loop reverse transcription polymerase chain reaction.Results. 29 and 10 miRNA pairs were differentially expressed in plasma extracellular vesicles and plasma of lung cancer patients and donors. Thus, plasma extracellular vesicles are characterized by greater potential as a source for miRNA based lung cancer diagnostic panels in comparison with blood plasma. Diagnostic algorithm based on aberrant miRNA expression of 8 different miRNAs (miRNA-30e, -1, -125b, -133, -222, -374, -425, -660) composed in 6 pairs was designed. This algorithm allows to diagnose 100 % of patients with lung cancer stages II–IV.Conclusion. Extracellular plasma vesicles represent a promising source of diagnostically significant microRNAs compared to plasma microRNAs. For the diagnosis of patients with non-small cell lung cancer with 100 % sensitivity and specificity, a panel of 8 microRNAs (6 miRNA pairs) was proposed.

Glioblastoma (GBM) is the most aggressive and lethal form of brain tumor. Extracellular vesicles (EVs) released by tumor cells play a critical role in cellular communication in the tumor microenvironment promoting tumor progression and invasion. We hypothesized that GBM EVs possess unique characteristics which exert effects on endogenous CNS cells including neurons, producing dose-dependent neuronal cytotoxicity. We purified EVs from the plasma of 20 GBM patients, 20 meningioma patients, and 21 healthy controls, and characterized EV phenotypes by electron microscopy, nanoparticle tracking analysis, protein concentration, and proteomics. We evaluated GBM EV functions by determining their cytotoxicity in primary neurons and the neuroblastoma cell line SH-SY5Y. In addition, we determined levels of IgG antibodies in the plasma in GBM (n = 82), MMA (n = 83), and controls (non-tumor CNS disorders and healthy donors, n = 50) with capture ELISA. We discovered that GBM plasma EVs are smaller in size and had no relationship between size and concentration. Importantly, GBM EVs purified from both plasma and tumor cell lines produced IgG-mediated, complement-dependent apoptosis and necrosis in primary human neurons, mouse brain slices, and neuroblastoma cells. The unique phenotype of GBM EVs may contribute to its neuronal cytotoxicity, providing insight into its role in tumor pathogenesis.