Separation of high-purity plasma extracellular vesicles for investigating proteomic signatures in diabetic retinopathy.

Extracellular vesicles (EVs) play a multifaceted role in intercellular communication and hold significant promise as bio-functional indicators for clinical diagnosis. Although plasma samples represent one of the most critical sources of circulating EVs, the existing technical challenges associated with plasma-EV isolation have restricted their application in disease diagnosis and biomarker discovery. In this study, we introduce a two-step purification method utilizing ultracentrifugation (UC) to isolate crude extracellular vesicle (EV) samples, followed by a phospholipid affinity-based technique for the selective isolation of small EVs, ensuring a high level of purity for downstream proteomic analysis. Our research demonstrates that the UC & TiO2-coated magnetic bead (TiMB) purification system significantly improves the purity of EVs when compared to conventional UC or TiMB along. We further revealed that proteomic alterations in plasma EVs effectively reflect key gene ontology components associated with diabetic retinopathy (DR) pathogenesis, including the VEGF-activated neuropilin pathway, positive regulation of angiogenesis, angiogenesis, cellular response to vascular endothelial growth factor stimulus, and immune response. By employing a comprehensive analytical approach, which incorporates both time-series analysis (cluster analysis) and differential analysis, we have identified three potential protein signatures including LGALS3, MYH10, and CPB2 that closely associated with the retinopathy process. These proteins exhibit promising diagnostic and severity-classification capabilities for DR disease. This adaptable EV isolation system can be regarded as an effective analytical tool for enhancing plasma-based liquid biopsies toward clinical applications.

Efficient preparation of high-purity and intact mesenchymal stem cell-derived extracellular vesicles.

Mesenchymal stem cell-derived extracellular vesicles (MSC-EVs) have shown great promise for regeneration and immunomodulation. However, efficient and scalable methods for their preparation are still lacking. In this study, we present the adoption of a label-free technique known as "EXODUS" to isolate and purify MSC-EVs from the conditioned medium. Our findings indicate that EXODUS can rapidly isolate EVs from 10 mL of conditioned medium with a 5-fold higher yield compared to conventional approaches, including ultracentrifugation (UC) and polyethylene glycol precipitation (PEG) methods. Additionally, pre-storing the conditioned medium at 4°C for 1 week resulted in a ~2-fold higher yield of MSC-EVs compared to the freshly prepared medium. However, storing the purified EV particles at 4°C for 1 month led to a 2-fold reduction in particle concentration. Furthermore, we found that MSC-EVs isolated using EXODUS exhibit higher expression levels of EV markers such as Alix, Flotillin1, CD81, and TSG101 in comparison to PEG and UC methods. We also discovered that MSC-EVs isolated using EXODUS are enriched in response to cytokine, collagen-containing extracellular matrix, and calcium ion binding compared to PEG method and enriched in extracellular structure organization, extracellular matrix, and extracellular matrix structure constituents compared to UC. Finally, we demonstrated that MSC-EVs isolated using EXODUS exhibit greater potential in animal organ development, tissue development, and anatomical structure morphogenesis compared to the UC. These findings suggest that EXODUS is a suitable method for the large-scale preparation of high-quality MSC-EVs for various clinical applications.

Metabolic signatures of tear extracellular vesicles caused by herpes simplex keratitis.

PURPOSE: Herpes simplex keratitis (HSK), caused by type 1 herpes simplex virus (HSV) reactivation, is a severe infectious disease that leads to vision loss. HSV can trigger metabolic reprogramming in the host cell and change the extracellular vesicles (EV) cargos; however, little is known about the EV metabolic signatures during ocular HSV infection. Here, we aimed to depict the EV-associated metabolic landscape in HSK patients' tears. METHODS: We collected 82 samples from 41 participants with unilateral HSK (contralateral unaffected tears were set as negative control), including subtype cohorts of 13 epithelial, 20 stromal, and 8 endothelial HSK. We isolated tear EVs via our previously established platform and conducted metabolic analysis using LC-MS/MS. The metabolic signatures for recognizing HSK and subtypes were assessed through differential analysis and machine learning algorithms. RESULTS: Hypopsia and increased extracellular CD63 levels were observed in affected eyes. We identified 339 metabolites based on sEVs isolated from tears. Differential analysis revealed alterations in energy and amino acid metabolism, as well as the infectious microenvironment. Furthermore, we observed dysregulated metabolite such as methyldopa, which is associated with inappropriate neovascularization and corneal sensation loss, contributing to the HSK severity particularly in the stromal subtype. Moreover, machine learning classification also suggested a set of EV metabolic signatures that have potential for pan-keratitis detection. CONCLUSIONS: Our findings demonstrate that tear EV metabolites can serve as valuable indicators for comprehending the underlying pathological mechanisms. This knowledge is expected to facilitate the development of liquid biopsy means and therapeutic target discovery.

A pH-Mediated Highly Selective System Enabling Simultaneous Analysis of Circulating RNAs Carried by Extracellular Vesicles and Lipoproteins.

Extracellular vesicles (EVs) and lipoproteins (LPPs) serve as important carriers of circulating miRNAs in peripheral blood, offering immense potential for disease diagnosis and therapeutic interventions. Due to their shared physicochemical attributes, EVs and LPPs are frequently coisolated, potentially leading to misunderstandings regarding their distinct functional roles in physiological and pathological processes. Here, we report a highly selective magnetic system based on the pH-mediated affinity displayed by cibacron blue (CB) toward EVs and LPPs, enabling successful separation and collection of these two nanoparticles without cross-contamination for subsequent circulating RNA analysis. First, we found that CB-modified magnetic beads (CBMBs) exhibit a strong affinity toward LPP particles while displaying little interaction with EVs in standard samples under physiological pH conditions. We further demonstrate that the affinity between CB molecules and bionanoparticles in plasma samples is highly pH-dependent. Specifically, CBMBs show affinities for both LPP and EV particles under neutral and acidic conditions. However, at basic pH levels, CB molecules selectively bind only to LPP particles. Consequently, the remaining EV particles present in plasma are subsequently isolated by using titanium dioxide-modified beads (TiMBs) through phospholipid affinity. The simultaneous analysis of the transcriptomic contents of EV and LPP reveals clear differences in their small RNA profiles, with the differentially expressed RNAs reflecting distinct biological processes. Significantly, in a proof-of-concept study, we successfully demonstrated a strong correlation between miRNAs carried by both EV and LPP particles with the occurrence of ocular neovascularization during the progression of diabetic retinopathy. The involved miRNAs may serve as potential biomarkers for DR diagnostics and severity classification. To sum up, this pH-mediated separation system is not only user-friendly but also highly compatible, rendering it a potent tool for probing the molecular compositions, biomarkers, and underlying biological mechanisms of EVs and LPPs.

Metabolomic investigation of urinary extracellular vesicles for early detection and screening of lung cancer.

Lung cancer is a prevalent cancer type worldwide that often remains asymptomatic in its early stages and is frequently diagnosed at an advanced stage with a poor prognosis due to the lack of effective diagnostic techniques and molecular biomarkers. However, emerging evidence suggests that extracellular vesicles (EVs) may promote lung cancer cell proliferation and metastasis, and modulate the anti-tumor immune response in lung cancer carcinogenesis, making them potential biomarkers for early cancer detection. To investigate the potential of urinary EVs for non-invasive detection and screening of patients at early stages, we studied metabolomic signatures of lung cancer. Specifically, we conducted metabolomic analysis of 102 EV samples and identified metabolome profiles of urinary EVs, including organic acids and derivatives, lipids and lipid-like molecules, organheterocyclic compounds, and benzenoids. Using machine learning with a random forest model, we screened for potential markers of lung cancer and identified a marker panel consisting of Kanzonol Z, Xanthosine, Nervonyl carnitine, and 3,4-Dihydroxybenzaldehyde, which exhibited a diagnostic potency of 96% for the testing cohort (AUC value). Importantly, this marker panel also demonstrated effective prediction for the validation set, with an AUC value of 84%, indicating the reliability of the marker screening process. Our findings suggest that the metabolomic analysis of urinary EVs provides a promising source of non-invasive markers for lung cancer diagnostics. We believe that the EV metabolic signatures could be used to develop clinical applications for the early detection and screening of lung cancer, potentially improving patient outcomes.

Robust Acute Pancreatitis Identification and Diagnosis: RAPIDx.

The occurrence of acute pancreatitis (AP) is increasing significantly worldwide. However, current diagnostic methods of AP do not provide a clear clinical stratification of severity, and the prediction of complications in AP is still limited. Here, we present a robust AP identification and diagnosis (RAPIDx) method by the proteomic fingerprinting of intact nanoscale extracellular vesicles (EVs) from clinical samples. By tracking analysis of circulating biological nanoparticles released by cells (i.e., EVs) via bottom-up proteomics, we obtain close phenotype connections between EVs, cell types, and multiple tissues based on their specific proteomes and identify the serum amyloid A (SAA) proteins on EVs as potential biomarkers that are differentially expressed from AP patients significantly. We accomplish the quantitative analysis of EVs fingerprints using MALDI-TOF MS and find the SAA proteins (SAA1-1, desR-SAA1-2, SAA2, SAA1-2) with areas under the curve (AUCs) from 0.92 to 0.97, which allows us to detect AP within 30 min. We further realize that SAA1-1 and SAA2, combined with two protein peaks (5290.19, 14032.33 m/z), can achieve an AUC of 0.83 for classifying the severity of AP. The RAPIDx platform will facilitate timely diagnosis and treatment of AP before severity development and persistent organ failure and promote precision diagnostics and the early diagnosis of pancreatic cancer.

Identification and detection of plasma extracellular vesicles-derived biomarkers for esophageal squamous cell carcinoma diagnosis.

Esophageal cancer is a malignant tumor with two-thirds of patients having a local recurrence or distant metastasis. To date, diagnostic biomarkers with high sensitivity and specificity are lacking. Extracellular vesicles (EVs) have shown their potential values as disease biomarkers as they carry specific proteins and RNAs derived from cancer cells. In this study, we investigate ESCC precision diagnostics from the insights of circulating EVs, and integrate the ultrafast EV isolation approach (EXODUS) and ELISA for fast detection and screening of ESCC patients. First, we isolate and characterize the high-purity plasma EVs with EXODUS and identify 401 proteins and 372 proteins from ESCC patient and healthy individuals, respectively. Further looking into the differentially expressed proteins (DEPs) of ESCC patients and enriched KEGG pathways, we discover EV-CD14 as a potential diagnostic biomarker for ESCC, which has been further validated as a significantly differentially expressed protein by Western Blot and immunogold labelling TEM. For fast screening and detection of ESCC towards clinical applications, we apply ELISA method to diagnose ESCC from 60 clinical samples based on circulating EV-CD14, which shows a high AUC value up to 96.0% for detection of ESCC in a test set (30 samples), and displays a high accuracy rate up to 90% for prediction of ESCC in a screening test (30 samples). Our results suggest that the circulating EV-CD14 may highly be related to the initiation and progression of ESCC, providing a novel method for the diagnosis and prognosis of ESCC towards clinical translations.

Highly Selective Purification of Plasma Extracellular Vesicles Using Titanium Dioxide Microparticles for Depicting the Metabolic Signatures of Diabetic Retinopathy.

Extracellular vesicle (EV) cargos with regular fluctuations hold the potential for providing chemical predictors toward clinical diagnosis and prognosis. A plasma sample is one of the most important sources of circulating EVs, yet the technical barrier and cost consumption in plasma-EV isolation still limit its application in disease diagnosis and biomarker discovery. Here, we introduced an easy-to-use strategy that allows selectively purifying small EVs (sEVs) from human plasma and detecting their metabolic alternations. Fe3O4@TiO2 microbeads with a rough island-shaped surface have proven the capability of performing efficient and reversible sEV capture owing to the phospholipid affinity, enhanced binding sites, and size-exclusion-like effect of the rough TiO2 shell. The proposed system can also shorten the separation procedure from hours to 20 min when compared with the ultracentrifugation method and yield approximately 108 sEV particles from 100 µL of plasma. Metabolome variations of sEVs among progressive diabetic retinopathy subjects were finally studied, observing a cluster of metabolites with elevated levels and suggesting potential roles of these sEV chemicals in diabetic retinopathy onset and progression. Such a scalable and flexible EV capture system can be seen as an effective analytical tool for facilitating plasma-based liquid biopsies.

Study on Key Technologies of Virtual Interactive Surgical Simulation for 3D Reconstruction of Medical Images.

Medical computed tomography (CT), nuclear magnetic resonance imaging (MRI), and other imaging facility produce a large amount of medical image data which has great diagnosis value. Traditional three-dimensional reconstruction of medical images has high requirements for graphics acceleration hardware and the processing speed. In this study, VxWorks embedded real-time process feature is used for CT or MRI DICOM data to real restoration and establishment of virtual three-dimensional model for realizing volume reconstruction, maximum density projection, multiplane reconstruction, dynamic interactive cutting of any surface, dynamic display of three-dimensional model and two-dimensional sectional image, surgical path planning and interactive surgical simulation, and determine the best surgical scheme. The practical application shows that the virtual simulation environment supports the seamless transplantation of code, function debugging, and interaction and solves the issue of high requirements for hardware. It can meet the needs of scientific research and teaching for clinicians and medical imaging workers and can be widely used in the training of virtual surgical anatomy for medical students.

Lipidomic identification of urinary extracellular vesicles for non-alcoholic steatohepatitis diagnosis.

BACKGROUND AND AIMS: Non-alcoholic fatty liver disease (NAFLD) is a usual chronic liver disease and lacks non-invasive biomarkers for the clinical diagnosis and prognosis. Extracellular vesicles (EVs), a group of heterogeneous small membrane-bound vesicles, carry proteins and nucleic acids as promising biomarkers for clinical applications, but it has not been well explored on their lipid compositions related to NAFLD studies. Here, we investigate the lipid molecular function of urinary EVs and their potential as biomarkers for non-alcoholic steatohepatitis (NASH) detection. METHODS: This work includes 43 patients with non-alcoholic fatty liver (NAFL) and 40 patients with NASH. The EVs of urine were isolated and purified using the EXODUS method. The EV lipidomics was performed by LC-MS/MS. We then systematically compare the EV lipidomic profiles of NAFL and NASH patients and reveal the lipid signatures of NASH with the assistance of machine learning. RESULTS: By lipidomic profiling of urinary EVs, we identify 422 lipids mainly including sterol lipids, fatty acyl lipids, glycerides, glycerophospholipids, and sphingolipids. Via the machine learning and random forest modeling, we obtain a biomarker panel composed of 4 lipid molecules including FFA (18:0), LPC (22:6/0:0), FFA (18:1), and PI (16:0/18:1), that can distinguish NASH with an AUC of 92.3%. These lipid molecules are closely associated with the occurrence and development of NASH. CONCLUSION: The lack of non-invasive means for diagnosing NASH causes increasing morbidity. We investigate the NAFLD biomarkers from the insights of urinary EVs, and systematically compare the EV lipidomic profiles of NAFL and NASH, which holds the promise to expand the current knowledge of disease pathogenesis and evaluate their role as non-invasive biomarkers for NASH diagnosis and progression.

Quantitative metabolic analysis of plasma extracellular vesicles for the diagnosis of severe acute pancreatitis.

BACKGROUND: Severe acute pancreatitis (SAP) is the most common gastrointestinal disease and is associated with unpredictable seizures and high mortality rates. Despite improvements in the treatment of acute pancreatitis, the timely and accurate diagnosis of SAP remains highly challenging. Previous research has shown that extracellular vesicles (EVs) in the plasma have significant potential for the diagnosis of SAP since the pancreas can release EVs that carry pathological information into the peripheral blood in the very early stages of the disease. However, we know very little about the metabolites of EVs that might play a role in the diagnosis of SAP. METHODS: Here, we performed quantitative metabolomic analyses to investigate the metabolite profiles of EVs isolated from SAP plasma. We also determined the metabolic differences of EVs when compared between healthy controls, patients with SAP, and those with mild acute pancreatitis (MAP). RESULTS: A total of 313 metabolites were detected, mainly including organic acids, amino acids, fatty acids, and bile acids. The results showed that the metabolic composition of EVs derived from SAP and MAP was significantly different from those derived from healthy controls and identified specific differences between EVs derived from patients with SAP and MAP. On this basis, we identified four biomarkers from plasma EVs for SAP detection, including eicosatrienoic acid (C20:3), thiamine triphosphate, 2-Acetylfuran, and cis-Citral. The area under the curve (AUC) was greater than 0.95 for both discovery (n = 30) and validation (n = 70) sets. CONCLUSIONS: Our data indicate that metabolic profiling analysis of plasma EVs and the screening of potential biomarkers are of significant potential for improving the early diagnosis and severity differentiation of acute pancreatitis.

Metabolomic analysis of exosomal-markers in esophageal squamous cell carcinoma.

Esophageal squamous cell carcinoma (ESCC) is a worldwide malignancy with high mortality rates and poor prognosis due to the lack of effective biomarkers for early detection. Exosomes have been extensively explored as attractive biomarkers for cancer diagnosis and treatment. However, little is known about exosome metabolomics and their roles in ESCC. Here, we performed a targeted metabolomic analysis of plasma exosomes and identified 196 metabolites, mainly including lipid fatty acids, benzene, amino acids, organic acids, carbohydrates and fatty acyls. We systematically compared metabolome patterns of exosomes via machine learning from patients with recrudescence and patients without recrudescence and demonstrated a marker set consisting of 3'-UMP, palmitoleic acid, palmitaldehyde, and isobutyl decanoate for predicting ESCC recurrence with an AUC of 98%. These metabolome signatures of exosomes retained a high absolute fold change value at all ESCC stages and were very likely associated with cancer metabolism, which could be potentially applied as novel biomarkers for diagnosis and prognosis of ESCC.

The genetic source tracking of human urinary exosomes.

The genetic origins of nanoscale extracellular vesicles in our body fluids remains unclear. Here, we perform a tracking analysis of urinary exosomes via RNA sequencing, revealing that urine exosomes mostly express tissue-specific genes for the bladder and have close cell-genetic relationships to the endothelial cell, basal cell, monocyte, and dendritic cell. Tracking the differentially expressed genes of cancers and corresponding enrichment analysis show urine exosomes are intensively involved in immune activities, indicating that they may be harnessed as reliable biomarkers of noninvasive liquid biopsy in cancer genomic diagnostics and precision medicine.

Ultrafiltration combing with phospholipid affinity-based isolation for metabolomic profiling of urinary extracellular vesicles.

Recent years have seen the field of extracellular vesicle (EV) studies burgeoning. This is mainly because EV constituents including nucleic acid, proteins, lipids, and metabolites are promising sources towards disease biomarker discovery. However, EV study remains challenging due to the complexity of biofluids as well as technical limitations during sample preparation. Here, we proposed a simple method combing ultrafiltration (UF) and phospholipid affinity to collect high purity EVs from 30 mL of urine sample for their metabolomic profiling. Ultracentrifugation (UC) for EV isolation was applied as a reference method. Western blot (WB) analysis, nanoparticles tracking analysis (NTA) and electron microscopy (EM) were used to assess EV protein markers and to characterize vesicle size and morphology. The results revealed that more than 1010 EV particles could be isolated from a 30 mL urine sample by the proposed method, and the resulting EVs carry specific protein markers and had a typical "cup shape" morphology. This suggests that our method is suitable for EV isolation and can provide sufficient EV quantity to ensure downstream analysis. Further untargeted metabolomic profiling of isolated EVs by UHPLC-QTOF-MS detected 433 metabolites by our methods and 432 metabolites by UC with a MS/MS similarity score greater than 0.7. We then applied the lipid metabolites-targeted method using UHPLC-QTrap-MS with the MRM mode, which successfully detected 467 compounds from urine EVs. This indicates that UF integrating phospholipid affinity is a reliable method for metabolic analysis of urinary EVs, which holds the potential for EV clinical application towards biomarker investigation from their metabolites.

Exosome detection via the ultrafast-isolation system: EXODUS.

Exosomes have shown great potential in disease diagnostics and therapeutics. However, current isolation approaches are burdensome and suffer from low speed, yield and purity, limiting basic research and clinical applications. Here, we describe an efficient exosome detection method via the ultrafast-isolation system (EXODUS) that allows automated label-free purification of exosomes from varied biofluids. We obtained the ultra-efficient purification of exosomes by negative pressure oscillation and double coupled harmonic oscillator-enabled membrane vibration. Our two coupled oscillators generate dual-frequency transverse waves on the membranes, enabling EXODUS to outperform other isolation techniques in speed, purity and yield. We demonstrated EXODUS by purifying exosomes from urine samples of 113 patients and validated the practical relevance in exosomal RNA profiling with the high-resolution capability and high-throughput analysis.

Deep dive on the proteome of salivary extracellular vesicles: comparison between ultracentrifugation and polymer-based precipitation isolation.

Salivary extracellular vesicles (EVs), as novel functional carriers and potential biomarkers, are usually obtained by ultracentrifugation (UC) and polyethylene glycol (PEG)-based precipitation methods. However, salivary EVs obtained by these two methods have not been systematically compared. Here, we perform an in-depth analysis on EVs isolated by these two methods using proteomics. Both methods obtain EVs ranging from 40 to 210 nm, with the PEG method resulting in a wider size distribution. PEG-separated products were irregularly shaped and aggregated, while UC-separated ones were monodispersed and teacup-shaped. Additionally, the expression of EV-specific markers was higher in UC-separated EVs. Using tandem mass spectrometry proteomics, we identified and quantified 1217 kinds of saliva exosomal proteins and 361 kinds of differential proteins, showing that UC can isolate more EV-related proteins. These results offer some guidance for EV separating and provide potential direction for the use of EVs in non-invasive diagnosis.

A facile, rapid, high-throughput extracellular vesicles analytical platform for cancer detection.

Cell-derived nanoparticles, so called Extracellular Vesicles (EVs), can reflect the physiological or pathological conditions of donor cells and can provide promising biomarkers for the non-invasive diagnosis of cancers. Size-based purification method is one of the common strategies for rapid extracting EVs from biosamples, but the downstream clinical studies still remain challenges in EV enrichment with high purity and high yield. Here, such challenges could be fulfilled through the development of an arrayed Exosome Purification and Operation System (Exo-POS) for efficiently isolating EVs from complex biofluids. Human urinary EVs with mean size of approximately 170 nm were isolated successfully from donors within 30 min, and the purification of individual samples were performable in parallel. Samples purified by Exo-POS showed detectable EV-specific biomarkers and less protein impurities than that by ultrafiltration method. The results also demonstrate the great purification ability of Exo-POS to discriminate between the EV-derived proteomic and genomic expressions of cancer patients and healthy controls. The developed platform can easily be adapted to retrieve EVs from biological samples for the downstream analysis, demonstrating its potential for both rapid clinical diagnosis and biomarker discovery.

An integrative microfluidic device for isolation and ultrasensitive detection of lung cancer-specific exosomes from patient urine.

Exosomes, derived from various biofluids, may serve as potential biomarkers for cancer early detection. Nevertheless, exosome clinical translation remains challenging due to the lack of reliable isolation and detection methods. Herein, we present a novel integrated microfluidic device specifically designed for isolation and in-situ detection of lung cancer-specific exosomes collected from patient's urine. The new device has been fabricated using polymethyl methacrylate (PMMA) and a nanoporous gold (Au) nanocluster membrane modified with the capture antibody. The second antibody-conjugated Au nanorod probe was then loaded to identify and quantify lung cancer-specific exosomes using a dark field microscope. AuNC-Exosome-AuR complex produces a significant scattering wavelength shift and an improved scattering intensity due to resonance Rayleigh scattering, which enables the ultrasensitive detection of exosomes with a LOD below 1000 particles/mL. The proteomic analysis revealed that the high-purity exosomes has been isolated by the device. We then validated this method with 500 µL urine samples from lung cancer patients and controls, which showed great promise for differentiating early-stage lung cancer patients from healthy individuals. Taken together, the presented method is fast and ultrasensitive and can be easily adapted for the isolation and detection of cancer specific exosomes from other malignant tumors.

High-Efficiency Separation of Extracellular Vesicles from Lipoproteins in Plasma by Agarose Gel Electrophoresis.

Isolation and purification of extracellular vesicles (EVs) from plasma is essential to understand the EV circulation mechanism and discover biomarkers for the early detection of diseases. However, the size range of lipoprotein particles such as high density lipoprotein (HDL), low density lipoprotein (LDL), and very low density lipoprotein (VLDL) overlap that of EVs, making it difficult to remove lipoproteins from EVs. Here, we propose a method for the high efficiency separation of EVs in plasma using agarose gel electrophoresis based on their differences in size and zeta potential properties. Electrophoresis track assays revealed that EVs propagate more slowly than HDL but more quickly than LDL and VLDL in 1% agarose gel with pH 7.4 Tris-Acetate-EDTA (TAE) buffer. The size and morphology of the electrophoresis-recovered products were characterized to be consistent with typical EVs. In addition, the biological function of recovered EVs was investigated with cell uptake tests. The feasibility of this method was further verified with human plasma samples. In summary, this technique has the potential to become a convenient and efficient approach for high-purity EV separation.