Single Extracellular Vesicles Research Articles

Mechanical property estimation of sarcoma-relevant extracellular vesicles using transmission electron microscopy.

Analysis of single extracellular vesicles (EVs) has the potential to yield valuable label-free information on their morphological structure, biomarkers and therapeutic targets, though such analysis is hindered by the lack of reliable and quantitative measurements of the mechanical properties of these compliant nanoscale particles. The technical challenge in mechanical property measurements arises from the existing tools and methods that offer limited throughput, and the reported elastic moduli range over several orders of magnitude. Here, we report on a flow-based method complemented by transmission electron microscopy (TEM) imaging to provide a high throughput, whole EV deformation analysis for estimating the mechanical properties of liposarcoma-derived EVs as a function of their size. Our study includes extracting morphological data of EVs from a large dataset of 432 TEM images, with images containing single to multiple EVs, and implementing the thin-shell deformation theory. We estimated the elastic modulus, E = 0.16 ± 0.02 MPa (mean±SE) for small EVs (sEVs; 30-150nm) and E = 0.17 ± 0.03 MPa (mean±SE) for large EVs (lEVs; >150 nm). To our knowledge, this is the first report on the mechanical property estimation of LPS-derived EVs and has the potential to establish a relationship between EV size and EV mechanical properties.

Integrated Antigenic and Nucleic Acid Detection in Single Virions and Extracellular Vesicles with Viral Content.

Virion-mediated outbreaks are imminent and despite rapid responses, continue to cause adverse symptoms and death. Therefore, tunable, sensitive, high-throughput assays are needed to help diagnose future virion-mediated outbreaks. Herein, it is developed a tunable in situ assay to selectively enrich virions and extracellular vesicles (EVs) and simultaneously detect antigens and nucleic acids at a single-particle resolution. The Biochip Antigen and RNA Assay (BARA) enhanced sensitivities compared to quantitative reverse-transcription polymerase chain reaction (qRT-PCR), enabling the detection of virions in asymptomatic patients, genetic mutations in single virions, and enabling the continued long-term expression of viral RNA in the EV-enriched subpopulation in the plasma of patients with post-acute sequelae of the coronavirus disease of 2019 (COVID-19). BARA revealed highly accurate diagnoses of COVID-19 by simultaneously detecting the spike glycoprotein and nucleocapsid-encoding RNA in saliva and nasopharyngeal swab samples. Altogether, the single-particle detection of antigens and viral RNA provides a tunable framework for the diagnosis, monitoring, and mutation screening of current and future outbreaks.

Heterogeneous Analysis of Extracellular Vesicles for Osteosarcoma Diagnosis.

Osteosarcoma (OS) is the most prevalent primary tumor of bones, often diagnosed late with a poor prognosis. Currently, few effective biomarkers or diagnostic methods have been developed for early OS detection with high confidence, especially for metastatic OS. Tumor-derived extracellular vesicles (EVs) are emerging as promising biomarkers for early cancer diagnosis through liquid biopsy. Here, we report a plasmonic imaging-based biosensing technique, termed subpopulation protein analysis by single EV counting (SPASEC), for size-dependent EV subpopulation analysis. In our SPASEC platform, EVs are accurately sized and counted on plasmonic sensor chips coated with OS-specific antibodies. Subsequently, EVs are categorized into distinct subpopulations based on their sizes, and the membrane proteins of each size-dependent subpopulation are profiled. We measured the heterogeneous expression levels of the EV markers (CD63, BMP2, GD2, and N-cadherin) in each of the EV subsets from both OS cell lines and clinical plasma samples. Using the linear discriminant analysis (LDA) model, the combination of four markers is applied to classify the healthy donors (n = 37), nonmetastatic OS patients (n = 13), and metastatic patients (n = 12) with an area under the curve of 0.95, 0.92, and 0.99, respectively. SPASEC provides accurate EV sensing technology for early OS diagnosis.

Plasmonic nano-aperture label-free imaging of single small extracellular vesicles for cancer detection

BackgroundSmall extracellular vesicle (sEV) analysis can potentially improve cancer detection and diagnostics. However, this potential has been constrained by insufficient sensitivity, dynamic range, and the need for complex labeling.MethodsIn this study, we demonstrate the combination of PANORAMA and fluorescence imaging for single sEV analysis. The co-acquisition of PANORAMA and fluorescence images enables label-free visualization, enumeration, size determination, and enables detection of cargo microRNAs (miRs).ResultsAn increased sEV count is observed in human plasma samples from patients with cancer, regardless of cancer type. The cargo miR-21 provides molecular specificity within the same sEV population at the single unit level, which pinpoints the sEVs subset of cancer origin. Using cancer cells-implanted animals, cancer-specific sEVs from 20 µl of plasma can be detected before tumors were palpable. The level plateaus between 5–15 absolute sEV count (ASC) per µl with tumors ≥8 mm3. In healthy human individuals (N = 106), the levels are on average 1.5 ASC/µl (+/− 0.95) without miR-21 expression. However, for stage I–III cancer patients (N = 205), nearly all (204 out of 205) have levels exceeding 3.5 ASC/µl with an average of 12.2 ASC/µl (±9.6), and a variable proportion of miR-21 labeling among different tumor types with 100% cancer specificity. Using a threshold of 3.5 ASC/µl to test a separate sample set in a blinded fashion yields accurate classification of healthy individuals from cancer patients.ConclusionsOur techniques and findings can impact the understanding of cancer biology and the development of new cancer detection and diagnostic technologies.

Poloxamer-188 as a wetting agent for microfluidic resistive pulse sensing measurements of extracellular vesicles.

Microfluidic resistive pulse sensing (MRPS) can determine the concentration and size distribution of extracellular vesicles (EVs) by measuring the electrical resistance of single EVs passing through a pore. To ensure that the sample flows through the pore, the sample needs to contain a wetting agent, such as bovine serum albumin (BSA). BSA leaves EVs intact but occasionally results in unstable MRPS measurements. Here, we aim to find a new wetting agent by evaluating Poloxamer-188 and Tween-20. An EV test sample was prepared using an outdated erythrocyte blood bank concentrate. The EV test sample was diluted in Dulbecco's phosphate-buffered saline (DPBS) or DPBS containing 0.10% BSA (w/v), 0.050% Poloxamer-188 (v/v) or 1.00% Tween-20 (v/v). The effect of the wetting agents on the concentration and size distribution of EVs was determined by flow cytometry. To evaluate the precision of sample volume determination with MRPS, the interquartile range (IQR) of the particles transit time through the pore was examined. To validate that DPBS containing Poloxamer-188 yields reliable MRPS measurements, the repeatability of MRPS in measuring blood plasma samples was examined. Flow cytometry results show that the size distribution of EVs in Tween 20, in contrast to Poloxamer-188, differs from the control measurements (DPBS and DPBS containing BSA). MRPS results show that Poloxamer-188 improves the precision of sample volume determination compared to BSA and Tween-20, because the IQR of the transit time of EVs in the test sample is 11 μs, which is lower than 56 μs for BSA and 16 μs for Tween-20. Furthermore, the IQR of the transit time of particles in blood samples with Poloxamer-188 are 14, 16, and 14 μs, which confirms the reliability of MRPS measurements. The solution of 0.050% Poloxamer-188 in DPBS does not lyse EVs and results in repeatable and unimpeded MRPS measurements.

305 Extracellular Vesicular mRNA and miRNA Biomarkers in Patients With Glioblastoma

INTRODUCTION: Glioblastoma (GBM) is a highly lethal primary brain cancer that is resistant to conventional immunotherapies. However, currently, there is a lack of reliable biomarkers for clinical practice. Extracellular vesicles (EVs) present in biofluids are a potential biomarker for GBM diagnosis. METHODS: EVs from cell culture medium, healthy donor and GBM patient serum were purified using tangential flow filtration (TFF). Morphology, size, and numbers of EVs were characterized by TEM and qNano, respectively. EVs from healthy donor and GBM patient serum were compared against other cancer EV samples using bulk mRNA and miRNA sequencing analysis. Selected mRNA and miRNA name are not mentioned due to patents being drafted. mRNA and miRNA expression in EVs were quantified by a fluorescent nucleic acid detection method using a biochip platform at a single-EV level. Fluorescence images were recorded using high-resolution total internal reflection fluorescence (TIRF) microscopy. RESULTS: From bulk mRNA and miRNA sequencing and bioinformatics analysis, three miRNA and two mRNA were selected as potential candidate biomarkers for GBM. Subsequently, EVs from the serum of GBM patients (n = 39) were evaluated through our biochip platform. mRNA and miRNA expression of these biomarkers were positive in single EVs, for which we detected colocalization of protein/RNAs or multiple RNAs in subpopulations of EVs. Our platform was benchmarked with a standard PCR method showing 100X more sensitivity. Moreover, levels of expression of the different RNAs were compared to a cohort of serum from healthy donors (n=20), and showing significant differences between the two groups. CONCLUSIONS: Our non-invasive biochip assay outperformed qRT-PCR for EV RNA detection. Selected mRNAs and miRNAs in subpopulations of EVs could be used as potential GBM biomarkers.

Precise analysis of single small extracellular vesicles using flow cytometry

Methods that enable specific and sensitive quantification of small extracellular vesicles (sEVs) using flow cytometry are still under development. Aggregation or adsorption of antibodies causes sub-nano sized particles or non-specific binding and largely affects the results of flow cytometric analysis of single sEVs. Comparison of control IgG and target-specific IgG is inappropriate because they have different characters. Here, we evaluate four preparation methods for flow cytometry, including ultracentrifugation, density gradient centrifugation, size exclusion chromatography (SEC), and the TIM4-affinity method by using tetraspanin-deficient sEVs. The ultracentrifugation or density gradient centrifugation preparation method has large false-positive rates for tetraspanin staining. Conversely, preparation methods using SEC or the TIM4-affinity method show specific detection of single sEVs, which elucidate the roles of sEV biogenesis regulators in the generation of sEV subpopulations. The methods are also useful for the detection of rare disease-related markers, such as PD-L1. Flow cytometric analysis using SEC or the TIM4-affinity method could accelerate research into sEV biogenesis and the development of sEV-based diagnostics and therapies.

Abstract 1066: Colocalization of ovarian cancer-associated biomarkers in tumors, cancer cells and extracellular vesicles

Abstract Introduction: We have previously shown that ovarian cancer can be detected with high specificity and sensitivity by interrogating colocalized membrane-associated cell-surface biomarkers on single tumor-associated extracellular vesicles (EVs). Our blood based Ovarian Cancer Test (OC Test) is composed of 5 cancer-associated biomarkers (BST-2, FOLR1, MUC-1, MUC-16, and sialylated Thomsen-nouveau antigen (sTn)) known to be overexpressed in ovarian cancer relative to healthy tissues. The test employs these 5 biomarkers in 3 combinations to capture and detect EVs from human plasma to distinguish high-grade serous ovarian carcinoma (HGSC) from both benign ovarian tumors and normal samples. Using super-resolution microscopy on ovarian cancer cell lines and EVs and multiplex immunohistochemistry of ovarian benign and tumor tissue, our study aimed to show that the 5 ovarian cancer biomarkers show 1) colocalization on ovarian cancer cells, 2) colocalization on the surface of EVs, and 3) colocalization on cancer cells within ovarian tumor biopsies. Methods: Super-resolution microscopy was employed to visualize stained biomarkers on the cell surface of a collection of ovarian cancer cell lines as well as on the surface of extracellular vesicles released by these cell lines. 58 HGSC (17 Stage I, 30 Stage II, and 10 Stage III) and 17 benign ovarian tumor K2EDTA plasma samples and paired FFPE tissue were sourced from the Ovarian Cancer Research Program (OVCARE, Vancouver BC, Canada). A tumor microarray from identical tissue samples was stained with an Opal multiplex-IHC (mIHC) assay comprised of antibodies to BST2, MUC1, sTn, and pan-cytokeratin and a DAPI counterstain. Machine learning based image analysis was utilized to determine colocalization of the biomarkers at the cellular level within benign and tumor tissue. RNA sequencing was carried out to assess expression levels of the biomarkers in each FFPE tissue sample. OC Tests were run on each plasma sample. Results: Super-resolution microscopy showed colocalization of OC Test combination biomarkers on ovarian cancer cells and EVs derived therefrom. Machine learning-based image analysis of tumor and benign tissue microarrays stained for BST2/MUC1/sTn using mIHC, showed strong evidence for colocalization of these biomarkers on single cells within ovarian tumor tissue, particularly in early-stage tumors, but not on benign tissue. RNA sequencing analysis showed good correlation of biomarker RNA expression levels and with the OC Test results. Conclusions: The results presented here demonstrate that the OC Test biomarkers are colocalized on tumor tissue, ovarian cancer cells, and EVs from ovarian cancer cell lines. RNA and protein expression levels show good correlation with OC Test results. These results provide further evidence that colocalized biomarkers on EVs can be utilized for early detection of cancer. Citation Format: Anthony D. Couvillon, Emily S. Winn-Deen, Shelby Thornton, Shuhong Liu, Michael DeRan, Maciej Pacula, Sanchari Banerjee, Daniel Gusenleitner, Laura T. Bortolin, Jonian Grosha, Timothy Santos-Heiman, Gabrielle Sangiuliano, Kelly M. Biette, Christopher R. Sedlak, Bilal Hamzeh, Delaney M. Byrne, Peter A. Duff, Julie Ho, Dongxia Gao, Amy Jamieson, Lauren T. Cuoco, MacKenzie S. King, Dawn R. Mattoon, Toumy Guettouche, Jessica N. McAlpine, David Huntsman. Colocalization of ovarian cancer-associated biomarkers in tumors, cancer cells and extracellular vesicles [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1066.

Deep Learning Promotes Profiling of Multiple miRNAs in Single Extracellular Vesicles for Cancer Diagnosis.

Extracellular vesicle microRNAs (EV miRNAs) are critical noninvasive biomarkers for early cancer diagnosis. However, accurate cancer diagnosis based on bulk analysis is hindered by the heterogeneity among EVs. Herein, we report an approach for profiling single-EV multi-miRNA signatures by combining total internal reflection fluorescence (TIRF) imaging with a deep learning (DL) algorithm for the first time. This innovative technique allows for the precise characterization of EV miRNAs at the single-vesicle level, overcoming the challenges posed by EV heterogeneity. TIRF with high resolution and a signal-to-noise ratio can simultaneously detect multi-miRNAs in situ in individual EVs. DL algorithm avoids complicated and inaccurate artificial feature extraction, achieving automated high-resolution image analysis. Using this approach, we reveal that the main variation of EVs from 5 cancer cells and normal plasma is the triple-positive EV subpopulation, and the classification accuracy of single triple-positive EVs from 6 sources can reach above 95%. In the clinical cohort, 20 patients (5 lung cancer, 5 breast cancer, 5 cervical cancer, and 5 colon cancer) and 5 healthy controls are predicted with an overall accuracy of 100%. This single-EV strategy provides new opportunities for exploring more specific EV biomarkers to achieve cancer diagnosis and classification.

Abstract A040: Improving specificity for ovarian cancer screening using a novel extracellular vesicle-based blood test

Abstract Introduction: The low incidence of ovarian cancer (OC) in average risk individuals dictates any OC screening method needs to be both highly sensitive and highly specific. Even using the multimodal OC screening strategy combining longitudinal serum CA125 and transvaginal ultrasound, 16% of OCs were not detected and false positives due to benign ovarian tumors, which are 5-10 times more prevalent than OC, remained high. Due to lack of mortality benefit, population screening is still not recommended. We hypothesized that detecting multiple colocalized protein or glycosylation epitopes (PGEs) on single tumor-derived extracellular vesicles (EVs) would increase specificity and sensitivity for detection of OC. Methods: The Mercy Halo™ OC test employs immunoaffinity capture and proximity-ligation qPCR to detect combinations of up to 3 PGE biomarkers to maximize specificity and measures multiple combinations to maximize sensitivity. We selected 3 PGE combinations to form a panel test to distinguish high-grade serous ovarian cancer (HGSC) from benign ovarian tumors and normal patients and compared performance of our test to CA125. We used a case-control training set from university-associated Canadian biobanks comprised of EDTA plasma from 124 healthy controls, 89 HGSC cases (17 Stage I, 35 Stage II, 37 Stage III)(10 BRCA+), and 192 benign adnexal masses to lock down the test, the data interpretation algorithm, and the cut-off between cancer and non-cancer. Performance was verified in an independent blinded diagnostic case-control set of 397 serum samples from 138 healthy controls, 111 benign adnexal masses, 20 borderlines, 66 HGSC and 62 non-HGSC from the UK Ovarian Cancer Population Study. For comparison, a CA125 ELISA was run for all samples in both studies. Results: Using the locked assay, algorithm, and cut-off established in the training cohort, our test showed a specificity in healthy controls of 92.8% (128/138; 95% CI: 0.87-0.96), a sensitivity in HGSC of 97.0% (64/66; 95% CI: 0.90-0.99), and an AUC of 0.92 (95% CI 0.89-0.95) in the verification cohort. We correctly identified 11/13 Stage I and all 54 Stage II/III/IV HGSC cases. Our test also detected 76.8% (63/82) of the borderline and non-HGSC histotypes. CA125 detected 92.4% (61/66) of the HGSC cases and 84.1% (69/82) of the borderlines and non-HGSC. The greatest improvement in test performance relative to CA125 was seen with benign adnexal masses. In the verification cohort 35.1% (39/111) of the benign samples were CA125 positive compared to 21.6% (24/111) with our test. This reduction in false positives was seen across 10 benign histotypes tested. Conclusions: These results demonstrate that our test using colocalized PGEs on EVs can detect OC, especially HGSC, in both plasma and serum with high sensitivity and specificity. The combinations of 5 biomarkers rather than CA125 alone also decreased benign false positives. The test performance suggests that our test may be useful in average and/or high-risk ovarian cancer screening. Future studies will explore this in asymptomatic populations. Citation Format: Emily S. Winn-Deen, Sanchari Banerjee, Daniel Gusenleitner, Laura T. Bortolin, Jonian Grosha, Kelly M. Biette, Karen Copeland, Christopher R. Sedlak, Bilal Hamzeh, Anthony D. Couvillon, Delaney M. Byrne, Peter A. Duff, Lauren T. Cuoco, MacKenzie S. King, Aleksandra Gentry-Maharaj, Sophia Apostolidou, Dawn R. Mattoon, Christine D. Berg, David F. Ransohoff, Steven J. Skates, Usha Menon. Improving specificity for ovarian cancer screening using a novel extracellular vesicle-based blood test [abstract]. In: Proceedings of the AACR Special Conference on Ovarian Cancer; 2023 Oct 5-7; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(5 Suppl_2):Abstract nr A040.

Colocalization of protein and microRNA markers reveals unique extracellular vesicle subpopulations for early cancer detection.

Extracellular vesicles (EVs) play important roles in cell-cell communication but are highly heterogeneous, and each vesicle has dimensions smaller than 200 nm with very limited amounts of cargos encapsulated. The technique of NanOstirBar (NOB)-EnabLed Single Particle Analysis (NOBEL-SPA) reported in the present work permits rapid inspection of single EV with high confidence by confocal fluorescence microscopy, thus enables colocalization assessment for selected protein and microRNA (miRNA) markers in the EVs produced by various cell lines, or present in clinical sera samples. EV subpopulations marked by the colocalization of unique protein and miRNA combinations were discovered to be able to detect early-stage (stage I or II) breast cancer (BC). NOBEL-SPA can be adapted to analyze other types of cargo molecules or other small submicron biological particles. Study of the sorting of specific cargos to heterogeneous vesicles under different physiological conditions can help discover distinct vesicle subpopulations valuable in clinical examination and therapeutics development and gain better understanding of their biogenesis.

Optical Imaging of Single Extracellular Vesicles: Recent Progress and Prospects

Optical Imaging of Single Extracellular Vesicles: Recent Progress and Prospects

Nanoprojectile Secondary Ion Mass Spectrometry Enables Multiplexed Analysis of Individual Hepatic Extracellular Vesicles.

Extracellular vesicles (EVs) are nanoscale lipid bilayer particles secreted by cells. EVs may carry markers of the tissue of origin and its disease state, which makes them incredibly promising for disease diagnosis and surveillance. While the armamentarium of EV analysis technologies is rapidly expanding, there remains a strong need for multiparametric analysis with single EV resolution. Nanoprojectile (NP) secondary ion mass spectrometry (NP-SIMS) relies on bombarding a substrate of interest with individual gold NPs resolved in time and space. Each projectile creates an impact crater of 10-20 nm in diameter while molecules emitted from each impact are mass analyzed and recorded as individual mass spectra. We demonstrate the utility of NP-SIMS for statistical analysis of single EVs derived from normal liver cells (hepatocytes) and liver cancer cells. EVs were captured on antibody (Ab)-functionalized gold substrate and then labeled with Abs carrying lanthanide (Ln) MS tags (Ab@Ln). These tags targeted four markers selected for identifying all EVs, and specific to hepatocytes or liver cancer. NP-SIMS was used to detect Ab@Ln-tags colocalized on the same EV and to construct scatter plots of surface marker expression for thousands of EVs with the capability of categorizing individual EVs. Additionally, NP-SIMS revealed information about the chemical nanoenvironment where targeted moieties colocalized. Our approach allowed analysis of population heterogeneity with single EV resolution and distinguishing between hepatocyte and liver cancer EVs based on surface marker expression. NP-SIMS holds considerable promise for multiplexed analysis of single EVs and may become a valuable tool for identifying and validating EV biomarkers of cancer and other diseases.

Optical Microfiber Coated with WS2‐Supported Gold Nanobipyramids: Ultrasensitive Detecting Prostate Cancer Extracellular Vesicles in Complex Human Samples

AbstractUltrasensitive and rapid detection of single extracellular vesicles (EV) in complex biological samples requires a biosensor that possesses remarkable specificity, cost‐effectiveness, and ease of use; however, this remains a big challenge owing to the small size and high heterogeneity of EV in samples. Although reported biosensors can detect biomarkers in complex biological samples, tedious preprocessing procedures, low sensitivity, and expensive and complicated production hinder their use in early‐stage cancer diagnostics. To address these issues, an optical microfiber modified with WS2‐supported gold nanobipyramid (Au NBPs) nanointerfaces for the ultrasensitive assay of prostate cancer (PC) exosomes is proposed. Owing to the strong localized surface plasmon resonance (LSPR) hotspots of the Au NBPs coupled with the WS2 nanointerface, the sensor could ultrasensitively detect exosomes with an ultralow limit of detection (LODs) of 23.5 particles mL−1 in phosphate‐buffered saline (PBS) solution and 570.6 particles mL−1 in 10% serum. The LOD of 23.5 particles mL−1 is two orders of magnitude lower than those reported for other cutting‐edge techniques. Based on this sensing scheme, the sensor can distinguish PC serum from healthy serum, and successfully diagnose patients’ whole blood by capturing and sizing a single bioparticle. This work presents a novel method for analyzing whole blood with ultrahigh sensitivity.

Brain-derived extracellular vesicles: Potential diagnostic biomarkers for central nervous system diseases.

Extracellular vesicles (EVs) are membrane-enclosed nanovesicles secreted by cells into the extracellular space and contain functional biomolecules, e.g. signaling receptors, bioactive lipids, nucleic acids, and proteins, which can serve as biomarkers. Neurons and glial cells secrete EVs, contributing to various physiological and pathological aspects of brain diseases. EVs confer their role in the bidirectional crosstalk between the central nervous system (CNS) and the periphery owing to their distinctive ability to cross the unique blood-brain barrier (BBB). Thus, EVs in the blood, cerebrospinal fluid (CSF), and urine can be intriguing biomarkers, enabling the minimally invasive diagnosis of CNS diseases. Although there has been an enormous interest in evaluating EVs as promising biomarkers, the lack of ultra-sensitive approaches for isolating and detecting brain-derived EVs (BDEVs) has hindered the development of efficient biomarkers. This review presents the recent salient findings of exosomal biomarkers, focusing on brain disorders. We summarize highly sensitive sensors for EV detection and state-of-the-art methods for single EV detection. Finally, the prospect of developing advanced EV analysis approaches for the non-invasive diagnosis of brain diseases is presented.

Engineering a tunable micropattern-array assay to sort single extracellular vesicles and particles to detect RNA and protein in situ.

The molecular heterogeneity of extracellular vesicles (EVs) and the co-isolation of physically similar particles, such as lipoproteins (LPs), confounds and limits the sensitivity of EV bulk biomarker characterization. Herein, we present a single-EV and particle (siEVP) protein and RNA assay (siEVP PRA) to simultaneously detect mRNAs, miRNAs, and proteins in subpopulations of EVs and LPs. The siEVP PRA immobilizes and sorts particles via positive immunoselection onto micropatterns and focuses biomolecular signals in situ. By detecting EVPs at a single-particle resolution, the siEVP PRA outperformed the sensitivities of bulk-analysis benchmark assays for RNA and protein. To assess the specificity of RNA detection in complex biofluids, EVs from various glioma cell lines were processed with small RNA sequencing, whereby two mRNAs and two miRNAs associated with glioblastoma multiforme (GBM) were chosen for cross-validation. Despite the presence of single-EV-LP co-isolates in serum, the siEVP PRA detected GBM-associated vesicular RNA profiles in GBM patient siEVPs. The siEVP PRA effectively examines intravesicular, intervesicular, and interparticle heterogeneity with diagnostic promise.

Assessing Extracellular Vesicles in Human Biofluids Using Flow-Based Analyzers.

Extracellular vesicles (EVs) are increasingly being analyzed by flow cytometry. Yet their minuscule size and low refractive index cause the scatter intensity of most EVs to fall below the detection limit of most flow cytometers. A new class of devices, known as spectral flow analyzers, are becoming standards in cell phenotyping studies, largely due to their unique capacity to detect a vast panel of markers with higher sensitivity for light scatter detection. Another class of devices, known as nano-analyzers, provides high-resolution detection of sub-micron-sized particles. Here, the EV phenotyping performance between the Aurora (Cytek) spectral cell analyzer and the NanoFCM (nFCM) nanoflow analyzer are compared. These two devices are specifically chosen given their lead in becoming gold standards in their respective fields. Immune cell-derived EVs remain poorly characterized despite their clinical potential. Therefore, B- and T-cell line-derived EVs and donor-matched human biofluid-derived EVs from plasma, urine, and saliva are used in combination with a panel of established immune markers for this comparative study. A comparative evaluation of both cytometry platforms is performed, discussing their potential and suitability for different applications. It is found that nFCM can accurately i) analyze small EVs (40-200 nm) matching the size accuracy of electron microscopy; ii) measure the concentration of a single EV particle per volume; iii) identify underrepresented EV marker subsets; and iv) provide co-localization of EV surface markers. It can also be shown that human sample biofluids have unique EV marker signatures that can have future clinical relevance. Finally, nFCM and Aurora have their unique strength, preferred fashion of data acquisition, and visualization to fit different research interests.

Double Digital Assay for Single Extracellular Vesicle and Single Molecule Detection.

Extracellular vesicles (EVs) have emerged as a promising source of biomarkers for disease diagnosis. However, current diagnostic methods for EVs present formidable challenges, given the low expression levels of biomarkers carried by EV samples, as well as their complex physical and biological properties. Herein, a highly sensitive double digital assay is developed that allows for the absolute quantification of individual molecules from a single EV. Because the relative abundance of proteins is low for a single EV, tyramide signal amplification (TSA) is integrated to increase the fluorescent signal readout for evaluation. With the integrative microfluidic technology, the technology's ability to compartmentalize single EVs is successfully demonstrated, proving the technology's digital partitioning capacity. Then the device is applied to detect single PD-L1 proteins from single EVs derived from a melanoma cell line and it is discovered that there are ≈2.7 molecules expressed per EV, demonstrating the applicability of the system for profiling important prognostic and diagnostic cancer biomarkers for therapy response, metastatic status, and tumor progression. The ability to accurately quantify protein molecules of rare abundance from individual EVs will shed light on the understanding of EV heterogeneity and discovery of EV subtypes as new biomarkers.

Monitoring of single extracellular vesicle heterogeneity in cancer progression and therapy.

Cancer cells actively release lipid bilayer extracellular vesicles (EVs) that affect their microenvironment, favoring their progression and response to extracellular stress. These EVs contain dynamically regulating molecular cargos (proteins and nucleic acids) selected from their parental cells, representing the active biological functionality for cancer progression. These EVs are heterogeneous according to their size and molecular composition and are usually defined based on their biogenetic mechanisms, such as exosomes and ectosomes. Recent single EV detection technologies, such as nano-flow cytometry, have revealed the dynamically regulated molecular diversity within bulk EVs, indicating complex EV heterogeneity beyond classical biogenetic-based EV subtypes. EVs can be changed by internal oncogenic transformation or external stress such as chemotherapy. Among the altered combinations of EV subtypes, only a specific set of EVs represents functional molecular cargo, enabling cancer progression and immune modulation in the tumor microenvironment through their altered targeting efficiency and specificity. This review covers the heterogeneity of EVs discovered by emerging single EV analysis technologies, which reveal the complex distribution of EVs affected by oncogenic transformation and chemotherapy. Encouragingly, these unique molecular signatures in individual EVs indicate the status of their parental cancer cells. Thus, precise molecular profiling of circulating single EVs would open new areas for in-depth monitoring of the cancer microenvironment and shed new light on non-invasive diagnostic approaches using liquid biopsy.

Extracellular Vesicles: Techniques and Biomedical Applications Related to Single Vesicle Analysis.

Extracellular vesicles (EVs) are extensively dispersed lipid bilayer membrane vesicles involved in the delivery and transportation of molecular payloads to certain cell types to facilitate intercellular interactions. Their significant roles in physiological and pathological processes make EVs outstanding biomarkers for disease diagnosis and treatment monitoring as well as ideal candidates for drug delivery. Nevertheless, differences in the biogenesis processes among EV subpopulations have led to a diversity of biophysical characteristics and molecular cargos. Additionally, the prevalent heterogeneity of EVs has been found to substantially hamper the sensitivity and accuracy of disease diagnosis and therapeutic monitoring, thus impeding the advancement of clinical applications. In recent years, the evolution of single EV (SEV) analysis has enabled an in-depth comprehension of the physical properties, molecular composition, and biological roles of EVs at the individual vesicle level. This review examines the sample acquisition tactics prior to SEV analysis, i.e., EV isolation techniques, and outlines the current state-of-the-art label-free and label-based technologies for SEV identification. Furthermore, the challenges and prospects of biomedical applications based on SEV analysis are systematically discussed.