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To outsmart cancer, we must move beyond treating disease to anticipating and intercepting it. Extracellular vesicles hold transformative potential: by decoding the signals they carry, we can uncover cancer at its earliest and most curable stages; by reengineering them, we can reshape communication between tumors and the immune system; and by harnessing them as precision delivery vehicles, we may turn cancer’s own biology into its greatest vulnerability. In this presentation, we will highlight the emerging opportunities extracellular vesicles offer for cancer detection, immune modulation, and targeted therapy.

We have developed a multi-parametric high-throughput and high-sensitivity flow-based method for counting single molecules, and applied this method to the analysis of individual extracelluar vesicles and particles (EVPs). EVPs are promising biomarkers but they are highly heterogeneous and comprise a diverse set of surface proteins as well as intra-vesicular cargoes. Yet, current approaches to the study of EVPs lack the necessary sensitivity and precision to fully characterize and understand the make-up and the distribution of various EVP sub-populations that may be present. Digital flow cytometry (dFC) provides single-fluorophore sensitivity and enables multiparameter characterization of EVPs, including single-EVP phenotyping, the absolute quantitation of EVP concentrations, and biomarker copy numbers. dFC has a broad range of applications, from analysis of single EVPs such as exosomes or RNA-binding proteins to the characterization of therapeutic lipid nano¬particles, viruses, and proteins. dFC also provides absolute quantitation of non-EVP samples such as for the quality control of antibodies (Ab), including the concentration of individual and aggregated Ab-dye conjugates and the Ab-to-dye ratio.

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Semen has one of the highest concentrations of extracellular vesicles of any biofluid. For the past twelve years we have been investigating how EVs and soluble fractions in semen shape infection and immunity in the recipient genital tract. We have investigated inhibition of viral and bacterial infection by semen EVs. We also study the immunological effects of semen EVs, finding that they induce a tolerogenic program in dendritic cells, impairing antigen presentation. Following interaction with T cells, EV-exposed DCs induce a population of long-lived regulatory T cells which are poised to support pregnancy.

Extracellular vesicles (EVs) are continuously released by cells and represent promising candidates for disease biomarkers. However, EVs remain challenging to characterize due to their heterogeneity in size, origin, and molecular composition. To advance EVs in biology and biomedicine, tools for biophysical assessment of EVs that do not require highly specialized equipment are urgently needed. We recently developed a Single Extracellular VEsicle Nanoscopy-Universal Protocol (SEVEN-UP) platform that employs affinity isolation of EVs with super-resolution microscopy imaging and machine learning data analysis. This platform works with a wide range of microscopes and fluorescent probes. We further improved data analysis, automated batch processing, and employed a user-friendly Python application that leverages GPU hardware acceleration. We show that our approach can robustly quantify EVs with improved image resolution and EV detection. Moreover, these enhancements allowed us to assess shape of EVs; this is particularly important for disease-enriched EVs that often have unique morphology. Thus, this user-friendly technique shows great promise for single EV characterization with potential to help advance new biomarkers.

Differentiating Alzheimer’s disease (AD) from both Dementia with Lewy bodies (DLB) and Parkinson’s disease (AD) is difficult due to their clinical similarities. Plasma biomarkers offer an alternative to neuroimaging and cerebrospinal fluid analysis; however, there are limitations with respect to differential diagnosis of AD, PD, and DLB with current clinical assays. Thus, there is a need to develop new plasma biomarker assays. Here we obtained human plasma samples from 83 healthy cognitive controls (NC), 87 AD, 100 PD, and 20 DLB to assay 57 AD-associated miRNAs, and used predictive models generated by three independent machine learning methods that were evaluated by cross-validated ROC curves (cvAUC). We will show the outcome of individual assessment of the 57 miRNAs, ridge logistic regression predictive models with 10 AD-specific miRNAs, and a predictive model using data from all 57 miRNAs and elastic-net regression. We also used linear discriminant analysis with all 57 miRNAs to identify a model that best separates AD from PD+DLB participants and DLB from AD+PD participants. Target prediction and Ingenuity Pathway Analysis with specific miRNAs identified highly relevant mRNA targets associated with tauopathy, dementia, movement disorders, and sleep disorders. These data demonstrate that predictive modeling using plasma miRNA expression data may improve the differential diagnosis of AD from PD from DLB.

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Extracellular vesicles (EVs) are nanoscale and highly heterogeneous, making them challenging to analyze with conventional methods. In this presentation, we showcase ONI’s fully integrated, end-to-end workflow for EV analysis, combining standardized sample preparation, automated fluidics, super-resolution imaging, and quantitative data analysis within a single platform. By enabling nanoscale visualization and multiplexed characterization of EV populations with high reproducibility and throughput, ONI transforms EV analysis from a fragmented, labor-intensive process into a streamlined, scalable solution for both fundamental research and translational applications.

Liquid biopsy technologies offer unprecedented opportunities for noninvasive disease detection, monitoring, and biological insight. In this talk, I will present our development of a comprehensive suite of liquid biopsy approaches designed to interrogate multiple layers of cell-free biology. These include reconstruction of chromatin landscapes from fragmented cell-free nucleosomes and cfDNA, cell-free transcriptome sequencing, and extracellular vesicle (EV) characterization and molecular profiling. By integrating these complementary platforms, we enable high-resolution analysis of epigenetic, transcriptional, and vesicle-associated signals in body fluids. We demonstrate the clinical potential of these approaches across several applications: monitoring and early detection of cancer in high-risk individuals and patients with suspected malignancies; determination of cancer subtypes and dynamic tracking of subtype evolution over time; and prediction of pregnancy complications through noninvasive profiling of maternal circulation. Together, these technologies establish a multi-dimensional framework for liquid biopsy that advances precision diagnostics and real-time disease monitoring in oncology and maternal health.

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According to the MISEV2023 guidelines, the precise determination of extracellular vesicle (EV) concentration, size distribution, and biochemical composition is essential for reliable EV characterization and downstream cargo analysis. The ZetaView® Evolution serves as a precision instrument designed specifically to meet and exceed these rigorous analytical requirements. Equipped with advanced Concentration Scanning Technology, the instrument significantly improves statistical robustness and measurement reproducibility by increasing the measurement volume (from 33.3nl to 220nl) and analyzing up to 20,000 particles in just 30 seconds. This makes it highly effective for monitoring downstream process performance, allowing researchers to track EV concentration, sizing and stability calibration free. Furthermore, the system features a sensitivity-improved CMOS camera that enables the highly sensitive detection of fluorescent particles (detecting <20 AF488 molecules). This eliminates the need to rely solely on scatter-based analysis, which can often be confounded by non-vesicular extracellular particles. By combining this advanced fluorescence detection with dedicated F-NTA Tetraspanin Detection Kits, the ZetaView® Evolution allows for straightforward and reliable phenotype assessment by quantifying specific vesicles of interest through CD9-, CD63-, and CD81-positive EV subpopulations.

Ultimately, this integrated approach provides researchers with the precision tools needed for robust assessment of EV purity, specific phenotypes, and the foundational metrics required for accurate cargo delivery evaluation, ensuring full compliance with MISEV2023 recommendations.

Demeter EVPrep is a novel EV-isolation tool that separates EVs using dielectrophoresis and semiconductor technology. The Demeter EVPrep system provides a fully automated, hands-off method for obtaining high quality EV preps from various sample types. Early adopters of this technology observe improved yield and purity of EV isolations, as well as compatibility with downstream analyses like mass spectrometry.

The diagnosis of pancreatic cancer is a complex task that can benefit from liquid biopsy approaches. This requires a series of orthogonal blood-based biomarkers to account for heterogeneity in the patient population. Circulating biological nanoparticles, such as extracellular vesicles (EVs) and cell-free DNA (cfDNA), both actively and passively secreted by solid tumors, carry disease-related biomarkers that can be used to detect pancreatic cancer. To overcome the challenges associated with EV recovery and characterization, we developed a dielectrophoresis (DEP) based microfluidic platform technology that simultaneously recovers multiple nanoparticle types from undiluted plasma, including known types such as EVs and cfDNA nanoparticles. We have now shown that an additional EV subtype, consisting of organelle fragments resulting from hypoxic and necrotic-mediated cellular lysis in solid pancreatic tumors, can also be collected by DEP. These organelle fragments provide a new subset of biomarkers distinct from those carried by traditional EVs. DEP selectively isolates and concentrates EVs, cfDNA, and organelle fragments from human plasma onto microelectrodes for on-chip fluorescent quantification of surface biomarkers, thereby enabling orthogonal biomarker analysis within a single system. In addition, this DEP approach enables the fabrication of high-throughput microelectrode well-plate devices that allow simultaneous isolation and analysis of up to 384 samples. This platform successfully distinguishes early-stage pancreatic cancer from benign diseases and precancerous lesions with an AUC of 0.93, outperforming endoscopic ultrasound-guided biopsy (AUC 0.79) and demonstrating DEP's potential for clinically translatable nanoparticle-based diagnostics.

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