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Extracellular vesicles (EVs) are highly heterogeneous and comprise a diverse set of surface protein markers as well as intra-vesicular cargoes. To fully understand the heterogeneity of EVs and to identify the relevant EV subpopulations of interest, it is important to study EVs using combination biomarkers with single-EV sensitive techniques. Here, I describe and discuss the current state-of-the-art in single-EV analysis, with emphasis on flow and imaging based approaches.

Research on extracellular vesicles (EVs) and particles (EPs) has exploded in recent years. Originally seen as curiosities found outside of cells and in blood or other biofluids, these entities are now the subject of intense study in nearly every biologic system and almost any disease we know of. Areas of research range from basic biology (fundamental origins of EV-Ps), to cargo characterization of EV-Ps, to their effects as purveyors of either pathologies or therapies. This talk will highlight examples of EV-Ps driving pathology, with a focus on glial scarring and fibrosis, and then delve into therapeutic strategies involving EV-Ps with an eye towards both their pros and cons.

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Extracellular vesicles (EVs) are membrane-encapsulated nanoparticles shed from all cells. In addition to their critical roles in cellular functions, EVs are emerging as exciting therapeutic delivery vectors and biomarker candidates. Our group has recently developed Single Extracellular VEsicle Nanoscopy (SEVEN), which combines affinity isolation of EVs with super-resolution microscopy to enable a multiparametric characterization of EVs. With ~7 nm precision and molecular sensitivity, this method can robustly provide critical parameters of individual EVs: concentration, size, shape, molecular cargo content, and heterogeneity. We show that SEVEN can specifically detect rare EV populations, including tissue-associated and disease-associated EVs. Because it attains nanoscale precision, SEVEN can also detect small non-vesicular extracellular nanoparticles. Importantly, EV studies may be democratized via a new method that employs super-resolution microscopy and advanced algorithms supported by machine learning-based EV assessment. Our approach – which we termed SEVEN-universal protocol (SEVEN-UP) – can robustly assess EV concentration, size, and molecular content. Altogether, SEVEN and SEVEN-UP may be important tools for significantly propelling our knowledge-base of EVs forward.

Nanoparticle Tracking Analysis (NTA) has emerged as a fast and vital characterization technology for Extracellular Vesicles (EVs), Exosomes and other biological material in the size range from 30 nm to 1 μm. While classic NTA scatter operation feeds back the size and total particle concentration, the user typically cannot discriminate whether the particle is a vesicle, protein aggregate, cellular trash or an inorganic precipitate. The fluorescence detection capabilities of f-NTA however enables the user to gain specific biochemical information for phenotyping of all kinds of vesicles and viruses. Alignment-free switching between excitation wavelengths and measurement modes (scatter and fluorescence) allow quantification of biomarker ratios such as the tetraspanins (CD63, CD81 and CD9) within minutes. Furthermore, specific colocalization studies using c-NTA gives a deeper understanding of the composition of biomarker on single particle.

Extracellular vesicles (EV) have been gaining attention as a promising biomarker for cancer diagnostics and therapeutic monitoring. For example, in recipients of PD1/PD-L1 immune checkpoint inhibitors – a key pillar of cancer immunotherapy, EV-derived PD-L1 molecules are increasingly recognized as a non-invasive key indicator of therapeutic responsiveness. Conventional methods to measure EV-derived PD-L1 molecules requires ultracentrifugation to separate EVs and quantify those that express PD-L1 using nanoscale flow cytometry. These are tedious processes that require trained technologists in centralized facilities, which is a key barrier to the close monitoring of these EV-based indicators of therapeutic responsiveness. However, frequently evaluating the immune responsiveness to the immunotherapies is essential, because prolonged use of a futile therapy can lead to the loss of time in the race with the fatal disease, as well as financial burden on patients, healthcare systems and economy. To bridge the gap, we develop a proof-of-concept microfluidic device that can separate EVs directly from biofluids and quantify PD-L1-positive EVs automatedly. In this device, EVs can be extracted by immunomagnetic beads, and the PD-L1 expression level can be quantified with the electrochemical sensor on-chip < 2 hours. Meanwhile, the device can enable 10X increase in the number of EVs extracted compared with in-tube extraction, with the ability to distinguish 10X differences in EV concentration. This tool can ultimately help to identify futile therapies quickly and prompt personalized alternatives, as well as to collect densely time-longitudinal data points so that we can extrapolate future response trajectories and thus inform strategies to curve the outcome.

Multiple Sclerosis is a chronic inflammatory demyelinating disorder of the central nervous system (CNS). Pediatric-onset MS represents 3-10% of all MS cases. The cause of MS is unknown, however it is driven by a complex interplay of genetic and environmental factors. Interestingly, childhood obesity has been shown to be an independent risk factor for MS but the underlying cellular and molecular mechanisms remain enigmatic. Extracellular vesicles (EVs) have gained increased attention as novel orchestrators of intercellular communication and key mediators of CNS neuroimmune cross-talk and adipose-tissue metabolic cross-talk. We present a systematic approach for establishing a tissue-specific EV signature which can then be applied to circulating biomarker discovery.

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Extracellular vesicles include a range of submicron particles, including small exosomes derived from endosomal biogenesis, larger microvesicles that bud directly from the cell membrane, and fragments of cells undergoing necrosis and apoptosis. These lipid-encapsulated EVs contain a variety of RNA species (including microRNAs) that provide insights into their cell source and potential function. Methods like size exclusion chromatography (SEC) provide a rich heterogeneous mixture of EVs from a variety of cell sources, and in a variety of sizes, but it lacks cell-specificity. Affinity capture lacks size-specificity, which is important because larger microvesicles have entirely different cargo than small EVs and larger EV contents are present in exponentially greater amounts than EVs with smaller diameters. In collaboration with BD Biosciences, we have developed a multiplex nanoscale high resolution flow cytometry (nanoFACS) platform to image, count, and isolate EVs. We can flow sort cell- and size-specific EVs and compare their contents with 100nm liposomes spiked into the same biofluid to control for background contamination inherent in any other EV isolation method. We have also developed a customized 50um nanoFACS flow sorting nozzle with protocol refinements that significantly advanced our methodological capabilities to isolate cell- and size-specific EVs more efficiently for cargo and functional analyses.

Viral infections and resultant inflammation are major risk factors for miscarriage and later obstetrical complications including preeclampsia and intrauterine growth restriction. The Tong lab is interested in examining the impact of virus-induced inflammation on the preparation of the maternal endometrium for implantation and the roles that endometrial extracellular vesicles may play in dictating early trophoblast function and subsequent pregnancy success.

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Cancer-derived extracellular vesicle (EV) nanoparticles released into circulation carry with them a wide range of important biomarkers. One of the main challenges of using them for clinical diagnostic applications is the difficulty of recovering them from plasma, which is a critical step for biomarker quantification. Here we demonstrate the use of dielectrophoresis (DEP) microelectrode array chip technology to recover these nanoparticles directly from plasma in a label free way that relies on the dielectric properties of the EVs as whole particles rather than on specific biochemical characteristics. Furthermore, we demonstrate the ability to analyze several different classes of biomarkers directly on the chip after EV collection and purification. This includes the overall concentration of cell-free DNA with its cancer-related mutations, protein biomarkers that are over-expressed by the tumor, and protease activity associated with the nanoparticles. We demonstrate the efficacy of this diagnostic technique through the combination of the cell-free DNA and the protein biomarkers to successfully differentiate patients with pancreatic cancer from patients with benign pancreatic disease. Using a support vector machine learning algorithm analysis, we show that the AUC of this DEP-based technique is 0.93 which increases to 0.97 when applied to patients over the age of 50. This is compared to the standard invasive endoscopic ultrasound-guided fine needle aspiration diagnostic procedure that has an AUC of 0.79. We show that protease activity on the DEP isolated EVs can be used to successfully different between patients with pancreatic cancer and patients with benign pancreatic cysts. We also show the collection of cf-DNA nanoparticles that contain mutant KRAS fragments. Together, the ability to quickly and efficiently recover EV nanoparticles directly from plasma and analyze biomarker content directly on the chip demonstrates the necessary characteristics for translation to the clinical laboratory setting.

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This talk will present data to support that microRNAs in cerebrospinal fluid from living donors can be used as biomarkers for Alzheimer’s disease.

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This talk will present data to support that extracellular vesicles can be isolated from small volumes of fed- and digested human milk and show that isolated vesicles are absorbed by primary neonatal epithelial cells via endocytosis.

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