The last decade has seen a sharp increase in the number of scientific publications describing physiological and pathological functions of extracellular vesicles (EVs), a collective term covering various subtypes of cell‐released, membranous structures, called exosomes, microvesicles, microparticles, ectosomes, oncosomes, apoptotic bodies, and many other names. However, specific issues arise when working with these entities, whose size and amount often make them difficult to obtain as relatively pure preparations, and to characterize properly. The International Society for Extracellular Vesicles (ISEV) proposed Minimal Information for Studies of Extracellular Vesicles (“MISEV”) guidelines for the field in 2014. We now update these “MISEV2014” guidelines based on evolution of the collective knowledge in the last four years. An important point to consider is that ascribing a specific function to EVs in general, or to subtypes of EVs, requires reporting of specific information beyond mere description of function in a crude, potentially contaminated, and heterogeneous preparation. For example, claims that exosomes are endowed with exquisite and specific activities remain difficult to support experimentally, given our still limited knowledge of their specific molecular machineries of biogenesis and release, as compared with other biophysically similar EVs. The MISEV2018 guidelines include tables and outlines of suggested protocols and steps to follow to document specific EV‐associated functional activities. Finally, a checklist is provided with summaries of key points.

Motivation: Extracellular vesicles (EVs) are spherical bilayered proteolipids, harboring various bioactive molecules. Due to the complexity of the vesicular nomenclatures and components, online searches for EV-related publications and vesicular components are currently challenging. Results: We present an improved version of EVpedia, a public database for EVs research. This community web portal contains a database of publications and vesicular components, identification of orthologous vesicular components, bioinformatic tools and a personalized function. EVpedia includes 6879 publications, 172 080 vesicular components from 263 high-throughput datasets, and has been accessed more than 65 000 times from more than 750 cities. In addition, about 350 members from 73 international research groups have participated in developing EVpedia. This free web-based database might serve as a useful resource to stimulate the emerging field of EV research. Availability and implementation: The web site was implemented in PHP, Java, MySQL and Apache, and is freely available at http://evpedia.info. Contact:ysgho@postech.ac.kr

Background: Lung cancer is one of the leading causes of cancer‐related death. At the time of diagnosis, more than half of the patients will have disseminated disease and, yet, diagnosing can be challenging. New methods are desired to improve the diagnostic work‐up. Exosomes are cell‐derived vesicles displaying various proteins on their membrane surfaces. In addition, they are readily available in blood samples where they constitute potential biomarkers of human diseases, such as cancer. Here, we examine the potential of distinguishing non‐small cell lung carcinoma (NSCLC) patients from control subjects based on the differential display of exosomal protein markers.Methods: Plasma was isolated from 109 NSCLC patients with advanced stage (IIIa–IV) disease and 110 matched control subjects initially suspected of having cancer, but diagnosed to be cancer free. The Extracellular Vesicle Array (EV Array) was used to phenotype exosomes directly from the plasma samples. The array contained 37 antibodies targeting lung cancer‐related proteins and was used to capture exosomes, which were visualised with a cocktail of biotin‐conjugated CD9, CD63 and CD81 antibodies.Results: The EV Array analysis was capable of detecting and phenotyping exosomes in all samples from only 10 µL of unpurified plasma. Multivariate analysis using the Random Forests method produced a combined 30‐marker model separating the two patient groups with an area under the curve of 0.83, CI: 0.77–0.90. The 30‐marker model has a sensitivity of 0.75 and a specificity of 0.76, and it classifies patients with 75.3% accuracy.Conclusion: The EV Array technique is a simple, minimal‐invasive tool with potential to identify lung cancer patients.

Cells release lipid-bound extracellular vesicles (EVs; exosomes, microvesicles and apoptotic bodies) containing proteins, lipids and RNAs into the circulation. Vesicles mediate intercellular communication between both neighboring and distant cells. There is substantial interest in using EVs as biomarkers for age-related diseases including cancer, and neurodegenerative, metabolic and cardiovascular diseases. The majority of research focuses on identifying differences in EVs when comparing disease states and matched controls. Here, we analyzed circulating plasma EVs in a cross-sectional and longitudinal study in order to address age-related changes in community-dwelling individuals. We found that EV concentration decreases with advancing age. Furthermore, EVs from older individuals were more readily internalized by B cells and increased MHC-II expression on monocytes compared with EVs from younger individuals, indicating that the decreased concentration of EVs with age may be due in part to increased internalization. EVs activated both monocytes and B cells, and activation of B cells by LPS enhanced EV internalization. We also report a relative stability of EV concentration and protein amount in individual subjects over time. Our data provide important information towards establishing a profile of EVs with human age, which will further aid in the development of EV-based diagnostics for aging and age-related diseases.

Physical activity initiates a wide range of multi‐systemic adaptations that promote mental and physical health. Recent work demonstrated that exercise triggers the release of extracellular vesicles (EVs) into the circulation, possibly contributing to exercise‐associated adaptive systemic signalling. Circulating EVs comprise a heterogeneous collection of different EV‐subclasses released from various cell types. So far, a comprehensive picture of the parental and target cell types, EV‐subpopulation diversity and functional properties of EVs released during exercise (ExerVs) is lacking. Here, we performed a detailed EV‐phenotyping analysis to explore the cellular origin and potential subtypes of ExerVs.Healthy male athletes were subjected to an incremental cycling test until exhaustion and blood was drawn before, during, and immediately after the test. Analysis of total blood plasma by EV Array suggested endothelial and leukocyte characteristics of ExerVs. We further purified ExerVs from plasma by size exclusion chromatography as well as CD9‐, CD63‐ or CD81‐immunobead isolation to examine ExerV‐subclass dynamics. EV‐marker analysis demonstrated increasing EV‐levels during cycling exercise, with highest levels at peak exercise in all EV‐subclasses analysed. Phenotyping of ExerVs using a multiplexed flow‐cytometry platform revealed a pattern of cell surface markers associated with ExerVs and identified lymphocytes (CD4, CD8), monocytes (CD14), platelets (CD41, CD42, CD62P), endothelial cells (CD105, CD146) and antigen presenting cells (MHC‐II) as ExerV‐parental cells. We conclude that multiple cell types associated with the circulatory system contribute to a pool of heterogeneous ExerVs, which may be involved in exercise‐related signalling mechanisms and tissue crosstalk.