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Extracellular vesicles (EVs), membrane-encapsulated nanoparticles shed from all cells, are tightly involved in critical cellular functions. Moreover, EVs have recently emerged as exciting therapeutic modalities, delivery vectors, and biomarker sources. However, EVs are difficult to characterize, because they are typically small and heterogeneous in size, origin, and molecular content. Recent advances in single EV methods have addressed some of these challenges by providing sensitive tools for assessing individual vesicles; one example is our recently developed Single Extracellular VEsicle Nanoscopy (SEVEN) approach. However, these tools are typically not universally available to the general research community, as they require highly specialized equipment. Here, we show how single EV studies may be democratized via a novel method that employs super-resolution radial fluctuations (SRRF) microscopy and advanced data analysis. SRRF is compatible with a wide range of microscopes and fluorophores. We herein quantified individual EVs by combining affinity isolation (analytical protocol based on SEVEN) with SRRF microscopy and new analysis algorithms supported by machine learning-based EV assessment. Using SEVEN, we first optimized the workflow and validated the data obtained on wide-field and total internal reflection fluorescence microscopes. We further demonstrated that our approach, which we call the SEVEN-Universal Protocol (SEVEN-UP), can robustly assess the number, size, and content of plasma and recombinant EVs. Finally, we used the platform to assess RNA in EVs from conditioned cell culture media. Using SYTO RNASelect dye, we found that 18% of EVs from HEK 293T cells appear to contain RNA; these EVs were significantly larger compared with the general EV population. Altogether, we developed an economical, multiparametric, single EV characterization approach for the research community.

Background: Patients with HER2-positive breast cancer can significantly benefit from HER2-directed therapy - such as the monoclonal antibody trastuzumab. However, some patients can develop therapy resistance or change HER2 status. Thus, we urgently need new, noninvasive strategies to monitor patients frequently. Extracellular vesicles (EVs) secreted from tumor cells are emerging as potential biomarker candidates. These membrane-delimited nanoparticles harbor molecular signatures of their origin cells; report rapidly on changes to cellular status; and can be frequently sampled from accessible biofluids., Results: Using Single Extracellular VEsicle Nanoscopy (SEVEN) platform that combines affinity isolation of EVs with super-resolution microscopy, here we provide multiparametric characterization of EVs with ~ 8 nm precision and molecular sensitivity. We first interrogated cell culture EVs affinity-enriched in tetraspanins CD9, CD63, and CD81; these transmembrane proteins are commonly found on EV membranes. SEVEN robustly provided critical parameters of individual, tetraspanin-enriched EVs: concentration, size, shape, molecular cargo content, and heterogeneity. Trastuzumab-resistant cells (vs. trastuzumab-sensitive) secreted more EVs. Additionally, EVs from trastuzumab-resistant cells had lower tetraspanin density and higher HER2 density. We also evaluated EVs affinity-enriched in HER2; we found that these EVs (vs. tetraspanin-enriched) were larger and more elongated. We further optimized analytical sample processing to assess a rare population of HER2-enriched EVs from patient plasma. In breast cancer patients with elevated HER2 protein expression (vs. controls), HER2-enriched EVs had distinct characteristics including typically increased number of tetraspanin molecules and larger size. Importantly, these EVs were on average 25-fold more abundant compared to no cancer controls., Conclusions: SEVEN revealed unique characteristics of HER2-enriched EVs in cultured cells and complex biological fluid. In combination with current clinical approaches, this method is well poised to support precise therapeutic decisions., (© 2024. The Author(s).)

Alzheimer's disease (AD), due to its multifactorial nature and complex etiology, poses challenges for research, diagnosis, and treatment, and impacts millions worldwide. To address the need for minimally invasive, repeatable measures that aid in AD diagnosis and progression monitoring, studies leveraging RNAs associated with extracellular vesicles (EVs) in human biofluids have revealed AD-associated changes. However, the validation of AD biomarkers has suffered from the collection of samples from differing points in the disease time course or a lack of confirmed AD diagnoses. Here, we integrate clinical diagnosis and postmortem pathology data to form more accurate experimental groups and use small RNA sequencing to show that EVs from plasma can serve as a potential source of RNAs that reflect disease-related changes. Importantly, we demonstrated that these changes are identifiable in the EVs of preclinical patients, years before symptom manifestation, and that machine learning models based on differentially expressed RNAs can help predict disease conversion or progression. This research offers critical insight into early disease biomarkers and underscores the significance of accounting for disease progression and pathology in human AD studies.

Extracellular vesicles (EVs) and their cargo constitute novel biomarkers. EV subpopulations have been defined not only by abundant tetraspanins (e.g., CD9, CD63 and CD81) but also by specific markers derived from their source cells. However, it remains a challenge to robustly isolate and characterize EV subpopulations. Here, we combined affinity isolation with super-resolution imaging to comprehensively assess EV subpopulations from human plasma. Our Single Extracellular VEsicle Nanoscopy (SEVEN) assay successfully quantified the number of affinity-isolated EVs, their size, shape, molecular tetraspanin content, and heterogeneity. The number of detected tetraspanin-enriched EVs positively correlated with sample dilution in a 64-fold range (for SEC-enriched plasma) and a 50-fold range (for crude plasma). Importantly, SEVEN robustly detected EVs from as little as ∼0.1 μL of crude plasma. We further characterized the size, shape and molecular tetraspanin content (with corresponding heterogeneities) for CD9-, CD63- and CD81-enriched EV subpopulations. Finally, we assessed EVs from the plasma of four pancreatic ductal adenocarcinoma patients with resectable disease. Compared to healthy plasma, CD9-enriched EVs from patients were smaller while IGF1R-enriched EVs from patients were larger, rounder and contained more tetraspanin molecules, suggestive of a unique pancreatic cancer-enriched EV subpopulation. This study provides the method validation and demonstrates that SEVEN could be advanced into a platform for characterizing both disease-associated and organ-associated EV subpopulations., (© 2023 The Authors. Journal of Extracellular Vesicles published by Wiley Periodicals, LLC on behalf of the International Society for Extracellular Vesicles.)

The Extracellular RNA Communication Consortium (ERCC) was launched to accelerate progress in the new field of extracellular RNA (exRNA) biology and to establish whether exRNAs and their carriers, including extracellular vesicles (EVs), can mediate intercellular communication and be utilized for clinical applications. Phase 1 of the ERCC focused on exRNA/EV biogenesis and function, discovery of exRNA biomarkers, development of exRNA/EV-based therapeutics, and construction of a robust set of reference exRNA profiles for a variety of biofluids. Here, we present progress by ERCC investigators in these areas, and we discuss collaborative projects directed at development of robust methods for EV/exRNA isolation and analysis and tools for sharing and computational analysis of exRNA profiling data., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Cells secrete extracellular RNA (exRNA) to their surrounding environment and exRNA has been found in many body fluids such as blood, breast milk and cerebrospinal fluid. However, there are conflicting results regarding the nature of exRNA. Here, we have separated 2 distinct exRNA profiles released by mast cells, here termed high-density (HD) and low-density (LD) exRNA. The exRNA in both fractions was characterized by microarray and next-generation sequencing. Both exRNA fractions contained mRNA and miRNA, and the mRNAs in the LD exRNA correlated closely with the cellular mRNA, whereas the HD mRNA did not. Furthermore, the HD exRNA was enriched in lincRNA, antisense RNA, vault RNA, snoRNA, and snRNA with little or no evidence of full-length 18S and 28S rRNA. The LD exRNA was enriched in mitochondrial rRNA, mitochondrial tRNA, tRNA, piRNA, Y RNA, and full-length 18S and 28S rRNA. The proteomes of the HD and LD exRNA-containing fractions were determined with LC-MS/MS and analyzed with Gene Ontology term finder, which showed that both proteomes were associated with the term extracellular vesicles and electron microscopy suggests that at least a part of the exRNA is associated with exosome-like extracellular vesicles. Additionally, the proteins in the HD fractions tended to be associated with the nucleus and ribosomes, whereas the LD fraction proteome tended to be associated with the mitochondrion. We show that the 2 exRNA signatures released by a single cell type can be separated by floatation on a density gradient. These results show that cells can release multiple types of exRNA with substantial differences in RNA species content. This is important for any future studies determining the nature and function of exRNA released from different cells under different conditions.

Extracellular microvesicles (EVs) have been recognized for many potential clinical applications including biomarkers for disease diagnosis. In this study, we identified a major population of EVs by simply screening fluid samples with a nanosizer. Unlike other EVs, this extracellular nanovesicle (named HG-NV, HG-NV stands for HomoGenous nanovesicle as well as for Huang-Ge- nanovesicle) can be detected with a nanosizer with minimal in vitro manipulation and are much more homogenous in size (8-12 nm) than other EVs. A simple filtration platform is capable of separating HG-NVs from peripheral blood or cell culture supernatants. In comparison with corresponding exosome profiles, HG-NVs released from both mouse and human breast tumor cells are enriched with RNAs. Tumor derived HG-NVs are more potent in promoting tumor progression than exosomes. In summary, we identified a major subset of EVs as a previously unrecognized nanovesicle. Tumor cell derived HG-NVs promote tumor progression. Molecules predominantly present in breast tumor HG-NVs have been identified and characterized. This discovery may have implications in advancing both microvesicle biology research and clinical management including potential used as a biomarker., Competing Interests: No potential conflicts of interest were disclosed by the author.