Fluorescence-Based Nanoparticle Tracking Analysis and Flow Cytometry for Characterization of Endothelial Extracellular Vesicle Release

Abstract

As extracellular vesicles (EVs) have become a prominent topic in life sciences, a growing number of studies are published on a regular basis addressing their biological relevance and possible applications. Nevertheless, the fundamental question of the true vesicular nature as well as possible influences on the EV secretion behavior have often been not adequately addressed. Furthermore, research regarding endothelial cell-derived EVs (EndoEVs) often focused on the large vesicular fractions comprising of microvesicles (MV) and apoptotic bodies. In this study we aimed to further extend the current knowledge of the influence of pre-isolation conditions, such as cell density and conditioning time, on EndoEV release from human umbilical vein endothelial cells (HUVECs). We combined fluorescence nanoparticle tracking analysis (NTA) and the established fluorescence-triggered flow cytometry (FT-FC) protocol to allow vesicle-specific detection and characterization of size and surface markers. We found significant effects of cell density and conditioning time on both abundance and size distribution of EndoEVs. Additionally, we present detailed information regarding the surface marker display on EVs from different fractions and size ranges. Our data provide crucial relevance for future projects aiming to elucidate EV secretion behavior of endothelial cells. Moreover, we show that the influence of different conditioning parameters on the nature of EndoEVs has to be considered.

Dysfunctional Vascular Endothelium as a Driver of Atherosclerosis: Emerging Insights Into Pathogenesis and Treatment

“…The continued integration of EV profiling in patients with atherosclerosis (i.e., local EVs in plaque; circulating EVs in plasma) with experimental models of EV activity will shed light on the causal role of EVs and EV-contents in the setting of atherosclerotic CVD. Such models will require the transition from in vitro co-culture towards in vivo tracking approaches to allow for a robust, physiological characterization of EV activity, which will be made possible with advances in the visualization of EV release and uptake ( Durak-Kozica et al, 2018 ; Oesterreicher et al, 2020 ). With respect to the diagnostic potential of vascular EVs, the application of machine learning strategies to predict CVD from circulating EV biomarkers has garnered recent interest ( Burrello et al, 2020 ; Castellani et al, 2020 ) and will likely play an important role in the development of precision cardiovascular medicine over the coming decades.…”

Characterisation of Extracellular Vesicles from Equine Mesenchymal Stem Cells

“…UC produced more particles of the desired size range (≤200 nm) per millilitre of supernatant than SEC due to its higher isolation efficacy. The size cutoffs for FT-FC were chosen based on a gating with fluorescence-labelled silica beads of defined sizes, which most closely approximated the dimensions of the different EV subtypes (small ≤200 nm, intermediate 200–500 nm and large >500 nm) [ 60 ]. The beads served as a size reference for the distinction of the EV subtypes to separate exosomes (the particles of interest for this study) from the other subtypes because exosomes (30–150 nm) have a defined mode of biogenesis and are highly interesting for regenerative applications, because they have been shown to play essential roles in intercellular communication [ 61 , 62 ].…”

Characterization of Extracellular Vesicles from Bronchoalveolar Lavage Fluid and Plasma of Patients with Lung Lesions Using Fluorescence Nanoparticle Tracking Analysis

“…Similar observations were made by other researchers. Oesterreicher et al detected higher EV-specific CD63 and CD81 marker expression in the small vesicle range (<200 nm) than in the intermediate and large ranges [ 42 ]. Staining with quantum dots for CD9 of canine MSC-derived EVs resulted in the detection of small particles ranging in size from 30–100 nm, which were significantly less numerous in scatter mode [ 43 ].…”