Subpopulations of extracellular vesicles from human metastatic melanoma tissue identified by quantitative proteomics after optimized isolation. Issue 1 (1st January 2020) - Crescitelli, Rossella; Lässer, Cecilia; Jang, Su Chul; Cvjetkovic, Aleksander; Malmhäll, Carina; Karimi, Nasibeh; Höög, Johanna L.; Johansson, Iva; Fuchs, Johannes; Thorsell, Annika; Gho, Yong Song; Olofsson Bagge, R.; et al.
ABSTRACT: The majority of extracellular vesicle (EV) studies conducted to date have been performed on cell lines with little knowledge on how well these represent the characteristics of EVs  in vivo . The aim of this study was to establish a method to isolate and categorize subpopulations of EVs isolated directly from tumour tissue. First we established an isolation protocol for subpopulations of EVs from metastatic melanoma tissue, which included enzymatic treatment (collagenase D and DNase). Small and large EVs were isolated with differential ultracentrifugation, and these were further separated into high and low-density (HD and LD) fractions. All EV subpopulations were then analysed in depth using electron microscopy, Bioanalyzer®, nanoparticle tracking analysis, and quantitative mass spectrometry analysis. Subpopulations of EVs with distinct size, morphology, and RNA and protein cargo could be isolated from the metastatic melanoma tissue. LD EVs showed an RNA profile with the presence of 18S and 28S ribosomal subunits. In contrast, HD EVs had RNA profiles with small or no peaks for ribosomal RNA subunits. Quantitative proteomics showed that several proteins such as flotillin-1 were enriched in both large and small LD EVs, while ADAM10 were exclusively enriched in small LD EVs. In contrast, mitofilin was enriched only in the large EVs. We conclude that enzymatic treatments improve EV isolation from dense fibrotic tissue without any apparent effect on molecular or morphological characteristics. By providing a detailed categorization of several subpopulations of EVs isolated directly from tumour tissues, we might better understand the function of EVs in tumour biology and their possible use in biomarker discovery.