Dataset Information

Optimizing extracellular vesicles' isolation from chronic lymphocytic leukemia patient plasma and cell line supernatant.

ABSTRACT: In chronic lymphocytic leukemia (CLL) and very likely all cancer types, extracellular vesicles (EVs) are a common mechanism by which intercellular messages are communicated between normal, diseased, and transformed cells. Studies of EVs in CLL and other cancers have great variability and often lack reproducibility. For CLL patient plasma and cell lines, we sought to characterize current approaches used in isolating EV products and understand whether cell culture-conditioned media or complex biological fluids confound results. Utilizing nanoparticle tracking analysis, protein quantification, and electron microscopy, we show that ultracentrifugation with an OptiPrep cushion can effectively minimize contaminants from starting materials including plasma and conditioned media of CLL cell lines grown in EV-depleted complete RPMI media but not grown in the serum-free media AIM V commonly used in CLL experimental work. Moreover, we confirm the benefit of including 25 mM trehalose in PBS during EV isolation steps to reduce EV aggregation, to preserve function for downstream applications and characterization. Furthermore, we report the highest particles/μg EVs were obtained from our CLL cell lines utilizing the CELLine bioreactor flask. Finally, we optimized a proliferation assay that offers a functional evaluation of our EVs with minimal sample requirements.

SUBMITTER: Elgamal S

PROVIDER: S-EPMC8410027 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Extracellular vesicles represent a rich source of novel biomarkers in the diagnosis and prognosis of disease. However, there is currently limited information elucidating the most efficient methods for obtaining high yields of pure exosomes, a subset of extracellular vesicles, from cell culture supernatant and complex biological fluids such as plasma. To this end, we comprehensively characterize a variety of exosome isolation protocols for their efficiency, yield and purity of isolated exosomes. Repeated ultracentrifugation steps can reduce the quality of exosome preparations leading to lower exosome yield. We show that concentration of cell culture conditioned media using ultrafiltration devices results in increased vesicle isolation when compared to traditional ultracentrifugation protocols. However, our data on using conditioned media isolated from the Non-Small-Cell Lung Cancer (NSCLC) SK-MES-1 cell line demonstrates that the choice of concentrating device can greatly impact the yield of isolated exosomes. We find that centrifuge-based concentrating methods are more appropriate than pressure-driven concentrating devices and allow the rapid isolation of exosomes from both NSCLC cell culture conditioned media and complex biological fluids. In fact to date, no protocol detailing exosome isolation utilizing current commercial methods from both cells and patient samples has been described. Utilizing tunable resistive pulse sensing and protein analysis, we provide a comparative analysis of 4 exosome isolation techniques, indicating their efficacy and preparation purity. Our results demonstrate that current precipitation protocols for the isolation of exosomes from cell culture conditioned media and plasma provide the least pure preparations of exosomes, whereas size exclusion isolation is comparable to density gradient purification of exosomes. We have identified current shortcomings in common extracellular vesicle isolation methods and provide a potential standardized method that is effective, reproducible and can be utilized for various starting materials. We believe this method will have extensive application in the growing field of extracellular vesicle research.

Dataset Information

Optimizing extracellular vesicles' isolation from chronic lymphocytic leukemia patient plasma and cell line supernatant.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets