Cellular crosstalk between hematopoietic stem/progenitor cells and the bone marrow (BM) niche is vital for the development and maintenance of myeloid malignancies. These compartments can communicate via bidirectional transfer of extracellular vesicles (EVs). EV trafficking in acute myeloid leukemia (AML) plays a crucial role in shaping the BM microenvironment into a leukemia-permissive niche. Although several EV isolation methods have been developed, it remains a major challenge to define the most accurate and reliable procedure. Here, we tested the efficacy and functional assay compatibility of four different EV isolation methods in leukemia-derived EVs: (1) membrane affinity-based: exoEasy Kit alone and (2) in combination with Amicon filtration; (3) precipitation: ExoQuick-TC; and (4) ultracentrifugation (UC). Western blot analysis of EV fractions showed the highest enrichment of EV marker expression (e.g., CD63, HSP70, and TSG101) by precipitation with removal of overabundant soluble proteins [e.g., bovine serum albumin (BSA)], which were not discarded using UC. Besides the presence of damaged EVs after UC, intact EVs were successfully isolated with all methods as evidenced by highly maintained spherical- and cup-shaped vesicles in transmission electron microscopy. Nanoparticle tracking analysis of EV particle size and concentration revealed significant differences in EV isolation efficacy, with exoEasy Kit providing the highest EV yield recovery. Of note, functional assays with exoEasy Kit-isolated EVs showed significant toxicity towards treated target cells [e.g., mesenchymal stromal cells (MSCs)], which was abrogated when combining exoEasy Kit with Amicon filtration. Additionally, MSC treated with green fluorescent protein (GFP)-tagged exoEasy Kit-isolated EVs did not show any EV uptake, while EV isolation by precipitation demonstrated efficient EV internalization. Taken together, the choice of EV isolation procedure significantly impacts the yield and potential functionality of leukemia-derived EVs. The cheapest method (UC) resulted in contaminated and destructed EV fractions, while the isolation method with the highest EV yield (exoEasy Kit) appeared to be incompatible with functional assays. We identified two methods (precipitation-based ExoQuick-TC and membrane affinity-based exoEasy Kit combined with Amicon filtration) yielding pure and intact EVs, also suitable for application in functional assays. This study highlights the importance of selecting the right EV isolation method depending on the desired experimental design.
In this work, we introduce a new workflow to solve portfolio optimization problems on annealing platforms. We combine a classical preprocessing step with a modified unconstrained binary optimization (QUBO) model and evaluate it using simulated annealing (classical computer), digital annealing (Fujitsu’s Digital Annealing Unit), and quantum annealing (D-Wave Advantage). Starting from Markowitz’s theory on portfolio optimization, our classical preprocessing step finds the most promising assets within a set of possible assets to choose from. We then modify existing QUBO models for portfolio optimization, such that there are no limitations on the number of assets that can be invested in. Furthermore, our QUBO model enables an investor to also place an arbitrary amount of money into each asset. We apply this modified QUBO to the set of promising asset candidates we generated previously via classical preprocessing. A solution to our QUBO model contains information about what percentage of the whole available capital should be invested into which asset. For the evaluation, we have used publicly available real-world data sets of stocks of the New York Stock Exchange as well as common ETFs. Finally, we have compared the respective annealing results with randomly generated portfolios by using the return, variance, and diversification of the created portfolios as measures. The results show that our QUBO formulation is capable of creating well-diversified portfolios that respect certain criteria given by an investor, such as maximizing return, minimizing risk, or sticking to a certain budget.
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