Browsing by Author "Ma, Lihua"

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    High throughput and rapid isolation of extracellular vesicles and exosomes with purity using size exclusion liquid chromatography
    (Elsevier, 2024) Kapoor, Kshipra S.; Harris, Kristen; Arian, Kent A.; Ma, Lihua; Schueng Zancanela, Beatriz; Church, Kaira A.; McAndrews, Kathleen M.; Kalluri, Raghu
    Extracellular vesicles (EVs) have emerged as potential biomarkers for diagnosing a range of diseases without invasive procedures. Extracellular vesicles also offer advantages compared to synthetic vesicles for delivery of various drugs; however, limitations in segregating EVs from other particles and soluble proteins have led to inconsistent EV retrieval rates with low levels of purity. Here, we report a new high-yield (88.47 %) and rapid (<20 min) EV isolation method termed size exclusion – fast protein liquid chromatography (SE-FPLC). We show SE-FPLC can effectively isolate EVs from multiple sources including EVs derived from human and mouse cells and serum samples. The results indicate that SE-FPLC can successfully remove highly abundant protein contaminants such as albumin and lipoprotein complexes, which can represent a major hurdle in large scale isolation of EVs. The high-yield nature of SE-FPLC allows for easy industrial scaling up of EV production for various clinical utilities. SE-FPLC also enables analysis of small volumes of blood for use in point-of-care diagnostics in the clinic. Collectively, SE-FPLC offers many advantages over current EV isolation methods and offers rapid clinical translation.
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    Inferring metabolic networks using the Bayesian adaptive graphical lasso with informative priors
    (International Press, 2013) Peterson, Christine; Vannucci, Marina; Karakas, Cemal; Choi, William; Ma, Lihua; Maletic-Savatic, Mirjana
    Metabolic processes are essential for cellular function and survival. We are interested in inferring a metabolic network in activated microglia, a major neuroimmune cell in the brain responsible for the neuroinflammation associated with neurological diseases, based on a set of quantified metabolites. To achieve this, we apply the Bayesian adaptive graphical lasso with informative priors that incorporate known relationships between covariates. To encourage sparsity, the Bayesian graphical lasso places double exponential priors on the off-diagonal entries of the precision matrix. The Bayesian adaptive graphical lasso allows each double exponential prior to have a unique shrinkage parameter. These shrinkage parameters share a common gamma hyperprior. We extend this model to create an informative prior structure by formulating tailored hyperpriors on the shrinkage parameters. By choosing parameter values for each hyperprior that shift probability mass toward zero for nodes that are close together in a reference network, we encourage edges between covariates with known relationships. This approach can improve the reliability of network inference when the sample size is small relative to the number of parameters to be estimated. When applied to the data on activated microglia, the inferred network includes both known relationships and associations of potential interest for further investigation.