2 research outputs found

    New insights into the intracellular distribution pattern of cationic amphiphilic drugs

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    Cationic amphiphilic drugs (CADs) comprise a wide variety of different substance classes such as antidepressants, antipsychotics, and antiarrhythmics. It is well recognized that CADs accumulate in certain intracellular compartments leading to specific morphological changes of cells. So far, no adequate technique exists allowing for ultrastructural analysis of CAD in intact cells. Azidobupramine, a recently described multifunctional antidepressant analogue, allows for the first time to perform high-resolution studies of CADs on distribution pattern and morphological changes in intact cells. We showed here that the intracellular distribution pattern of azidobupramine strongly depends on drug concentration and exposure time. The mitochondrial compartment (mDsRed) and the late endolysosomal compartment (CD63-GFP) were the preferred localization sites at low to intermediate concentrations (i.e. 1 mu M, 5 mu M). In contrast, the autophagosomal compartment (LC3-GFP) can only be reached at high concentrations (10 mu M) and long exposure times (72 hrs). At the morphological level, LC3-clustering became only prominent at high concentrations (10 mu M), while changes in CD63 pattern already occurred at intermediate concentrations (5 mu M). To our knowledge, this is the first study that establishes a link between intracellular CAD distribution pattern and morphological changes. Therewith, our results allow for gaining deeper understanding of intracellular effects of CADs

    Automatic Bayesian single molecule identification for localization microscopy

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    Single molecule localization microscopy (SMLM) is on its way to become a mainstream imaging technique in the life sciences. However, analysis of SMLM data is biased by user provided subjective parameters required by the analysis software. To remove this human bias we introduce here the Auto-Bayes method that executes the analysis of SMLM data automatically. We demonstrate the success of the method using the photoelectron count of an emitter as selection characteristic. Moreover, the principle can be used for any characteristic that is bimodally distributed with respect to false and true emitters. The method also allows generation of an emitter reliability map for estimating quality of SMLM-based structures. The potential of the Auto-Bayes method is shown by the fact that our first basic implementation was able to outperform all software packages that were compared in the ISBI online challenge in 2015, with respect to molecule detection (Jaccard index)
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