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DDC: 570 × Subject: live cell imaging ×

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    Involvement of two uptake mechanisms of gold and iron oxide nanoparticles in a co-exposure scenario using mouse macrophages
    (Frankfurt am Main : Beilstein-Institut, 2017) Vanhecke, Dimitri; Kuhn, Dagmar A.; de Aberasturi, Dorleta Jimenez; Balog, Sandor; Milosevic, Ana; Urban, Dominic; Peckys, Diana; de Jonge, Niels; Parak, Wolfgang J.; Petri-Fink, Alke; Rothen-Rutishauser, Barbara
    Little is known about the simultaneous uptake of different engineered nanoparticle types, as it can be expected in our daily life. In order to test such co-exposure effects, murine macrophages (J774A.1 cell line) were incubated with gold (AuNPs) and iron oxide nanoparticles (FeOxNPs) either alone or combined. Environmental scanning electron microscopy revealed that single NPs of both types bound within minutes on the cell surface but with a distinctive difference between FeOxNPs and AuNPs. Uptake analysis studies based on laser scanning microscopy, transmission electron microscopy, and inductively coupled plasma optical emission spectrometry revealed intracellular appearance of both NP types in all exposure scenarios and a time-dependent increase. This increase was higher for both AuNPs and FeOxNPs during co-exposure. Cells treated with endocytotic inhibitors recovered after co-exposure, which additionally hinted that two uptake mechanisms are involved. Cross-talk between uptake pathways is relevant for toxicological studies: Co-exposure acts as an uptake accelerant. If the goal is to maximize the cellular uptake, e.g., for the delivery of pharmaceutical agents, this can be beneficial. However, co-exposure should also be taken into account in the case of risk assessment of occupational settings. The demonstration of co-exposure-invoked pathway interactions reveals that synergetic nanoparticle effects, either positive or negative, must be considered for nanotechnology and nanomedicine in particular to develop to its full potential.
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    Object detection networks and augmented reality for cellular detection in fluorescence microscopy
    (New York, NY : Rockefeller Univ. Press, 2020) Waithe, Dominic; Brown, Jill M.; Reglinski, Katharina; Diez-Sevilla, Isabel; Roberts, David; Eggeling, Christian
    Object detection networks are high-performance algorithms famously applied to the task of identifying and localizing objects in photography images. We demonstrate their application for the classification and localization of cells in fluorescence microscopy by benchmarking four leading object detection algorithms across multiple challenging 2D microscopy datasets. Furthermore we develop and demonstrate an algorithm that can localize and image cells in 3D, in close to real time, at the microscope using widely available and inexpensive hardware. Furthermore, we exploit the fast processing of these networks and develop a simple and effective augmented reality (AR) system for fluorescence microscopy systems using a display screen and back-projection onto the eyepiece. We show that it is possible to achieve very high classification accuracy using datasets with as few as 26 images present. Using our approach, it is possible for relatively nonskilled users to automate detection of cell classes with a variety of appearances and enable new avenues for automation of fluorescence microscopy acquisition pipelines. © 2020 Waithe et al.