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Author: Eggeling, Christian × End date: 2024 × Start date: 2020 × DDC: 570 × Has files: Yes × Subject: fluorescence microscopy ×

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    Aggregation and mobility of membrane proteins interplay with local lipid order in the plasma membrane of T cells
    (Chichester : Wiley, 2021) Urbančič, Iztok; Schiffelers, Lisa; Jenkins, Edward; Gong, Weijian; Santos, Ana Mafalda; Schneider, Falk; O'Brien-Ball, Caitlin; Vuong, Mai Tuyet; Ashman, Nicole; Sezgin, Erdinc; Eggeling, Christian
    To disentangle the elusive lipid-protein interactions in T-cell activation, we investigate how externally imposed variations in mobility of key membrane proteins (T-cell receptor [TCR], kinase Lck, and phosphatase CD45) affect the local lipid order and protein colocalisation. Using spectral imaging with polarity-sensitive membrane probes in model membranes and live Jurkat T cells, we find that partial immobilisation of proteins (including TCR) by aggregation or ligand binding changes their preference towards a more ordered lipid environment, which can recruit Lck. Our data suggest that the cellular membrane is poised to modulate the frequency of protein encounters upon alterations of their mobility, for example in ligand binding, which offers new mechanistic insight into the involvement of lipid-mediated interactions in membrane-hosted signalling events.
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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.