Subdiffraction‐Resolution Fluorescence Imaging with Conventional Fluorescent ProbesMike Heilemann, Sebastian van de Linde, Mark Schüttpelz et al.|Angewandte Chemie International Edition|2008 Eagle eyes: dSTORM uses conventional photoswitchable fluorescent dyes that can be reversibly cycled between a fluorescent and a dark state by irradiation with light of different wavelengths (see picture). This elegant approach can visualize cellular structures with a resolution of approximately 20 nm, far beyond the diffraction limit of light, without the need of an activator molecule.
Direct stochastic optical reconstruction microscopy with standard fluorescent probesA Reducing and Oxidizing System Minimizes Photobleaching and Blinking of Fluorescent DyesJan Vogelsang, Robert Kasper, Christian Steinhauer et al.|Angewandte Chemie International Edition|2008 On the ROXS: Photobleaching represents one of the main limitations of modern fluorescence microscopy. A reducing and oxidizing system (ROXS) has been introduced that recovers triplet states as well as charge-separated states through electron-transfer reactions (see picture). Thus the blinking and photobleaching of fluorophores is strikingly reduced.
Democratising deep learning for microscopy with ZeroCostDL4MicDeep Learning (DL) methods are powerful analytical tools for microscopy and can outperform conventional image processing pipelines. Despite the enthusiasm and innovations fuelled by DL technology, the need to access powerful and compatible resources to train DL networks leads to an accessibility barrier that novice users often find difficult to overcome. Here, we present ZeroCostDL4Mic, an entry-level platform simplifying DL access by leveraging the free, cloud-based computational resources of Google Colab. ZeroCostDL4Mic allows researchers with no coding expertise to train and apply key DL networks to perform tasks including segmentation (using U-Net and StarDist), object detection (using YOLOv2), denoising (using CARE and Noise2Void), super-resolution microscopy (using Deep-STORM), and image-to-image translation (using Label-free prediction - fnet, pix2pix and CycleGAN). Importantly, we provide suitable quantitative tools for each network to evaluate model performance, allowing model optimisation. We demonstrate the application of the platform to study multiple biological processes.
Full length RTN3 regulates turnover of tubular endoplasmic reticulum via selective autophagyThe turnover of endoplasmic reticulum (ER) ensures the correct biological activity of its distinct domains. In mammalian cells, the ER is degraded via a selective autophagy pathway (ER-phagy), mediated by two specific receptors: FAM134B, responsible for the turnover of ER sheets and SEC62 that regulates ER recovery following stress. Here, we identified reticulon 3 (RTN3) as a specific receptor for the degradation of ER tubules. Oligomerization of the long isoform of RTN3 is sufficient to trigger fragmentation of ER tubules. The long N-terminal region of RTN3 contains several newly identified LC3-interacting regions (LIR). Binding to LC3s/GABARAPs is essential for the fragmentation of ER tubules and their delivery to lysosomes. RTN3-mediated ER-phagy requires conventional autophagy components, but is independent of FAM134B. None of the other reticulon family members have the ability to induce fragmentation of ER tubules during starvation. Therefore, we assign a unique function to RTN3 during autophagy.