Human ATG4 autophagy proteases counteract attachment of ubiquitin-like LC3/GABARAP proteins to other cellular proteins.

Microtubule-associated protein 1 light chain 3 α (LC3)/GABA type A receptor-associated protein (GABARAP) comprises a family of ubiquitin-like proteins involved in (macro)autophagy, an important intracellular degradation pathway that delivers cytoplasmic material to lysosomes via double-membrane vesicles called autophagosomes. The only currently known cellular molecules covalently modified by LC3/GABARAP are membrane phospholipids such as phosphatidylethanolamine in the autophagosome membrane. Autophagy-related 4 cysteine peptidase (ATG4) proteases process inactive pro-LC3/GABARAP before lipidation, and the same proteases can also deconjugate LC3/GABARAP from lipids. To determine whether LC3/GABARAP has other molecular targets, here we generated a pre-processed LC3B mutant (Q116P) that is resistant to ATG4-mediated deconjugation. Upon expression in human cells and when assessed by immunoblotting under reducing and denaturing conditions, deconjugation-resistant LC3B accumulated in multiple forms and at much higher molecular weights than free LC3B. We observed a similar accumulation when pre-processed versions of all mammalian LC3/GABARAP isoforms were expressed in ATG4-deficient cell lines, suggesting that LC3/GABARAP can attach also to other larger molecules. We identified ATG3, the E2-like enzyme involved in LC3/GABARAP lipidation, as one target of conjugation with multiple copies of LC3/GABARAP. We show that LC3B-ATG3 conjugates are distinct from the LC3B-ATG3 thioester intermediate formed before lipidation, and we biochemically demonstrate that ATG4B can cleave LC3B-ATG3 conjugates. Finally, we determined ATG3 residue Lys-243 as an LC3B modification site. Overall, we provide the first cellular evidence that mammalian LC3/GABARAP post-translationally modifies proteins akin to ubiquitination ("LC3ylation"), with ATG4 proteases acting like deubiquitinating enzymes to counteract this modification ("deLC3ylation").

Artificial intelligence for microscopy: what you should know.

Artificial Intelligence based on Deep Learning (DL) is opening new horizons in biomedical research and promises to revolutionize the microscopy field. It is now transitioning from the hands of experts in computer sciences to biomedical researchers. Here, we introduce recent developments in DL applied to microscopy, in a manner accessible to non-experts. We give an overview of its concepts, capabilities and limitations, presenting applications in image segmentation, classification and restoration. We discuss how DL shows an outstanding potential to push the limits of microscopy, enhancing resolution, signal and information content in acquired data. Its pitfalls are discussed, along with the future directions expected in this field.

DeepBacs for multi-task bacterial image analysis using open-source deep learning approaches.

This work demonstrates and guides how to use a range of state-of-the-art artificial neural-networks to analyse bacterial microscopy images using the recently developed ZeroCostDL4Mic platform. We generated a database of image datasets used to train networks for various image analysis tasks and present strategies for data acquisition and curation, as well as model training. We showcase different deep learning (DL) approaches for segmenting bright field and fluorescence images of different bacterial species, use object detection to classify different growth stages in time-lapse imaging data, and carry out DL-assisted phenotypic profiling of antibiotic-treated cells. To also demonstrate the ability of DL to enhance low-phototoxicity live-cell microscopy, we showcase how image denoising can allow researchers to attain high-fidelity data in faster and longer imaging. Finally, artificial labelling of cell membranes and predictions of super-resolution images allow for accurate mapping of cell shape and intracellular targets. Our purposefully-built database of training and testing data aids in novice users' training, enabling them to quickly explore how to analyse their data through DL. We hope this lays a fertile ground for the efficient application of DL in microbiology and fosters the creation of tools for bacterial cell biology and antibiotic research.

Democratising deep learning for microscopy with ZeroCostDL4Mic.

Deep 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.

Automated cell tracking using StarDist and TrackMate.

The ability of cells to migrate is a fundamental physiological process involved in embryonic development, tissue homeostasis, immune surveillance, and wound healing. Therefore, the mechanisms governing cellular locomotion have been under intense scrutiny over the last 50 years. One of the main tools of this scrutiny is live-cell quantitative imaging, where researchers image cells over time to study their migration and quantitatively analyze their dynamics by tracking them using the recorded images. Despite the availability of computational tools, manual tracking remains widely used among researchers due to the difficulty setting up robust automated cell tracking and large-scale analysis. Here we provide a detailed analysis pipeline illustrating how the deep learning network StarDist can be combined with the popular tracking software TrackMate to perform 2D automated cell tracking and provide fully quantitative readouts. Our proposed protocol is compatible with both fluorescent and widefield images. It only requires freely available and open-source software (ZeroCostDL4Mic and Fiji), and does not require any coding knowledge from the users, making it a versatile and powerful tool for the field. We demonstrate this pipeline's usability by automatically tracking cancer cells and T cells using fluorescent and brightfield images. Importantly, we provide, as supplementary information, a detailed step-by-step protocol to allow researchers to implement it with their images.