Creating a platform for the democratisation of Deep Learning in microscopy

One of the major technological success stories of the last decade has been the advent of deep learning (DL), which has touched almost every aspect of modern life after a breakthrough performance in an image detection challenge in 2012. The bioimaging community quickly recognised the prospect of the automated ability to make sense of image data with near-human performance as potentially ground-breaking. In the decade since, hundreds of publications have used this technology to tackle many problems related to image analysis, such as labelling or counting cells, identifying cells or organelles of interest in large image datasets, or removing noise or improving the resolution of images. However, the adoption of DL tools in large parts of the bioimaging community has been slow, and many tools have remained in the hands of developers. In this project, I have identified key barriers which have prevented many bioimage analysts and microscopists from accessing existing DL technology in their field and have, in collaboration with colleagues, developed the ZeroCostDL4Mic platform, which aims to address these barriers. This project is inspired by the observation that the most significant impact technology can have in science is when it becomes ubiquitous, that is, when its use becomes essential to address the community’s questions. This work represents one of the first attempts to make DL tools accessible in a transparent, code-free, and affordable manner for bioimage analysis to unlock the full potential of DL via its democratisation for the bioimaging community

M1/M2 Macrophage Polarity in Normal and Complicated Pregnancy

Tissue macrophages play an important role in all stages of pregnancy, including uterine stromal remodeling (decidualization) before embryo implantation, parturition, and postpartum uterine involution. The activation state and function of utero-placental macrophages are largely dependent on the local tissue microenvironment. Thus, macrophages are involved in a variety of activities such as regulation of immune cell activities, placental cell invasion, angiogenesis, and tissue remodeling. Disruption of the uterine microenvironment, particularly during the early stages of pregnancy (decidualization, implantation, and placentation) can have profound effects on macrophage activity and subsequently impact pregnancy outcome. In this review, we will provide an overview of the temporal and spatial regulation of utero-placental macrophage activation during normal pregnancy in humans and rodents with a focus on more recent findings. We will also discuss the role of M1/M2 dysregulation within the intrauterine environment during adverse pregnancy outcomes

Automated cell tracking using StarDist and TrackMate [version 1; peer review: awaiting peer review]

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

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

Microtubule-associated protein 1 light chain 3 alpha (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 4A 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 preprocessed 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 preprocessed 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 K243 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’)

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. </div

An integrated assessment of nitrogen source, transformation and fate within an intensive dairy system to inform management change

From an environmental perspective optimised dairy systems, which follow current regulations, still have low nitrogen (N) use efficiency, high N surplus (kg N ha-1) and enable ad-hoc delivery of direct and indirect reactive N losses to water and the atmosphere. The objective of the present study was to divide an intensive dairy farm into N attenuation capacity areas based on this ad-hoc delivery. Historical and current spatial and temporal multi-level datasets (stable isotope and dissolved gas) were combined and interpreted. Results showed that the farm had four distinct attenuation areas: high N attenuation: characterised by ammonium-N (NH4+-N) below 0.23 mg NH4+-N l-1 and nitrate (NO3--N) below 5.65 mg NO3--N l-1 in surface, drainage and groundwater, located on imperfectly to moderately-well drained soils with high denitrification potential and low nitrous oxide (N2O) emissions (av. 0.0032 mg N2O-N l-1); moderate N attenuation: characterised by low NO3--N concentration in drainage water but high N2O production (0.0317 mg N2O-N l-1) and denitrification potential lower than group 1 (av. δ15N-NO3-: 16.4‰, av. δ18O-NO3-: 9.2‰), on well to moderately drained soils; low N attenuation—area 1: characterised by high NO3--N (av. 6.90 mg NO3--N l-1) in drainage water from well to moderately-well drained soils, with low denitrification potential (av. δ15N-NO3-: 9.5‰, av. δ18O-NO3-: 5.9‰) and high N2O emissions (0.0319 mg N2O l-1); and low N attenuation—area 2: characterised by high NH4+-N (av. 3.93 mg NH4+-N l-1 and high N2O emissions (av. 0.0521 mg N2O l-1) from well to imperfectly drained soil. N loads on site should be moved away from low attenuation areas and emissions to air and water should be assessed

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.DeepBacs guides users without expertise in machine learning methods to leverage state-of-the-art artificial neural networks to analyse bacterial microscopy images

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. Deep learning methods show great promise for the analysis of microscopy images but there is currently an accessibility barrier to many users. Here the authors report a convenient entry-level deep learning platform that can be used at no cost: ZeroCostDL4Mic