Phenotype-preserving metric design for high-content image reconstruction by generative inpainting

In the past decades, automated high-content microscopy demonstrated its ability to deliver large quantities of image-based data powering the versatility of phenotypic drug screening and systems biology applications. However, as the sizes of image-based datasets grew, it became infeasible for humans to control, avoid and overcome the presence of imaging and sample preparation artefacts in the images. While novel techniques like machine learning and deep learning may address these shortcomings through generative image inpainting, when applied to sensitive research data this may come at the cost of undesired image manipulation. Undesired manipulation may be caused by phenomena such as neural hallucinations, to which some artificial neural networks are prone. To address this, here we evaluate the state-of-the-art inpainting methods for image restoration in a high-content fluorescence microscopy dataset of cultured cells with labelled nuclei. We show that architectures like DeepFill V2 and Edge Connect can faithfully restore microscopy images upon fine-tuning with relatively little data. Our results demonstrate that the area of the region to be restored is of higher importance than shape. Furthermore, to control for the quality of restoration, we propose a novel phenotype-preserving metric design strategy. In this strategy, the size and count of the restored biological phenotypes like cell nuclei are quantified to penalise undesirable manipulation. We argue that the design principles of our approach may also generalise to other applications.Comment: 8 pages, 3 figures, conference proceeding

The nuclear export factor CRM1 controls juxta-nuclear microtubule-dependent virus transport

Transport of large cargo through the cytoplasm requires motor proteins and polarized filaments. Viruses that replicate in the nucleus of post-mitotic cells use microtubules and the dynein/dynactin motor to traffic to the nuclear membrane, and deliver their genome through nuclear pore complexes (NPCs) into the nucleus. How virus particles (virions) or cellular cargo are transferred from microtubules to the NPC is unknown. Here, we analyzed trafficking of incoming cytoplasmic adenoviruses by single particle tracking and super-resolution microscopy. We provide evidence for a regulatory role of CRM1/XPO1 (chromosome-region-maintenance-1, exportin-1) in juxta-nuclear microtubule-dependent adenovirus transport. Leptomycin B (LMB) abolishes nuclear targeting of adenovirus. It binds to CRM1, precludes CRM1-cargo binding and blocks signal-dependent nuclear export. LMB-inhibited CRM1 did not compete with adenovirus for binding to the nucleoporin Nup214 at the NPC. Instead, CRM1 inhibition selectively enhanced virion association with microtubules, and boosted virion motions on microtubules less than about 2 µm from the nuclear membrane. The data show that the nucleus provides positional information for incoming virions to detach from microtubules, engage a slower microtubule-independent motility to the NPC and enhance infection

Microscopy image reconstruction with physics-informed denoising diffusion probabilistic model

Light microscopy is a widespread and inexpensive imaging technique facilitating biomedical discovery and diagnostics. However, light diffraction barrier and imperfections in optics limit the level of detail of the acquired images. The details lost can be reconstructed among others by deep learning models. Yet, deep learning models are prone to introduce artefacts and hallucinations into the reconstruction. Recent state-of-the-art image synthesis models like the denoising diffusion probabilistic models (DDPMs) are no exception to this. We propose to address this by incorporating the physical problem of microscopy image formation into the model's loss function. To overcome the lack of microscopy data, we train this model with synthetic data. We simulate the effects of the microscope optics through the theoretical point spread function and varying the noise levels to obtain synthetic data. Furthermore, we incorporate the physical model of a light microscope into the reverse process of a conditioned DDPM proposing a physics-informed DDPM (PI-DDPM). We show consistent improvement and artefact reductions when compared to model-based methods, deep-learning regression methods and regular conditioned DDPMs.Comment: 16 pages, 5 figure

Mimicry Embedding Facilitates Advanced Neural Network Training for Image-Based Pathogen Detection.

The use of deep neural networks (DNNs) for analysis of complex biomedical images shows great promise but is hampered by a lack of large verified data sets for rapid network evolution. Here, we present a novel strategy, termed "mimicry embedding," for rapid application of neural network architecture-based analysis of pathogen imaging data sets. Embedding of a novel host-pathogen data set, such that it mimics a verified data set, enables efficient deep learning using high expressive capacity architectures and seamless architecture switching. We applied this strategy across various microbiological phenotypes, from superresolved viruses to in vitro and in vivo parasitic infections. We demonstrate that mimicry embedding enables efficient and accurate analysis of two- and three-dimensional microscopy data sets. The results suggest that transfer learning from pretrained network data may be a powerful general strategy for analysis of heterogeneous pathogen fluorescence imaging data sets.IMPORTANCE In biology, the use of deep neural networks (DNNs) for analysis of pathogen infection is hampered by a lack of large verified data sets needed for rapid network evolution. Artificial neural networks detect handwritten digits with high precision thanks to large data sets, such as MNIST, that allow nearly unlimited training. Here, we developed a novel strategy we call mimicry embedding, which allows artificial intelligence (AI)-based analysis of variable pathogen-host data sets. We show that deep learning can be used to detect and classify single pathogens based on small differences

High-content, arrayed compound screens with rhinovirus, influenza A virus and herpes simplex virus infections

Viruses are genetically and structurally diverse, and outnumber cells by orders of magnitude. They can cause acute and chronic infections, suppress, or exacerbate immunity, or dysregulate survival and growth of cells. To identify chemical agents with pro- or antiviral effects we conducted arrayed high-content image-based multi-cycle infection screens of 1,280 mainly FDA-approved compounds with three human viruses, rhinovirus (RV), influenza A virus (IAV), and herpes simplex virus (HSV) differing in genome organization, composition, presence of an envelope, and tropism. Based on Z’-factors assessing screening quality and Z-scores ranking individual compounds, we identified potent inhibitors and enhancers of infection: the RNA mutagen 5-Azacytidine against RV-A16; the broad-spectrum antimycotic drug Clotrimazole inhibiting IAV-WSN; the chemotherapeutic agent Raltitrexed blocking HSV-1; and Clobetasol enhancing HSV-1. Remarkably, the topical antiseptic compound Aminacrine, which is clinically used against bacterial and fungal agents, inhibited all three viruses. Our data underscore the versatility and potency of image-based, full cycle virus propagation assays in cell-based screenings for antiviral agents

Bisbenzimide compounds inhibit the replication of prototype and pandemic potential poxviruses

We previously identified the bisbenzimide Hoechst 33342 (H42) as a potent multi-stage inhibitor of the prototypic poxvirus, the vaccinia virus (VACV), and several parapoxviruses. A recent report showed that novel bisbenzimide compounds similar in structure to H42 could prevent human cytomegalovirus replication. Here, we assessed whether these compounds could also serve as poxvirus inhibitors. Using virological assays, we show that these bisbenzimide compounds inhibit VACV spread, plaque formation, and the production of infectious progeny VACV with relatively low cell toxicity. Further analysis of the VACV lifecycle indicated that the effective bisbenzimide compounds had little impact on VACV early gene expression but inhibited VACV late gene expression and truncated the formation of VACV replication sites. Additionally, we found that bisbenzimide compounds, including H42, can inhibit both monkeypox and a VACV mutant resistant to the widely used anti-poxvirus drug TPOXX (Tecovirimat). Therefore, the tested bisbenzimide compounds were inhibitors of both prototypic and pandemic potential poxviruses and could be developed for use in situations where anti-poxvirus drug resistance may occur. Additionally, these data suggest that bisbenzimide compounds may serve as broad-activity antiviral compounds, targeting diverse DNA viruses such as poxviruses and betaherpesviruses

The zebrafish as a novel model for the in vivo study of Toxoplasma gondii replication and interaction with macrophages.

Toxoplasma gondii is an obligate intracellular parasite capable of invading any nucleated cell. Three main clonal lineages (type I, II, III) exist and murine models have driven the understanding of general and strain-specific immune mechanisms underlying Toxoplasma infection. However, murine models are limited for studying parasite-leukocyte interactions in vivo, and discrepancies exist between cellular immune responses observed in mouse versus human cells. Here, we developed a zebrafish infection model to study the innate immune response to Toxoplasma in vivo By infecting the zebrafish hindbrain ventricle, and using high-resolution microscopy techniques coupled with computer vision-driven automated image analysis, we reveal that Toxoplasma invades brain cells and replicates inside a parasitophorous vacuole to which type I and III parasites recruit host cell mitochondria. We also show that type II and III strains maintain a higher infectious burden than type I strains. To understand how parasites are cleared in vivo, we further analyzed Toxoplasma-macrophage interactions using time-lapse microscopy and three-dimensional correlative light and electron microscopy (3D CLEM). Time-lapse microscopy revealed that macrophages are recruited to the infection site and play a key role in Toxoplasma control. High-resolution 3D CLEM revealed parasitophorous vacuole breakage in brain cells and macrophages in vivo, suggesting that cell-intrinsic mechanisms may be used to destroy the intracellular niche of tachyzoites. Together, our results demonstrate in vivo control of Toxoplasma by macrophages, and highlight the possibility that zebrafish may be further exploited as a novel model system for discoveries within the field of parasite immunity.This article has an associated First Person interview with the first author of the paper