The nanoscale molecular assembly of mammalian viruses during their infectious life cycle remains poorly understood. Their small dimensions, generally bellow the 300nm diffraction limit of light microscopes, has limited most imaging studies to electron microscopy. The recent development of super-resolution (SR) light microscopy now allows the visualisation of viral structures at resolutions of tens of nanometers. In addition, these techniques provide the added benefit of molecular specific labelling and the capacity to investigate viral structural dynamics using live-cell microscopy. However, there is a lack of robust analytical tools that allow for precise mapping of viral structure within the setting of infection. Here we present an open-source analytical framework that combines super-resolution imaging and naïve single-particle analysis to generate unbiased molecular models. This tool, VirusMapper, is a high-throughput, user-friendly, ImageJ-based software package allowing for automatic statistical mapping of conserved multi-molecular structures, such as viral substructures or intact viruses. We demonstrate the usability of VirusMapper by applying it to SIM and STED images of vaccinia virus in isolation and when engaged with host cells. VirusMapper allows for the generation of accurate, high-content, molecular specific virion models and detection of nanoscale changes in viral architecture.

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.

All poxviruses contain a set of proteinaceous structures termed lateral bodies (LB) that deliver viral effector proteins into the host cytosol during virus entry. To date, the spatial proteotype of LBs remains unknown. Using the prototypic poxvirus, vaccinia virus (VACV), we employed a quantitative comparative mass spectrometry strategy to determine the poxvirus LB proteome. We identified a large population of candidate cellular proteins, the majority being mitochondrial, and 15 candidate viral LB proteins. Strikingly, one-third of these are VACV redox proteins whose LB residency could be confirmed using super-resolution microscopy. We show that VACV infection exerts an anti-oxidative effect on host cells and that artificial induction of oxidative stress impacts early and late gene expression as well as virion production. Using targeted repression and/or deletion viruses we found that deletion of individual LB-redox proteins was insufficient for host redox modulation suggesting there may be functional redundancy. In addition to defining the spatial proteotype of VACV LBs, these findings implicate poxvirus redox proteins as potential modulators of host oxidative anti-viral responses and provide a solid starting point for future investigations into the role of LB resident proteins in host immunomodulation.