Elucidation of synaptonemal complex organization by super-resolution imaging with isotropic resolution.
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ABSTRACT:
SUBMITTER: Schucker K
PROVIDER: S-EPMC4343094 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:During meiosis, homologous chromosomes must recognize and adhere to one another to allow for their correct segregation. One of the key events that secures the interaction of homologous chromosomes is the assembly of the synaptonemal complex (SC) in meiotic prophase I. Even though there is little sequence homology between protein components within the SC among different species, the general structure of the SC has been highly conserved during evolution. In electron micrographs, the SC appears as a tripartite, ladder-like structure composed of lateral elements or axes, transverse filaments, and a central element. However, precisely identifying the localization of individual components within the complex by electron microscopy to determine the molecular structure of the SC remains challenging. By contrast, fluorescence microscopy allows for the identification of individual protein components within the complex. However, since the SC is only ~100 nm wide, its substructure cannot be resolved by diffraction-limited conventional fluorescence microscopy. Thus, determining the molecular architecture of the SC requires super-resolution light microscopy techniques such as structured illumination microscopy (SIM), stimulated-emission depletion (STED) microscopy, or single-molecule localization microscopy (SMLM). To maintain the structure and interactions of individual components within the SC, it is important to observe the complex in an environment that is close to its native environment in the germ cells. Therefore, we demonstrate an immunohistochemistry and imaging protocol that enables the study of the substructure of the SC in intact, extruded Caenorhabditis elegans germline tissue with SMLM and STED microscopy. Directly fixing the tissue to the coverslip reduces the movement of the samples during imaging and minimizes aberrations in the sample to achieve the high resolution necessary to visualize the substructure of the SC in its biological context.
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Project description:Single-molecule localization microscopy provides insights into the nanometer-scale spatial organization of proteins in cells, however it does not provide information on their conformation and orientation, which are key functional signatures. Detecting single molecules' orientation in addition to their localization in cells is still a challenging task, in particular in dense cell samples. Here, we present a polarization-splitting scheme which combines Stochastic Optical Reconstruction Microscopy (STORM) with single molecule 2D orientation and wobbling measurements, without requiring a strong deformation of the imaged point spread function. This method called 4polar-STORM allows, thanks to a control of its detection numerical aperture, to determine both single molecules' localization and orientation in 2D and to infer their 3D orientation. 4polar-STORM is compatible with relatively high densities of diffraction-limited spots in an image, and is thus ideally placed for the investigation of dense protein assemblies in cells. We demonstrate the potential of this method in dense actin filament organizations driving cell adhesion and motility.
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Project description:Four different SYP proteins (SYP-1, SYP-2, SYP-3, and SYP-4) have been proposed to form the central region of the synaptonemal complex (SC) thereby bridging the axes of paired meiotic chromosomes in Caenorhabditis elegans. Their interdependent localization suggests that they may interact within the SC. Our studies reveal for the first time how these SYP proteins are organized in the central region of the SC. Yeast two-hybrid and co-immunoprecipitation studies show that SYP-1 is the only SYP protein that is capable of homotypic interactions, and is able to interact with both SYP-2 and SYP-3 directly, whereas SYP-2 and SYP-3 do not seem to interact with each other. Specifically, the coiled-coil domain of SYP-1 is required both for its homotypic interactions and its interaction with the C-terminal domain of SYP-2. Meanwhile, SYP-3 interacts with the C-terminal end of SYP-1 via its N-terminal domain. Immunoelectron microscopy analysis provides insight into the orientation of these proteins within the SC. While the C-terminal domain of SYP-3 localizes in close proximity to the chromosome axes, the N-terminal domains of both SYP-1 and SYP-4, as well as the C-terminal domain of SYP-2, are located in the middle of the SC. Taking into account the different sizes of these proteins, their interaction abilities, and their orientation within the SC, we propose a model of how the SYP proteins link the homologous axes to provide the conserved structure and width of the SC in C. elegans.
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Project description:Premise:High-resolution cameras are very helpful for plant phenotyping as their images enable tasks such as target vs. background discrimination and the measurement and analysis of fine above-ground plant attributes. However, the acquisition of high-resolution images of plant roots is more challenging than above-ground data collection. An effective super-resolution (SR) algorithm is therefore needed for overcoming the resolution limitations of sensors, reducing storage space requirements, and boosting the performance of subsequent analyses. Methods:We propose an SR framework for enhancing images of plant roots using convolutional neural networks. We compare three alternatives for training the SR model: (i) training with non-plant-root images, (ii) training with plant-root images, and (iii) pretraining the model with non-plant-root images and fine-tuning with plant-root images. The architectures of the SR models were based on two state-of-the-art deep learning approaches: a fast SR convolutional neural network and an SR generative adversarial network. Results:In our experiments, we observed that the SR models improved the quality of low-resolution images of plant roots in an unseen data set in terms of the signal-to-noise ratio. We used a collection of publicly available data sets to demonstrate that the SR models outperform the basic bicubic interpolation, even when trained with non-root data sets. Discussion:The incorporation of a deep learning-based SR model in the imaging process enhances the quality of low-resolution images of plant roots. We demonstrate that SR preprocessing boosts the performance of a machine learning system trained to separate plant roots from their background. Our segmentation experiments also show that high performance on this task can be achieved independently of the signal-to-noise ratio. We therefore conclude that the quality of the image enhancement depends on the desired application.
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