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Protein localization in electron micrographs using fluorescence nanoscopy.

ABSTRACT: A complete portrait of a cell requires a detailed description of its molecular topography: proteins must be linked to particular organelles. Immunocytochemical electron microscopy can reveal locations of proteins with nanometer resolution but is limited by the quality of fixation, the paucity of antibodies and the inaccessibility of antigens. Here we describe correlative fluorescence electron microscopy for the nanoscopic localization of proteins in electron micrographs. We tagged proteins with the fluorescent proteins Citrine or tdEos and expressed them in Caenorhabditis elegans, fixed the worms and embedded them in plastic. We imaged the tagged proteins from ultrathin sections using stimulated emission depletion (STED) microscopy or photoactivated localization microscopy (PALM). Fluorescence correlated with organelles imaged in electron micrographs from the same sections. We used these methods to localize histones, a mitochondrial protein and a presynaptic dense projection protein in electron micrographs.

SUBMITTER: Watanabe S

PROVIDER: S-EPMC3059187 | biostudies-literature | 2011 Jan

REPOSITORIES: biostudies-literature

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Protein localization in electron micrographs using fluorescence nanoscopy.

Watanabe Shigeki S Punge Annedore A Hollopeter Gunther G Willig Katrin I KI Hobson Robert John RJ Davis M Wayne MW Hell Stefan W SW Jorgensen Erik M EM

Nature methods 20101121 1

A complete portrait of a cell requires a detailed description of its molecular topography: proteins must be linked to particular organelles. Immunocytochemical electron microscopy can reveal locations of proteins with nanometer resolution but is limited by the quality of fixation, the paucity of antibodies and the inaccessibility of antigens. Here we describe correlative fluorescence electron microscopy for the nanoscopic localization of proteins in electron micrographs. We tagged proteins with ...[more]

PMID: 21102453

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Project description:BackgroundIdentification and selection of protein particles in cryo-electron micrographs is an important step in single particle analysis. In this study, we developed a deep learning-based particle picking network to automatically detect particle centers from cryoEM micrographs. This is a challenging task due to the nature of cryoEM data, having low signal-to-noise ratios with variable particle sizes, shapes, distributions, grayscale variations as well as other undesirable artifacts.ResultsWe propose a double convolutional neural network (CNN) cascade for automated detection of particles in cryo-electron micrographs. This approach, entitled Deep Regression Picker Network or "DRPnet", is simple but very effective in recognizing different particle sizes, shapes, distributions and grayscale patterns corresponding to 2D views of 3D particles. Particles are detected by the first network, a fully convolutional regression network (FCRN), which maps the particle image to a continuous distance map that acts like a probability density function of particle centers. Particles identified by FCRN are further refined to reduce false particle detections by the second classification CNN. DRPnet's first CNN pretrained with only a single cryoEM dataset can be used to detect particles from different datasets without retraining. Compared to RELION template-based autopicking, DRPnet results in better particle picking performance with drastically reduced user interactions and processing time. DRPnet also outperforms the state-of-the-art particle picking networks in terms of the supervised detection evaluation metrics recall, precision, and F-measure. To further highlight quality of the picked particle sets, we compute and present additional performance metrics assessing the resulting 3D reconstructions such as number of 2D class averages, efficiency/angular coverage, Rosenthal-Henderson plots and local/global 3D reconstruction resolution.ConclusionDRPnet shows greatly improved time-savings to generate an initial particle dataset compared to manual picking, followed by template-based autopicking. Compared to other networks, DRPnet has equivalent or better performance. DRPnet excels on cryoEM datasets that have low contrast or clumped particles. Evaluating other performance metrics, DRPnet is useful for higher resolution 3D reconstructions with decreased particle numbers or unknown symmetry, detecting particles with better angular orientation coverage.

Project description:BackgroundCharacterization of the structural morphology of virus particles in electron micrographs is a complex task, but desirable in connection with investigation of the maturation process and detection of changes in viral particle morphology in response to the effect of a mutation or antiviral drugs being applied. Therefore, we have here developed a procedure for describing and classifying virus particle forms in electron micrographs, based on determination of the invariant characteristics of the projection of a given virus structure. The template for the virus particle is created on the basis of information obtained from a small training set of electron micrographs and is then employed to classify and quantify similar structures of interest in an unlimited number of electron micrographs by a process of correlation.ResultsPractical application of the method is demonstrated by the ability to locate three diverse classes of virus particles in transmission electron micrographs of fibroblasts infected with human cytomegalovirus. These results show that fast screening of the total number of viral structures at different stages of maturation in a large set of electron micrographs, a task that is otherwise both time-consuming and tedious for the expert, can be accomplished rapidly and reliably with our automated procedure. Using linear deformation analysis, this novel algorithm described here can handle capsid variations such as ellipticity and furthermore allows evaluation of properties such as the size and orientation of a virus particle.ConclusionOur methodological procedure represents a promising objective tool for comparative studies of the intracellular assembly processes of virus particles using electron microscopy in combination with our digitized image analysis tool. An automated method for sorting and classifying virus particles at different stages of maturation will enable us to quantify virus production in all stages of the virus maturation process, not only count the number of infectious particles released from un infected cell.

Project description:MotivationWhile multi-channel fluorescence microscopy is a vital imaging method in biological studies, the number of channels that can be imaged simultaneously is limited by technical and hardware limitations such as emission spectra cross-talk. One solution is using deep neural networks to model the localization relationship between two proteins so that the localization of one protein can be digitally predicted. Furthermore, the input and predicted localization implicitly reflect the modeled relationship. Accordingly, observing the response of the prediction via manipulating input localization could provide an informative way to analyze the modeled relationships between the input and the predicted proteins.ResultsWe propose a protein localization prediction (PLP) method using a cGAN named 4D Reslicing Generative Adversarial Network (4DR-GAN) to digitally generate additional channels. 4DR-GAN models the joint probability distribution of input and output proteins by simultaneously incorporating the protein localization signals in four dimensions including space and time. Because protein localization often correlates with protein activation state, based on accurate PLP, we further propose two novel tools: digital activation (DA) and digital inactivation (DI) to digitally activate and inactivate a protein, in order to observing the response of the predicted protein localization. Compared with genetic approaches, these tools allow precise spatial and temporal control. A comprehensive experiment on six pairs of proteins shows that 4DR-GAN achieves higher-quality PLP than Pix2Pix, and the DA and DI responses are consistent with the known protein functions. The proposed PLP method helps simultaneously visualize additional proteins, and the developed DA and DI tools provide guidance to study localization-based protein functions.Availability and implementationThe open-source code is available at https://github.com/YangJiaoUSA/4DR-GAN.Supplementary informationSupplementary data are available at Bioinformatics online.

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Protein localization in electron micrographs using fluorescence nanoscopy.

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Protein localization in electron micrographs using fluorescence nanoscopy.

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