Single particle cryogenic electron micrographs of isolated mouse mitochondrial supercomplex I2+III2
Ontology highlight
ABSTRACT:
SUBMITTER: James Anthony Letts
PROVIDER: EMPIAR-11705 | biostudies-other |
REPOSITORIES: biostudies-other
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Project description:Advances in cryogenic transmission electron microscopy have revolutionised the determination of many macromolecular structures at atomic or near-atomic resolution. This method is based on conventional defocused phase contrast imaging. However, it has limitations of weaker contrast for small biological molecules embedded in vitreous ice, in comparison with cryo-ptychography, which shows increased contrast. Here we report a single-particle analysis based on the use of ptychographic reconstruction data, demonstrating that three dimensional reconstructions with a wide information transfer bandwidth can be recovered by Fourier domain synthesis. Our work suggests future applications in otherwise challenging single particle analyses, including small macromolecules and heterogeneous or flexible particles. In addition structure determination in situ within cells without the requirement for protein purification and expression may be possible.
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Project description:Cryogenic single-particle photoluminescence (PL) spectroscopy has been used with great success to directly observe the heterogeneous photophysical states present in a population of luminescent particles. Cryogenic electron tomography provides complementary nanometer scale structural information to PL spectroscopy, but the two techniques have not been correlated due to technical challenges. Here, we present a method for correlating single-particle information from these two powerful microscopy modalities. We simultaneously observe PL brightness, emission spectrum, and in-plane excitation dipole orientation of CdSSe/ZnS quantum dots suspended in vitreous ice. Stable and fluctuating emitters were observed, as well as a surprising splitting of the PL spectrum into two bands with an average energy separation of 80 meV. In some cases, the onset of the splitting corresponded to changes in the in-plane excitation dipole orientation. These dynamics were assigned to structures of individual quantum dots and the excitation dipoles were visualized in the context of structural features.
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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.
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Project description:Here we present for the first time a three-dimensional cryo-EM map of the Saccharomyces cerevisiae respiratory supercomplex composed of dimeric complex III flanked on each side by one monomeric complex IV. A precise fit of the existing atomic x-ray structures of complex III from yeast and complex IV from bovine heart into the cryo-EM map resulted in a pseudo-atomic model of the three-dimensional structure for the supercomplex. The distance between cytochrome c binding sites of complexes III and IV is about 6 nm, which supports proposed channeling of cytochrome c between the individual complexes. The opposing surfaces of complexes III and IV differ considerably from those reported for the bovine heart supercomplex as determined by cryo-EM. A closer association between the individual complex domains at the aqueous membrane interface and larger spaces between the membrane-embedded domains where lipid molecules may reside are also demonstrated. The supercomplex contains about 50 molecules of cardiolipin (CL) with a fatty acid composition identical to that of the inner membrane CL pool, consistent with CL-dependent stabilization of the supercomplex.
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