In situ cryo-electron tomograms #1-#2 of human brain organoid from SOLIST procedure.
Ontology highlight
ABSTRACT:
SUBMITTER: Philipp S Erdmann
PROVIDER: EMPIAR-12130 | biostudies-other |
REPOSITORIES: biostudies-other
Publications
Serialized on-grid lift-in sectioning for tomography (SOLIST) enables a biopsy at the nanoscale.
Nguyen Ho Thuy Dung HTD Perone Gaia G Klena Nikolai N Vazzana Roberta R Kaluthantrige Don Flaminia F Silva Malan M Sorrentino Simona S Swuec Paolo P Leroux Frederic F Kalebic Nereo N Coscia Francesca F Erdmann Philipp S PS
Nature methods 20240913 9
Cryo-focused ion beam milling has substantially advanced our understanding of molecular processes by opening windows into cells. However, applying this technique to complex samples, such as tissues, has presented considerable technical challenges. Here we introduce an innovative adaptation of the cryo-lift-out technique, serialized on-grid lift-in sectioning for tomography (SOLIST), addressing these limitations. SOLIST enhances throughput, minimizes ice contamination and improves sample stabilit ...[more]
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| EMPIAR-12128 | biostudies-other
Project description:Although acknowledged to be variable and subjective, manual annotation of cryo-electron tomography data is commonly used to answer structural questions and to create a "ground truth" for evaluation of automated segmentation algorithms. Validation of such annotation is lacking, but is critical for understanding the reproducibility of manual annotations. Here, we used voxel-based similarity scores for a variety of specimens, ranging in complexity and segmented by several annotators, to quantify the variation among their annotations. In addition, we have identified procedures for merging annotations to reduce variability, thereby increasing the reliability of manual annotation. Based on our analyses, we find that it is necessary to combine multiple manual annotations to increase the confidence level for answering structural questions. We also make recommendations to guide algorithm development for automated annotation of features of interest.
| S-EPMC5111626 | biostudies-literature
Project description:Imaging of fully hydrated, vitrified biological samples by electron tomography yields structural information about cellular protein complexes in situ. Here we present a computational procedure that removes artifacts of three-dimensional reconstruction caused by contamination present in samples during imaging by electron microscopy. Applying the procedure to phantom data and electron tomograms of cellular samples significantly improved the resolution and the interpretability of tomograms. Artifacts caused by surface contamination associated with thinning by focused ion beam, as well as those arising from gold fiducial markers and from common, lower contrast contamination, could be removed. Our procedure is widely applicable and is especially suited for applications that strive to reach a higher resolution and involve the use of recently developed, state-of-the-art instrumentation.
| S-EPMC4775855 | biostudies-literature
Project description:MotivationSince 2017, an increasing amount of attention has been paid to the supervised deep learning-based macromolecule in situ structural classification (i.e. subtomogram classification) in cellular electron cryo-tomography (CECT) due to the substantially higher scalability of deep learning. However, the success of such supervised approach relies heavily on the availability of large amounts of labeled training data. For CECT, creating valid training data from the same data source as prediction data is usually laborious and computationally intensive. It would be beneficial to have training data from a separate data source where the annotation is readily available or can be performed in a high-throughput fashion. However, the cross data source prediction is often biased due to the different image intensity distributions (a.k.a. domain shift).ResultsWe adapt a deep learning-based adversarial domain adaptation (3D-ADA) method to timely address the domain shift problem in CECT data analysis. 3D-ADA first uses a source domain feature extractor to extract discriminative features from the training data as the input to a classifier. Then it adversarially trains a target domain feature extractor to reduce the distribution differences of the extracted features between training and prediction data. As a result, the same classifier can be directly applied to the prediction data. We tested 3D-ADA on both experimental and realistically simulated subtomogram datasets under different imaging conditions. 3D-ADA stably improved the cross data source prediction, as well as outperformed two popular domain adaptation methods. Furthermore, we demonstrate that 3D-ADA can improve cross data source recovery of novel macromolecular structures.Availability and implementationhttps://github.com/xulabs/projects.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC6612867 | biostudies-literature
Project description:BackgroundDespite recent advances in cellular cryo-electron tomography (CET), developing automated tools for macromolecule identification in submolecular resolution remains challenging due to the lack of annotated data and high structural complexities. To date, the extent of the deep learning methods constructed for this problem is limited to conventional Convolutional Neural Networks (CNNs). Identifying macromolecules of different types and sizes is a tedious and time-consuming task. In this paper, we employ a capsule-based architecture to automate the task of macromolecule identification, that we refer to as 3D-UCaps. In particular, the architecture is composed of three components: feature extractor, capsule encoder, and CNN decoder. The feature extractor converts voxel intensities of input sub-tomograms to activities of local features. The encoder is a 3D Capsule Network (CapsNet) that takes local features to generate a low-dimensional representation of the input. Then, a 3D CNN decoder reconstructs the sub-tomograms from the given representation by upsampling.ResultsWe performed binary and multi-class localization and identification tasks on synthetic and experimental data. We observed that the 3D-UNet and the 3D-UCaps had an [Formula: see text]score mostly above 60% and 70%, respectively, on the test data. In both network architectures, we observed degradation of at least 40% in the [Formula: see text]-score when identifying very small particles (PDB entry 3GL1) compared to a large particle (PDB entry 4D8Q). In the multi-class identification task of experimental data, 3D-UCaps had an [Formula: see text]-score of 91% on the test data in contrast to 64% of the 3D-UNet. The better [Formula: see text]-score of 3D-UCaps compared to 3D-UNet is obtained by a higher precision score. We speculate this to be due to the capsule network employed in the encoder. To study the effect of the CapsNet-based encoder architecture further, we performed an ablation study and perceived that the [Formula: see text]-score is boosted as network depth is increased which is in contrast to the previously reported results for the 3D-UNet. To present a reproducible work, source code, trained models, data as well as visualization results are made publicly available.ConclusionQuantitative and qualitative results show that 3D-UCaps successfully perform various downstream tasks including identification and localization of macromolecules and can at least compete with CNN architectures for this task. Given that the capsule layers extract both the existence probability and the orientation of the molecules, this architecture has the potential to lead to representations of the data that are better interpretable than those of 3D-UNet.
| S-EPMC9429335 | biostudies-literature
Project description:MotivationCryo electron tomography (CryoET) produces 3D density maps of biological specimen in its near native states. Applied to small cells, cryoET produces 3D snapshots of the cellular distributions of large complexes. However, retrieving this information is non-trivial due to the low resolution and low signal-to-noise ratio in tomograms. Current pattern recognition methods identify complexes by matching known structures to the cryo electron tomogram. However, so far only a small fraction of all protein complexes have been structurally resolved. It is, therefore, of great importance to develop template-free methods for the discovery of previously unknown protein complexes in cryo electron tomograms.ResultsHere, we have developed an inference method for the template-free discovery of frequently occurring protein complexes in cryo electron tomograms. We provide a first proof-of-principle of the approach and assess its applicability using realistically simulated tomograms, allowing for the inclusion of noise and distortions due to missing wedge and electron optical factors. Our method is a step toward the template-free discovery of the shapes, abundance and spatial distributions of previously unknown macromolecular complexes in whole cell tomograms.Contactalber@usc.edu
| S-EPMC3117359 | biostudies-literature
Project description:Cryo-electron tomography (cryo-ET) enables observation of macromolecular complexes in their native, spatially contextualized cellular environment. Cryo-ET processing software to visualize such complexes at nanometer resolution via iterative alignment and averaging are well developed but rely upon assumptions of structural homogeneity among the complexes of interest. Recently developed tools allow for some assessment of structural diversity but have limited capacity to represent highly heterogeneous structures, including those undergoing continuous conformational changes. Here we extend the highly expressive cryoDRGN (Deep Reconstructing Generative Networks) deep learning architecture, originally created for single-particle cryo-electron microscopy analysis, to cryo-ET. Our new tool, tomoDRGN, learns a continuous low-dimensional representation of structural heterogeneity in cryo-ET datasets while also learning to reconstruct heterogeneous structural ensembles supported by the underlying data. Using simulated and experimental data, we describe and benchmark architectural choices within tomoDRGN that are uniquely necessitated and enabled by cryo-ET. We additionally illustrate tomoDRGN's efficacy in analyzing diverse datasets, using it to reveal high-level organization of human immunodeficiency virus (HIV) capsid complexes assembled in virus-like particles and to resolve extensive structural heterogeneity among ribosomes imaged in situ.
| S-EPMC11655136 | biostudies-literature
Project description:Cryo-electron tomography (cryo-ET) allows one to observe macromolecular complexes in their native, spatially contextualized environment. Tools to visualize such complexes at nanometer resolution via iterative alignment and averaging are well-developed but rely on assumptions of structural homogeneity among the complexes under consideration. Recently developed downstream analysis tools allow for some assessment of macromolecular diversity but have limited capacity to represent highly heterogeneous macromolecules, including those undergoing continuous conformational changes. Here, we extend the highly expressive cryoDRGN deep learning architecture, originally created for cryo-electron microscopy single particle analysis, to sub-tomograms. Our new tool, tomoDRGN, learns a continuous low-dimensional representation of structural heterogeneity in cryo-ET datasets while also learning to reconstruct a large, heterogeneous ensemble of structures supported by the underlying data. Using simulated and experimental data, we describe and benchmark architectural choices within tomoDRGN that are uniquely necessitated and enabled by cryo-ET data. We additionally illustrate tomoDRGN's efficacy in analyzing an exemplar dataset, using it to reveal extensive structural heterogeneity among ribosomes imaged in situ.
| S-EPMC10312494 | biostudies-literature