Accurate membrane tracing in three-dimensional reconstructions from electron cryotomography data

Cryo-EM tomography and automatic segmentation delineate modular structures in the postsynaptic density

Postsynaptic densities (PSDs) are large protein complexes associated with the postsynaptic membrane of excitatory synapses important for synaptic function including plasticity. Conventional electron microscopy (EM) typically depicts PSDs as compact disk-like structures of hundreds of nanometers in size. Biochemically isolated PSDs were also similar in dimension revealing a predominance of proteins with the ability to polymerize into an extensive scaffold; several EM studies noted their irregular contours with often small granular structures (<30 nm) and holes. Super-resolution light microscopy studies observed clusters of PSD elements and their activity-induced lateral movement. Furthermore, our recent EM study on PSD fractions after sonication observed PSD fragments (40–90 nm in size) separate from intact PSDs; however, such structures within PSDs remained unidentified. Here we examined isolated PSDs by cryo-EM tomography with our new approach of automatic segmentation that enables delineation of substructures and their quantitative analysis. The delineated substructures broadly varied in size, falling behind 30 nm or exceeding 100 nm and showed that a considerable portion of the substructures (>38%) in isolated PSDs was in the same size range as those fragments. Furthermore, substructures spanning the entire thickness of the PSD were found, large enough to contain both membrane-associated and cytoplasmic proteins of the PSD; interestingly, they were similar to nanodomains in frequency. The structures detected here appear to constitute the isolated PSD as modules of various compositions, and this modular nature may facilitate remodeling of the PSD for proper synaptic function and plasticity.

High Rac1 activity is functionally translated into cytosolic structures with unique nanoscale cytoskeletal architecture

Rac1 activation is at the core of signaling pathways regulating polarized cell migration. So far, it has not been possible to directly explore the structural changes triggered by Rac1 activation at the molecular level. Here, through a multiscale imaging workflow that combines biosensor imaging of Rac1 dynamics with electron cryotomography, we identified, within the crowded environment of eukaryotic cells, a unique nanoscale architecture of a flexible, signal-dependent actin structure. In cell regions with high Rac1 activity, we found a structural regime that spans from the ventral membrane up to a height of ∼60 nm above that membrane, composed of directionally unaligned, densely packed actin filaments, most shorter than 150 nm. This unique Rac1-induced morphology is markedly different from the dendritic network architecture in which relatively short filaments emanate from existing, longer actin filaments. These Rac1-mediated scaffold assemblies are devoid of large macromolecules such as ribosomes or other filament types, which are abundant at the periphery and within the remainder of the imaged volumes. Cessation of Rac1 activity induces a complete and rapid structural transition, leading to the absence of detectable remnants of such structures within 150 s, providing direct structural evidence for rapid actin filament network turnover induced by GTPase signaling events. It is tempting to speculate that this highly dynamical nanoscaffold system is sensitive to local spatial cues, thus serving to support the formation of more complex actin filament architectures-such as those mandated by epithelial-mesenchymal transition, for example-or resetting the region by completely dissipating.

Convolutional neural networks for automated annotation of cellular cryo-electron tomograms

Cellular Electron Cryotomography (CryoET) offers the ability to look inside cells and observe macromolecules frozen in action. A primary challenge for this technique is identifying and extracting the molecular components within the crowded cellular environment. We introduce a method using neural networks to dramatically reduce the time and human effort required for subcellular annotation and feature extraction. Subsequent subtomogram classification and averaging yields in-situ structures of molecular components of interest.

Assumption-free morphological quantification of single anisotropic nanoparticles and aggregates

Characterizing the morphometric parameters of noble metal nanoparticles for sensing and catalysis is a persistent challenge due to their small size and complex shape. Herein, we present an approach to determine the volume, surface area, and curvature of non-symmetric anisotropic nanoparticles using electron tomography and design-based stereology without the use of segmentation tools or modeling of the particles. Finally, we apply these tools to aggregates to estimate their fractal dimension.

Nano-scale actin-network characterization of fibroblast cells lacking functional Arp2/3 complex

Arp2/3 complex is thought to be the primary protrusive force generator in cell migration by controlling the assembly and turnover of the branched filament network that pushes the leading edge of moving cells forward. However, mouse fibroblasts without functional Arp2/3 complex migrate at rates similar to wild-type cells, contradicting this paradigm. We show by correlative fluorescence and large-scale cryo-tomography studies combined with automated actin-network analysis that the absence of functional Arp2/3 complex has profound effects on the nano-scale architecture of actin networks. Our quantitative analysis at the single-filament level revealed that cells lacking functional Arp2/3 complex fail to regulate location-dependent fine-tuning of actin filament growth and organization that is distinct from its role in the formation and regulation of dendritic actin networks.

Quantifying Variability of Manual Annotation in Cryo-Electron Tomograms

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.

A validated active contour method driven by parabolic arc model for detection and segmentation of mitochondria

Recent studies reveal that mitochondria take substantial responsibility in cellular functions that are closely related to aging diseases caused by degeneration of neurons. These studies emphasize that the membrane and crista morphology of a mitochondrion should receive attention in order to investigate the link between mitochondrial function and its physical structure. Electron microscope tomography (EMT) allows analysis of the inner structures of mitochondria by providing highly detailed visual data from large volumes. Computerized segmentation of mitochondria with minimum manual effort is essential to accelerate the study of mitochondrial structure/function relationships. In this work, we improved and extended our previous attempts to detect and segment mitochondria from transmission electron microcopy (TEM) images. A parabolic arc model was utilized to extract membrane structures. Then, curve energy based active contours were employed to obtain roughly outlined candidate mitochondrial regions. Finally, a validation process was applied to obtain the final segmentation data. 3D extension of the algorithm is also presented in this paper. Our method achieved an average F-score performance of 0.84. Average Dice Similarity Coefficient and boundary error were measured as 0.87 and 14nm respectively.