Efficient Extraction of Macromolecular Complexes from Electron Tomograms Based on Reduced Representation Templates

Deciphering the molecular mechanisms of actin cytoskeleton regulation in cell migration using cryo-EM

The actin cytoskeleton plays a key role in cell migration and cellular morphodynamics in most eukaryotes. The ability of the actin cytoskeleton to assemble and disassemble in a spatiotemporally controlled manner allows it to form higher-order structures, which can generate forces required for a cell to explore and navigate through its environment. It is regulated not only via a complex synergistic and competitive interplay between actin-binding proteins (ABP), but also by filament biochemistry and filament geometry. The lack of structural insights into how geometry and ABPs regulate the actin cytoskeleton limits our understanding of the molecular mechanisms that define actin cytoskeleton remodeling and, in turn, impact emerging cell migration characteristics. With the advent of cryo-electron microscopy (cryo-EM) and advanced computational methods, it is now possible to define these molecular mechanisms involving actin and its interactors at both atomic and ultra-structural levels in vitro and in cellulo. In this review, we will provide an overview of the available cryo-EM methods, applicable to further our understanding of the actin cytoskeleton, specifically in the context of cell migration. We will discuss how these methods have been employed to elucidate ABP- and geometry-defined regulatory mechanisms in initiating, maintaining, and disassembling cellular actin networks in migratory protrusions.

Morphological control enables nanometer-scale dissection of cell-cell signaling complexes

Protein micropatterning enables robust control of cell positioning on electron-microscopy substrates for cryogenic electron tomography (cryo-ET). However, the combination of regulated cell boundaries and the underlying electron-microscopy substrate (EM-grids) provides a poorly understood microenvironment for cell biology. Because substrate stiffness and morphology affect cellular behavior, we devised protocols to characterize the nanometer-scale details of the protein micropatterns on EM-grids by combining cryo-ET, atomic force microscopy, and scanning electron microscopy. Measuring force displacement characteristics of holey carbon EM-grids, we found that their effective spring constant is similar to physiological values expected from skin tissues. Despite their apparent smoothness at light-microscopy resolution, spatial boundaries of the protein micropatterns are irregular at nanometer scale. Our protein micropatterning workflow provides the means to steer both positioning and morphology of cell doublets to determine nanometer details of punctate adherens junctions. Our workflow serves as the foundation for studying the fundamental structural changes governing cell-cell signaling.

Quantitative Cryo-Electron Tomography

The three-dimensional organization of biomolecules important for the functioning of all living systems can be determined by cryo-electron tomography imaging under native biological contexts. Cryo-electron tomography is continually expanding and evolving, and the development of new methods that use the latest technology for sample thinning is enabling the visualization of ever larger and more complex biological systems, allowing imaging across scales. Quantitative cryo-electron tomography possesses the capability of visualizing the impact of molecular and environmental perturbations in subcellular structure and function to understand fundamental biological processes. This review provides an overview of current hardware and software developments that allow quantitative cryo-electron tomography studies and their limitations and how overcoming them may allow us to unleash the full power of cryo-electron tomography.

Computational toolbox for ultrastructural quantitative analysis of filament networks in cryo-ET data

A precise quantitative description of the ultrastructural characteristics underlying biological mechanisms is often key to their understanding. This is particularly true for dynamic extra- and intracellular filamentous assemblies, playing a role in cell motility, cell integrity, cytokinesis, tissue formation and maintenance. For example, genetic manipulation or modulation of actin regulatory proteins frequently manifests in changes of the morphology, dynamics, and ultrastructural architecture of actin filament-rich cell peripheral structures, such as lamellipodia or filopodia. However, the observed ultrastructural effects often remain subtle and require sufficiently large datasets for appropriate quantitative analysis. The acquisition of such large datasets has been enabled by recent advances in high-throughput cryo-electron tomography (cryo-ET) methods. This also necessitates the development of complementary approaches to maximize the extraction of relevant biological information. We have developed a computational toolbox for the semi-automatic quantification of segmented and vectorized filamentous networks from pre-processed cryo-electron tomograms, facilitating the analysis and cross-comparison of multiple experimental conditions. GUI-based components simplify the processing of data and allow users to obtain a large number of ultrastructural parameters describing filamentous assemblies. We demonstrate the feasibility of this workflow by analyzing cryo-ET data of untreated and chemically perturbed branched actin filament networks and that of parallel actin filament arrays. In principle, the computational toolbox presented here is applicable for data analysis comprising any type of filaments in regular (i.e. parallel) or random arrangement. We show that it can ease the identification of key differences between experimental groups and facilitate the in-depth analysis of ultrastructural data in a time-efficient manner.

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.

Improved deep learning-based macromolecules structure classification from electron cryo-tomograms

Cellular processes are governed by macromolecular complexes inside the cell. Study of the native structures of macromolecular complexes has been extremely difficult due to lack of data. With recent breakthroughs in Cellular Electron Cryo-Tomography (CECT) 3D imaging technology, it is now possible for researchers to gain accesses to fully study and understand the macro-molecular structures single cells. However, systematic recovery of macromolecular structures from CECT is very difficult due to high degree of structural complexity and practical imaging limitations. Specifically, we proposed a deep learning-based image classification approach for large-scale systematic macromolecular structure separation from CECT data. However, our previous work was only a very initial step toward exploration of the full potential of deep learning-based macromolecule separation. In this paper, we focus on improving classification performance by proposing three newly designed individual CNN models: an extended version of (Deep Small Receptive Field) DSRF3D, donated as DSRF3D-v2, a 3D residual block-based neural network, named as RB3D, and a convolutional 3D (C3D)-based model, CB3D. We compare them with our previously developed model (DSRF3D) on 12 datasets with different SNRs and tilt angle ranges. The experiments show that our new models achieved significantly higher classification accuracies. The accuracies are not only higher than 0.9 on normal datasets, but also demonstrate potentials to operate on datasets with high levels of noises and missing wedge effects presented.

A convolutional autoencoder approach for mining features in cellular electron cryo-tomograms and weakly supervised coarse segmentation

Cellular electron cryo-tomography enables the 3D visualization of cellular organization in the near-native state and at submolecular resolution. However, the contents of cellular tomograms are often complex, making it difficult to automatically isolate different in situ cellular components. In this paper, we propose a convolutional autoencoder-based unsupervised approach to provide a coarse grouping of 3D small subvolumes extracted from tomograms. We demonstrate that the autoencoder can be used for efficient and coarse characterization of features of macromolecular complexes and surfaces, such as membranes. In addition, the autoencoder can be used to detect non-cellular features related to sample preparation and data collection, such as carbon edges from the grid and tomogram boundaries. The autoencoder is also able to detect patterns that may indicate spatial interactions between cellular components. Furthermore, we demonstrate that our autoencoder can be used for weakly supervised semantic segmentation of cellular components, requiring a very small amount of manual annotation.

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.