Tracing Actin Filament Bundles in Three-Dimensional Electron Tomography Density Maps of Hair Cell Stereocilia

Cryo-electron tomography (cryo-ET) is a powerful method of visualizing the three-dimensional organization of supramolecular complexes, such as the cytoskeleton, in their native cell and tissue contexts. Due to its minimal electron dose and reconstruction artifacts arising from the missing wedge during data collection, cryo-ET typically results in noisy density maps that display anisotropic XY versus Z resolution. Molecular crowding further exacerbates the challenge of automatically detecting supramolecular complexes, such as the actin bundle in hair cell stereocilia. Stereocilia are pivotal to the mechanoelectrical transduction process in inner ear sensory epithelial hair cells. Given the complexity and dense arrangement of actin bundles, traditional approaches to filament detection and tracing have failed in these cases. In this study, we introduce BundleTrac, an effective method to trace hundreds of filaments in a bundle. A comparison between BundleTrac and manually tracing the actin filaments in a stereocilium showed that BundleTrac accurately built 326 of 330 filaments (98.8%), with an overall cross-distance of 1.3 voxels for the 330 filaments. BundleTrac is an effective semi-automatic modeling approach in which a seed point is provided for each filament and the rest of the filament is computationally identified. We also demonstrate the potential of a denoising method that uses a polynomial regression to address the resolution and high-noise anisotropic environment of the density map

\u3ci\u3eSpaghetti Tracer\u3c/i\u3e: A Framework for Tracing Semiregular Filamentous Densities in 3D Tomograms

Within cells, cytoskeletal filaments are often arranged into loosely aligned bundles. These fibrous bundles are dense enough to exhibit a certain regularity and mean direction, however, their packing is not sufficient to impose a symmetry between—or specific shape on—individual filaments. This intermediate regularity is computationally difficult to handle because individual filaments have a certain directional freedom, however, the filament densities are not well segmented from each other (especially in the presence of noise, such as in cryo-electron tomography). In this paper, we develop a dynamic programming-based framework, Spaghetti Tracer, to characterizing the structural arrangement of filaments in the challenging 3D maps of subcellular components. Assuming that the tomogram can be rotated such that the filaments are oriented in a mean direction, the proposed framework first identifies local seed points for candidate filament segments, which are then grown from the seeds using a dynamic programming algorithm. We validate various algorithmic variations of our framework on simulated tomograms that closely mimic the noise and appearance of experimental maps. As we know the ground truth in the simulated tomograms, the statistical analysis consisting of precision, recall, and F1 scores allows us to optimize the performance of this new approach. We find that a bipyramidal accumulation scheme for path density is superior to straight-line accumulation. In addition, the multiplication of forward and backward path densities provides for an efficient filter that lifts the filament density above the noise level. Resulting from our tests is a robust method that can be expected to perform well (F1 scores 0.86–0.95) under experimental noise conditions

Image Analysis and Multiphase Bioreactors

The applications of visualisation and image analysis to bioreactors can be found in two main areas: the characterisation of biomass (fungi, bacteria, yeasts, animal and plant cells, etc), in terms of size, morphology and physiology, that is the far most developed, and the characterisation of the multiphase behaviour of the reactors (flow patterns, velocity fields, bubble size and shape distribution, foaming), that may require sophisticated visualisation techniques

Structural characterization of Ebola virus uncoating

Viruses initiate infection of host cells by entering through a variety of different pathways. Their entry is concluded by the release of the viral genome into the cytoplasm, where the cellular machinery gets repurposed for virus replication. Prerequisite for genome release is the uncoating of the viral particles, a process which requires the destabilization of interactions established during virus assembly. Ebola viruses (EBOVs) are highly pathogenic, enveloped RNA viruses of remarkable filamentous morphology. Their shape is dictated by the viral matrix protein VP40, which forms a tubular scaffold underneath the viral envelope and confers stability to the particles during EBOV transmission. EBOVs enter host cells via the endocytic pathway and release their genome into the cytoplasm after fusion of their envelope with the endosomal membrane. The first line of defence against a viral infection is blocking viral entry, and EBOV entry has accordingly been well investigated with respect to receptor engagement and potential membrane fusion triggers. However, key mechanisms governing the final step of virus entry are still unknown, including the central question of how these unusually shaped virions undergo uncoating. Whether and how the VP40 matrix disassembles to enable membrane fusion; whether uncoating involves additional triggers; and finally, how and where the viral genome gets released from the viral particles and nucleocapsids remains to be elucidated. In this thesis, I investigate EBOV uncoating during entry into host cells and shed light on the fate of the most abundant and versatile viral protein, VP40. As a main tool, I use in situ cryo-electron tomography and provide structural insights into EBOV uncoating both in vitro and in infected host cells at molecular resolution. I discover that at low endosomal pH, the VP40 matrix detaches from the viral envelope and disassembles. This is caused by the disruption of electrostatic interactions between membrane lipids and anionic amino acids exposed on the surface of VP40 dimers, which I show are the structural units of the VP40 matrix. The strong effect of low pH on the integrity of the VP40 matrix is a consequence of acidification of the viral lumen, which I further investigate to uncover its mechanism. I show that protons diffuse passively across the viral envelope independently of a dedicated ion channel, which might be relevant for other late-penetrating viruses lacking viroporins. Finally, I provide the first high-resolution images of Ebola virions in endolysosomal compartments of infected cells. These images confirm the disassembly of the VP40 matrix in virions located in acidified compartments while clearly showing that their nucleocapsids remain intact. Together, these findings reveal that VP40 matrix disassembly is an essential step during EBOV uncoating, which precedes membrane fusion and genome release from the nucleocapsids. Overall, this thesis extends the current understanding of virus uncoating and indicates that pH-driven structural remodeling of viral matrix proteins may act as a switch coupling matrix uncoating to membrane fusion during host cell entry of enveloped viruses

Three-dimensional X-ray imaging and analysis of fungi on and in wood

As wood is prone to fungal degradation, fundamental research is necessary to increase our knowledge aiming at product improvement. Several imaging modalities are capable of visualizing fungi, but the X-ray equipment presented in this paper can envisage fungal mycelium in wood non-destructively in three dimensions with sub-micron resolution. Four types of wood subjected to the action of the white rot fungus Coriolus versicolor (Linnaeus) Quélet (CTB 863 A) were scanned using an X-ray based approach. Comparison of wood volumes before and after fungal exposure, segmented manually or semi-automatically, showed the presence of the fungal mass on and in the wood samples and therefore demonstrated the usefulness of computed X-ray tomography for mycological and wood research. Further improvements to the experimental set-up are necessary in order to resolve individual hyphae and enhance segmentation

High-resolution insights into macromolecular assembly: a yeast’s survival strategy

Cells grow in environments that can change suddenly. To cope with unpredictable perturbations, they have evolved mechanisms to adjust their metabolism according to the various types of environmental stress. Cells experiencing starvation, for example, have low energy levels and are forced to lower their metabolism and enter a protective quiescent state to survive until nutrients become available again. Recently, it has been shown that starved yeast cells experience a marked acidification of the cytoplasm, due to a passive influx of protons. This pH drop causes multiple rearrangements in the cytoplasm: increased crowding, reduced mobility of intracellular components and formation of stress-induced non-membrane bound compartments of specific metabolic enzymes. Cytoplasm rearrangements are required for cell survival and can be reversed upon replenishment of energy. However, there is little understanding of how cytoplasmic components reorganize in stressed quiescent cells. Using high-pressure freezing, correlative light and electron microscopy (CLEM) and electron tomography, coupled to high-resolution 3D-reconstruction techniques, I investigate the structural modi cations that happen in situ in yeast cells undergoing quiescence. I observe that the cytoplasm becomes increasingly crowded, due to a massive rearrangement of membranous structures, including accumulation of intracellular vesicles and pronounced invaginations in the plasma membrane. This is proved by quantification of the difference in ribosome densities between stressed and not stressed cells. The increased crowding, coupled to cytoplasm acidification, leads to the formation of non-membrane bound enzyme compartments, that appear as foci and elongated structures of fluorescently tagged enzymes. I prove that the fluorescent structures correspond to bundles of filaments. Among many essential enzymes, known to form mesoscale structure in stressed yeast, I demonstrate that the eukaryotic translation initiation factor 2B (eIF2B) forms bundles of filaments in situ, and the evolutionary conserved glutamine synthetase (Gln1) self-assembles into filaments in vitro. The present study on the energy depleted cytoplasm and the structural analysis of filament-forming enzymes provides insights into an unexplored survival strategy that is used by yeast, as well as other organisms, to cope with extreme environmental conditions and stress.:1 Introduction 1 Stress, survival and quiescence 2 1.1 Cytoplasm and cellular compartments 2 1.2 Membraneless compartmentalization in the cell 3 1.3 Stress-induced non-membrane bound assemblies 4 The quiescent sleeping yeast 6 1.4 The yeast S.cerevisiae as model organism 7 1.5 Growth and metabolism of yeast 9 1.5.1 Yeast eukaryotic translation initiation factor 2B: eIF2B 11 1.5.2 Yeast glutamine synthetase: Gln1 12 3D electron microscopy 14 Aims of the Thesis 18 2 Materials and methods 21 Room temperature electron microscopy (EM) 21 2.1 Yeast strains, media and energy depletion 21 2.2 High-pressure freezing of yeast cells 22 2.2.1 EM sample preparation for untagged eIF2B yeast strains 22 2.2.2 EM sample preparation for GFP-tagged eIF2B yeast strains 23 2.3 Electron tomography 23 2.4 Subtomogram averaging 24 2.5 Fiji script for automated ribosome counting 25 2.6 Immunofluorescence of eIF2B in yeast 26 2.7 Western-blot on yeast ribosomes 27 Single particle procedures 30 2.8 Protein purification protocols 30 2.8.1 Baculovirus-insect cell expression and purification of eIF2B 30 2.8.2 Gradient of fixation for fragile complexes 31 2.8.3 Yeast expression and purification of Gln1 33 2.9 Negative staining 34 2.9.1 Image acquisition and analysis—eIF2B 35 2.9.2 Image acquisition and analysis—Gln1 36 Cryo-electronmicroscopy(cryo-EM) 37 2.10 Plunge freezing 37 2.11 Image acquisition and 3D reconstruction 37 3 Results Visualizing yeast’s cytoplasmic reorganization 39 3.1 Quiescence is accompanied by reorganization of the cytoplasm 40 3.2 Ribosome density proves cytoplasmic crowding in starved cells 42 3.3 eIF2B organizes in bundles of filaments in energy-depleted cells 45 3.4 eIF2B filaments are polymers of the eIF2B complex 47 3.5 Filaments are found in wild-type energy-depleted cells 49 Structural analysis of filament forming enzymes 51 3.6 Purification of eIF2B complexes 51 3.7 Single particle analysis of eIF2B 53 3.8 Purification of Gln1 complexes 55 3.9 Single particle analysis of Gln1 56 3.10 Gln1 forms filaments in vitro 58 4 Discussion and Outlook 59 4.1 Yeast cytoplasm reorganizes in response of stress 59 4.2 Ribosomes density is a measure of increased macromolecular crowding 60 4.3 eIF2B forms filaments as a survival strategy 62 4.4 Molecular analysis of filament forming enzymes 64 4.5 Outlook 65 Appendix 67 Bibliography 8

Correlative Microscopy: a tool for understanding soil weathering in modern analogues of early terrestrial biospheres

Correlative imaging provides a method of investigating complex systems by combining analytical (chemistry) and imaging (tomography) information across dimensions (2D-3D) and scales (centimetres-nanometres). We studied weathering processes in a modern cryptogamic ground cover from Iceland, containing early colonizing, and evolutionary ancient, communities of mosses, lichens, fungi, and bacteria. Targeted multi-scale X-ray Microscopy of a grain in-situ within a soil core revealed networks of surficial and internal features (tunnels) originating from organic-rich surface holes. Further targeted 2D grain characterisation by optical microscopy (OM), scanning electron microscopy (SEM), and energy dispersive X-ray spectroscopy (SEM–EDS), following an intermediate manual correlative preparation step, revealed Fe-rich nodules within the tunnels. Finally, nanotomographic imaging by focussed ion beam microscopy (FIB-SEM) revealed coccoid and filamentous-like structures within subsurface tunnels, as well as accumulations of Fe and S in grain surface crusts, which may represent a biological rock varnish/glaze. We attribute these features to biological processes. This work highlights the advantages and novelty of the correlative imaging approach, across scales, dimensions, and modes, to investigate biological weathering processes. Further, we demonstrate correlative microscopy as a means of identifying fingerprints of biological communities, which could be used in the geologic rock record and on extra-terrestrial bodies

Making microscopy count: quantitative light microscopy of dynamic processes in living plants

First published: April 2016This is the author accepted manuscript. The final version is available from the publisher via the DOI in this record.Cell theory has officially reached 350 years of age as the first use of the word ‘cell’ in a biological context can be traced to a description of plant material by Robert Hooke in his historic publication “Micrographia: or some physiological definitions of minute bodies”. The 2015 Royal Microscopical Society Botanical Microscopy meeting was a celebration of the streams of investigation initiated by Hooke to understand at the sub-cellular scale how plant cell function and form arises. Much of the work presented, and Honorary Fellowships awarded, reflected the advanced application of bioimaging informatics to extract quantitative data from micrographs that reveal dynamic molecular processes driving cell growth and physiology. The field has progressed from collecting many pixels in multiple modes to associating these measurements with objects or features that are meaningful biologically. The additional complexity involves object identification that draws on a different type of expertise from computer science and statistics that is often impenetrable to biologists. There are many useful tools and approaches being developed, but we now need more inter-disciplinary exchange to use them effectively. In this review we show how this quiet revolution has provided tools available to any personal computer user. We also discuss the oft-neglected issue of quantifying algorithm robustness and the exciting possibilities offered through the integration of physiological information generated by biosensors with object detection and tracking

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. However, 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 filamentous networks from cryo-ET datasets to facilitate the analysis and cross-comparison of multiple experimental conditions. GUI-based components simplify the manipulation 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