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Following axon pathfinding, growth cones transition from stochastic filopodial exploration to the formation of a limited number of synapses. How the interplay of filopodia and synapse assembly ensures robust connectivity in the brain has remained a challenging problem. Here, we developed a new 4D analysis method for filopodial dynamics and a data-driven computational model of synapse formation for R7 photoreceptor axons in developing Drosophila brains. Our live data support a 'serial synapse formation' model, where at any time point only a single 'synaptogenic' filopodium suppresses the synaptic competence of other filopodia through competition for synaptic seeding factors. Loss of the synaptic seeding factors Syd-1 and Liprin-α leads to a loss of this suppression, filopodial destabilization and reduced synapse formation, which is sufficient to cause the destabilization of entire axon terminals. Our model provides a filopodial 'winner-takes-all' mechanism that ensures the formation of an appropriate number of synapses.
The role of titanium surface nanotopography on preosteoblast morphology, adhesion and migration
(2017)
Surface structuring of titanium-based implants with appropriate nanotopographies can significantly modulate their impact on the biological behavior of cells populating these implants. Implant assisted bone tissue repair and regeneration require functional adhesion and expansion of bone progenitors. The surface nanotopography of implant materials used to support bone healing and its effect on cell behavior, in particular cell adhesion, spreading, expansion, and motility, is still not clearly understood. The aim of this study is to investigate preosteoblast proliferation, adhesion, morphology, and migration on different titanium materials with similar surface chemistry, but distinct nanotopographical features. Sonochemical treatment and anodic oxidation were employed to fabricate disordered – mesoporous titania (TMS), and ordered – titania nanotubular (TNT) topographies respectively. The morphological evaluation revealed a surface dependent shape, thickness, and spreading of cells owing to different adherence behavior. Cells were polygonal-shaped and well-spread on glass and TMS, but displayed an elongated fibroblast-like morphology on TNT surfaces. The cells on glass however, were much flatter than on nanostructured surfaces. Both nanostructured surfaces impaired cell adhesion, but TMS was more favorable for cell growth due to its support of cell attachment and spreading in contrast to TNT. Quantitative wound healing assay in combination with live-cell imaging revealed that cells seeded on TMS surfaces migrated in close proximity to neighboring cells and less directed when compared to the migratory behavior on other surfaces. The results indicate distinctly different cell adhesion and migration on ordered and disordered titania nanotopographies, providing important information that could be used in optimizing titanium-based scaffold design to foster bone tissue growth and repair.
Tracing microtubule centerlines in serial section electron tomography requires microtubules to be stitched across sections, that is lines from different sections need to be aligned, endpoints need to be matched at section boundaries to establish a correspondence between neighboring sections, and corresponding lines need to be connected across multiple sections. We present computational methods for these tasks: 1) An initial alignment is computed using a distance compatibility graph. 2) A fine alignment is then computed with a probabilistic variant of the iterative closest points algorithm, which we extended to handle the orientation of lines by introducing a periodic random variable to the probabilistic formulation. 3) Endpoint correspondence is established by formulating a matching problem in terms of a Markov random field and computing the best matching with belief propagation. Belief propagation is not generally guaranteed to converge to a minimum. We show how convergence can be achieved, nonetheless, with minimal manual input. In addition to stitching microtubule centerlines, the correspondence is also applied to transform and merge the electron tomograms. We applied the proposed methods to samples from the mitotic spindle in C. elegans, the meiotic spindle in X. laevis, and sub-pellicular microtubule arrays in T. brucei. The methods were able to stitch microtubules across section boundaries in good agreement with experts’ opinions for the spindle samples. Results, however, were not satisfactory for the microtubule arrays. For certain experiments, such as an analysis of the spindle, the proposed methods can replace manual expert tracing and thus enable the analysis of microtubules over long distances with reasonable manual effort.
This work introduces a novel streamline seeding technique based on dual streamlines that are orthogonal to the vector field, instead of tangential. The greedy algorithm presented here produces a net of orthogonal streamlines that is iteratively refined resulting in good domain coverage and a high degree of continuity and uniformity. The algorithm is easy to implement and efficient, and it naturally extends to curved surfaces.
In oocytes of many organisms, meiotic spindles form in the absence of centrosomes [1–5]. Such female meiotic spindles have a pointed appearance in metaphase with microtubules focused at acentrosomal spindle poles. At anaphase, the microtubules of acentrosomal spindles then transition to an inter- chromosomal array, while the spindle poles disappear. This transition is currently not understood. Previous studies have focused on this inter- chromosomal microtubule array and proposed a pushing model to drive chromosome segregation [6, 7]. This model includes an end-on orientation of microtubules with chromosomes. Alternatively, chromosomes were thought to associate along bundles of microtubules [8, 9]. Starting with metaphase, this second model proposed a pure lateral chromosome-to-microtubule association up to the final meiotic stages of anaphase. Here we applied large-scale electron tomography [10] of staged C. elegans oocytes in meiosis to analyze the orientation of microtubules in respect to chromosomes. We show that microtubules at metaphase I are primarily oriented laterally to the chromosomes and that microtubules switch to an end-on orientation during progression through anaphase. We further show that this switch in microtubule orientation involves a kinesin-13 microtubule depolymerase, KLP-7, which removes laterally associated microtubules around chromosomes. From this we conclude that both lateral and end-on modes of microtubule-to-chromosome orientations are successively used in C. elegans oocytes to segregate meiotic chromosomes.
This work introduces novel internal and external memory algorithms for computing voxel skeletons of massive voxel objects with complex network-like architecture and for converting these voxel skeletons to piecewise linear geometry, that is triangle meshes and piecewise straight lines. The presented techniques help to tackle the challenge of visualizing and analyzing 3d images of increasing size and complexity, which are becoming more and more important in, for example, biological and medical research. Section 2.3.1 contributes to the theoretical foundations of thinning algorithms with a discussion of homotopic thinning in the grid cell model. The grid cell model explicitly represents a cell complex built of faces, edges, and vertices shared between voxels. A characterization of pairs of cells to be deleted is much simpler than characterizations of simple voxels were before. The grid cell model resolves topologically unclear voxel configurations at junctions and locked voxel configurations causing, for example, interior voxels in sets of non-simple voxels. A general conclusion is that the grid cell model is superior to indecomposable voxels for algorithms that need detailed control of topology. Section 2.3.2 introduces a noise-insensitive measure based on the geodesic distance along the boundary to compute two-dimensional skeletons. The measure is able to retain thin object structures if they are geometrically important while ignoring noise on the object's boundary. This combination of properties is not known of other measures. The measure is also used to guide erosion in a thinning process from the boundary towards lines centered within plate-like structures. Geodesic distance based quantities seem to be well suited to robustly identify one- and two-dimensional skeletons. Chapter 6 applies the method to visualization of bone micro-architecture. Chapter 3 describes a novel geometry generation scheme for representing voxel skeletons, which retracts voxel skeletons to piecewise linear geometry per dual cube. The generated triangle meshes and graphs provide a link to geometry processing and efficient rendering of voxel skeletons. The scheme creates non-closed surfaces with boundaries, which contain fewer triangles than a representation of voxel skeletons using closed surfaces like small cubes or iso-surfaces. A conclusion is that thinking specifically about voxel skeleton configurations instead of generic voxel configurations helps to deal with the topological implications. The geometry generation is one foundation of the applications presented in Chapter 6. Chapter 5 presents a novel external memory algorithm for distance ordered homotopic thinning. The presented method extends known algorithms for computing chamfer distance transformations and thinning to execute I/O-efficiently when input is larger than the available main memory. The applied block-wise decomposition schemes are quite simple. Yet it was necessary to carefully analyze effects of block boundaries to devise globally correct external memory variants of known algorithms. In general, doing so is superior to naive block-wise processing ignoring boundary effects. Chapter 6 applies the algorithms in a novel method based on confocal microscopy for quantitative study of micro-vascular networks in the field of microcirculation.
Kurzfassung. Durch die Alkalität des Betons wird Betonstahl dauerhaft vor
Korrosion geschützt. Infolge von Chlorideintrag kann dieser Schutz nicht länger
aufrechterhalten werden und führt zu Lochkorrosion. Die zerstörungsfreie Prüfung
von Stahlbetonproben mit 3D-CT bietet die Möglichkeit, eine Probe mehrfach
gezielt vorzuschädigen und den Korrosionsfortschritt zu untersuchen. Zur Quantifizierung
des Schädigungsgrades müssen die bei dieser Untersuchung anfallenden
großen Bilddaten mit Bildverarbeitungsmethoden ausgewertet werden. Ein wesentlicher
Schritt dabei ist die Segmentierung der Bilddaten, bei der zwischen Korrosionsprodukt
(Rost), Betonstahl (BSt), Beton, Rissen, Poren und Umgebung
unterschieden werden muss. Diese Segmentierung bildet die Grundlage für statistische
Untersuchungen des Schädigungsfortschritts. Hierbei sind die Änderung
der BSt-Geometrie, die Zunahme von Korrosionsprodukten und deren Veränderung
über die Zeit sowie ihrer räumlichen Verteilung in der Probe von Interesse. Aufgrund
der Größe der CT-Bilddaten ist eine manuelle Segmentierung nicht durchführbar,
so dass automatische Verfahren unabdingbar sind. Dabei ist insbesondere
die Segmentierung der Korrosionsprodukte in den Bilddaten ein schwieriges
Problem. Allein aufgrund der Grauwerte ist eine Zuordnung nahezu unmöglich,
denn die Grauwerte von Beton und Korrosionsprodukt unterscheiden sich kaum.
Eine formbasierte Suche ist nicht offensichtlich, da die Korrosionsprodukte in Beton
diffuse Formen haben.
Allerdings lässt sich Vorwissen über die Ausbreitung der Korrosionsprodukte
nutzen. Sie bilden sich in räumlicher Nähe des BSt (in Bereichen vorheriger
Volumenabnahme des BSt), entlang von Rissen sowie in Porenräumen, die direkt
am BSt und in dessen Nahbereich liegen. Davon ausgehend wird vor der
Korrosionsprodukterkennung zunächst eine BSt-Volumen-, Riss- und Porenerkennung
durchgeführt. Dieser in der Arbeit näher beschriebene Schritt erlaubt es, halbautomatisch
Startpunkte (Seed Points) für die Korrosionsprodukterkennung zu
finden. Weiterhin werden verschiedene in der Bildverarbeitung bekannte
Algorithmen auf ihre Eignung untersucht werden.
Mitotic and meiotic spindles are microtubule-based structures to faithfully segregate chromosomes. Electron tomography is currently the method of choice to analyze the three-dimensional architecture of both types of spindles. Over the years, we have developed methods and software for automatic segmentation and stitching of microtubules in serial sections for large-scale reconstructions. Three-dimensional reconstruction of microtubules, however, is only the first step towards biological insight. The second step is the analysis of the structural data to derive measurable spindle properties. Here, we present a comprehensive set of techniques to quantify spindle parameters. These techniques provide quantitative analyses of specific microtubule classes and are applicable to a variety of tomographic reconstructions of spindles from different organisms.
In this paper, we present a software-assisted workflow for the alignment and matching of filamentous structures across a stack of 3D serial image sections. This is achieved by a combination of automatic methods, visual validation, and interactive correction. After an initial alignment, the user can continuously improve the result by interactively correcting landmarks or matches of filaments. This is supported by a quality assessment that visualizes regions that have been already inspected and, thus, allows a trade-off between quality and manual labor.
The software tool was developed in collaboration with biologists who investigate microtubule-based spindles during cell division. To quantitatively understand the structural organization of such spindles, a 3D reconstruction of the numerous microtubules is essential. Each spindle is cut into a series of semi-thick physical sections, of which electron tomograms are acquired. The sections then need to be stitched, i.e. non-rigidly aligned; and the microtubules need to be traced in each section and connected across section boundaries. Experiments led to the conclusion that automatic methods for stitching alone provide only an incomplete solution to practical analysis needs. Automatic methods may fail due to large physical distortions, a low signal-to-noise ratio of the images, or other unexpected experimental difficulties. In such situations, semi-automatic validation and correction is required to rescue as much information as possible to derive biologically meaningful results despite of some errors related to data collection.
Since the correct stitching is visually not obvious due to the number of microtubules (up to 30k) and their dense spatial arrangement, these are difficult tasks. Furthermore, a naive inspection of each microtubule is too time consuming. In addition, interactive visualization is hampered by the size of the image data (up to 100 GB). Based on the requirements of our collaborators, we present a practical solution for the semi-automatic stitching of serial section image stacks with filamentous structures.
Im Rahmen der biomechanischen Simulation knöcherner Organe ist die Frage nach einer befriedigenden Materialbeschreibung nach wie vor ungelöst. Computertomographische Datensätze liefern eine räumliche Verteilung der (Röntgen-)Dichte und ermöglichen damit eine gute Darstellung der individuellen Geometrie. Weiter können die verschiedenen Materialbestandteile des Knochens, Spongiosa und Kortikalis, voneinander getrennt werden. Aber die richtungsabängige Information der Materialanisotropie ist verloren. In dieser Arbeit wird ein Ansatz für eine anisotrope Materialbeschreibung vorgestellt, die es ermöglicht, den Einfluss der individuellen knöchernen Struktur auf das makroskopische Materialverhalten abzuschätzen.