Doctor of Philosophy

dissertationElectron microscopy can visualize synapses at nanometer resolution, and can thereby capture the fine structure of these contacts. However, this imaging method lacks three key elements: temporal information, protein visualization, and large volume reconstruction. For my dissertation, I developed three methods in electron microscopy that overcame these limitations. First, I developed a method to freeze neurons at any desired time point after a stimulus to study synaptic vesicle cycle. Second, I developed a method to couple super-resolution fluorescence microscopy and electron microscopy to pinpoint the location of proteins in electron micrographs at nanometer resolution. Third, I collaborated with computer scientists to develop methods for semi-automated reconstruction of nervous system. I applied these techniques to answer two fundamental questions in synaptic biology. Which vesicles fuse in response to a stimulus? How are synaptic vesicles recovered at synapses after fusion? Only vesicles that are in direct contact with plasma membrane fuse upon stimulation. The active zone in C. elegans is broad, but primed vesicles are concentrated around the dense projection. Following exocytosis of synaptic vesicles, synaptic vesicle membrane was recovered rapidly at two distinct locations at a synapse: the dense projection and adherens junctions. These studies suggest that there may be a novel form of ultrafast endocytosis

Towards increased efficiency and automation in fluorescence micrograph analysis based on hand-labeled data

Held CH. Towards increased efficiency and automation in fluorescence micrograph analysis based on hand-labeled data. Bielefeld: Universität Bielefeld; 2013.In the past decade, automation in fluorescence microscopy has strongly increased, particularly in regards to image acquisition and sample preparation, which results in a huge volume of data. The amount of time required for manual assessment of an experiment is hence mainly determined by the amount of time required for data analysis. In addition, manual data analysis is often a task with poor reproducibility and lack of objectivity. Using automated image analysis software, the time required for data analysis can be reduced while quality and reproducibility of the evaluation are improved. Most image analysis approaches are based on a segmentation of the image. By arranging several image processing methods in a so-called segmentation pipeline, and by adjusting all parameters, a broad range of fluorescence image data can be segmented. The drawback of available software tools is the long time required to calibrate the segmentation pipeline for an experiment, particularly for researchers with little knowledge of image processing. As a result, many experiments that could benefit from automated image analysis are still evaluated manually. In order to reduce the amount of time users have to spend in adapting automated image analysis software to their data, research was carried out on a novel image analysis concept based on hand-labeled data. Using this concept, the user is required to provide hand-labeled cells, based on which an efficient combination of image processing methods and their parameterization is automatically calibrated, without further user input. The development of a segmentation pipeline that allows high-quality segmentation of a broad range of fluorescence micrographs in short time poses a challenge. In this work, a three-stage segmentation pipeline consisting of exchangeable preprocessing, figure-ground separation and cell-splitting methods was developed. These methods are mainly based on the state of the art, whereas some of them represent contributions to this status. Discretization of parameters must be performed carefully, as a broad range of fluorescence image data shall be supported. In order to allow calibration of the segmentation pipeline in a short time, discretization with equidistant as well as nonlinear step sizes was implemented. Apart from parameter discretization, quality of the calibration strongly depends on choice of the parameter optimization technique. In order to reduce calibration runtime, exploratory parameter space analysis was performed for different segmentation methods. This experiment showed that parameter spaces are mostly monotonous, but also show several local performance maxima. The comparison of different parameter optimization techniques indicated that the coordinate descent method results in a good parameterization of the segmentation pipeline in a small amount of time. In order to minimize the amount of time spent by the user in calibration of the system, correlation between the number of hand-labeled reference samples and the resulting segmentation performance was investigated. This experiment demonstrates that as few as ten reference samples often result in a good parameterization of the segmentation pipeline. Due to the low number of cells required for automatic calibration of the segmentation pipeline, as well as its short runtime, it can be concluded that the investigated method improves automation and efficiency in fluorescence micrograph analysis

Ultrastructural analysis of adult mouse neocortex comparing aldehyde perfusion with cryo fixation

Analysis of brain ultrastructure using electron microscopy typically relies on chemical fixation. However, this is known to cause significant tissue distortion including a reduction in the extracellular space. Cryo fixation is thought to give a truer representation of biological structures, and here we use rapid, high-pressure freezing on adult mouse neocortex to quantify the extent to which these two fixation methods differ in terms of their preservation of the different cellular compartments, and the arrangement of membranes at the synapse and around blood vessels. As well as preserving a physiological extracellular space, cryo fixation reveals larger numbers of docked synaptic vesicles, a smaller glial volume, and a less intimate glial coverage of synapses and blood vessels compared to chemical fixation. The ultrastructure of mouse neocortex therefore differs significantly comparing cryo and chemical fixation conditions

Learning Discriminative Features and Structured Models for Segmentation in Microscopy and Natural Images

Segmenting images is a significant challenge that has drawn a lot of attention from different fields of artificial intelligence and has many practical applications. One such challenge addressed in this thesis is the segmentation of electron microscope (EM) imaging of neural tissue. EM microscopy is one of the key tools used to analyze neural tissue and understand the brain, but the huge amounts of data it produces make automated analysis necessary. In addition to the challenges specific to EM data, the common problems encountered in image segmentation must also be addressed. These problems include extracting discriminative features from the data and constructing a statistical model using ground-truth data. Although complex models appear to be more attractive because they allow for more expressiveness, they also lead to a higher computational complexity. On the other hand, simple models come with a lower complexity but less faithfully express the real world. Therefore, one of the most challenging tasks in image segmentation is in constructing models that are expressive enough while remaining tractable. In this work, we propose several automated graph partitioning approaches that address these issues. These methods reduce the computational complexity by operating on supervoxels instead of voxels, incorporating features capable of describing the 3D shape of the target objects and using structured models to account for correlation in output variables. One of the non-trivial issues with such models is that their parameters must be carefully chosen for optimal performance. A popular approach to learning model parameters is a maximum-margin approach called Structured SVM (SSVM) that provides optimality guarantees but also suffers from two main drawbacks. First, SSVM-based approaches are usually limited to linear kernels, since more powerful nonlinear kernels cause the learning to become prohibitively expensive. In this thesis, we introduce an approach to “kernelize” the features so that a linear SSVM framework can leverage the power of nonlinear kernels without incurring their high computational cost. Second, the optimality guarentees are violated for complex models with strong inter-relations between the output variables. We propose a new subgradient-based method that is more robust and leads to improved convergence properties and increased reliability. The different approaches presented in this thesis are applicable to both natural and medical images. They are able to segment mitochondria at a performance level close to that of a human annotator, and outperform state-of-the-art segmentation techniques while still benefiting from a low learning time

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

Automated Analysis of Biomedical Data from Low to High Resolution

Recent developments of experimental techniques and instrumentation allow life scientists to acquire enormous volumes of data at unprecedented resolution. While this new data brings much deeper insight into cellular processes, it renders manual analysis infeasible and calls for the development of new, automated analysis procedures. This thesis describes how methods of pattern recognition can be used to automate three popular data analysis protocols: Chapter 1 proposes a method to automatically locate bimodal isotope distribution patterns in Hydrogen Deuterium Exchange Mass Spectrometry experiments. The method is based on L1-regularized linear regression and allows for easy quantitative analysis of co-populations with different exchange behavior. The sensitivity of the method is tested on a set of manually identified peptides, while its applicability to exploratory data analysis is validated by targeted follow-up peptide identification. Chapter 2 develops a technique to automate peptide quantification for mass spectrometry experiments, based on 16O/18O labeling of peptides. Two different spectrum segmentation algorithms are proposed: one based on image processing and applicable to low resolution data and one exploiting the sparsity of high resolution data. The quantification accuracy is validated on calibration datasets, produced by mixing a set of proteins in pre-defined ratios. Chapter 3 provides a method for automated detection and segmentation of synapses in electron microscopy images of neural tissue. For images acquired by scanning electron microscopy with nearly isotropic resolution, the algorithm is based on geometric features computed in 3D pixel neighborhoods. For transmission electron microscopy images with poor z-resolution, the algorithm uses additional regularization by performing several rounds of pixel classification with features computed on the probability maps of the previous classification round. The validation is performed by comparing the set of synapses detected by the algorithm against a gold standard detection by human experts. For data with nearly isotropic resolution, the algorithm performance is comparable to that of the human experts

Polarity and Competition in the Development of the Calyx of Held Terminal in the Medial Nucleus of the Trapezoid Body in the Mouse

In the auditory brainstem, the connection between globular bushy cells of the anteroventral cochlear nucleus and principal cells (PCs) of the medial nucleus of the trapezoid body (MNTB) is created by one of the largest nerve terminals in the central nervous system, the calyx of Held (CH). The characteristics of the CH:MNTB connection—a short developmental period (48-72 hours), accessibility for recording from pre- and postsynaptic components, and clear monoinnervated end point—make this system an ideal model system for studying nervous system development. Model systems undergo stereotyped stages of development, including exuberant overinnervation, competition between terminals, and a refinement of innervation through removal of weak inputs. However, unlike other similar model systems (climbing fiber:Purkinje cell, retinal ganglion cells:dorsolateral geniculate nucleus), it has been a long-standing question whether the CH:MNTB system undergoes competition. We investigated the innervation state of PCs using the novel technique of segmentation and 3D reconstruction of PCs and their associated inputs across important developmental timepoints (postnatal days (P)2,3,4,6,9,30). This was accomplished by application of serial block-face scanning electron microscopy (SBEM), a method of serial section electron microscopy providing high spatial resolution (~4-10nm) and a high degree of alignment between images with very little section loss. Applying this technique, we show early exuberant innervation of PCs (P2), establish that competition is a common process, and pinpoint the 24-hour period from P3-P4 as a uniquely active day in CH growth during which terminal contact with PCs increases at a rate exceeding 200 µm2/day. Common morphological characteristics of the CH:MNTB connection also became qualitatively evident based on 3D reconstructions, particularly an eccentric PC nucleus and preference for polarized terminal growth. Based on these observations, we undertook a quantitative study of polarity in CH:MNTB development using our 3D reconstructions. The results of this investigation demonstrate a novel polarity in development of both the CH and PC; developing PCs are characterized by eccentrically placed nuclei that establish an “intrasomatic polarity” that persists through young adulthood (P30). This polarity appears to define a unique territory opposite of the nuclear location that is amenable to growth of the calyx, is enriched in dendrites, and is selectively enlarged as the principal cell matures to create glia-free surface area for innervation. To our knowledge, this is the first report of a polarity program in the coordinated pre- and postsynaptic development of a non-laminar brain region. Additionally, our findings have codified a progression of dendritic pruning in the maturation of principal cells that may influence and be influenced by the developmental state of the cell. Taken in totality, these results indicate a highly polarized, systemic competitive process in the MNTB during the development of the calyx of Held and suggests potential mediators of competition that deserve further study