Automated dendritic spine tracking on 2-photon microscopic images (2-Foton mikroskopi görüntülerinde otomatik dendritik diken takibi)

The rapid and spontaneous morphological changes of dendritic spines have been an important observation to understand how information is stored in brain. Manual assessment of spine structure has been a useful tool to understand the differences between wild type (normal) and diseased cases. In order to perform a more through analysis, automatic tools need to be developed due to the immense amount of image data collected throughout the experiments. Additionally, dendritic spines are very dynamic structures and florescence microscopy contains high level of noise, blur and shift due to the optical properties. In this study, we track locations of dendritic spines in a full series of a time-lapse two photon microscopic images. To achieve this we propose a combined detection and tracking framework. For the detection we use a SIFT based algorithm, while the tracking requires a combination of registration and distance based spine matching. Experimental results show that this technique helps to track detected spines in time series even though the noise or blur deformed the image and complicated the detection

Model and Appearance Based Analysis of Neuronal Morphology from Different Microscopy Imaging Modalities

The neuronal morphology analysis is key for understanding how a brain works. This process requires the neuron imaging system with single-cell resolution; however, there is no feasible system for the human brain. Fortunately, the knowledge can be inferred from the model organism, Drosophila melanogaster, to the human system. This dissertation explores the morphology analysis of Drosophila larvae at single-cell resolution in static images and image sequences, as well as multiple microscopy imaging modalities. Our contributions are on both computational methods for morphology quantification and analysis of the influence of the anatomical aspect. We develop novel model-and-appearance-based methods for morphology quantification and illustrate their significance in three neuroscience studies. Modeling of the structure and dynamics of neuronal circuits creates understanding about how connectivity patterns are formed within a motor circuit and determining whether the connectivity map of neurons can be deduced by estimations of neuronal morphology. To address this problem, we study both boundary-based and centerline-based approaches for neuron reconstruction in static volumes. Neuronal mechanisms are related to the morphology dynamics; so the patterns of neuronal morphology changes are analyzed along with other aspects. In this case, the relationship between neuronal activity and morphology dynamics is explored to analyze locomotion procedures. Our tracking method models the morphology dynamics in the calcium image sequence designed for detecting neuronal activity. It follows the local-to-global design to handle calcium imaging issues and neuronal movement characteristics. Lastly, modeling the link between structural and functional development depicts the correlation between neuron growth and protein interactions. This requires the morphology analysis of different imaging modalities. It can be solved using the part-wise volume segmentation with artificial templates, the standardized representation of neurons. Our method follows the global-to-local approach to solve both part-wise segmentation and registration across modalities. Our methods address common issues in automated morphology analysis from extracting morphological features to tracking neurons, as well as mapping neurons across imaging modalities. The quantitative analysis delivered by our techniques enables a number of new applications and visualizations for advancing the investigation of phenomena in the nervous system

Automated Reconstruction of Evolving Curvilinear Tree Structures

Curvilinear networks are prevalent in nature and span many different scales, ranging from micron-scale neural structures in the brain to petameter-scale dark-matter arbors binding massive galaxy clusters. Reliably reconstructing them in an automated fashion is of great value in many different scientific domains. However, it remains an open Computer Vision problem. In this thesis we focus on automatically delineating curvilinear tree structures in images of the same object of interest taken at different time instants. Unlike virtually all of the existing methods approaching the task of tree structures delineation we process all the images at once. This is useful in the more ambiguous regions and allows to reason for the tree structure that fits best to all the acquired data. We propose two methods that utilize this principle of temporal consistency to achieve results of higher quality compared to single time instant methods. The first, simpler method starts by building an overcomplete graph representation of the final solution in all time instants while simultaneously obtaining correspondences between image features across time. We then define an objective function with a temporal consistency prior and reconstruct the structures in all images at once by solving a mathematical optimization. The role of the prior is to encourage solutions where for two consecutive time instants corresponding candidate edges are either both retained or both rejected from the final solution. The second multiple time instant method uses the same overcomplete graph principle but handles the temporal consistency in a more robust way. Instead of focusing on the very local consistency of single edges of the overcomplete graph we propose a method for describing topological relationships. This favors solutions whose connectivity is consistent over time. We show that by making the temporal consistency more global we achieve additional robustness to errors in the initial features matching step, which is shared by both the approaches. In the end, this yields superior performance. Furthermore, an added benefit of both our approaches is the ability to automatically detect places where significant changes have occurred over time, which is challenging when considering large amounts of data. We also propose a simple single time instant method for delineating tree structures. It computes a Minimum Spanning Arborescence of an initial overcomplete graph and proceeds to optimally prune spurious branches. This yields results of lower but still competitive quality compared to the mathematical optimization based methods, while keeping low computational complexity. Our methods can applied to both 2D and 3D data. We demonstrate their performance in 3D on microscopy volumes of mouse brain and rat brain. We also test them in 2D on time-lapse images of a growing runner bean and aerial images of a road network

Reconstructing Evolving Tree Structures in Time Lapse Sequences by Enforcing Time-Consistency

We propose a novel approach to reconstructing curvilinear tree structures evolving over time, such as road networks in 2D aerial images or neural structures in 3D microscopy stacks acquired in vivo. To enforce temporal consistency, we simultaneously process all images in a sequence, as opposed to reconstructing structures of interest in each image independently. We formulate the problem as a Quadratic Mixed Integer Program and demonstrate the additional robustness that comes from using all available visual clues at once, instead of working frame by frame. Furthermore, when the linear structures undergo local changes over time, our approach automatically detects them

Introducing Geometry in Active Learning for Image Segmentation

We propose an Active Learning approach to training a segmentation classifier that exploits geometric priors to streamline the annotation process in 3D image volumes. To this end, we use these priors not only to select voxels most in need of annotation but to guarantee that they lie on 2D planar patch, which makes it much easier to annotate than if they were randomly distributed in the volume. A simplified version of this approach is effective in natural 2D images. We evaluated our approach on Electron Microscopy and Magnetic Resonance image volumes, as well as on natural images. Comparing our approach against several accepted baselines demonstrates a marked performance increase

Muerte de neuronas colinérgicas de la región basal por necrosis y apoptosis, así como alteración de la densidad de espinas dendríticas tras la exposición aguda y a largo plazo a clorpirifos: Implicaciones legales del uso del perfil toxicogenómico como biomarcador de efectos dañinos inducidos a dosis subclínicas

Introduction: Chlorpyrifos (CPF) is an organophosphate insecticide reported to induce both after acute and repeated exposure learning and memory dysfunctions, although the mechanism is not completely known. CPF produces basal forebrain cholinergic neuronal loss, involved on learning and memory regulation, which could be the cause of such cognitive disorders. This effect was reported to be mediated through apoptotic process, although neuronal necrosis was also described after CPF exposure. Otherwise, neuronal dendritic spines were reported to be also involved on learning and memory process regulation and their alteration could also contribute to this effect. In this regard, CPF has been reported to induce an alteration in the dendritic spines density in the prefrontal cortex and hippocampus after acute and repeated exposure to subclinical doses respectively, thus their alteration in basal forebrain cholinergic neurons could also mediate cognitive disorders. Objectives and methods: Accordingly, we hypothesized that CPF induces basal forebrain cholinergic dendritic spine alteration at low concentrations and at higher concentrations produces necrotic and apoptotic cell death. We evaluated in septal SN56 basal forebrain cholinergic neurons, the CPF effect after 24 h and 14 days exposure on dendritic spines, the necrosis induction and the apoptotic and necrotic gene expression pathways. Results: This study shows that CPF induces after acute and long-term exposure an alteration of dendritic spines at lower concentrations than which induces cell death. Evaluation of cell death pathways and genes related to dendritic spine plasticity revealed that some of them are altered at lower concentrations than which produces the effects observed and below the No Observed Adverse Effect (NOAEL). Conclusions: The present finding suggest that the use of gene expression profile could be a more sensitive and accurate way to determine the NOAEL.Introducción: El clorpirifos (CPF) es un insecticida organofosforado que tras la exposición aguda y repetida, induce disfunciones de los procesos de aprendizaje y memoria, aunque el mecanismo por el cual se produce este efecto no se conoce por completo. El CPF produce en la región cerebral basal anterior la pérdida de neuronas colinérgicas, que participan en la regulación de los procesos de aprendizaje y la memoria, pudiendo ser esta la causa de tales trastornos cognitivos. Se ha observado que este efecto está mediado a través del proceso de apoptosis, aunque también se ha descrito que se produce necrosis neuronal tras la exposición a CPF. Por otra parte, también se ha demostrado que las espinas dendríticas participan en la regulación de los procesos de aprendizaje y memoria y su disrupción también podría contribuir a la alteración de dichos procesos. En este sentido, se ha descrito que el CPF altera la densidad de las espinas dendríticas en la corteza prefrontal y el hipocampo tras la exposición aguda y repetida a dosis subclínicas, respectivamente, por lo que su perturbación en las neuronas colinérgicas de la región basal anterior también podría mediar estos trastornos cognitivos. Objetivos y métodos: De acuerdo con lo expuesto, nosotros hipotetizamos que el CPF induce, en las neuronas colinérgicas de la región basal anterior, una alteración de las espinas dendríticas a bajas concentraciones y a concentraciones más altas produce muerte celular por apoptosis y necrosis. Evaluamos en neuronas colinérgicas SN56 de la región basal anterior, el efecto del CPF después de 24 horas y 14 días de exposición sobre las espinas dendríticas, la inducción de necrosis y las vías de expresión génica que median la inducción de apoptosis y necrosis. Resultados: Este estudio demuestra que el CPF induce, tras la exposición aguda y a largo plazo, una alteración de las espinas dendríticas, a concentraciones más bajas de aquellas a las que induce la muerte celular. La evaluación de las vías de muerte celular y los genes relacionados con la plasticidad de la espina dendrítica reveló que algunos de estos genes están alterados a concentraciones más bajas de aquellas a las que producen muerte celular o alteración de las espinas dendríticas y por debajo del Nivel sin efecto adverso observable (NOAEL) . Conclusiones: El presente studio sugiere que el uso del perfil de expresión génica podría ser una manera más sensible y precisa para la determinación del NOAEL

AUTOMATED ANALYSIS OF NEURONAL MORPHOLOGY: DETECTION, MODELING AND RECONSTRUCTION

Ph.DDOCTOR OF PHILOSOPH

STED Nanoscopy to Illuminate New Avenues in Cancer Research – From Live Cell Staining and Direct Imaging to Decisive Preclinical Insights for Diagnosis and Therapy

Molecular imaging is established as an indispensable tool in various areas of cancer research, ranging from basic cancer biology and preclinical research to clinical trials and medical practice. In particular, the field of fluorescence imaging has experienced exceptional progress during the last three decades with the development of various in vivo technologies. Within this field, fluorescence microscopy is primarily of experimental use since it is especially qualified for addressing the fundamental questions of molecular oncology. As stimulated emission depletion (STED) nanoscopy combines the highest spatial and temporal resolutions with live specimen compatibility, it is best-suited for real-time investigations of the differences in the molecular machineries of malignant and normal cells to eventually translate the acquired knowledge into increased diagnostic and therapeutic efficacy. This thesis presents the application of STED nanoscopy to two acute topics in cancer research of direct or indirect clinical interest. The first project has investigated the structure of telomeres, the ends of the linear eukaryotic chromosomes, in intact human cells at the nanoscale. To protect genome integrity, a telomere can mask the chromosome end by folding back and sequestering its single-stranded 3’-overhang in an upstream part of the double-stranded DNA repeat region. The formed t-loop structure has so far only been visualized by electron microscopy and fluorescence nanoscopy with cross-linked mammalian telomeric DNA after disruption of cell nuclei and spreading. For the first time, this work demonstrates the existence of t-loops within their endogenous nuclear environment in intact human cells. The identification of further telomere conformations has laid the groundwork for distinguishing cancerous cells that use different telomere maintenance mechanisms based on their individual telomere populations by a combined STED nanoscopy and deep learning approach. The population difference was essentially attributed to the promyelocytic leukemia (PML) protein that significantly perturbs the organization of a subpopulation of telomeres towards an open conformation in cancer cells that employ a telomerase-independent, alternative telomere lengthening mechanism. Elucidating the nanoscale topology of telomeres and associated proteins within the nucleus has provided new insight into telomere structure-function relationships relevant for understanding the deregulation of telomere maintenance in cancer cells. After understanding the molecular foundations, this newly gained knowledge can be exploited to develop novel or refined diagnostic and treatment strategies. The second project has characterized the intracellular distribution of recently developed prostate cancer tracers. These novel prostate-specific membrane antigen (PSMA) inhibitors have revolutionized the treatment regimen of prostate cancer by enabling targeted imaging and therapy approaches. However, the exact internalization mechanism and the subcellular fate of these tracers have remained elusive. By combining STED nanoscopy with a newly developed non-standard live cell staining protocol, this work confirmed cell surface clustering of the targeted membrane antigen upon PSMA inhibitor binding, subsequent clathrin-dependent endocytosis and endosomal trafficking of the antigen-inhibitor complex. PSMA inhibitors accumulate in prostate cancer cells at clinically relevant time points, but strikingly and in contrast to the targeted antigen itself, they eventually distribute homogenously in the cytosol. This project has revealed the subcellular fate of PSMA/PSMA inhibitor complexes for the first time and provides crucial knowledge for the future application of these tracers including the development of new strategies in the field of prostate cancer diagnostics and therapeutics. Relying on the photostability and biocompatibility of the applied fluorophores, the performance of live cell STED nanoscopy in the field of cancer research is boosted by the development of improved fluorophores. The third project in this thesis introduces a biocompatible, small molecule near-infrared dye suitable for live cell STED imaging. By the application of a halogen dance rearrangement, a dihalogenated fluorinatable pyridinyl rhodamine could be synthesized at high yield. The option of subsequent radiolabeling combined with excellent optical properties and a non-toxic profile renders this dye an appropriate candidate for medical and bioimaging applications. Providing an intrinsic and highly specific mitochondrial targeting ability, the radiolabeled analogue is suggested as a vehicle for multimodal (positron emission tomography and optical imaging) medical imaging of mitochondria for cancer diagnosis and therapeutic approaches in patients and biopsy tissue. The absence of cytotoxicity is not only a crucial prerequisite for clinically used fluorophores. To guarantee the generation of meaningful data mirroring biological reality, the absence of cytotoxicity is likewise a decisive property of dyes applied in live cell STED nanoscopy. The fourth project in this thesis proposes a universal approach for cytotoxicity testing based on characterizing the influence of the compound of interest on the proliferation behavior of human cell lines using digital holographic cytometry. By applying this approach to recently developed live cell STED compatible dyes, pronounced cytotoxic effects could be excluded. Looking more closely, some of the tested dyes slightly altered cell proliferation, so this project provides guidance on the right choice of dye for the least invasive live cell STED experiments. Ultimately, live cell STED data should be exploited to extract as much biological information as possible. However, some information might be partially hidden by image degradation due the dynamics of living samples and the deliberate choice of rather conservative imaging parameters in order to preserve sample viability. The fifth project in this thesis presents a novel image restoration method in a Bayesian framework that simultaneously performs deconvolution, denoising as well as super-resolution, to restore images suffering from noise with mixed Poisson-Gaussian statistics. Established deconvolution or denoising methods that consider only one type of noise generally do not perform well on images degraded significantly by mixed noise. The newly introduced method was validated with live cell STED telomere data proving that the method can compete with state-of-the-art approaches. Taken together, this thesis demonstrates the value of an integrated approach for STED nanoscopy imaging studies. A coordinated workflow including sample preparation, image acquisition and data analysis provided a reliable platform for deriving meaningful conclusions for current questions in the field of cancer research. Moreover, this thesis emphasizes the strength of iteratively adapting the individual components in the operational chain and it particularly points towards those components that, if further improved, optimize the significance of the final results rendering live cell STED nanoscopy even more powerful

Long-term stability of the hippocampal neural code as a substrate for episodic memory

The hippocampus supports the initial formation and recall of episodic memories, as well as the consolidation of short-term into long-term memories. The ability of hippocampal neurons to rapidly change their connection strengths during learning and maintain these changes over long time-scales may provide a mechanism supporting memory. However, little evidence currently exists concerning the long-term stability of information contained in hippocampal neuronal activity, likely due to limitations in recording extracellular activity in vivo from the same neurons across days. In this thesis I employ calcium imaging in freely moving mice to longitudinally track the activity of large ensembles of hippocampal neurons. Using this technology, I explore the proposal that long-term stability of hippocampal information provides a substrate for episodic memory in three different ways. First, I tested the hypothesis that hippocampal activity should remain stable across days in the absence of learning. I found that place cells – hippocampal neurons containing information about a mouse’s position – maintain a coherent map relative to each other across long time-scales but exhibit instability in how they anchor to the external world. Furthermore, I found that coherent maps were frequently used to represent a different environment and incorporated learning via changes in a subset of neurons. Next, I examined how learning a spatial alternation task impacts neuron stability. I found that splitter neurons whose activity patterns reflected an animal’s future or past trajectory emerged relatively slowly when compared to place cells. However, splitter neurons remained more consistently active and relayed more consistent spatial information across days than did place cells, suggesting that the utility of information provided by a neuron influences its long term stability. Last, I investigated how protein synthesis, known to be necessary for long-term maintenance of changes in hippocampal neuron connection strengths and for proper memory consolidation, influences their activity patterns across days. I found that along with blocking memory consolidation, inhibiting protein synthesis induced a profound, long-lasting decrease in neuronal activity up to two days later. These results combined demonstrate the importance of rapid, lasting changes in the hippocampal neuronal code to supporting long-term memory