Data Models in Neuroinformatics

Advancements in integrated neuroscience are often characterized with data-driven approaches for discovery; these progressions are the result of continuous efforts aimed at developing integrated frameworks for the investigation of neuronal dynamics at increasing resolution and in varying scales. Since insights from integrated neuronal models frequently rely on both experimental and computational approaches, simulations and data modeling have inimitable roles. Moreover, data sharing across the neuroscientific community has become an essential component of data-driven approaches to neuroscience as is evident from the number and scale of ongoing national and multinational projects, engaging scientists from diverse branches of knowledge. In this heterogeneous environment, the need to share neuroscientific data as well as to utilize it across different simulation environments drove the momentum for standardizing data models for neuronal morphologies, biophysical properties, and connectivity schemes. Here, I review existing data models in neuroinformatics, ranging from flat to hybrid object-hierarchical approaches, and suggest a framework with which these models can be linked to experimental data, as well as to established records from existing databases. Linking neuronal models and experimental results with data on relevant articles, genes, proteins, disease, etc., might open a new dimension for data-driven neuroscience

Ferramentas de processamento digital de imagem para imagens neuronais

Mestrado em Biomedicina MolecularOs neurónios são celulas especializadas do Sistema Nervoso, cujas funções se baseiam na correta formação de três compartimentos subcelulares primários – corpo celular, axónio e dendrites – e na rede neuronal que formam para passar a informação entre si. A análise quantitativa das características destas estruturas pode ser usada para estudar a relação entre a morfologia e função neuronal, e monitorizar alterações que ocorram em células individuais ou ao nível da rede, que se possam correlacionar com doenças neurológicas. Nesta tese foi efetuada uma pesquisa de ferramentas digitais disponíveis dedicadas ao processamento e análise de imagens neuronais, com enfoque na sua aplicabilidade para analisar as nossas bioimagens neuronais de fluorescência adquiridas no dia-a-dia. Nos programas selecionados (NeuronJ, NeurphologyJ e NeuriteQuant) foi primeiro avaliada a necessidade de preprocessamento, e os programas foram subsequentemente utilizados em conjuntos de imagens de culturas primárias de córtex de rato para comparar a sua eficácia no processamento destas bioimagens. Os dados obtidos com os vários programas foram comparados com a análise manual usando o ImageJ como ferramenta de análise. Os resultados demonstraram que o programa que aparenta funcionar melhor com as nossas imagens de fluorescência é o NeuriteQuant, porque é automático e dá resultados globalmente semelhantes aos da análise manual, especialmente na avaliação do Comprimento das Neurites por célula. Uma das desvantagens é que a quantificação da ramificação das neurites não dá resultados satisfatórios e deve continuar a ser realizada manualmente. Também realizamos uma pesquisa de ferramentas de processamento de imagem dedicada a imagens de contraste de fase, mas poucos programas foram encontrados. Estas imagens são mais fáceis de obter e mais acessíveis economicamente, contudo são mais difíceis de analisar devido às suas características intrínsecas. Para contornar esta lacuna, estabeleceu-se e otimizou-se uma sequência de processamento e análise para melhor extrair informação neuronal relevante de imagens de contraste de fase utilizando o programa ImageJ. A sequência desenvolvida, na forma de uma macro do ImageJ designada NeuroNet, foi aplicada a imagens de contraste de fase de culturas neuronais em diferentes dias de diferenciação, na presença ou ausência de um inibidor farmacológico, com o objetivo de responder a uma questão científica. A macro NeuroNet desenvolvida provou ser útil para analisar estas bioimagens, existindo contudo espaço para ser aperfeiçoada.Neurons are specialized cells of the Nervous System, with their function being based on the formation of the three primary sub cellular compartments – soma, axons, and dendrites – and on the neuritic network they form to contact and pass information to each other. The quantitative analysis of the characteristics of these structures can be used to study the relation between neuronal morphology and function, and to monitor distortions occurring in individual cells or at the network level that may correlate with neurological diseases. In this thesis a survey of freely available digital tools dedicated to neuronal images processing and analysis was made with an interest in their applicability to analyse our routinely acquired neuronal fluorescent bioimages. The selected program´ (NeuronJ, NeurphologyJ and NeuriteQuant) preprocessing requirements were first evaluated, and the programs were subsequently applied to a set of images of rat cortical neuronal primary cultures in order to compare their effectiveness in bioimage processing. Data obtained with the various programs was compared to the manual analysis of the images using the ImageJ analysis tool. The result show that the program that seems to work better with our fluorescence images is NeuriteQuant, since it is automatic and gives overall results more similar to the manual analysis. This is particularly true for the evaluation of the Neurite Length per Cell. One of the drawbacks is that the quantification of neuritic ramification does not give satisfactory results and is better to be performed manually. We also performed a survey of digital image processing tools dedicated to phase contrast microphotographs, but very few programs were found. These images are easier to obtain and more affordable in economic terms, however they are harder to analyse due to their intrinsic characteristics. To surpass this gap we have established and optimized a sequence of steps to better extract relevant information of neuronal phase contrast images using ImageJ. The work-flow developed, in the form of an ImageJ macro named NeuroNet, was then used to answer a scientific question by applying it to phase contrast images of neuronal cultures at different differentiating days, in the presence or absence of a pharmacological inhibitor. The developed macro NeuroNet proved to be useful to analyse the images however there is still space to improvement

Caracterização do papel da Gαo na neuritogénese: um destaque para o complexo Gαo-Proteina percursora de amilóide

Doutoramento em BiomedicinaGαo is the most abundant Gα subunit present in the brain, however, its specific functions are still far from clear. Studies of the signaling pathways modulated by Gαo have uncovered potential roles for Gαo in the development of the nervous system, especially in neuritogenesis. The characterization of Gαo interactome has also been crucial for the better understanding of this protein’s functions. One of the Gαo interacting proteins is the amyloid precursor protein (APP), a protein that is involved in several physiological functions, such as cell survival, neuronal migration, and neuronal differentiation. APP is also best known for its involvement in Alzheimer’s Disease (AD). APP binds and activates Gαo, an interplay that was associated with neuronal migration and AD. However, so far, no published study has investigated the effects of the APP-Gαo interaction on neuritogenesis. The main goal of this work was thus to characterize Gαo role on neuritogenesis by focusing the research on the neuritogenic effects of the Gαo-APP complex. First, by using SH-SY5Y neuroblastoma cells, we studied the impact of APP serine 655 (S655) phosphorylation on the APP-Gαo interaction. Through the use of two APP mutants mimicking the phosphorylated and dephosphorylated state of S655, SE and SA APP respectively, we have demonstrated that S655 phosphorylation increases APP efficiency to bind and activate Gαo. Moreover, we present evidence that APP modulates Gαo neuritogenic effects in a phosphodependent mechanism. STAT3 and ERK1/2 signaling displayed a sequential activation on this neuritogenic mechanism, with STAT3 being mainly involved in the formation of new processes, while ERK1/2 was more involved in neuritic elongation. We also present data supporting a role for the APP-Gαo complex on dendritogenesis in rat primary neuronal cultures. The second part of this work focused on unraveling the mechanisms involved in the control of APP and Gαo cellular protein levels. We identified the lysosome as a new pathway by which Gαo is degraded, as an effect of SA APP overexpression. We also provide evidence that this degradation mechanism might be part of chaperone-mediated autophagy, through which APP-Gαo signaling might be regulated. Finally, due to our interest in studying neuronal differentiation and a lack of reliable tools to analyze phase contrast images, we developed NeuronRead, an ImageJ macro capable of semi-automated analysis of both phase contrast and fluorescence neuronal images. NeuronRead was extensively validated and used to monitor SH-SY5Y differentiation upon modulation of Gαo activity. With this work, we delivered new data that advances knowledge on the function and regulation of the Gαo-APP complex in a neuronal context, and provided the scientific community with a new tool for the study of neuronal differentiation.Gαo é a subunidade Gα mais abundante no cérebro, no entanto, as suas funções especificas ainda estão longe de serem claras. Estudos das vias de sinalização moduladas pela Gαo têm exposto potenciais papéis para a Gαo no desenvolvimento do sistema nervoso, especialmente em neuritogénese. A caracterização do interactoma da Gαo também tem sido crucial para uma melhor compreensão das funções desta proteína. Uma das proteínas interatoras da Gαo é a proteina precursora de amiloide (APP), uma proteina que se encontra envolvida em várias funções fisiológicas, como sobrevivência celular, migração neuronal, e diferenciação neuronal. APP também é mais conhecida pelo seu envolvimento da Doença de Alzheimer (AD). APP liga-se e ativa a Gαo, uma interação que tem sido associada com migração neuronal e AD. No entanto, até agora, não existem estudos publicados que investiguem a interação APP-Gαo na neuritogénese. O principal objetivo deste trabalho foi então caracterizar o papel da Gαo na neuritogénese através do foco na investigação dos efeitos neuritogénico do complexo Gαo-APP. Primeiro, através do uso de células de neuroblastoma SH-SY5Y, estudámos o impacto da fosforilação da serina 655 (S655) da APP na interação APP-Gαo. Através do uso de dois mutantes da APP que mimetizam o estado fosforilado e desfosforilado da S655, SE e SA APP respetivamente, demonstrámos que a fosforilação da S655 aumenta a eficiência da APP em ligar e ativar a Gαo. Além disso, apresentamos provas de que a APP modula os efeitos neuritogénicos da Gαo num mecanismo fosfo-dependente. Neste mecanismo neuritogénico, a sinalização da STAT3 e ERK1/2 exibiram uma ativação sequencial, com a STAT3 participando na formação de novos processos e a ERK1/2 na elongação dos mesmos. Apresentamos ainda dados que suportam um papel da APP-Gαo na dendritogénese em culturas neuronais primárias. A segunda parte deste trabalho focou-se na investigação de mecanismos envolvidos no controlo dos níveis proteicos celulares da APP e Gαo. Identificámos o lisossoma como um novo processo pelo qual a Gαo é degradada em consequência da sobre expressão da SA APP. Também mostramos provas de que este mecanismo pode fazer parte de autofagia mediada por chaperonas, através do qual a sinalização da APP-Gαo poderá estar a ser regulada. Finalmente, devido ao nosso interesse em estudar diferenciação neuronal e à falta de ferramentas para este estudo em imagens de contraste de fase, criámos o NeuronRead, uma macro do ImageJ capaz de analisar de forma semiautomática imagens neuronais de contraste de fase e fluorescência. NeuronRead foi extensivamente validado, e usado para monitorizar a diferenciação de células SH-SY5Y após modulação da atividade da Gαo. Com este trabalho contribuímos com novos dados que ajudam na compreensão da função e regulação do complexo Gαo-APP, e disponibilizamos para a comunidade cientifica uma nova ferramenta para o estudo da diferenciação neurona

Development of a complete advanced computational workflow for high-resolution LDI-MS metabolomics imaging data processing and visualization

La imatge per espectrometria de masses (MSI) mapeja la distribució espacial de les molècules en una mostra. Això permet extreure informació Metabolòmica espacialment corralada d'una secció de teixit. MSI no s'usa àmpliament en la metabolòmica espacial a causa de diverses limitacions relacionades amb les matrius MALDI, incloent la generació d'ions que interfereixen en el rang de masses més baix i la difusió lateral dels compostos. Hem desenvolupat un flux de treball que millora l'adquisició de metabòlits en un instrument MALDI utilitzant un "sputtering" per dipositar una nano-capa d'Au directament sobre el teixit. Això minimitza la interferència dels senyals del "background" alhora que permet resolucions espacials molt altes. S'ha desenvolupat un paquet R per a la visualització d'imatges i processament de les dades MSI, tot això mitjançant una implementació optimitzada per a la gestió de la memòria i la programació concurrent. A més, el programari desenvolupat inclou també un algoritme per a l'alineament de masses que millora la precisió de massa.La imagen por espectrometría de masas (MSI) mapea la distribución espacial de las moléculas en una muestra. Esto permite extraer información metabolòmica espacialmente corralada de una sección de tejido. MSI no se usa ampliamente en la metabolòmica espacial debido a varias limitaciones relacionadas con las matrices MALDI, incluyendo la generación de iones que interfieren en el rango de masas más bajo y la difusión lateral de los compuestos. Hemos desarrollado un flujo de trabajo que mejora la adquisición de metabolitos en un instrumento MALDI utilizando un “sputtering” para depositar una nano-capa de Au directamente sobre el tejido. Esto minimiza la interferencia de las señales del “background” a la vez que permite resoluciones espaciales muy altas. Se ha desarrollado un paquete R para la visualización de imágenes y procesado de los datos MSI, todo ello mediante una implementación optimizada para la gestión de la memoria y la programación concurrente. Además, el software desarrollado incluye también un algoritmo para el alineamiento de masas que mejora la precisión de masa.Mass spectrometry imaging (MSI) maps the spatial distributions of molecules in a sample. This allows extracting spatially-correlated metabolomics information from tissue sections. MSI is not widely used in spatial metabolomics due to several limitations related with MALDI matrices, including the generation of interfering ions and in the low mass range and the lateral compound delocalization. We developed a workflow to improve the acquisition of metabolites using a MALDI instrument. We sputter an Au nano-layer directly onto the tissue section enabling the acquisition of metabolites with minimal interference of background signals and ultra-high spatial resolution. We developed an R package for image visualization and MSI data processing, which is optimized to manage datasets larger than computer’s memory using a mutli-threaded implementation. Moreover, our software includes a label-free mass alignment algorithm for mass accuracy enhancement

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

Modeling and Simulation in Engineering

This book provides an open platform to establish and share knowledge developed by scholars, scientists, and engineers from all over the world, about various applications of the modeling and simulation in the design process of products, in various engineering fields. The book consists of 12 chapters arranged in two sections (3D Modeling and Virtual Prototyping), reflecting the multidimensionality of applications related to modeling and simulation. Some of the most recent modeling and simulation techniques, as well as some of the most accurate and sophisticated software in treating complex systems, are applied. All the original contributions in this book are jointed by the basic principle of a successful modeling and simulation process: as complex as necessary, and as simple as possible. The idea is to manipulate the simplifying assumptions in a way that reduces the complexity of the model (in order to make a real-time simulation), but without altering the precision of the results

Dockomatic: An Emerging Resource to Manage Molecular Docking

The application of computational modeling to rationally design drugs and characterize macro-biomolecular receptors has proven increasingly useful due to the accessibility of computing clusters and clouds. AutoDock is a well-known and powerful software program used to model ligand to receptor binding interactions. A limitation of AutoDock is the inability of a user to automatically create ligands and manage the input and output of data when dealing with large numbers of simulations; a problem that arises in High Throughput Virtual Screening (HTVS) or Inverse Virtual Screening (IVS). We have designed DockoMatic, a user friendly Graphical User Interface (GUI) application that constructs peptide-based ligands, integrates with the software program TreePack to create user defined peptide analogs, and automates the creation and management of AutoDock jobs for HTVS of ligand to receptor interactions. DockoMatic is a valuable tool for studying complex systems such as conotoxins, from the genus Conus, and their interactions with the well-characterized molecular receptor, Aplysia californica acetylcholine binding protein (Ac-AChBP)

Exploring the role of A-type lamins in cellular oxidative stress

Every cell contains the genetic information needed to create an entire organism. This blueprint is stored in the cell nucleus. The nucleus continuously regulates the accessibility of this information based on ever-changing intra- and extracellular stimuli. Therefore, proper functioning of the nucleus is crucial for cellular and organismal survival. The nuclear lamina, a perinuclear network composed of type V intermediate filaments called lamins, is emerging as key regulator in nuclear organization. It physically shapes the nucleus, influences gene expression and modulates cell differentiation. A recent addition to the expanding list of functions of the nuclear lamina is an apparent involvement in cellular redox homeostasis. Indeed, cells from patients suffering from various laminopathies display increased levels of intracellular reactive oxygen species (ROS) and often show a higher susceptibility towards induced ROS. The underlying pathways however, remain poorly understood. The goal of this PhD dissertation was to obtain a better insight in this novel putative pathogenic feature. Chapter 1 comprises a general introduction into lamin biology and the state of the art with respect to their involvement in redox biology, next to a guide into fluorescence microscopy of redox-related processes. In Chapter 2, a quantitative comparison and characterization is presented of various experimental perturbations to interfere with lamin A metabolism in primary fibroblast cells. Based on the results shown in this chapter, perturbations were selected to create the models that were used in the experiments in chapters 4 & 5. In chapter 3, the development and benchmarking of a novel high-content microscopy method for the simultaneous measurement of intracellular ROS levels and mitochondrial function is outlined, together with a complementary automated analysis pipeline. The application of the newly developed method from Chapter 3 on the selected models from Chapter 2 culminated in the discovery that distinct lamin variants induce divergent oxidative responses, eventually resulting in different cell fates (Chapter 4), and pointed to the involvement of perturbed protein degradation pathways as a causal factor for oxidative stress (Chapter 5)