Cell Nuclear Morphology Analysis Using 3D Shape Modeling, Machine Learning and Visual Analytics

Quantitative analysis of morphological changes in a cell nucleus is important for the understanding of nuclear architecture and its relationship with cell differentiation, development, proliferation, and disease. Changes in the nuclear form are associated with reorganization of chromatin architecture related to altered functional properties such as gene regulation and expression. Understanding these processes through quantitative analysis of morphological changes is important not only for investigating nuclear organization, but also has clinical implications, for example, in detection and treatment of pathological conditions such as cancer. While efforts have been made to characterize nuclear shapes in two or pseudo-three dimensions, several studies have demonstrated that three dimensional (3D) representations provide better nuclear shape description, in part due to the high variability of nuclear morphologies. 3D shape descriptors that permit robust morphological analysis and facilitate human interpretation are still under active investigation. A few methods have been proposed to classify nuclear morphologies in 3D, however, there is a lack of publicly available 3D data for the evaluation and comparison of such algorithms. There is a compelling need for robust 3D nuclear morphometric techniques to carry out population-wide analyses. In this work, we address a number of these existing limitations. First, we present a largest publicly available, to-date, 3D microscopy imaging dataset for cell nuclear morphology analysis and classification. We provide a detailed description of the image analysis protocol, from segmentation to baseline evaluation of a number of popular classification algorithms using 2D and 3D voxel-based morphometric measures. We proposed a specific cross-validation scheme that accounts for possible batch effects in data. Second, we propose a new technique that combines mathematical modeling, machine learning, and interpretation of morphometric characteristics of cell nuclei and nucleoli in 3D. Employing robust and smooth surface reconstruction methods to accurately approximate 3D object boundary enables the establishment of homologies between different biological shapes. Then, we compute geometric morphological measures characterizing the form of cell nuclei and nucleoli. We combine these methods into a highly parallel computational pipeline workflow for automated morphological analysis of thousands of nuclei and nucleoli in 3D. We also describe the use of visual analytics and deep learning techniques for the analysis of nuclear morphology data. Third, we evaluate proposed methods for 3D surface morphometric analysis of our data. We improved the performance of morphological classification between epithelial vs mesenchymal human prostate cancer cells compared to the previously reported results due to the more accurate shape representation and the use of combined nuclear and nucleolar morphometry. We confirmed previously reported relevant morphological characteristics, and also reported new features that can provide insight in the underlying biological mechanisms of pathology of prostate cancer. We also assessed nuclear morphology changes associated with chromatin remodeling in drug-induced cellular reprogramming. We computed temporal trajectories reflecting morphological differences in astroglial cell sub-populations administered with 2 different treatments vs controls. We described specific changes in nuclear morphology that are characteristic of chromatin re-organization under each treatment, which previously has been only tentatively hypothesized in literature. Our approach demonstrated high classification performance on each of 3 different cell lines and reported the most salient morphometric characteristics. We conclude with the discussion of the potential impact of method development in nuclear morphology analysis on clinical decision-making and fundamental investigation of 3D nuclear architecture. We consider some open problems and future trends in this field.PHDBioinformaticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/147598/1/akalinin_1.pd

Brain-Inspired Computing

This open access book constitutes revised selected papers from the 4th International Workshop on Brain-Inspired Computing, BrainComp 2019, held in Cetraro, Italy, in July 2019. The 11 papers presented in this volume were carefully reviewed and selected for inclusion in this book. They deal with research on brain atlasing, multi-scale models and simulation, HPC and data infra-structures for neuroscience as well as artificial and natural neural architectures

A visual analytics system for neuronal morphology analysis of zebrafish brain

Department of Computer Science and EngineeringThe electron microscope is capable of capturing tissue in three dimensions up to the nanometer scale. This makes it possible to analyze the shape of brain cells beyond conventional analysis throughout the brain region. Morphological analysis of brain regions requires tracking the morphological features of each cell and applying techniques for visualizing electronic microscope (EM) volume data and brain cells at the same time, as well as brain region information such as diencephalon and mesencephalon. However, morphological analysis of cells in the brain region has been challenging. Because terabytes of EM volume data must be processed, brain cell segmentation and accurate brain subregion information down to 120 nm are required. In this thesis, I present a novel system for visualizing and analyzing EM volume data and extracted brain cell regions. The system shows an EM volume data view and a histogram of the morphological features, and it provides a graph-based user interface. This system classifies the whole brain cell cluster into various brain cell selection methods according to the type and expresses statistical information of the selected cell cluster. This allows the user to mask only the desired brain regions or desired brain cells and to perform a morphological analysis of the brain cell population. Using this system, users can quickly analyze the brain cell type and the role of the group by using the user interface. By using this system, I can know the shape of the brain cell by brain area and analyze brain cell morphology and function by known brain area function. I also propose an efficient data structure of terabytes of data to construct a system. I performed several case studies, including zebrafish whole brain cells analysis, diencephalon and habenula analysis, mesencephalon tectum neuropil analysis, and optic nerve analysis with brain expert using 120 nm/px isotropic zebrafish EM data.clos

VIOLA - A multi-purpose and web-based visualization tool for neuronal-network simulation output

Neuronal network models and corresponding computer simulations are invaluable tools to aid the interpretation of the relationship between neuron properties, connectivity and measured activity in cortical tissue. Spatiotemporal patterns of activity propagating across the cortical surface as observed experimentally can for example be described by neuronal network models with layered geometry and distance-dependent connectivity. The interpretation of the resulting stream of multi-modal and multi-dimensional simulation data calls for integrating interactive visualization steps into existing simulation-analysis workflows. Here, we present a set of interactive visualization concepts called views for the visual analysis of activity data in topological network models, and a corresponding reference implementation VIOLA (VIsualization Of Layer Activity). The software is a lightweight, open-source, web-based and platform-independent application combining and adapting modern interactive visualization paradigms, such as coordinated multiple views, for massively parallel neurophysiological data. For a use-case demonstration we consider spiking activity data of a two-population, layered point-neuron network model subject to a spatially confined excitation originating from an external population. With the multiple coordinated views, an explorative and qualitative assessment of the spatiotemporal features of neuronal activity can be performed upfront of a detailed quantitative data analysis of specific aspects of the data. Furthermore, ongoing efforts including the European Human Brain Project aim at providing online user portals for integrated model development, simulation, analysis and provenance tracking, wherein interactive visual analysis tools are one component. Browser-compatible, web-technology based solutions are therefore required. Within this scope, with VIOLA we provide a first prototype.Comment: 38 pages, 10 figures, 3 table

An Artificial Intelligence Method to Describe the Onset and Transition from Stochastic to Coordinated Neural Activity in Danionella Translucida Embryo

Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceNos últimos anos, a aprendizagem profunda tem se tornado cada vez mais bem-sucedida quando aplicada para lidar com diferentes questões em diversos campos. Na análise de bioimagem, tem sido usada para extrair informações significativas de imagens microscópicas, onde aplicamos aprendizagem profunda a dados de microscopia de light-sheet para compreender o desenvolvimento inicial do sistema nervoso. Atualmente, sabe-se que o cérebro é responsável pela maioria de nossas ações voluntárias e involuntárias e que regula os processos fisiológicos em todo o corpo. No entanto, as barreiras técnicas deixaram muitas questões em aberto em relação ao desenvolvimento e função dos circuitos neuronais. Imagiologia provou ser uma técnica poderosa para responder a essas perguntas, embora as dificuldades em segmentar e rastrear neurônios individuais tenham retardado o progresso. Danionella translucida foi recentemente introduzida como um poderoso organismo modelo para estudos neurocientíficos devido a ter o menor cérebro de vertebrado conhecido e não desenvolver um crânio completo na idade adulta, tornando-a facilmente acessível para estudos de imagem. No entanto, o surgimento da atividade neural e subsequente montagem de circuitos neurais no desenvolvimento inicial do embrião não foi ainda caracterizado. Esta dissertação pretende fornecer uma descrição inicial de todo o processo de resolução celular, utilizando técnicas avançadas de microscopia e um método de inteligência artificial para segmentar e analisar os dados. Usamos microscopia de fluorescência de light-sheet para obter imagens do início e da coordenação da atividade neuronal da medula espinhal da Danionella translucida com alta resolução temporal e por longos períodos de tempo. Além disso, analisamos os dados com um algoritmo baseado em aprendizagem profunda para detetar, segmentar e rastrear no espaço e no tempo o sinal de cada neurônio. Focamos nossa análise nos picos de intensidade do sinal, ou seja, no momento em que os neurónios estavam a disparar, e encontramos mais atividade na região inferior do embrião, sugerindo uma correspondência com a extensão da cauda. Este trabalho demonstra que a combinação de métodos utilizados foi capaz de gerar imagens e analisar os dados com sucesso. Abre as possibilidades para um estudo mais aprofundado da rede neuronal da Danionella translucida, e para estudar sinais de imagens aglomeradas com resolução de célula única que, de outra forma, seriam muito complexas para serem analisadas

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

From cognitive maps to spatial schemas

A schema refers to a structured body of prior knowledge that captures common patterns across related experiences. Schemas have been studied separately in the realms of episodic memory and spatial navigation across different species and have been grounded in theories of memory consolidation, but there has been little attempt to integrate our understanding across domains, particularly in humans. We propose that experiences during navigation with many similarly structured environments give rise to the formation of spatial schemas (for example, the expected layout of modern cities) that share properties with but are distinct from cognitive maps (for example, the memory of a modern city) and event schemas (such as expected events in a modern city) at both cognitive and neural levels. We describe earlier theoretical frameworks and empirical findings relevant to spatial schemas, along with more targeted investigations of spatial schemas in human and non-human animals. Consideration of architecture and urban analytics, including the influence of scale and regionalization, on different properties of spatial schemas may provide a powerful approach to advance our understanding of spatial schemas

Multimodal Biomedical Data Visualization: Enhancing Network, Clinical, and Image Data Depiction

In this dissertation, we present visual analytics tools for several biomedical applications. Our research spans three types of biomedical data: reaction networks, longitudinal multidimensional clinical data, and biomedical images. For each data type, we present intuitive visual representations and efficient data exploration methods to facilitate visual knowledge discovery. Rule-based simulation has been used for studying complex protein interactions. In a rule-based model, the relationships of interacting proteins can be represented as a network. Nevertheless, understanding and validating the intended behaviors in large network models are ineffective and error prone. We have developed a tool that first shows a network overview with concise visual representations and then shows relevant rule-specific details on demand. This strategy significantly improves visualization comprehensibility and disentangles the complex protein-protein relationships by showing them selectively alongside the global context of the network. Next, we present a tool for analyzing longitudinal multidimensional clinical datasets, that we developed for understanding Parkinson's disease progression. Detecting patterns involving multiple time-varying variables is especially challenging for clinical data. Conventional computational techniques, such as cluster analysis and dimension reduction, do not always generate interpretable, actionable results. Using our tool, users can select and compare patient subgroups by filtering patients with multiple symptoms simultaneously and interactively. Unlike conventional visualizations that use local features, many targets in biomedical images are characterized by high-level features. We present our research characterizing such high-level features through multiscale texture segmentation and deep-learning strategies. First, we present an efficient hierarchical texture segmentation approach that scales up well to gigapixel images to colorize electron microscopy (EM) images. This enhances visual comprehensibility of gigapixel EM images across a wide range of scales. Second, we use convolutional neural networks (CNNs) to automatically derive high-level features that distinguish cell states in live-cell imagery and voxel types in 3D EM volumes. In addition, we present a CNN-based 3D segmentation method for biomedical volume datasets with limited training samples. We use factorized convolutions and feature-level augmentations to improve model generalization and avoid overfitting