COMPUTATIONAL APPROACHES TO UNDERSTAND PHENOTYPIC STRUCTURE AND CONSTITUTIVE MECHANICS RELATIONSHIPS OF SINGLE CELLS

The goal of this work is to better understand the relationship between the structure and function of biological cells by simulating their nonlinear mechanical behavior under static and dynamic loading using image structure-based finite element modeling (FEM). Vascular smooth muscle cells (VSMCs) are chosen for this study due to the strong correlation of the geometric arrangement of their structural components on their mechanical behavior and the implications of that behavior on diseases such as atherosclerosis. VSMCs are modeled here using a linear elastic material model together with truss elements, which simulate the cytoskeletal fiber network that provides the cells with much of their internal structural support. Geometric characterization of single VSMCs of two physiologically relevant phenotypes in 2D cell culture is achieved using confocal microscopy in conjunction with novel image processing techniques. These computer vision techniques use image segmentation, 2D frequency analysis, and linear programming approaches to create representative 3D model structures consisting of the cell nucleus, cytoplasm, and actin stress fiber network of each cell. These structures are then imported into MSC Patran for structural analysis with Marc. Mechanical characterization is achieved using atomic force microscopy (AFM) indentation. Material properties for each VSMC model are input based on values individually obtained through experimentation, and the results of each model are compared against those experimental values. This study is believed to be a significant step towards the viability of finite element models in the field of cellular mechanics because the geometries of the cells in the model are based on confocal microscopy images of actual cells and thus, the results of the model can be compared against experimental data for those same cells

Removing striping artifacts in light-sheet fluorescence microscopy: a review

In recent years, light-sheet fluorescence microscopy (LSFM) has found a broad application for imaging of diverse biological samples, ranging from sub-cellular structures to whole animals, both in-vivo and ex-vivo, owing to its many advantages relative to point-scanning methods. By providing the selective illumination of sample single planes, LSFM achieves an intrinsic optical sectioning and direct 2D image acquisition, with low out-of-focus fluorescence background, sample photo-damage and photo-bleaching. On the other hand, such an illumination scheme is prone to light absorption or scattering effects, which lead to uneven illumination and striping artifacts in the images, oriented along the light sheet propagation direction. Several methods have been developed to address this issue, ranging from fully optical solutions to entirely digital post-processing approaches. In this work, we present them, outlining their advantages, performance and limitations

La-images: A Software For Elemental Distribution Bioimaging Using La-icp-ms Data

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)The spatial distribution of chemical elements in different types of samples is an important field in several research areas such as biology, paleontology or biomedicine, among others. Elemental distribution imaging by laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) is an effective technique for qualitative and quantitative imaging due to its high spatial resolution and sensitivity. By applying this technique, vast amounts of raw data are generated to obtain high-quality images, essentially making the use of specific LA-ICP-MS imaging software that can process such data absolutely mandatory. Since existing solutions are usually commercial or hard-to-use for average users, this work introduces LA-iMageS, an open-source, free-to-use multiplatform application for fast and automatic generation of high-quality elemental distribution bioimages from LA-ICP-MS data in the PerkinElmer Elan XL format, whose results can be directly exported to external applications for further analysis. A key strength of LA-iMageS is its substantial added value for users, with particular regard to the customization of the elemental distribution bioimages, which allows, among other features, the ability to change color maps, increase image resolution or toggle between 2D and 3D visualizations.8Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP, Sao Paulo, Brazil)Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq, Brasilia, Brazil)Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES, Brasilia, Brazil)Provincial Council of Ourense [INOU-16-05]national funds from FCT/MEC [UID/QUI/50006/2013, UID/Multi/04378/2013]ERDF under the PT2020 Partnership Agreement [POCI-01-0145-FEDER-007265, POCI-01-0145-FEDER-007728]Unidade de Ciencias Biomoleculares Aplicadas-UCIBIOXunta de GaliciaFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES

Development of scanning ion conductance microscopy for biomedical applications

Imperial Users onl

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

Automated Vascular Smooth Muscle Segmentation, Reconstruction, Classification and Simulation on Whole-Slide Histology

Histology of the microvasculature depicts detailed characteristics relevant to tissue perfusion. One important histologic feature is the smooth muscle component of the microvessel wall, which is responsible for controlling vessel caliber. Abnormalities can cause disease and organ failure, as seen in hypertensive retinopathy, diabetic ischemia, Alzheimer’s disease and improper cardiovascular development. However, assessments of smooth muscle cell content are conventionally performed on selected fields of view on 2D sections, which may lead to measurement bias. We have developed a software platform for automated (1) 3D vascular reconstruction, (2) detection and segmentation of muscularized microvessels, (3) classification of vascular subtypes, and (4) simulation of function through blood flow modeling. Vessels were stained for α-actin using 3,3\u27-Diaminobenzidine, assessing both normal (n=9 mice) and regenerated vasculature (n=5 at day 14, n=4 at day 28). 2D locally adaptive segmentation involved vessel detection, skeletonization, and fragment connection. 3D reconstruction was performed using our novel nucleus landmark-based registration. Arterioles and venules were categorized using supervised machine learning based on texture and morphometry. Simulation of blood flow for the normal and regenerated vasculature was performed at baseline and during demand based on the structural measures obtained from the above tools. Vessel medial area and vessel wall thickness were found to be greater in the normal vasculature as compared to the regenerated vasculature (p\u3c0.001) and a higher density of arterioles was found in the regenerated tissue (p\u3c0.05). Validation showed: a Dice coefficient of 0.88 (compared to manual) for the segmentations, a 3D reconstruction target registration error of 4 μm, and area under the receiver operator curve of 0.89 for vessel classification. We found 89% and 67% decreases in the blood flow through the network for the regenerated vasculature during increased oxygen demand as compared to the normal vasculature, respectively for 14 and 28 days post-ischemia. We developed a software platform for automated vasculature histology analysis involving 3D reconstruction, segmentation, and arteriole vs. venule classification. This advanced the knowledge of conventional histology sampling compared to whole slide analysis, the morphological and density differences in the regenerated vasculature, and the effect of the differences on blood flow and function

Modeling and Analysis of Subcellular Protein Localization in Hyper-Dimensional Fluorescent Microscopy Images Using Deep Learning Methods

Hyper-dimensional images are informative and become increasingly common in biomedical research. However, the machine learning methods of studying and processing the hyper-dimensional images are underdeveloped. Most of the methods only model the mapping functions between input and output by focusing on the spatial relationship, whereas neglect the temporal and causal relationships. In many cases, the spatial, temporal, and causal relationships are correlated and become a relationship complex. Therefore, only modeling the spatial relationship may result in inaccurate mapping function modeling and lead to undesired output. Despite the importance, there are multiple challenges on modeling the relationship complex, including the model complexity and the data availability. The objective of this dissertation is to comprehensively study the mapping function modeling of the spatial-temporal and the spatial-temporal-causal relationship in hyper-dimensional data with deep learning approaches. The modeling methods are expected to accurately capture the complex relationships in class-level and object-level so that new image processing tools can be developed based on the methods to study the relationships between targets in hyper-dimensional data. In this dissertation, four different cases of relationship complex are studied, including the class-level spatial-temporal-causal relationship and spatial-temporal relationship modeling, and the object-level spatial-temporal-causal relationship and spatial-temporal relationship modeling. The modelings are achieved by deep learning networks that implicitly model the mapping functions with network weight matrix. For spatial-temporal relationship, because the cause factor information is unavailable, discriminative modeling that only relies on available information is studied. For class-level and object-level spatial-temporal-causal relationship, generative modeling is studied with a new deep learning network and three new tools proposed. For spatial-temporal relationship modeling, a state-of-the-art segmentation network has been found to be the best performer over 18 networks. Based on accurate segmentation, we study the object-level temporal dynamics and interactions through dynamics tracking. The multi-object portion tracking (MOPT) method allows object tracking in subcellular level and identifies object events, including object born, dead, split, and fusion. The tracking results is 2.96% higher on consistent tracking accuracy and 35.48% higher on event identification accuracy, compared with the existing state-of-the-art tracking methods. For spatial-temporal-causal relationship modeling, the proposed four-dimensional reslicing generative adversarial network (4DR-GAN) captures the complex relationships between the input and the target proteins. The experimental results on four groups of proteins demonstrate the efficacy of 4DR-GAN compared with the widely used Pix2Pix network. On protein localization prediction (PLP), the predicted localization from 4DR-GAN is more accurate in subcellular localization, temporal consistency, and dynamics. Based on efficient PLP, the digital activation (DA) and digital inactivation (DI) tools allow precise spatial and temporal control on global and local localization manipulation. They allow researchers to study the protein functions and causal relationships by observing the digital manipulation and PLP output response

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