Targeted computational analysis of the C3HEB/FEJ mouse model for drug efficacy testing

2020 Spring.Includes bibliographical references.Efforts to develop effective and safe drugs for the treatment of tuberculosis (TB) require preclinical evaluation in animal models. Alongside efficacy testing of novel therapies, effects on pulmonary pathology and disease progression are monitored by using histopathology images from these infected animals. To compare the severity of disease across treatment cohorts, pathologists have historically assigned a semi-quantitative histopathology score that may be subjective in terms of their training, experience, and personal bias. Manual histopathology, therefore, has limitations regarding reproducibility between studies and pathologists, potentially masking successful treatments. This report describes a pathologist-assistive software tool that reduces these user limitations while providing a rapid, quantitative scoring system for digital histopathology image analysis. The software, called 'Lesion Image Recognition and Analysis' (LIRA), employs convolutional neural networks to classify seven different pathology features, including three different lesion types from pulmonary tissues of the C3HeB/FeJ tuberculosis mouse model. LIRA was developed to improve the efficiency of histopathology analysis for mouse tuberculosis infection models. The model approach also has broader applications to other diseases and tissues. This also includes animals that are undergoing anti-mycobacterial treatment and host immune system modulation. A complimentary software package called 'Mycobacterial Image Analysis' (MIA) had also been developed that characterizes the varying bacilli characteristics such as density, aggregate/planktonic bacilli size, fluorescent intensity, and total counts. This further groups the bacilli characteristic data depending on the seven different classifications that are selected by the user. Using this approach allows for an even more targeted analysis approach that can determine how therapy and microenvironments influence the Mtb response

Automated Correlative Light and Electron Microscopy using FIB-SEM as a tool to screen for ultrastructural phenotypes

In Correlative Light and Electron Microscopy (CLEM), two imaging modalities are combined to take advantage of the localization capabilities of light microscopy (LM) to guide the capture of high-resolution details in the electron microscope (EM). However, traditional approaches have proven to be very laborious, thus yielding a too low throughput for quantitative or exploratory studies of populations. Recently, in the electron microscopy field, FIB-SEM (Focused Ion Beam -Scanning Electron Microscope) tomography has emerged as a flexible method that enables semi-automated 3D volume acquisitions. During my thesis, I developed CLEMSite, a tool that takes advantage of the semi-automation and scanning capabilities of the FIB-SEM to automatically acquire volumes of adherent cultured cells. CLEMSite is a combination of computer vision and machine learning applications with a library for controlling the microscope ( product from a collaboration with Carl Zeiss GmbH and Fibics Inc.). Thanks to this, the microscope was able to automatically track, find and acquire cell regions previously identified in the light microscope. More specifically, two main modules were implemented. First, a correlation module was designed to detect and record reference points from a grid pattern present on the culture substrate in both modalities (LM and EM). Second, I designed a module that retrieves the regions of interest in the FIB-SEM and that drives the acquisition of image stacks between different targets in an unattended fashion. The automated CLEM approach is demonstrated on a project where 3D EM volumes are examined upon multiple siRNA treatments for knocking down genes involved in the morphogenesis of the Golgi apparatus. Additionally, the power of CLEM approaches using FIB-SEM is demonstrated with the detailed structural analysis of two events: the breakage of the nuclear envelope within constricted cells and an intriguing catastrophic DNA Damage Response in binucleated cells. Our results demonstrate that executing high throughput volume acquisition in electron microscopy is possible and that EM can provide incredible insights to guide new biological discoveries

Multivariate modelling of cognitive function and brain structural data

archiv

THE ROLE OF NEURONAL CALCIUM SENSOR PROTEIN VILIP-1 IN Aβ-INDUCED NEURONAL DEATH IN ALZHEIMER DISEASE

Alzheimer disease (AD) is the most prevalent form of dementia in the United States affecting an estimated 5.3 million individuals in 2015. Clinically, AD presents with progressive memory loss, decline in cognitive abilities, and behavioral changes. Neuropathologically, increased synaptic pathology and neuronal loss correlate with cognitive impairment in AD. While the specific neurobiological mechanisms underlying AD neuronal loss are not fully understood, a growing pool of evidence implicates soluble amyloid-β (Aβ) oligomers as a primary neurotoxic agent. Previous work has demonstrated that Aβ disrupts neuronal Ca2+ homeostasis, initiating a cascade of pathological events that ultimately culminate in widespread neuronal death. Recently, neuronal calcium sensor protein visinin-like protein 1 (Vilip-1) has been identified an AD-specific peripheral biomarker, however little is known about Vilip-1 in the AD brain. Previous work has alluded to associations between Vilip-1, Aβ, and neuronal death. VSNL1, the gene that encodes Vilip-1, coexpresses with genes related to AD throughout normal aging, notably amyloid precursor protein (APP), which is cleaved in the pathogenesis of AD to form Aβ. Vilip-1 immunoreactivity also associates with neuritic plaques in the neocortex of the human AD brain. Finally, overexpression of Vilip-1 in a cell line increased death rates following a Ca2+ challenge, suggesting Vilip-1 may play a functional role in neuronal loss. To determine if Vilip-1 plays a causal role in AD, first we investigated Vilip-1 levels in two regions of the human AD brain. Then we used model systems to evaluate the impact of Aβ on Vilip-1 expression and determined whether manipulation of Vilip-1 expression affected Aβ-induced neuronal death. We demonstrated that Vilip-1 is reduced within brain regions characterized by prominent neuronal loss in both AD and frontotemporal lobar degeneration (FTLD). We reported that Vilip-1 expression is not driven by Aβ. Finally, we found that Aβ-initiated neuron death was unaffected by the extent of Vilip-1 expression. Together, these data suggest that Vilip-1 is a general marker for neuronal loss in brain tissue rather than participant in an AD-specific neuronal death process. In addition, Vilip-1 may have value as a novel marker for neuronal integrity and loss in human brain tissue

Anisotropy Across Fields and Scales

This open access book focuses on processing, modeling, and visualization of anisotropy information, which are often addressed by employing sophisticated mathematical constructs such as tensors and other higher-order descriptors. It also discusses adaptations of such constructs to problems encountered in seemingly dissimilar areas of medical imaging, physical sciences, and engineering. Featuring original research contributions as well as insightful reviews for scientists interested in handling anisotropy information, it covers topics such as pertinent geometric and algebraic properties of tensors and tensor fields, challenges faced in processing and visualizing different types of data, statistical techniques for data processing, and specific applications like mapping white-matter fiber tracts in the brain. The book helps readers grasp the current challenges in the field and provides information on the techniques devised to address them. Further, it facilitates the transfer of knowledge between different disciplines in order to advance the research frontiers in these areas. This multidisciplinary book presents, in part, the outcomes of the seventh in a series of Dagstuhl seminars devoted to visualization and processing of tensor fields and higher-order descriptors, which was held in Dagstuhl, Germany, on October 28–November 2, 2018

Anisotropy Across Fields and Scales

This open access book focuses on processing, modeling, and visualization of anisotropy information, which are often addressed by employing sophisticated mathematical constructs such as tensors and other higher-order descriptors. It also discusses adaptations of such constructs to problems encountered in seemingly dissimilar areas of medical imaging, physical sciences, and engineering. Featuring original research contributions as well as insightful reviews for scientists interested in handling anisotropy information, it covers topics such as pertinent geometric and algebraic properties of tensors and tensor fields, challenges faced in processing and visualizing different types of data, statistical techniques for data processing, and specific applications like mapping white-matter fiber tracts in the brain. The book helps readers grasp the current challenges in the field and provides information on the techniques devised to address them. Further, it facilitates the transfer of knowledge between different disciplines in order to advance the research frontiers in these areas. This multidisciplinary book presents, in part, the outcomes of the seventh in a series of Dagstuhl seminars devoted to visualization and processing of tensor fields and higher-order descriptors, which was held in Dagstuhl, Germany, on October 28–November 2, 2018