Novel application of stochastic modeling techniques to long-term, high-resolution time-lapse microscopy of cortical axons

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Brain and Cognitive Sciences, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 64-70).Axons exhibit a rich variety of behaviors, such as elongation, turning, branching, and fasciculation, all in service of the complex goal of wiring up the brain. In order to quantify these behaviors, I have developed a system for in vitro imaging of axon growth cones with time-lapse fluorescence microscopy. Image tiles are automatically captured and assembled into a mosaic image of a square millimeter region. GFP-expressing mouse cortical neurons can be imaged once every few minutes for up to weeks if phototoxicity is minimized. Looking at the data, the trajectories of axon growth cones seem to alternate between long, straight segments and sudden turns. I first rigorously test the idea that the straight segments are generated from a biased random walk by analyzing the correlation between growth cone steps in the time and frequency domain. To formalize and test the intuition that sharp turns join straight segments, I fit a hidden Markov model to time series of growth cone velocity vectors.(cont.) The hidden state variable represents the bias direction of a biased random walk, and specifies the mean and variance of a Gaussian distribution from which velocities are drawn. Rotational symmetry is used to constrain the transition probabilities of the hidden variable, as well as the Gaussian distributions for the hidden states. Maximum likelihood estimation of the model parameters shows that the most probable behavior is to remain in the same hidden state. The second most probable behavior is to turn by about 40 degrees. Smaller angle turns are highly improbable, consistent with the idea that the axon makes sudden turns. When the same hidden Markov model was applied to artificially generated meandering trajectories, the transition probabilities were significant only for small angle turns. This novel application of stochastic models to growth cone trajectories provides a quantitative framework for testing interventions (eg. pharmacological, activity-related, etc.) that can impact axonal growth cone movement and turning. For example, manipulations that inhibit actin polymerization increase the frequency and angle of turns made by the growth cone. More generally, axon behaviors may be useful in deducing computational principles for wiring up circuits.by Neville Espi Sanjana.Ph.D

Detecting cells and analyzing their behaviors in microscopy images using deep neural networks

The computer-aided analysis in the medical imaging field has attracted a lot of attention for the past decade. The goal of computer-vision based medical image analysis is to provide automated tools to relieve the burden of human experts such as radiologists and physicians. More specifically, these computer-aided methods are to help identify, classify and quantify patterns in medical images. Recent advances in machine learning, more specifically, in the way of deep learning, have made a big leap to boost the performance of various medical applications. The fundamental core of these advances is exploiting hierarchical feature representations by various deep learning models, instead of handcrafted features based on domain-specific knowledge. In the work presented in this dissertation, we are particularly interested in exploring the power of deep neural network in the Circulating Tumor Cells detection and mitosis event detection. We will introduce the Convolutional Neural Networks and the designed training methodology for Circulating Tumor Cells detection, a Hierarchical Convolutional Neural Networks model and a Two-Stream Bidirectional Long Short-Term Memory model for mitosis event detection and its stage localization in phase-contrast microscopy images”--Abstract, page iii

Modelling and Identification of Immune Cell Migration during the Inflammatory Response

Neutrophils are the white blood cells that play a crucial role in the response of the innate immune system to tissue injuries or infectious threats. Their rapid arrival to the damaged area and timely removal from it define the success of the inflammatory process. Therefore, understanding neutrophil migratory behaviour is essential for the therapeutic regulation of multiple inflammation-mediated diseases. Recent years saw rapid development of various in vivo models of inflammation that provide a remarkable insight into the neutrophil function. The main drawback of the \textit{in vivo} microscopy is that it usually focuses on the moving cells and obscures the external environment that drives their migration. To evaluate the effect of a particular treatment strategy on neutrophil behaviour, it is necessary to recover the information about the cell responsiveness and the complex extracellular environment from the limited experimental data. This thesis addresses the presented inference problem by developing a dynamical modelling and estimation framework that quantifies the relationship between an individual migrating cell and the global environment. \par The first part of the thesis is concerned with the estimation of the hidden chemical environment that modulates the observed cell migration during the inflammatory response in the injured tail fin of zebrafish larvae. First, a dynamical model of the neutrophil responding to the chemoattractant concentration is developed based on the potential field paradigm of object-environment interaction. This representation serves as a foundation for a hybrid model that is proposed to account for heterogeneous behaviour of an individual cell throughout the migration process. An approximate maximum likelihood estimation framework is derived to estimate the hidden environment and the states of multiple hybrid systems simultaneously. The developed framework is then used to analyse the neutrophil tracking data observed in vivo under the assumption that each neutrophil at each time can be in one of three migratory modes: responding to the environment, randomly moving, and stationary. The second part of the thesis examines the process of neutrophil migration at the subcellular scale, focusing on the subcellular mechanism that translates the local environment sensing into the cell shape change. A state space model is formulated based on the hypothesis that links the local protrusions of the cell membrane and the concentration of the intracellular pro-inflammatory signalling protein. The developed model is tested against the local concentration data extracted from the in vivo time-lapse images via the classical expectation-maximisation algorithm

Quantitative automated analysis of host-pathogen interactions

This work aims to broaden knowledge about neutrophil biology in their interaction with fungi species that most frequently cause invasive fungal diseases (IFD). The questions addressed include the alteration of neutrophil morphology after interaction with Candida albicans or C. glabrata, revealing factors that modulate the production and composition of neutrophil-derived extracellular vesicles (EVs) obtained in confrontation assay with conidia of Aspergillus fumigatus and analysing EVs activity against this fungus. Alongside fundamental interests, those questions have important applied aspects in the medicine of IFD. In particular, for diagnostic purposes and infection process monitoring. The results of this work include: 1 a novel segmentation and tracking algorithm which is capable of working with low-contrast cell images, producing accurate cell contours and providing data about positions of clusters, which would improve further analysis; 2 a novel workflow algorithm for analysis of neutrophil continuous morphological spectrum without consensus-based manual annotation; 3 quantitative evidence that morphodynamics of isolated neutrophils depends on the infectious agent (C. albicans or C. glabrata) used in whole blood infection assay; 4 quantitative evidence that neutrophil-derived extracellular vesicles, obtained in confrontation assays with conidia of A. fumigatus could inhibit hyphae development and damage hyphae cell wall; 5 quantitative evidence that EVs inhibition activity is strain-specific

Virtual cardiac monolayers for electrical wave propagation

The complex structure of cardiac tissue is considered to be one of the main determinants of an arrhythmogenic substrate. This study is aimed at developing the first mathematical model to describe the formation of cardiac tissue, using a joint in silico-in vitro approach. First, we performed experiments under various conditions to carefully characterise the morphology of cardiac tissue in a culture of neonatal rat ventricular cells. We considered two cell types, namely, cardiomyocytes and fibroblasts. Next, we proposed a mathematical model, based on the Glazier-Graner-Hogeweg model, which is widely used in tissue growth studies. The resultant tissue morphology was coupled to the detailed electrophysiological Korhonen-Majumder model for neonatal rat ventricular cardiomyocytes, in order to study wave propagation. The simulated waves had the same anisotropy ratio and wavefront complexity as those in the experiment. Thus, we conclude that our approach allows us to reproduce the morphological and physiological properties of cardiac tissue

A Dynamical Systems Modelling Framework for Breast Cancer Cell Motility and Morphology Analysis

Cancer is a worldwide disease and, in the UK, breast cancer is the most common. Compared to healthy cells, cancer cells migrate abnormally, associated with alterations in cell motility and morphology. The development of biomedical imaging techniques result in the production of large amounts of data. The analysis of such large data, the variety of cancer cell shapes and the potential links between cell motility and morphology present a challenge for cell migration study: how to analyse cell motility and morphology simultaneously. This thesis proposes a computational framework to address integrated cancer cell migration analysis. Firstly, automated tracking of cell boundaries is undertaken by a DWNA kinematic model of cell boundaries, described by B-spline active contours. The tracked cell states intrinsically links cell morphology to motility features. As a result, cell centroid and boundary dynamics are successfully tracked, followed by quantitative motility analysis. A module to quantitatively analyse cell morphology is proposed after tracking. Cell shapes are described by a 2D descriptor. Accordingly, cell morphodynamics are modelled as a hidden Markov process, along with three shape states: round, elongated and teardrop. In order to explore the potential interactions between cell shapes and motility, cell centroid motility characteristics are associated to the identified shape states. When the analysis was applied to breast cancer control cells, the identified shape states showed distinct motility characteristics. Finally, the proposed framework is adapted to the comparison of MDA-MB-231 cell behaviours with regulating migration-associated proteins: i) Blebbistatin and Y-27632, which are chemical inhibitors of two different proteins working on the same pathway, showed identical, but different degrees of effects on the motility and morphology characteristics of MDA-MB-231 cells. ii) The absence of FA-associated genes, including FAK, RhoE and beta-PIX, respectively showed distinct effects on cell migrations

Image Analysis and Platform Development for Automated Phenotyping in Cytomics

This thesis is dedicated to the empirical study of image analysis in HT/HC screen study. Often a HT/HC screening produces extensive amounts that cannot be manually analyzed. Thus, an automated image analysis solution is prior to an objective understanding of the raw image data. Compared to general application domain, the efficiency of HT/HC image analysis is highly subjected to image quantity and quality. Accordingly, this thesis will address two major procedures, namely image segmentation and object tracking, in the image analysis step of HT/HC screen study. Moreover, this thesis focuses on expending generic computer science and machine learning theorems into the design of dedicated algorithms for HT/HC image analysis. Additionally, this thesis exemplifies a practical implementation of image analysis and data analysis workflow via empirical case studies with different image modalities and experiment settings. However, the data analysis theorem will be generally illustrated without further expansions. Finally, the thesis will briefly address supplementary infrastructures for end-user interaction and data visualization.Netherlands Bioinformatics CentreComputer Systems, Imagery and Medi