Computational image analysis of subcellular dynamics in time-lapse fluorescence microscopy

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, February 2005.Includes bibliographical references (p. 69-73).The use of image segmentation and motion tracking algorithms was adapted for analyzing time-lapse data of cells with fluorescently labeled protein. Performance metrics were devised and algorithm parameters were matched to hand-created ground-truth data. The performance of these algorithms in this domain was compared. Finally, the optimal algorithms were selected and used to acquire statistics on existing data, in order to reproduce previous studies on the cell cytoskeleton. New data was acquired to extend previous results and further test the algorithms on a different cell line, under both widefield and confocal microscope conditions.by Austin V. Huang.S.M

SPOT DETECTION METHODS IN FLUORESCENCE MICROSCOPY IMAGING: A REVIEW

Fluorescence microscopy imaging has become one of the essential tools used by biologists to visualize and study intracellular particles within a cell. Studying these particles is a long-term research effort in the field of microscopy image analysis, consisting of discovering the relationship between the dynamics of particles and their functions. However, biologists are faced with challenges such as the counting and tracking of these intracellular particles. To overcome the issues faced by biologists, tools which can extract the location and motion of these particles are essential. One of the most important steps in these analyses is to accurately detect particle positions in an image, termed spot detection. The detection of spots in microscopy imaging is seen as a critical step for further quantitative analysis. However, the evaluation of these microscopic images is mainly conducted manually, with automated methods becoming popular. This work presents some advances in fluorescence microscopy image analysis, focusing on the detection methods needed for quantifying the location of these spots. We review several existing detection methods in microscopy imaging, along with existing synthetic benchmark datasets and evaluation metrics

Automatic approach for spot detection in microscopy imaging based on image processing and statistical analysis

Abstract: In biological research, fluorescence microscopy has become one of the vital tools used for observation, allowing researchers to study, visualise and image the details of intracel-lular structures which result in better understanding of biology. However, analysis of large numbers of samples is often required to draw statistically verifiable conclusions. Automated methods for analysis of microscopy image data make it possible to handle large datasets, and at the same time reduce the risk of bias imposed by manual techniques in the image analysis pipeline. This work covers automated methods for extracting quan-titative measurements from microscopy images, enabling the detection of spots resulting from different experimental conditions. The work resulted in four main significant con-tributions developed around the microscopy image analysis pipeline. Firstly, an investiga-tion into the importance of spot detection within the automated image analysis pipeline is conducted. Experimental findings show that poor spot detection adversely affected the remainder of the processing pipeline...D.Ing. (Electrical and Electronic Engineering

Approximate Matching in Genomic Sequence Data

Ph.DDOCTOR OF PHILOSOPH

Complexity in Developmental Systems: Toward an Integrated Understanding of Organ Formation

During animal development, embryonic cells assemble into intricately structured organs by working together in organized groups capable of implementing tightly coordinated collective behaviors, including patterning, morphogenesis and migration. Although many of the molecular components and basic mechanisms underlying such collective phenomena are known, the complexity emerging from their interplay still represents a major challenge for developmental biology. Here, we first clarify the nature of this challenge and outline three key strategies for addressing it: precision perturbation, synthetic developmental biology, and data-driven inference. We then present the results of our effort to develop a set of tools rooted in two of these strategies and to apply them to uncover new mechanisms and principles underlying the coordination of collective cell behaviors during organogenesis, using the zebrafish posterior lateral line primordium as a model system. To enable precision perturbation of migration and morphogenesis, we sought to adapt optogenetic tools to control chemokine and actin signaling. This endeavor proved far from trivial and we were ultimately unable to derive functional optogenetic constructs. However, our work toward this goal led to a useful new way of perturbing cortical contractility, which in turn revealed a potential role for cell surface tension in lateral line organogenesis. Independently, we hypothesized that the lateral line primordium might employ plithotaxis to coordinate organ formation with collective migration. We tested this hypothesis using a novel optical tool that allows targeted arrest of cell migration, finding that contrary to previous assumptions plithotaxis does not substantially contribute to primordium guidance. Finally, we developed a computational framework for automated single-cell segmentation, latent feature extraction and quantitative analysis of cellular architecture. We identified the key factors defining shape heterogeneity across primordium cells and went on to use this shape space as a reference for mapping the results of multiple experiments into a quantitative atlas of primordium cell architecture. We also propose a number of data-driven approaches to help bridge the gap from big data to mechanistic models. Overall, this study presents several conceptual and methodological advances toward an integrated understanding of complex multi-cellular systems

Dynamics of Marine Microbial Metabolism and Physiology at Station Aloha.

Ph.D. Thesis. University of Hawaiʻi at Mānoa 2017

Bioinformatics Discovery And Functional Characterization Of Lipid-Binding Lov Photoreceptors

The light–oxygen–voltage sensitive (LOV) domain subset of the PAS superfamily is a ubiquitous photoreceptor class that enables organisms across multiple kingdoms to sense blue light. LOV photoreceptors are modular flavin-binding proteins that consist of discrete sensor and effector domains. Blue-light drives a LOV sensor to trigger a conformational change in photoreceptor structure that ultimately regulates the biochemical function of one or more of its fused effectors. The nature by which LOV photoreceptors vary in their sensor-effector domain combinations allows for light-gated regulation by a single-photoreceptor class of diverse physiological processes across species in varied ecological settings that underlie circadian rhythms, virulence, phototropism, and stress responses. Here, we report the bioinformatics identification of over 6,700 candidate LOV domains and their annotation for sensor-effector topology and inferred ontological function. In addition to nearly tripling the number of reported LOV sequences, we identified several classes of LOV proteins with predicted sensor-effector pairings that were previously unknown or considered rare and thus have yet to be functionally characterized, including photoreceptors with LOV sensors and Regulator of G-protein signaling (RGS) and PAS homology effectors (“RGS-LOV-PAS”) in dikarya fungi and brown algae. We report the experimental characterization of two bioinformatics-identified fungal RGS-LOV-PAS photoreceptors, BcRGS5 from B cinerea and CeRGS from C. europaea, that rapidly localize from cytoplasm to the plasma membrane upon blue-light illumination in a heterologous mammalian cell expression system. Dynamic membrane localization by BcRGS5 is mediated by a light-switchable high affinity electrostatic interaction with anionic phospholipids. The seconds-timescale membrane-recruitment, likely driven by a conserved lipid-binding amphipathic helix, may serve to potentiate RGS effector activity on membrane-bound binding partners in the native fungal organism. As neither LOV photoreceptors nor RGS proteins nor PAS sensory proteins are known to traffic by light-gated and direct association with phospholipids, this work establishes a novel photosensory signaling mechanism for multiple protein classes and highlights the value of applying genomic technologies to diverse organisms to capture photosensory protein diversity that is vastly important in adaptation, photobiology, and optogenetics

Investigations into Convective Deposition from Fundamental and Application-Driven Perspectives

Crystalline particle coatings can provide critical enhancement to wide-ranging energy and biomedical device applications. One method by which ordered particle arrays can be assembled is convective deposition. In convective deposition, particles flow to a surface via evaporation-driven convection, then order through capillary interactions. This thesis will serve to investigate convective deposition from fundamental and application-driven perspectives. Motivations for this work include the development of point-of-care diagnostic devices, macroporous membranes, and various energy applications. Immunoaffinity cell capture devices display enhanced diagnostic capabilities with intelligently varied surface roughness in the form of particle coatings. Relatedly, highly crystalline particle coatings can be used to template the fabrication of macroporous polymer membranes. These membranes display highly monodisperse pores at particle contact points. In addition, ordered areas of particles, acting as microlenses, can enhance LED performance by 2.66-fold and DSSC efficiency by 30%. Previous research has targeted the formation of crystalline monolayers of particles. However, much insight can be gleaned from imperfect coatings. The analysis of submonolayer coatings, exhibiting significant void spaces, provides insight as to the specific mechanisms and timescales for flow and crystallization. A pair of competing deposition modes, termed ballistic and locally-ordered, enables the intelligent design of experiments and enables significant enhancement in control of resultant thin film morphology. Surface tension-driven particle assembly is subject to a number of native instabilities and macroscale defects that can irreversibly compromise coating uniformity. These include the formation of three-dimensional streaks, where surface tension-driven flow spurs on the nucleation of large imperfections. These imperfections, once nucleated, exhibit a feedback loop of dramatically enhanced evaporation and resultant flow. In addition, thick nanoparticle coatings, subject to enormous drying stresses, exhibit highly uniform crack formation and spacing in an attempt to minimize system energy. Both these imperfections yield insight on convective deposition as a fundamental phenomenon, and intelligent design of experiments moving forward. Cracking can be suppressed through layer-by-layer particle assembly, whereas streaking can be controlled via several significant process enhancements. Process enhancements include the addition of smaller constituent, as packing aids, to suspension, the application of lateral vibration, and the reversal of relevant surface tension gradients. The transition from unary to binary suspensions represents a significant improvement to convective deposition as a process. Nanoparticles act as packing, and flow, aids, wholly suppress macroscale defects under ideal conditions. A relative deficiency or excess of nanoparticles can generate complex coating morphologies including multilayers and transverse stripes. The application of lateral vibration to convective deposition allows the assembly of monolayer particle coatings under a larger range of operating conditions and at a faster rate. Macroscale defect formation can increased through an enhancement of the natural condition, where evaporative cooling generates a thermal gradient in drying droplets. Conversely, these defects can be suppressed with a reversal of this gradient, which will reverse the direction of surface tension-driven recirculation. These fundamental developments in understanding, and associated process enhancements, are critical in current efforts to scale up convective deposition. As convective deposition evolves from laboratory-scale batch experiments to continuous, large scale, coatings, repeatability and robustness, as well as an ability to controllably change thin film morphology, will be essential

Cascade of classifier ensembles for reliable medical image classification

Medical image analysis and recognition is one of the most important tools in modern medicine. Different types of imaging technologies such as X-ray, ultrasonography, biopsy, computed tomography and optical coherence tomography have been widely used in clinical diagnosis for various kinds of diseases. However, in clinical applications, it is usually time consuming to examine an image manually. Moreover, there is always a subjective element related to the pathological examination of an image. This produces the potential risk of a doctor to make a wrong decision. Therefore, an automated technique will provide valuable assistance for physicians. By utilizing techniques from machine learning and image analysis, this thesis aims to construct reliable diagnostic models for medical image data so as to reduce the problems faced by medical experts in image examination. Through supervised learning of the image data, the diagnostic model can be constructed automatically. The process of image examination by human experts is very difficult to simulate, as the knowledge of medical experts is often fuzzy and not easy to be quantified. Therefore, the problem of automatic diagnosis based on images is usually converted to the problem of image classification. For the image classification tasks, using a single classifier is often hard to capture all aspects of image data distributions. Therefore, in this thesis, a classifier ensemble based on random subspace method is proposed to classify microscopic images. The multi-layer perceptrons are used as the base classifiers in the ensemble. Three types of feature extraction methods are selected for microscopic image description. The proposed method was evaluated on two microscopic image sets and showed promising results compared with the state-of-art results. In order to address the classification reliability in biomedical image classification problems, a novel cascade classification system is designed. Two random subspace based classifier ensembles are serially connected in the proposed system. In the first stage of the cascade system, an ensemble of support vector machines are used as the base classifiers. The second stage consists of a neural network classifier ensemble. Using the reject option, the images whose classification results cannot achieve the predefined rejection threshold at the current stage will be passed to the next stage for further consideration. The proposed cascade system was evaluated on a breast cancer biopsy image set and two UCI machine learning datasets, the experimental results showed that the proposed method can achieve high classification reliability and accuracy with small rejection rate. Many computer aided diagnosis systems face the problem of imbalance data. The datasets used for diagnosis are often imbalanced as the number of normal cases is usually larger than the number of the disease cases. Classifiers that generalize over the data are not the most appropriate choice in such an imbalanced situation. To tackle this problem, a novel one-class classifier ensemble is proposed. The Kernel Principle Components are selected as the base classifiers in the ensemble; the base classifiers are trained by different types of image features respectively and then combined using a product combining rule. The proposed one-class classifier ensemble is also embedded into the cascade scheme to improve classification reliability and accuracy. The proposed method was evaluated on two medical image sets. Favorable results were obtained comparing with the state-of-art results