74 research outputs found

    Regulation of Single-Cell Bacterial Gene Expression at the Stage of Transcription Initiation

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    One of the qualities that allow bacterial cells to survive in diverse, fluctuating environments is phenotypic plasticity, which is the ability to exhibit different phenotypes depending on the environmental conditions. Phenotypic plasticity arises via coordinated work of small genetic circuits that provide the cell with the means for decision-making. The behavior of these circuits depends, among other factors, on the ability of protein numbers to cross certain thresholds for a sufficient amount of time. In bacteria, RNA numbers largely define protein numbers and thus can be used to study the decision-making processes. Previous research outlined the effects of mean and variance in RNA or protein numbers on the behavior of small genetic circuits. However, noise in gene expression is often highly asymmetric. This could impact the threshold-crossing abilities of molecular numbers in a way that is not detectable by considering only their mean and variance. The focus of this thesis is to study the regulation of multi-step kinetics of bacterial gene expression in live bacteria and its effects on the shape of the distribution of RNA or protein levels. In particular, the thesis investigates how the rate-limiting steps in bacterial transcription, such as closed and open complex formation, intermittent inactive states, and promoter escape contribute to the dynamics of RNA numbers, and how this dynamics propagates to the distribution of protein levels in a cell population. This study made use of already existing techniques such as measurements at the single-RNA level and dynamically accurate stochastic modeling, complemented by the novel methodology developed in this work. First, the thesis introduced a new method for estimating the numbers of fluorescently tagged molecules present in a cell from time series data obtained by microscopy. This method allows improving the accuracy of the estimation when fluorescently tagged molecules are absent from the cell image for time intervals comparable with cell lifetime. Second, the new methodology for dissecting in vivo kinetics of rate-limiting steps in transcription initiation was proposed. Applying this methodology to study initiation kinetics at lac/ara-1 promoter provided insights on the amount, duration, and reversibility of the rate-limiting steps in this process. Further, the thesis investigated the kinetics of transcription activation of lac/ara-1 promoter at various temperatures. The results indicate that additional rate-limiting steps emerge in inducer intake kinetics as temperature decreases from optimal (37 °C). Finally, the focus was shifted specifically to quantifying the asymmetry and tailedness in RNA and protein level distributions, since these features are relevant for determining threshold crossing propensities. Here, these features were found to depend both on promoter sequence and on regulatory molecules, thus being evolvable and adaptable. Overall, the work conducted in this thesis suggests that asymmetries in RNA and protein numbers may be crucial for decision-making in bacteria, since they can be regulated by promoter sequence, regulatory molecules levels, and temperature shifts. The thesis also contributes to the pool of existing methodology for studying in vivo bacterial gene expression using single-cell biology approach. These findings should be of use both for better understanding of natural systems and for fine-tuning behavior of synthetic gene circuits

    Unraveling the Thousand Word Picture: An Introduction to Super-Resolution Data Analysis

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    Super-resolution microscopy provides direct insight into fundamental biological processes occurring at length scales smaller than light’s diffraction limit. The analysis of data at such scales has brought statistical and machine learning methods into the mainstream. Here we provide a survey of data analysis methods starting from an overview of basic statistical techniques underlying the analysis of super-resolution and, more broadly, imaging data. We subsequently break down the analysis of super-resolution data into four problems: the localization problem, the counting problem, the linking problem, and what we’ve termed the interpretation problem

    Feature-preserving image restoration and its application in biological fluorescence microscopy

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    This thesis presents a new investigation of image restoration and its application to fluorescence cell microscopy. The first part of the work is to develop advanced image denoising algorithms to restore images from noisy observations by using a novel featurepreserving diffusion approach. I have applied these algorithms to different types of images, including biometric, biological and natural images, and demonstrated their superior performance for noise removal and feature preservation, compared to several state of the art methods. In the second part of my work, I explore a novel, simple and inexpensive super-resolution restoration method for quantitative microscopy in cell biology. In this method, a super-resolution image is restored, through an inverse process, by using multiple diffraction-limited (low) resolution observations, which are acquired from conventional microscopes whilst translating the sample parallel to the image plane, so referred to as translation microscopy (TRAM). A key to this new development is the integration of a robust feature detector, developed in the first part, to the inverse process to restore high resolution images well above the diffraction limit in the presence of strong noise. TRAM is a post-image acquisition computational method and can be implemented with any microscope. Experiments show a nearly 7-fold increase in lateral spatial resolution in noisy biological environments, delivering multi-colour image resolution of ~30 nm

    Single Particle Tracking: Analysis Techniques for Live Cell Nanoscopy.

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    Single molecule experiments are a set of experiments designed specifically to study the properties of individual molecules. It has only been in the last three decades where single molecule experiments have been applied to the life sciences; where they have been successfully implemented in systems biology for probing the behaviors of sub-cellular mechanisms. The advent and growth of super-resolution techniques in single molecule experiments has made the fundamental behaviors of light and the associated nano-probes a necessary concern among life scientists wishing to advance the state of human knowledge in biology. This dissertation disseminates some of the practices learned in experimental live cell microscopy. The topic of single particle tracking is addressed here in a format that is designed for the physicist who embarks upon single molecule studies. Specifically, the focus is on the necessary procedures to generate single particle tracking analysis techniques that can be implemented to answer biological questions. These analysis techniques range from designing and testing a particle tracking algorithm to inferring model parameters once an image has been processed. The intellectual contributions of the author include the techniques in diffusion estimation, localization filtering, and trajectory associations for tracking which will all be discussed in detail in later chapters. The author of this thesis has also contributed to the software development of automated gain calibration, live cell particle simulations, and various single particle tracking packages. Future work includes further evaluation of this laboratory\u27s single particle tracking software, entropy based approaches towards hypothesis validations, and the uncertainty quantification of gain calibration

    Super-resolution microscopy live cell imaging and image analysis

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    Novel fundamental research results provided new techniques going beyond the diffraction limit. These recent advances known as super-resolution microscopy have been awarded by the Nobel Prize as they promise new discoveries in biology and live sciences. All these techniques rely on complex signal and image processing. The applicability in biology, and particularly for live cell imaging, remains challenging and needs further investigation. Focusing on image processing and analysis, the thesis is devoted to a significant enhancement of structured illumination microscopy (SIM) and super-resolution optical fluctuation imaging (SOFI)methods towards fast live cell and quantitative imaging. The thesis presents a novel image reconstruction method for both 2D and 3D SIM data, compatible with weak signals, and robust towards unwanted image artifacts. This image reconstruction is efficient under low light conditions, reduces phototoxicity and facilitates live cell observations. We demonstrate the performance of our new method by imaging long super-resolution video sequences of live U2-OS cells and improving cell particle tracking. We develop an adapted 3D deconvolution algorithm for SOFI, which suppresses noise and makes 3D SOFI live cell imaging feasible due to reduction of the number of required input images. We introduce a novel linearization procedure for SOFI maximizing the resolution gain and show that SOFI and PALM can both be applied on the same dataset revealing more insights about the sample. This PALM and SOFI concept provides an enlarged quantitative imaging framework, allowing unprecedented functional exploration of the sample through the estimation of molecular parameters. For quantifying the outcome of our super-resolutionmethods, the thesis presents a novel methodology for objective image quality assessment measuring spatial resolution and signal to noise ratio in real samples. We demonstrate our enhanced SOFI framework by high throughput 3D imaging of live HeLa cells acquiring the whole super-resolution 3D image in 0.95 s, by investigating focal adhesions in live MEF cells, by fast optical readout of fluorescently labelled DNA strands and by unraveling the nanoscale organization of CD4 proteins on a plasma membrane of T-cells. Within the thesis, unique open-source software packages SIMToolbox and SOFI simulation tool were developed to facilitate implementation of super-resolution microscopy methods

    Structural Organization and Chemical Activity Revealed by New Developments in Single-Molecule Fluorescence and Orientation Imaging

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    Single-molecule (SM) fluorescence and its localization are important and versatile tools for understanding and quantifying dynamical nanoscale behavior of nanoparticles and biological systems. By actively controlling the concentration of fluorescent molecules and precisely localizing individual single molecules, it is possible to overcome the classical diffraction limit and achieve \u27super-resolution\u27 with image resolution on the order of 10 nanometers. Single molecules also can be considered as nanoscale sensors since their fluorescence changes in response to their local nanoenvironment. This dissertation discusses extending this SM approach to resolve heterogeneity and dynamics of nanoscale materials and biophysical structures by using positions and orientations of single fluorescent molecules. I first present an SM approach for resolving spatial variations in the catalytic activity of individual photocatalysts. Quantitative colocalization of chemically triggered molecular probes reveals the role of structural defects on the activity of catalytic nanoparticles. Next, I demonstrate a new engineered optical point spread function (PSF), called the Duo-spot PSF, for SM orientation measurements. This PSF exhibits high sensitivity for estimating orientations of dim fluorescent molecules. This dissertation also discusses a new amyloid imaging method, transient amyloid binding (TAB) microscopy, for studying heterogeneous organization of amyloid structures, which are associated with various aging-related neurodegenerative diseases. Continuous transient binding of dye molecules to amyloid structures generates photon bursts for SM localization over hours to days with minimal photobleaching, yielding about 40% more localizations than standard immunolabeling. Finally, I augment TAB imaging to simultaneously measure positions and orientations of fluorescent molecules bound to amyloid surfaces. This new method, termed single-molecule orientation localization microscopy (SMOLM), robustly and sensitively measures the in-plane (xy) orientations of fluorophores (approximately 9 degree precision in azimuthal angle) near a refractive index interface and reveals structural heterogeneities along amyloid fibrillar networks that cannot be resolved by SM localization alone

    Contributions to Statistical Image Analysis for High Content Screening.

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    Images of cells incubated with fluorescent small molecule probes can be used to infer where the compounds distribute within cells. Identifying the spatial pattern of compound localization within each cell is very important problem for which adequate statistical methods do not yet exist. First, we asked whether a classifier for subcellular localization categories can be developed based on a training set of manually classified cells. Due to challenges of the images such as uneven field illumination, low resolution, high noise, variation in intensity and contrast, and cell to cell variability in probe distributions, we constructed texture features for contrast quantiles conditioning on intensities, and classifying on artificial cells with same marginal distribution but different conditional distribution supported that this conditioning approach is beneficial to distinguish different localization distributions. Using these conditional features, we obtained satisfactory performance in image classification, and performed to dimension reduction and data visualization. As high content images are subject to several major forms of artifacts, we are interested in the implications of measurement errors and artifacts on our ability to draw scientifically meaningful conclusions from high content images. Specifically, we considered three forms of artifacts: saturation, blurring and additive noise. For each type of artifacts, we artificially introduced larger amount, and aimed to understand the bias by `Simulation Extrapolation' (SIMEX) method, applied to the measurement errors for pairwise centroid distances, the degree of eccentricity in the class-specific distributions, and the angles between the dominant axes of variability for different categories. Finally, we briefly considered the analysis of time-point images. Small molecule studies will be more focused. Specifically, we consider the evolving patterns of subcellular staining from the moment that a compound is introduced into the cell culture medium, to the point that steady state distribution is reached. We construct the degree to which the subcellular staining pattern is concentrated in or near the nucleus as the features of timecourse data set, and aim to determine whether different compounds accumulate in different regions at different times, as characterized in terms of their position in the cell relative to the nucleus.Ph.D.StatisticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/91460/1/liufy_1.pd

    Particle Filtering Methods for Subcellular Motion Analysis

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    Advances in fluorescent probing and microscopic imaging technology have revolutionized biology in the past decade and have opened the door for studying subcellular dynamical processes. However, accurate and reproducible methods for processing and analyzing the images acquired for such studies are still lacking. Since manual image analysis is time consuming, potentially inaccurate, and poorly reproducible, many biologically highly relevant questions are either left unaddressed, or are answered with great uncertainty. The subject of this thesis is particle filtering methods and their application for multiple object tracking in different biological imaging applications. Particle filtering is a technique for implementing recursive Bayesian filtering by Monte Carlo sampling. A fundamental concept behind the Bayesian approach for performing inference is the possibility to encode the information about the imaging system, possible noise sources, and the system dynamics in terms of probability density functions. In this thesis, a set of novel PF based metho
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