Image Processing and Simulation Toolboxes of Microscopy Images of Bacterial Cells

Recent advances in microscopy imaging technology have allowed the characterization of the dynamics of cellular processes at the single-cell and single-molecule level. Particularly in bacterial cell studies, and using the E. coli as a case study, these techniques have been used to detect and track internal cell structures such as the Nucleoid and the Cell Wall and fluorescently tagged molecular aggregates such as FtsZ proteins, Min system proteins, inclusion bodies and all the different types of RNA molecules. These studies have been performed with using multi-modal, multi-process, time-lapse microscopy, producing both morphological and functional images. To facilitate the finding of relationships between cellular processes, from small-scale, such as gene expression, to large-scale, such as cell division, an image processing toolbox was implemented with several automatic and/or manual features such as, cell segmentation and tracking, intra-modal and intra-modal image registration, as well as the detection, counting and characterization of several cellular components. Two segmentation algorithms of cellular component were implemented, the first one based on the Gaussian Distribution and the second based on Thresholding and morphological structuring functions. These algorithms were used to perform the segmentation of Nucleoids and to identify the different stages of FtsZ Ring formation (allied with the use of machine learning algorithms), which allowed to understand how the temperature influences the physical properties of the Nucleoid and correlated those properties with the exclusion of protein aggregates from the center of the cell. Another study used the segmentation algorithms to study how the temperature affects the formation of the FtsZ Ring. The validation of the developed image processing methods and techniques has been based on benchmark databases manually produced and curated by experts. When dealing with thousands of cells and hundreds of images, these manually generated datasets can become the biggest cost in a research project. To expedite these studies in terms of time and lower the cost of the manual labour, an image simulation was implemented to generate realistic artificial images. The proposed image simulation toolbox can generate biologically inspired objects that mimic the spatial and temporal organization of bacterial cells and their processes, such as cell growth and division and cell motility, and cell morphology (shape, size and cluster organization). The image simulation toolbox was shown to be useful in the validation of three cell tracking algorithms: Simple Nearest-Neighbour, Nearest-Neighbour with Morphology and DBSCAN cluster identification algorithm. It was shown that the Simple Nearest-Neighbour still performed with great reliability when simulating objects with small velocities, while the other algorithms performed better for higher velocities and when there were larger clusters present

Nucleus segmentation : towards automated solutions

Single nucleus segmentation is a frequent challenge of microscopy image processing, since it is the first step of many quantitative data analysis pipelines. The quality of tracking single cells, extracting features or classifying cellular phenotypes strongly depends on segmentation accuracy. Worldwide competitions have been held, aiming to improve segmentation, and recent years have definitely brought significant improvements: large annotated datasets are now freely available, several 2D segmentation strategies have been extended to 3D, and deep learning approaches have increased accuracy. However, even today, no generally accepted solution and benchmarking platform exist. We review the most recent single-cell segmentation tools, and provide an interactive method browser to select the most appropriate solution.Peer reviewe

Multilevel Aggregation Methods for Small-World Graphs with Application to Random-Walk Ranking

We describe multilevel aggregation in the specific context of using Markov chains to rank the nodes of graphs. More generally, aggregation is a graph coarsening technique that has a wide range of possible uses regarding information retrieval applications. Aggregation successfully generates efficient multilevel methods for solving nonsingular linear systems and various eigenproblems from discretized partial differential equations, which tend to involve mesh-like graphs. Our primary goal is to extend the applicability of aggregation to similar problems on small-world graphs, with a secondary goal of developing these methods for eventual applicability towards many other tasks such as using the information in the hierarchies for node clustering or pattern recognition. The nature of small-world graphs makes it difficult for many coarsening approaches to obtain useful hierarchies that have complexity on the order of the number of edges in the original graph while retaining the relevant properties of the original graph. Here, for a set of synthetic graphs with the small-world property, we show how multilevel hierarchies formed with non-overlapping strength-based aggregation have optimal or near optimal complexity. We also provide an example of how these hierarchies are employed to accelerate convergence of methods that calculate the stationary probability vector of large, sparse, irreducible, slowly-mixing Markov chains on such small-world graphs. The stationary probability vector of a Markov chain allows one to rank the nodes in a graph based on the likelihood that a long random walk visits each node. These ranking approaches have a wide range of applications including information retrieval and web ranking, performance modeling of computer and communication systems, analysis of social networks, dependability and security analysis, and analysis of biological systems

Reverse-Engineering Self-Organized Behavior in Myxococcus xanthus Biofilms

Myxococcus xanthus ( M. xanthus ) is a gram-negative, rod-shaped soil-dwelling predatory bacterium. It can move on solid surfaces forming cooperative single-species biofilm in which various self-organizing patterns are observed. Under distinct environmental conditions, these bacteria can swarm outward, form travelling waves or aggregate into fruiting bodies as a result of diverse intercellular interactions, signaling and coordinated cell motility. M. xanthus colony actively expands when food is plentiful, but stops this under nutritional stress and thereafter aggregates into fruiting bodies where individual cells transform into spores. When in direct contact with their prey, M. xanthus cells form traveling cell-density waves called ripples to facilitate their predation. These patterns play an important role in maximizing M. xanthus adaption to the changing environment. While these phenomena have been studied using traditional experimental microbiology and genetics, recently it is becoming clear that system biology approach greatly complements traditional laboratory work. This thesis shows my effort to deepen the understanding of self-organization in microorganisms using statistical image processing techniques and agent-based modeling. Statistical image processing results illustrate that aggregation into fruiting bodies is a highly non-monotonic yet spontaneous process without long-range signal transduction. The agent-based model of aggregation accurately reproduces the final steady states of an aggregation process but fails to reproduce the experimental dynamics. The agent-based modeling for predatory ripples quantitatively reproduces all observed patterns based on three simple experimentally observed rules: regular cellular reversals, side-to-side contact induced early reversals and refractory period after each cellular reversal. Moreover, the agent-based model predicts that predatory ripples speed up the swarm expansion into the prey region and keep individual M. xanthus cells in the prey region longer. These predictions are all quantitatively verified by experimental observations. The combination of statistical image analysis and agent-based modeling brings greater understanding of self-organizing patterns in M. xanthus and will be essential for further research on similar patterns in other microorganisms and higher organisms

Micro/Nano Devices for Blood Analysis, Volume II

The development of micro- and nanodevices for blood analysis continues to be a growing interdisciplinary subject that demands the careful integration of different research fields. Following the success of the book “Micro/Nano Devices for Blood Analysis”, we invited more authors from the scientific community to participate in and submit their research for a second volume. Researchers from different areas and backgrounds cooperated actively and submitted high-quality research, focusing on the latest advances and challenges in micro- and nanodevices for diagnostics and blood analysis; micro- and nanofluidics; technologies for flow visualization and diagnosis; biochips, organ-on-a-chip and lab-on-a-chip devices; and their applications to research and industry

Computational analysis of single-cell dynamics: protein localisation, cell cycle, and metabolic adaptation

Cells need to be able to adapt quickly to changes in nutrient availability in their environment in order to survive. Budding yeasts constitute a convenient model to study how eukaryotic cells respond to sudden environmental change because of their fast growth and relative simplicity. Many of the intracellular changes needed for adaptation are spatial and transient; they can be captured experimentally using fluorescence time-lapse microscopy. These data are limited when only used for observation, and become most powerful when they can be used to extract quantitative, dynamic, single-cell information. In this thesis we describe an analysis framework heavily based on deep learning methods that allows us to quantitatively describe different aspects of cells’ response to a new environment from microscopy data. chapter 2 describes a start-to-finish pipeline for data access and preprocessing, cell segmentation, volume and growth rate estimation, and lineage extraction. We provide benchmarks of run time and describe how to speed up analysis using parallelisation. We then show how this pipeline can be extended with custom processing functions, and how it can be used for real-time analysis of microscopy experiments. In chapter 3 we develop a method for predicting the location of the vacuole and nucleus from bright field images. We combine this method with cell segmentation to quantify the timing of three aspects of the cells’ response to a sudden nutrient shift: a transient change in transcription factor nuclear localisation, a change in instantaneous growth rate, and the reorganisation of the plasma membrane through the endocytosis of certain membrane proteins. In particular, we quantify the relative timing of these processes and show that there is a consistent lag between the perception of the stress at the level of gene expression and the reorganisation of the cell membrane. In chapter 4 we evaluate several methods to obtain cell cycle phase information in a label-free manner. We begin by using the outputs of cell segmentation to predict cytokinesis with high accuracy. We then predict cell cycle phase at a higher granularity directly from bright field images. We show that bright field images contain information about the cell cycle which is not visible by eye. We use these methods to quantify the relationship between cell cycle phase length and growth rate. Finally, in chapter 5 we look beyond microscopy to the bigger picture. We sketch an abstract description of how, at a genome-scale, cells might choose a strategy for adapting to a nutrient shift based on limited, noisy, and local information. Starting from a constraint-based model of metabolism, we propose an algorithm to navigate through metabolic space using only a lossy encoding of the full metabolic network. We show how this navigation can be used to adapt to a changing environment, and how its results differ from the global optimisation usually applied to metabolic models

Novel approaches for image analysis of in vitro epithelial cultures with application to silver nanoparticle toxicity

A novel imaging approach was developed for the purpose of counting cells from phase contrast microscopy images of laboratory grown (in vitro} cultures of epithelial cells. Validation through comparison with standard laboratory cell counting techniques showed this approach provided consistent and comparable results, whilst overcoming limitations of these existing techniques, such as operator variability and sample destruction. The imaging approach was subsequently applied to investigate the effects of silver nanoparticles (AgNP} on H400 oral keratinocytes. Concurrent investigations into antimicrobial effects of AgNP were performed on Escherichia coli, Staphylococcus aureus and Streptococcus mutans to provide models for Gram-positive and Gram-negative infection, and to compare with the literature and oral keratinocyte toxicity. It was found that AgNP elicit size-, dose- and time-dependent growth inhibition in both human cells and bacteria, although bacterial inhibition was not achieved without significant cytotoxicity at the same concentrations

Mitochondrial function and dynamics in demyelinated axons

Demyelination is a pathological process which causes profound changes in the physiology of axons. Post mortem evidence suggests that mitochondrial content is increased in demyelinated axons which has led to the widely accepted theory that loss of myelin causes an increase in axonal energy demand which in turn can be satisfied by a larger number of mitochondria. However, demyelinated axons are known to undergo a series of changes from conduction block early on in demyelination to altered modes of conduction and finally remyelination and return of normal conduction. Little is known about how these different states influence axonal mitochondria. In this thesis, mitochondrial function and dynamics were investigated throughout the time course of lysolecithin-mediated de- and remyelination in the saphenous nerve in vivo. First, the time course of anatomical and electrophysiological changes after application of lysolecithin was mapped out using semi-thin resin sections and recordings of compound action potentials. Then, mitochondrial dynamics and membrane potential were measured at various time points during the onset and resolution of demyelination. Mitochondrial transport was significantly reduced during the first week of demyelination, preceding the accumulation of stationary mitochondria in the axon. At the same time, mitochondrial membrane potential was significantly increased, particularly at the earliest time points investigated (days 2 and 4). Interestingly, all changes including the abovementioned accumulation of mitochondria took place before the return of conduction and putative increase in energy demand. In order to understand better the processes underlying these changes, the role of two known modifiers of mitochondrial dynamics were investigated: action potential conduction and intra-axonal calcium. A 6h conduction block was induced using bupivacaine and confirmed using electrophysiological stimulation but did not lead to any of the changes seen in the early phases of demyelination. On the other hand, calcium imaging using the genetically encoded calcium sensor Tn-XXL revealed a slight but consistent increase in intra-axonal calcium in demyelinated axons, both at the time point with the highest increase in mitochondrial membrane potential (day 2) and at the time point with the highest mitochondrial density (day 8). Taken together, these findings point to impaired axonal calcium homeostasis, rather than changes in energy demand, as the main driving force behind mitochondrial changes in demyelination. The fact that mitochondrial transport remained impaired until later in the remyelination process may have implications for the long term survival of chronically demyelinated axons

Fuzzy systems and unsupervised computing: exploration of applications in biology

In this thesis we will explore the use of fuzzy systems theory for applications in bioinformatics. The theory of fuzzy systems is concerned with formulating decision problems in data sets that are ill-defined. It supports the transfer from a subjective human classification to a numerical scale. In this manner it affords the testing of hypothesis and separation of the classes in the data. We first formulate problems in terms of a fuzzy system and then develop and test algorithms in terms of their performance with data from the domain of the life-sciences. From the results and the performance, we will learn about the usefulness of fuzzy systems for the field, as well as the applicability to the kind of problems and practicality for the computation itself. Computer Systems, Imagery and Medi