Automatically Improving Cell Segmentation in Time-Lapse Microscopy Images Using Temporal Context From Tracking and Lineaging

Over the past decade biologists and microscopists have produced truly amazing movies, showing in wonderful detail the dynamics of living cells and subcellular structures. Access to this degree of detail in living cells is a key aspect of current biological research. This wealth of data and potential discovery is constrained by a lack of software tools. The standard approach to biological image analysis begins with segmentation to identify individual cells, tracking to maintain cellular identities over time, and lineaging to identify parent-daughter relationships. This thesis presents new algorithms for improving the segmentation, tracking and lineaging of live cell time-lapse microscopy images. A new ''segmentation from lineage'' algorithm feeds lineage or other high-level behavioral information back into segmentation algorithms along with temporal context provided by the multitemporal association tracker to create a powerful iterative learning algorithm that significantly improves segmentation and tracking results. A tree inference algorithm is used to improve automated lineage generation by integrating known cellular behavior constraints as well as fluorescent signals if available. The ''learn from edits'' technique uses tracking information to propagate user corrections to automatically correct further tracking mistakes. Finally, the new pixel replication algorithm is used for accurately partitioning touching cells using elliptical shape models. These algorithms are integrated into the LEVER lineage editing and validation software, providing user interfaces for automated segmentation, tracking and lineaging, as well as the ability to easily correct the automated results. These algorithms, integrated into LEVER, have identified key behavioral differences in embryonic and adult neural stem cells. Edit-based and functional validation techniques are used to evaluate and compare the new algorithms with current state of the art segmentation and tracking approaches. All the software as well as the image data and analysis results are released under a new open source/open data model built around Gitlab and the new CloneView interactive web tool.Ph.D., Electrical Engineering -- Drexel University, 201

Automated identification of Fos expression

The concentration of Fos, a protein encoded by the immediate-early gene c-fos, provides a measure of synaptic activity that may not parallel the electrical activity of neurons. Such a measure is important for the difficult problem of identifying dynamic properties of neuronal circuitries activated by a variety of stimuli and behaviours. We employ two-stage statistical pattern recognition to identify cellular nuclei that express Fos in two-dimensional sections of rat forebrain after administration of antipsychotic drugs. In stage one, we distinguish dark-stained candidate nuclei from image background by a thresholding algorithm and record size and shape measurements of these objects. In stage two, we compare performance of linear and quadratic discriminants, nearest-neighbour and artificial neural network classifiers that employ functions of these measurements to label candidate objects as either Fos nuclei, two touching Fos nuclei or irrelevant background material. New images of neighbouring brain tissue serve as test sets to assess generalizability of the best derived classification rule, as determined by lowest cross-validation misclassification rate. Three experts, two internal and one external, compare manual and automated results for accuracy assessment. Analyses of a subset of images on two separate occasions provide quantitative measures of inter- and intra-expert consistency. We conclude that our automated procedure yields results that compare favourably with those of the experts and thus has potential to remove much of the tedium, subjectivity and irreproducibility of current Fos identification methods in digital microscopy

The cluster structure function

For each partition of a data set into a given number of parts there is a partition such that every part is as much as possible a good model (an "algorithmic sufficient statistic") for the data in that part. Since this can be done for every number between one and the number of data, the result is a function, the cluster structure function. It maps the number of parts of a partition to values related to the deficiencies of being good models by the parts. Such a function starts with a value at least zero for no partition of the data set and descents to zero for the partition of the data set into singleton parts. The optimal clustering is the one chosen to minimize the cluster structure function. The theory behind the method is expressed in algorithmic information theory (Kolmogorov complexity). In practice the Kolmogorov complexities involved are approximated by a concrete compressor. We give examples using real data sets: the MNIST handwritten digits and the segmentation of real cells as used in stem cell research

Advances in quantitative microscopy

Microscopy allows us to peer into the complex deeply shrouded world that the cells of our body grow and thrive in. With the emergence of automated digital microscopes and software for anlysing and processing the large numbers of image that they produce; quantitative microscopy approaches are now allowing us to answer ever larger and more complex biological questions. In this thesis I explore two trends. Firstly, that of using quantitative microscopy for performing unbiased screens, the advances made here include developing strategies to handle imaging data captured from physiological models, and unsupervised analysis screening data to derive unbiased biological insights. Secondly, I develop software for analysing live cell imaging data, that can now be captured at greater rates than ever before and use this to help answer key questions covering the biology of how cells make the decision to arrest or proliferate in response to DNA damage. Together this thesis represents a view of the current state of the art in high-throughput quantitative microscopy and details where the field is heading as machine learning approaches become ever more sophisticated.Open Acces

Bioinspired Designs and Biomimetic Applications of Triboelectric Nanogenerators

The emerging novel power generation technology of triboelectric nanogenerators (TENGs) is attracting increasing attention due to its unlimited prospects in energy harvesting and self-powered sensing applications. The most important factors that determine TENGs’ electrical and mechanical performance include the device structure, surface morphology and the type of triboelectric material employed, all of which have been investigated in the past to optimize and enhance the performance of TENG devices. Amongst them, bioinspired designs, which mimic structures, surface morphologies, material properties and sensing/power generation mechanisms from nature, have largely benefited in terms of enhanced performance of TENGs. In addition, a variety of biomimetic applications based on TENGs have been explored due to the simple structure, self-powered property and tunable output of TENGs. In this review article, we present a comprehensive review of various researches within the specific focus of bioinspired TENGs and TENG enabled biomimetic applications. The review begins with a summary of the various bioinspired TENGs developed in the past with a comparative analysis of the various device structures, surface morphologies and materials inspired from nature and the resultant improvement in the TENG performance. Various ubiquitous sensing principles and power generation mechanisms in use in nature and their analogous artificial TENG designs are corroborated. TENG-enabled biomimetic applications in artificial electronic skins and neuromorphic devices are discussed. The paper concludes by providing a perspective towards promising directions for future research in this burgeoning field of study

Structure and Dynamics of Replication Domains in Single Chromosome Territories of Interphase Nuclei

Knowing the three-dimensional organization of chromatin sets the framework for understanding genome regulation. Our picture of higher order chromatin structure insitu however remains fragmentary at many scales, since it is not directly accessible by imaging technologies available today. The recently revealed domain organization of chromatin subunits into sub-megabasepair sized topologically associating domains (TADs), enabled by chromosome conformation capture based techniques, marks a significant advancement in understanding chromatin architecture. Similarly quantitative methods for the analysis of global structure and dynamics of chromatin in single living cells are currently lacking, leaving it unclear how TADs are manifested within a single nucleus and how dynamic topological chromatin interactions are in living cells. To start to address this gap in our knowledge, I set out to systematically probe the basic polymer features of chromatin at the level of replication domains (RDs) in single cells as a basis for a model of higher order chromatin organization. I have addressed both structural and dynamic aspects of RD organization during interphase. Using super-resolution microscopy, I was able to investigate RD organization at unprecedented resolution. I found that the median RD diameter is ~150 nm, significantly smaller than the ~270 nm distance to the nearest neighbor, which leaves sufficient physical space for extended linker regions between RDs. By quantifying correlated motion of neighboring RDs, I could reveal the typical elastic coupling range between RDs to be ~500 nm. Combining super-resolution microscopy with a perturbation experiment I could further obtain evidence for the model that chromatin compaction upon ATP depletion is predominantly mediated by preferential compaction of linker regions between RDs, rather than by compaction of RDs themselves. In addition to these structural parameters of RD organization, I also characterized the diffusional behavior of interphase RDs of single chromosome territories. Tracking 1,372 RDs of 141 chromosome territories allowed me to obtain a global and statistically robust view of interphase chromatin dynamics across the entire nucleus. My data confirms that heterochromatin chromatin is immobile within a few hundred nanometers of the nuclear membrane and nucleolar surface over the time scale of several minutes and that nucleoplasmic dynamics is characterized by anomalous diffusion. I did not observe reproducible directed motion of RDs on the timescale of seconds to a minute. I observed a systematic reduction in chromatin motion as the cell cycle progressed from G1 to late S-phase and an increase in mobility if I artificially increased nuclear volume by allowing cells to grow when DNA replication was inhibited. My observations on native and perturbed chromatin structure and dynamics in nuclei of living cells allow me to propose a comprehensive model of higher order chromatin organization in single cells, that consists of stable structuring units of RDs, which are connected by extended flexible linker domains, whose dynamics are limited by attachment to the nuclear periphery and nucleoli and the available free volume inside the nucleus

An Adaptable Single-cell Trapping Device for a Wide Range of Cell Sizes

In the last two decades many microfluidic devices have been developed in order to provide better and more reliable tools for biological assays and analysis. Microfluidic technology has proven its functionality and advantages over conventional cellomics methods such as flow cytometry (FC) and laser scanning cytometry (LSC) in particular for processes that require the analysis of single cells. Different microfluidic platforms capable of capturing, positioning, and sorting single cells have been developed; however, such devices are incapable of working with various sizes of cells. Therefore, once a device has been designed and fabricated it is not possible to modify its dimensions so a new device is required for each different cell size for analysis. In an effort to overcome the limitation of adaptability of microfluidic cell trapping devices, this thesis presents a new microfluidic single cell-trapping device capable of capturing cells of various diameters. This thesis conducts a review and analysis of several microfluidic cell trapping devices under the FCBPSS (function-context-behavior-principle-state-structure) framework to have a better understanding and classification of the most relevant microfluidic devices, followed by the design of a new device capable of trapping large batches of single cells and modifying its physical features in order to work with multiple sizes of cells. The design process of the new device is based on and guided by the Axiomatic Design Theory. From the thorough review of the literature, it was concluded that the most suitable structure to demonstrate the concept proposed on this thesis an array of single cell trappers, and the best tuning method would be a mechanical stretching to generate a uniform distributed strain on the device. After designing and modeling the new device, it was imperative for this research to fabricate a device which could be tested in accordance with the literature. The final device consists of two thin layers of polydimethylsiloxane (PDMS), one of which bears trapezoidal microstructures (traps) to physically capture cells. The size of the traps can be modified by stretching the device via a uniform distributed force, which is applied using a stretching apparatus. Finally, the performance of the new device was assessed by conducting two main experiments. The first experiment consisted of characterizing the mechanical behavior of the device when different strains were applied. It has been found that all the traps of the device have a uniform deformation when a strain is applied, and the minimum size increment permitted by the stretching apparatus is of 2μm.The second experiment was done in order to characterize the hydrodynamical and trapping behavior of the device. By using water-in-oil microspheres of various sizes the trapping of particles was demonstrated; it was determined that the device can capture particles between 20μm and 30μm. To demonstrate cell viability, the device was tested using melanoma cells. No visible damage onto the cells was observed after the experiments using the device; therefore, it is suitable for biological applications where various sizes of cells are required for analysis

Modeling and Simulation of Damage In The Brazilian Indirect Tension Test Using The Finite-Discrete Element Method

The Brazilian indirect tension test is used to investigate possible correlations between progressive damage and associated permeability changes. The test offers ease of replicability with a damage behavior known to lead to fracture openings as a tool for interpreting the indirect tensile strength of concrete and rocks. This behavior can lend insight into the nature of tensile damage and fracture progression in association with changes in permeability. The nature of these brittle materials is known to exhibit rapid failures in the Brazilian indirect tension test and require a method to retard the progression of damage for the possibility of acquiring permeability measurements. Experimental results are replicated and investigated using the finite-discrete element method, which allows for the replication of both the elastic and post-peak fractured behaviors seen within the test. The model results are used to interpret the progression of damage and its type. Investigations of a sample pre-damaged with a drop tower is also made to observe a differing damage mechanism. The model validates the methodology behind what is called the stiff Brazilian indirect tension test and results from the models indicate that damage progression initiates near the contact points as shear separations, quickly followed by tensile separations along the line of fracture. The FDEM code used is observed to be a useful method for continued investigations into future modifications to the Brazilian indirect tension test for a broader damage and permeability correlation objective

Image analysis and statistical modeling for applications in cytometry and bioprocess control

Today, signal processing has a central role in many of the advancements in systems biology. Modern signal processing is required to provide efficient computational solutions to unravel complex problems that are either arduous or impossible to obtain using conventional approaches. For example, imaging-based high-throughput experiments enable cells to be examined at even subcellular level yielding huge amount of image data. Cytometry is an integral part of such experiments and involves measurement of different cell parameters which requires extraction of quantitative experimental values from cell microscopy images. In order to do that for such large number of images, fast and accurate automated image analysis methods are required. In another example, modeling of bioprocesses and their scale-up is a challenging task where different scales have different parameters and often there are more variables than the available number of observations thus requiring special methodology. In many biomedical cell microscopy studies, it is necessary to analyze the images at single cell or even subcellular level since owing to the heterogeneity of cell populations the population-averaged measurements are often inconclusive. Moreover, the emergence of imaging-based high-content screening experiments, especially for drug design, has put single cell analysis at the forefront since it is required to study the dynamics of single-cell gene expressions for tracking and quantification of cell phenotypic variations. The ability to perform single cell analysis depends on the accuracy of image segmentation in detecting individual cells from images. However, clumping of cells at both nuclei and cytoplasm level hinders accurate cell image segmentation. Part of this thesis work concentrates on developing accurate automated methods for segmentation of bright field as well as multichannel fluorescence microscopy images of cells with an emphasis on clump splitting so that cells are separated from each other as well as from background. The complexity in bioprocess development and control crave for the usage of computational modeling and data analysis approaches for process optimization and scale-up. This is also asserted by the fact that obtaining a priori knowledge needed for the development of traditional scale-up criteria may at times be difficult. Moreover, employment of efficient process modeling may provide the added advantage of automatic identification of influential control parameters. Determination of the values of the identified parameters and the ability to predict them at different scales help in process control and in achieving their scale-up. Bioprocess modeling and control can also benefit from single cell analysis where the latter could add a new dimension to the former once imaging-based in-line sensors allow for monitoring of key variables governing the processes. In this thesis we exploited signal processing techniques for statistical modeling of bioprocess and its scale-up as well as for development of fully automated methods for biomedical cell microscopy image segmentation beginning from image pre-processing and initial segmentation to clump splitting and image post-processing with the goal to facilitate the high-throughput analysis. In order to highlight the contribution of this work, we present three application case studies where we applied the developed methods to solve the problems of cell image segmentation and bioprocess modeling and scale-up