Model-based Curvilinear Network Extraction and Tracking toward Quantitative Analysis of Biopolymer Networks

Curvilinear biopolymer networks pervade living systems. They are routinely imaged by fluorescence microscopy to gain insight into their structural, mechanical, and dynamic properties. Image analysis can facilitate understanding the mechanisms of their formation and their biological functions from a quantitative viewpoint. Due to the variability in network geometry, topology and dynamics as well as often low resolution and low signal-to-noise ratio in images, segmentation and tracking networks from these images is challenging. In this dissertation, we propose a complete framework for extracting the geometry and topology of curvilinear biopolymer networks, and also tracking their dynamics from multi-dimensional images. The proposed multiple Stretching Open Active Contours (SOACs) can identify network centerlines and junctions, and infer plausible network topology. Combined with a $k$-partite matching algorithm, temporal correspondences among all the detected filaments can be established. This work enables statistical analysis of structural parameters of biopolymer networks as well as their dynamics. Quantitative evaluation using simulated and experimental images demonstrate its effectiveness and efficiency. Moreover, a principled method of optimizing key parameters without ground truth is proposed for attaining the best extraction result for any type of images. The proposed methods are implemented into a usable open source software ``SOAX\u27\u27. Besides network extraction and tracking, SOAX provides a user-friendly cross-platform GUI for interactive visualization, manual editing and quantitative analysis. Using SOAX to analyze several types of biopolymer networks demonstrates the potential of the proposed methods to help answer key questions in cell biology and biophysics from a quantitative viewpoint

Compression-induced structural and mechanical changes of fibrin-collagen composites

Fibrin and collagen as well as their combinations play an important biological role in tissue regeneration and are widely employed in surgery as fleeces or sealants and in bioengineering as tissue scaffolds. Earlier studies demonstrated that fibrin-collagen composite networks displayed improved tensile mechanical properties compared to the isolated protein matrices. Unlike previous studies, here unconfined compression was applied to a fibrin-collagen filamentous polymer composite matrix to study its structural and mechanical responses to compressive deformation. Combining collagen with fibrin resulted in formation of a composite hydrogel exhibiting synergistic mechanical properties compared to the isolated fibrin and collagen matrices. Specifically, the composite matrix revealed a one order of magnitude increase in the shear storage modulus at compressive strains>0.8 in response to compression compared to the mechanical features of individual components. These material enhancements were attributed to the observed structural alterations, such as network density changes, an increase in connectivity along with criss-crossing, and bundling of fibers. In addition, the compressed composite collagen/fibrin networks revealed a non-linear transformation of their viscoelastic properties with softening and stiffening regimes. These transitions were shown to depend on protein concentrations. Namely, a decrease in protein content drastically affected the mechanical response of the networks to compression by shifting the onset of stiffening to higher degrees of compression. Since both natural and artificially composed extracellular matrices experience compression in various (patho)physiological conditions, our results provide new insights into the structural biomechanics of the polymeric composite matrix that can help to create fibrin-collagen sealants, sponges, and tissue scaffolds with tunable and predictable mechanical properties

Fibrin structural and diffusional analysis suggests that fibers are permeable to solute transport

Fibrin hydrogels are promising carrier materials in tissue engineering. They are biocompatible and easy to prepare, they can bind growth factors and they can be prepared from a patient’s own blood. While fibrin structure and mechanics have been extensively studied, not much is known about the relation between structure and diffusivity of solutes within the network. This is particularly relevant for solutes with a size similar to that of growth factors. A novel methodological approach has been used in this study to retrieve quantitative structural characteristics of fibrin hydrogels, by combining two complementary techniques, namely confocal fluorescence microscopy with a fiber extraction algorithm and turbidity measurements. Bulk rheological measurements were conducted to determine the impact of fibrin hydrogel structure on mechanical properties. From these measurements it can be concluded that variations in the fibrin hydrogel structure have a large impact on the rheological response of the hydrogels (up to two orders of magnitude difference in storage modulus) but only a moderate influence on the diffusivity of dextran solutes (up to 25% difference). By analyzing the diffusivity measurements by means of the Ogston diffusion model we further provide evidence that individual fibrin fibers can be semi-permeable to solute transport, depending on the average distance between individual protofibrils. This can be important for reducing mass transport limitations, for modulating fibrinolysis and for growth factor binding, which are all relevant for tissue engineering

Imaging the pulmonary extracellular matrix

The pulmonary extracellular matrix (ECM) plays an important role in the structure and function of the lung. In many respiratory diseases the profile of the ECM reflects pathological changes. The capacity to visualize the ECM and its alterations is of considerable importance to facilitate a better understanding of pulmonary diseases and eventually augment therapeutic solutions. This short review summarizes the current and novel possibilities for imaging the pulmonary ECM by the use of computed tomography (CT), optical coherence tomography (OCT), confocal laser endomicroscopy (CLE) and molecular imaging. While not all these techniques are as yet implemented in standard clinical practice, we address their main features along with the key possibilities for the future

An algorithm for extracting the network geometry of three-dimensional collagen gels

The geometric structure of a biopolymer network impacts its mechanical and biological properties. In this paper, we develop an algorithm for extracting the network architecture of three-dimensional (3d) fluorescently labeled collagen gels, building on the initial work of Wu et al., (2003) . Using artificially generated images, the network extraction algorithm is then validated for its ability to reconstruct the correct bulk properties of the network, including fiber length, persistence length, cross-link density, and shear modulus.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/75476/1/j.1365-2818.2008.02141.x.pd

Molecular Imaging

The present book gives an exceptional overview of molecular imaging. Practical approach represents the red thread through the whole book, covering at the same time detailed background information that goes very deep into molecular as well as cellular level. Ideas how molecular imaging will develop in the near future present a special delicacy. This should be of special interest as the contributors are members of leading research groups from all over the world

An Image Processing Approach to Determine the Morphological Changes in Cell Nucleus

Confocal imaging has been a powerful tool for scientists over the decades for visualization of cellular architecture and behavior. However, the quantitative inference drawn from the confocal images typically relies upon image processing. So far many image processing tools are available that can quantify various image parameters and image characteristics i.e., intensity, area, shape, volume, perimeter, etc.. However the success of the existing techniques depends on high picture clarity and efficient noise removal. Now in this regard, lots of scoperemains over the increase of the quality of the native image. Moreover, the existing procedure of image processing fails to quantify morphology distortion properly. Though pattern recognition algorithms can often be used to measure the changes, but is seldom able to provide reliable data when the input elements are taken from a pool of varying diversity. Keeping this perspective in mind, we have developed a MATLAB image analysis program for processing of universal confocal images and quantification of cell shape factors. The confocal images were properly processed for efficient noise removal and then subjected to skeletonization algorithm for quantification of cell nucleus shape change. The program was tested to quantify the nuclear mechanotransduction. The nucleus is a key component of the cell and its shape changes with a change in environment. Scientists have now discovered that such variation leads to altered cellular and nuclear function

Doctor of Philosophy

dissertationTreatment and management of heart disease is challenging due to the heart's limited ability to self-repair. Although current approaches to manage heart disease, such as pharmacotherapy, medical devices, lifestyle changes, and heart transplantation, have improved and extended the quality of life for millions of individuals, they have inherent shortcomings. Future strategies to manage heart disease will likely be based upon a combination of biological and engineering approaches through cell therapy and tissue engineering strategies, both of which have the potential to regenerate the myocardium and improve cardiac function. However, a key hurdle in applying biological approaches is our limited ability to produce reliable tissue to study disease progression and tissue development, therapeutic intervention, drug discovery, or tissue replacement. Establishing hallmarks of the native myocardium in engineered cardiac tissue is a central goal and appears to be required for creating functional tissue that can serve as a surrogate for in vitro testing or the eventual replacement of diseased or injured myocardium. The objective of this research was to apply an engineering approach to develop tools and methods to produce engineered cardiac tissue and characterize both native and engineered cardiac tissue. Three phases of research included: 1) the development and utilization of a framework to characterize microstructure in living cardiac tissue using confocal microscopy and local dye delivery, 2) the development a next-generation bioreactor capable of continuously monitoring force-displacement in engineered tissue, and 3) the application of confocal imaging and image analysis to quantitatively describe features of the native myocardium, focusing on myocyte geometry and spatial distribution of a major gap junction protein connexin-43, in both engineered tissue and native tissue