Indirect microfabrication of biomimetic materials for locomotor tissues regeneration

Tissue Engineering is a new field of the scientific research with a final aim to develop techniques for regeneration, repair, maintenance and growth of tissues or organs to overcome the limitations intrinsic to current therapeutic strategies. A fundamental element of this approach is the scaffold. The scaffold is a 2D and 3D structure, made with natural or synthetic material, that emulates the extracellular matrix, that is it offers mechanical, topological, biochemical and chemical stimuli to promote cellular organization, growth and differentiation to create a tissue with adequate functional and morphological characteristic. Scaffolds are therefore characterized by peculiar features (e.g. porosity, mechanical properties) determined by the material and by the manufacture process. Nowadays, the additive Rapid Prototyping (RP) techniques are the best approach to realize complex structures, because overcome all the problem of conventional (subtractive) techniques. Despite the high potential, RP techniques are not always compatible with all materials. In particular, hydrogels, an elective class of biomaterial for scaffolds realization because the lot of features in common with the extracellular matrix, results very difficult to be processed. To overcome these limitations and take advantage of all benefits of rapid prototyping, indirect rapid prototyping (iRP) was developed, that is the realization of scaffold or other structures starting from sacrificial molds realized by RP. The iRP offers the benefits to fabricate composite scaffold realized with different materials, with less waste and high fidelity in the realization of the designed structure. One of the critical aspect of this class of realization process is the extraction of the final object from the mold. A possible solution, proposed in this research, is to realize the mold with low melting point materials, dissolving the mold at the end of the process without damaging the scaffold. Moving in this direction, the attention of this research is focused on two classes of materials, low melting point waxes and agarose. Two alternative RP techniques have been evaluated: new modules of the PAM^2, a continuous flow system, and a inkjet-based device have been designed and realized to test the feasibility of this approach. In addiction, an alternative approach to fabricate agarose microstructure, by exploiting the different agarose gelling ability in DMSO and water, has been proposed. In a future perspective, casting of the desired material, which may include also cells, should be performed directly in the surgery room using an anatomical shaped mold designed on the patient needs. Following this approach, two plugins for bioimages de-noising and segmentation, based on the ITK library, have been implemented for the OsiriX software. To further test the versatility of the two microfabrication devices, other applications have been explored, such as the realization of microfluidic circuits using PAM^2 or printing carbon nanotubes suspension for polymeric actuators

3D Reconstruction of Neural Circuits from Serial EM Images

A basic requirement for reconstructing and understanding complete circuit diagrams of neuronal processing units is the availability of electron microscopic 3D data sets of large ensembles of neurons. A recently developed technique, "Serial Block Face Scanning Electron Microscopy" (SBFSEM, Denk and Horstmann 2004) allows automatic sectioning and imaging of biological tissue inside the vacuum chamber of a scanning electron microscope. Image stacks generated with this technology have a resolution sucient to distinguish different cellular compartments, including synaptic structures. Such an image stack contains thousands of images and is recorded with a voxel size of 23 nm in the x- and y-directions and 30 nm in the z-direction. Consequently a tissue block of 1 mm3 produces 63 terabytes of data. Therefore new concepts for managing large data sets and automated image processing are required. I developed an image segmentation and 3D reconstruction software, which allows precise contour tracing of cell membranes and simultaneously displays the resulting 3D structure. The software contains two stand-alone packages: Neuron2D and Neuron3D, both oering an easy-to-operate graphical user interface (GUI). The software package Neuron2D provides the following image processing functions: • Image Registration: Combination of multiple SBFSEM image tiles. • Image Preprocessing: Filtering of image stacks. Implemented are Gaussian and Non-Linear-Diusion lters in 2D and 3D. This step enhances the contrast between contour lines and image background, leading to a higher signal-to-noise ratio, thus further improving detection of membrane borders. • Image Segmentation: The implemented algorithms extract contour lines from the preceding image and automatically trace the contour lines in the following images (z-direction), taking into account the previous image segmentation. They also permit image segmentation starting at any position in the image stack. In addition, manual interaction is possible. To visualize 3D structures of neuronal circuits the additional software Neuron3D was developed. The program relies on the contour line information provided by Neuron2D to implement a surface reconstruction algorithm based on dynamic time warping. Additional rendering techniques, such as shading and texture mapping, are provided. The detailed anatomical reconstruction provides a framework for computational models of neuronal circuits. For example in ies, where moving retinal images lead to appropriate course control signals, the circuit reconstruction of motion-sensitive neurons can help to further understand the neural processing of visual motion in ies

Automated retinal layer segmentation and pre-apoptotic monitoring for three-dimensional optical coherence tomography

The aim of this PhD thesis was to develop segmentation algorithm adapted and optimized to retinal OCT data that will provide objective 3D layer thickness which might be used to improve diagnosis and monitoring of retinal pathologies. Additionally, a 3D stack registration method was produced by modifying an existing algorithm. A related project was to develop a pre-apoptotic retinal monitoring based on the changes in texture parameters of the OCT scans in order to enable treatment before the changes become irreversible; apoptosis refers to the programmed cell death that can occur in retinal tissue and lead to blindness. These issues can be critical for the examination of tissues within the central nervous system. A novel statistical model for segmentation has been created and successfully applied to a large data set. A broad range of future research possibilities into advanced pathologies has been created by the results obtained. A separate model has been created for choroid segmentation located deep in retina, as the appearance of choroid is very different from the top retinal layers. Choroid thickness and structure is an important index of various pathologies (diabetes etc.). As part of the pre-apoptotic monitoring project it was shown that an increase in proportion of apoptotic cells in vitro can be accurately quantified. Moreover, the data obtained indicates a similar increase in neuronal scatter in retinal explants following axotomy (removal of retinas from the eye), suggesting that UHR-OCT can be a novel non-invasive technique for the in vivo assessment of neuronal health. Additionally, an independent project within the computer science department in collaboration with the school of psychology has been successfully carried out, improving analysis of facial dynamics and behaviour transfer between individuals. Also, important improvements to a general signal processing algorithm, dynamic time warping (DTW), have been made, allowing potential application in a broad signal processing field.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Automated retinal layer segmentation and pre-apoptotic monitoring for three-dimensional optical coherence tomography

The aim of this PhD thesis was to develop segmentation algorithm adapted and optimized to retinal OCT data that will provide objective 3D layer thickness which might be used to improve diagnosis and monitoring of retinal pathologies. Additionally, a 3D stack registration method was produced by modifying an existing algorithm. A related project was to develop a pre-apoptotic retinal monitoring based on the changes in texture parameters of the OCT scans in order to enable treatment before the changes become irreversible; apoptosis refers to the programmed cell death that can occur in retinal tissue and lead to blindness. These issues can be critical for the examination of tissues within the central nervous system. A novel statistical model for segmentation has been created and successfully applied to a large data set. A broad range of future research possibilities into advanced pathologies has been created by the results obtained. A separate model has been created for choroid segmentation located deep in retina, as the appearance of choroid is very different from the top retinal layers. Choroid thickness and structure is an important index of various pathologies (diabetes etc.). As part of the pre-apoptotic monitoring project it was shown that an increase in proportion of apoptotic cells in vitro can be accurately quantified. Moreover, the data obtained indicates a similar increase in neuronal scatter in retinal explants following axotomy (removal of retinas from the eye), suggesting that UHR-OCT can be a novel non-invasive technique for the in vivo assessment of neuronal health. Additionally, an independent project within the computer science department in collaboration with the school of psychology has been successfully carried out, improving analysis of facial dynamics and behaviour transfer between individuals. Also, important improvements to a general signal processing algorithm, dynamic time warping (DTW), have been made, allowing potential application in a broad signal processing field

Doctor of Philosophy

dissertationMagnetic Resonance (MR) is a relatively risk-free and flexible imaging modality that is widely used for studying the brain. Biophysical and chemical properties of brain tissue are captured by intensity measurements in T1W (T1-Weighted) and T2W (T2-Weighted) MR scans. Rapid maturational processes taking place in the infant brain manifest as changes in co{\tiny }ntrast between white matter and gray matter tissue classes in these scans. However, studies based on MR image appearance face severe limitations due to the uncalibrated nature of MR intensity and its variability with respect to changing conditions of scan. In this work, we develop a method for studying the intensity variations between brain white matter and gray matter that are observed during infant brain development. This method is referred to by the acronym WIVID (White-gray Intensity Variation in Infant Development). WIVID is computed by measuring the Hellinger Distance of separation between intensity distributions of WM (White Matter) and GM (Gray Matter) tissue classes. The WIVID measure is shown to be relatively stable to interscan variations compared with raw signal intensity and does not require intensity normalization. In addition to quantification of tissue appearance changes using the WIVID measure, we test and implement a statistical framework for modeling temporal changes in this measure. WIVID contrast values are extracted from MR scans belonging to large-scale, longitudinal, infant brain imaging studies and modeled using the NLME (Nonlinear Mixed Effects) method. This framework generates a normative model of WIVID contrast changes with time, which captures brain appearance changes during neurodevelopment. Parameters from the estimated trajectories of WIVID contrast change are analyzed across brain lobes and image modalities. Parameters associated with the normative model of WIVID contrast change reflect established patterns of region-specific and modality-specific maturational sequences. We also detect differences in WIVID contrast change trajectories between distinct population groups. These groups are categorized based on sex and risk/diagnosis for ASD (Autism Spectrum Disorder). As a result of this work, the usage of the proposed WIVID contrast measure as a novel neuroimaging biomarker for characterizing tissue appearance is validated, and the clinical potential of the developed framework is demonstrated

High Resolution, Quantitative Optical Coherence Tomography for Tissue Imaging

Master'sMASTER OF SCIENC

Mechanisms of Early Brain Morphogenesis

In structures with obvious mechanical function, like the heart and bone, the relationship of mechanical forces to growth and development has been well studied. In contrast, other than the problem of neurulation: formation of the neural tube), developmental mechanisms in the nervous system have received relatively little attention. The central aim of this research is to characterize the biophysical mechanisms that shape the early embryonic brain. Experiments were performed primarily in the chicken brain, which is morphologically similar to humans during early stages of development. Proposed mechanisms were tested using computational models to ensure that hypotheses are consistent with physical law. The brain initially forms as a straight epithelial tube in the embryo: approximately 3 weeks gestation in humans). We first investigated a potential role for mechanical feedback in regulating the development of this structure. We find that the neuroepithelium actively stiffens under decreased loading and softens under increased loading. Nuclear shapes are elongated in stiffer brains and circular in softer brains, consistent with changes in cytoskeletal contractility and wall stress. These results suggest a role for stress-based mechanical feedback in regulating epithelial development. We next investigated the more specific role of cytoskeletal contraction in forming the primary brain vesicles and rhombomeres that subdivide the primitive brain tube. We show that a combination of circumferential contraction in the boundary regions and isotropic contraction between boundaries can generate realistic vesicle morphologies, whereas longitudinal contraction between boundaries likely causes rhombomere formation. Models are used to show how regional variations in contraction may be a function of brain geometry and morphogenetic plasticity. As an extension of the previous study, we show that enhancing contractility in the embryonic chicken brain induces morphologies reminiscent of more primitive species such as frog and fish. In particular, brain cross sections that are relatively circular transform into diamonds, triangles, and narrow slits, shapes that are present in normal zebrafish and Xenopus brains at comparable stages of development. Models show that these shapes are likely produced by locally elevated cytoskeletal contraction, indicating a potential role for differential contractility in early brain development and evolution. In summary, results from this thesis should improve our understanding of the biophysical mechanisms that establish and regulate phenotype in the developing brain. The research begins to establish the framework necessary to connect early-stage mechanisms to interspecies differences in brain morphogenesis that occur during later development

Three dimensional cell reconstructions for morphological analysis and modelling

It is highly desirable to devise a systematic approach to predict cell – material interactions, especially for novel biomaterial surfaces, and to further understanding in the complex area of attachment and spreading. The aim of this research was to produce a new method of studying morphology in real time, whereby data from live spreading cells can be collected for mathematical modelling. There is an abundance of models for sub-cellular elements, however, there are few calibrated models of whole cells; in particular, three-dimensional models predicting attachment, spreading and cell morphology have yet to be produced. Live HOS cells were imaged using LavaCell membrane stain and CLSM every 5 min for a period of 75 min in this study, capturing sufficient detail to produce three dimensional representations of cells during initial attachment and spreading. In order for the contact line to be measured, the interface between the cell membrane and the substrate had to be imaged in sufficient resolution for accurate measurements of the angles to be made. An image processing algorithm developed using Matlab was able to detect the edge of cells in the CLSM z-stack optical sections. These were then used to create contour plots onto which a surface representing the cell membrane could be added. These reconstructions of cells can be easily manipulated to enable the dynamic contact line of attaching cells to be measured for a model based on two-phase poroviscous flow equations. The three dimensional representations not only showed the changing morphology of spreading cells, but gave data on contact radius and area, contact angle and cell height. The main modelling prediction is a near contact line law, which is given by; Θ3 - Φ3 = 3 µ(n)ln(R/λ) (3nV - J(V,n,... )) γ where Θ is the dynamic contact angle (which remains to be determined by experimental means as the cell is spreading), Φ is the static contact angle, n the network density at the contact-line, J is the mass transfer rate from G- to F-actin at contact line and V equals the outward normal velocity of contact line. Once the method had been developed for glass surfaces, the influence on attachment and spreading of various material substrate and protein conditioning layers was investigated. This was achieved by using transparent thin film coated surfaces of titanium nitride and titanium oxide and pre-coating glass with fibronectin and albumin respectively. Three dimensional representations showed the ability to reproduce the different cell response to each surface and gave comparable morphologies to cells fixed for SEM and immunocytochemical staining