Versatile Optical Imaging Technique for Dynamic Monitoring and Quantitative Analysis in Tissue Engineering

Department of Biomedical EngineeringMany researches in the tissue engineering are investigating the development of technologies that have covered a broad range of applications and closely associated with the tissue regeneration and replacement of lost or damaged tissues as well as tissue manipulation. However, there are challenges regarding monitoring and assessing outcomes to analyze a variety of morphological and structural changes in tissue engineering applications. Most tissue engineering studies have utilized histopathological techniques for morphological analysis and evaluation of the tissues. Although the conventional methods provided a high definition and clear distinction under optical microscopy, it has still limitations in the visualization of tissue constructs without destruction of the tissues. Also, these methods have not allowed a volumetric assessment and functional information. Due to the destructive process and limited information in a two-dimensional approach, the conventional methods were difficult not only to analyze the specimens at continuous time points but also to compare inconsistencies of the results between different samples. For these reasons, there are clear needs for the development of advanced optical imaging techniques available for non-invasive and consistent observation and quantitative analysis in tissue engineering applications. Optical coherence tomography (OCT) has emerged as appropriate candidate for studying tissue morphology dynamically and quantitatively. OCT equips optimal imaging characteristics for dynamic monitoring because it offers cross-sectional, high-resolution, real-time tissue imaging in a non-invasive manner. Unlike most optical imaging techniques, OCT does not require any contrast agent or labeling process even it provides a deep penetration depth of about 2 mm in the tissue. Here, we utilized OCT technique to carry out application for the tissue engineering research ranging from the observation of biological tissues, dynamic monitoring, and quantitative analysis, as well as fabrication of image-guided engineered tissue. In the chapter 2, we utilized 3D OCT imaging to observe the tissue regeneration after laser irradiation, epidermal biopsy, and skin incision in in vitro and in vivo skin model. We utilized OCT system to monitor and analyze the wound recovery process after laser irradiation on the engineered skin. Also, we presented a quantitative evaluation of drug efficiency that affect the wound recovery on the engineered skin model after epidermal biopsy. Next, we analyzed quantitatively a recovery process of the wound width and depth in skin incised rat model in vivo with tissue adhesives treatment under the OCT monitoring. In the chapter 3, we utilized optical coherence microscopy (OCM) imaging modality to observe and quantitatively analyze the morphological changes of biological tissue in subcellular level. We introduced depth trajectory-tracking technique to acquire homogenous quality OCM images regardless of the height difference of the sample surface. Also, we developed the serial block-face OCM (SB-OCM) system to acquire the whole tissue information by repeating tissue sectioning and image acquisition using the serial block-face imaging technique. In the chapter 4, we developed the hand-held probe based portable OCT system for convenience in human target studies. We monitored and quantitatively analyzed various changes in the human skin using the hand-held probe based portable OCT system. Especially, we studied quantitative analysis of human skin wrinkle in terms of depth and volume as well as roughness parameters in comparison with conventional platforms. In the chapter 5, we suggested the feasibility to fabricate the engineered tissue based on a volumetric information of optical imaging. Here, we studied a fabrication of wrinkle mimicked engineered skin for anti-aging assessment and a protocol of imaging guided personalized engineered cornea for cornea transplantation. In conclusion, we confirmed that OCT system was able to provide various quantitative information from the biological tissues by its advantages such as high-resolution, non-invasive, label-free, deep penetration depth with real-time imaging. These characteristics of OCT imaging enables the quantitative analysis of tissue recovery and replacement as well as tissue manipulation in the tissue engineering research.clos

Nanowired Human Cardiac Spheroids for Cardiac Regenerative Medicine

3D scaffold-free spherical micro-tissue (spheroids) holds great potential in tissue engineering as building blocks to fabricate the functional tissues or organs in vitro. To date, agarose based hydrogel molds have been extensively used to facilitate fusion process of tissue spheroids. As a molding material, agarose typically requires low temperature plates for gelation and/or heated dispenser units. Here, we developed an alginate-based, direct 3D mold-printing technology: 3D printing micro-droplets of alginate solution into biocompatible, bio-inert alginate hydrogel molds for the fabrication of scaffold-free tissue engineering constructs. Specifically, we developed a 3D printing technology to deposit micro-droplets of alginate solution on calcium containing substrates in a layer-by-layer fashion to prepare ring-shaped 3D agarose hydrogel molds. Tissue spheroids composed of 50% human endothelial cells and 50% human smooth muscle cells were robotically dispensed into the 3D printed alginate molds using a 3D printer, and were found to rapidly fuse into toroid-shaped tissue units. Histological and immunofluorescence analysis indicated that the cells secreted collagen type I playing a critical role in promoting cell-cell adhesion, tissue formation and maturation. The current inability to derive mature cardiomyocytes (CMs) from human pluripotent stem cells (hiPSC) has been the limiting step for transitioning this powerful technology into clinical therapies. To address this, scaffold-based tissue engineering approaches have been utilized to mimic heart development in vitro and promote maturation of CMs derived from hiPSC. While scaffolds can provide 3D microenvironments, current scaffolds lack the matched physical/chemical/biological properties of native extracellular environments. On the other hand, scaffold-free, 3D cardiac spheroids prepared by seeding CMs into agarose microwells were shown to improve cardiac functions. However, CMs within the spheroids could not assemble in a controlled manner and led to compromised, unsynchronized contractions. Here we show, for the first time, that incorporation of a trace amount (i.e., ~0.004% w/v) of electrically conductive silicon nanowires (e-SiNWs) in otherwise scaffold-free cardiac spheroids can form an electrically conductive network, leading to synchronized and significantly enhanced contraction (i.e., \u3e55% increase in average contraction amplitude), resulting in significantly more advanced cellular structural and contractile maturation. Our previous results showed addition of e-SiNWs effectively enhanced the functions of the cardiac spheroids and improved the cellular maturation of hiPSC-CMs. Here, we examined two important factors that can affect functions of the nanowired hiPSC cardiac spheroids: (1) cell number per spheroid (i.e., size of the spheroids), and (2) the electrical conductivity of the e-SiNWs. To examine the first factor, we prepared hiPSC cardiac spheroids with four different sizes by varying cell number per spheroid (~0.5k, ~1k, ~3k, ~7k cells/spheroid). Spheroids with ~3k cells/spheroid was found to maximize the beneficial effects of the 3D spheroid microenvironment. This result was explained with a semi-quantitative theory that considers two competing factors: 1) the improved 3D cell-cell adhesion, and 2) the reduced oxygen supply to the center of spheroids with the increase of cell number. Also, the critical role of electrical conductivity of silicon nanowires has been confirmed in improving tissue function of hiPSC cardiac spheroids. These results lay down a solid foundation to develop suitable nanowired hiPSC cardiac spheroids as an innovative cell delivery system to treat cardiovascular diseases. We reasoned that the presence of e-SiNWs in the injectable spheroids improves their ability to receive exogenous electromechanical pacing from the host myocardium to enhance their integration with host tissue post-transplantation. In this study, we examined the cardiac biocompatibility of the e-SiNWs and cell retention, engraftment and integration after injection of the nanowired hiPSC cardiac spheroids into adult rat hearts. Our results showed that the e-SiNWs caused minimal toxicity to rat adult hearts after intramyocardial injection. Further, the nanowired spheroids were shown to significantly improve cell retention and engraftment, when compared to dissociated hiPSC-CMs and unwired spheroids. The 7-days-old nanowired spheroid grafts showed alignment with the host myocardium and development of sarcomere structures. The 28-days-old nanowired spheroid grafts showed gap junctions, mechanical junctions and vascular integration with host myocardium. Together, our results clearly demonstrate the remarkable potential of the nanowired spheroids as cell delivery vehicles to treat cardiovascular diseases

3D Architectural Analysis of Neurons, Astrocytes, Vasculature & Nuclei in the Motor and Somatosensory Murine Cortical Columns

Characterization of the complex cortical structure of the brain at a cellular level is a fundamental goal of neuroscience which can provide a better understanding of both normal function as well as disease state progression. Many challenges exist however when carrying out this form of analysis. Immunofluorescent staining is a key technique for revealing 3-dimensional structure, but subsequent fluorescence microscopy is limited by the quantity of simultaneous targets that can be labeled and intrinsic lateral and isotropic axial point-spread function (PSF) blurring during the imaging process in a spectral and depth-dependent manner. Even after successful staining, imaging and optical deconvolution, the sheer density of filamentous processes in the neuropil significantly complicates analysis due to the difficulty of separating individual cells in a highly interconnected network of tightly woven cellular arbors. In order to solve these problems, a variety of methodologies were developed and validated for improved analysis of cortical anatomy. An enhanced immunofluorescent staining and imaging protocol was utilized to precisely locate specific functional regions within brain slices at high magnification and collect four-channel, complete cortical columns. A powerful deconvolution routine was established which collected depth variant PSFs using an optical phantom for image restoration. Fractional volume analysis (FVA) was used to provide preliminary data of the proportions of each stained component in order to statistically characterize the variability within and between the functional regions in a depth-dependent and depth-independent manner. Finally, using machine learning techniques, a supervised learning model was developed that could automatically classify neuronal and astrocytic nuclei within the large cortical column datasets based on perinuclear fluorescence. These annotated nuclei were then used as seed points within their corresponding fluorescent channel for cell individualization in a highly interconnected network. For astrocytes, this technique provides the first method for characterization of complex morphology in an automated fashion over large areas without laborious dye filling or manual tracing

Modelling Neuron Morphology: Automated Reconstruction from Microscopy Images

Understanding how the brain works is, beyond a shadow of doubt, one of the greatest challenges for modern science. Achieving a deep knowledge about the structure, function and development of the nervous system at the molecular, cellular and network levels is crucial in this attempt, as processes at all these scales are intrinsically linked with higher-order cognitive functions. The research in the various areas of neuroscience deals with advanced imaging techniques, collecting an increasing amounts of heterogeneous and complex data at different scales. Then, computational tools and neuroinformatics solutions are required in order to integrate and analyze the massive quantity of acquired information. Within this context, the development of automaticmethods and tools for the study of neuronal anatomy has a central role. The morphological properties of the soma and of the axonal and dendritic arborizations constitute a key discriminant for the neuronal phenotype and play a determinant role in network connectivity. A quantitative analysis allows the study of possible factors influencing neuronal development, the neuropathological abnormalities related to specific syndromes, the relationships between neuronal shape and function, the signal transmission and the network connectivity. Therefore, three-dimensional digital reconstructions of soma, axons and dendrites are indispensable for exploring neural networks. This thesis proposes a novel and completely automatic pipeline for neuron reconstruction with operations ranging from the detection and segmentation of the soma to the dendritic arborization tracing. The pipeline can deal with different datasets and acquisitions both at the network and at the single scale level without any user interventions or manual adjustment. We developed an ad hoc approach for the localization and segmentation of neuron bodies. Then, various methods and research lines have been investigated for the reconstruction of the whole dendritic arborization of each neuron, which is solved both in 2D and in 3D images

Systems microscopy approaches to understand cancer cell migration and metastasis

Cell migration is essential in a number of processes, including wound healing, angiogenesis and cancer metastasis. Especially, invasion of cancer cells in the surrounding tissue is a crucial step that requires increased cell motility. Cell migration is a well-orchestrated process that involves the continuous formation and disassembly of matrix adhesions. Those structural anchor points interact with the extra-cellular matrix and also participate in adhesion-dependent signalling. Although these processes are essential for cancer metastasis, little is known about the molecular mechanisms that regulate adhesion dynamics during tumour cell migration. In this review, we provide an overview of recent advanced imaging strategies together with quantitative image analysis that can be implemented to understand the dynamics of matrix adhesions and its molecular components in relation to tumour cell migration. This dynamic cell imaging together with multiparametric image analysis will help in understanding the molecular mechanisms that define cancer cell migration

Microscale Measurements of Cell and Tissue Mechanics in Three Dimensions

Two-dimensional (2D) studies have revealed that mechanical forces drive cell migration and can feedback to regulate proliferation, differentiation and the synthesis/remodeling of extracellular matrix (ECM) proteins. Whether these observations can be translated to clinical settings or be utilized for tissue engineering will depend critically on our ability to translate these findings into physiologically relevant three-dimensional (3D) environments. The general goal of this dissertation has been to develop and apply new technologies capable of extending studies of cell and tissue mechanics into 3D environments. In the first project, we measured both shear and normal traction forces exerted by cells cultured on planar substrates. We observed that focal adhesions serve as pivots about which cells generate rotational moments. In the second project, we combined enzymatically degradable synthetic hydrogels with finite element models to measure the mechanical tractions exerted by cells fully encapsulated within 3D matrices. We found that cells reach out thin protrusions and pull back inward towards the cell body with the highest forces at the tip. Cellular extensions that were invading into the surrounding matrix displayed a strong inward force 10-15 microns behind the leading tip, suggesting that growing extensions may establish a contractile waypoint, before invading further. To study the forces cells exert during tissue remodeling, we utilized photolithograpy to generate arrays of microtissues consisting of cells encapsulated in 3D collagen matrices. Microcantilevers were used to constrain the remodeling of the collagen gel and to report the forces generated during this process. We used this technique to explore the effects of boundary stiffness and matrix density within model tendon and cardiac tissues. Finally, we combined this system with a Foerster radius energy transfer (FRET) based biosensor of fibronectin conformation to reveal how tissue geometry and cell-genereated tractions cooperate to pattern matrix conformation during tissue remodeling. Together, these studies highlight novel approaches to understand the nature of cell-ECM interactions in 3D matrices. Such mechanical insights will help us to understand how physical forces drive cell migration and behavior within physiologically relevant environments

Automating the Reconstruction of Neuron Morphological Models: the Rivulet Algorithm Suite

The automatic reconstruction of single neuron cells is essential to enable large-scale data-driven investigations in computational neuroscience. The problem remains an open challenge due to various imaging artefacts that are caused by the fundamental limits of light microscopic imaging. Few previous methods were able to generate satisfactory neuron reconstruction models automatically without human intervention. The manual tracing of neuron models is labour heavy and time-consuming, making the collection of large-scale neuron morphology database one of the major bottlenecks in morphological neuroscience. This thesis presents a suite of algorithms that are developed to target the challenge of automatically reconstructing neuron morphological models with minimum human intervention. We first propose the Rivulet algorithm that iteratively backtracks the neuron fibres from the termini points back to the soma centre. By refining many details of the Rivulet algorithm, we later propose the Rivulet2 algorithm which not only eliminates a few hyper-parameters but also improves the robustness against noisy images. A soma surface reconstruction method was also proposed to make the neuron models biologically plausible around the soma body. The tracing algorithms, including Rivulet and Rivulet2, normally need one or more hyper-parameters for segmenting the neuron body out of the noisy background. To make this pipeline fully automatic, we propose to use 2.5D neural network to train a model to enhance the curvilinear structures of the neuron fibres. The trained neural networks can quickly highlight the fibres of interests and suppress the noise points in the background for the neuron tracing algorithms. We evaluated the proposed methods in the data released by both the DIADEM and the BigNeuron challenge. The experimental results show that our proposed tracing algorithms achieve the state-of-the-art results