Reconstructing neural circuits using multiresolution correlated light and electron microscopy

Correlated light and electron microscopy (CLEM) can be used to combine functional and molecular characterizations of neurons with detailed anatomical maps of their synaptic organization. Here we describe a multiresolution approach to CLEM (mrCLEM) that efficiently targets electron microscopy (EM) imaging to optically characterized cells while maintaining optimal tissue preparation for high-throughput EM reconstruction. This approach hinges on the ease with which arrays of sections collected on a solid substrate can be repeatedly imaged at different scales using scanning electron microscopy. We match this multiresolution EM imaging with multiresolution confocal mapping of the aldehyde-fixed tissue. Features visible in lower resolution EM correspond well to features visible in densely labeled optical maps of fixed tissue. Iterative feature matching, starting with gross anatomical correspondences and ending with subcellular structure, can then be used to target high-resolution EM image acquisition and annotation to cells of interest. To demonstrate this technique and range of images used to link live optical imaging to EM reconstructions, we provide a walkthrough of a mouse retinal light to EM experiment as well as some examples from mouse brain slices

Biopolymeric microbeads as a 3D scaffold for soft tissue engineering

The increase of different types of cell cultures, which can be used for the in vitro studies of physiological and/or pathological processes, has introduced the need to improve culture techniques through the use of materials and culture media that promote growth, recreating a cellular micro-environment that can be asserted in in vivo condition. Therefore, it is important to design and develop new biologically sustainable methods, such as to contribute to the \u201ccloser-to-in vivo\u201d condition. In particular, the design of a 3D in vitro model of neuronal culture is an important step to better understand the mechanisms of cell-cell communication, synaptogenesis and neurophysiological circuits. In order to mimic the ECM environment, a granular, porous and soft structure is preferred in the design of an artificial neural network. The granular structure is preferred due to the fact that CNS tissue seems to be organized as a greater proportion of the microscale tissue, that can be thought of as granular. For this reason, the thesis is focused on the production and characterization of bipolymeric microbeads as a 3D scaffold for soft tissue engineering. The biopolymer Chitosan is presented as an alternative adhesion factor and support for 2D and 3D neuronal cell cultures. Chitosan is a copolymer of glucosamine and N-acetyl-glucosamine, obtained by the deacetylation of chitin; it is well known for its low-cost, biocompatibility, biodegradability, muco-adhesiveness, antibacterial activity as well as its bioaffinity. Chitosan backbone shows positive charges of primary ammines that favor the electrostatic interactions with the negatively charged cell membranes promoting cell adhesion and growth. The standard studies focoused on the development of nervous system, have been performed using traditional monolayer culture onto supports modified by extracellular matrix components or synthetic biopolymers such as poly-ornithine and poly-lysine which are expressed at stages critical for neuronal differentiation in situ and are functional in neurite outgrowth in vitro, acting as adhesion proteins. Morphological and functional characterization of 2D neuronal culture grew up onto chitosan susbtrates are carried out and compared with the gold standard reported in literature, in order to validate the ability of chitosan to support neuronal adhesion, networks development and the differentiation capacity. 3D cultured neurons on chitosan microbeads based-scaffold, showed a structural development of a functional network that are more representative of the in vivo environment. The studies reported in this thesis, successfully demonstrate the alternative use of the polysaccharide chitosan as adhesion factor and as a structural component for 2D/3D neuronal cultures. Definitely, thanks to its low cost and versatility, it could be easily functionalized for the fabrication of personalized of in vitro models. In this thesis, a new technology to converts monodisperse microbead hydrogels to fine powders, is reported. Microengineered emulsion-to-powder (MEtoP) technology generates microgels with all the molecular, colloidal, and bulk characteristics of fresh microbeas upon resuspension in aqueous media. GelMA microbeads are fabricated by microfluidic technique, that is one of the most effective techniques, and allows precise tuning of the compositions and geometrical characteristics of microbeads. Gelatin-methacryloyl (GelMA) is a semi-synthetic hydrogel which consists of gelatin derivatized with methacrylamide and methacrylate groups. These hydrogels provide cells with an optimal biological environment (e.g., RGD motifs for adhesion) and can be quickly photo-crosslinked, which provide shape fidelity and stability at physiological temperature. MEtoP technology is based on protecting the dispersed phase of an emulsion to preserve its physical and chemical cues during harsh freezing and lyophilization procedures. This technology avoids the persistent problems of colloids, including difficulty in sterilization, bacterial and viral contamination, impaired stability, high processing costs, and difficult packaging and transportation

Model and Appearance Based Analysis of Neuronal Morphology from Different Microscopy Imaging Modalities

The neuronal morphology analysis is key for understanding how a brain works. This process requires the neuron imaging system with single-cell resolution; however, there is no feasible system for the human brain. Fortunately, the knowledge can be inferred from the model organism, Drosophila melanogaster, to the human system. This dissertation explores the morphology analysis of Drosophila larvae at single-cell resolution in static images and image sequences, as well as multiple microscopy imaging modalities. Our contributions are on both computational methods for morphology quantification and analysis of the influence of the anatomical aspect. We develop novel model-and-appearance-based methods for morphology quantification and illustrate their significance in three neuroscience studies. Modeling of the structure and dynamics of neuronal circuits creates understanding about how connectivity patterns are formed within a motor circuit and determining whether the connectivity map of neurons can be deduced by estimations of neuronal morphology. To address this problem, we study both boundary-based and centerline-based approaches for neuron reconstruction in static volumes. Neuronal mechanisms are related to the morphology dynamics; so the patterns of neuronal morphology changes are analyzed along with other aspects. In this case, the relationship between neuronal activity and morphology dynamics is explored to analyze locomotion procedures. Our tracking method models the morphology dynamics in the calcium image sequence designed for detecting neuronal activity. It follows the local-to-global design to handle calcium imaging issues and neuronal movement characteristics. Lastly, modeling the link between structural and functional development depicts the correlation between neuron growth and protein interactions. This requires the morphology analysis of different imaging modalities. It can be solved using the part-wise volume segmentation with artificial templates, the standardized representation of neurons. Our method follows the global-to-local approach to solve both part-wise segmentation and registration across modalities. Our methods address common issues in automated morphology analysis from extracting morphological features to tracking neurons, as well as mapping neurons across imaging modalities. The quantitative analysis delivered by our techniques enables a number of new applications and visualizations for advancing the investigation of phenomena in the nervous system

Construction of carbon-based three-dimensional neural scaffolds and their structural regulation

Motivation The brain is formed by an intricate assembly of cellular networks, where neurons are embedded in an extracellular matrix (ECM) consisting of an intricate three-dimensional (3D) mesh of proteins that provides complex chemical, electrical and mechanical signalling.1 Given this complexity as well as the limitations of in vivo studies,2 it is important to develop in vitro models able to recapitulate the brain connectivity at various levels and ultimately, provide a mimic of the human brain suitable for preclinical applications.3 By reproducing cell to cell and cell to ECM interactions so to mimic the in vivo microenvironment, 3D tissue engineering promotes more physiological responses than conventional 2D cultures.4 Toward this goal, several 3D supporting materials or scaffolds have been developed, tested and applied.5 Among them, emerging carbon-based materials, such as carbon nanotubes (CNTs) 6, graphene oxide 7 and graphene foam (GF) 8 have high mechanical stability, high porosity and dense interconnectivity, providing a 3D microenvironment beneficial for cell growth and interaction.9 My Work In my Ph.D., I aimed to construct 3D neural scaffolds based on carbon materials especially graphene and carbon nanotubes (CNTs) and explore the regulation of these scaffolds for specific application in neural cultures. To achieve these goals, I combined chemical vapor deposition (CVD) and nano-fabrication for the preparation of different kinds of scaffolds and then used these scaffolds for the neural cultures. In the characterization of neural culture part, I mainly used optical imaging methods, particularly immunochemistry and calcium imaging, to investigate the neuronal network morphology and electrical dynamics of reconstructed 3D primary cultures from rats. These are my main results: 1) By using Fe nanoparticles confined to the interlamination of graphite as catalyst, we have obtained a fully 3D interconnected CNT web through the pores of graphene foam (GCNT web) by in situ chemical vapor deposition. This 3D GCNT web has a thickness up to 1.5 mm and a completely geometric, mechanical and electrical interconnectivity. Dissociated cortical cells cultured inside the GCNT web form a functional 3D cortex-like network exhibiting a spontaneous electrical activity that is closer to what is observed in vivo. Moreover, we have explored the application of this functional 3D cortex-like network: 2) By co-culturing and fluorescently labelling glioma and healthy cortical cells with different colours, a new in vitro model is obtained to investigate malignant glioma infiltration. This model allows reconstruction of the 3D trajectories and velocity distribution of individual infiltrating glioma with an unprecedented precision. The model is cost-effective and allows a quantitative and rigorous screening of anti-cancer drugs. 3) We have fabricated a 3D free-standing ordered graphene (3D-OG) network with the pore size of 20 \u3bcm, the skeleton width of 20 \u3bcm and an exact 90\ub0 orientation angle between the building blocks. Extensive interconnectivity of graphene sheets allows 3D-OG scaffolds to be free-standing and to be easily manipulated. When primary cortical cells are cultured on 3D-OG scaffolds, the cells form well-defined 3D connections with a cellular density similar to that observed when cells were cultured on 2D coverslip. In contrast to the 2D coverslips culture, astrocytes cultured on 3D-OG scaffolds did not have a flat morphology but had a more ramified shape similar to that seen in vivo conditions. Moreover, neurons on 3D-OG scaffolds had axons and dendrites aligned along the graphene skeleton allowing the formation of neuronal networks with highly controlled connections. Neuronal networks grown on 3D-OG scaffolds had a higher electrical activity with functional signaling over a long distance. 4) We have constructed a novel scaffold of three-dimensional bacterial cellulose-graphene foam (3D-BC/G) for neural stem cells (NSCs) in vitro, which was prepared via in situ bacterial cellulose interfacial polymerization on the skeleton surface of porous graphene foam. We found that 3D-BC/G can not only support NSCs growth and adhesion, but also keep NSCs stemness and enhanced its proliferative capacity. Further phenotypic analysis indicated that 3D-BC/G can induce NSCs selectively to differentiate into neurons, forming a neural network in short time. It was also meanwhile demonstrated to have good biocompatibility for primary cortical neurons and enhanced neuronal network activities by measuring calcium transient

Generalizable automated pixel-level structural segmentation of medical and biological data

Over the years, the rapid expansion in imaging techniques and equipments has driven the demand for more automation in handling large medical and biological data sets. A wealth of approaches have been suggested as optimal solutions for their respective imaging types. These solutions span various image resolutions, modalities and contrast (staining) mechanisms. Few approaches generalise well across multiple image types, contrasts or resolution. This thesis proposes an automated pixel-level framework that addresses 2D, 2D+t and 3D structural segmentation in a more generalizable manner, yet has enough adaptability to address a number of specific image modalities, spanning retinal funduscopy, sequential fluorescein angiography and two-photon microscopy. The pixel-level segmentation scheme involves: i ) constructing a phase-invariant orientation field of the local spatial neighbourhood; ii ) combining local feature maps with intensity-based measures in a structural patch context; iii ) using a complex supervised learning process to interpret the combination of all the elements in the patch in order to reach a classification decision. This has the advantage of transferability from retinal blood vessels in 2D to neural structures in 3D. To process the temporal components in non-standard 2D+t retinal angiography sequences, we first introduce a co-registration procedure: at the pairwise level, we combine projective RANSAC with a quadratic homography transformation to map the coordinate systems between any two frames. At the joint level, we construct a hierarchical approach in order for each individual frame to be registered to the global reference intra- and inter- sequence(s). We then take a non-training approach that searches in both the spatial neighbourhood of each pixel and the filter output across varying scales to locate and link microvascular centrelines to (sub-) pixel accuracy. In essence, this \link while extract" piece-wise segmentation approach combines the local phase-invariant orientation field information with additional local phase estimates to obtain a soft classification of the centreline (sub-) pixel locations. Unlike retinal segmentation problems where vasculature is the main focus, 3D neural segmentation requires additional exibility, allowing a variety of structures of anatomical importance yet with different geometric properties to be differentiated both from the background and against other structures. Notably, cellular structures, such as Purkinje cells, neural dendrites and interneurons, all display certain elongation along their medial axes, yet each class has a characteristic shape captured by an orientation field that distinguishes it from other structures. To take this into consideration, we introduce a 5D orientation mapping to capture these orientation properties. This mapping is incorporated into the local feature map description prior to a learning machine. Extensive performance evaluations and validation of each of the techniques presented in this thesis is carried out. For retinal fundus images, we compute Receiver Operating Characteristic (ROC) curves on existing public databases (DRIVE & STARE) to assess and compare our algorithms with other benchmark methods. For 2D+t retinal angiography sequences, we compute the error metrics ("Centreline Error") of our scheme with other benchmark methods. For microscopic cortical data stacks, we present segmentation results on both surrogate data with known ground-truth and experimental rat cerebellar cortex two-photon microscopic tissue stacks.Open Acces

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

Use of Enabling Technologies in Combination with Human Pluripotent Stem Cells to Study Neural Differentiation and Neurite Outgrowth

Regeneration of human central nervous system (CNS) neurons is limited due to the inhibitory environment that forms post injury known as the glial scar. Reactive astrocytes within the glial scar produce both inhibitory and permissive extracellular matrix molecules (ECM) into the local environment. Chondroitin sulfate proteoglycans (CSPGs) are a component of the ECM which have been shown in vitro and in vivo to inhibit neurite regeneration. Physiologically relevant in vitro models of the glial scar are essential in developing new therapeutics and understanding the cellular processes that underpin neural regeneration. In this study human pluripotent stem cells were differentiated using the highly potent and stable synthetic retinoid EC23. A concentration dependent profile of the action of EC23 on stem cell differentiation was determined, furthermore, the mechanisms for the enhanced biological activity of EC23 were investigated. This study used the well described small molecule EC23 to form aggregates of neural progenitors which were characterised and used in a two dimension (2D) and three dimension (3D) model of neurite outgrowth. Next, the neurite outgrowth substrate was manipulated to represent the inhibitory ECM of the glial scar using the CSPG Aggrecan. The presence of Aggrecan inhibited neurite development and was used to assess small molecules that could enhance outgrowth in 2D and 3D. Small molecule modulators of; rho-associated protein kinase (ROCK); retinoic acid receptor β2; glycogen synthase kinase 3β and protein tyrosine phosphatase σ were shown to at least partially enhance neurite outgrowth on Aggrecan in 2D and 3D. Furthermore, the bacterial enzyme Chondroitinase ABC was used to cleave chondroitin sulphate glycosaminoglycan side chains (GAG) from Aggrecan to further aid neurite outgrowth in this model. In addition a 2D and 3D co-culture system was developed using the human astroglioma cell line U118MG and human stem cell-derived neural progenitors described previously. This model demonstrated inhibition of neurite outgrowth by U118MG which could be overcome by ROCK inhibition. This thesis describes the development of a novel model of neurite outgrowth using human stem cell-derived neurons. The model described was used to investigate Aggrecan induced neurite inhibition and to investigate pathways involved in neural regeneration

Microstimulation and multicellular analysis: A neural interfacing system for spatiotemporal stimulation

Willfully controlling the focus of an extracellular stimulus remains a significant challenge in the development of neural prosthetics and therapeutic devices. In part, this challenge is due to the vast set of complex interactions between the electric fields induced by the microelectrodes and the complex morphologies and dynamics of the neural tissue. Overcoming such issues to produce methodologies for targeted neural stimulation requires a system that is capable of (1) delivering precise, localized stimuli a function of the stimulating electrodes and (2) recording the locations and magnitudes of the resulting evoked responses a function of the cell geometry and membrane dynamics. In order to improve stimulus delivery, we developed microfabrication technologies that could specify the electrode geometry and electrical properties. Specifically, we developed a closed-loop electroplating strategy to monitor and control the morphology of surface coatings during deposition, and we implemented pulse-plating techniques as a means to produce robust, resilient microelectrodes that could withstand rigorous handling and harsh environments. In order to evaluate the responses evoked by these stimulating electrodes, we developed microscopy techniques and signal processing algorithms that could automatically identify and evaluate the electrical response of each individual neuron. Finally, by applying this simultaneous stimulation and optical recording system to the study of dissociated cortical cultures in multielectode arrays, we could evaluate the efficacy of excitatory and inhibitory waveforms. Although we found that the proximity of the electrode is a poor predictor of individual neural excitation thresholds, we have shown that it is possible to use inhibitory waveforms to globally reduce excitability in the vicinity of the electrode. Thus, the developed system was able to provide very high resolution insight into the complex set of interactions between the stimulating electrodes and populations of individual neurons.Ph.D.Committee Chair: Stephen P. DeWeerth; Committee Member: Bruce Wheeler; Committee Member: Michelle LaPlaca; Committee Member: Robert Lee; Committee Member: Steve Potte

Engineered environments for biomedical applications: anisotropic nanotopographies and microfluidic devices

During the last two decades micro- and nano-fabrication techniques originally developed for electronic engineering have directed their attention towards life sciences. The increase of analytical power of diagnostic devices and the creation of more biomimetic scaffolds have been strongly desired by these fields, in order to have a better insight into the complexity of physiological systems, while improving the ability to model them in vitro. Technological innovations worked to fill such a gap, but the integration of these fields of science is not progressing fast enough to satisfy the expectations. In this thesis I present novel devices which exploit the unique features of the micro- and nanoscale and, at the same time, match the requirements for successful application in biomedical research. Such biochips were used for optical detection of water-dispersed nanoparticles in microchannels, for highly controlled cell-patterning in closed microreactors, and for topography-mediated regulation of cell morphology and migration. Moreover, pilot experiments on the pre-clinical translation of micropatterned scaffolds in a rat model of peripheral nerve transaction were initiated and are ongoing. Given these results, the devices presented here have the potential to achieve clinical translation in a short/medium time, contributing to the improvement of biomedical technologies