A Pipeline for Volume Electron Microscopy of the Caenorhabditis elegans Nervous System.

The "connectome," a comprehensive wiring diagram of synaptic connectivity, is achieved through volume electron microscopy (vEM) analysis of an entire nervous system and all associated non-neuronal tissues. White et al. (1986) pioneered the fully manual reconstruction of a connectome using Caenorhabditis elegans. Recent advances in vEM allow mapping new C. elegans connectomes with increased throughput, and reduced subjectivity. Current vEM studies aim to not only fill the remaining gaps in the original connectome, but also address fundamental questions including how the connectome changes during development, the nature of individuality, sexual dimorphism, and how genetic and environmental factors regulate connectivity. Here we describe our current vEM pipeline and projected improvements for the study of the C. elegans nervous system and beyond

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

STED microscopy reveals dendrite-specificity of spines in turtle cortex

Dendritic spines are key structures for neural communication, learning and memory. Spine size and shape probably reflect synaptic strength and learning. Imaging with superresolution STED microscopy the detailed shape of the majority of the spines of individual neurons in turtle cortex (Trachemys scripta elegans) revealed several distinguishable shape classes. Dendritic spines of a given class were not distributed randomly, but rather decorated significantly more often some dendrites than others. The individuality of dendrites was corroborated by significant inter-dendrite differences in other parameters such as spine density and length. In addition, many spines were branched or possessed spinules. These findings may have implications for the role of individual dendrites in this cortex

The natverse, a versatile toolbox for combining and analysing neuroanatomical data.

To analyse neuron data at scale, neuroscientists expend substantial effort reading documentation, installing dependencies and moving between analysis and visualisation environments. To facilitate this, we have developed a suite of interoperable open-source R packages called the natverse. The natverse allows users to read local and remote data, perform popular analyses including visualisation and clustering and graph-theoretic analysis of neuronal branching. Unlike most tools, the natverse enables comparison across many neurons of morphology and connectivity after imaging or co-registration within a common template space. The natverse also enables transformations between different template spaces and imaging modalities. We demonstrate tools that integrate the vast majority of Drosophila neuroanatomical light microscopy and electron microscopy connectomic datasets. The natverse is an easy-to-use environment for neuroscientists to solve complex, large-scale analysis challenges as well as an open platform to create new code and packages to share with the community

The natverse, a versatile toolbox for combining and analysing neuroanatomical data.

To analyse neuron data at scale, neuroscientists expend substantial effort reading documentation, installing dependencies and moving between analysis and visualisation environments. To facilitate this, we have developed a suite of interoperable open-source R packages called the natverse. The natverse allows users to read local and remote data, perform popular analyses including visualisation and clustering and graph-theoretic analysis of neuronal branching. Unlike most tools, the natverse enables comparison across many neurons of morphology and connectivity after imaging or co-registration within a common template space. The natverse also enables transformations between different template spaces and imaging modalities. We demonstrate tools that integrate the vast majority of Drosophila neuroanatomical light microscopy and electron microscopy connectomic datasets. The natverse is an easy-to-use environment for neuroscientists to solve complex, large-scale analysis challenges as well as an open platform to create new code and packages to share with the community

Fuzzy-Logic Based Detection and Characterization of Junctions and Terminations in Fluorescence Microscopy Images of Neurons

Digital reconstruction of neuronal cell morphology is an important step toward understanding the functionality of neuronal networks. Neurons are tree-like structures whose description depends critically on the junctions and terminations, collectively called critical points, making the correct localization and identification of these points a crucial task in the reconstruction process. Here we present a fully automatic method for the integrated detection and characterization of both types of critical points in fluorescence microscopy images of neurons. In view of the majority of our current studies, which are based on cultured neurons, we describe and evaluate the method for application to two-dimensional (2D) images. The method relies on directional filtering and angular profile analysis to extract essential features about the main streamlines at any location in an image, and employs fuzzy logic with carefully designed rules to reason about the feature values in order to make well-informed decisions about the presence of a critical point and its type. Experiments on simulated as well as real images of neurons demonstrate the detection performance of our method. A comparison with the output of two existing neuron reconstruction methods reveals that our method achieves substantially higher detection rates and could provide beneficial information to the reconstruction process

Extraction of protein profiles from primary neurons using active contour models and wavelets

AbstractThe function of complex networks in the nervous system relies on the proper formation of neuronal contacts and their remodeling. To decipher the molecular mechanisms underlying these processes, it is essential to establish unbiased automated tools allowing the correlation of neurite morphology and the subcellular distribution of molecules by quantitative means.We developed NeuronAnalyzer2D, a plugin for ImageJ, which allows the extraction of neuronal cell morphologies from two dimensional high resolution images, and in particular their correlation with protein profiles determined by indirect immunostaining of primary neurons. The prominent feature of our approach is the ability to extract subcellular distributions of distinct biomolecules along neurites. To extract the complete areas of neurons, required for this analysis, we employ active contours with a new distance based energy. For locating the structural parts of neurons and various morphological parameters we adopt a wavelet based approach. The presented approach is able to extract distinctive profiles of several proteins and reports detailed morphology measurements on neurites.We compare the detected neurons from NeuronAnalyzer2D with those obtained by NeuriteTracer and Vaa3D-Neuron, two popular tools for automatic neurite tracing. The distinctive profiles extracted for several proteins, for example, of the mRNA binding protein ZBP1, and a comparative evaluation of the neuron segmentation results proves the high quality of the quantitative data and proves its practical utility for biomedical analyses

Automated Reconstruction of Neuronal Morphology Based on Local Geometrical and Global Structural Models

Digital reconstruction of neurons from microscope images is an important and challenging problem in neuroscience. In this paper, we propose a model-based method to tackle this problem. We first formulate a model structure, then develop an algorithm for computing it by carefully taking into account morphological characteristics of neurons, as well as the image properties under typical imaging protocols. The method has been tested on the data sets used in the DIADEM competition and produced promising results for four out of the five data sets