Single Layer Graphene Biointerface: Studying Neuronal Network Development and Monitoring Cell Behavior over Time

The objective of my Ph.D. thesis is the investigation of the role of Single Layer Graphene (SLG) as a biointerface for its possible future exploitation in various biomedical applications; in particular for the development of biosensors, substrates for regenerative medicine, interfacing platforms for better recording of electrophysiological activity of neuronal networks, among others. This Ph.D. project is multidisciplinary involving both the material transfer and characterization part from one side and the biological part from another side. The material part offers an in-depth explanation of SLG synthesis, transfer, characterization and functionalization while the biological section sheds light on the studies performed for investigation of the behavior of different types of cell lines on SLG substrates. For better understanding of the sequence of the performed work, I have divided this thesis into separate chapters. In the beginning and end of every chapter, I added an introduction and conclusions related to it. Chapter 1 acts as a general introduction to graphene and graphene-related materials where a detailed explanation on the evolution of those materials as a cell interface is provided leading to the introduction of SLG in the end of this chapter along with its production process. Chapter 2 is oriented on the surface characterization of SLG substrates; in this chapter, I described the SLG transfer method, creation of the micrometric ablated geometric patterns on the transferred substrates using excimer laser micromachining, a technique developed in our lab, then further functionalization of the substrates and finally all the techniques employed for their physicochemical characterization. Chapter 3 is dedicated to the biological part of the project; i.e. studying the behavior of different cell lines on the SLG substrates. In this chapter, I have described and explained the interest of using the selected cell lines and the experiments that were performed on them. Chapter 4 has been devoted to a complete and separate project that I performed in collaboration with the Neuroscience and Brain Technologies department. The main focus of the project was the functionalization of the commercial multi-electrode arrays (MEAs) with SLG and studying the neuronal network activity on them throughout the complete network development. Although the main focus of my Ph.D. project was studying SLG biointerface, I have also been involved in side projects, among which, studying the neuronal-like response of mouse neuroblastoma (N2a) living cells to nanoporous patterns of thin supported anodic alumina which I have described in Appendix A, and studying the surface potential of graphene by polyelectrolyte coating which I have presented in Appendix B. To summarize, this thesis reports an original investigation, since, to the best of our knowledge, there is no report yet about the study of the effect of SLG functionalized MEA on the neuronal network activity throughout the complete network maturation. Furthermore, proliferation curves of different cell lines on SLG versus control substrates have been presented; in addition to physicochemical characterization of ablated and functionalized SLG substrates as means of possible explanation of a certain cellular behavior on graphene

EXPLORING DEEP LEARNING METHODS FOR LOW NUMERICAL APERTURE TO HIGH NUMERICAL APERTURE RESOLUTION ENHANCEMENT IN CONFOCAL MICROSCOPY

Confocal microscopy is a widely used tool that provides valuable morphological and functional information within cells and tissues. A major advantage of confocal microscopy is its ability to record multi-color and optically sectioned images. A major drawback to confocal microscopy is its diffraction-limited spatial resolution. Though techniques have been developed that break this limit in confocal microscopy, they require additional hardware or accurate estimates of the system’s impulse response (e.g., point spread function). Here we investigate two deep learning-based models, the cGAN and cycleGAN, trained with low-resolution (LR) and high-resolution (HR) confocal images to improve spatial resolution in confocal microscopy. Our findings conclude that the cGAN can accurately produce HR images if the training set contains images with a high signal-to-noise ratio. We have also found that the cycleGAN model has the potential to perform as the cGAN model but without the requirement of using paired inputs

A Smart Region-Growing Algorithm for Single-Neuron Segmentation From Confocal and 2-Photon Datasets

Accurately digitizing the brain at the micro-scale is crucial for investigating brain structure-function relationships and documenting morphological alterations due to neuropathies. Here we present a new Smart Region Growing algorithm (SmRG) for the segmentation of single neurons in their intricate 3D arrangement within the brain. Its Region Growing procedure is based on a homogeneity predicate determined by describing the pixel intensity statistics of confocal acquisitions with a mixture model, enabling an accurate reconstruction of complex 3D cellular structures from high-resolution images of neural tissue. The algorithm’s outcome is a 3D matrix of logical values identifying the voxels belonging to the segmented structure, thus providing additional useful volumetric information on neurons. To highlight the algorithm’s full potential, we compared its performance in terms of accuracy, reproducibility, precision and robustness of 3D neuron reconstructions based on microscopic data from different brain locations and imaging protocols against both manual and state-of-the-art reconstruction tools

Development of multifunctional nano-probes for neuroscience research

The contribution of nanotechnology to the field of Neuroscience is increasing exponentially. In order to understand the relationship of structure to function at the cellular level, and to decipher the mysteries of nervous system, development of new tools to manipulate and measure cellular function at a local level is necessary. It is a continuing challenge to develop easily fabricated, multipurpose nano-probes which are able to target neural nanostructures for the local manipulation and measurement of functional responses. This thesis is focused on the fabrication, characterisation and implementation of a nano-pipette on a Scanning Ion Conductance Microscopy (SICM). The nano-pipette mounted on a SICM set-up acts as a proximity sensor for non-contact imaging of cellular features. SICM platform to accommodate electrochemical experiments is discussed. In particular, the development of a novel electrochemical probe, fabricated by pyrolytic decomposition of carbon within a quartz nano-pipette is discussed. This method is simple and carbon nano-electrodes of variable size can be fabricated in a single step. The nano-pipette‘s distance controlled feedback system was exploited for local delivery of chemicals to neuronal structures. Experimental and theoretical data are compared in order to calculate the concentration of molecules at the tip of the nano-pipette as a function of the driving force (voltage or pressure) and distance. The quantitative delivery of molecules from a 100 nm nano-pipette is demonstrated. In particular capsaicin-filled nano-pipette is used to trigger capsaicin-sensitive TRPV1 receptors in sensory neurons and transfected cells. Finally some preliminary results for the future development and potential application of nano-pipettes are shown. The nano-pipette is easily fabricated and is shown to be multi-functional. It provides an invaluable tool in the investigation of the nano-physiology of neurons. The SICM multipoint delivery competence can contribute to the various endeavours in drug discovery and to the yield of in vitro pharmacological assays.Open Acces

Graphene Microelectrode Arrays to Combine Electrophysiology with Fluorescence Imaging of Amyloid Proteins

Alzheimer's disease (AD) and Parkinson's diseases (PD) are neurodegenerative diseases that affect $\sim$60\,million people worldwide. Both diseases are linked to the misfolding of proteins from their native conformational state into $\beta$-sheeted amyloid fibrils. In AD the implicated proteins are amyloid-$\beta$ and tau, and for PD the implicated protein is $\alpha$-synuclein (aSyn). The motivation for this work is to develop and use physical techniques to better understand the role of amyloid proteins in neurodegenerative diseases. Two techniques used in amyloid research are fluorescence microscopy, to map the protein location and aggregation state, and electrophysiology, to examine the effect of the proteins on neurons. To enable these techniques to be combined, a transparent graphene microelectrode array (MEA) was designed, fabricated and characterised. The active electrode site was graphene since it is electrically conductive, optically transparent and biocompatible. The graphene MEA was characterised using Raman spectroscopy to check the graphene quality, and electrochemical impedance spectroscopy (EIS) to probe the electrode-electrolyte interface. The graphene MEAs enabled voltage trace recordings from cultured neurons to be combined with widefield, confocal fluorescence and fluorescence lifetime imaging microscopy (FLIM). Combining fluorescence imaging and electrophysiology will allow amyloid aggregation to be correlated with neuronal firing patterns. Another physical technique used was Fourier transform infrared spectroscopy (FTIR). A script was written to estimate the protein secondary structure content, and used to investigate polymorphism in the monomeric amyloid protein aSyn.Engineering and Physical Sciences Research Council, Wellcome Trust, Medical Research Counci

Light Sheet Microscopy and Image Analysis of Neural Development and Programmed Cell Death in C. Elegans Embryos

The positioning of neuronal cell bodies and neurites is critical for intact functioning of the nervous system. Mapping the positions of the soma and neurites in the brains of developing embryos as important central nervous system structures are being created may yield novel insight into the role of distinct cell groups in creating these structures. New developments in microscopy have made this an excellent time to study neural development in the C. elegans embryo. In the past decade, implementations of highly light efficient methods such as single plane illumination microscopy have rendered it possible to follow development of embryonic structures in 3D with excellent temporal resolution (Huisken et al., 2004) and low phototoxicity. Recent work has resulted in quantitative characterization of the outgrowth of a single neurite in the late, rapidly moving three-fold stage of the C. elegans embryo for the first time (Christensen et al., 2015). In this thesis, I first describe the construction and programming of a single plane illumination microscope (SPIM) based on a design from Hari Shroff\u27s lab (Wu et al., 2011). The microscope is developed especially for use with C. elegans embryos and permits fast image acquisition without excessive photodamage, compared to other forms of microscopy. Second, I describe the use of the SPIM microscope to image the development of a subset of sublateral neurons, the earliest known entrants to the nerve ring (Rapti et al, in preparation), into which they grow in the 1.5-fold stage. I describe an algorithm for automatically aligning developing embryos onto one another until the beginning of the rapid embryonic movements known as twitching, which begin at the start of the twofold stage. I employ my algorithm to align a group of identically imaged embryos onto one another and deduce information about the positioning of the nerve ring in an approximately uniform coordinate system. I determine that nerve rings are precisely positioned in the embryo to within about a micrometer while the cell bodies that grow into the nerve ring are positioned over a much wider distance. My work suggests that the nerve ring grows out towards the ALA neuron as an anchor, and that twitching may begin when the developing nerve ring reaches the ALA. I additionally describe observation of new phenotypes related to the cam-1 mutation, which was previously identified as a regulator of anterior-posterior placement of the nerve ring (Kennerdell et al., 2009). Third, I describe an application of the SPIM microscope for imaging the death of the tail spike cell, a complex, multi-compartment differentiated cell which dies over a period of hours during the three-fold stage, when the animal is rapidly moving in its shell, and cannot be imaged otherwise than with a rapid, light efficient microscope such as the one described here. I determined the time course and confirmed the sequence of events of wild type tail spike cell death. Additionally, I report stronger phenotypes for some known tail spike cell death genes when imaged in the embryo, suggesting that eff-1 plays a stronger role than previously known in clearance of the distal part of the tail spike cell process, and additionally that ced-5 has a strong role in clearance of the same compartment (in addition to its known role in soma clearance). In an appendix I describe work beginning on an extension of the microscope, which will hopefully see the microscope used as a tool for selectively inducing fluorescence in individual cells and following the development of those cells in time. My results demonstrate the utility of single plane illumination microscopy for study of C. elegans embryogenesis and establish fundamental facts about the variability of the C. elegans central nervous system by making direct comparisons between animals. This work contributes to our understanding of the C. elegans nervous system by establishing fundamental bounds on the range of nerve ring positioning between individuals

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