3D-printer visualization of neuron models

Neurons come in a wide variety of shapes and sizes. In a quest to understand this neuronal diversity, researchers have three-dimensionally traced tens of thousands of neurons; many of these tracings are freely available through online repositories like NeuroMorpho.Org and ModelDB. Tracings can be visualized on the computer screen, used for statistical analysis of the properties of different cell types, used to simulate neuronal behavior, and more. We introduce the use of 3D printing as a technique for visualizing traced morphologies. Our method for generating printable versions of a cell or group of cells is to expand dendrite and axon diameters and then to transform the wireframe tracing into a 3D object with a neuronal surface generating algorithm like Constructive Tessellated Neuronal Geometry (CTNG). We show that 3D printed cells can be readily examined, manipulated, and compared with other neurons to gain insight into both the biology and the reconstruction process. We share our printable models in a new database, 3DModelDB, and encourage others to do the same with cells that they generate using our code or other methods. To provide additional context, 3DModelDB provides a simulatable version of each cell, links to papers that use or describe it, and links to associated entries in other databases

Developmental Steps for a Functional Three-Dimensional Cell Culture System for the Study of Asymmetrical Division of Neural Stem Cells

Stem cells are a cell type present during and following development, which possess self- renewal properties, as well as the ability to differentiate into specific cells. Asymmetrical division is the cellular process that allows stem cells to produce one differentiated and one un-differentiated daughter cell during the same mitotic event. Insights in the molecular mechanisms of such process are minimal, due to the absence of effective methods for its targeted study. Currently, traditional methods of investigation include monolayer cell culture and animal models. The first poses structural limitations to the accurate representation of human tissue and cell structures, while animal models pose restrictions on the ability to visualize and manipulate the experiment performed, as well as being highly susceptible to error during data analysis and interpretation. Three-dimensional cell culture models are a novel approach to the study of cell mechanisms and disease, and are able to overcome many of the limitations of monolayer cell cultures and animal models. It is our goal to devise a three-dimensional cell culture system suitable for the in vitro study of asymmetrical division of neural stem cells at a single cell resolution. Current methods based on the culture of cells embedded in extracellular matrix-derived substrates have been efficient tools for the understanding of cellular mechanisms. However, currently available three-dimensional cell culture systems lack important qualities for an efficient application to the study of asymmetrical division of neural stem cells. A defining quality of cells of the neural lineage is the generation of spontaneous electrical activity, necessary for the transmission of signals in the organism. Current systems lack the ability to perform electrophysiological measurements and characterize the functionality of the cells in culture. A second limitation is posed by the adoption of non-specific substrates for cell culture able to recapitulate the natural cellular environment, as it is known that this has an impact on cell fate determination. Finally, current methods lack the ability to place and inject a controlled number of cells within the three-dimensional substrate, preventing the ability to perform studies at lower-cells resolutions. In this project, I developed three Aims with the goal of overcoming the three major limitations of three-dimensional cell culture systems. In Aim 1, I evaluated the efficiency of Microelectrode Arrays systems to perform electrophysiological measurements of neural stem cells and neuronal networks in vitro. In Aim 2, I fabricated and characterized a porcine brain extracellular matrix-derived hydrogel, and I tested its ability to promote stem cells survival and proliferation. Finally, in Aim 3, I optimized the parameters of a custom 3D extrusion-based bioprinter to perform single cell and single beads resolution printing. Combined, the development of these Aims allowed me to lay the foundations for the development of a functional three-dimensional cell culture system applicable to the study of asymmetrical division in neural stem cells at a single cell resolution level, and which holds great potential for the uncovering of cellular mechanisms characteristic of stem cells and neurodegenerative disease

Engineering of Ideal Systems for the Study and Direction of Stem Cell Asymmetrical Division and Fate Determination

The cellular microenvironment varies significantly across tissues, and it is constituted by both resident cells and the macromolecules they are exposed to. Cues that the cells receive from the microenvironment, as well as the signaling transmitted to it, affect their physiology and behavior. This notion is valid in the context of stem cells, which are susceptible to biochemical and biomechanical signaling exchanged with the microenvironment, and which plays a fundamental role in establishing fate determination and cell differentiation events. The definition of the molecular mechanisms that drive stem cell asymmetrical division, and how these are modulated by microenvironmental signaling, is challenging. Important findings have been described in recent years, corroborating the idea that external stimuli play a fundamental role in development and stem cell physiology. However, speedy progress is hindered by the lack of adequate and highly efficient tools for the study of cellular mechanisms at the single cell level and within a defined and highly controllable environment. The work presented in this dissertation focuses on the engineering of ideal techniques for the study of the processes that define stem cell asymmetrical division and fate determination, devising systems that overcome the current limitations of this research field. The first goal of this project was the engineering of a 3D bioprinting system in combination with tissue-specific substrates for the establishment of a biomimetic, highly accurate, three-dimensional cell culture system for the study of extracellular matrix impact on stem cell physiology. Particular focus was posed on the development of an optimal system for the study of the influence of a brain-specific environment on embryonic and neural stem cells’ differentiation potential. The second goal of this project was the optimization of a system for the delivery of single cells or single cell – single beads complexes into three-dimensional substrates to enable the performance of high throughput experiments at the single cell resolution. Particular focus was posed on the development of an optimal system for the study of asymmetrical stem cell division driven by discrete signals

Evaluating Appearance Differences of Color 3D Printed Objects

Color 3D printing is a relatively young technology with several exciting applications and challenges yet to be explored. One of those challenges is the effect that three dimensional surface geometries have on appearance. The appearance of 3D objects is complex and can be affected by the interaction between several visual appearance parameters such as color, gloss and surface texture. Since traditional printing is only 2D, several of these challenges have either been solved or never needed to be addressed. Complicating matters further, different color 3D printing technologies and materials come with their own inherent material appearance properties, necessitating the study of these appearance parameters on an individual case by case basis. Neural networks are powerful tools that are finding their way into just about every field imaginable, and the world of color science is no exception. A process described by previous researchers provides a method for picking out color sensitive neurons in a given layer of a convolutional neural network (CNN). Typically, CNNs are used for image classification but can also be used for image comparison. A siamese CNN was built and shown to be a good model for appearance differences using textured color patches designed to simulate the appearance of color 3D printed objects. A direct scaling psychophysical experiment was done to create an interval scale of perceptual appearance between color 3D printed objects printed at different angles. The objects used for this experiment were printed with an HP® Jet Fusion 580 color 3D printer. The objects exhibit print angle dependent surface textures inherent to the layered printing process itself. The preliminary siamese CNN showed that perceptual differences in the prints were likely to exist and could be modeled using a neural network. However, the results of the psychophysical experiment indicated that CIELAB color differences were extremely strong predictors of observer perceptions, even with variable surface texture in uncontrolled lighting conditions

Fluorescent and photo-oxidizing TimeSTAMP tags track protein fates in light and electron microscopy.

Protein synthesis is highly regulated throughout nervous system development, plasticity and regeneration. However, tracking the distributions of specific new protein species has not been possible in living neurons or at the ultrastructural level. Previously we created TimeSTAMP epitope tags, drug-controlled tags for immunohistochemical detection of specific new proteins synthesized at defined times. Here we extend TimeSTAMP to label new protein copies by fluorescence or photo-oxidation. Live microscopy of a fluorescent TimeSTAMP tag reveals that copies of the synaptic protein PSD95 are synthesized in response to local activation of growth factor and neurotransmitter receptors, and preferentially localize to stimulated synapses in rat neurons. Electron microscopy of a photo-oxidizing TimeSTAMP tag reveals new PSD95 at developing dendritic structures of immature neurons and at synapses in differentiated neurons. These results demonstrate the versatility of the TimeSTAMP approach for visualizing newly synthesized proteins in neurons

Automatic Generation of Custom Image Recognition Models

In this thesis, we examine the viability of training a convolutional neural network using synthetic data. The cnn is used for image recognition in an rms. We create a program that can render 3D models from STL files as images with varied backgrounds. The process of creating and training an image recognition model is also automated. Lastly, the model is used for image recognition. The report compares different methods and hyperparameters used in training a model. Transfer learning is found to be suited for synthetic datasets. Using the pre-trained feature extraction layers of the VGG-16 model, we train an image recognition model with better than 90% accuracy in the laboratory. We then demonstrate the use of this model for object detection, and suggest avenues for further development

Engineering a multicompartment in vitro model for dorsal root ganglia phenotypic assessment

Despite the significant global prevalence of chronic pain, current methods to identify pain therapeutics often fail translation to the clinic. Phenotypic screening platforms rely on modeling and assessing key pathologies relevant to chronic pain, improving predictive capability. Patients with chronic pain often present with sensitization of primary sensory neurons (that extend from dorsal root ganglia [DRG]). During neuronal sensitization, painful nociceptors display lowered stimulation thresholds. To model neuronal excitability, it is necessary to maintain three key anatomical features of DRGs to have a physiologically relevant platform: (1) isolation between DRG cell bodies and neurons, (2) 3D platform to preserve cell–cell and cell-matrix interactions, and (3) presence of native non-neuronal support cells, including Schwann cells and satellite glial cells. Currently, no culture platforms maintain the three anatomical features of DRGs. Herein, we demonstrate an engineered 3D multicompartment device that isolates DRG cell bodies and neurites and maintains native support cells. We observed neurite growth into isolated compartments from the DRG using two formulations of collagen, hyaluronic acid, and laminin-based hydrogels. Further, we characterized the rheological, gelation and diffusivity properties of the two hydrogel formulations and found the mechanical properties mimic native neuronal tissue. Importantly, we successfully limited fluidic diffusion between the DRG and neurite compartment for up to 72 h, suggesting physiological relevance. Lastly, we developed a platform with the capability of phenotypic assessment of neuronal excitability using calcium imaging. Ultimately, our culture platform can screen neuronal excitability, providing a more translational and predictive system to identify novel pain therapeutics to treat chronic pain

A Tangible Educative 3D Printed Atlas of the Rat Brain

[EN] In biology and neuroscience courses, brain anatomy is usually explained using Magnetic Resonance (MR) images or histological sections of different orientations. These can show the most important macroscopic areas in an animals¿ brain. However, this method is neither dynamic nor intuitive. In this work, an anatomical 3D printed rat brain with educative purposes is presented. Hand manipulation of the structure, facilitated by the scale up of its dimensions, and the ability to dismantle the ¿brain¿ into some of its constituent parts, facilitates the understanding of the 3D organization of the nervous system. This is an alternative method for teaching students in general and biologists in particular the rat brain anatomy. The 3D printed rat brain has been developed with eight parts, which correspond to the most important divisions of the brain. Each part has been fitted with interconnections, facilitating assembling and disassembling as required. These solid parts were smoothed out, modified and manufactured through 3D printing techniques with poly(lactic acid) (PLA). This work presents a methodology that could be expanded to almost any field of clinical and pre-clinical research, and moreover it avoids the need for dissecting animals to teach brain anatomy.This work was supported in part by the Spanish Ministerio de Economia y Competitividad (MINECO) and FEDER funds under grants BFU2015-64380-C2-2-R (D.M.) and BFU2015-64380-C2-1-R and EU Horizon 2020 Program 668863-SyBil-AA grant (S.C.). S.C. acknowledges financial support from the Spanish State Research Agency, through the "Severo Ochoa" Programme for Centres of Excellence in R&D (ref. SEV-2013-0317). 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A Computational Design Pipeline to Fabricate Sensing Network Physicalizations

Interaction is critical for data analysis and sensemaking. However, designing interactive physicalizations is challenging as it requires cross-disciplinary knowledge in visualization, fabrication, and electronics. Interactive physicalizations are typically produced in an unstructured manner, resulting in unique solutions for a specific dataset, problem, or interaction that cannot be easily extended or adapted to new scenarios or future physicalizations. To mitigate these challenges, we introduce a computational design pipeline to 3D print network physicalizations with integrated sensing capabilities. Networks are ubiquitous, yet their complex geometry also requires significant engineering considerations to provide intuitive, effective interactions for exploration. Using our pipeline, designers can readily produce network physicalizations supporting selection-the most critical atomic operation for interaction-by touch through capacitive sensing and computational inference. Our computational design pipeline introduces a new design paradigm by concurrently considering the form and interactivity of a physicalization into one cohesive fabrication workflow. We evaluate our approach using (i) computational evaluations, (ii) three usage scenarios focusing on general visualization tasks, and (iii) expert interviews. The design paradigm introduced by our pipeline can lower barriers to physicalization research, creation, and adoption.Comment: 11 pages, 8 figures; to be published in Proceedings of IEEE VIS 202