This research is funded by The National Health and Medical Research Council Project Grant (Grant Number 1109056).

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Welcome to Spyder's Documentation - Spyder 5 documentation Available from: <a target="_blank" href="https://docs.spyder-idle.org/current/index.html">https://docs.spyder-idle.org/current/index.html</a> (2022)</li><li>Rattay, F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Ways+to+approximate+current-distance+relations+for+electrically+stimulated+fibers.">Ways to approximate current-distance relations for electrically stimulated fibers.</a> Journal of Theoretical Biology. 125 (3), 339-349 (1987).</li><li>Tsai, D., Morley, J. W., Suaning, G. J., Lovell, N. 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H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Frequency-dependent+reduction+of+voltage-gated+sodium+current+modulates+retinal+ganglion+cell+response+rate+to+electrical+stimulation.">Frequency-dependent reduction of voltage-gated sodium current modulates retinal ganglion cell response rate to electrical stimulation.</a> Journal of Neural Engineering. 8 (6), 066007 (2011).</li><li>Joucla, S., Gli&#232;re, A., Yvert, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Current+approaches+to+model+extracellular+electrical+neural+microstimulation.">Current approaches to model extracellular electrical neural microstimulation.</a> Frontiers in Computational Neuroscience. 8, 13 (2014).</li><li>. 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The authors declare no competing interests.

In this paper, we have demonstrated a modeling workflow that combined finite element and biophysical neuron modeling. The model is highly flexible, as it can be modified in its complexity to fit different purposes, and it provides a way to validate the results against empirical findings. We also demonstrated how we parameterized the model to enable automation.
The two-step modeling method combines the advantages of using FEM and neuron computational suite to solve the neuron's cable equation i...

Visual neuroprostheses are a group of devices that deliver stimulations (electrical, light, etc.) to the neural cells in the visual pathway to create phosphenes or sensation of seeing the light. It is a treatment strategy that has been in clinical use for almost a decade for people with permanent blindness caused by degenerative retinal diseases. Typically, a complete system would include an external camera that captures the visual information around the user, a power supply and computing unit to process and translate the image to a series of electrical pulses, and an implanted electrode array that interfaces the neural tissue and deliver the electrical pulses to the neural cells. The working principle allows a visual neuroprosthesis to be placed in different sites along the visual pathway from the retina to the visual cortex, as long as it is downstream from the damaged tissue. A majority of current research in visual neuroprostheses focuses on increasing the efficacy of the stimulation and improving the spatial acuity to provide a more natural vision.
In the efforts to improve the efficacy of the stimulation, computational modeling has been a cost- and time-effective method to validate a prosthesis design and simulate its visual outcome. Computational modeling in this field gained popularity since 1999 as Greenberg1 modeled the response of a retinal ganglion cell to extracellular electrical stimuli. Since then, computational modeling has been used to optimize the parameters of the electrical pulse2,3 or the geometrical design of the electrode4,5. Despite the variation in complexity and research questions, these models work by determining the electrical voltage distribution in the medium (e.g., neural tissue) and estimating the electrical response that the neurons in the vicinity will produce due to the electrical voltage.
The electrical voltage distribution in a conductor can be found by solving the Poisson equations6 at all locations:
<img alt="Equation 1" src="/files/ftp_upload/63792/63792eq01.jpg" style="margin: auto;" />
<img alt="Equation 2" src="/files/ftp_upload/63792/63792eq02.jpg" style="margin: auto;" />
where E is the electric field, V the electric potential, J the current density, and&#160;&#963; is the electrical conductivity. The <img alt="Equation 12" src="/files/ftp_upload/63792/63792eq12.jpg" style="margin: auto;" /> in the equation indicates a gradient operator. In the case of stationary current, the following boundary conditions are imposed on the model:
<img alt="Equation 3" src="/files/ftp_upload/63792/63792eq3.jpg" style="margin: auto;" />
<img alt="Equation 4" src="/files/ftp_upload/63792/63792eq04.jpg" style="margin: auto;" />
where n is the normal to the surface, &#937; represents the boundary, and I0 represents the specific current. Together, they create electrical insulation at the external boundaries and create a current source for a selected boundary. If we assume a monopolar point source in a homogeneous medium with an isotropic conductivity, the extracellular electric potential at an arbitrary location can be calculated by7:
<img alt="Equation 5" src="/files/ftp_upload/63792/63792eq05.jpg" style="margin: auto;" />
where Ie&#160;is the current and is the distance between the electrode and the point of measurement. When the medium is inhomogeneous or anisotropic, or the electrode array has multiple electrodes, a computational suite to numerically solve the equations can be convenient. A finite-element modeling software6 breaks up the volume conductor into small sections known as &#39;elements&#39;. The elements are interconnected with each other such that the effects of change in one element influence change in others, and it solves the physical equations that serve to describe these elements. With the increasing computational speed of modern computers, this process can be completed within seconds. Once the electric potential is calculated, one can then estimate the electrical response of the neuron.
A neuron sends and receives information in the form of electrical signals. Such signals come in two forms - graded potentials and action potentials. Graded potentials are temporary changes to the membrane potential wherein the voltage across the membrane becomes more positive (depolarization) or negative (hyperpolarization). Graded potentials typically have localized effects. In cells that produce them, action potentials are all-or-nothing responses that can travel long distances along the length of an axon. Both graded and action potentials are sensitive to the electrical as well as the chemical environment. An action potential spike can be produced by various neuronal cell types, including the retinal ganglion cells, when a threshold transmembrane potential is crossed. The action potential spiking and propagation then trigger synaptic transmission of signals to downstream neurons. A neuron can be modeled as a cable that is divided into cylindrical segments, where each segment has capacitance and resistance due to the lipid bilayer membrane8. A neuron computational programme9 can estimate the electrical activity of an electrically-excitable cell by discretizing the cell into multiple compartments and solving the mathematical model10:
<img alt="Equation 6" src="/files/ftp_upload/63792/63792eq06.jpg" style="margin: auto;" />
In this equation, Cm is the membrane capacitance, Ve,n is the extracellular potential at node n, Vi,n the intracellular potential at node n, Rn the intracellular (longitudinal) resistance at node n, and Iion is the ionic current going through the ion channels at node n. The values of V from the FEM model are implemented as Ve,n for all nodes in the neuron when the stimulation is active.
The transmembrane currents from ion channels can be modeled by using Hodgkin-Huxley formulations11:
<img alt="Equation 7" src="/files/ftp_upload/63792/63792eq07.jpg" style="margin: auto;" />
where gi is the specific conductance of the channel, Vm the transmembrane potential (Vi,n - Ve,n) and Eion the reversal potential of the ion channel. For voltage-gated channels, such as Na channel, dimensionless parameters, m, and h, that describe the probability of opening or closing of the channels are introduced:
<img alt="Equation 8" src="/files/ftp_upload/63792/63792eq08.jpg" style="margin: auto;" />
where <img alt="Equation 9" src="/files/ftp_upload/63792/63792eq09.jpg" style="margin: auto;" /> is the maximum membrane conductance for the particular ion channel, and the values of parameters m and h are defined by differential equations:
<img alt="Equation 10" src="/files/ftp_upload/63792/63792eq10.jpg" style="margin: auto;" />
where &#945;x and&#160;&#946;x are voltage-dependent functions that define the rate constants of the ion channel. They generally take the form:
<img alt="Equation 11" src="/files/ftp_upload/63792/63792eq11v2.jpg" style="margin: auto;" />
The values of the parameters in these equations, including maximal conductance, as well as the constants A, B, C, and D, were typically found from empirical measurements.
With these building blocks, models of different complexities can be built by following the steps described. An FEM software is useful when the Poisson equation cannot be solved analytically, such as in the case of inhomogeneous or anisotropic conductance in the volume conductor or when the geometry of the electrode array is complex. After the extracellular potential values have been solved, the neuron cable model can then be numerically solved in the neuron computational software. Combining the two software enables the computation of a complex neuron cell or network to a non-uniform electric field.
A simple two-step model of a retinal ganglion cell under a suprachoroidal stimulation will be built using the aforementioned programs. In this study, the retinal ganglion cell will be subjected to a range of magnitudes of electrical current pulses. The location of the cell relative to the stimulus is also varied to show the distance-threshold relationship. Moreover, the study includes a validation of the computational result against an in vivo study of the cortical activation threshold using different sizes of stimulation electrode12, as well as an in vitro study showing the relationship between the electrode-neuron distance and the activation threshold13.

<table><tbody><tr><td>Computer workstation</td><td>N/A</td><td>N/A</td><td>Windows 64-bit operating system, at least 4GB of RAM, at least 3 GB of disk space</td></tr><tr><td>Anaconda Python</td><td>Anaconda Inc.</td><td>Version 3.9</td><td>The open source Individual Edition containing Python 3.9 and preinstalled packages to perform data manipulation, as well as Spyder Integrated Development Environment. It could be used to control the simulation, as well as to display and analyse the simulation data.</td></tr><tr><td>COMSOL Multiphysics</td><td>COMSOL</td><td>Version 5.6</td><td>The simulation suite to perform finite element modelling. The licence for the AC/DC module should be purchased. The Application Builder capability should be included in the licence to follow the automation tutorial.</td></tr><tr><td>NEURON</td><td>NEURON</td><td>Version 8.0</td><td>A freely-distributed software to perform the computation of neuronal cells and/or neural networks.</td></tr></tbody></table>

computational modeling of retinal neurons for visual prosthesis research - fundamental approaches

Computational modeling has become an increasingly important method in neural engineering due to its capacity to predict behaviors of in vivo and in vitro systems. This has the key advantage of minimizing the number of animals required in a given study by providing an often very precise prediction of physiological outcomes. In the field of visual prosthesis, computational modeling has an array of practical applications, including informing the design of an implantable electrode array and prediction of visual percepts that may be elicited through the delivery of electrical impulses from the said array. Some models described in the literature combine a three-dimensional (3D) morphology to compute the electric field and a cable model of the neuron or neural network of interest. To increase the accessibility of this two-step method to researchers who may have limited prior experience in computational modeling, we provide a video of the fundamental approaches to be taken in order to construct a computational model and utilize it in predicting the physiological and psychophysical outcomes of stimulation protocols deployed via a visual prosthesis. The guide comprises the steps to build a 3D model in a finite element modeling (FEM) software, the construction of a retinal ganglion cell model in a multi-compartmental neuron computational software, followed by the amalgamation of the two. A finite element modeling software to numerically solve physical equations would be used to solve electric field distribution in the electrical stimulations of tissue. Then, specialized software to simulate the electrical activities of a neural cell or network was used. To follow this tutorial, familiarity with the working principle of a neuroprosthesis, as well as neurophysiological concepts (e.g., action potential mechanism and an understanding of the Hodgkin-Huxley model), would be required.

We conducted two simulation protocols to demonstrate the use of the model. The first protocol involved varying the electrode size while keeping the location of the neuron and the electrical pulse parameters the same. The second protocol involved shifting the neuron in the x-direction in 100 &#181;m steps, while the size of the electrode remained constant. For both protocols, the pulse used was a single cathodic-first biphasic pulse of 0.25 ms width with a 0.05 ms interphase gap. For the first protocol, the radius of the ...

Watch this Scientific Journal Video about Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches at JoVE.com

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches

We summarize a workflow to computationally model a retinal neuron's behaviors in response to electrical stimulation. The computational model is versatile and includes automation steps that are useful in simulating a range of physiological scenarios and anticipating the outcomes of future in vivo/in vitro studies.

Computational modeling has become an increasingly important method in neural engineering due to its capacity to predict behaviors of in vivo and in vitro ...

computational-modeling-retinal-neurons-for-visual-prosthesis-research

Research

JoVE Journal

Neuroscience

1.7K Views.  The University of Sydney. Dit protocol helpt de reactie van neurale cellen op verschillende stimuli te voorspellen, waardoor we nieuwe ideeën in neurale stimulatie kunnen verkennen zonder fysiek prototype of levensweefsel nodig te hebben. Het kan worden gebruikt als een voorstudie om concepten te testen die een uitdaging zijn om fysiek te emuleren, en het is kosten- en tijdsefficiënt, waardoor het testen van grote aantallen parameters mogelijk is. Deze methode kan de neurale respons voorspellen in andere neurale sti...

Video: Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches

Optic Nerve Transection: A Model of Adult Neuron Apoptosis in the Central Nervous System

Retinal ganglion cells (RGCs) are CNS neurons that output visual information from the retina to the brain, via the optic nerve. 
The optic nerve can be accessed within the orbit of the eye and completely transected (axotomized), cutting the axons of the entire RGC population. Optic nerve 
transection is a reproducible model of apoptotic neuronal cell death in the adult CNS 1-4. This model is particularly attractive because the vitreous
 chamber of the eye acts as a capsule for drug delivery to the retina, permitting experimental manipulations via intraocular injections. The diffusion of chemicals 
through the vitreous fluid ensures that they act upon the entire RGC population. Moreover, RGCs can be selectively transfected by applying short interfering RNAs 
(siRNAs), plasmids, or viral vectors to the cut end of the optic nerve 5-7 or injecting vectors into their target, the superior colliculus 8. 
This allows researchers to study apoptotic mechanisms in the desired neuronal population without confounding effects on other bystander neurons or surrounding glia. 
An additional benefit is the ease and accuracy with which cell survival can be quantified after injury. The retina is a flat, layered tissue and RGCs are localized in 
the innermost layer, the ganglion cell layer. The survival of RGCs can be tracked over time by applying a fluorescent tracer (3% Fluorogold) to the cut end of the 
optic nerve at the time of axotomy, or by injecting the tracer into the superior colliculus (RGC target) one week prior to axotomy. The tracer is retrogradely transported, labeling 
the entire RGC population. Because the ganglion cell layer is a monolayer (one cell thick), RGC densities can be quantified in flat-mounted tissue, without the need 
for stereology. Optic nerve transection leads to the apoptotic death of 90% of injured RGCs within 14 days postaxotomy 9-11. RGC apoptosis has a 
characteristic time-course whereby cell death is delayed 3-4 days postaxotomy, after which the cells rapidly degenerate. This provides a time window for 
experimental manipulations directed against pathways involved in apoptosis.

Optic Nerve transection is a widely used model of adult CNS injury. Ninety percent of retinal ganglion cells (RGCs) whose axons are completely transected (axotomy) die within 14 days after axotomy. This model is easily amenable to experimental manipulations and highly reproducible.

Retinal ganglion cells (RGCs) are CNS neurons that output visual information from the retina to the brain, via the optic nerve. 
The optic nerve can be ...

An Isolated Retinal Preparation to Record Light Response from Genetically Labeled Retinal Ganglion Cells

The first steps in vertebrate vision take place when light stimulates the rod and cone photoreceptors of the retina 1. This information is then segregated into what are known as the ON and OFF pathways. The photoreceptors signal light information to the bipolar cells (BCs), which depolarize in response to increases (On BCs) or decreases (Off BCs) in light intensity. This segregation of light information is maintained at the level of the retinal ganglion cells (RGCs), which have dendrites stratifying in either the Off sublamina of the inner plexiform layer (IPL), where they receive direct excitatory input from Off BCs, or stratifying in the On sublamina of the IPL, where they receive direct excitatory input from On BCs. This segregation of information regarding increases or decreases in illumination (the On and Off pathways) is conserved and signaled to the brain in parallel.
The RGCs are the output cells of the retina, and are thus an important cell to study in order to understand how light information is signaled to visual nuclei in the brain. Advances in mouse genetics over recent decades have resulted in a variety of fluorescent reporter mouse lines where specific RGC populations are labeled with a fluorescent protein to allow for identification of RGC subtypes 2 3 4 and specific targeting for electrophysiological recording. Here, we present a method for recording light responses from fluorescently labeled ganglion cells in an intact, isolated retinal preparation. This isolated retinal preparation allows for recordings from RGCs where the dendritic arbor is intact and the inputs across the entire RGC dendritic arbor are preserved. This method is applicable across a variety of ganglion cell subtypes and is amenable to a wide variety of single-cell physiological techniques.

This article provides a description of how to dissect and record from the isolated retinal preparation in mouse. In particular, we describe how to record light responses from a fluorescently labeled ganglion cell population and subsequently identify and analyze its morphology.

The first steps in vertebrate vision take place when light stimulates the rod and cone photoreceptors of the retina 1.  This information is then ...

Techniques for Processing Eyes Implanted With a Retinal Prosthesis for Localized Histopathological Analysis

With the recent development of retinal prostheses, it is important to develop reliable techniques for assessing the safety of these devices in preclinical studies. However, the standard fixation, preparation, and automated histology procedures are not ideal. Here we describe new procedures for evaluating the health of the retina directly adjacent to an implant. Retinal prostheses feature electrode arrays in contact with eye tissue. Previous methods have not been able to spatially localize the ocular tissue adjacent to individual electrodes within the array. In addition, standard histological processing often results in gross artifactual detachment of the retinal layers when assessing implanted eyes. Consequently, it has been difficult to assess localized damage, if present, caused by implantation and stimulation of an implanted electrode array. Therefore, we developed a method for identifying and localizing the ocular tissue adjacent to implanted electrodes using a (color-coded) dye marking scheme, and we modified an eye fixation technique to minimize artifactual retinal detachment. This method also rendered the sclera translucent, enabling localization of individual electrodes and specific parts of an implant. Finally, we used a matched control to increase the power of the histopathological assessments. In summary, this method enables reliable and efficient discrimination and assessment of the retinal cytoarchitecture in an implanted eye.

Techniques for visualizing retinal cytoarchitecture directly adjacent to individual electrodes within a retinal stimulator.

With  the recent development of retinal prostheses, it is important to develop  reliable techniques for assessing the safety of these devices in ...

Medicine

Using Looming Visual Stimuli to Evaluate Mouse Vision

The visual system in the central nervous system processes diverse visual signals. Although the overall structure has been characterized from the retina through the lateral geniculate nucleus to the visual cortex, the system is complex. Cellular and molecular studies have been conducted to elucidate the mechanisms underpinning visual processing and, by extension, disease mechanisms. These studies may contribute to the development of artificial visual systems. To validate the results of these studies, behavioral vision testing is necessary. Here, we show that the looming stimulation experiment is a reliable mouse vision test that requires a relatively simple setup. The looming experiment was conducted in a large enclosure with a shelter in one corner and a computer monitor located on the ceiling. A CCD camera positioned next to the computer monitor served to observe mouse behavior. A mouse was placed in the enclosure for 10 minutes and allowed to acclimate to and explore the surroundings. Then, the monitor projected a program-derived looming stimulus 10 times. The mouse responded to the stimuli either by freezing or by fleeing to the hiding place. The mouse&#39;s behavior before and after the looming stimuli was recorded, and the video was analyzed using motion tracking software. The velocity of the mouse movement significantly changed after the looming stimuli. In contrast, no reaction was observed in blind mice. Our results demonstrate that the simple looming experiment is a reliable test of mouse vision.

To examine mouse vision, we conducted a looming test. Mice were placed in a large square arena with a monitor on its ceiling. The looming visual stimulus consistently evoked freezing or flight reactions in the mice.

The visual system in the central nervous system processes diverse visual signals. Although the overall structure has been characterized from the retina ...

Behavior

A Standardized Obstacle Course for Assessment of Visual Function in Ultra Low Vision and Artificial Vision

We describe an indoor, portable, standardized course that can be used to evaluate obstacle avoidance in persons who have ultralow vision. Six sighted controls and 36 completely blind but otherwise healthy adult male (n=29) and female (n=13) subjects (age range 19-85 years), were enrolled in one of three studies involving testing of the BrainPort sensory substitution device. Subjects were asked to navigate the course prior to, and after, BrainPort training. They completed a total of 837 course runs in two different locations. Means and standard deviations were calculated across control types, courses, lights, and visits. We used a linear mixed effects model to compare different categories in the PPWS (percent preferred walking speed) and error percent data to show that the course iterations were properly designed. The course is relatively inexpensive, simple to administer, and has been shown to be a feasible way to test mobility function. Data analysis demonstrates that for the outcome of percent error as well as for percentage preferred walking speed, that each of the three courses is different, and that within each level, each of the three iterations are equal. This allows for randomization of the courses during administration.
Abbreviations: 
preferred walking speed (PWS) 
course speed (CS) 
percentage preferred walking speed (PPWS)

We describe an indoor, portable, standardized course that can be used to evaluate obstacle avoidance in persons who have ultralow vision. The course is relatively inexpensive, simple to administer, and has been shown to be reliable and reproducible.

We describe an indoor, portable, standardized course that can be used to evaluate obstacle avoidance in persons who have ultralow vision. Six sighted ...

Techniques for Processing Eyes Implanted with a Retinal Prosthesis for Localized Histopathological Analysis: Part 2 Epiretinal Implants with Retinal Tacks

Retinal prostheses for the treatment of certain forms of blindness are gaining traction in clinical trials around the world with commercial devices currently entering the market. In order to evaluate the safety of these devices, in preclinical studies, reliable techniques are needed. However, the hard metal components utilised in some retinal implants are not compatible with traditional histological processes, particularly in consideration for the delicate nature of the surrounding tissue. Here we describe techniques for assessing the health of the eye directly adjacent to a retinal implant secured epiretinally with a metal tack.
Retinal prostheses feature electrode arrays in contact with eye tissue. The most commonly used location for implantation is the epiretinal location (posterior chamber of the eye), where the implant is secured to the retina with a metal tack that penetrates all the layers of the eye. Previous methods have not been able to assess the proximal ocular tissue with the tack in situ, due to the inability of traditional histological techniques to cut metal objects. Consequently, it has been difficult to assess localized damage, if present, caused by tack insertion.
Therefore, we developed a technique for visualizing the tissue around a retinal tack and implant. We have modified an established technique, used for processing and visualizing hard bony tissue around a cochlear implant, for the soft delicate tissues of the eye. We orientated and embedded the fixed eye tissue, including the implant and retinal tack, in epoxy resin, to stabilise and protect the structure of the sample. Embedded samples were then ground, polished, stained, and imaged under various magnifications at incremental depths through the sample. This technique allowed the reliable assessment of eye tissue integrity and cytoarchitecture adjacent to the metal tack.

Here we describe histological techniques for visualising ocular tissue directly adjacent to a metal epiretinal tack and retinal prosthesis.

Retinal prostheses for the treatment of certain forms of blindness are gaining traction in clinical trials around the world with commercial devices ...

Methodology for Biomimetic Chemical Neuromodulation of Rat Retinas with the Neurotransmitter Glutamate In Vitro

Photoreceptor degenerative diseases cause irreparable blindness through the progressive loss of photoreceptor cells in the retina. Retinal prostheses are an emerging treatment for photoreceptor degenerative diseases that seek to restore vision by artificially stimulating the surviving retinal neurons in the hope of eliciting comprehensible visual perception in patients. Current retinal prostheses have demonstrated success in restoring limited vision to patients using an array of electrodes to electrically stimulate the retina but face substantial physical barriers in restoring high acuity, natural vision to patients. Chemical neurostimulation using native neurotransmitters is a biomimetic alternative to electrical stimulation and could bypass the fundamental limitations associated with retinal prostheses using electrical neurostimulation. Specifically, chemical neurostimulation has the potential to restore more natural vision with comparable or better visual acuities to patients by injecting very small quantities of neurotransmitters, the same natural agents of communication used by retinal chemical synapses, at much finer resolution than current electrical prostheses. However, as a relatively unexplored stimulation paradigm, there is no established protocol for achieving chemical stimulation of the retina in vitro. The purpose of this work is to provide a detailed framework for accomplishing chemical stimulation of the retina for investigators who wish to study the potential of chemical neuromodulation of the retina or similar neural tissues in vitro. In this work, we describe the experimental setup and methodology for eliciting retinal ganglion cell (RGC) spike responses similar to visual light responses in wild-type and photoreceptor-degenerated wholemount rat retinas by injecting controlled volumes of the neurotransmitter glutamate into the subretinal space using glass micropipettes and a custom multiport microfluidic device. This methodology and protocol are general enough to be adapted for neuromodulation using other neurotransmitters or even other neural tissues.

Methodology for Biomimetic Chemical Neuromodulation of Rat Retinas with the Neurotransmitter Glutamate In Vitro

This protocol describes a novel method for investigating a form of chemical neurostimulation of wholemount rat retinas in vitro with the neurotransmitter glutamate. Chemical neurostimulation is a promising alternative to the conventional electrical neurostimulation of retinal neurons for treating irreversible blindness caused by photoreceptor degenerative diseases.

Photoreceptor degenerative diseases cause irreparable blindness through the progressive loss of photoreceptor cells in the retina. Retinal prostheses are ...

Bioengineering

Generating Retinal Degeneration to Investigate Cellular Repair Strategies in Xenopus

Generating Retinal Injury Models in Xenopus Tadpoles

Retinal neurodegenerative diseases are the leading causes of blindness. Among numerous therapeutic strategies being explored, stimulating self-repair recently emerged as particularly appealing. A cellular source of interest for retinal repair is the M&#252;ller glial cell, which harbors stem cell potential and an extraordinary regenerative capacity in anamniotes. This potential is, however, very limited in mammals. Studying the molecular mechanisms underlying retinal regeneration in animal models with regenerative capabilities should provide insights into how to unlock the latent ability of mammalian M&#252;ller cells to regenerate the retina. This is a key step for the development of therapeutic strategies in regenerative medicine. To this aim, we developed several retinal injury paradigms in Xenopus: a mechanical retinal injury, a transgenic line allowing for nitroreductase-mediated photoreceptor conditional ablation, a retinitis pigmentosa model based on CRISPR/Cas9-mediated rhodopsin knockout, and a cytotoxic model driven by intraocular CoCl2 injections. Highlighting their advantages and disadvantages, we describe here this series of protocols that generate various degenerative conditions and allow the study of retinal regeneration in Xenopus.

Author Spotlight: Exploring Retinal Regeneration Mechanisms in the Xenopus Frog 

We have developed several protocols to induce retinal damage or retinal degeneration in Xenopus laevis tadpoles. These models offer the possibility of studying retinal regeneration mechanisms.

Retinal neurodegenerative diseases are the leading causes of blindness. Among numerous therapeutic strategies being explored, stimulating self-repair ...

Developmental Biology

Subretinal Transplantation of Human Embryonic Stem Cell-Derived Retinal Tissue in a Feline Large Animal Model

Retinal degenerative (RD) conditions associated with photoreceptor loss such as age-related macular degeneration (AMD), retinitis pigmentosa (RP) and Leber Congenital Amaurosis (LCA) cause progressive and debilitating vision loss. There is an unmet need for therapies that can restore vision once photoreceptors have been lost. Transplantation of human pluripotent stem cell (hPSC)-derived retinal tissue (organoids) into the subretinal space of an eye with advanced RD brings retinal tissue sheets with thousands of healthy mutation-free photoreceptors and has a potential to treat most/all blinding diseases associated with photoreceptor degeneration with one approved protocol. Transplantation of fetal retinal tissue into the subretinal space of animal models and people with advanced RD has been developed successfully but cannot be used as a routine therapy due to ethical concerns and limited tissue supply. Large eye inherited retinal degeneration (IRD) animal models are valuable for developing vision restoration therapies utilizing advanced surgical approaches to transplant retinal cells/tissue into the subretinal space. The similarities in globe size, and photoreceptor distribution (e.g., presence of macula-like region area centralis) and availability of IRD models closely recapitulating human IRD would facilitate rapid translation of a promising therapy to the clinic. Presented here is a surgical technique of transplanting hPSC-derived retinal tissue into the subretinal space of a large animal model allowing assessment of this promising approach in animal models.

Presented here is a surgical technique for transplanting human pluripotent stem cell (hPSC)-derived retinal tissue into the subretinal space of a large animal model.

Retinal degenerative (RD) conditions associated with photoreceptor loss such as age-related macular degeneration (AMD), retinitis pigmentosa (RP) and ...

Modeling and Analyzing Retinal Degeneration

LipidUNet-Machine Learning-Based Method of Characterization and Quantification of Lipid Deposits Using iPSC-Derived Retinal Pigment Epithelium

The retinal pigment epithelium (RPE) is a monolayer of hexagonal cells located at the back of the eye. It provides nourishment and support to photoreceptors and choroidal capillaries, performs phagocytosis of photoreceptor outer segments (POS), and secretes cytokines in a polarized manner for maintaining the homeostasis of the outer retina. Dysfunctional RPE, caused by mutations, aging, and environmental factors, results in the degeneration of other retinal layers and causes vision loss. A hallmark phenotypic feature of degenerating RPE is intra and sub-cellular lipid-rich deposits. These deposits are a common phenotype across different retinal degenerative diseases. To reproduce the lipid deposit phenotype of monogenic retinal degenerations in vitro, induced pluripotent stem cell-derived RPE (iRPE) was generated from patients&#39; fibroblasts. Cell lines generated from patients with Stargardt and Late-onset retinal degeneration (L-ORD) disease were fed with POS for 7 days to replicate RPE physiological function, which caused POS phagocytosis-induced pathology in these diseases. To generate a model for age-related macular degeneration (AMD), a polygenic disease associated with alternate complement activation, iRPE was challenged with alternate complement anaphylatoxins. The intra and sub-cellular lipid deposits were characterized using Nile Red, boron-dipyrromethene (BODIPY), and apolipoprotein E (APOE). To quantify the density of lipid deposits, a machine learning-based software, LipidUNet, was developed. The software was trained on maximum-intensity projection images of iRPE on culture surfaces. In the future, it will be trained to analyze three-dimensional (3D) images and quantify the volume of lipid droplets. The LipidUNet software will be a valuable resource for discovering drugs that decrease lipid accumulation in disease models.

Author Spotlight: Unraveling the Pathogenesis of Age-Related Macular Degeneration and Discovering Potential Therapies 

Degenerative eye diseases that affect the retinal pigment epithelium layer of the eye have monogenic and polygenic origins. Several disease models and a software application, LipidUNet, have been developed to study mechanisms of disease, as well as potential therapeutic interventions.

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches

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