Incorporating microglia‐like cells in human induced pluripotent stem cell‐derived retinal organoids

Microglia are the primary resident immune cells in the retina. They regulate neuronal survival and synaptic pruning making them essential for normal development. Following injury, they mediate adaptive responses and under pathological conditions they can trigger neurodegeneration exacerbating the effect of a disease. Retinal organoids derived from human induced pluripotent stem cells (hiPSCs) are increasingly being used for a range of applications, including disease modelling, development of new therapies and in the study of retinogenesis. Despite many similarities to the retinas developed in vivo, they lack some key physiological features, including immune cells. We engineered an hiPSC co-culture system containing retinal organoids and microglia-like (iMG) cells and tested their retinal invasion capacity and function. We incorporated iMG into retinal organoids at 13 weeks and tested their effect on function and development at 15 and 22 weeks of differentiation. Our key findings showed that iMG cells were able to respond to endotoxin challenge in monocultures and when co-cultured with the organoids. We show that retinal organoids developed normally and retained their ability to generate spiking activity in response to light. Thus, this new co-culture immunocompetent in vitro retinal model provides a platform with greater relevance to the in vivo human retina

Doctor of Philosophy

dissertationProgressive retinal ganglion cell (RGC) degeneration in glaucoma, the leading cause of permanent vision loss, is commonly caused by elevated intraocular pressure (IOP). Neuroprotective treatments complementing current IOP-reducing therapies could improve glaucoma management (Chapters 1, 3). IOP elevations induce glial reactivity (Chapter 4) and dysregulate RGC calcium (Chapters 2-3, 5-7), contributing to RGC degeneration (Chapters 5, 7), but it is unknown how glaucomatous forces perturb RGC and glial Ca2+ homeostasis. We discovered that mouse RGCs and Müller glia respond to osmotic pressure and tensile stretch with a cytosolic Ca2+ elevation that is primarily mediated by opening of the mechanosensitive cation channel transient receptor potential vanilloid 4 (TRPV4; Chapters 5-7). We therefore hypothesized that TRPV4 activation by glaucomatous forces drives RGC excitotoxicity. Consistent with this, intraocular injection of a selective TRPV4 agonist (GSK1016790A) induced mouse RGC loss (Chapter 7). This was prevented by systemic administration of a selective TRPV4 antagonist (HC-067047). Sustained exposure to glaucomatous mechanical strain caused RGC apoptosis, which was rescued by Ca2+ chelation or pharmacological/genetic TRPV4 antagonism, indicating that Ca2+ influx via TRPV4 is required for mechanical excitotoxicity (Chapter 7). Furthermore, RGCs and Müller glia swell during the progression of glaucoma and other blinding conditions, indicating the presence of aberrant osmotic gradients and loss of volume control. We found that RGC and Müller iv cell swelling is exacerbated by TRPV4-dependent Ca2+ influx. Swelling differentially activated TRPV4 in neurons and glia, the later of which required phospholipase A2- dependent production of 5,6-EET, an endogenous TRPV4 agonist. The water channel aquaporin 4 (AQP4) facilitated water entry, which enhanced glial TRPV4 activation (Chapter 6). Finally, we found that TRPV4 antagonism in mouse and primate glaucoma models lowered IOP to normal levels, potentially by promoting fluid drainage from the eye via the trabecular meshwork (TM). Although IOP elevation for eight weeks caused mouse RGC loss, this was prevented by daily treatment with a TRPV4 antagonist (Chapter 7). TRPV4 inhibition, therefore, simultaneously lowers IOP and increases RGC resilience. This, together with our finding that TRPV4 is expressed in human RGCs, Müller glia and TM cells (Chapters 6, 7), makes TRPV4 an attractive therapeutic target for prevention of glaucoma

Data analysis of retinal recordings from multi-electrode arrays under in situ electrical stimulation

The development of retinal implants has become an important field of study in recent years, with increasing numbers of people falling victim to legal or physical blindness as a result of retinal damage. Important weaknesses in current retinal implants include a lack of the resolution necessary to give a patient a viable level of visual acuity, question marks over the amount of power and energy required to deliver adequate stimulation, and the removal of eye movements from the analysis of the visual scene. This thesis documents investigations by the author into a new CMOS stimulation and imaging chip with the potential to overcome these difficulties. An overview is given of the testing and characterisation of the componments incorporated in the device to mimic the normal functioning of the human retina. Its application to in situ experimental studies of frog retina is also described, as well as how the data gathered from these experiments enables the optimisation of the geometry of the electrode array through which the device will interface with the retina. Such optimisation is important as the deposit of excess electrical charge and energy can lead to detrimental medical side effects. Avoidance of such side effects is crucial to the realisation of the next generation of retinal implants

Parameter optimization of evolving spiking neural network with dynamic population particle swarm optimization

Evolving Spiking Neural Network (ESNN) is widely used in classification problem. However, ESNN like any other neural networks is incapable to find its own parameter optimum values, which are crucial for classification accuracy. Thus, in this study, ESNN is integrated with an improved Particle Swarm Optimization (PSO) known as Dynamic Population Particle Swarm Optimization (DPPSO) to optimize the ESNN parameters: the modulation factor (Mod), similarity factor (Sim) and threshold factor (C). To find the optimum ESNN parameter value, DPPSO uses a dynamic population that removes the lowest particle value in every pre-defined iteration. The integration of ESNN-DPPSO facilitates the ESNN parameter optimization searching during the training stage. The performance analysis is measured by classification accuracy and is compared with the existing method. Five datasets gained from University of California Irvine (UCI) Machine Learning Repository are used for this study. The experimental result presents better accuracy compared to the existing technique and thus improves the ESNN method in optimising its parameter values

Computational Intelligence in Healthcare

This book is a printed edition of the Special Issue Computational Intelligence in Healthcare that was published in Electronic

Parameter optimization of evolving spiking neural network with dynamic population particle swarm optimization

Evolving Spiking Neural Network (ESNN) is widely used in classification problem. However, ESNN like any other neural networks is incapable to find its own parameter optimum values, which are crucial for classification accuracy. Thus, in this study, ESNN is integrated with an improved Particle Swarm Optimization (PSO) known as Dynamic Population Particle Swarm Optimization (DPPSO) to optimize the ESNN parameters: the modulation factor (Mod), similarity factor (Sim) and threshold factor (C). To find the optimum ESNN parameter value, DPPSO uses a dynamic population that removes the lowest particle value in every pre-defined iteration. The integration of ESNN-DPPSO facilitates the ESNN parameter optimization searching during the training stage. The performance analysis is measured by classification accuracy and is compared with the existing method. Five datasets gained from University of California Irvine (UCI) Machine Learning Repository are used for this study. The experimental result presents better accuracy compared to the existing technique and thus improves the ESNN method in optimising its parameter values

Computational Intelligence in Healthcare

The number of patient health data has been estimated to have reached 2314 exabytes by 2020. Traditional data analysis techniques are unsuitable to extract useful information from such a vast quantity of data. Thus, intelligent data analysis methods combining human expertise and computational models for accurate and in-depth data analysis are necessary. The technological revolution and medical advances made by combining vast quantities of available data, cloud computing services, and AI-based solutions can provide expert insight and analysis on a mass scale and at a relatively low cost. Computational intelligence (CI) methods, such as fuzzy models, artificial neural networks, evolutionary algorithms, and probabilistic methods, have recently emerged as promising tools for the development and application of intelligent systems in healthcare practice. CI-based systems can learn from data and evolve according to changes in the environments by taking into account the uncertainty characterizing health data, including omics data, clinical data, sensor, and imaging data. The use of CI in healthcare can improve the processing of such data to develop intelligent solutions for prevention, diagnosis, treatment, and follow-up, as well as for the analysis of administrative processes. The present Special Issue on computational intelligence for healthcare is intended to show the potential and the practical impacts of CI techniques in challenging healthcare applications

Echo state network‐based feature extraction for efficient color image segmentation

Image segmentation plays a crucial role in many image processing and understanding applications. Despite the huge number of proposed image segmentation techniques, accurate segmentation remains a significant challenge in image analysis. This article investigates the viability of using echo state network (ESN), a biologically inspired recurrent neural network, as features extractor for efficient color image segmentation. First, an ensemble of initial pixel features is extracted from the original images and injected into the ESN reservoir. Second, the internal activations of the reservoir neurons are used as new pixel features. Third, the new features are classified using a feed forward neural network as a readout layer for the ESN. The quality of the pixel features produced by the ESN is evaluated through extensive series of experiments conducted on real world image datasets. The optimal operating range of different ESN setup parameters for producing competitive quality features is identified. The performance of the proposed ESN‐based framework is also evaluated on a domain‐specific application, namely, blood vessel segmentation in retinal images where experiments are conducted on the widely used digital retinal images for vessel extraction (DRIVE) dataset. The obtained results demonstrate that the proposed method outperforms state‐of‐the‐art general segmentation techniques in terms of performance with an F‐score of 0.92 ± 0.003 on the segmentation evaluation dataset. In addition, the proposed method achieves a comparable segmentation accuracy (0.9470) comparing with reported techniques of segmentation of blood vessels in images of retina and outperform them in terms of processing time. The average time required by our technique to segment one retinal image from DRIVE dataset is 8 seconds. Furthermore, empirically derived guidelines are proposed for adequately setting the ESN parameters for effective color image segmentation