Investigation of Human Stem Cell-Derived Network Development Using In Vitro Microelectrode Arrays
Abstract
Disease modeling has enabled scientists to study a range of human diseases in the lab, offering the potential to translate basic science into clinical applications. Despite this potential, there is a significant gap, often referred to as the valley of death, between laboratory research and effective human treatments. This gap arises due to challenges in reproducibility, clinical relevance, and systemic issues such as regulatory hurdles, and incentives to translate findings from preclinical to clinical stages. To overcome these challenges, scientists are increasingly collaborating across disciplines, utilizing advancements in stem cell patterning, genome editing, and high-throughput functional assays to achieve detailed, patient-specific disease insights. In my research, I’ve leveraged microelectrode arrays (MEAs) to study the electrophysiological activity of 2D and 3D human stem cell-derived neuronal networks in models of Rett syndrome (RTT) and Alzheimer’s disease (AD) – two neurological disorders with distinct origins and manifestations. RTT, primarily caused by mutations in the MECP2 gene, is a neurodevelopmental disorder that emerges early in life. In contrast, AD is an age-related neurodegenerative disease leading to severe cognitive decline, with, in most cases, no single known cause. My findings demonstrate that RTT neurons are hyperexcitable and produce hyperactive monolayer networks, which may predispose them to hypersynchronous states, such as seizures. Furthermore, I demonstrate that this is likely due to recurrent excitation producing elevated pre-synaptic Ca2+ that can be rescued. In modeling AD using astrocyte-enriched organoids, I observed AD networks that were hyperactive and disordered – unable to organize spiking and bursting activity into complex patterns of network bursts. Additionally, AD network bursts adapted more quickly and were resilient to excitability-inducing treatment. MEAs provide unparalleled access to evaluate network circuitry in in vitro neuronal networks in a non-destructive manner. Through the work presented in this thesis, I illustrate that in both monolayer and organoid networks, the electrophysiologist’s toolbox can be expanded to capture the dynamics recorded by MEAs and measure phenotypes caused by gene mutations. This can provide a testing ground for pharmacological or gene therapy interventions to correct altered phenotypes, moving us closer to crossing the valley of death in translational research.