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Thesis Format

Integrated Article


Doctor of Philosophy




Martinez-Trujillo, Julio

2nd Supervisor

Muller, Lyle



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.

Summary for Lay Audience

The human brain is arguably the most complex organ in the human body. However, before birth and over the lifetime of an individual, changes to the instructions for building and maintaining a living organism can undergo unpredictable changes. When the process of building the nervous system goes awry, it is known as a neurodevelopment disorder. Over time, when the nervous system abnormally breaks down, it is known as a neurodegenerative disease. Rett syndrome is caused by a single change that makes a single protein called MeCP2 dysfunctional. Patients appear to develop normally; however, as early as 6 months of age, the period of normal infancy is followed by regression. Individuals with Rett syndrome often develop several other problems, such as seizures. On the other end of the spectrum, Alzheimer’s disease is a neurodegenerative disease that is typically associated with aging. In the later years of adulthood, Alzheimer’s disease slowly destroys cognitive skills, eventually making it difficult to carry out simple tasks. Inside the brain, it is caused by the buildup of toxic proteins that disrupt the normal functioning of brain cells. Despite decades of research, early detection of Alzheimer’s disease and treatment is still a challenge. Scientists use lab models to study diseases and aim to develop treatments. However, bridging the gap from lab findings to real treatments, known as the "valley of death," is challenging. To tackle this, scientists from various fields are collaborating. In my research, I’ve used microelectrode array technology to study the electrical activity of brain models derived from individuals with Rett syndrome and Alzheimer’s disease. These patient- specific models offer a glimpse into the re-enactment of disease states. My research shows that Rett syndrome brain networks are composed of hyperactive brain cells that produce hyperactive brain networks that may lead to epilepsy, while Alzheimer’s disease is characterized by disorganized, hyperactive brain networks, likely due to impaired cell communication. My hope is that the work detailed in this thesis contributes to a better understanding of Rett syndrome, Alzheimer’s disease, and how multielectrode arrays can be leveraged to study diseases and assist in finding patient-specific treatments.

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

Available for download on Friday, August 01, 2025