Modeling Connectomic Deficits in TSC with Cerebral Organoids

The Human brain is immensly complex and contains roughly the same number of neurons as there are in the Milky Way galaxy, around 100 billion, with many times that number of neuronal connections. The sum of neuronal connections that make up the brain is called the connectome, and its proper formation is essential for normal cognitive function. Many neurological diseases occur when connectivity is in some way disturbed , such as in epileptic disorders. However, mapping neuronal connections and understanding how connectivity is disrupted in disease is challenging for several reasons. First, animal models often do not mimic the human specific aspects of brain development, specifically the massive expansion of the human cortex, or of neurological disorders. Additionally, since live brain tissue is extremely difficult to obtain, modeling disease in a human specific manner requires new in vitro systems that can mimic its structure. Second, the brain is immensely complex and even in relatively simple models the scale and complexity of neuronal connections makes traditional mapping strategies inadequate for charting large numbers of individual neuronal networks. Third, when asking questions about changes in connectivity within the disease context, the readout is not simply whether a cell is alive, dying, or dead; but rather how the nature of neuronal networks is perturbed. Therefore, the analysis of how connectivity is disrupted in disease must have high-enough resolution to allow researchers to identify how a neuron changes its number and/or type of synaptic partner(s) while at the same time giving us insight as to what might be causing such changes. My proposed work aims to overcome these challenges and develop a computationally based system that can map thousands of individual neuronal networks while simultaneously collecting the gene expression data from the neurons that make up said networks. I will use this technique to map the connectome of the most advanced and high-fidelity model of the human brain, human induced pluripotent stem cell (hiPSC)- derived cerebral organoids and investigate how neuronal connectivity is altered in Tuberous Sclerosis Complex (TSC), a disease which alters the connectivity of patients neurons resulting in epileptic seizures. In sum, I aim to map thousands of human neuronal networks within single organoids while simultaneously collecting the information that tell us what genes are responsible for the generation of those networks. Additionally, I will apply this technique to TSC to try and understand how neuronal networks are altered in epilepsy and what gene expression changes may be responsible for such a change. Together, this work will dramatically alter how we view human neuronal networks and can provide mechanistic information on how gene expression controls their development, maintenance, and disruption in neurological disease.

The sum of neuronal connections that make up the brain is called the connectome, and its proper formation is essential for normal cognitive function. Many neurological diseases occur when connectivity is in some way disturbed, such as in epilepsy. However, mapping neuronal connections and understanding how connectivity is disturbed in human disease is challenging for several reasons. First, traditional animal models often do not recapitulate the human specifics aspects of neurological disorders, and since live brain tissue is extremely difficult to obtain, modeling disease in a human specific manner requires novel in vitro systems. Second, the human brain is immensely complex, consisting of roughly 100 billion neurons with each neuron making thousands of connections. Even in relatively simplified models the scale and complexity of neuronal connections makes traditional mapping strategies inadequate for charting large numbers of individual neuronal networks. Third, when asking questions about changes in connectivity within the disease context, the readout is not simply whether a cell is alive, dying, or dead; but rather how the nature of neuronal networks is perturbed. Therefore, the analysis of how connectivity is disrupted in disease must have high-enough resolution to allow researchers to identify how a neuron changes its number and/or type of synaptic partner(s) while at the same time giving researchers insight as to what might be causing such changes. Here, I developed a method to overcome these challenges and created a computationally based system that takes advantage of single-cell RNA sequencing to map thousands of individual neuronal networks while simultaneously collecting the gene expression data from the neurons that make up said networks. I am using this technique to map the connectome of the most advanced and high-fidelity model of the human brain, human induced pluripotent stem cell (hiPSC)-derived cerebral organoids and investigate how neuronal connectivity is altered in Tuberous Sclerosis Complex (TSC), a disease which alters the connectivity of patient's neurons resulting in epileptic seizures. In sum, I am aiming to map thousands of human neuronal networks within single organoids while simultaneously collecting the information that tell us what genes are responsible for the generation of those networks. Additionally, I am applying this technique to TSC to try and understand how neuronal networks are altered in epilepsy and what gene expression changes may be responsible for such a change. These analyses are still ongoing. However, the information derived from these experiments will dramatically alter how we view human neuronal networks and provide mechanistic information on how gene expression controls their development, maintenance, and disruption in neurological disease.