Creating an All-optical, Mechanobiology-guided, and Machine-learning-powered High-throughput Framework to Elucidate Neural Dynamics

The ability to change cell membrane voltage is essential for neurons, nerve cells that send messages across the body. Voltage changes serve to regulate the behavior of the neuron and can be an indicator of health and disease. Like other cells in the body, neurons sense their surroundings and adapt. Current methods to study neurons often do not mimic the environments they encounter in the body and cannot measure their behavior at a high enough speed. The goal of this project is to create a new strategy that leverages advanced high-throughput microscopy and machine learning to understand neural behavior at unprecedented speeds, lower costs, and in environments that mimic those encountered in the body. Through a range of educational activities at the University of Florida, such as the Student Science Training Program (SSTP) at the Center for Precollegiate Education and Training (CPET) and Course-based Undergraduate Research Experience (CURE), this project will broaden participation of historically underserved groups in STEM fields. Training and hands-on research opportunities will be provided to all students to better prepare them for successful future careers. This project will develop and validate an experimental-computational framework that enables optical interrogation of neuron membrane voltage dynamics in a high-throughput, mechanobiology-guided, and non-invasive manner. The overarching goal is that a framework can be created by integrating wide-area voltage imaging, large tissue-mimicking hydrogel-based culture, patterned crosstalk-free optogenetic stimulation, and machine-learning powered closed-loop control to elucidate the membrane voltage dynamics of hundreds of neurons simultaneously, filling technological gaps. The project will explore novel oblique light-sheet illumination and crosstalk-free optogenetic stimulation approaches to enable exploration of plasma membrane voltage dynamics in several hundreds of neurons simultaneously. Scientific findings in this project are expected to provide a new fundamental understanding of electrophysiology and mechanobiology of neurons and inform development of new strategies to treat human diseases. The framework is scalable and can be applied to investigate other cell types, such as cardiomyocytes and muscle cells. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.