In Vivo Phenotyping of Dopaminergic Neurodegeneration in Zebrafish Larvae Using Behavioral Analysis and High-Content Imaging.

Drug discovery research in neurodegeneration is constrained by the high cost and low throughput of traditional mammalian models. This bottleneck is particularly observed in Parkinson's disease research, where rigorous and scalable dopaminergic (DA) neurodegeneration studies remain slow and resource intensive. To address this gap, we present a standardized, high-throughput phenotyping pipeline using a transgenic zebrafish model expressing nitroreductase in DA neurons to study DA neuron loss within five days post-fertilization. Zebrafish offer key advantages for translational neuroscience, including rapid larval development, optical transparency that enables in vivo whole-brain imaging, strong conservation of Parkinson's disease-relevant genes and pathways, and intact neural circuitry not accessible in cell culture models. Our protocol integrates chemogenetic ablation and high-content imaging to generate rapid datasets for screening. DA neurons are selectively ablated using metronidazole (MTZ), producing specific and tunable neurodegeneration. MTZ treatment produces dose-dependent reductions in locomotion consistent with bradykinesia-like phenotypes, providing a robust behavioral correlate to DA cell loss. Since Parkinson's disease is fundamentally a motor disorder, we pair anatomical measurements with functional behavioral readouts. Locomotor activity is recorded directly in a plate and quantified using automated tracking, extracting metrics including total distance traveled, swim bout frequency, and burst initiation. Zebrafish provide an efficient and scalable model system in which hundreds of larvae can be assayed simultaneously with minimal handling. We optimized a high-throughput, plate-based drug screening protocol designed to minimize experimental variance in undergraduate research. This standardized, plate-based format for screening candidate compounds for neuroprotection or functional rescue ensures that data collected by different researchers remains statistically comparable and effective for identifying neuroprotective candidates, improving reproducibility. Together, this methodology provides a robust, scalable framework for neurodegeneration research.