UC Berkeley

Imaging the dynamic behavior of neuromodulatory neurotransmitters in the extracelluar space that arise from individual quantal release events would constitute a major advance in neurochemical imaging. Spatial and temporal resolution of these highly stochastic neuromodulatory events requires concurrent advances in the chemical development of optical nanosensors selective for neuromodulators in concert with advances in imaging methodologies to capture millisecond neurotransmitter release. Herein, we develop and implement a stochastic model to describe dopamine dynamics in the extracellular space (ECS) of the brain dorsal striatum to guide the design and implementation of fluorescent neurochemical probes that record neurotransmitter dynamics in the ECS. Our model is developed from first-principles and simulates release, diffusion, and reuptake of dopamine in a 3D simulation volume of striatal tissue. We find that in vivo imaging of neuromodulation requires simultaneous optimization of dopamine nanosensor reversibility and sensitivity: dopamine imaging in the striatum or nucleus accumbens requires nanosensors with an optimal dopamine dissociation constant (K_d) of 1 μM, whereas K_ds above 10 μM are required for dopamine imaging in the prefrontal cortex. Furthermore, as a result of the probabilistic nature of dopamine terminal activity in the striatum, our model reveals that imaging frame rates of 20 Hz are optimal for recording temporally resolved dopamine release events. Our work provides a modeling platform to probe how complex neuromodulatory processes can be studied with fluorescent nanosensors and enables direct evaluation of nanosensor chemistry and imaging hardware parameters. Our stochastic model is generic for evaluating fluorescent neurotransmission probes, and is broadly applicable to the design of other neurotransmitter fluorophores and their optimization for implementation in vivo.