A Microfluidic System for Investigating Anticipatory Medication Effects on Dopamine Homeostasis in Dopaminergic Cells.

Dopamine (DA) homeostasis influences emotions, neural circuit development, cognition, and the reward system. Dysfunctions in DA regulation can lead to neurological disorders, including depression, developmental disorders, and addiction. DA homeostasis disruption is a primary cause of Parkinson's Disease (PD). Therefore, understanding the relationship between DA homeostasis and PD progression may clarify the mechanisms for pharmacologically treating PD. This study developed a novel in vitro DA homeostasis platform which consists of three main parts: (1) a microfluidic device for culturing DAergic neurons, (2) an optical detection system for reading DA levels, and (3) an automatic closed-loop control system that establishes when and how much medication to infuse; this uses a microfluidic device that can cultivate DAergic neurons, perfuse solutions, perform in vitro PD modeling, and continuously monitor DA concentrations. The automatically controlled closed-loop control system simultaneously monitors pharmacological PD treatment to support long-term monitoring of DA homeostasis. SH-SY5Y neuroblastoma cells were chosen as DAergic neurons. They were cultivated in the microfluidic device, and real-time cellular DA level measurements successfully achieved long-term monitoring and modulation of DA homeostasis. When applied in combination with multiday cell culture, this advanced system can be used for drug screening and fundamental biological studies.

Second-Derivative-Based Background Drift Removal for a Tonic Dopamine Measurement in Fast-Scan Cyclic Voltammetry.

The dysregulation of dopamine, a neuromodulator, is associated with a broad spectrum of brain disorders, including Parkinson's disease, addiction, and schizophrenia. Quantitative measurements of dopamine are essential for understanding dopamine functional dynamics. Fast-scan cyclic voltammetry (FSCV) is the most popular electrochemical technique for measuring real-time in vivo dopamine level changes. Standard FSCV has only analyzed "phasic dopamine" (changes in seconds) because the gradual generation of background charging current is inevitable and is the primary noise source in the low-frequency band. Although "tonic dopamine" (changes in minutes to hours) is critical for understanding the dopamine system, an electrochemical technique capable of simultaneously measuring phasic and tonic dopamine in an in vivo environment has not been established. Several modified voltammetric techniques have been developed for measuring tonic dopamine; however, the sampling rates (0.1-0.05 Hz) are too low to be useful. Further investigation of the in vivo applicability of previously developed background drift removal methods for measuring tonic dopamine levels is required. We developed a second-derivative-based background removal (SDBR) method for simultaneously measuring phasic and tonic neurotransmitter levels in real-time. The performance of this technique was tested via in silico and in vitro tonic dopamine experiments. Furthermore, its applicability was tested in vivo. SDBR is a simple, robust, postprocessing technique that can extract tonic neurotransmitter levels from all FSCV data. As SDBR is calculated in individual-scan voltammogram units, it can be applied to any real-time closed-loop system that uses a neurotransmitter as a biomarker.

Continuous long-range measurement of tonic dopamine with advanced FSCV for pharmacodynamic analysis of levodopa-induced dyskinesia in Parkinson's disease.

Levodopa, a dopamine prodrug, alleviates the motor symptoms of Parkinson's disease (PD), but its chronic use gives rise to levodopa-induced dyskinesia (LID). However, it remains unclear whether levodopa pharmacodynamics is altered during the progressive onset of LID. Using in vivo fast-scan cyclic voltammetry and second-derivative-based background drift removal, we continuously measured tonic dopamine levels using high temporal resolution recording over 1-h. Increases to tonic dopamine levels following acute levodopa administration were slow and marginal within the naïve PD model. However, these levels increased faster and higher in the LID model. Furthermore, we identified a strong positive correlation of dyskinetic behavior with the rate of dopamine increase, but much less with its cumulative level, at each time point. Here, we identified the altered signature of striatal DA dynamics underlying LID in PD using an advanced FSCV technique that demonstrates the long-range dynamics of tonic dopamine following drug administration.

Simultaneous Estimation of Tonic Dopamine and Serotonin with High Temporal Resolution In Vitro Using Deep Learning.

Quantitative measurement of the phasic (changes in several seconds) and tonic (changes in minutes to hours) level changes of neurotransmitters is an essential technique for understanding brain functions and brain diseases regulated by the neurotransmitters. However, monitoring phasic and tonic levels of multiple neurotransmitters is still a challenging technology. Microdialysis can measure the tonic levels of multiple neurotransmitters simultaneously but has a low temporal resolution (minute) to analyze precisely. Fast-scan cyclic voltammetry (FSCV) has high temporal resolution and high sensitivity, but it was not able to simultaneously measure the tonic level of multiple neurotransmitters. The recently proposed deep learning-based FSCV method was still only capable of phasic concentration estimation of neurotransmitters. In this study, we estimate the tonic levels of dopamine and serotonin simultaneously by training a deep-learning network with the extracted tonic information from the FSCV. The proposed deep learning model was validated in vitro to simultaneously estimate tonic concentrations of two neurotransmitters with statistically significantly higher accuracy than previously proposed background subtraction-based models (p<0.001). In particular, in the case of serotonin concentration estimation error, the proposed model showed higher prediction performance than the background subtraction-based model (48 nM and 73 nM, respectively). We expect that the proposed technique will help simultaneous measurement of the phasic and tonic levels of numerous neurotransmitters in vivo soon.Clinical Relevance- This study proposes a method to simultaneously measure tonic dopamine and tonic serotonin with high temporal resolution with a single electrode in the brain.

ICoRD: iterative correlation-based ROI detection method for the extraction of neural signals in calcium imaging.

Objective. In vivocalcium imaging is a standard neuroimaging technique that allows selective observation of target neuronal activities. In calcium imaging, neuron activation signals provide key information for the investigation of neural circuits. For efficient extraction of the calcium signals of neurons, selective detection of the region of interest (ROI) pixels corresponding to the active subcellular region of the target neuron is essential. However, current ROI detection methods for calcium imaging data exhibit a relatively low signal extraction performance from neurons with a low signal-to-noise power ratio (SNR). This is problematic because a low SNR is unavoidable in many biological experiments.Approach.Therefore, we propose an iterative correlation-based ROI detection (ICoRD) method that robustly extracts the calcium signal of the target neuron from a calcium imaging series with severe noise.Main results.ICoRD extracts calcium signals closer to the ground-truth calcium signal than the conventional method from simulated calcium imaging data in all low SNR ranges. Additionally, this study confirmed that ICoRD robustly extracts activation signals against noise, even withinin vivoenvironments.Significance.ICoRD showed reliable detection from neurons with a low SNR and sparse activation, which were not detected by conventional methods. ICoRD will facilitate our understanding of neural circuit activity by providing significantly improved ROI detection in noisy images.