Computational modeling of extracellular dopamine kinetics suggests low probability of neurotransmitter release

Dopamine release, diffusion and uptake: A computational model for synaptic and volume transmission

Computational modeling of dopamine transmission is challenged by complex underlying mechanisms. Here we present a new computational model that (I) simultaneously regards release, diffusion and uptake of dopamine, (II) considers multiple terminal release events and (III) comprises both synaptic and volume transmission by incorporating the geometry of the synaptic cleft. We were able to validate our model in that it simulates concentration values comparable to physiological values observed in empirical studies. Further, although synaptic dopamine diffuses into extra-synaptic space, our model reflects a very localized signal occurring on the synaptic level, i.e. synaptic dopamine release is negligibly recognized by neighboring synapses. Moreover, increasing evidence suggests that cognitive performance can be predicted by signal variability of neuroimaging data (e.g. BOLD). Signal variability in target areas of dopaminergic neurons (striatum, cortex) may arise from dopamine concentration variability. On that account we compared spatio-temporal variability in a simulation mimicking normal dopamine transmission in striatum to scenarios of enhanced dopamine release and dopamine uptake inhibition. We found different variability characteristics between the three settings, which may in part account for differences in empirical observations. From a clinical perspective, differences in striatal dopaminergic signaling contribute to differential learning and reward processing, with relevant implications for addictive- and compulsive-like behavior. Specifically, dopaminergic tone is assumed to impact on phasic dopamine and hence on the integration of reward-related signals. However, in humans DA tone is classically assessed using PET, which is an indirect measure of endogenous DA availability and suffers from temporal and spatial resolution issues. We discuss how this can lead to discrepancies with observations from other methods such as microdialysis and show how computational modeling can help to refine our understanding of DA transmission.

Plasticity in striatal dopamine release is governed by release-independent depression and the dopamine transporter

Mesostriatal dopaminergic neurons possess extensively branched axonal arbours. Whether action potentials are converted to dopamine output in the striatum will be influenced dynamically and critically by axonal properties and mechanisms that are poorly understood. Here, we address the roles for mechanisms governing release probability and axonal activity in determining short‐term plasticity of dopamine release, using fast‐scan cyclic voltammetry in the ex vivo mouse striatum. We show that brief short‐term facilitation and longer short term depression are only weakly dependent on the level of initial release, i.e. are release insensitive. Rather, short-term plasticity is strongly determined by mechanisms which govern axonal activation, including K⁺‐gated excitability and the dopamine transporter, particularly in the dorsal striatum. We identify the dopamine transporter as a master regulator of dopamine short‐term plasticity, governing the balance between release‐dependent and independent mechanisms that also show region‐specific gating.

Dopamine release in the striatum has important roles in action selection and in disorders such as Parkinson’s disease. The authors here show that short-term plasticity of dopamine release is strongly determined by axonal activation and dopamine transporters.

Computational cell analysis for label-free detection of cell properties in a microfluidic laminar flow

Although a flow cytometer, being one of the most popular research and clinical tools for biomedicine, can analyze cells based on the cell size, internal structures such as granularity, and molecular markers, it provides little information about the physical properties of cells such as cell stiffness and physical interactions between the cell membrane and fluid. In this paper, we propose a computational cell analysis technique using cells' different equilibrium positions in a laminar flow. This method utilizes a spatial coding technique to acquire the spatial position of the cell in a microfluidic channel and then uses mathematical algorithms to calculate the ratio of cell mixtures. Most uniquely, the invented computational cell analysis technique can unequivocally detect the subpopulation of each cell type without labeling even when the cell type shows a substantial overlap in the distribution plot with other cell types, a scenario limiting the use of conventional flow cytometers and machine learning techniques. To prove this concept, we have applied the computation method to distinguish live and fixed cancer cells without labeling, count neutrophils from human blood, and distinguish drug treated cells from untreated cells. Our work paves the way for using computation algorithms and fluidic dynamic properties for cell classification, a label-free method that can potentially classify over 200 types of human cells. Being a highly cost-effective cell analysis method complementary to flow cytometers, our method can offer orthogonal tests in companion with flow cytometers to provide crucial information for biomedical samples.

Heterogeneities in Axonal Structure and Transporter Distribution Lower Dopamine Reuptake Efficiency

Efficient clearance of dopamine (DA) from the synapse is key to regulating dopaminergic signaling. This role is fulfilled by DA transporters (DATs). Recent advances in the structural characterization of DAT from Drosophila (dDAT) and in high-resolution imaging of DA neurons and the distribution of DATs in living cells now permit us to gain a mechanistic understanding of DA reuptake events in silico. Using electron microscopy images and immunofluorescence of transgenic knock-in mouse brains that express hemagglutinin-tagged DAT in DA neurons, we reconstructed a realistic environment for MCell simulations of DA reuptake, wherein the identity, population and kinetics of homology-modeled human DAT (hDAT) substates were derived from molecular simulations. The complex morphology of axon terminals near active zones was observed to give rise to large variations in DA reuptake efficiency, and thereby in extracellular DA density. Comparison of the effect of different firing patterns showed that phasic firing would increase the probability of reaching local DA levels sufficiently high to activate low-affinity DA receptors, mainly owing to high DA levels transiently attained during the burst phase. The experimentally observed nonuniform surface distribution of DATs emerged as a major modulator of DA signaling: reuptake was slower, and the peaks/width of transient DA levels were sharper/wider under nonuniform distribution of DATs, compared with uniform. Overall, the study highlights the importance of accurate descriptions of extrasynaptic morphology, DAT distribution, and conformational kinetics for quantitative evaluation of dopaminergic transmission and for providing deeper understanding of the mechanisms that regulate DA transmission.

Kinetic Diversity of Striatal Dopamine: Evidence from a Novel Protocol for Voltammetry

In vivo voltammetry reveals substantial diversity of dopamine kinetics in the rat striatum. To substantiate this kinetic diversity, we evaluate the temporal distortion of dopamine measurements arising from the diffusion-limited adsorption of dopamine to voltammetric microelectrodes. We validate two mathematical procedures for correcting adsorptive distortion, both of which substantiate that dopamine's apparent kinetic diversity is not an adsorption artifact.