![]() ![]() The red fill pattern indicates the percentage of NE and DA relative to the highest measured value. ( b) RdLight1 validations: (i) dual-color fiber photometry of VTA terminals in the NAc using the green axon-targeted calcium sensor axon-GCaMP6f and local DA release using RdLight1 following unexpected, audible reward deliveries (dotted line) and (ii) fiber photometry imaging of DA release using RdLight1 in the NAc following optogenetic stimulation (1–32 pulses 2 seconds) of DA cell bodies in the VTA using the green opsin ChR2.ĭistribution of norepinephrine (NE) (left hemispheres) and dopamine (DA) (right hemispheres) neurotransmitter levels as measured by enzyme isotope biochemistry assays in micropunches of the rat brain. orange) at the Go cue (vertical line) and increases in fluorescence upon reward delivery (dark green) but not reward omission (light green) (population data) (middle). ( a) dLight1 variants were validated in multiple imaging and experimental modalities: (i) dual-color fiber photometry of nucleus accumbens (NAc) cells using the red-shifted calcium sensor jRGECO1a and local DA release using dLight1.1 following unpredictable shock exposure (dotted line) (ii) fiber photometry imaging of DA release using dLight1.1 in the NAc following optogenetic stimulation (5–20 Hz, 2 seconds) of DA cell bodies in the ventral tegmental area (VTA) using the red-shifted opsin ChrimsonR (iii) two-photon imaging of DA release (dLight1.2) (top) across heterogeneous sites in motor cortex (M1/M2) across 17 μM large regions of interest (= red square ROIs, bottom), here showing ROIs responsive to locomotion/reward expectation vs. Graphs show normalized fluorescent responses (dFF). In vivo applications of dLight1 and RdLight1 dopamine (DA) sensors in mice. Altogether this review should act as a tool to guide DA sensor choice for end-users.īehavior dopamine drug screening fiber photometry fluorescent biosensor genetically encoded in vivo fluorescent imaging neuromodulator pharmacology. We then outline a map of DA heterogeneity across the brain and provide a guide for optimal sensor choice and implementation based on local DA levels and other experimental parameters. In this review, we use DA as an example we briefly summarize old and new techniques to monitor DA release, including DA biosensors. Molecular specificity, sensor kinetics, spectral properties, brightness, sensor scaffold and pharmacology can further influence sensor choice depending on the experimental question. Sensor properties, most importantly their affinity and dynamic range, must be carefully chosen to match local DA levels. When implementing these tools in the laboratory, it is important to consider there is not a 'one-size-fits-all' sensor. Combined with rapid developments in in vivo imaging, these sensors have the potential to transform the field of DA sensing and DA-based drug discovery. Recently, red and green genetically encoded sensors for DA (dLight, GRAB-DA) were developed and now provide the ability to track release dynamics at a subsecond resolution, with submicromolar affinity and high molecular specificity. Understanding how dopamine (DA) encodes behavior depends on technologies that can reliably monitor DA release in freely-behaving animals. ![]()
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