of 22
Ultrafast neuronal imaging of dopamine dynamics with designed
genetically encoded sensors
Tommaso Patriarchi
†,1
,
Jounhong Ryan Cho
†,2
,
Katharina Merten
3
,
Mark W. Howe
4
,
Aaron
Marley
5
,
Wei-Hong Xiong
6
,
Robert W. Folk
3
,
Gerard Joey Broussard
1
,
Ruqiang Liang
1
,
Min
Jee Jang
2
,
Haining Zhong
6
,
Daniel Dombeck
4
,
Mark von Zastrow
5
,
Axel Nimmerjahn
3
,
Viviana Gradinaru
2
,
John T. Williams
6
, and
Lin Tian
1,*
1
Department of Biochemistry and Molecular Medicine, University of California Davis, Davis, CA,
USA
2
Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA
91125, USA
3
Waitt Advanced Biophotonics Center, The Salk Institute for Biological Studies, 10010 North
Torrey Pines Road, La Jolla, CA 92037, USA
4
Department of Neurobiology, Northwestern University, Evanston, IL 60208, USA
5
Department of Cellular and Molecular Pharmacology, University of California, San Francisco,
San Francisco, CA, 94131, USA
6
Vollum Institute, Oregon Health & Science University, Portland, OR 97239, USA
Abstract
Neuromodulatory systems exert profound influences on brain function. Understanding how these
systems modify the operating mode of target circuits requires measuring spatiotemporally precise
neuromodulator release. We developed dLight1, an intensity-based genetically encoded dopamine
indicator, to enable optical recording of dopamine dynamics with high spatiotemporal resolution in
behaving mice. We demonstrated the utility of dLight1 by imaging dopamine dynamics
simultaneously with pharmacological manipulation, electrophysiological or optogenetic
*
Corresponding author. lintian@ucdavis.edu.
equal contribution
Author contributions:
L.T. and T.P conceived the project. T.P. and L.T. designed, screened and optimized sensors and characterized
them in mammalian cells and cultured neurons. R.L. simulated structure of the sensor. A.M. and M.V.Z. characterized signaling
properties of the sensor. W-H. X. and H.Z. characterized the sensor in organotypic brain slices. J.W. performed characterization in
acute brain slices. M.H. and D.D. characterized the sensor in vivo in the dorsal striatum. J.R.C. and V.G. performed fiber photometry
recordings coupled with optogenetic manipulations, calcium imaging and behavioral experiments in NAc, analyzed the data and
prepared the related figures and text. M.J.J. performed hybridization chain reaction experiments and prepared relevant figures and text
with input from J.R.C and V.G. K.M., R.F. and A. N. performed the two-photon imaging experiments in the cortex of behaving mice,
analyzed the data and prepared the related figures and text. All authors analyzed the data. L.T. led the project. L.T., T.P. wrote the
paper with contributions from all authors.
Competing interests:
L.T., R.L. and T.P. submitted a provisional patent application on sensor engineering.
Data and materials availability:
All DNA and viruses have been deposited in NCBI (accession number MH244549-MH244561),
ADDGENE and The University of Pennsylvania Vector Core. Computer codes are deposited in github (
https://github.com/
GradinaruLab/dLight1/
). All other data needed to evaluate the conclusion in the paper are present in the paper or the supplementary
materials.
List of Supplementary Materials:
Materials and Methods Figures S1–S16 Data S1–S3 References (
37
67
)
HHS Public Access
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Science
. 2018 June 29; 360(6396): . doi:10.1126/science.aat4422.
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stimulation, and calcium imaging of local neuronal activity. dLight1 enabled chronic tracking of
learning-induced changes in millisecond dopamine transients in striatum. Further, we used dLight1
to image spatially distinct, functionally heterogeneous dopamine transients relevant to learning
and motor control in cortex. We also validated our sensor design platform for developing
norepinephrine, serotonin, melatonin, and opioid neuropeptide indicators.
Animal behavior is influenced by the release of neuromodulators, such as dopamine (DA),
which signal behavioral variables that are relevant to the functioning of circuits brainwide.
Projections from dopaminergic nuclei to the striatum and cortex, for example, play
important roles in reinforcement learning, decision-making and motor control. Loss of DA
or dysfunction of its target circuits has been linked to disorders such as Parkinson’s disease,
schizophrenia, and addiction (
1
3
).
Much work has been devoted to determining how neural representations of behavioral states
are encoded in the firing patterns of neuromodulatory neurons (
4
9
), but very little is known
about how the precise release of neuromodulators alters the function of their target circuits
(
10
,
11
). To address this problem, one essential step is to monitor the spatiotemporal
dynamics of neuromodulatory signals in target circuits, while also measuring and
manipulating the elements of the circuit during behavior.
Analytical techniques such as microdialysis and electrochemical micro-sensors, have
provided useful insights about neuromodulator presence (
12
,
13
), but suffer from poor
spatial and/or temporal resolution, and cannot be targeted to cells of interest. Optical
approaches, such as injected cell-based systems (CNiFERs) (
14
) and reporter gene-based
iTango (
15
) can reveal dopamine release with high molecular specificity. However, these
systems are limited by poor temporal resolution (seconds to hours), preventing direct
detection of DA release events that occur on a sub-second time scale (
16
,
17
).
High-quality single fluorescence protein (FP)-based sensors that report calcium or glutamate
transients with subsecond temporal resolution have recently been developed and are widely
used (
18
,
19
). Here we report the development of a set of single-FP-based dopamine sensors,
named dLight1, that enables imaging of DA transients with high spatiotemporal resolution
in behaving animals.
Sensor engineering
Sensitive optical readout of changes in DA concentration was achieved by directly coupling
the DA-binding-induced conformational changes in human DA receptors to changes in the
fluorescence intensity of circularly permuted (cp) GFP. We did this by replacing the third
intracellular loop (IL3) of the human Dopamine D1 (DRD1), D2 (DRD2) and D4 receptors
(DRD4) with a cpGFP module from GCaMP6 (Fig. 1A).
To determine the insertion site of cpGFP in IL3 that produces maximal coupling of ligand-
induced conformational changes to cpGFP fluorescence, we aligned the sequences of DRD1
and DRD4 with that of
β
2
adrenergic receptor (B2AR) (Fig. 1B), for which both active and
inactive structure are available (
20
). The initial variant, obtained by inserting a cpGFP
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module (LSSLE-cpGFP-LPDQL) between K232 and K269 of DRD1, was well-expressed at
the plasma membrane of human embryonic kidney (HEK293) cells and showed a
fluorescence decrease (ΔF/F
max
= - 19.4 ± 0.02 %) in response to puffed DA (fig. S1A). To
obtain a positive-response sensor, we screened a library of 585 variants in HEK cells (Fig.
1C and fig. S1B). The variant with the largest positive fluorescence response (max ΔF/F
max
=
230 ± 9 %) and excellent membrane localization was named dLight1.1 (Fig. 1D). In situ DA
titration on HEK cells revealed sub-micromolar apparent affinity of dLight1.1 (330 ± 30 nM,
Fig 1E).
We next sought to further tune the dynamic range and affinity of the sensor. Mutation of
Phe129, a highly conserved residue among many GPCRs (
21
), into alanine (dLight1.2)
slightly increased dynamic range (max ΔF/F
max
= 340 ± 20%, apparent affinity: 770 ± 10
nM, Fig. 1D and E). Optimizing the cpGFP insertion site in dLight1.1 and dLight1.2 (fig.
S1C to G) greatly increased the dynamic range, but also reduced the affinity to micromolar
range (dLight1.3a: ΔF/F
max
= 660 ± 30 %, apparent affinity: 2300 ± 20 nM, fig S2A and B;
dLight1.3b: ΔF/F
max
= 930 ± 30 %, apparent affinity: 1680± 10 nM, Fig. 1D and E).
Insertion of the cpGFP module into DRD4 and DRD2 respectively produced dLight1.4 and
dLight1.5 respectively, which exhibited nanomolar affinity with a relatively small dynamic
range (dLight 1.4: ΔF/F
max
= 170 ± 10 %, apparent affinity: 4.1 ± 0.2 nM, Fig. 1B, D and E;
dLight1.5: ΔF/F
max
to DA= 180 ± 10 %, apparent affinity to DA: 110 ± 10 nM, and ΔF/F
max
to Quinpirole= 124 ± 19%, fig S2A to C). In addition, we engineered a control sensor by
incorporating a D103A mutation in dLight1.1 to abolish DA binding (ΔF/F= 0.4 ± 4 %, Fig.
1E) (
22
). Because dLight1.1 and dLight1.2 produced large responses at low DA
concentration (e.g.100 nM) without approaching response saturation (Fig. 1E inset) and had
submicromolar affinity, we further characterized these two sensors.
Sensor characterization
These two sensors showed peak emissions at 516 nm and 920 nm for one- and two-photon
illumination in HEK cells, respectively (fig. S3). In situ titration on dissociated hippocampal
neurons showed similar apparent affinities to DA as on HEK293 cells (fig. S4A to C, Fig
1E). Single pulses (5ms) of uncaged dopamine were robustly detected on the dendrites of
cultured neurons, and the fluorescence response tracked uncaging pulse duration (fig. S4D to
F). In cultured hippocampal slices, dLight1 could reliably detect sub-micromolar DA
concentration changes at dendrites and single dendritic spines (fig. S4G to I).
We then investigated the endogenous and pharmacological molecular specificity of the
sensor. dLight1 was ~70 and ~40 times less sensitive to norepinephrine and epinephrine than
to DA; negligible responses were observed to all other neuromodulators tested (fig. S5). The
amplitude of the response to each pharmacological compound reflected the efficacy of drugs
on the wild-type receptors, with the largest response to the full-agonist, dihydrexidine
(ΔF/F= 300 ± 10%), followed by partial agonists (Fig. 1F). The response to DA was
abolished in the presence of the DRD1 antagonists SKF-83566 and SCH-233990, but was
unaffected by the DRD2 antagonists haloperidol and sulpiride (Fig. 1F).
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To investigate the possible interference of sensor expression with G-protein signaling, we
first measured the effect of sensor expression on the ligand-induced cyclic AMP (cAMP)
response (fig. S6)(
23
). Transiently transfected dLight1.1 and dLight1.2 triggered no
significant cAMP response in HEK cells, similar to the negative control (EGFP); whereas
wild-type DRD1 did (**p<0.01; ***p<0.001, fig. S6A). The conversion of DRD1 to a
fluorescent sensor thus apparently blocked the scaffold’s ability to bind G-protein and
trigger the signaling cascade. When introduced into a cell line that endogenously expressed
DRD1 (U2OS), dLight1 did not significantly alter the dose-response curve to DA (p=0.96,
fig. S6B). dLight1 was also defective in agonist-induced internalization, a readout of DRD1
engagement of
β
-arrestin (
24
), when compared to wild type DRD1(**p<0.01, fig. S6C).
Total internal reflectance fluorescence (TIRF) imaging verified that dLight1 remained
diffusely distributed in the plasma membrane, without any detectable internalization, during
a complete cycle of ligand-dependent fluorescence change (fig. S6 D–F). Taken together,
these results indicate that the dLight sensors are suitable for use on the cell membrane
without affecting endogenous signaling through G-proteins or engagement of
β
-arrestins.
Versatile application to other neuromodulators
We next applied the design strategy of dLight1 to modularly develop a class of intensity-
based sensors for various neuromodulators and neuropeptides. We selected a subset of
GPCRs, including Gs-coupled
β
1
and
β
2
adrenergic receptors (B1AR and B2AR); Gi-
coupled k and μ-type opioid receptors (KOR, MOR), and
α
2
adrenergic receptor (A2AR);
and G
q
-coupled 5-hydroxytryptamine (serotonin) receptor-2A (5HT2A) and melatonin
type-2 receptor (MT2). As with dLight1, we replaced IL3 with cpGFP, with insertion sites
chosen to preserve the conserved positive charges (fig. S7A). All sensors localized to the
membrane and showed positive fluorescence responses to their respective agonists (fig.
S7B).
Two-photon imaging of dopamine release in dorsal striatum ex vivo and in
vivo
We next used dLight1 to measure the time course and concentration of endogenous
dopamine release triggered by electrical stimulation and drug modification in acute striatal
slices with 2P imaging (Fig. 2A). Two to four weeks after injection of an adeno-associated
virus encoding dLight1 (AAV9.
hSynapsin
1.dLight1.2) into the dorsal striatum, we observed
both broadly distributed and localized fluorescence transients across the field of view (Fig.
2B and C, fig. S8A to C) in response to a single electrical stimulus. Fast line scan at these
hotspots (Fig 2C) revealed a rapid onset of fluorescence increase (rise
τ
1/2
= 9.5 ± 1.1 ms)
followed by a plateaued peak (averaged ΔF/F= 220 ± 50%) for about 150 ms, which decayed
to baseline in about 400 ms (decay
τ
1/2
= 90 ± 11ms, Fig. 2D). We observed robust and
reproducible fluorescent transients to low-frequency stimuli over a prolonged imaging
period, whereas subsequent higher frequency stimuli elicited much smaller responses
(p<0.0001, Fig. 2E and F), indicating strong depression from an initially high probability of
release. Blockade of dopamine reuptake with cocaine significantly prolonged the decay of
fluorescence from peak to baseline (p<0.05, Fig. 2G and H), but with equivocal effect on
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response amplitude (P=0.056, Fig. 2G and H). Application of the competitive antagonist
SKF83566 eliminated the responses (fig. S8F), confirming that fluorescent signals are
indeed due to DA binding.
We next used dLight1 to estimate released DA concentration induced by a brief electrical
stimulus. By comparison with a concentration-response curve (fig. S8D, E and G), the
fluorescence response suggested a 10–30 μM DA release (Fig. 2I), which is 1–2 orders of
magnitude higher than previously reported in ventral striatum using fast-scan cyclic
voltammetry (FSCV) (
25
) and is similar to that reported by measuring DRD2 activation
(
26
). Addition of saturating amphetamine (10 μM, in the presence of 400 μM sulpiride)
increased tonic dopamine to 3.3 μM (fig. S8F and G).
We then examined the action of known modulators of dopamine release using dLight1 (Fig.
2J to L). Activation of D2-autoreceptors with quinpirole decreased the electrically evoked
fluorescence transients, which was reversed by the application of sulpiride (p<0.01, Fig. 2J).
Perfusion with a k-opioid receptor agonist (U69,593) caused a small decrease in the
amplitude (P<0.05, Fig. 2J), which was completely blocked by naloxone (Fig. 2J). We then
imaged the effects of nicotinic receptors activation in mediating the probability of dopamine
release. Blockade of nicotinic receptors with hexamethonium profoundly reduced the
fluorescence transient, which depended on the number of stimuli (*P<0.05, ***P<0.001,
Fig. 2K, L). In the absence of hexamethonium, the amplitude of the fluorescence remained
consistent regardless of the stimulation protocol (P=0.06, Fig. 2K, L) (
27
).
Next we asked whether dLight1 could reliably report dopamine signals associated with
mouse locomotion in dorsal striatum, which was labeled with
AAV1.
hSynapsin
1.dLight1.1/1.2 and AAV1.
hSynapsin
1.flex.tdTomato. We measured
dopamine transients with 2P imaging during rest and self-initiated locomotion (fig. S9).
Consistent with
in-vivo
2P calcium imaging of substantia nigra pars compacta (SNc) axon
terminals in dorsal striatum (
10
), dLight1 reliably showed widespread and synchronous sub-
second transients associated with spontaneous locomotion, which was clearly
distinguishable from motion artifacts (fig. S9A to E). The dopamine transients were rapidly
and bi-directionally modulated with respect to locomotion. Accelerations were associated
with an increase and decelerations with a decrease (peak mean cross-correlation 240 ms,
P<0.001, fig. S9F to L).
In summary, dLight1 faithfully and directly reports the time course and concentration of
local dopamine release and drug-dependent modulatory effects on dopamine release in acute
striatum slice. In addition, dLight1 enables direct visualization of locomotion-triggered
dopamine release in behaving mice.
Deep-brain recording of DA dynamics simultaneously with optogenetics or
calcium imaging
The nucleus accumbens (NAc) receives projections from dopaminergic neurons in the
ventral tegmental area (VTA). To directly probe DA release in freely moving mice, we
delivered AAV9.
CAG
.dLight1.1 or AAV9.
CAG
.control_sensor in the NAc, followed by
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fiber photometry imaging (Fig. 3, fig. S10A and B). dLight1 revealed visible spontaneous
dopamine transients, which were absent in the imaging sessions using the control sensor
(fig. S10C).
To optically activate VTA DAergic neurons, we infected VTA of
TH
::IRES-Cre mice with
AAV5.
hSynapsin
1.flex.ChrimsonR.tdTomato (
28
) (Fig. 3A to C, fig. S11A and B, fig. S12A
and D). The high temporal resolution of dLight1 enabled detection of individual peaks of
dopamine transients in response to 5, 10 and 20 Hz photostimulation (Fig. 3D, fig. S13A to
C). The amplitude of fluorescence increase was correlated with the frequency of
photostimulation (Fig. 3D and F). In contrast, no fluorescence changes were observed with
the control sensor using 20Hz stimuli (Fig. 3D and E). Compared to saline-injected controls,
systemic administration of SCH-23390 significantly reduced optogenetically-induced
dLight1 responses, whereas the reuptake inhibitor GBR-12909 enhanced them (Fig. 3G to
H).
Next, we examined whether dLight1 can report inhibition of DA transients. To induce
transient inhibition of VTA DAergic neurons, we optogenetically stimulated VTA
GABAergic neurons in
VGAT
::IRES-Cre mice (
29
) (Fig. 3I). Histology confirmed
ChrimsonR expression in VTA GABAergic neurons (fig. S12B, C and E). We observed
rapid and reversible reductions in dLight1 fluorescence in response to VTA GABAergic
neuron photoactivation at 40 Hz (Fig. 3J and K, fig. S13D), indicating that dLight1 can
report bidirectional changes in local DA release.
Motivationally salient stimuli modify dopamine neuron firing and downstream NAc activity
(
9
,
29
,
30
). To link the DA release to local neuronal activity, we performed dual-color
measurements with dLight1 and the red-shifted calcium indicator jRGECO1a (
31
) in lateral
core/shell regions (Fig. 3L, fig. S10B, S11A and S12F). When mice voluntarily consumed
reward (50 μL of 5% sucrose), we observed a concordant increase of DA concentration and
local population activity (Fig. 3M, N, and S13E), similar to a class of NAc single units
showing excitation upon reward (
32
). In contrast, footshocks suppressed DA release while
enhancing local neuronal activity, indicating dissociation between DA dynamics and local
circuit activity (Fig. 3O, P and S13F).
Chronic imaging of dopamine dynamics throughout cue-reward learning
We next examined the utility of dLight1 in reporting modulation of DA signaling in response
to conditioned (CS) and unconditioned (US) stimuli throughout Pavlovian conditioning (Fig.
4A)(
8
,
33
,
34
). Mice successfully learned to associate the predictive cues to the reward as
shown by increasing numbers of licks during CS over the course of training, and decreasing
licks during extinction learning (Fig. 4B).
Repeated fiber photometry recordings in the NAc revealed two types of DA transients
modulated during associative learning: increased DA response to the predictive cues and
decreased response to reward consumption across sessions. In the first session, a small and
time-locked phasic DA signal was present at the CS onset, while after US the DA signal was
larger and also more temporally spread (Fig. 4C and D), consistent with US consumption
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onsets being highly variable at early stages (Fig. S14A and B). Aligning to consumption
onset revealed large DA signal to the US at the first session (Fig. 4C and S14A). Upon
repeated cue-reward pairings, the amplitude of CS response significantly increased (Fig 4C,
D, F, H and S14C). On the other hand, US response, when aligned to the consumption onset,
showed linear decrease across learning sessions. (Fig. 4G and H). This suggests that DA
response gradually shifted from US to CS as animals acquired cue-reward association (Fig.
4H, and S14D) (
9
,
33
). During extinction, we observed an attenuated phasic CS response
(Fig. 4E, F and H). The amplitude of the phasic CS response was correlated with CS-
triggered licking behavior during both learning and extinction sessions (Fig. S14E),
suggesting that the CS response predicted the animal’s vigor to obtain reward.
We further investigated whether dLight1 can report signals correlated with “reward
prediction error” (RPE)(
4
). After the animals had fully learned CS-US association, mice
underwent “unexpected reward availability” sessions (in which the US was occasionally
made available without the CS) in between normal paired trials (Fig. 4I). Unexpected reward
consumption elicited significantly higher fluorescence compared to expected consumption
(Fig. 4J and K). In the “unexpected reward omission” session, where the US was
occasionally omitted after the predictive CS, fluorescence decreased below the pre-CS
baseline after the time at which the US would have normally become available CS
presentation (Fig. 4L and M).
Cellular-level imaging of functionally-heterogeneous dopamine transients
in mouse cortex
Finally, we tested whether 2P imaging with dLight1 can reveal the spatiotemporal release of
DA associated with reward in the cortex. The cortex receives projection axons from both
SNc and VTA. Inputs from these nuclei carry distinct dopaminergic signals, influencing
motor control and reward learning, respectively (
10
,
35
). To demonstrate the utility of
dLight1 in detecting behavior-related DA signals, we broadly labeled frontal/motor cortex
with AAV9.
hSynapsin
1.dLight1.2, followed by 2P imaging of dLight1-expressing layer 2/3
neurons in head-fixed mice. The animals had fully learned a visuomotor association task that
required them to run in response to a visual “Go” cue in order to receive a water reward (Fig.
5A and B). We observed task-related DA transients, distinguishable from motion artifacts
(fig. S15), across cell-sized regions of interest (ROIs) across the field of view (Fig. 5C, fig.
S16).
Aligning the DA transients to trial/stand still phase onset, we found two types of task
relevant DA responses during the reward expectation and reward delivery intervals. An
average of 63% of responsive ROIs showed significantly increased DA transients that
correlated with reward, which were abolished by unexpected reward omission (20% of
randomly selected trials) (Fig. 5D right panel). A subset of ROIs (~37%) showed
significantly increased DA transients that lasted during the short phase of “Go” stimulus
presentation for both rewarded and non-rewarded trials (Fig. 5D, left panel). These transient
increases during the stimulus presentation phase were not caused by the stimulus appearance
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itself, because no significant increase in DA levels was observed during miss trials during
which the animal saw the stimulus, but did not respond (Fig. 5D; yellow traces).
To investigate whether these early responses shown in 37% of ROIs reflect increased DA
levels during reward expectation or correlate with locomotion, we aligned the trials at
running onset (Fig. 5E, group averages; fig. S16G, single ROIs) and compared the DA
transients of runs triggered by the “Go” stimulus (when the animals expected a reward) with
spontaneous runs that erroneously occurred during the stand-still phase (with no reward
expectation).
A small subset of responsive ROIs (5%) showed significant increases in DA transients
during reward expectation but not spontaneous running (Fig. 5E, middle panel), whereas the
other 32% of ROIs correlated with locomotion (Fig. 5E, left panel). The 63% of ROIs
responsive to reward only (Fig. 5D, right panel) also showed increased DA transients during
the early stimulus presentation phase consisting of both locomotion or reward expectation
related responses (Fig. 5E, right panel). All three types of responses were consistently seen
across animals. Comparing the heterogeneity of response transients between layer 1 and
layers 2/3 of cortical area M1 (fig. S16E and F) we found that layer 2/3 showed more ROIs
active during reward. A similar number of ROIs responded to locomotion and reward
expectation in both layers (fig. S16H). Mesocortical dopaminergic projections are thus
spatially intermingled and activation of these inputs leads to spatiotemporally heterogeneous
DA signals in the cortex whose dynamics depends on motor behavior, reward expectation,
and consumption.
Conclusion
We developed and applied a new class of genetically encoded indicators that overcome
major barriers of current methods to permit high-resolution imaging of dopamine dynamics
in acute brain slices and in behaving mice. The sub-micromolar affinity and fast kinetics of
dLight1 offer fast temporal resolution (10 ms on and 100 ms off) to detect the
physiologically or behaviorally relevant DA transients with higher molecular specificity
compared to existing electrochemical or cell-based probes (
14
). For example, in the NAc of
freely behaving mice, longitudinal measurements revealed different changes in time-
resolved dopamine signals encoding either predictive cue or reward consumption across
learning.
The disparate contributions of synaptic, extra-synaptic, and spillover dopamine events to
circuit function are not addressable without fast, robust and genetically encoded sensors. In
dorsal striatal slice, dLight1 reliably detected the concentration and time-course of dopamine
transients and their modifications by pharmacological compounds. The rapid rise of
fluorescence (10ms) and the peak concentration (
10
30
μM) of dopamine following
electrical stimulation indicates that the initial measures of dopamine are closely associated
with the site of release (
26
). The decline of fluorescence, particularly in the presence of
cocaine, results primarily from reuptake and diffusion of dopamine away from release sites.
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dLight1 also permits high spatial resolution measurement of functionally-heterogeneous
dopamine transients at the cellular level. In the cortex, 2P imaging with dLight1 revealed a
dopamine transient map with spatially distributed, functionally heterogeneous dopamine
signals during a visuomotor learning task. Simultaneous calcium imaging can further
determine how spatiotemporal differences in DA levels relate to ongoing neural activity and
influence associative learning or goal-directed behavior. dLight1.1 and dLight1.2 represent
optimized sensor variants to be immediately applied to ex vivo or in vivo studies, as they
offer a good balance between dynamic range and affinity. Other dLight variants may be
suitable for measuring synaptic release (dLight 1.3) or tonic DA transients (dLight 1.4).
Given the broadly tunable affinity and dynamic range of dLight1, protein engineering and
high-throughput screening efforts can further optimize SNR and molecular specificity (
36
),
as well as performance of other neuromodulator indicators.
In combination with calcium imaging and optogenetics, our sensors are well poised to
permit direct functional analysis of how the spatiotemporal coding of neuromodulatory
signaling mediates the plasticity and function of target circuits.
Supplementary Material
Refer to Web version on PubMed Central for supplementary material.
Acknowledgments
We thank Brian Paul McGrew for assistance during in vitro sensor library screening and Huaiyang Cheng for
producing AAV virus. Luke Lavis (Janelia Research Campus) kindly provide us NV-caged DA. Erin Carey
performed cortical viral vector injections. Dr. Brett Mensh provided critical advice for writing and revising the
manuscript. This project was made possible with generous help from Dr. Loren Looger (HHMI Janelia Research
Campus).
Funding:
This work was supported by funding to L.T. (BRAIN Inititiave U01NS090604 and U01NS013522,
DP2MH107056 from National Institute of Health), to A.N. (DP2NS083038, R01NS085938, P30CA014195), J.W.,
and M.V.Z (BRAIN Initiative U01NS013522 from National Institute of Health), to H.Z. (BRAIN Initiative
U01NS094247 and R01NS104944), to D.D. (1R01MH110556 from National Institute of Health), and to V.G. (NIH
DP2NS087949, NIH/NIA R01AG047664). K.M. is a DFG research fellow and recipient of a Catharina Foundation
postdoctoral scholar award. V.G. is a Heritage Principal Investigator supported by the Heritage Medical Research
Institute.
References and Notes
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One sentence summary
We developed and applied genetically-encoded dopamine sensors for high spatiotemporal
optical measurement of dopamine release in vivo.
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Fig. 1. Development of dLight1 and versatile applications to other neuromodulators
(
A
) Simulated structure of dLight1 consisting DRD1 and cpGFP module. (
B
) Sequence
alignment of transmembrane (TM) domain 5 and 6 in
β
2
AR, DRD1 and DRD4. Library
design is shown. (
C
) Screening result of 585 linker variants. Fluorescence changes (ΔF/F) to
10 μM DA in vertical bar and significance values of ΔF/F in colored bar (n=3 trials, two-
tailed t test). (
D
) Expression of dLight variants in HEK cells. Fluorescence intensity and
signal-to-noise ratio of apo and sat state were shown. Scale bars: 10 μm. (
E
) In situ titration
of DA on HEK cells. Data were fitted with the Hill Equation (n=5). (
F
) Pharmacological
specificity of dLight1.1. DRD1 full agonist (Dihydrexidine, 295 ± 8% ΔF/F, n=5); DRD1
partial agonists (SKF81297, 230 ± 7.7%, n=5; A77636, 153 % 7.8%, n=7; Apomorphine, 22
± 0.8%, n=6); DRD1 antagonists (SCH-23390 - 0.04 ± 0.01%; SKF-83566, 0.04 ± 0.03%);
DRD2 antagonists (Sulpiride, 213 ± 5.1%, n=5; Haloperidol, 219 ± 11%, n=6). All data
shown as mean ± SEM. ****p<0.0001, One-way ANOVA, Dunnett’s post-test.
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Fig. 2. Imaging electrically evoked and pharmacologically modulated dopamine release in acute
dorsal striatum slices
(
A
) Schematics of experimental setup. (
B
) Single-trial fluorescence response (average in
black) in response to a single stimulus (0.5 ms). Images acquired at 15Hz using 2-photon
light at 920nm. Averaged ΔF/F= 182 ± 21% across 7 trials, mean ± SEM. Scale bar: 20 μm.
(
C
) Representative hotspot (ΔF/F) for line-scan. Scale bar: 20 μm. (
D
) Individual
fluorescence traces during line-scan (500Hz) in response to a single stimulus (average in
black across 13 trials). Inset shows zoomed-in view of the fluorescence plateau. (
E
)
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Fluorescence responses to low and high frequency stimuli (left 0.2Hz, right 1Hz) quantified
in (
F
) (Fold change in ΔF/F = 0.506 ± 0.061 at 1Hz across 5 trials,). (
G
) Single trial
fluorescence response in the presence of cocaine (10 μM) triggered by a single stimulus
overlayed with trace without cocaine. (
H
) Quantification of fold change in peak fluorescence
amplitude (1.056±0.095, n=7, P=0.056) and duration (3.15±0.213, n=4,). (
I
) Estimation of
released DA concentration (single trial trace shown). (
J
) Quantification of fold change in
peak fluorescence in the presence of bath applied sulpiride (400nM) (0.437 ± 0.052, n=5),
quinpirole (1 μM) (0.926 ± 0.070, P<0.01, n=5), U69,593 (1 μM) (0.838 ± 0.042, n=4,) and
Naloxone (1 μM) (1.022 ± 0.053, n=4). (
K
) Single-trial fluorescence response to either a
single pulse (black) or a train of 5 pulses at 40Hz (red) in the absence (left) and presence
(right) of the nicotinic acetylcholine receptor blocker hexamethonium (200 μM). (
L
)
Quantification of fold change in peak fluorescence response in
K
(Hex/Control: 0.561
± 0.038, n=10,; control 5stim/1stim: 1.13 ± 0.069, n=7; Hex 5stim/1stim: 1.76±0.16, n=6).
*p<0.05, **p<0.01, ***p<0.001, ****p<0.0001, paired t test.
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Fig. 3. Deep brain imaging of DA release triggered by optogenetic stimulation and combined
with calcium imaging in freely behaving mice
(
A
) Schematics showing fiber photometry recording of dLight1.1 or control sensor in the
NAc while stimulating VTA DA neurons by optogenetics. (
B
) Expression of dLight1.1 in the
NAc around fiber tip location and ChrimsonR expressing axons from midbrain. (
C
)
ChrimsonR-expressing TH
+
DA neurons in the VTA. (
D
) Averaged fluorescence increase in
response to optogenetic stimuli (n = 5 mice)
(E)
Quantification of peak fluorescence at
20Hz.
(F)
Fluorescence fold changes relative to 5Hz. (
G–H
) Optogenetically induced
fluorescence increase of dLight1.1 after systemic administration of saline, D1 antagonist
(SCH-23390, 0.25 mg/kg) and DA reuptake inhibitor (GBR-12909, 10 mg/kg) (n = 5 mice).
(
I
) Schematics showing fiber photometry recording of dLight1.1 in the NAc and optogenetic
stimulation of VTA GABA neurons that inhibits VTA DA neurons. (
J–K
) Averaged
fluorescence decrease in response to optogenetic stimulation at 40 Hz (n = 4 mice) and mean
fluorescence quantified in
K
.
(L–P)
Dual-color fiber photometry recording of DA release
with dLight1.1 and local neuronal activity with jRGECO1a. (
M–N
) Increase of dLight1.1
(green) and jRGECO1a (magenta) fluorescence during 5% sucrose consumption with lick
rate (black, n = 5 mice). Mean fluorescence was quantified in (N). (
O–P
) Fluorescence
decrease in dLight1.1 (green) and increase in jRGECO1a (red) during unpredictable
footshock delivery (0.6 mA for 1 sec, n = 5 mice). Mean fluorescence was quantified in (P).
Data shown are means ± SEM. Significance was calculated by means of paired or unpaired
t
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tests for two-group comparisons and one-way ANOVA by post hoc Tukey’s test for
multiple-group comparisons. * p < 0.05, ** p < 0.01, *** p < 0.001.
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Fig. 4. Dynamic changes of NAc DA signaling during appetitive Pavlovian conditioning and
reward prediction error
(
A
) Pavlovian conditioning procedures involved learning to associate neutral cues (CS;
house light and 5 kHz tone) with a sucrose reward (US; 50μL of 5% sucrose), and
subsequent extinction. (
B
) Change of CS-evoked licks across cue-reward learning (left) and
extinction (right). (
C–D
) dLight1.1 dynamics in response to CS and US in first and last
sessions of cue-reward learning, shown in single (gray) and averaged (blue) trials (n=20
trials) from a single animal (C) or averaged across all trials and animals (n=5 mice) (D).
Lick rate shown in black. (
E
) Same as (D), of cue-reward extinction (n = 5 mice). In D and
E, the dotted lines indicate CS onset, US onset and CS offset respectively. (
F–H
) Evolution
of CS- (F) and US- evoked (G left panel) average fluorescence and US triggered licks (G
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right panel) across learning and extinction sessions. Quantification of peak fluorescence
across learning and extinction shown in (H).
(I)
Reward prediction error procedure. (
J
)
Fluorescence response during expected (red) versus unexpected (black) reward consumption
(n = 4 mice). (
K
) Peak fluorescence evoked by expected (red) and unexpected (black)
reward consumption. (
L
) Fluorescence response during expected (red) versus unexpected
reward omission (brown) (n = 4 mice). Second and third dotted line indicates US onset and
CS offset. (
M
) Mean fluorescence during baseline and after unexpected reward omission.
Data shown are means ± SEM. Significance was calculated from Pearson’s correlation
coefficient and with paired t-test. * p < 0.05, ** p < 0.01.
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Fig. 5. Spatially resolved imaging of cortical dopamine release during a visuomotor association
task
(
A
) Schematics of experimental setup. (
B
) To initiate a trial, mice were required to stand still
for 10s following a visual cue (blue square). If mice started to run during the stimulus phase
(“Hit trials”), a water reward was given. In 20% of randomly selected “Hit trials” the reward
was withheld. If no run was triggered by stimulus presentation, the trials were counted as
“Miss trials”. Erroneous/spontaneous runs during the stand-still phase ended the trial (no
“Go” cue or reward). (
C
) Top, dorsal view of mouse cortex with the chronic cranial window
(circle) and imaging location indicated (square). Bottom, heatmap of dLight1.2 expression
pattern in layer 2/3 of M1 cortex. The image is overlaid with computationally defined
regions of interest (ROIs, ~17×17μm). Colored ROIs indicate the type of fluorescence
responses observed during the task. (
D
) Population data (N = 4 mice, n = 19 recording
sessions) showing average task-related dLight1.2 transients (bottom) and mouse running
velocity (top) aligned to trial/stand-still cue onset (0s). The solid vertical line indicates “Go”
cue onset. The dotted line marks the end of the reward expectation phase during unrewarded
Hit and Miss trials. The period during which running velocity-dependent reward
consumption occurred is indicated by the horizontal line. Left, ROIs showing significantly
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