1Department of Physiology and Neuroscience, New York University School of Medicine, New York, New York 10016; and 2University Department of Pharmacology, Oxford OX1 3QT, United Kingdom
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ABSTRACT |
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Cragg, Stephanie J.,
Charles Nicholson,
June Kume-Kick,
Lian Tao, and
Margaret E. Rice.
Dopamine-Mediated Volume Transmission in Midbrain Is Regulated by
Distinct Extracellular Geometry and Uptake.
J. Neurophysiol. 85: 1761-1771, 2001.
Somatodendritic
release of dopamine (DA) in midbrain is, at least in part, nonsynaptic;
moreover, midbrain DA receptors are predominantly extrasynaptic. Thus
somatodendritic DA mediates volume transmission, with an efficacy
regulated by the diffusion and uptake characteristics of the local
extracellular microenvironment. Here, we quantitatively evaluated
diffusion and uptake in substantia nigra pars compacta (SNc) and
reticulata (SNr), ventral tegmental area (VTA), and cerebral cortex in
guinea pig brain slices. The geometric parameters that govern
diffusion, extracellular volume fraction () and tortuosity (
),
together with linear uptake (k'), were determined for
tetramethylammonium (TMA+), and for DA, using
point-source diffusion combined with ion-selective and carbon-fiber
microelectrodes. TMA+-diffusion measurements
revealed a large
of 30% in SNc, SNr, and VTA, which was
significantly higher than the 22% in cortex. Values for
and
k' for TMA+ were similar among
regions. Point-source DA-diffusion curves fitted theory well with
linear uptake, with significantly higher values of k' for DA
in SNc and VTA (0.08-0.09
s
1) than in SNr (0.006 s
1), where DA processes
are sparser. Inhibition of DA uptake by GBR-12909 caused a greater
decrease in k' in SNc than in VTA. In addition, DA uptake
was slightly decreased by the norepinephrine transport inhibitor,
desipramine in both regions, although this was statistically
significant only in VTA. We used these data to model the radius of
influence of DA in midbrain. Simulated release from a 20-vesicle point
source produced DA concentrations sufficient for receptor activation up
to 20 µm away with a DA half-life at this distance of several hundred
milliseconds. Most importantly, this model showed that diffusion
rather than uptake was the most important determinant of DA time course
in midbrain, which contrasts strikingly with the striatum where uptake
dominates. The issues considered here, while specific for DA in
midbrain, illustrate fundamental biophysical properties relevant for
all extracellular communication.
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INTRODUCTION |
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When considering how dopamine (DA) mediates volume transmission, the question, "how far does dopamine diffuse?" always arises. As framed, however, the question is incomplete. Without uptake or metabolism to remove DA from the extracellular space, released molecules could diffuse infinitely far. Because extracellular DA concentration ([DA]o) will decrease with increasing distance from the source, however, the sphere of DA influence would be limited by the initial number of molecules released and the sensitivity of local DA receptors. Importantly, [DA]o would also be influenced by the extracellular volume into which DA is initially released. Moreover, the half-life of [DA]o at a given distance from a release site would be governed by the apparent diffusion coefficient of DA. In dopaminergic regions, the sphere of influence of released DA could also be significantly constrained by regionally distinct DA uptake. Here, we determined how these factors regulate DA diffusion in the substantia nigra pars compacta (SNc) and pars reticulata (SNr) and the ventral tegmental area (VTA).
Somatodendritic release of DA in midbrain (Cragg et al.
1997; Geffen et al. 1976
; Nieoullon et
al. 1977
; Rice et al. 1997
) may arise from both
synaptic and nonsynaptic sites; DA synapses are few in number in
midbrain (Bayer and Pickel 1990
; Juraska et al.
1977
; Wassef et al. 1981
; Wilson et al.
1977
), and nonsynaptic release from DA soma has been
demonstrated (Jaffe et al. 1998
). In addition, DA
receptors and the DA transporter (DAT) on DA cell bodies and dendrites
are largely extrasynaptic (Nirenberg et al. 1996
,
1997
; Sesack et al. 1994
; Yung et
al. 1995
), as are D1 receptors on
nondopaminergic terminals in these regions (Cameron and Williams 1993
; Yung et al. 1995
). Thus
somatodendritically released DA relies on extracellular diffusion to
reach its sites of action, a process known as volume transmission
(Fuxe and Agnati 1991
; Rice 2000
).
The geometric parameters that govern diffusion, outlined above, are the
extracellular volume fraction () and the tortuosity (
) of
diffusion paths in tissue (Nicholson and Syková
1998
). In many brain regions, including cerebral cortex and
neostriatum,
is about 0.2, or 20% of brain volume (Cserr et
al. 1991
; Lehmenkühler et al. 1993
;
Pérez-Pinzón et al. 1995
; Rice and
Nicholson 1991
). Tortuosity,
, reflects the increased
path-length molecules encounter as they diffuse around cellular
elements, compared with that in free solution (Nicholson and
Syková 1998
). A consequence of tortuosity is that the
apparent diffusion coefficient in tissue, D*, is decreased from that in solution, D, such that D* = D/
2.
The concentration of a diffusing substance will also be influenced by
uptake. For small, nonbiological molecules, like tetramethylammonium (TMA+), uptake is low and can be described by the
linear uptake term, k' (Nicholson 1992;
Nicholson and Phillips 1981
). Diffusion of biological molecules, like DA, however, can be profoundly limited by
saturable, nonlinear uptake, especially in axon terminal regions like
neostriatum (Garris and Wightman 1995
;
Wightman et al. 1988
). Fitting point-source DA diffusion
records from striatum to theory therefore necessitates incorporation of
Michaelis-Menten uptake parameters (Nicholson 1995
;
Rice and Nicholson 1995
). Somatodendritic uptake of DA
in midbrain, however, appears to be less avid than that in striatum
(Cragg et al. 1997
).
Here, the structural characteristics of midbrain and cortex were
defined using the TMA+ method of diffusion
analysis (Nicholson 1993; Nicholson and
Syková 1998
) in guinea pig brain slices. Then the unique
properties of DA diffusion in midbrain were elucidated using fast-scan
cyclic voltammetry (FCV) at carbon-fiber microelectrodes (Rice
and Nicholson 1995
); specific uptake systems that remove
extracellular DA were evaluated pharmacologically. Having established
these fundamental characteristics of DA diffusion, we modeled the
sphere of influence of somatodendritically released DA.
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METHODS |
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Brain slice preparation
Male guinea pigs (Hartley strain), 150-250 g, were anesthetized
with 40 mg kg1 ip pentobarbital sodium and decapitated,
following National Institutes of Health guidelines and with approval by
the New York University School of Medicine Institutional Animal Care
and Use Committee. Coronal midbrain slices, 400 µm thick, were
prepared as described previously (Rice et al. 1997
)
using a vibrating-blade microtome (Campden Vibroslice, WPI, Sarasota,
FL), with slice coordinates between 7.3 and 8.3 mm anterior to the
interaural line (Smits et al. 1990
). The presence and
location of the accessory optic tract was used to define the
appropriate region of midbrain and to delineate SNc (lateral to tract)
versus VTA (medial). In some experiments, coronal slices of cerebral
cortex (400 µm thick, frontoparietal, motor area) were also prepared.
Slices were cut in ice-cold HEPES-buffered physiological saline
containing (in mM) 120 NaCl, 20 NaHCO3, 10 glucose, 6.7 HEPES acid, 5 KCl, 3.3 HEPES sodium salt, 2 CaCl2, and 2 MgSO4,
saturated with 95% O2-5% CO2; the slices were then maintained in this
solution at room temperature for at least 1 h before
experimentation (Rice et al. 1994
).
Experiments were performed in a submersion recording chamber at 32°C,
superfused at 1.3 mL min1 with bicarbonate-buffered
physiological saline, containing (in mM) 124 NaCl, 26 NaHCO3, 10 glucose, 2.4 CaCl2, 3.7 KCl, 1.3 MgSO4, and 1.3 KH2PO4; saturated
with 95% O2-5% CO2. For
TMA+-diffusion measurements, 0.5 mM TMA-Cl was
added to the solution. For evaluation of the effect of DA and
norepinephrine (NE) transport inhibitors on DA uptake, the selective
DAT inhibitor, GBR-12909 (2 µM; RBI, Natick, MA), or the selective NE
transport (NET) inhibitor, desipramine (2 µM; Sigma, St. Louis, MO),
was added.
Recording positions for measurements in SN and VTA were referenced to a
series of coronal midbrain sections processed for tyrosine hydroxylase
immunoreactivity (TH-ir) (Cragg et al. 1997; Rice
et al. 1997
). For this histology, slices were fixed overnight in 4% paraformaldehyde with 75% saturated picric acid in 0.1 M phosphate-buffered saline (pH 7.4) at 4°C. Fixed slices were then resectioned at 50 µm and processed for TH-ir, as described previously (Nedergaard and Greenfield 1992
).
TMA+ diffusion
Geometric diffusion parameters were determined using the
TMA+ method (Nicholson 1993;
Nicholson and Syková 1998
), which is based on the
analysis of TMA+ diffusion curves monitored with
an ion-selective microelectrode (ISM) placed at a fixed, known distance
from an iontophoresis or pressure ejection source of
TMA+. Here TMA+ was
introduced by iontophoresis. TMA+-ISMs were
prepared from theta glass and calibrated using the fixed interference
method, as previously described (Nicholson 1993
;
Nicholson and Rice 1988
). The ion exchanger was Corning 477317 (presently available from WPI as Liquid Ion Exchanger type IE
190); the ion-sensing barrel was back-filled with 150 mM TMA-Cl and the
reference barrel with 150 mM NaCl. Potentials were recorded using a
custom-built amplifier; potentials at the reference barrel were
continuously subtracted from the ion signal using a CyberAmp 320 (Axon
Instruments, Foster City, CA). Reference and subtracted ion signals
were monitored on a chart recorder and on a digital oscilloscope.
Iontophoresis micropipettes were prepared from theta glass, with tip sizes of 1-3 µm, and contained 150 mM TMA-Cl. Iontophoresis parameters were typically a +100-nA current step on a +20-nA bias, applied using an Axoprobe 1A (Axon Instruments, Foster City, CA). The shank of the iontophoresis micropipette was bent to an angle of roughly 30° before filling, so that the shanks of the pipette and the ISM were parallel when the iontophoresis pipette and ISM were glued together with dental cement. Tip separation (r) was 100-160 µm.
Diffusion records for TMA+ were first obtained in
0.3% agarose (NuSieve; Rockland, ME) in 150 mM NaCl with 0.5 mM
TMA+ to obtain the transport number,
nt, for the iontophoresis pipette in the
recording chamber at 32°C. Concentration-time profiles recorded on
the digital oscilloscope were transferred to a PC and fitted to the
diffusion equation for an iontophoretic point source (Nicholson
1993) using locally written software (VOLTORO and WALTER, both
programs written by and available from Nicholson). Values for
,
,
and k' were determined from diffusion curves in brain slices
(Nicholson 1993
; Nicholson and Syková
1998
; Rice and Nicholson 1991
) using the
following expression
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(1) |
DA diffusion
Carbon-fiber microelectrodes (CFMs) were spark-etched to a tip
diameter of 2-4 µm and had an active surface extending 30-50 µm
below the insulating glass sheath (MPB Electrodes, Queen Mary and
Westfield College, London, UK) (see Millar 1992).
Electrodes were connected to an EI400 potentiostat (currently available
through Cypress Systems, Lawrence, KS). Scan rate for FCV was 900 V/s with a sampling frequency of 4 Hz; scan range was
0.7 to +1.3 V
versus Ag/AgCl. The current at the oxidation peak potential for DA
(typically +0.6 V vs. Ag/AgCl) was monitored continuously on a chart
recorder; DA-diffusion curves were recorded on a digital oscilloscope
and transferred to a PC for subsequent analysis using VOLTORO and/or
WALTER. Subtraction voltammograms for DA were recorded periodically to
confirm signal identity. CFMs were calibrated with 0.5-5 µM DA in
bicarbonate-buffered physiological saline in the slice chamber at
32°C; GBR-12909 and desipramine had no effect on electrode
sensitivity for DA at the concentrations of these transport inhibitors
used (2 µM). The absolute response time of the electrodes was not
determined, however, measurements of DA diffusion in 0.3% agarose with
these electrodes yielded the predicted D for DA (not
illustrated). That the electrodes could follow the faster rates of
change in [DA] in agarose indicates that they should also accurately
record diffusion kinetics in tissue.
DA was introduced into the tissue by pressure ejection using a Picospritzer (General Valve, Fairfield, NJ). Pressure ejection, rather than iontophoresis, was used to minimize the duration of DA diffusion curves as well as limit the maximum [DA]o in the tissue. Ejection pipettes were prepared from 1 mm OD glass capillary tubing, with tip diameters of 3-5 µm. The shanks of the pipettes were bent; DA backfill concentration (Cf) was 400 µM DA in 150 mM NaCl, with 1 mM thiourea included to prevent DA oxidation. The pipette and the carbon-fiber microelectrode were mounted with shanks parallel and spacing, r, of typically 100 µm on a dual electrode holder attached to a three-dimensional manipulator (HMD-2 and WR-91, Narishige, Tokyo). Pulse pressure and duration were adjusted to give average ejected volumes (U) that produced [DA]o at the CFM of approximately 2-3 µM, although a range of [DA]o was obtained at each site.
Diffusion of DA was evaluated in SNc, SNr, and VTA. Pressure ejection
drop volume for each curve was calculated using the value for obtained from TMA+-diffusion measurements in each
region. Concentration-time profiles for DA were fitted using the
solution to the diffusion equation for pressure ejection
(Nicholson 1985
, 1992
), again using the program WALTER
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(2) |
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As the volume injected becomes small, i.e., b 0, and
assuming that nDA remains constant,
Eq. 2 tends to the well-known expression for an
instantaneous point source (Nicholson 1985
,
1992
)
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(3) |
Dextran diffusion
To visualize the ejection of DA and enable further analysis, 1 mM 3000 Mr dextran labeled with the
fluorescent probe, Texas Red (Molecular Probes, Eugene, OR) was added
to the DA backfill solution, so that DA and dextran co-diffused.
Two-dimensional images of dextran diffusion were obtained using the
Integrative Optical Imaging (IOI) method, as described by
Nicholson and Tao (1993). The imaging system consisted
of a Zeiss Standard microscope equipped with a Nikon ×10 water
immersion objective and an epifluorescence illuminator with appropriate
excitation and barrier filters for Texas Red. Images were recorded by a
charge-coupled device (CCD) camera [Model CH250, Photometrics (now
Roper Scientific), Tucson, AZ], cooled to
40°C. The camera was
equipped with a Thompson CCD array with 576 × 384 pixels and
14-bit resolution. Images were transferred directly to a PC for
analysis, using software written by Tao and Nicholson. The image
intensity along a line through the center of each two-dimensional
images, recorded with a 50-ms exposure at 8 to 10-s intervals,
was fitted with a modified form of Eq. 3 that took into
account the defocused point spread function of the microscope objective
(Nicholson and Tao 1993
). This curve fitting permitted
the apparent diffusion coefficient of the dextran to be measured, and
hence
, but in the present paper, the fitting was simply used to
provide qualitative comparisons of ejected volumes under different conditions.
Electrode placement and orientation
Electrode arrays were lowered at an angle of 30° into the slice to a vertical depth of 200 µm from the slice surface. The diffusion path between electrodes was along a dorsal-ventral axis (approximately a 45° angle to the ventromedial-dorsolateral band of cell bodies in the SNc). Exact electrode spacing for each set of diffusion records was determined from images of the electrode array in solution, obtained with the imaging system.
Calculation of DA clearance rates
To estimate clearance rates, the maximum value of
|dC/dt| (in µM s1) on the
falling phase of diffusion curves was determined both for experimental
and theoretical records, as described further in RESULTS.
The term "clearance" is frequently used to describe removal of DA
(or another compound) from the vicinity of a measurement location. The
measure of clearance used here is similar to the chord slope of the
change in [DA]o between two time points on the
falling phase used previously by Gerhardt and co-workers (e.g., Hoffman et al. 1998
; Zahniser et al.
1999
), but the present measure offered analytical advantages.
Both measures of clearance have some limitations, which will be
addressed later in the paper. It should be noted, however, that these
limitations are relevant primarily for discussions of diffusion from a
point source; clearance will have a different significance when other
types of release geometry are examined, including stimulated release
from a large number of synapses surrounding a measurement site (e.g.,
Garris and Wightman 1995
; Wightman et al.
1988
). The present clearance calculations and all analyses
using WALTER were carried out with MATLAB (MathWorks, Natick, MA).
Data analysis and statistics
All data are means ± SE; n is the number of measurements, typically three per region per slice, 2-4 slices per animal. Statistical significance was evaluated using one-way ANOVA, with post hoc, pair-wise Tukey test using SigmaStat (SPSS, Chicago, IL); significance was considered to be P < 0.05.
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RESULTS |
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TMA+ diffusion: ,
, and k' in midbrain and cortex
Geometric factors that govern extracellular diffusion in SNc, SNr,
and VTA were evaluated from TMA+ diffusion in
midbrain slices, monitored using a TMA+-ISM;
comparative measurements were made in cortical slices. Experimental data were fitted to the diffusion equation for iontophoresis (Eq. 1) to extract ,
, and k' for each record; the
smooth line through each indicates the theoretical curve that fits the
data (Fig. 1). In midbrain,
was
generally 0.30 (Fig. 2A), with
average values of 0.30 ± 0.01 in SNc (mean ± SE,
n = 38), 0.29 ± 0.01 in SNr (n = 34), and 0.30 ± 0.01 in VTA (n = 26). These data
contrasted sharply with the
in cortex, 0.22 ± 0.01 (n = 71), so that
was nearly 40% greater in the
midbrain (P < 0.001).
|
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The other geometric parameter, , was more nearly equal between
midbrain and cortex (Fig. 2B). In SNc,
= 1.58 ± 0.01 (n = 38), in VTA, 1.62 ± 0.02 (n = 26), and in cortex,
= 1.59 ± 0.02 (n = 71). The value of
for SNr, however, was
somewhat higher than in SNc, VTA, or cortex (P < 0.001), with an average value of 1.69 ± 0.02 (n = 34).
In contrast to these variations in and
, there were no
significant differences in TMA+ uptake among the
regions examined, with average k' values of 0.005-0.007
s
1 (Fig. 2C).
Theoretical diffusion curves for iontophoretically introduced
TMA+ were generated using Eq. 1 with
the average values of ,
, and k' for each region (Fig.
2D). These simulated concentration-time profiles indicate
how regional differences in diffusion parameters affected the time
course and extracellular concentration of TMA+
([TMA+]o). When all other
parameters (i.e., diffusion distance, iontophoresis transport number)
were held constant, the similar geometric diffusion parameters in SNc
and VTA led to nearly identical TMA+-diffusion
curves (Fig. 2D). In simulated diffusion curves for SNr,
however, [TMA+]o was
initially slightly lower than in SNc and VTA, but exceeded those in SNc
and VTA at later times because of the higher
in SNr (Fig.
2B). By contrast,
[TMA+]o in each midbrain
region was lower at most times than in cortex, because of the larger
in midbrain (Fig. 2A). Interestingly, the larger
in
SNr compared with cortex caused
[TMA+]o in SNr to exceed
that in cortex at later time points in these records (Fig.
2D).
DA diffusion in midbrain: , k', and U
Concentration-time curves for DA after local pressure ejection
were recorded in SNc, SNr, and VTA (Fig.
3A). These diffusion records
were fitted to theory using the basic diffusion equation for pressure
ejection (Eq. 3), with the theoretical fit indicated by the
smooth line superimposed on each record (Fig. 3A). The average value of determined for each region from
TMA+-diffusion measurements was used to fit the
DA data, so that the ejected drop volume, U, for each record
could be calculated. Independent parameters determined in this
analysis, therefore were
, k', and U.
|
Average values of determined for DA diffusion in SNc, SNr, and VTA
were slightly (~6%), but significantly, higher than those for
TMA+ diffusion in each midbrain region
(P < 0.001). Reasons for this are unclear, but it has
been suggested that hydroxylated, positively charged amines, including
DA, might diffuse more slowly in brain tissue than nonpolar
TMA+ because of cell-surface interactions
(Rice et al. 1985
). Average
values were 1.68 ± 0.02 (n = 52) in SNc, 1.80 ± 0.02 (n = 24) in SNr, and 1.72 ± 0.02 (n = 46) in VTA. This pattern of regional differences
in
was similar to that seen with TMA+: the
average value of
was significantly higher in SNr than in either SNc
(P < 0.001) or VTA (P < 0.05), but
did not differ between SNc and VTA.
Diffusion profiles for DA in SNc, SNr, and VTA could be fitted well to
Eq. 3 using the linear uptake term, k' (Fig.
3A). Although linear uptake was also appropriate for fitting
TMA+ curves, the k' values for DA were
over 10-fold larger than for TMA+ (Fig.
3C; P < 0.001), at least in SNc and VTA. In
these regions, average DA k' was 0.085 ± 0.005 s1 (n = 53) in SNc and 0.093 ± 0.006 s
1 (n = 45) in VTA (P > 0.05 for SNc compared with VTA). By
contrast, DA diffusion curves in SNr required a k' of only
0.006 ± 0.001 s
1
(n = 24), which was significantly lower than in either
SNc or VTA (P < 0.001) and indistinguishable from that
for nonspecific TMA+ uptake (Fig. 3C).
Consistent with a linear uptake process, k' values at a given site were independent of ejection drop volume, U, and hence [DA]o over the range examined (Fig. 3B). Indeed, with overall values of U that ranged from 3 to 150 pL in these experiments, there was no correlation between U and k' for DA in any midbrain region [R2 = 0.01 (n = 53) in SNc; R2 = 0.05 (n = 24) in SNr; and R2 = 0.22 (n = 45) in VTA]. Maximal [DA]o recorded in these regions was on average 2-3 µM, 100 µm away from the point source, although the absolute range was much greater. Conversely, the influence of k' on U was indicated by the larger average U required in SNc and VTA than in SNr to reach similar peak [DA]o (Fig. 3, A and C). Average values for U were 41 ± 4 pL (n = 52) in SNc; 25 ± 3 pL in SNr (n = 24); and 52 ± 8 pL (n = 46) in VTA.
DA uptake by DA and NE transporters
To determine the relative contributions of DAT- and NET-mediated processes to uptake of diffusing DA in SNc and VTA, DA diffusion curves were recorded in these regions in the presence of either GBR-12909 or desipramine. Because the k' for DA in SNr was already similar to that for nonspecific uptake of TMA+, the inhibitors were not tested in that region. Images of co-diffusing Texas Red-labeled dextran were simultaneously recorded to show the relative size of ejected volumes during the experiment; actual values of U were calculated from post hoc analysis of DA records, as above. The role of the DAT in regulating [DA]o in SNc and VTA was indicated qualitatively by the increased duration of DA diffusion profiles in the presence of GBR-12909 and quantified by the corresponding decrease in k' (Fig. 4). In the experiment illustrated for SNc (Fig. 4A), decreased DA uptake was also indicated by the smaller U required in the presence of the DAT inhibitor to give the same maximum [DA]o as in the control record (19 pL compared with 27 pL). The difference in U can also be seen in the companion dextran images (Fig. 4B).
|
Uptake of DA in VTA was also sensitive to GBR-12909 (Fig. 4C). In the illustrated experiment, U was kept constant between control and uptake-inhibited conditions (31 and 33 pL, respectively), so that the decrease in DA uptake caused an increase in [DA]o amplitude throughout the curve in GBR-12909 (Fig. 4C). It should be noted that the falling phases of these records, which reflected the rate of [DA]o clearance, were more nearly parallel than those of the normalized records in Fig. 4A, as discussed further below. Images of co-ejected dextran confirmed the similarity of the ejected volumes in these VTA records (Fig. 4D).
The overall effect of DAT inhibition on k' for DA in SNc and VTA is summarized in Fig. 5A. Although GBR-12909 decreased k' significantly in both regions (P < 0.001), DAT inhibition was more effective in SNc. In the presence of GBR-12909, the DA k' in SNc decreased to the same level as that seen in SNr (P > 0.05), whereas that in VTA remained significantly higher than in SNr (P < 0.001; Fig. 5A). The difference between SNc and VTA was further shown by the significantly lower k' in SNc compared with that in VTA (P < 0.05) in GBR-12909.
|
The effect of the NET inhibitor desipramine was less than that of GBR in both SNc and VTA. The slight decrease in the DA k' in SNc did not reach significance (Fig. 5B). On the other hand, desipramine caused a significant (P < 0.05) decrease in the DA k' in VTA (Fig. 5B), showing that uptake of [DA]o in this region is due in part to the NET, as well as the DAT.
To illustrate the effect of these uptake systems on
[DA]o, simulated DA diffusion curves were
generated using a standardized drop volume (30 pL), average and
values for DA diffusion in SNc (Fig. 5C) and VTA (Fig.
5D), and DA k' values determined under control
conditions or in the presence of GBR-12909 or desipramine. Although the
DAT had the greater effect on DA behavior in both regions (Fig. 5,
C and D), in VTA especially, NET inhibition also increased amplitude and prolonged the time course of DA
concentration-time curves (Fig. 5D).
Contributions of , k', and diffusion to [DA]o
clearance
The two most unique characteristics of DA diffusion in midbrain
cell body regions were the large and the DAT-dependent
k'. To understand better the relative contributions of these
parameters, we modeled their effects on DA diffusion after pressure
ejection in SNc (Fig. 6). Values of
used were 0.30 (SNc) and 0.22 (cortex), and those for k'
were 0 s
1 (zero uptake),
0.0075 s
1 (average uptake
for DA in SNc in the presence of GBR-12909), and 0.085 s
1 (average control DA
uptake in SNc).
|
As predicted from Eq. 3, the larger of SNc
compared with cortex caused [DA]o to be lower
at all times, with amplitudes that were proportional to 1/
(Fig.
6A). These concentration-time curves, in which k' = 0, also illustrate the role of diffusion alone on clearance of
[DA]o from a site of a local elevation.
When and k' were varied simultaneously in this model
(Fig. 6B), an interesting pattern emerged that showed how
these factors, along with diffusion, differentially regulate
[DA]o. Increases in either
and
k' will cause the amplitude of [DA]o
to decrease (Fig. 5, A and B) when other
parameters (e.g., amount released) are constant. With constant
, the
time course of the return to baseline, or clearance rate, of an
increase in [DA]o will be influenced by
k'. For example, when the curve generated for DA diffusion in SNc using
= 0.3 and k' = 0.085 s
1 (Fig. 6B)
is compared with that generated using
= 0.3 and k' = 0 (Fig. 6A), an obvious consequence of the higher
k' is that [DA]o returned to
baseline more rapidly than when diffusion alone (k' = 0) was
involved. Indeed, when k' was 0.085 s
1,
[DA]o decayed to 80% of its maximum by 17 s after ejection (Fig. 6B), whereas when return to baseline
involved only diffusion (k' = 0),
[DA]o had decayed by only 40% at this
time and did not fall to 80% until 41 s after ejection (Fig.
6A).
From these observations, the idea easily arises that the rate of fall
(or time derivative of the decay phase) following the initial increase
in [DA]o resulting from a brief, localized
release, could be used as a measure of clearance. One can define a
maximum clearance rate for [DA]o as the
absolute value of the minimum of
d[DA]o/dt (measured in µM
s1) on the falling phase of the
[DA]o curve. This minimum will occur at time
tmin. For this type of curve, this
measure of clearance is the same as the maximum value of
|d[DA]o/dt|, which also occurs at
tmin. Importantly, "clearance" in this case
reflects the combination of diffusion and uptake (Fig. 6C).
Comparison of |d[DA]o/dt| at tmin for the various combinations of
and k' examined here indicates that the rate of
clearance is concentration-dependent, even when governed by diffusion
alone (k' = 0): maximum
|d[DA]o/dt| was higher when
= 0.22 than when
= 0.30 (compare open circles in Fig.
6C) simply from the higher [DA]o
when
= 0.22 (Fig. 6A).
Sphere of influence of DA after local release in SNc and SNr
Knowing the geometric constraints on DA diffusion and the
regionally distinct characteristics of DA uptake in midbrain, we could
then address how these parameters might influence
[DA]o following local release in SNc and SNr.
Because diffusion parameters determined here for SNc and VTA were
similar, calculations for SNc would also apply to VTA. Average ,
, and k' for SNc and SNr were used in Eq. 3 to
generate theoretical curves. The point source was considered to be a
site of DA vesicle fusion on a DA cell body or dendrite. Based on an
estimated 14,000 DA molecules per vesicle in a SNc neuron, as
previously reported by Jaffe et al. (1998)
, if 20 vesicles were released, the amount of DA entering the extracellular
volume would be 280,000 molcules, or 4.6 × 10
19 moles. The
simulation further used an intravesicular [DA] of 350 mM
(Cf) in a vesicle volume of 6.5 × 10
20 L (U),
calculated from a vesicle radius of 25 nm (e.g., Nirenberg et
al. 1997
), which was similar to the 300-mM intravesicular
[DA] estimated previously for cultured DA neurons by Pothos et
al. (1998)
. The diffusion distance selected for these
calculations was 20 µm, reflecting a typical distance between DA
dendrites in SNr (Fig. 7). The maximum
[DA]o after "release" under these conditions was 13.7 nM in SNc at tmax = 239 ms and 14.4 nM in SNr at
tmax = 276 ms (Fig. 7). These
concentrations were comparable to known EC50
values for D2 receptor activation (1-20 nM)
(Levant 1997
). Maximum clearance rate,
|d[DA]o/dt|, in SNc was 0.020 µM s
1 and in SNr was 0.016 µM s
1. The
difference in clearance rate between SNc and SNr also influenced clearance times after the [DA]o peak: the time
required for the [DA]o maximum to decay by 50%
(t1/2) was 490 ms in SNc and 580 ms in
SNr, with 1.29 s required for 80% decay in SNc and 1.60 s in
SNr.
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DISCUSSION |
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After somatodendritic release in midbrain, DA acts at
extrasynaptic receptors (Sesack et al. 1994; Yung
et al. 1995
) via diffusion-based volume transmission. We
describe here the factors that govern the lifetime and concentration of
DA in SNc, SNr, and VTA. Both geometric parameters and specific DA
uptake were quantified. Together, these data provided a complete
picture of DA regulation in midbrain that permitted us to model the
behavior of [DA]o after simulated vesicular release.
TMA+ diffusion
From TMA+ diffusion data, the extracellular
volume fraction of midbrain was shown to be 0.30. This value of was
40-50% larger than the usual 0.18-0.22 found in cerebral cortex,
both in the present studies in guinea pig and in previous studies in
rat (Cserr et al. 1991
; Lehmenkühler et al.
1993
; Pérez-Pinzón et al. 1995
).
Moreover,
is also about 0.2 in neostriatum, cerebellum, hippocampus, and spinal cord across species (Nicholson and
Phillips 1981
; Pérez-Pinzón et al.
1995
; Rice and Nicholson 1991
; Rice et
al. 1993
; Svoboda and Syková 1991
).
Subregional values can differ from 0.2, however, with an
of about
0.3 in the cerebellar molecular layer (Rice et al. 1993
)
and in the hippocampal stratum pyramidale (Mazel et al.
1998
). Even higher values of
occur in immature rat cortex,
where 40% of cortical volume is extracellular during the first days of
postnatal development (Lehmenkühler et al. 1993
).
At that stage, neurons are not yet ensheathed in the proteoglycan
complexes that comprise perineuronal nets (Celio and Blumcke
1984
). A contributing factor to the large
of midbrain therefore might be that catecholamine neurons, including midbrain DA
cells, do not have perineuronal nets, even at maturity (Hobohm et al. 1998
).
The strikingly higher value of in midbrain will cause DA behavior
to differ significantly from that in forebrain. Whenever a given number
of molecules enter the larger extracellular space of midbrain after
release from either an exogenous or endogenous source, the resultant
[DA]o will be lower than in forebrain. This has
obvious implications for experimental comparisons of
[DA]o between midbrain and forebrain
structures. More importantly, this means that concentration-dependent
receptor activation will be lower in midbrain after equivalent DA release.
Despite the large of midbrain regions, the corresponding tortuosity
factors were similar to that of cortex. This relative constancy in
might seem surprising; however, it should be noted that
and
are
mathematically independent parameters (Nicholson and Phillips
1981
). A similar invariance in
is also seen in developing
rat cortex, in which
is constant as cortex matures from the
neonatal period to adulthood, while
decreases from 0.4 to 0.2 (Lehmenkühler et al. 1993
). Within midbrain,
was significantly higher in SNr than in SNc or VTA. Although the
difference was <7%, this had subtle effects on DA time course (Fig.
2).
DA diffusion in midbrain
DA-diffusion curves in midbrain could be fitted well to the
diffusion equation (Eq. 3) using linear uptake,
k', and, thus did not require more elaborate fitting
procedures incorporating nonlinear Michaelis-Menten uptake kinetics
(e.g., Nicholson 1995). By contrast, similar
DA-diffusion data from striatum cannot be fitted with k',
and require Michaelis-Menten kinetic parameters (Rice and
Nicholson 1995
). The suitability of linear uptake to describe
the present data would suggest that the concentrations examined (low
micromolar) were within a range in which the relationship between
uptake rate and concentration was approximately linear. One condition
under which the relationship between Michaelis-Menten uptake rate and
concentration is linear is when [DA]o is
substantially below Km (i.e.,
C
Km), such that the
nonlinear uptake term
VmaxC/(C + Km) simplifies to
(Vmax/Km)C
(i.e., k' = Vmax/Km).
In the present experiments, this would mean that
Km was
2-3 µM, which was our average peak [DA]o. A
Km of this magnitude seems unlikely,
however, given that the usual DAT Km
is about 0.2 µM [although values >1 µM have also been reported
(Giros and Caron 1993
; Horn 1990
)]. On
the other hand, it is relevant to note that Michaelis-Menten kinetics
describe initial uptake rates, a condition that may not be met in many
experimental paradigms nor during normal release in situ.
The appropriateness of using linear uptake to fit DA diffusion curves
in the present studies in midbrain was confirmed by the independence of
k' from U, such that similar k' values
were obtained in each of a family of DA curves of different amplitude (Fig. 3B). Moreover, the physiological relevance of
k' was indicated by its sensitivity to transporter
inhibition (Figs. 4 and 5). Experiments with DAT and NET inhibitors
indicated that most DA uptake in SNc and VTA was via the DAT. This is
consistent with the high density of DA cell bodies and dendrites
expressing the DAT in these areas (Ciliax et al. 1995;
Freed et al. 1995
; Nirenberg et al. 1996
,
1997
). Previously, we reported that DAT inhibition enhanced maximum evoked [DA]o in SNc, but had
no effect in VTA (Cragg et al. 1997
). The present
studies showed that active DA uptake does occur in VTA, with
control values of k' that were similar to those in SNc.
Nonetheless, here, too, DAT inhibition was more effective in SNc than
in VTA, demonstrating a functional consequence of the lower density of
DAT expression in VTA (e.g., Ciliax et al. 1995
). In our
previous study (Cragg et al. 1997
), the depolarizing
stimulus used to evoke DA release was presumably sufficient to inhibit
the voltage-sensitive DAT (Hoffman et al. 1999
), thus
masking additional inhibitory effects of GBR-12909. This was more
evident in VTA, where DAT activity is lower, than in SNc. In contrast
to DAT inhibition, inhibition of the NET only slightly decreased DA
k' in VTA and SNc, reaching significance only in VTA. Uptake
of DA by the NET (Cragg et al. 1997
; Simon and
Ghetti 1993
) in VTA appears to be by en passant NE processes (Cragg et al. 1997
).
In contrast to SNc and VTA, in SNr k' for DA was
indistinguishable from that for TMA+. Although
the DAT is expressed on DA dendrites in SNr, as in SNc, the
distribution of TH-ir-positive processes is sparser in SNr (Fig. 7).
Consequently, DA can diffuse for relatively large distances in SNr
without encountering uptake sites, as indicated by our experimental
results (Fig. 3, A and C). This geometric organization is somewhat analogous to that in retina, where DA is
released from dopaminergic amacrine cells that are oriented in a plane
defined by the inner plexiform layer. Once DA escapes these cells, it
is free to diffuse to DA receptors on horizontal cells and
photoreceptors 50-100 µm away (Rice 2000;
Witkovsky et al. 1993
).
Relevance for DA volume transmission
The effects of and k' on DA diffusion in SNc were
modeled to evaluate how these parameters influence
[DA]o and DA clearance rate. For these
comparisons, clearance was taken as the maximum |d[DA]o/dt| of the falling phase
of a diffusion curve. A previous study (Hoffman et al.
1998
) also examined DA clearance following pressure ejection
and found surprisingly similar values for clearance rate, despite a
threefold greater diffusion distance. In those experiments, DA uptake
inhibitors caused an increase in [DA]o amplitude and time course, but paradoxically did not alter clearance rate (Hoffman et al. 1998
). The present studies, in
which diffusion parameters and DA uptake were evaluated independently,
resolve this paradox. Here we show a clear decrease in DA k'
in the presence of a DAT inhibitor; we also show that the concentration
dependence of DA clearance rate precludes its use for adequate
assessment of uptake blockade. Moreover, our models of DA diffusion
indicated that for a given amount of "released" DA, a region with a
larger
or k' would have lower
[DA]o. Although a large k' increased |d[DA]o/dt|, a large
, with
constant k', decreased it because of the lower
[DA]o. A larger k' also decreased
clearance time, whereas different
values did not affect this. The
overall effect of the large
of midbrain, therefore would be to
decrease |d[DA]o/dt|, effectively damping changes in [DA]o.
When all now-known diffusion characteristics were combined with
specific DA uptake, we could then model DA diffusion in SNc and SNr
following vesicular release (Fig. 7). For this model, we assumed a
20-vesicle point source, with 14,000 DA molecules per vesicle
(Jaffe et al. 1998). This gave a peak
[DA]o of 14 nM that occurred roughly 250 ms
after release, 20 µm away, whether in SNc or SNr. This concentration
is similar to affinity constants for the high-affinity state of all DA
receptor subtypes (Neve and Neve 1997
) and to
EC50 values for D1-like
(Jackson et al. 2000
) and D2-like
(Levant 1997
) receptor activation in vitro. Consequently, 14 nM could be sufficient to activate most high-affinity receptors within this radius. Equally important, in both regions, these
concentrations would be available for a sufficient time for
DA receptor activation (Cragg and Greenfield
1997
): t1/2 was nearly
500 ms in SNc and 600 ms in SNr.
The most significant result of this modeling, however, was the
surprising similarity of the t1/2
values in SNc and SNr, despite the 10-fold difference in k'
in these structures. This shows, for the first time, that diffusion
rather than uptake is the most important determinant of DA time course
in midbrain. The dominance of diffusion contrasts completely with the
case in striatum in which uptake dominates (Garris
and Wightman 1995; Giros et al. 1996
).
Conclusions
The large and limited, linear uptake of DA in midbrain will
act as "oscillation dampers" to decrease the amplitude and rate of
fluctuation of [DA]o to which extracellular DA
receptors are exposed. These findings not only allow us to predict the
behavior of somatodendritically released DA, but also have implications for all biogenic amine systems in midbrain. Indeed, these novel results
illuminate concepts that are relevant for any substance that mediates
volume transmission.
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ACKNOWLEDGMENTS |
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These studies were funded by National Institute of Neurological Disorders and Stroke Grants NS-28642 (to C. Nicholson) and NS-36362 (to M. E. Rice). S. J. Cragg is currently supported by an E. P. Abraham Research Fellowship (Keble College, Oxford) and by Novartis Pharma.
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FOOTNOTES |
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Address for reprint requests: M. E. Rice, Dept. of Physiology and Neuroscience, New York University School of Medicine, 550 First Ave., New York, NY 10016 (E-mail: margaret.rice{at}nyu.edu).
Received 19 September 2000; accepted in final form 14 November 2000.
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REFERENCES |
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