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The Journal of Neuroscience, January 15, 2002, 22(2):413-427
Molecular Phenotyping of Retinal Ganglion Cells
Robert E.
Marc and
Bryan W.
Jones
John Moran Eye Center, University of Utah School of Medicine, Salt
Lake City, Utah 84132
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ABSTRACT |
Classifying all of the ganglion cells in the mammalian retina has
long been a goal of anatomists, physiologists, and cell biologists. The
rabbit retinal ganglion cell layer was phenotyped using intrinsic small
molecule signals (aspartate, glutamate, glycine, glutamine, GABA, and
taurine) and glutamate receptor-gated 1-amino-4-guanidobutane
excitation signals as the clustering dimensions for formal
classification. Intrinsic signals alone yielded 7 ganglion cell
superclasses and 1 amacrine cell superclass; the addition of excitation
signals ultimately resolved 14 natural ganglion cell classes and 3 amacrine cell classes. Ganglion cells comprise two-thirds to
three-quarters of the cells in the ganglion cell layer and exhibited
distinct metabolic, coupling, and excitation phenotypes, as well as
characteristic sizes, population fractions, and patterns. Metabolic
signatures (mixtures of glutamate, aspartate, glutamine, and GABA)
chemically discriminated ganglion from amacrine cells. Coupling
signatures reflected heterologous coupling states across ganglion
cells: (1) uncoupled, (2) coupled to GABAergic amacrine cells, and (3)
coupled to glycinergic amacrine cells. Excitation signatures reflected
differential channel permeation rates across classes after AMPA
activation. Extraction of unique size and patterning features from the
data sets further validated the robustness of the classification.
Because the classifications were explicitly blinded to structure, this
is strong evidence that molecular phenotype classes are natural
classes. Correspondences of molecular phenotype classes to functional
classes were inferred from size, coupling, encounter, and physiological
attributes. Ganglion cell classes display markedly different ionotropic
drives, which may partly explain the physiological brisk-sluggish
spectrum of ganglion cell spiking patterns.
Key words:
neuronal classification; molecular phenotyping; 1-amino-4-guanidobutane; AGB; retina; ganglion cells; amacrine cells; patterning
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INTRODUCTION |
Efforts to assess the classes,
numbers, and distributions of retinal ganglion cells have engaged
diverse metrics: cell count, size, shape, axon diameter, and
physiological encounter rates. Subsets of mammalian ganglion cell
classes project to an array of central targets (Fukuda and Stone, 1974 ;
Farmer and Rodieck, 1982; Leventhal et al., 1985; Rodieck and Watanabe,
1993; Pu et al., 1994; Rodieck, 1998), but a unified description
of all classes and distributions in the ganglion cell layer has
remained elusive. Several summaries of how neuronal typologies might be
abstracted have emerged, some accompanied by debates regarding methods,
definitions, and results (Rowe and Stone, 1977; Hughes, 1979; Holden,
1981; Rodieck and Brening, 1982; Famiglietti, 1992; Wingate et
al., 1992; Cook, 1998; Masland and Raviola, 2000). We have
performed a formal multispectral classification (Swain and Davis, 1978) of the rabbit ganglion cell layer using molecular signals as the dimensions of the multispectral space. The attributes of such a
classification were precisely articulated by Famiglietti (1992): ". . .the ability to label neurons by neurotransmitter molecules. . .has
provided a new means of identifying neurons repeatedly with some
reliability. Nevertheless such labels are not unique markers. As a
consequence of their very large range of diversity, neurons
are likely to be identified and characterized as unique types by
quantifying the expression of overlapping sets of markers, rather than
by absolutely unique indicators. . . .The plausible hypothesis has been
advanced that `natural' cell types emerge as distinct clusters of
points in a parametric space. . ."
Although Famiglietti and others (Rodieck and Brening, 1982;
Cook, 1998) generally conceived this parametric space to be
morphological, mixtures of small molecules do constitute distinctive
signatures for many natural classes of retinal cells (Marc et al.,
1995, 1998; Kalloniatis et al., 1996). On the basis of previous
analyses, we expected that the signatures of natural ganglion cell
classes would be poorly separable (Marc, 1999a,b), but when we expanded the set of molecular targets to include aspartate, glutamate, glycine,
glutamine, GABA, and taurine signals, strongly heterogeneous multispectral signatures emerged in the ganglion cell layer, suggesting that detailed phenotyping was indeed possible. Excitation signatures based on mapping AMPA-activated 1-amino-4-guanidobutane (AGB) permeation (Marc, 1999b) proved decisive. Fusion of excitation and
intrinsic signals permitted the demonstration that the rabbit retinal
ganglion cell layer contains no fewer than 14 natural types of ganglion
cells, and some are further characterized by cell size, patterning, and
population fraction. This separability arises from three forms of
molecular phenotype variation: (1) some ganglion cells display
different intrinsic glutamatergic metabolite signatures; (2) other
cells express different degrees of heterologous coupling with amacrine
cells, leading to separable patterns of GABA and glycine leakage into
the ganglion cells; and (3) each cell class expresses a characteristic
AMPA receptor-mediated drive.
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MATERIALS AND METHODS |
Terminology. We define five retinal cell types
as major cellular divisions traditionally distinguished by position and
shape: photoreceptors, bipolar cells, horizontal cells, amacrine cells, and ganglion cells. We define superclasses and classes as statistical groups within a type. Thus we are concerned with discovering the natural classes of the ganglion cell type. A statistical class is
defined by its position and dispersion in N-dimensional space. If it is
well separated from its fellow classes and cannot be separated
further, it may be a natural class. That hypothesis is strengthened if
the statistical class can be shown to possess additional features
associated with presumed natural classes. In this process, we will see
that some natural classes form intermediate statistical groups as superclasses.
Isolated retinal preparations. These methods follow those
described in Marc (1999a). Light-adapted adult male and female
pigmented rabbits were tranquilized with intramuscular
ketamine/xylazine, deeply anesthetized with intraperitoneal urethane in
saline, and euthanized by thoracotamy, all in accord with institutional
animal care and use guidelines. Both eyes were rapidly removed and
hemisected, and large retinal pieces were mounted on cellulose acetate
filter discs, then bathed in 35°C Ames medium (Ames and Nesbett,
1981) equilibrated with 95% O2/5%
CO2 or HEPES-based modified Ames medium equilibrated with 100% O2. Single 2 × 3 mm
retinal chips taken within 2 mm of the visual streak were razor cut
from the large pieces and incubated for 10 min at 35°C under gas in
100 µl droplets of Ames or HEPES-based medium containing 5 mM AGB and 25 µl AMPA. All incubations were
performed under fluorescent room lighting as described in Marc (1999a).
Samples from four rabbits (cases 2366, 2578, 2780, and 2781) were
similarly processed and classified (see below). Horizontal sections
from retinas of >20 other specimens with and without AGB or agonists
were also analyzed. All retinas were fixed by immersing chips in room
temperature 1% paraformaldehyde, 2.5% glutaraldehyde, 3% sucrose,
0.01% CaCl2, in 0.1 M
phosphate buffer, pH 7.4. All tissue was processed as described
previously (Marc et al., 1990).
Specimen preparation and immunocytochemical visualization.
Each chip was flat embedded in epoxy resin on a glass slide; a small
rectangular sample was scribed from the slide (Stell and Lightfoot,
1975) and sectioned serially at 250 nm onto 12-spot Teflon-coated
slides (Cel-Line, Fisher Scientific). The immunocytochemical and
IgG production procedures were as described previously (Marc et al.,
1990, 1995) using the silver-intensification protocol of Kalloniatis
and Fletcher (1993). The samples were serially probed with IgGs
targeting aspartate, glutamate, glutamine, glycine, GABA, taurine, and
AGB obtained from Signature Immunologics Inc. (Salt Lake City, UT).
Primary IgG signals were detected with goat anti-rabbit IgGs adsorbed
to 1 nm gold particles (Amersham Biosciences) and visualized with
silver intensification. The silvering process was run at 30°C for 240 sec before quenching. All preparations received identical probing and
visualization treatments, yielding two orders of magnitude of detection
range with differential concentration sensitivity as low as 40 µM based on artificial standards. Single-letter amino acid codes were sometimes used to denote AGB (B), aspartate (D),
glutamate (E), glycine (G), glutamine (Q), taurine ( ), and GABA
( ). The selectivities of each of these probes have been characterized extensively. This is particularly critical for the IgG
targeting GABA, because we interpret variations in signal strength as
reflecting true GABA content arising from different sources. It is
important to know the degree to which small signals could reflect
contamination. Full cross-reactivity data for the Signature
Immunologics YY100 anti-GABA have been obtained for the most plausible
contaminating species: alanine,  -alanine, citrulline, cysteine,
aspartate, glutamate, glycine, isoleucine, glutathione, lysine,
leucine, methionine, asparagine, arginine, ornithine, proline,
glutamine, serine, threonine, valine, tryptophan, tyrosine, ATP, ADP,
AMP, cAMP, and cGMP, as well as all macromolecules expressed in retina.
With the exception of -alanine, the YY100 IgG is selective for GABA
at >10,000:1. The selectivity for -alanine (the three-carbon analog
of GABA) is 80:1. This means that low levels of GABA-like
immunoreactivity could be produced by -alanine only if it were
present in 80-fold excess over the inferred GABA level: a 0.1 mM YY100 signal could be produced by 8 mM -alanine. However, this is an impossible
level, because the ganglion cell layer shows little endogenous
-alanine immunoreactivity (<0.5 mM maximum)
using our own -alanine-selective IgG, and the cells in question
(those showing the least amount of GABA signal, ~0.1-1 mM) have no detectable -alanine
immunoreactivity. Thus we are confident that the GABA signal is
accurate and uncontaminated. Similar verifications hold for each of the
IgGs used in this study.
Image analysis. All images of immunoreactivity were captured
as 8-bit 1536 pixel × 1152 line frames under constant flux light with feedback regulation and fixed CCD camera gain and gamma as described previously (Marc, 1999a). Silver visualization produces density-scaled images, and linear image inversion produces
intensity-scaled images that are linear with
log10(concentration) over a range of 0.05-10
mM. Serial images were aligned to >250 nm
root-mean-square error with registration algorithms from PCI Geomatics
(Richmond, Hill, Ontario, Canada). Image analysis and morphometry were
performed with Image-Pro Plus 2.0 (Media Cybernetics Inc., Silver
Spring, MD).
Classification followed these steps: (1) capture of N molecular signal
channels, (2) registration of channels, (3) isodata clustering of N
channels, (4) theme map generation, (5) histogram and scatter plot
exploration, and if necessary, (6) deconvolution and theme map
correction. After theme map generation (step 4), univariate and
bivariate signal histograms were explored for each emergent class. In
cases in which a signal was clearly multimodal, the histogram was
deconvolved, and each cell was recoded. This is roughly equivalent to
another form of classification based on watershed filtering of N-space
histograms to find decision boundaries (Narendra and Goldberg, 1977).
Classes completely resolved by clustering were formally statistically
separable, and those resolved by deconvolution were formally
statistically significant (see below).
Classifications were performed using the isodata algorithm (PCI
Geomatics), and data were explored with applications written in IDL
(Research Systems Inc., Boulder, CO). An overview of and reference
lists for simple classical pattern recognition methods are provided in
Marc et al. (1995). Isodata resembles the simpler K-means clustering
method but adds heuristic splitting of high variance classes and
merging of highly overlapping classes (Ball and Hall, 1967).
Statistical separability indicates that the means and covariances for a
set of N-dimensional data allow classification of a sample of those
data into distinct classes. The probability of error
(pe) in classification is estimated
from the transformed divergences of the classes assuming equal a
priori probability densities. Cell classes described as separable
have pe  0.01. Separable classes are
also inherently statistically significant classes. Natural classes need
not be inherently separable by clustering and could have signature
overlaps greater than required for pe 0.01 yet still be statistically significant classes. In the separations
of class 5 from 9, and class 10 from 11, we used deconvolution methods
[previously detailed in Marc et al. (1995)] to detect signals arising
from distinct classes that clustering failed to resolve completely.
These cells were reassigned as classes associated with a mode of a
bimodal distribution by thresholding above and below the mode crossover point.
The multidimensional signatures of classes were visualized as selected
rgb maps (encoding three molecular signals as red, green, and blue
signals, respectively) and as superimposed bivariate 2N-plots. Single
bivariate plots (Marc et al., 1995) display the area of concentration
space occupied by a class in a two-dimensional chemical space. 2N-plots
superimpose several bivariate plots, capturing most of the features
discriminating cell groupings in an N-space. This method was inspired
by the parallel coordinate space described by Inselberg and Dimsdale
(1990). Pairs of signals were displayed as class means bounded by 2 SD
margins. The x-y axes spanned 0.1-10
mM with logarithmic scaling, and the
[x,y] pairs were color coded: [AGB, AGB]
gray; [E, ] orange; [D, Q] cyan; and [G, ] magenta.
Average cell diameters and cell spacings were acquired with the object
utilities in ImagePro Plus 2.0 (Media Cybernetics). Cell sizes in each
sample were measured only from glutamate signals in individual 250 nm
sections, so cell size estimates contain no registration errors and
require no use of dissector methods. Some classified cells often
appeared to form regular arrays, and these were numerically
characterized by their conformity ratios (CRs; also known as regularity
indices): the ratio of the class mean nearest-neighbor distance to its
SD. The significance of the deviation of a conformity ratio from that
predicted for a random pattern was determined from significance charts
in Cook (1996).
Agents and sources. Ames medium either was purchased from
Sigma (St. Louis, MO) or made according to Ames and Nesbett (1981) and
modified as needed by equimolar Na+
replacement with 15 mM HEPES and reduction of
NaHCO3 to 1 mM. AGB and
AMPA were added without adjustment. All solutions were made before each
preparation. AGB (agmatine sulfate) was obtained from Sigma, and
AMPA was from Research Biochemicals International (Natick, MA).
Figure preparation. All images are digital, assembled from
the raw data captured by CCD camera (see Image analysis above). Selected frames of raw Tagged Image Format (*.tif) files were extracted
for display, each sharpened by unsharp masking, and after entire images
were assembled as a single figure, contrasts were adjusted with linear
remapping to correct for out-of-gamut effects during printing. All
final images were prepared in Adobe PhotoShop 5.0. Combinations of
registered channels were viewed as true-color rgb mappings, as
described previously (Marc et al., 1995).
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RESULTS |
GABA signals demonstrate intrinsic heterogeneity in the
ganglion cell layer
A common error in immunocytochemistry is the imputation, via
imagery, of binary states for small molecules: present versus absent.
This is almost never true for amino acids because absolute contents
vary across cell types. The error is often technical, arising from
nonlinear and excessive amplification during enzyme-linked visualization or saturation of signals (fluorescence or absorption) during image capture or later processing. Silver visualization of
immunogold signals on thin sections offers unparalleled signal differentiation. A spectrum of GABA signals, displayed in a single 250 nm section through the plane of the ganglion cell layer in the central
streak of the rabbit retina (Fig.
1A) and ranging from
<50 µM to ~10 mM,
characterizes different compartments. The basic reasons for this
differentiation are that some ganglion cells contain no GABA; displaced
starburst amacrine cells contain GAD65 (Brandon and Criswell,
1995) and synthesize high GABA levels; Müller cells import
GABA via a high-affinity GAT-3 transporter (Johnson et al., 1996) and
metabolize it, yet have a persistent low level; and many ganglion cells
engage in heterologous dye coupling with amacrine cells (Xin and
Bloomfield, 1997), and GABA could clearly leak into ganglion cells. The
concentration scaling of this image reveals that Müller cells
appear to contain ~85 µM GABA (uncorrected
for fixation loss) and that some large neuronal cells clearly contain
measurably less than that, whereas others contain much more. Some cells
in the ganglion cell layer contain roughly the same amount of GABA as
the Müller cells and are nearly invisible against the glial
background (Fig. 1A, ellipse). An example
of the sensitivity of the silvering method is also indicated, where a
clear difference between Müller cell and ganglion cell labeling
can be detected with as little as 40 µM
calculated difference between the two compartments (Fig.
1A, box). Müller cell signals do
vary across preparations, primarily because the rate of GABA conversion
to succinate semi-aldehyde is an explicitly aerobic reaction. This
field was chosen for its slightly higher-than-average Müller cell
signal, which highlights the range of ganglion cell GABA signals. All
of the signal diversity in Figure 1 is caused by intercellular
variations in GABA content; none can be caused by corruption of the
anti-GABA IgG signal with any known targets. This same pattern of GABA
signals has been observed in >150 rabbit retinas, at all loci, with
variation in cell proportions. Similarly, fascicles of ganglion cell
axons just beneath the layer of somas contain streaks of strong GABA
signal (Fig. 1B) that correspond to glutamate
immunoreactive axons (Fig. 1C). Regardless of whether the
GABA signal in ganglion cells arises from coupling leakage or
synthesis, it appears in axons as well.
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Figure 1.
The diversity of GABA signals in the
ganglion cell layer. A, Differential GABA content of
cells in the ganglion cell layer of the rabbit retinal area centralis
visualized by quantitative GABA mapping on a 250 nm section. Scaling
was determined from artificial standards. Cellular contents range from
<50 µM to ~10 mM. Some ganglion cells are
literally invisible in the array because they have the same GABA
content as the surrounding Müller cells (ellipse).
Other cells can be differentiated from the Müller cells by having
contents roughly 40 µM higher (box); a
strongly immunoreactive starburst amacrine cell is also contained in
the box. Image is density scaled. Scale bar, 25 µm.
B, C, Precision registered images of GABA
and glutamate immunoreactivity in serial 250 nm sections through the
optic fiber layer just proximal to the ganglion cell somas.
Streaks of GABA immunoreactivity represent axons of varying caliber,
and all GABA-immunoreactive axons are contained in fascicles of
glutamate-immunoreactive axons. The grid overlay permits tracking of
common points in both images. Images are density scaled. Scale bars, 25 µm.
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Full classification of the ganglion cell layer
The notion that one might be able to formally phenotype the
ganglion cell layer by applying pattern recognition algorithms to sets
of molecular signatures (Marc et al., 1995) gains credence from images
probed for excitation signals (Fig.
2A) and intrinsic small
molecules (Fig. 2B-H) in a
comprehensive data set obtained ~2 mm below the visual streak.
Aspartate levels vary across cell types (Fig. 2B),
even among cells displaying similar glutamate signals (Fig.
2C). This variation is not random, and strong aspartate signals are biased toward large cells. Glutamine contents (Fig. 2E,F) vary in a pattern
similar but not identical to aspartate. The high glutamine contents of
Müller cells form a background against which many neurons are
invisible, but masking out Müller cells with their own signature
class (Marc et al., 1990, 1995) exposes the full spectrum of neuronal
glutamine contents. Glycine signals in the ganglion cell layer are weak
(Fig. 2D), never exceeding a few hundred micromolar
and never reaching the 5-10 mM levels achieved
by glycinergic amacrine cells (Fig. 2D,
inset). Nevertheless, certain cells always display glycine
content higher than their neighbors. As in most vertebrates, taurine
signals of the ganglion cell layer are restricted to Müller cells
(Fig. 2G). Although none of these additional signals alone
exhibits differential patterning as dramatic as that of GABA (Fig.
2H), separation improves with every dimension that
adds even small correlations. Use of these intrinsic signals alone
never permitted a complete segregation of all likely natural classes,
but isodata clustering in five normal and four excitation-mapped
retinas resolved eight neuronal superclasses, labeled according to
their dominant molecular signals: one amacrine cell and seven ganglion
cell superclasses. At the simplest level, we can resolve two global
N-dimensional clusters that distinguish amacrine and ganglion cells as
fully separable types, the details of which will be discussed below.
The superclasses of ganglion cells arise in part from fully separating
or detecting strong three-dimensional modes of metabolic signatures
dominated by glutamate (E) and characterized by increasing glutamine
(Q) and aspartate (D) content: superclasses E, EQ, and EDQ. These basic
superclass signatures also are separable from their counterparts that
also possess a GABA signal: superclasses E, EQ, and EDQ. As
seen below, however, the EQ and EDQ modes do not completely separate, because a bridging cell population overlaps the two, although
it more appropriately fits in the EQ superclass. Two variations on
this simple theme emerge. First, one population of cells in the E
classification space further separates by evidencing coupling to
glycinergic amacrine cells: superclass EG. Finally, some members of
the E and EQ classification spaces contain GABA levels that are
indistinguishable from those of bona fide amacrine cells. Several
superclasses are composites of natural classes.
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Figure 2.
Excitation and intrinsic molecular signals in the
ganglion cell layer visualized from an array of seven serial 250 nm
sections (sequence: D G Q B E). All cells are embedded in a
matrix of Müller cell cytoplasm. The reticulated patch at the
top left of A and H
represents intrusion of the inner plexiform layer into the
plane. Sets of the same circled cells appear in every
image: labels 1-12 indicate ganglion cell classes,
labels dA1-3 indicate amacrine cell classes, and
m indicates misplaced amacrine cells. All images are
density scaled. Scale bar, 25 µm. A displays
AMPA-activated AGB excitation signals, and subsequent panels represent
intrinsic signals: B, aspartate; C,
glutamate; D, glycine; E, glutamine;
F, glutamine with Müller cells masked;
G, taurine; H, GABA. The
inset in the glycine image (D)
represents an array of glycine-immunoreactive amacrine cells from the
same specimen. Ganglion cells contain a spectrum of glutamine signals
(E, F), and many cells, such as
classes 5 and 9, closely match the glutamine contents of Müller
cells. Some classes are significantly higher (class 1a), whereas others
are substantially lower (classes 3, dA1, dA2, and dA3). In
G, the black taurine background is
Müller cell cytoplasm, revealing that taurine levels in ganglion
cells are virtually zero.
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The addition of an excitation signal proved decisive, enabling a robust
estimate of the minimum number of ganglion cell natural classes and
some of their morphological attributes. AGB is an organic cation that
permeates glutamate-gated ion channels, accumulates in activated
neurons, and can be detected with quantitative immunochemical protocols
(Marc, 1999a). AGB mapping thus reports recent excitation history. We
have shown previously that activation of ionotropic glutamate receptors
in the ganglion cell layer yields stable, heterogeneous patterns of AGB
signals, implying intrinsic differences in the AMPA receptor properties
of ganglion cells. We chose to analyze the excitation patterns
generated by 25 µM AMPA in the ganglion cell layer in
combination with the signals that permit detection of the eight
superclasses. Four normal adult rabbit retinas were incubated for 10 min in Ames medium containing 5 mM AGB and 25 µM AMPA. In all samples, the ganglion cell layer displayed a spectrum of AMPA-driven responses (Fig.
2A). These signal differences must arise from
variations in either the numbers or types of ionotropic glutamate
receptors expressed by neurons and not simply cell size variation
(Marc, 1999a). Both the very largest and smallest of cells show the
strongest AMPA responsivity, whereas both large and small cell types
also show very weak responses.
Inclusion of the AMPA-driven excitation signal in the AGB signal in the
classification set resulted in enhanced differentiation of the ganglion
cell layer, revealing molecular phenotype classes visualized as rgb
maps (Fig. 3A), theme maps
(Fig. 3B), and signature arrays (Fig.
4) of the constituent cells. Because the
15-color theme map is complex, it is practical to decompose it into its constituent cell patterns in Figure 5.
The molecular phenotype data suggest the existence of at least 14 natural classes of ganglion cells and 3 natural classes of displaced
amacrine cells. In general, rgb maps suggest the existence of multiple
cells classes by revealing a spectrum of hues that correspond to a
spectrum of molecular mixtures. Visual examination of such maps is not
a proof, however, and classification is essential, revealing both
classes and their signature spaces.
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Figure 3.
A summary rgb triplet and theme map of the
ganglion cell layer. A, One of 35 possible rgb triplet
mappings visualized from a registered array of 7 serial 250 nm
sections: GABA  red, AGB green, glutamate blue. Cells
exhibit a vast spectrum of hues associated with their varying intrinsic
GABA contents and AMPA-activated AGB excitation signals. For example,
class 1 cells (1a, 1b, 1c) are all bright cyan, reflecting
strong responses to AMPA and negligible GABA content. At another limit
one finds mauve class 12 cells with weak AMPA responses and high GABA
content. Class dA1 starburst amacrine cells are yellowish, with high
AMPA responses and high GABA content and low glutamate content. Image
is intensity scaled. Scale bar, 25 µm. B, The theme
map displays the results of isodata clustering and deconvolution to
extract all distinct neuronal molecular phenotypes in the ganglion cell
layer. The class color code can be read directly from the image:
1, light red; 2,
yellow; 3, cyan;
4, dark blue; 5,
light tan; 6, bright
green; 7, olive;
8, sea green; 9,
dark tan; 10, magenta;
11, dark purple; 12,
dark red; dA1, lavender;
dA2, dark green; dA3,
orange; misplaced amacrine cells, m,
lemon yellow. Symbols as in Figure 2. Image is indexed.
Scale bar, 25 µm.
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Figure 4.
Bivariate signal plots (left
column) and class signature N-plots (right four
columns) that characterize classes of cells in the ganglion
cell layer (case 2366). Each molecular signal or signature plot is
placed on the same quantitative bivariate log millimolar scale
that maps directly to pixel intensity. Each colored spot
is the bivariate mean for a pair of values surrounded by a 2 SD border.
In x,y axis notation,
black is AGB AGB, gold is
E, cyan is DQ,
magenta is G. Gray
symbols map log population fraction (x = log N/Nt, where N = number of cells in class, Nt = total cells in all
classes) against mean cell diameter ± 2 SD
(y) on the scale in the bottom left
quadrant. Left signal column, The AGB AGB signal
plots (top left two frames, black
symbols) reveal that the AMPA response spectrum of classes is
large and index the positions in response space for each class. The
E signal plot shows the separation of amacrine cells from ganglion
cells, especially on the E vector, and the overlap of some ganglion
cells into the signal space of true amacrine cells. The DQ plot
similarly shows that amacrine cells occupy the lowest portion of the
signal space. This bivariate space alone is insufficient to completely
segregate amacrine cells but is part of a complete separation when
combined with the E space. The G plot reveals that class 7 ganglion cells separate on the G vector. The bottom right
plot demonstrates that the large cells are the rarest, small
cells are the most abundant, and all other cells are clustered between.
Right four signature columns, The class signature plots
are N-plots, encoding the same five signal pairs (AGB AGB, E, DQ,
G, fraction-size) for each of 15 classes.
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Figure 5.
Separated patterns of cell classes derived from
the registered array of seven serial 250 nm sections. Class 1 contains
a mixture of cell sizes that are separable by deconvolution into
classes 1a, 1b, and 1c. Class pairs 9 and 5 and 10 and 11 were not
completely separated by the isodata algorithm and were resolved by
deconvolution of their GABA signals.
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Amacrine and ganglion cells are separate cell types in signature space:
amacrine cells are notably deficient in glutamate, aspartate, and
glutamine signals, and they show enhanced GABA signals, identical to
GABA immunoreactive cells of the amacrine cell layer proper. The
classification decision boundaries for amacrine-ganglion cell
discrimination are shown on the E and DQ bivariate signal channel
summary plots (Fig. 4, left column) and the univariate
glutamate signal histogram of the ganglion cell layer (Fig.
6A). Although ganglion
cells express various GABA signals that may overlap into the amacrine
cell range, they are nevertheless completely separable from amacrine
cells in EDQ (glutamate-aspartate-glutamine) space.
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Figure 6.
Univariate histograms and histogram deconvolution.
A, Peak normalized probability density profiles for
glutamate signals in displaced amacrine cells (dotted
line) and ganglion cells (solid line).
B, Deconvolution of the GABA signal histogram
(dotted line) for superclass EDQ into Gaussian
components representing class 5 (EQ) and class 9 EDQ ganglion
cells (solid lines). C, Deconvolution of
the cell size histogram for superclass EDQ, class 1 ganglion cells into
three components of identical shape and varying peak height
(dotted lines).
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If molecular phenotype classes are natural classes, they must
eventually map onto functional and morphological classes of cells. As
an interim naming strategy, we chose to label classes in ordinal
sequence by increasing GABA signals: i.e., class 1 cells contain no
detectable GABA and class 12 cells have high GABA contents, with other
classes ordered between. The relationships among superclasses and
classes are summarized in Figure 7 and below.
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Figure 7.
A summary hierarchy of superclasses and classes
encoded as a metabolic signature (EDQ signal), a coupling/synthesis
signature ( signal), an excitation signature
(white = high, horizontal line = medium, black = low), and a size signature (disc
diameter).
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Superclass EDQ class 1
This superclass contains a single molecular phenotype, class 1 ganglion cells, characterized by strong DQ signals, no GABA coupling,
and three cell-size groups displaying nearly identical AMPA-driven AGB
signals. Classes 1a, 1b, and 1c were extracted by deconvolution of
cell-size histograms (discussed below in Structural correlates of classes).
Superclass EDQ class 9
This group resembled superclass EDQ but also showed significant
GABA content and slightly weaker AMPA responses. Another distinct molecular class that did not separate fully with the isodata algorithm also initially contaminated it. Overlapping modes of GABA, aspartate, and glutamine signals suggested that one class of cells bridged N-space
between superclasses EQ and EDQ, likely because of differing degree of coupling. This hypothesis was tested by deconvolving the
univariate histogram of GABA signals for superclass EDQ into Gaussian modes (Fig. 6B) and recoding each cell by
modal membership. These operations yielded two unique size classes:
class 5 (medium-sized cells), which properly belonged in superclass
EQ, and class 9 ganglion cells (the largest neurons in the retina)
as the sole member of superclass EDQ. Because classification and
deconvolution were blind to structure, the discovery of different size
classes is an independent validation of the separation method. Failure to completely separate using clustering algorithms is a common problem
in pattern recognition because natural classes need not respect
arbitrary separability criteria.
Superclass EQ classes 2 and 4
With high Q but low D signals, this superclass encompassed classes
of low (class 2) and high (class 4) AMPA responsivity.
Superclass EQ classes 5, 8, 10, and 11
Superclass EQ represents the high end of a continuum of GABA
content in ganglion cells. In particular, classes 11 and 12 represent
high and low AMPA response classes of ganglion cell subsets with GABA
contents that overlap those of bona fide GABAergic amacrine cells.
However, their absolute glutamate, aspartate, and glutamine values
place them outside the amacrine cell molecular phenotype. Classes 8 and
10 represent low and high AMPA response classes of ganglion cell
subsets in which GABA levels do not reach those of amacrine cells but
that have much higher GABA signals than any other superclass. Class 5 cells were originally mixed with class 9 cells in superclass EDQ but
were resolved by deconvolution.
Superclass E class 3
Superclass E contains class 3 ganglion cells with negligible GABA
signals, weak DQ signals, and medium AMPA responsivity.
Superclass E classes 6 and 12
Superclass E contains cells with both low and high GABA
contents, modest DQ signals, and it expresses either high (class 6) or
low (class 12) AMPA responsivity.
Superclass EG class 7
This superclass contained only a single population of GABA+ and
glycine+ ganglion cells with medium-strength AMPA responses.
Superclass classes dA1, dA2, and dA3
This amacrine cell superclass contained three distinctive
molecular phenotypes. Class dA1 (displaced amacrine cells) is composed entirely of the highly AMPA-responsive ON-center starburst amacrine cell cohort and represents 85% of superclass . The remainder is
composed of a class (dA2) with phenotype and size that resemble starburst amacrine cells but is significantly less responsive to AMPA,
and class dA3, a very small cell type displaying no AMPA response at
all. It is possible that class dA2 cells are a frequent misplaced
variety or true dA1 starburst amacrine cells that, for some reason,
have weaker AMPA responses. Class dA3 cells are smaller than any other
element and may not be amacrine cells at all, although they have high
GABA content. They closely resemble very small cells detected in (and
excluded from) the ganglion cell cohort by Oyster et al. (1981) and
would fit in the microneuron category of Wong and Hughes (1987), being
much larger than microglia.
Stability of classes
The superclasses can easily be found in every retina examined, but
can all the classes be extracted? AGB mapping is a physiological in vitro experiment, and assessment of variability of the
AMPA-induced AGB signal across cell types and individual retinas is
pivotal in validating classifications. Each preparation must yield
arrays of nearly flawless thin horizontal sections that can be
precisely registered, are flat enough to provide some hundreds of
ganglion cells in a high-resolution field, and behave similarly in
response to physiological stimulation. Four individual retinas (cases
2366, 2578, 2780, and 2781) were prepared and treated identically,
using 10 min of 25 µM AMPA activation in the
presence 5 mM AGB. All were processed identically
as serially probed 250 nm horizontal sections and analyzed as described
in Materials and Methods. Each represented a locus 2-4 mm below the
visual streak. Figure 8 displays both
GABA and AMPA-activated AGB signals from an example of every cell class
from each retina. GABA content and AMPA activation are fairly stable
across retinas, although there are clear variations in the amount of
basal GABA and AGB signals in the Müller cells. The ordinal
ranking of AMPA responsivity in ganglion cells classes is consistent
and suggests that AMPA responses are stable under our defined
experimental conditions. Two of the retinas yielded 14 ganglion cell
classes and two yielded 13, the latter lacking detectable examples of
class 10. Examination of five other retinas prepared at different AMPA
activation levels revealed one case in which class 7 was absent and two
more in which class 10 cells could not be found. We have no obvious
explanation for this except that the efficacy of heterologous coupling
with amacrine cells may vary. Decreases in glycine coupling would
collapse class 7 into class 6; a decrease in GABA coupling could
disperse class 10 into classes 5, 4 or maybe even 3 if glutamine signal
changed as well; increases in GABA coupling would seem less likely,
because contamination of the small classes 11 and 12 would be
noticeable. On the whole, separation into 14 classes in chemical space
seems to most reasonably account for the ganglion cell population. In every preparation, some cells appear to be bona fide misplaced amacrine
cells, and ~2-5% of the small ganglion cells could not be
classified, either because of section defects or disappearance from the
end of a series, or simply because the signature of a single cell did
not fit a single class well enough. Such cells could be damaged cells
or true additional classes with sparse distributions.
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Figure 8.
An array of GABA and AGB signals in four
retinas. All panels were captured under identical conditions from
horizontal 250 nm sections after registration and classification of
cells. The outline of each cell was captured in a
registered taurine channel (Fig. 2G) and used to track
its location in both AGB and GABA channels. AGB signals were activated
by exposure to 25 µM AMPA as described in
Results. Class 10 cells were not resolved in cases
2780 and 2781. Images are density scaled. Scale
bar, 25 µm.
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Structural correlates of classes
Do molecular phenotype classes represent stable natural classes?
They do for some other cell types. Horizontal cells (Marc et al., 1995;
Marc, 1999b) and starburst amacrine cells (Marc, 1999b) can be
extracted via their distinctive signatures, and other cell types
display strong superclasses; e.g., mammalian cone ON-center and
OFF-center bipolar cells have distinctive signatures (Kalloniatis et
al., 1996). This does not prove that all natural classes should have
characteristic signatures, even when excitation signals are added to
the data. Independent tests of identity are needed. The classification
methods that we used were blind to size differences because they were
pixel based and not object based. Thus size and patterning extracted
from the classifications become test statistics.
The mean diameter of every cell in the glutamate channel of dataset
2366 was determined, and the values for all classes were compared pair
wise [Student's t test (Table
1); true diameter measures from single
sections are better approximated by the mode or supremum, but
for spheres randomly sampled, the true diameter  measured
mean × shrinkage correction × 4/ ]. The mean suffices for tests of distribution differences. Most classes were significantly different in size from many other classes. For example, extraction of
molecular phenotype classes 2 and 4 from superclass EQ permits the
demonstration that they are also members of statistically different
size groups. Superclass EDQ was also initially contaminated, and
deconvolution exposed molecular phenotype classes that are vastly
different size groups. The only groups that did not completely segregate internally based on size were classes within superclasses EQ and . Even so, superclass still separates from all other superclasses in terms of size, and superclass EQ segregated
partially. On balance, the extraction of 12 ganglion cell and 3 amacrine cell molecular phenotype classes reveals the existence of
underlying structural correlates, which the pixel-based pattern
recognition that was applied could not have detected, a priori.
Class 1 (superclass EDQ) separated from all other classes based on
size, except for class 9. There was, however, cause to suspect that
molecular phenotype class 1 was not an ultimate, natural class: (1)
visual examination showed it to be more heterogeneous than any other
molecular phenotype class; (2) the class diameter variance was greater
than any other class; and (3) the diameter histogram was clearly
multimodal. We thus attempted to decompose the histogram into the most
probable component Gaussians with variances forced to match the mean
variances of all other single classes (Fig. 6C). This
assumption forces the shape of all Gaussians to be as broad as bona
fide natural classes. Only the sum of three such Gaussians no more, no
lessfits the envelope of the histogram with a correlation coefficient
of >0.8. Fits of 1, 2, or 4 or more Gaussians led to correlation
coefficients of <0.4. Furthermore, model distributions with more
adjustable parameters (e.g., Weibull functions, etc.) did no
better. Arguably, three natural populations comprise molecular
phenotype class 1, and they were extracted simply by segmenting the
population at the two minima in the histogram. This resulted in
three classes (1a, 1b, and 1c) that still differed in size from all
other cell types with the exception that classes 1a and 9 were not
different, although they are fully separable in molecular phenotype
space. Taken together, 80% of the 136 possible size comparisons among
the classes are significantly different at p 0.05 (Table 1). This would not be possible had not the underlying size
classes and molecular phenotypes been the same natural classes.
This array of size groupings allows a reconstitution of the theoretical
ganglion cell size histogram with higher resolution of its underlying
structure than previously possible. The cell-size Gaussian for each
class was calculated from its mean diameter and SD and weighted by the
frequency of the class (Fig. 9). Viewed on both logarithmic and linear probability density ordinates, the
envelope of all the individual classes resembles rabbit ganglion cell
layer density histograms acquired from whole mounts when the displaced
amacrine cells are included (Vaney, 1980; Tancred, 1981; Vaney et al.,
1981; Rowe and Dreher, 1982). Several points emerge from inspection of
the histograms. First, amacrine and ganglion cells are separable by
size. Second, six cell classes dominate the shape of the ganglion cell
size histogram: 1a, 1b, 1c, 3, 6, and 8. All other ganglion cell
classes are submerged beneath the envelope and are undetectable by
simple population counting and sizing. Third, no deconvolution of the
total cell size histogram of the ganglion cell layer can extract the
correct number of types, regardless of the underlying fundamental
shapes chosen.
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Figure 9.
Reconstruction of the ganglion cell
layer size histogram. The mean, population density, and variance of
each class of ganglion cell in case 2366 was used to control the
position, height, and width of a Gaussian distribution for that class.
All classes were summed to create the envelope distribution and plotted
on linear (A) or logarithmic
(B) ordinates. The linear envelope strongly
resembles typical soma size or soma area histograms plotted on linear
ordinates when starburst amacrine cells are included. The logarithmic
envelope and Gaussians demonstrate that the smooth peak of the envelope
belies the complex mixture of cell types comprising the ganglion cell
layer. At this eccentricity (~2 mm below the streak), all cells with
mean diameters <8 µm are amacrine cells on the basis of
signatures.
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Molecular phenotyping also uncovers hidden patterning. Quantitative
signatures must be extracted from serial thin sections of nearly
perfect horizontal orientations, strongly limiting cell numbers. Given
that there are so many different classes, any one class will occur with
low frequency, and the number of sampled cells in one comprehensive
data set is usually lower than practical for robust tests of
patterning. We cannot combine sets from other loci or retinas, and each
horizontal section array must stand alone. Even so, three classes
occurred with high enough frequencies in a single patch to test
patterning by measuring the CR (also known as the regularity index) and
testing significance (Cook, 1996). All three, (classes 3, 6, and dA1)
were patterned significantly at p < 0.01 (Fig.
10, Table 1). Classes were extracted by
signatures alone, so this demonstrates that molecular phenotyping
uncovers patterned classes, which would again be implausible were not
the underlying elements natural classes. We expected to see better patterning among the remaining classes, but this is partly a
consequence of the limited numbers of cells. If we presumed that the
patterning precision of each class was replicated over an area
subtending 100 cells of that class, then 10 of 18 classes would have
been statistically patterned. In addition, some patterns might
represent mixed "subclasses." For example, we know that ON-OFF
direction-selective (DS) ganglion cells exist as four vector subclasses
(Oyster and Barlow, 1967) but that they are not morphologically
distinguishable (Amthor et al., 1989b). Any class that contains
the ON-OFF DS cells should be represented by a mixture of patterns (a
"mixed" pattern). Furthermore, it is not clear that somatic
positions of all ganglion cell types must be well patterned, because
the territory of dendritic coverage is the critical tiling unit
(Wässle et al., 1981), and cells with wide, sparse,
asymmetrical dendritic arbors, such as many type "W" ganglion
cells, are likely to be tiled with rather asymmetrical Dirichlet
(Voronoi) domains. In addition, some cells in a single functional
class, such as ON DS ganglion cells (ignoring vector subtypes), can be
immediate neighbors (He and Masland, 1998), which will strongly break
any patterning statistics based on somatic spacings alone. This will again yield mixed patterns.
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Figure 10.
Superposition of class 3, class 6, class 1a
(white), class 9, and class dA1 cells on the array of
all cells in the ganglion cell layer. Classes 3, 6, and dA1 are
significantly patterned (p < 0.01). Classes
1a and 9 cells are too rare to test in this data set. Indexed cell
class image is superimposed on a digital "phase" image of the
glutamate channel. Scale bar, 25 µm.
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Finally, the fractions of some cell classes exposed by classification
roughly correspond to previously determined groups. Classes 1a and 9 clearly fit within the size group for  ganglion cells (Peichl et
al., 1987) alone and are also the rarest of types, comprising but
1-2% of all cells in the ganglion cell layer in all preparations.
Conversely, the well known starburst amacrine cells were the dominant
cell class comprising 20-35% of the ganglion cell layer depending on
eccentricity. As pointed out by Hughes (1985), the ganglion cell size
spectrum in rabbit is quite unimodal, and the remaining populations are
difficult to correlate with known types.
Attributes of different ganglion cell classes: coupling and
excitatory drive
Classification allows exploration of important structural and
functional properties of these cell types. Roughly three domains of
GABA signals can be assigned to ganglion cells (Fig. 7). Classes 1a,
1b, 1c, 2, 3, and 4 have little or no detectable GABA signals. This is
consistent with arguments that certain mammalian ganglion cells such as
a subset of cells are not tracer coupled to amacrine cells (Xin and
Bloomfield, 1997). A spectrum of classes including high population
density class 8 cells, large class 9 cells, medium class 5 cells, and
small class 10 cells show significant GABA signals,
suggesting varying degrees of steady-state leakage of GABA from coupled
amacrine cells. Class 7 in particular stands out by having measurable
glycine and GABA signals. Finally, ganglion cell classes 11 and 12 contain GABA signals indistinguishable from those of GABAergic amacrine
cells and might synthesize GABA directly. As previously argued,
however, class 11 and 12 cells possess a glutamate phenotype that
places them with ganglion cells and not amacrine cells. Furthermore,
GABA immunoreactive axons are abundant in the rabbit optic nerve but
always map to glutamate immunoreactive axons (Fig.
1B).
The most important feature of these classes, however, is likely to be
the fact that they vary greatly in their responses to AMPA activation
(Figs. 2A, 8), and the variation has no correlation with size or GABA content. Some of the smallest (class dA1) and largest
(classes 1a, 1b, and 1c) cells of the ganglion cell layer show
extremely high AMPA responsivity, so cell volume cannot be the
mechanism underlying high signals. Similarly, one of the smallest (class 8) and one of the largest (class 12) ganglion cells have weak
AMPA responses. We presume that class 9 ganglion cells correspond to
OFF- cells because they could be no other than an type based on
size, and their basal dendrites can be traced into the distal half of
the inner plexiform layer (Fig. 11).
However, it is clear that in every preparation, the AMPA-driven signals
of class 9 ganglion cells are slightly weaker than those of class 1a
ganglion cells.
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Figure 11.
Identification of class 9 ganglion cells as
probable OFF ganglion cells in semi-serial 250 nm vertical
sections. The top panel shows the characteristic
moderate GABA signal of a class 9 ganglion cell embedded in the
Müller cell foot pieces (m), along with a
strongly immunoreactive nearby starburst amacrine cell
(asterisk). A large-caliber proximal dendrite clearly
passes the midway point (dots) of the inner plexiform
layer (double-headed arrow). Bottom
panel, The glutamine signal validates the identity of the cell
as a member of superclass EDQ, and its dimensions require that it be
a member of the size group. The dendrite continues into the distal
inner plexiform layer. Scale bars, 25 µm.
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DISCUSSION |
Metabolic signatures
The molecular phenotypes of ganglion cells distinguish them from
all other retinal neurons (Sherry and Ulshafer, 1992;
Kalloniatis et al., 1994, 1996; Marc et al., 1995, 1998).
Classification shows that EDQ and EDQ cells are large
ganglion cells, although no biological reason for this specialization
is obvious. Of the remaining cells, EQ+EQ superclasses contain
larger ganglion cells than the E+E superclasses. We have documented
the association of the EDQ phenotype with large ganglion cells in >30
individual rabbit retinas; it appears to be a reliable index of
identity. Amacrine cells possess distinctively weaker EDQ signatures
than the true ganglion cells, and this, rather than their GABA signals
alone, enables separation of the superclass. We hypothesize that
signatures are tools for molecular profiling of cell types in all
tissues, in all states.
Coupling signatures
One-half of all ganglion cells in the rabbit retina contain
nontrivial GABA signals. Where does this GABA come from? A major source
must be heterologous coupling between ganglion cells and conventional
GABAergic amacrine cells. The evidence for amacrine-ganglion cell coupling in the mammalian retina is unequivocal (Rodieck and Haun,
1991; Vaney, 1991; Dacey and Brace, 1992; Penn et al., 1994; Jacoby et al., 1996; Stafford and Dacey, 1997; Xin
and Bloomfield, 1997). Most amacrine cells are GABA immunoreactive, and
GABA-immunoreactive dendrites comprise ~80% of the mass of the inner
plexiform layer (Marc, 1992, 1999b; Marc et al., 1995, 1998).
GABA is a 97 Da molecule and would clearly leak from GABA in amacrine
cells (containing ~10 mM GABA) through gap junctions at
nearly diffusion-limited rates into ganglion cells. A similar
phenomenon would explain glycine-rich class 7 ganglion cells. Ganglion
cells may possess other sources of GABA signals, such as transport or
synthesis, but the existence of a coupling leak for many cell classes
is certain, and we are satisfied that coupling patterns must shape molecular phenotypes of ganglion cells. Why do not all ganglion cells
coupled to amacrine cells eventually equilibrate to the same level as
their source cells? A GABA sink must exist in ganglion cells. All
eukaryotic cells are probably capable of metabolizing GABA (Michal,
1999), so it would not be surprising if a GABA leak from a "10
mM" amacrine cell yielded a stable steady-state 0.5 mM GABA level in a ganglion cell that slowly metabolizes
it. Coupling efficiency between ganglion cells and amacrine cells might
also vary. OFF-center ganglion cells display heterologous coupling to amacrine cells (Mills and Xia, 2000) but can appear uncoupled, likely because of changes in adaptation state (Hu and Bloomfield, 2000,
2001). We hypothesize that coupling signatures can be used to uncover
patterns of dendritic cofasciculation and costratification and to track
adaptation events.
Excitation signatures
As reported previously, the responses of retinal neurons to
glutamate agonists are not identical (Marc, 1999a,b), and ganglion cells show stereotyped differences in AGB permeation after activation with kainate, AMPA, NMDA, and glutamate itself. These differences are
not explained by cell size but must arise from cell-specific variations
in receptor affinity, macroscopic open-time kinetics, and/or number.
Rabbit retinal ganglion cell signal integration is dominated by AMPA
and NMDA receptors, with no significant kainate receptor involvement
(Marc, 1999c). Differences in AMPA-activated responses should reflect
the behaviors of AMPA receptors under native glutamate stimulation, and
we predict that glutamate drive from a given bipolar cell targeting
different ganglion cell classes may evoke different effects based on
AMPA receptor properties. Cells with high and low AMPA responsivity
should have fast and slow integration times, respectively. We
hypothesize that AMPA receptor type, and the absence or presence of
GluR2-edited subunits in particular, may control the brisk-sluggish
spectrum of ganglion cell responses.
Correlation with physiological classes
The apparent numbers of molecular phenotype classes and
physiological classes in rabbit are similar (Oyster et al., 1971; Amthor et al., 1989a,b,). Given data on coupling patterns (Xin and
Bloomfield, 1997; Mills and Xia, 2000; Hu and Bloomfield, 2001), the
tabulation of morphologies of physiological classes (Amthor et al.,
1989a,b), encounter frequencies (Oyster et al., 1971), and soma size
histograms (Provis, 1979; Vaney, 1980; Oyster et al., 1981; Vaney et
al., 1981), we can construct provisional matches between physiological
and molecular phenotype classes (Table
2). We have made the matches as specific
as possible and offer them as a target for argument and experiment.
Concentric brisk nonlinear transient cells
Classes 1a and 9 are the ganglion cells of the rabbit retina
(Peichl et al., 1987). Considering dye-coupling patterns (Mills and
Xia, 2000; Hu and Bloomfield, 2001) and our tracing of dendrites (Fig.
11), class 1a cells are ON-center and class 9 cells are OFF-center, corresponding directly to the concentric brisk nonlinear transient cells of Amthor et al. (1989a) and the Y cells of carnivores.
Concentric brisk linear sustained cells
A presumed X-like, -like class is one of the complex problems
in rabbit retina. Rabbit homologs have small somas (Amthor et al.,
1989a), excluding candidate classes 1b and 1c. Brisk sustained cells
are a common cell type (Levick, 1967; Oyster et al., 1971; Amthor et al., 1989a) and should have good AMPA responses. The well
patterned, frequent small cells of classes 3 and 6 seem the best
candidates. There are conceptual problems: class 6 is coupled, whereas ferret ganglion cells (Penn et al., 1994) and primate midget ganglion cells (Dacey and Brace, 1992) show no amacrine cell coupling. At present, we ignore this conflict. By analogy with
coupling patterns of ON and OFF cells, we propose class 3 as the
ON-center and class 6 as the OFF-center classes.
Concentric brisk linear transient cells
Amthor et al. (1989a) distinguished these medium-sized cells from
cells with caveats. Equating briskness and high AMPA responsivity, the best matches are classes 1b (ON) and 5 (OFF).
Concentric sluggish cells
Equating sluggishness with low AMPA responsivity and matching
sizes and encounter rates, the small sustained sluggish cells could be
members of class 2 or 8. The patterning of class 8 suggests that the
population could be mixed and may contain both ON and OFF varieties.
Transient sluggish cells are of medium size, matching best with class
12 ganglion cells.
ON-OFF DS cells
The ON-OFF DS cell class harbors four vector types (Oyster and
Barlow, 1967) and lies in the small-to-medium soma size spectrum (Amthor et al., 1989b). Because vector type might display a well patterned distribution (Vaney, 1994b) and a single class contains two
or more vector types, that class might show mixed distribution patterns. Some ON-OFF DS cells are coupled to amacrine cells and others
are not (Vaney, 1994a,b; Xin and Bloomfield, 1997). We match
small-medium class 10 and medium-sized 1b cells as the coupled and
uncoupled sets of ON-OFF DS cells.
ON DS cells
The vector types of ON DS cells project to the medial terminal
nucleus of the rabbit accessory optic system and are medium-large ganglion cells (Oyster et al., 1981). Xin and Bloomfield (1997) suggest
that this cell is not coupled. Class 4 seems the best fit for ON DS
ganglion cells.
Orientation-selective cells
These cells, with two orientation-preference subclasses, are among
the smallest cells in soma size (Amthor et al., 1989b). Of all the
small cells, they appear to have the most elongate, polygonal
somas, corresponding well to the mixed-pattern, -coupled class 11 ganglion cells.
Local edge detector cells
With complex physiologies and small somas, local edge detector
(LED) cells also have small, compact dendritic arbors. Xin and
Bloomfield (1997) illustrated small "narrow field" cells much like
LED cells injected by Amthor et al. (1989b), and they were clearly
coupled to amacrine cells. The only remaining group that seems to fit
is class 7, but Xin and Bloomfield (1997) speculated that the cell was
coupled only to a single type of amacrine cell, and class 7 is
apparently both and G coupled. Furthermore, if all ganglion cells
have coverage factors of at least 1, the class 7 distribution (Fig.
2D) does not suffice. LED cells are reported |
TOP
ABSTRACT
INTRODUCTION
