Postembedding silver-intensified immunogold procedures reveal high levels of glutamate immunoreactivity in "vertical" elements of the goldfish retina: (1) Red-sensitive and green-sensitive cones display strong glutamate immunoreactivity, especially in their synaptic terminals, but blue-sensitive cones are poorly immunoreactive. (2) All type Mb (on-center) and Ma (off-center) mixed rod-cone bipolar cells and all identifiable cone bipolar cells are highly glutamate immunoreactive. We find no evidence for bipolar cells that lack glutamate immunoreactivity. (3) The majority of the somas in the ganglion cell layer and certain large cells of the amacrine cell layer resembling displaced ganglion cells are strongly glutamate immunoreactive. (4) Despite their high affinity symport of acidic amino acids, the endogenous levels of glutamate in Müller's cells are among the lowest in the retina. (5) GABAergic neurons possess intermediate levels of glutamate immunoreactivity. Quantitative immunocytochemistry coupled with digital image analysis allows estimates of intracellular glutamate levels. Photoreceptors and bipolar and ganglion cells contain from 1 to 10 mM glutamate. The bipolar and ganglion cell populations maintain high intracellular glutamate concentrations, averaging about 5 mM, whereas red-sensitive and green-sensitive cones apparently maintain lower levels. Importantly, photoreceptor glutamate levels are extremely volatile, and in vitro maintenance is required to preserve cone glutamate immunoreactivity in the goldfish. GABAergic horizontal and amacrine cells contain about 0.3-0.7 mM glutamate, which matches the values predicted from the Km of glutamic acid decarboxylase. Müller's cells and non-GABAergic amacrine cells contain less than 0.1 mM glutamate. Though Müller's cells are known to possess potent glutamate symport, they clearly possess equally potent mechanisms for maintaining low intracellular glutamate concentrations.

Pattern recognition of amino acid signals partitions virtually all of the macaque retina into 16 separable biochemical theme classes, some further divisible by additional criteria. The photoreceptor→bipolar cell→ganglion cell pathway is composed of six separable theme classes, each possessing a characteristic glutamate signature. Neuronal aspartate and glutamine levels are always positively correlated with glutamate signals, implying that they largely represent glutamate precursor pools. Amacrine cells may be parsed into four glycine-dominated (including one glycine/GABA immunoreactive population) and four GABA-dominated populations. Horizontal cells in central retina possess a distinctive GABA signature, although their GABA content is constitutively lower than that of amacrine cells and shows both regional and sample variability. Finally, a taurine–glutamine signature defines Müller’s cells. We thus have established the fundamental biochemical signatures of the primate retina along with multiple metabolic subtypes for each neurochemical class and demonstrated that virtually all neuronal space can be accounted for by cells bearing characteristic glutamate, GABA, or glycine signatures.

Postembedding immunocytochemistry was used to determine the cellular localization of the amino acid neurotransmitters glutamate, aspartate, gamma-aminobutyric acid (GABA), and glycine in the avian retina. The through retinal pathway was glutamatergic, with all photoreceptors, bipolar cells, and ganglion cells being immunoreactive for glutamate. Bipolar cells displayed the highest level of glutamate immunoreactivity, with the cell bodies terminating just below the middle of the inner nuclear layer. All lateral elements, horizontal cells, amacrine cells, and interplexiform cells were immunoreactive for glycine or GABA. The GABAergic neurons consisted of two classes of horizontal cells and amacrine cells located in the lower part of the inner nuclear layer. GABA was also localized in displaced amacrine cells in the ganglion cell layer, and a population of ganglion cells that co-localize glutamate and GABA. Both the horizontal cells and GABAergic amacrine cells had high levels of glutamate immunoreactivity, which probably reflects a metabolic pool. At least two types of horizontal cells in the avian retina could be discriminated on the basis of the presence of aspartate immunoreactivity in the H2 horizontal cells. Glycine was contained in a subclass of amacrine cells, with their cell bodies located between the bipolar cells and GABAergic amacrine cells, two subclasses of bipolar cells, displaced amacrine cells in the ganglion cell layer, and ganglion cells that colocalize glutamate and glycine. Glycinergic amacrine cells had low levels of glutamate. We have also identified a new class of glycinergic interplexiform cell, with its stellate cell body located in the middle of the inner nuclear layer among the cell bodies of bipolar cells. Neurochemical signatures obtained by analyzing data from serial sections allowed the classification of subclasses of horizontal cells, bipolar cells, amacrine cells, and ganglion cells.

We established that converting to age-equivalent thresholds and application of dB* principle advantageously allows comparison of data sets across age and test size at different locations of the visual field. By identifying the Ac across the visual field, it is now possible to systematically determine threshold changes across the 30-2 locations in ocular disease and further characterize the importance of testing within complete spatial summation in standard automated perimetry.

Cell lineage analyses suggest that cortical neuroblasts are capable of undertaking both radial and tangential modes of cell movement. However, it is unclear whether distinct progenitors are committed to generating neuroblasts that disperse exclusively in either radial or tangential directions. Using highly unbalanced mouse stem cell chimeras, we have identified certain progenitors that are committed to one mode of cell dispersion only. Radially dispersed neurons expressed glutamate, the neurochemical signature of excitatory pyramidal cells. In contrast, tangential progenitors gave rise to widely scattered neurons that are predominantly GABAergic. These results suggest lineage-based mechanisms for early specification of certain progenitors to distinct dispersion pathways and neuronal phenotypes.

The mammalian retina contains as many as 50-60 unique cell types, many of which have been identified using various neurochemical markers. Retinal neurons express N-methyl-D-aspartate (NMDA), alpha-amino-3-hydroxyl-5-methylisoxazole-4-propionic acid (AMPA), and kainic acid (KA) receptor subunits in various mixtures, densities, and spatial distributions. Ionotropic glutamatergic drive in retinal neurons can be mapped using a cation channel permeant guanidinium analog called agmatine (1-amino-4-guanidobutane; AGB). This alternative approach to physiologically characterize neurons in the retina was introduced by Marc (1999, J Comp Neurol 407:47-64, 407:65-76), and allows the simultaneous mapping of responses of glutamate receptor-gated channels from an entire population of neurons. Unlike previous AGB studies, we colocalized AGB with various macromolecular markers using direct and indirect immunofluorescence to characterize the glutamate agonist sensitivities of specific cell types. Activation with NMDA, AMPA, and KA resulted in AGB entry into neurons in a dose-dependent manner and was consistent with previous receptor subunit localization studies. Consistent with the various morphological phenotypes encompassed by the calbindin and calretinin immunoreactive cells, we observed various functional phenotypes revealed by AGB labeling. Not all calbindin or calretinin immunoreactive cells showed ligand-evoked AGB permeation. A small proportion either did not possess functional glutamate receptors, required higher activation thresholds, or express functional channels impermeable to AGB. AMPA and KA activation of bipolar cells resulted in AGB permeation into the hyperpolarizing variety only. We also studied the glutamate ligand-gating properties of 3[alpha1-3]-fucosyl-N-acetyl-lactosamine (CD15) immunoreactive cells and show functional responses consistent with receptor subunit gene expression patterns. CD15-immunoreactive bipolar cells only responded to AMPA but not KA. The CD15 immunoreactive amacrine cells demonstrated an identical selectivity to AMPA activation, but were also responsive to NMDA. Finally, localization of AGB secondary to glutamate receptor activation was visualized with a permanent reaction product.

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PurposeTo characterize macular ganglion cell layer (GCL) changes with age and provide a framework to assess changes in ocular disease. This study used data clustering to analyze macular GCL patterns from optical coherence tomography (OCT) in a large cohort of subjects without ocular disease.MethodsSingle eyes of 201 patients evaluated at the Centre for Eye Health (Sydney, Australia) were retrospectively enrolled (age range, 20–85); 8 × 8 grid locations obtained from Spectralis OCT macular scans were analyzed with unsupervised classification into statistically separable classes sharing common GCL thickness and change with age. The resulting classes and gridwise data were fitted with linear and segmented linear regression curves. Additionally, normalized data were analyzed to determine regression as a percentage. Accuracy of each model was examined through comparison of predicted 50-year-old equivalent macular GCL thickness for the entire cohort to a true 50-year-old reference cohort.ResultsPattern recognition clustered GCL thickness across the macula into five to eight spatially concentric classes. F-test demonstrated segmented linear regression to be the most appropriate model for macular GCL change. The pattern recognition–derived and normalized model revealed less difference between the predicted macular GCL thickness and the reference cohort (average ± SD 0.19 ± 0.92 and −0.30 ± 0.61 μm) than a gridwise model (average ± SD 0.62 ± 1.43 μm).ConclusionsPattern recognition successfully identified statistically separable macular areas that undergo a segmented linear reduction with age. This regression model better predicted macular GCL thickness. The various unique spatial patterns revealed by pattern recognition combined with core GCL thickness data provide a framework to analyze GCL loss in ocular disease.