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Neuronal Diversity in the Retina
Summary: Richard Masland's laboratory studies the microcircuitry of the retina.
The retina is a complex sample of neural tissue, a microprocessor located in the eye. Specialized photoreceptor cells detect light and communicate synaptically with a network of subsequent neurons, ultimately leading to the transmission of nerve impulses down the optic nerve. Along the way, the visual input is shaped and modified for transmission to the brain's higher centers.
This recoding of the visual input is surprisingly sophisticated: certain features of the visual input are accentuated, and others are downplayed. We are still unraveling the intricacies of the retina's neural codes. The retinal hardware is concomitantly complex, but unraveling its intricacies is worth the effort because the retina, which is an extension of the central nervous system, is an accurate prototype of many brain nuclei—their neuronal structure is no less heterogeneous than that of the retina. The lessons learned from retinal structure, and the methods developed for its analysis, thus are applicable to other brain regions.
The Cell Populations of the Retina
About 10 years ago we set out to make a comprehensive accounting of the types of neurons that participate in the retina's computations. As a strategy, this was a distant cousin to the human genome project: the program is first to list the players—the gears and wheels that make the machine turn—and later find out how they are assembled.
The initial phase of this effort is now complete. A typical mammalian retina contains rod and cone photoreceptors, 2 types of horizontal cell, 13 types of bipolar cell (more about these below), ~30 types of amacrine cell, and a dozen types of ganglion cell. Each cell is a distinct computational element: it carries out a distinct job in the retina's circuitry. This shows, among other things, that the florid neuronal complexity described by the great turn-of-the-century anatomists is real, reflecting the actual components of an intricate computing machine. The next task is to make sense of it.
To understand a retina that contains 60 different computational elements seems at first a daunting problem, but the job becomes simpler once a central rule is recognized: the output of any individual cone photoreceptor is tapped by each of the dozen distinct types of cone bipolar cell. For example, retinal bipolar cells are divided into ON and OFF types. Each class of bipolar cell is further subdivided. For example, there are cells that respond best to sudden changes in illumination and other cells that respond best to steady levels of light. The retina has mixtures of a dozen output signals to represent the world's panoply of brightnesses, shapes, motion, and colors. Those individual signals are collected in different combinations by retinal ganglion cells, which then transmit a combinatorial message to the brain.
Ganglion Cells of the Mouse Retina
Because of molecular genetics, the mouse has major advantages as an experimental model. Unfortunately, very little is known of the retina in the mouse. We undertook a study of the structure of its neurons.
In traditional methods of classifying neurons, the shape, size, and pattern of the cell's branches are distinguished by informal criteria. This does not mean that they are less than rigorous: the human brain is an effective pattern analyzer and still the most general-purpose one. As a step toward more rigorous methods, though, we undertook a fully automated, computer-based classification of the types of retinal ganglion cells found in the mouse. To generate a large sample of filled ganglion cells, we used conventional methodologies for filling cells and/or a transgenic mouse line in which small numbers of retinal ganglion cells express one of several fluorescent proteins. The cells were imaged in three dimensions by confocal microscopy. We measured a number of structural parameters and applied automated cluster analysis.
The cluster analysis produced groupings of cells (cell types) very much like those that would have been defined by traditional inspection. This provided a classification that will be useful to us and to others in future studies. At the same time, however, the automated analysis made some important errors, notably where wide-field cells and cells present in very low numbers were concerned. The source of these errors can be attacked using current methodology, and it now seems practical to sort neurons automatically in this way.
Wide-Field Amacrine Cells in the Mouse Retina
Recent physiological studies have confirmed the findings of anatomical studies by showing that a great variety of effects spread laterally across the retina rather than simply being transmitted through it. This challenges our earlier belief, which was that any laterally spreading conduction should only degrade the fidelity of the image transmitted through the tissue—the final spatial resolution of the retina. Because of these newly discovered lateral effects, we undertook a survey of the wide-field amacrine cells present in the mouse retina. We used a transgenic mouse in which subsets of neurons express the green fluorescent protein (GFP). We found at least a half dozen types of wide-field cells were present. The cells are distinguished on the basis of their branching pattern and their level of stratification within the inner plexiform layer. This second criterion distinguishes cells with different patterns of connectivity and thus, by definition, different functions in the retina. It is remarkable that so much space is devoted to these cells. Even though they are relatively rare, their huge dendritic arbors (the arbor of a single cell can cover more than half of the mouse retina) fill up a substantial fraction of the volume of the inner plexiform layer. It is likely that further wide-field physiological interactions remain to be discovered.
Heterogeneity of Gap Junctions Among Retinal Neurons
A variety of gap junctional connections exist in the retina. These range from connections between different types of photoreceptor cells all the way down to gap junctional connections between ganglion cells. Most of these connections have been revealed only by dye coupling, and their functions are unknown. It seems clear, however, that highly specific types of regulation occur, as gap junctions between different cells are now shown to use different gap junctional proteins.
Rod signals are transmitted to ON retinal ganglion cells by means of gap junctions between AII amacrine cells and ON bipolars. The AII amacrine cells are known to express connexin36 (Cx36), but previous studies of Cx36 in ON cone bipolars have produced ambiguous results. We studied bipolar cells in a transgenic mouse line that expresses high levels of GFP in one type of ON cone bipolar cell. We found strong Cx36 immunostaining in the axon terminals of the GFP-labeled type 357 bipolar cells in both vertical sections and whole mounts of the retina. This finding was confirmed by single-cell immunostaining and single-cell reverse transcription–PCR (RT-PCR). As previously reported Stephan Maxeiner (University of Bonn) and his colleagues, Cx45 was found in some ON bipolar cells, but RT-PCR showed that Cx36 and not Cx45 is expressed by the type 357 bipolar cells. Some of the remaining GFP-negative bipolar cells expressed Cx45 but not Cx36. Different types of ON cone bipolar cells appear to express different connexins at their gap junctions with AII amacrine cells.
This research is supported in part by a grant from the National Institutes of Health.
Last updated January 03, 2006
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