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Publicly Available Published by De Gruyter May 16, 2017

Neuronal Diversity In The Retina

Philipp Berens and Thomas Euler
From the journal e-Neuroforum

Abstract

The retina in the eye performs complex computations, to transmit only behaviourally relevant information about our visual environment to the brain. These computations are implemented by numerous different cell types that form complex circuits. New experimental and computational methods make it possible to study the cellular diversity of the retina in detail – the goal of obtaining a complete list of all the cell types in the retina and, thus, its “building blocks”, is within reach. We review the current state of this endeavour and highlight possible directions for future research.

Introduction

The retina is a thin sheet of neural tissue which lines the back of the eye. Despite its superficial simplicity, the retina is a complex structure, transforming patterns of light into nerve signals and initiating visual processing. These light signals are not merely transmitted to the brain, but undergo considerable processing to isolate and enhance stimulus features – in stark contrast to a photo camera, which simply captures the brightness of the pixels in an image.

The retina performs these computations on neural hardware consisting of five classes of neurons (Fig. 1). Photoreceptors are light-sensitive cells in the outer retina. They transform light of particular wavelengths into electrochemical signals and forward these to the bipolar cells, which form the link between the outer and the inner retina. Bipolar cells contact retinal ganglion cells, which send the signal via their axons to the brain. This “vertical” pathway is modulated by lateral connections with horizontal cells and amacrine cells in the inner and outer retina, respectively. As we discuss below, amacrine cells in particular play an important though so far little understood role in retinal visual processing.

The basic structure of the retina appears to be comparatively simple – complexity arises from the fact that each of the five cell classes consists of up to several dozens of cell types, which interact in many different ways. There are, for example, four types of photoreceptors in humans, and three in mice. The cell types belonging to the same class share a particular position in the circuitry, but can differ substantially in their morphology, physiology, genetic profile and in their connections with other cells. The number of cell types increases through subsequent stages of processing – only few types of photoreceptors and horizontal cells are followed by more than a dozen bipolar cells and yet more amacrine and ganglion cells. This sequence suggests that the large increase in the number of types reflects the increasing divergence of signal processing pathways.

What is a cell type?

The retina, much like the cortex, is an incredibly complex structure and in order to understand it we must decompose it into its constituent parts – the cell types from which its circuitry is built. Once cell types are identified and described, one can begin exploring their interaction and contribution to the neural circuits. From this should emerge a more integrated understanding of the principles underlying the retinal system and its contribution to visual processing.

So far we have relied on the reader’s intuitive notion of a “type”, but this concept benefits from clear definition. In general, we refer to a group of cells as a type if they share particular features with respect to their morphology, function, physiology and genetics, in particular when these features distinguish them from other cells (Seung and Sümbül, 2014). We typically assume that cell types are discrete, such that they possess a robust set of common properties. A good example is the class of photoreceptors. Cone and rod photoreceptors have the same basic function (the transduction of light into electrochemical signals) and a similar structure (an inner and outer segment, a cell body and a synaptic terminal) – but they differ dramatically with respect to their light sensitivity. While the different types of cones cannot be differentiated anatomically, they show substantial differences in the wavelengths of light which drive their activity.

Ideally, cell types should be defined with respect to each of these criteria alone, although a degree of variation within a single type is permissible. For example, size and form of the dendritic arbours of ganglion cells are a function of their location on the retina. This is true for the mouse (Bleckert et al., 2014; Sümbül et al., 2014), but is even more pronounced in primates – the so-called “midget” ganglion cell in the fovea only has a single dendrite contacting via a single bipolar cell a single cone photoreceptor, whereas midget cells located in the periphery feature elaborate dendritic arbors with multiple bipolar cells each contacting several cones (Kolb and Marshak, 2003). Nevertheless, we speak of a single type. In addition, it is unclear whether connectivity is a type-defining criterion and if cells of one type can, in principle, show substantially different connectivity and projection patterns. Retinal ganglion cells are a prominent example. If two ganglion cell types differ only in their projection targets in the brain (Robles et al., 2014), should they be considered two independent types?

New experimental methods make it possible to study the different properties of cell types in detail. Single-cell transcriptomics allows us to measure with high accuracy which genes are expressed in individual cells (Macosko et al., 2015; Shekhar et al., 2016), while high-resolution electron microscopy (EM) enables the reconstruction of all cells in a piece of tissue (Helmstaedter et al., 2013). In combination with synthetic and genetic activity indicators, two-photon microscopy makes large scale functional studies in intact tissue possible (Baden et al., 2016; Franke et al., 2017).

Our understanding of retinal cell types has advanced considerably in recent years, not least because of these experimental advances, which allow large, quantitatively precise datasets to be collected; in addition, techniques from machine learning facilitate the processing of such large and complex datasets in a semi-automated way. The retina is particularly well suited to study the principle questions of cell type classification (Seung and Sümbül, 2014). Not only is the tissue comparably accessible for experimental investigation, it also possesses a clearly layered structure (Fig. 1). Helpfully, there is a clear, testable criterion: it is thought that each cell type covers the retina in a complete mosaic. The current success of this research program can nicely be illustrated in the bipolar cells of the mouse, where the current classification leads to 14 types with complete mosaics.

Fig. 1: Schematic of the retina, vertical section. Two plexiform layers with synaptic connections (OPL, outer plexiform layer; IPL, inner plexiform layer) separate three cellular layers, the inner and the outer nuclear layer (INL, ONL) and the ganglion cell layer (GCL). Cone and rod photoreceptors (yellow) send light signals to the dendrites of the bipolar cells (green), which form synapses with the dendrites of the ganglion cells (blue). Their axons from the optic nerve. Horizontal cells (purple) and amacrine cells (orange-red) modulate the signals of bipolar and ganglion cells in the OPL and the IPL, respectively.

Fig. 1:

Schematic of the retina, vertical section. Two plexiform layers with synaptic connections (OPL, outer plexiform layer; IPL, inner plexiform layer) separate three cellular layers, the inner and the outer nuclear layer (INL, ONL) and the ganglion cell layer (GCL). Cone and rod photoreceptors (yellow) send light signals to the dendrites of the bipolar cells (green), which form synapses with the dendrites of the ganglion cells (blue). Their axons from the optic nerve. Horizontal cells (purple) and amacrine cells (orange-red) modulate the signals of bipolar and ganglion cells in the OPL and the IPL, respectively.

Bipolar Cells

In mammals, bipolar cells form the third most diverse cell class in the retina, after amacrine and ganglion cells. In his seminal work “The Structure of the Mammalian Retina”, Santiago Ramón y Cajal drew bipolar cells with their characteristic morphological features, projecting dendrites in one direction, and an axon in the opposite. The term “bipolar cell”, though, dates back to a student of Golgi called Tartuferi (Tartuferi, 1887). Today, the classification of mouse bipolar cells can be considered complete at a count of 14 types, and classification in other mammalian model systems like rabbit, ground squirrel and macaque is very advanced (for an overview and discussion see Euler et al., 2014).

Typically, the first distinguishing feature of bipolar cells is whether they contact mostly cones or mostly rods (Fig. 2A). The latter bipolar cells form a single homogenous type, the rod bipolar cell (RBC). Cone bipolar cells (CBCs) can be separated further into On- and Off-CBCs: On-CBCs (as well as RBCs) depolarize to light increments, while Off-CBCs depolarize to light decrements (Fig. 2B, C). Today we distinguish five Off-CBC types and eight On-CBC types. Note, however, that the labels “On” and “Off” are a simplification that holds only for very simple light stimuli, and that responses to more complex stimuli can in fact be of the opposite polarity.

Besides response polarity, CBCs also differ in their morphology. Off-CBCs send their axons into the upper layers of the primary synaptic region of the retina, the inner plexiform layer (IPL; Fig. 2A,D). By contrast, axons of On-CBCs end in the lowest IPL layers. Further distinctions can be made according to the morphology of the cells in a straightforward way. For example, Off-CBC types 1 and 2 can be easily distinguished from types 3A/3B and 4, as their axons terminate deeper in the IPL (Fig. 2A). Other distinctions are more subtle. For example, types 3A, 3B and 4 send their axons to a very similar depth in the IPL, such that classification of single examples based on morphologies in very difficult. Eventually, these types were separated based on specific immunohistochemical markers (Mataruga et al., 2007). This classification was recently confirmed using single cell transcriptomics (Shekhar et al., 2016) and with a large set of reconstructed bipolar cells from EM-data, it was possible to find a robust anatomical correlate of these splits (Kim et al., 2014). Importantly, all three types (3A, 3B and 4) form complete mosaics (Fig. 2D).

As it is difficult to distinguish bipolar cell types with similar morphology, it is not surprising that the number of bipolar cell types has gone up in recent years due to the availability of new experimental techniques – only a decade or so ago, mere nine types had been described (Ghosh et al., 2004). Advances were first made through new immunohistochemical markers (see above and Wässle et al., 2009) and EM-based analysis, which were used to completely classify the On-CBC types in the middle of the IPL into types 5T, 5O, 5I and X (Helmstaedter et al., 2013; Greene et al., 2016).

Recently, functional as well as genetic „fingerprints” for all 14 BC types in the mouse were identified (Franke et al., 2017; Shekhar et al., 2016) (Fig. 2B and Fig. 2E,F, respectively) and their connectivity with photoreceptors was described in detail (Behrens et al., 2016). Neither transcriptome data nor functional measurements indicate that bipolar cell types need to be split further. This also fits with the “mosaic criterion” for cell types in the retina, as any further distinction would create sparse cell types which are not dense enough to cover the retina’s surface.

In addition to bipolar cells with classical morphology, recent studies have presented several peculiarities that do not fit the contemporary understanding of retinal cell classes. For example, there is a cell type in the mouse retina that exhibits the genetic profile of a bipolar cell as well as ribbon synapses, which also are typical for bipolar cells. In the adult animal, however, this cell type does not have dendrites and might more accurately be called a “monopolar” neuron (Della Santina et al., 2016; Shekhar et al., 2016). The terminal system of this glutamatergic monopolar interneuron (GluMI) has some resemblance to type 1 and type 2 CBCs. Not quite as extreme is the recently discovered X-type CBC (Helmstaedter et al., 2013), which, in contrast to its more “traditional” colleagues, contacts only a small proportion of the cone photoreceptors within the reach of its dendritic arbour (Behrens et al., 2016).

Ganglion Cells

Similar advances could also be achieved in classifying ganglion cells, although this cannot yet be considered complete. Similarly to bipolar cells, ganglion cells were classically divided by their response to light on- and/or offset, into On-, Off- and On-Off types. At the same time, many types of ganglion cells also show more complex response patterns (see also Gollisch and Meister, 2010). For example, one type responds quickly and vigorously to any luminance change in its receptive field (called Off alpha transient, see Fig. 3C,D, top ; van Wyk et al., 2009), while another type prefers small dark objects moving in the sky (On-Off „W3“, Fig. 3C,D, bottom; Zhang et al., 2012). While early studies identified between 10 and 20 types of ganglion cell, today it is generally agreed that there are well over 30 types (Sanes and Masland, 2015).

From a morphological point of view, ganglion cell types predominantly differ with respect to the IPL level in which their dendrites stratify. The dendrites of some types only ramify in a narrow band of the IPL (monostratified), while others form dendrites stratifying in two bands (bistratified) or diffusely. For this reason, anatomical studies have suggested that there should be more than 10 to 20 ganglion cell types (Sümbül et al., 2014). The full functional diversity of retinal ganglion cell types was recently demonstrated in a physiological study based on calcium imaging with two-photon microscopy. The responses of more than 11,000 cells to a standardized set of visual stimuli were recorded and then analysed with statistical methods. The results indicate that there are substantially more than 30 ganglion cell types in the mouse retina (Baden et al., 2016). Current anatomical studies confirm this emerging picture. On the website museum.eyewire.org one can explore the current state of a large-scale anatomical study which aims to reconstruct and classify ganglion cells from an EM data set (Fig. 3D).

Fig. 3: Functional and morphological characterization of retinal ganglion cells in the mouse. A: Ganglion cell layer of a mouse retina in top view: the colours indicate different functionally defined ganglion cell types (top). Below, the same region of the retina is shown as seen in the two-photon microscope, when cells have been “stained” with a fluorescent activity indicator. B: Light responses of more than 11,000 cells; each block indicates a ganglion cell type (red: high activity, blue: low activity). The ganglion cell layer includes also somata of amacrine cells, making up for about 1/3 of the total number of cells. C: Light responses (top left), soma distribution (top right) as well as morphology in top view (bottom left) and vertical section (bottom right) of two types of ganglion cell: transient Off alpha cells respond to any contrast change in their receptive field, while On-Off “W3” cells prefer small dark objects moving in the upper visual field. D: Mosaic of the two ganglion cell types (from C) in top view. The gaps at the boarders are caused by cells with the cell body outside of the reconstructed piece of tissue. Modified figures in A-C with permission from (Baden et al., 2016); Figure in D from museum.eyewire.org with permission from S. Seung).

Fig. 3:

Functional and morphological characterization of retinal ganglion cells in the mouse. A: Ganglion cell layer of a mouse retina in top view: the colours indicate different functionally defined ganglion cell types (top). Below, the same region of the retina is shown as seen in the two-photon microscope, when cells have been “stained” with a fluorescent activity indicator. B: Light responses of more than 11,000 cells; each block indicates a ganglion cell type (red: high activity, blue: low activity). The ganglion cell layer includes also somata of amacrine cells, making up for about 1/3 of the total number of cells. C: Light responses (top left), soma distribution (top right) as well as morphology in top view (bottom left) and vertical section (bottom right) of two types of ganglion cell: transient Off alpha cells respond to any contrast change in their receptive field, while On-Off “W3” cells prefer small dark objects moving in the upper visual field. D: Mosaic of the two ganglion cell types (from C) in top view. The gaps at the boarders are caused by cells with the cell body outside of the reconstructed piece of tissue. Modified figures in A-C with permission from (Baden et al., 2016); Figure in D from museum.eyewire.org with permission from S. Seung).

To integrate functional and anatomical cell types, we need data where both kinds of information are available, perhaps with cells reconstructed through light microscopy that have additionally been studied physiologically. This has been done by Baden et al. (2016) for a few exemplary types; but due to the considerable diversity and the variability between each example within a single type much more data are required to achieve a one-to-one mapping. A combination of functional two-photon imaging with subsequent EM-reconstruction of the tissue could be useful in this regard (Briggman et al., 2011). Similarly desirable is a precise characterization of the computations performed by specific ganglion cell types within their circuits – while the study of Baden and co-workers (2016) provided a “fingerprint” for each cell, this is only a coarse and incomplete description of the function of each type. For such studies, targeted single-cell physiology will probably be necessary, which could profit from the increasing availability of transgenic mouse lines in which certain types or small groups of types are genetically labelled (Rousso et al., 2016).

It is also an open question whether the number of ganglion cell types will increase further based on additional data modalities. For example, there has not yet been an exhaustive study based on single-cell transcriptomics. However, we expect the number of types to increase only slightly, as many of the reported types match the criteria of forming a mosaic. This does not preclude the existence of sparsely distributed or regionally specialized cell types (see also Outlook). Finally, it is unclear if ganglion cells can be further divided based on their projection patterns. Axons of ganglion cells project to widely differing brain areas, with more than 40 of such target area in the mouse (Morin and Studholme, 2014). It has been shown that in the zebrafish, ganglion cells with apparently identical dendritic morphology could be assigned to different projection types in the brain (Robles et al., 2014).

Amacrine Cells

Comparatively little is known about amacrine cells, although with more than 40 cell types, they likely form the most diverse class of cells in the retina (Masland, 2012). Originally, scientists believed that this class of interneurons would not have axons – the name has etymological roots in ancient Greek, where “amakrin” means “without long fibre”. Indeed, most amacrine cell types do not have an axon in the classical sense. Their dendrites receive input as well as make output synapses. Depending on the cell type, these can be spatially segregated or mixed on the same dendritic branch. One curious group, the polyaxonal amacrine cells, in fact possesses multiple axons (Lin and Masland, 2006). For simplicity, we will refer to the processes of amacrine cells as “dendrites”.

Barring a few exceptions, amacrine cells are inhibitory; they suppress the activity of other neurons in the inner retina. Depending on their neurotransmitter (glycine or GABA), they are roughly divided into “glycinergic” and “GABAergic” cells. In mammals, the dendritic trees of glycinergic cells are mostly small (narrow field), while the GABAergic cells are rather large (wide field) – in other vertebrates this may be different.

Wide-field amacrine cells can signal over large distances. The polyaxonal types belong to this group and have axons which extend for several millimetres (Lin and Masland, 2006). Wide-field amacrine cells are thus well suited for spatial computations. For example, they play a major role in setting up the functional response diversity of bipolar cells (Eggers and Lukasiewicz, 2011; Franke et al., 2017). In contrast, narrow field amacrine cells carry signals vertically through the inner retina, for example from the On- to the Off-layer of the IPL or vice versa (cross-over inhibition, Werblin, 2010). Their activity also influences the functional diversity of bipolar cells as they regulate the strength of GABAergic inhibition acting upon the bipolar cell axon terminals.

Within both groups of amacrine cells, several cell types can be distinguished (MacNeil and Masland, 1998). The exact function of most of these is completely unclear, not the least because amacrine cells are experimentally hard to access due to their location in the inner retina and their morphological diversity. It has only been possible for a few years to study amacrine cells and their function in detail using, for example, two-photon microscopy (Euler et al., 2002). This technique allows direct measurements of activity in amacrine cell dendrites, which in many cases can be considered the fundamental computational unit of the cell (Euler and Denk, 2001).

Among the better understood amacrine cell types are the AII and the A17 amacrine cells, both of which are at the heart of the processing of rod photoreceptor signals (Bloomfield and Dacheux, 2001). Similarly well studied is the starburst amacrine cell (SAC), which plays a major role in the circuits underlying the computation of direction selectivity observed in certain ganglion cell types (Borst and Euler, 2011). These ganglion cells respond vigorously when a moving stimulus crosses their receptive field in a certain direction, but hardly at all if it moves in the opposite (“null”) direction. The computation mainly takes place not in the ganglion cells, but through local processing on the SAC’s star-shaped dendritic branches. Each branch becomes active and releases GABA if a stimulus moves outwards from the SAC soma towards the tip of the dendrite (Euler et al., 2002). SACs are asymmetrically coupled via GABAergic synapses to the direction-selective ganglion cells (Briggman et al., 2011) and thereby determine their null direction: SACs inhibit the activity if the stimulus moves in the „wrong“ direction across the ganglion cell’s receptive field. Several mechanisms are likely involved in the computation of direction selectivity in the SAC-dendrites, including active membrane channels (Hausselt et al., 2007), reciprocal inhibition in SAC network (Ding et al., 2016) and possibly temporal differences in the inputs from bipolar cells (Kim et al., 2014; Greene et al., 2016). There is even evidence that SACs in different mammalian species might use distinct mechanisms (Ding et al., 2016).

A special feature of many amacrine cells is that they release a second neurotransmitter, such as dopamine or different neuropeptides. Some can even release both an excitatory and an inhibitory transmitter, such as GABA and acetylcholine in the case of the SACs. Likewise, so-called vGluT3 amacrine cells release glycine and glutamate (Haverkamp and Wässle, 2004). Recent studies of the vGluT3 amacrine cells show how specific and local amacrine cells can influence the networks of the inner retina (Lee et al., 2014, 2015; Tien et al., 2016). These cells stratify in the middle of the IPL and receive synaptic input from On- and Off-bipolar cells. They contribute to at least four different circuits: they inhibit ganglion cells responding to homogenous stimuli with glycine (uniformity detectors), and excite three other ganglion cell types with glutamate, among them the aforementioned direction-selective ganglion cells.

All amacrine cells studied so far play a very specific role in one or more neural circuits. Whether this also holds for the remaining 30 or so types is unclear, not the least because systematic surveys such as those conducted for bipolar and ganglion cells are still lacking. However, it is unlikely that the 5-10 types studied in detail so far were, quite by chance, the most exciting ones – we expect many interesting insights into neural processing in general by studying the remaining types of amacrine cell. Advances in single-cell transcriptomics and anatomy based on large EM-datasets promise future progress and make it conceivable that this elusive class of retinal cells can soon be better understood. Central to this endeavour will be our understanding of connectivity, as the synaptic networks formed by amacrine, bipolar and ganglion cells – and, in particular among different types of amacrine cell – can be extraordinarily complex.

Outlook

Roughly once a decade, the retina is declared “solved”. One reason for this impression is that the retina is a comparatively well-characterised model system, perhaps suggesting that only a few details remain to be figured out. But it is this level of detail that allows one to keep asking deep questions, in pursuit of more general principles of sensory processing. One example is the rod circuit (for an overview, see Bloomfield and Dacheux, 2001), whose connectivity and function has been described in such exquisite detail that it is possible to study the effect of molecular networks in cell compartments in the broader context of rod vision (Grimes et al., 2010). More generally, it is only possible to study the principles of neuronal processing at the cellular and circuit level in a system that is generally understood. Not neglecting the idiosyncrasies of the retina – such as the spatial arrangement in the eye necessitating a thin and transparent tissue, or the apparent lack of true synaptic plasticity in the fully-developed adult retina – this system can be considered a viable blueprint for the circuits and computations in other parts of the brain.

One question that has been investigated in the retina to perhaps the greatest level of detail, is the question of cell types and classes. As we have here discussed at length, new technologies have allowed for considerable and rapid development in cell type identification, and it seems within reach to arrive at a complete atlas of the fundamental building blocks of this system, including their connectivity, and their genetic, physiological, anatomical and functional features. On the other hand, we observe an increasing number of cases where the traditional definition of a cell type becomes more fluid. Consider for example the JAM-B ganglion cell (Kim et al., 2008). Its most characteristic morphological property is that when the cell is located in the dorsal retina, its dendrites are asymmetric with respect to the soma, pointing downwards (towards the ventral retina). Interestingly, this asymmetry disappears in more ventral locations. Functional studies have shown that JAM-B cells are weakly direction selective in the dorsal retina (Kim et al., 2008), while their ventral counter parts are not, but rather respond antagonistically to colour stimuli (Joesch and Meister, 2016). It is nevertheless assumed that genetically, JAM-B cells are one type that varies morphologically and functionally, depending on its location. It remains to be seen whether this type of ganglion cell indeed delivers different information to the brain, depending on its position in the upper and the lower visual field. This would contribute to the increasing evidence that less visually oriented animals, like mice, have functionally specialized retinal regions (Baden et al., 2013; Bleckert et al., 2014).

We can also expect surprises regarding amacrine cells, due to their well-known morphological diversity and the fact that each amacrine cell type studied in detail has been shown to carry out a highly specialized, unique function. In addition, it has been shown that certain amacrine cell types can change their function depending on context (e.g. ambient luminance). This has been shown for the AII amacrine cell, which is the main hub of rod signals in dim light, and forms an integral part of a circuit reporting dark, approaching objects in bright light conditions (“looming detector”, Münch et al., 2009).

In summary, we have reviewed recent progress in the understanding of cell type diversity in the mouse retina and have highlighted exciting paths moving forward.

Article note: The authors thank Luke Rogerson for help with the English version of the text.

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Published Online: 2017-5-16
Published in Print: 2017-5-24

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