Function first: classifying cell types and circuits of the retina

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ScienceDirect Function first: classifying cell types and circuits of the retina Anna L Vlasits1,2, Thomas Euler1,2,3 and Katrin Franke1,3 Cell type classification has been a major part of retina research for over one hundred years. In recent years, the ability to sample large populations of retinal cells has accelerated cell type classification based on different criteria like genetics, morphology, function, and circuitry. For example, recent work includes bipolar and retinal ganglion cell classifications based on single-cell transcriptomes, large-scale electron microscopy reconstruction, and population-level functional imaging. With comprehensive descriptions of several retinal cell classes now within reach, it is important to reflect on the priority of these different criteria to create an accurate and useful classification. Here, we argue that functional information about retinal cells should be prioritized over other criteria when addressing questions of visual function because this criterion provides the most meaningful information about how the retina works. Addresses 1 Institute for Ophthalmic Research, University of Tu¨bingen, Tu¨bingen, Germany 2 Centre for Integrative Neuroscience, University of Tu¨bingen, Tu¨bingen, Germany 3 Bernstein Center for Computational Neuroscience, University of Tu¨bingen, Germany Corresponding author: Euler, Thomas ([email protected])

Current Opinion in Neurobiology 2019, 56:8–15 This review comes from a themed issue on Neuronal identity Edited by Sacha Nelson and Oliver Hobert

https://doi.org/10.1016/j.conb.2018.10.011

of the five cell classes contains a set of cell types; in total, the mammalian retina likely contains 100 neuron types alone. These neuron types can be defined based on a wide array of criteria (modalities), from gene expression pattern, to morphology and anatomical connectivity, to functional response properties (Figure 1b). In an ideal world, using the different cell type criteria individually would ultimately lead to the same overall classification scheme. However, recent work has shown that a scheme resulting from one criterion can be puzzlingly misaligned with a scheme resulting from another criterion, in that they do not identify the same cell types or number of types. For instance, a genetically well-defined cell type in the mouse retina, the JAM-B RGC, appears to function as a direction-selective (DS) cell, a color opponent cell, and an orientation-selective cell, depending on the exact stimulus and location of the cell on the retina [2,3,4]. Are the genetically identified JAM-B cells therefore functionally of a single type, several distinct but regional types, or, perhaps, a spectrum of types? Or vice versa, the On–Off DS RGC is well-defined and consistent in terms of its function (reporting direction of motion) and circuitry [5], and yet among the numerous genetic markers that label DS RGCs there is none that clearly captures the functionally defined population of DS RGCs [6–8]. As more classification schemes based on different and/or refined criteria emerge, we must consider the priority of these criteria for defining cell types. In this paper, we argue that, although all criteria described above provide important information for cell type classification, functional criteria will best allow us to increase our knowledge about how the retina works, and should be prioritized over other criteria.

0959-4388/ã 2018 Elsevier Ltd. All rights reserved.

A case for functional classification

Defining cell types has been an extremely productive strategy for understanding the function of neural circuits since Santiago Ramon y Cajal began the project over a century ago [1]. In the retina, the five classes of neurons — photoreceptors, horizontal cells, bipolar cells (BCs), amacrine cells and retinal ganglion cells (RGCs) (Figure 1a) — as well as the basic flow of information through them were already defined in Cajal’s studies, which provided the first modern answer to the question: how does the retina work? Today we know, however, that the answer to that question is much more complex. Each Current Opinion in Neurobiology 2019, 56:8–15

In general, having a standardized classification scheme for cell types and circuits is useful in two different ways. First, as we learn more about the brain, a classification scheme provides scientists with working definitions of cell types (nominal definitions) that aid in the project of identifying, studying, and exchanging knowledge about them. Second, once a classification scheme is completely developed, this can reveal the essential features of each cell type — their real definitions. In the case of the brain, these real definitions would contain the information to answer the long-standing question ‘How does the brain work?’. The retina is a perfect substrate for developing both kinds of definitions: first, the tissue has one major source of www.sciencedirect.com

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Classifying retinal cell types. (a) Schematic illustrating the five neuron classes of the vertebrate retina: Photoreceptors (P, yellow) transduce the visual input into an electrical signal and feed into bipolar cells (B, red) which provide input to the retina’s output neurons, the retinal ganglion cells (G, blue). This vertical excitatory pathway is modulated by lateral inhibition from horizontal (H, orange) and amacrine cells (A, green) in two different synaptic layers. For each cell class except for horizontal cells, two different cell types are shown in distinct hues. (b) Illustration of four major criteria used so far for classifying cell types. Molecular, in particular genetic, classification of cell types may include characterizing protein expression (i.e. transcription factors and neurotransmitter-synthesis and release machinery) or profiling translated RNA (top). Statistical differences in levels of these molecules can then be used to create a classification schema or tree (bottom left). Morphological analysis of cell types includes analyzing the stratification profile of dendrites and, for retinal interneurons, axons, quantifying differences in features of the dendritic tree like spatial extent and branching patterns (top), and analyzing the size of the cell soma. In the inner plexiform layer of the retina, two sublayers immuno-positive for choline acetyltransferase (ChAT) are usually used as a reference for characterizing a cell’s stratification profile (bottom). Functional properties include measuring electrical or chemical responses to stimuli, including visual stimulation, direct stimulation or silencing (e. g. through electrical or optogenetic stimulation) of upstream circuit partners, and regulatory stimulation with hormones or other factors. Standardized stimuli can be used to compare functional responses of large populations of cells (top) while detailed analysis of the current (I)– voltage (V) relationship can be used to understand mechanisms behind functional responses. Last, methods including imaging immuno-stained electrical and chemical synaptic proteins as well as electron microscopy imaging of synapses or contact area between retinal neurons can be used to create connectivity matrices of types and numbers of connections (top). In addition, retrograde and anterograde labeling mechanisms can reveal projection patterns of genetically labeled RGC populations to the brain (bottom).

sensory input (visual stimulation of photoreceptors) and only one output (action potentials from RGC axons to the brain), second, the major neuron classes are already welldefined, third, a small patch of tissue is expected to contain most of the cell types arranged into a regular, layered circuit, and fourth, the circuit is still sufficiently www.sciencedirect.com

complex as to provide insights into fundamental mechanisms underlying neuronal computations in the brain. Thus, several classifications based on nominal definitions of retinal cell types have already been generated, and some of these classifications may be approaching the real classification scheme (e.g. for BCs, see below). Current Opinion in Neurobiology 2019, 56:8–15

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However, these existing classifications of cell types are revealing challenges when one attempts to merge schemes based on different criteria. As described above, classifications can be defined by mRNA transcription profile, morphology, connectivity and functional responses (Figure 1b), which in the end also provide insights about evolutionary similarity of cell types between species (see below). In some cases, the results of these classifications are quite well-aligned, as in the case of photoreceptors and BCs (see below) [9,10], and indeed the aggregate classification scheme that includes several kinds of classification criteria tends to be more robust than any individual criteria’s scheme on its own. But for other cell classes, the alignment is less clear. Sometimes parallel classification schemes based on different criteria can create unnecessary confusion, as for example in the case of melanopsin-positive RGCs.1 From a practical standpoint, the usefulness of a given classification depends on the specific inquiry a scientist chooses to undertake: to understand the development of circuits, prioritizing a connectomic classification would be essential. To understand differences in evolutionary origin of cell types or their protein expression, an mRNA transcription profile-based classification may be more useful than one based on cell morphology. Indeed, genetic history represents an important context in order to avoid problematic mistakes, such as incorrectly assuming that analogous types are homologous. For answering questions about how the retina computes information, we argue that functional criteria should be prioritized in deciding how cell types be classified. Especially in cases where the alignment between different classifications schemes is not clear, dividing up cell types based on functional responses and connectivity is expected to provide the most useful information. We do not pledge to disregard other markers and we are not arguing that a spiking amacrine cell should be counted as an RGC just because both spike, for instance. We are more interested in what happens in the final steps of dividing up cell types, which is where different classification schemes tend to clash. Should a cell type be divided into three types because of differences in gene expression, when the functional properties — the physiological response to a suite of stimuli, excitatory and 1 Only the intrinsically photosensitive RGCs (ipRGCs) M1 and possibly M2 express melanopsin levels that allow them to be directly driven by light under physiological conditions [55,56]. The remaining M cells represent classical RGCs, as their functional roles in the retinal circuit are not likely related to intrinsic photosensing and referring to them as ipRGCs is confusing. That said, a recent study revealed a specific physiological role for melanopsin signalling in low-melanopsin-expressing M4 RGCs (sustained On-alpha RGCs): increasing membrane excitability through potassium leak channel regulation [57]. Thus, melanopsin expression remains an important feature of these RGCs, even if their functional role may not be to intrinsically sense light.

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inhibitory input currents, spiking or release kinetics, and the receptive fields — are observed to be the same for the stimulus set used? We would currently argue that this is one cell type. Ultimately, neural circuits exist to generate functional responses and behaviors, not to generate mRNA transcription patterns. While both are valid criteria, we argue that classifying based on function gets to the heart of what we as systems neuroscientists ultimately want to know about the brain.

Functional solutions to classification problems One example of well-aligned retinal classification schemes are BCs of the mouse retina. For this cell class, recent large-scale studies using functional [11], morphological [12,13] or genetic criteria [14] converge onto the same number of 14 bipolar cell types (not including the recently described monopolar interneuron [15]). However, even for this case of matching classification schemes, the meaning of some of the cell type divisions is still unclear. For example, type 5 BCs were originally classified as a single type due to the lack of a clear classifying difference despite the unusually high coverage factor of their axon terminals [16]. Recently, independent re-evaluation of mouse BCs using first detailed morphological [12,13,17,18] and later mRNA transcription profiles [14] revealed the presence of three different type 5 BC ‘versions’ (types 5t, 5o and 5i). Interestingly, both morphologically and genetically, the type 5 BCs are very close together, which might explain the difficulty in identifying these as individual cell types. This genetic and morphological similarity suggests a recent evolutionary origin of type 5 BCs, which is in line with reports that their functional role in the circuit seems to be not (yet) strongly diverged — at least judging by current functional data [11,19]. If gene expression or morphology is prioritized in classifying these cells, a popular criterion at the moment [9], then there are three cell types. However, one could argue that type 5 BCs should be considered one functional type for the time being until a clear functional/ circuit-level criterion can be used to divide them up. Another issue that illustrates the importance of classifications based on functional criteria is the presence of continuous variation in the properties of cell types. As proposed recently, when a property (i.e. dendritic tree size or RNA levels) varies in a cell type, one would not arbitrarily divide this type further unless the property varied discontinuously [9]. Here again, functional criteria are important for understanding the meaning of the variability. One classic example is the decrease in RGC density and corresponding increase in dendritic arbor size with eccentricity in the primate retina [20,21]. In this case, while morphological properties vary continuously, functional responses (except for receptive field size [22]) appear not to vary, and there is no reason to think that the cell types should be further divided [23]. However, www.sciencedirect.com

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several recent studies have demonstrated that many types of RGCs in the mouse retina possess more or less continuously varying properties with more significant functional variability. In the case of the transient-Off alpha RGC, one study suggests that its functional properties change gradually along the dorso-ventral axis, with dorsal and ventral RGCs displaying very distinct response kinetics [24]. While dividing this genetically and morphologically defined cell type into separate functional types might be discussed, it is clear that defining the cell types’ role in retinal function requires a detailed description of this functional variation. An even stronger case could be made for dividing the JAM-B RGC into multiple (functional) cell types. These genetically identified cells can be clearly divided into two groups: color-opponent in the ventral retina and nonopponent in the dorsal retina [2]. In addition, the members of this molecular type that have asymmetric dendrites are direction-selective in certain stimulus contexts [3], while a new study reports that JAM-B RGCs are orientation-selective [4]. These results together present a complex, multidimensional functional description of a genetically defined and/or morphologically defined cell type, and how these properties combine to create the diverse functional responses is beginning to be explored [25]. For us, these results suggest that the circuit context of these cells, in particular their location in the dorsal versus ventral retina where cone inputs differ in their opsin expression, lead to important functional differences that one would want to recognize in a functional classification scheme. Thus, JAM-B cells would represent more than one functional cell type. This example illustrates that without observing the circuit context and functional responses, meaningful complexities of genetically or morphologically defined cell types are lost. Finally, we want to use classification schemes of retinal cell types to better understand observed differences in visual processing between different species. While the current main model organisms for studying the retina — fish, mouse, rabbit and primate — have clear differences in visual perception and behavior, a comparative approach will reveal general principles in retinal signal processing, including insights into why the retina functions as it does from an evolutionary perspective. By comparing function of cell types across species (rather than other criteria like morphology and genetics) these comparisons come into sharper focus, as function is a feature that natural selection operates upon. A recent example of this is the starburst amacrine cell (SAC) in the DS circuit, a cell type that has been extensively studied in both mouse and rabbit [5,26,27], and which has strikingly similar morphological and molecular properties in these species. A recent study revealed that there are specific differences in the connectivity of the presynaptic inputs onto the amacrine cells’ dendrites between mice and rabbits [28]. In www.sciencedirect.com

models of the two configurations, the authors reveal that the differences produce optimal motion velocity tuning given specific adaptations of the two species, namely the size of their eyes. Another example of comparisons between the mouse and rabbit are the recent studies on orientation selectivity of some RGCs. Here, different groups have studied orientation-selective RGCs in the two species, and several similarities in their functional and morphological properties and synaptic mechanisms are emerging, including a specific role for asymmetric dendritic arbors in establishing the cells’ tuning [4,29,30] and a possible role for gap junction-coupled amacrine cells in orientation selectivity [4,31]. Because genetic tools remain more limited in the rabbit, whether these two species’ cell types possess genetic relationships is currently unknown. In these and other examples, it is clear that functional information both revealed the meaning of morphological and molecular phenomena in these cell types and provided insights into what the cell types are for.

Scaling up classification: a focus on retinal ganglion cells Recently, several large-scale classifications have been published, and many recent datasets now include much larger populations of retinal cells than before (Figure 2). These include transcriptomic classifications using singlecell mRNA sequencing [14,32], cell morphological classifications based on sparsely filling or genetically labeling cells or based on electron microscopy [17,33,34], a systematic characterization of RGCs based on projection targets of their axons [35], and functional classifications based on fluorescent reporter imaging or electrical activity [11,36,37]. These studies are generally able to demonstrate examples of identified cell types that match up with other classifications or with cell types that have been previously characterized. However, in contrast to the almost perfect classifications alignment in the case of the BCs, the alignment of the different RGC classifications remains less clear. One encouraging sign is that several recent classifications using orthogonal criteria, like function, morphology and genetics, report 40 types of RGCs [32,33,36]. This consensus may represent a division of RGCs into their ‘true’ types. But in most cases, clustering into cell types was not achieved through entirely unsupervised methods, usually when well-known cell types did not cluster properly. For example, a recent anatomical study used unsupervised clustering followed by clustering based on secondary criteria specific to a given subcluster, in this case morphological features like dendritic length and arbor density, to divide clusters that contained multiple known cell types [33]. In a paper from our group, DS and nonDS RGCs were clustered independently in order to better identify RGC groups that are DS [36]. These choices, while guided by the literature and scientists’ domain Current Opinion in Neurobiology 2019, 56:8–15

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Contributions of small-scale and large-scale studies to retinal cell type classification: While large-scale studies (left) provide researchers with datasets describing large numbers and types of cells, small-scale studies (right) contribute more curated analysis of particular cell types. a, Largescale analysis of transcribed RNA can be used to classify all of the cell types in a retinal cell class, as was recently demonstrated for bipolar cells (BCs) [14] and retinal ganglion cells (RGCs) (a1, RGC clusters plotted and color-coded based on t-distributed stochastic neighbor embedding; adapted from Ref. [32]). A small-scale study that utilized transcriptomic data to classify four cell types was able to further characterize the morphologies, connectivity, and physiological features of the identified types (a2, the four cell types immuno-labeled for combinations of four identifying transcription factors on the right, with example reconstructions of their morphology on the left; adapted from Ref. [44]). b, A new Current Opinion in Neurobiology 2019, 56:8–15

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Classifying cell types and circuits of the retina Vlasits, Euler and Franke 13

knowledge, make it more difficult to determine if the trend towards 40 RGC types is unbiased or driven by the general wish for consensus and ultimate happiness. For almost all ‘cherry-picked’ examples of RGC types that align across classifications (e.g. alpha RGCs and On– Off DS RGCs), previous studies had extensively characterized those example cell types’ genetics, function, morphology, and/or connectivity. This points to an important and humbling truth about large-scale classifications: these datasets clearly profit from more focused and extensive studies of the individual cell types for a useful interpretation of retinal function (Figure 2). Here again, characterizing function is paramount because it gives meaning to otherwise abstract classifications. Retinal neurobiologists have been chipping away at this task for decades, but some relatively recent technological advances and a shift towards studying the mouse retina have made it possible to more easily align function with other features of cell types. These technologies include genetically encoded fluorescent reporters of activity combined with two-photon microscopy, pharmacogenetics and optogenetics, and genetically defined mouse lines. For instance, recent studies of the role of amacrine cells, horizontal cells, or BCs in RGC function used pharmacogenetics to reversibly hyperpolarize-specific, genetically defined cell types and measure the resulting change in RGC light responses [38,39,40]. In the case of the recent horizontal cell study, researchers observed six kinds of effects on distinct functional groups of RGCs. An important feature of single-cell level studies is the ability to characterize functional responses to complex and varied stimuli. Compared to large-scale surveys of function, which usually use relatively short and ‘general’ stimuli such as white noise and bars, single-cell studies can zero in on a more specific description of functional properties. In turn, such indepth functional characterization at the single-cell level might then help to select the appropriate visual stimuli

for large-scale functional surveys. Recent examples of such small-scale studies include characterization of RGCs that encode contrast and space linearly [41], small-receptive-field RGCs [42], orientation-selective RGCs [4,29,30], RGCs that detect looming objects [43], and DS cells [44,45], as well as several ‘new’ amacrine cell types [15,31,46–50]. These studies usually provide an exhaustive characterization, which may include morphology, receptive field properties, pharmacology, genetics, and synaptic connections and physiology, all of which contribute insights into the mechanisms behind the functional responses. Large-scale classifications ideally allow for more general characterization of the principles of retinal computations, such as defining distinctive morphological features of each RGC type or the diversity of functional responses across the RGC population in response to a small number of stimuli. Thus, producing an accurate classification may always entail toggling back and forth between larger-scale attempts at classification and small-scale studies of putative cell types.

Conclusion In our view, an ideal classification approach may require utilizing a set of classification criteria depending on the hypothesis in question [51,52]. Using function as the ‘primary’ criterion for classification does not mean that other criteria should be disregarded. In fact, all of the current classification efforts have an important role to play in achieving a better understanding of how the retina works. Ideally, other features like morphology and genetics can be used to guide classification in cases where functional features are not yet clear. In particular, transcriptomic classifications can contribute important insights into evolutionary history of cell types and molecules contributing to specific functions (i.e. channels and receptors), and can also provide scientists with markers or curated genetically labeled lines that might correspond to single functional types [9,32,44]. Characterizing axonal projections of RGCs to the brain provides important information about where functional information goes next

(Figure 2 Legend Continued) ‘museum’ of retinal cell morphology based on reconstructions of electron microscopy data is the most recent example of large-scale morphological classification (b1, [33]). Images show reconstructed cells of two example types, with each example cell presented in a different color (right, second from top). Images from the museum obtained from http://museum.eyewire.org with permission from Sebastian Seung. Detailed morphological analysis of individual cell types can reveal how morphology contributes to function, as in this orientationselective RGC type with asymmetric dendrites (b2, left; adapted from Ref. [4]). Comparing the angle of the dendritic asymmetry and orientation tuning reveals correspondence between these features (b2, right). c, Large-scale analysis of functional activity in response to visual stimuli is a powerful way to classify cell types. Here, our group imaged calcium activity in populations of RGCs (c1, left) and classified cell types based on their functional responses to specific stimuli, such as the full-field chirp (c1, right, top line depicts luminance, color-filled lines depict calcium responses; adapted from Ref. [36]). However, functional analysis of single cell RGC types often reveals more specific information. For instance, researchers recorded a ‘Pixel-encoder’ RGC’s excitatory (Exc.) and inhibitory (Inh.) synaptic conductances in response to light stimuli varying in spatial scale to understand mechanisms of functional responses and the cell’s presynaptic circuit elements (c2, left shows spikes and conductances in response to stimulation with circles of varying diameter, right shows plot of conductance as a function of stimulus diameter; adapted from Ref. [41]). d, A recent large-scale analysis of projection patterns from genetically defined populations of RGCs to the brain revealed complex and diverse targeting patterns (d1, left: projection of mouse brain with color-coded brain targets of RGC types, right: matrix of Cre mouse lines labeling RGCs versus their target brain regions, adapted from Ref. [35]). However, in many cases the labeled cell types are not yet fully characterized, and may not comprise single RGC types. On the other hand, detailed analysis of electron microscopy reconstructions of synapses between morphologically identified cell types illuminated the precise wiring details of a circuit (d2, left: a starburst amacrine cell (SAC) labeled with synapses from BCs. Right: plot of distribution of BC synapses onto SACs. BCs are color-coded by BC type; adapted with permission from Ref. [28]).

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14 Neuronal identity

after the retina, allowing for a connection between function and behavior [35,53,54]. Morphology-based and connectomics-based classifications provide crucial hints about how a type achieves its functional responses. All of these together, centered on function, will provide the best shot at answering our question: how does the retina work?

Conflict of interest statement Nothing declared.

Acknowledgements Many thanks to Justin Vlasits for insightful discussions about the history and purpose of classification as a method in epistemology. AV and TE: This work was supported by the German Research Foundation (DFG; SFB 1233 — Robust Vision: Inference Principles and Neural Mechanisms, TP 12; SPP2041 Computational Connectomics, EU 42/9-1). KF: This study is part of the research program of the Bernstein Center for Computational Neuroscience, Tuebingen, funded by the German Federal Ministry of Education and Research and the Max Planck Society (BMBF, FKZ: 01GQ1002; MPG M.FE.A.KYBE0004).

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Current Opinion in Neurobiology 2019, 56:8–15