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Retinal structure and function have been studied in many vertebrate orders, but molecular characterization has been largely confined to mammals. We used single-cell RNA sequencing (scRNA-seq) to generate a cell atlas of the chick retina. We identified 136 cell types plus 14 positional or developmental intermediates distributed among the six classes conserved across vertebrates – photoreceptor, horizontal, bipolar, amacrine, retinal ganglion, and glial cells. To assess morphology of molecularly defined types, we adapted a method for CRISPR-based integration of reporters into selectively expressed genes. For Müller glia, we found that transcriptionally distinct cells were regionally localized along the anterior-posterior, dorsal-ventral, and central-peripheral retinal axes. We also identified immature photoreceptor, horizontal cell, and oligodendrocyte types that persist into late embryonic stages. Finally, we analyzed relationships among chick, mouse, and primate retinal cell classes and types. Our results provide a foundation for anatomical, physiological, evolutionary, and developmental studies of the avian visual system. The evolutionary relationships of organisms and of genes have long been studied in various ways, including genome sequencing. More recently, the evolutionary relationships among the different types of cells that perform distinct roles in an organism, have become a subject of inquiry. High throughput single-cell RNA sequencing is a technique that allows scientists to determine what genes are switched on in single cells. This technique makes it possible to catalogue the cell types that make up a tissue and generate an atlas of the tissue based on what genes are switched on in each cell. The atlases can then be compared among species. The retina is a light-sensitive tissue that animals with a backbone, called vertebrates, use to see. The basic plan of the retina is very similar in vertebrates: five classes of neurons – the cells that make up the nervous system – are arranged into three layers. The chicken is a highly visual animal and it has frequently been used to study the development of the retina, from understanding how unspecialized embryonic cells become neurons to examining how circuits of neurons form. The structure and role of the retina have been studied in many vertebrates, but detailed descriptions of this tissue at the molecular level have been largely limited to mammals. To bridge this gap, Yamagata, Yan and Sanes generated the first cell atlas of the chicken retina. Additionally, they developed a gene editing-based technique based on CRISPR technology called eCHIKIN to label different cell types based on genes each type switched on selectively, providing a means of matching their shape and location to their molecular identity. Using these methods, it was possible to subdivide each of the five classes of neurons in the retina into multiple distinct types for a total of 136. The atlas provided a foundation for evolutionary analysis of how retinas evolve to serve the very different visual needs of different species. The chicken cell types could be compared to types previously identified in similar studies of mouse and primate retinas. Comparing the relationships among retinal cells in chickens, mice and primates revealed strong similarities in the overall cell classes represented. However, the results also showed big differences among species in the specific types within each class, and the genes that were switched on within each cell type. These findings may provide a foundation to study the anatomy, physiology, evolution, and development of the avian visual system. Until now, neural development of the chicken retina was being studied without comprehensive knowledge of its cell types or the developmentally important genes they express. The system developed by Yamagata, Yan and Sanes may be used in the future to learn more about vision and to investigate how neural cell types evolve to match the repertoire of each species to its environment.

The retina communicates with the brain using ≥30 parallel channels, each carried by axons of distinct types of retinal ganglion cells. In every mammalian retina one finds so-called \"alpha\" ganglion cells (αRGCs), identified by their large cell bodies, stout axons, wide and mono-stratified dendritic fields, and high levels of neurofilament protein. In the mouse, three αRGC types have been described based on responses to light steps: On-sustained, Off-sustained, and Off-transient. Here we employed a transgenic mouse line that labels αRGCs in the live retina, allowing systematic targeted recordings. We characterize the three known types and identify a fourth, with On-transient responses. All four αRGC types share basic aspects of visual signaling, including a large receptive field center, a weak antagonistic surround, and absence of any direction selectivity. They also share a distinctive waveform of the action potential, faster than that of other RGC types. Morphologically, they differ in the level of dendritic stratification within the IPL, which accounts for their response properties. Molecularly, each type has a distinct signature. A comparison across mammals suggests a common theme, in which four large-bodied ganglion cell types split the visual signal into four channels arranged symmetrically with respect to polarity and kinetics.

Sensitive and specific antibodies are essential for detecting molecules in cells and tissues. However, currently used polyclonal and monoclonal antibodies are often less specific than desired, difficult to produce, and available in limited quantities. A promising recent approach to circumvent these limitations is to employ chemically defined antigen-combining domains called “nanobodies,” derived from single-chain camelid antibodies. Here, we used nanobodies to prepare sensitive unimolecular detection reagents by genetically fusing cDNAs encoding nanobodies to enzymatic or antigenic reporters. We call these fusions between a reporter and a nanobody “RANbodies.” They can be used to localize epitopes and to amplify signals from fluorescent proteins. They can be generated and purified simply and in unlimited amounts and can be preserved safely and inexpensively in the form of DNA or digital sequence.

Most irreversible blindness results from retinal disease. To advance our understanding of the etiology of blinding diseases, we used single-cell RNA-sequencing (scRNA-seq) to analyze the transcriptomes of ~85,000 cells from the fovea and peripheral retina of seven adult human donors. Utilizing computational methods, we identified 58 cell types within 6 classes: photoreceptor, horizontal, bipolar, amacrine, retinal ganglion and non-neuronal cells. Nearly all types are shared between the two retinal regions, but there are notable differences in gene expression and proportions between foveal and peripheral cohorts of shared types. We then used the human retinal atlas to map expression of 636 genes implicated as causes of or risk factors for blinding diseases. Many are expressed in striking cell class-, type-, or region-specific patterns. Finally, we compared gene expression signatures of cell types between human and the cynomolgus macaque monkey, Macaca fascicularis . We show that over 90% of human types correspond transcriptomically to those previously identified in macaque, and that expression of disease-related genes is largely conserved between the two species. These results validate the use of the macaque for modeling blinding disease, and provide a foundation for investigating molecular mechanisms underlying visual processing.

The basic plan of the retina is conserved across vertebrates, yet species differ profoundly in their visual needs 1 . Retinal cell types may have evolved to accommodate these varied needs, but this has not been systematically studied. Here we generated and integrated single-cell transcriptomic atlases of the retina from 17 species: humans, two non-human primates, four rodents, three ungulates, opossum, ferret, tree shrew, a bird, a reptile, a teleost fish and a lamprey. We found high molecular conservation of the six retinal cell classes (photoreceptors, horizontal cells, bipolar cells, amacrine cells, retinal ganglion cells (RGCs) and Müller glia), with transcriptomic variation across species related to evolutionary distance. Major subclasses were also conserved, whereas variation among cell types within classes or subclasses was more pronounced. However, an integrative analysis revealed that numerous cell types are shared across species, based on conserved gene expression programmes that are likely to trace back to an early ancestral vertebrate. The degree of variation among cell types increased from the outer retina (photoreceptors) to the inner retina (RGCs), suggesting that evolution acts preferentially to shape the retinal output. Finally, we identified rodent orthologues of midget RGCs, which comprise more than 80% of RGCs in the human retina, subserve high-acuity vision, and were previously believed to be restricted to primates 2 . By contrast, the mouse orthologues have large receptive fields and comprise around 2% of mouse RGCs. Projections of both primate and mouse orthologous types are overrepresented in the thalamus, which supplies the primary visual cortex. We suggest that midget RGCs are not primate innovations, but are descendants of evolutionarily ancient types that decreased in size and increased in number as primates evolved, thereby facilitating high visual acuity and increased cortical processing of visual information. Single-cell and single-nucleus transcriptomic analysis of retina from 17 vertebrate species shows high conservation of retinal cell types and suggests that midget retinal ganglion cells in primates evolved from orthologous cells in ancestral mammals.

The retina reports the visual scene to the brain through many parallel channels, each carried by a distinct population of retinal ganglion cells. Among these, the population with the smallest and densest receptive fields encodes the neural image with highest resolution. In human retina, and those of cat and macaque, these high-resolution ganglion cells act as generic pixel encoders: They serve to represent many different visual inputs and convey a neural image of the scene downstream for further processing. Here we identify and analyze high-resolution ganglion cells in the mouse retina, using a transgenic line in which these cells, called “W3”, are labeled fluorescently. Counter to the expectation, these ganglion cells do not participate in encoding generic visual scenes, but remain silent during most common visual stimuli. A detailed study of their response properties showed that W3 cells pool rectified excitation from both On and Off bipolar cells, which makes them sensitive to local motion. However, they also receive unusually strong lateral inhibition, both pre- and postsynaptically, triggered by distant motion. As a result, the W3 cell can detect small moving objects down to the receptive field size of bipolar cells, but only if the background is featureless or stationary—an unusual condition. A survey of naturalistic stimuli shows that W3 cells may serve as alarm neurons for overhead predators.

Making connections Downs syndrome cell adhesion molecules (Dscams) are adhesion molecules of the immunoglolulin superfamily. Drosophila Dscams have been implicated in the organization of neural connectivity, but little is known about the functions of the closely related molecules in vertebrates. Masahito Yamagata and Joshua Sanes now demonstrate a role for Dscam and DscamL in patterning of lamina-specific connections in the chick retina. Two other adhesion molecules, called Sidekick-1 and Sidekick-2, act in a similar way. These molecules are widely distributed in the nervous system and may be part of an 'adhesive code' that patterns neural connections in the brain. Further evidence for the importance of Dscams in vertebrate neural patterning comes from Fuerst et al ., who identify a role for DSCAM in establishing neural circuits in the retina of mice. The computing power of the brain depends on its components, nerve cells, being wired to each other in very specific patterns. Some of the molecules involved in this specificity have been identified, and it is demonstrated that nerve cells miss-wire when these molecules are missing or, conversely, when they are present in the wrong place. Synaptic circuits in the retina transform visual input gathered by photoreceptors into messages that retinal ganglion cells (RGCs) send to the brain. Processes of retinal interneurons (amacrine and bipolar cells) form synapses on dendrites of RGCs in the inner plexiform layer (IPL). The IPL is divided into at least 10 parallel sublaminae; subsets of interneurons and RGCs arborize and form synapses in just one or a few of them 1 , 2 , 3 . These lamina-specific circuits determine the visual features to which RGC subtypes respond 3 , 4 , 5 . Here we show that four closely related immunoglobulin superfamily (IgSF) adhesion molecules—Dscam (Down’s syndrome cell adhesion molecule), DscamL (refs 6–9 ), Sidekick-1 and Sidekick-2 (ref. 10 )—are expressed in chick by non-overlapping subsets of interneurons and RGCs that form synapses in distinct IPL sublaminae. Moreover, each protein is concentrated within the appropriate sublaminae and each mediates homophilic adhesion. Loss- and gain-of-function studies in vivo indicate that these IgSF members participate in determining the IPL sublaminae in which synaptic partners arborize and connect. Thus, vertebrate Dscams, like Drosophila Dscams 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , play roles in neural connectivity. Together, our results on Dscams and Sidekicks suggest the existence of an IgSF code for laminar specificity in retina and, by implication, in other parts of the central nervous system.

Normal aging and neurodegenerative diseases both lead to structural and functional alterations in synapses. Comparison of synapses that are generally similar but respond differently to insults could provide the basis for discovering mechanisms that underlie susceptibility or resistance to damage. Here, we analyzed skeletal neuromuscular junctions (NMJs) in 16 mouse muscles to seek such differences. We find that muscles respond in one of three ways to aging. In some, including most limb and trunk muscles, age-related alterations to NMJs are progressive and extensive during the second postnatal year. NMJs in other muscles, such as extraocular muscles, are strikingly resistant to change. A third set of muscles, including several muscles of facial expression and the external anal sphinter, succumb to aging but not until the third postnatal year. We asked whether susceptible and resistant muscles differed in rostrocaudal or proximodistal position, source of innervation, motor unit size, or fiber type composition. Of these factors, muscle innervation by brainstem motor neurons correlated best with resistance to age-related decline. Finally, we compared synaptic alterations in normally aging muscles to those in a mouse model of amyotrophic lateral sclerosis (ALS). Patterns of resistance and susceptibility were strikingly correlated in the two conditions. Moreover, damage to NMJs in aged muscles correlated with altered expression and distribution of CRMP4a and TDP-43, which are both altered in motor neurons affected by ALS. Together, these results reveal novel structural, regional and molecular parallels between aging and ALS.

The related transmembrane proteins MEGF10 and MEGF11 are shown to have critical roles in the formation of mosaic arrangements in the retina. Building the retinal network In the retina and other nerve tissues, neural interactions are facilitated by the arrangement of the neurons in regular patterns. This paper addresses how these patterns emerge during development. Kay et al . show that in the mouse retina, where neurons of individual subtypes form arrays called mosaics, the related transmembrane proteins MEGF10 and MEGF11 are required for the formation of mosaics by starburst amacrine cells and horizontal cells. Like the related proteins CED-1 and Draper, from Caenorhabditis elegans and Drosophila respectively, MEGF10 and MEGF11 have until now been studied predominantly as receptors for cell engulfment. In many parts of the nervous system, neuronal somata display orderly spatial arrangements 1 . In the retina, neurons of numerous individual subtypes form regular arrays called mosaics: they are less likely to be near neighbours of the same subtype than would occur by chance, resulting in ‘exclusion zones’ that separate them 1 , 2 , 3 , 4 . Mosaic arrangements provide a mechanism to distribute each cell type evenly across the retina, ensuring that all parts of the visual field have access to a full set of processing elements 2 . Remarkably, mosaics are independent of each other: although a neuron of one subtype is unlikely to be adjacent to another of the same subtype, there is no restriction on its spatial relationship to neighbouring neurons of other subtypes 5 . This independence has led to the hypothesis that molecular cues expressed by specific subtypes pattern mosaics by mediating homotypic (within-subtype) short-range repulsive interactions 1 , 4 , 5 , 6 , 7 , 8 , 9 . So far, however, no molecules have been identified that show such activity, so this hypothesis remains untested. Here we demonstrate in mouse that two related transmembrane proteins, MEGF10 and MEGF11, have critical roles in the formation of mosaics by two retinal interneuron subtypes, starburst amacrine cells and horizontal cells. MEGF10 and 11 and their invertebrate relatives Caenorhabditis elegans CED-1 and Drosophila Draper have hitherto been studied primarily as receptors necessary for engulfment of debris following apoptosis or axonal injury 10 , 11 , 12 , 13 , 14 . Our results demonstrate that members of this gene family can also serve as subtype-specific ligands that pattern neuronal arrays.

Key Points Classification of neurons into types enables their reproducible identification across times, laboratories and conditions. Classification also facilitates genetic access for functional studies, as well as analyses of development, evolution and disease. Neuronal cell types must be defined by multiple criteria related to their morphological, physiological, molecular and connectional properties. Past efforts at neuronal classification were hindered by severe biases and under-sampling, but newly developed high-throughput techniques allow this limitation to be circumvented. For some regions of the central nervous system, particularly the retina and cerebral cortex, a complete cell census appears within reach. Principles derived from the well-developed field of species taxonomy (systematics) provide common-sense guidelines for cell-type classification. Attempts to group the cells of the nervous system into classes or types face technical and conceptual barriers. Zeng and Sanes consider the current approaches to classification and propose a strategy and set of principles to guide future classification efforts. Neurons have diverse molecular, morphological, connectional and functional properties. We believe that the only realistic way to manage this complexity — and thereby pave the way for understanding the structure, function and development of brain circuits — is to group neurons into types, which can then be analysed systematically and reproducibly. However, neuronal classification has been challenging both technically and conceptually. New high-throughput methods have created opportunities to address the technical challenges associated with neuronal classification by collecting comprehensive information about individual cells. Nonetheless, conceptual difficulties persist. Borrowing from the field of species taxonomy, we propose principles to be followed in the cell-type classification effort, including the incorporation of multiple, quantitative features as criteria, the use of discontinuous variation to define types and the creation of a hierarchical system to represent relationships between cells. We review the progress of classifying cell types in the retina and cerebral cortex and propose a staged approach for moving forward with a systematic cell-type classification in the nervous system.