Typology and Circuitry of Suppressed-by-Contrast Retinal Ganglion Cells

A chromatic feature detector in the retina signals visual context changes

The retina transforms patterns of light into visual feature representations supporting behaviour. These representations are distributed across various types of retinal ganglion cells (RGCs), whose spatial and temporal tuning properties have been studied extensively in many model organisms, including the mouse. However, it has been difficult to link the potentially nonlinear retinal transformations of natural visual inputs to specific ethological purposes. Here, we discover a nonlinear selectivity to chromatic contrast in an RGC type that allows the detection of changes in visual context. We trained a convolutional neural network (CNN) model on large-scale functional recordings of RGC responses to natural mouse movies, and then used this model to search in silico for stimuli that maximally excite distinct types of RGCs. This procedure predicted centre colour opponency in transient suppressed-by-contrast (tSbC) RGCs, a cell type whose function is being debated. We confirmed experimentally that these cells indeed responded very selectively to Green-OFF, UV-ON contrasts. This type of chromatic contrast was characteristic of transitions from ground to sky in the visual scene, as might be elicited by head or eye movements across the horizon. Because tSbC cells performed best among all RGC types at reliably detecting these transitions, we suggest a role for this RGC type in providing contextual information (i.e. sky or ground) necessary for the selection of appropriate behavioural responses to other stimuli, such as looming objects. Our work showcases how a combination of experiments with natural stimuli and computational modelling allows discovering novel types of stimulus selectivity and identifying their potential ethological relevance.

Awake responses suggest inefficient dense coding in the mouse retina

The structure and function of the vertebrate retina have been extensively studied across species with an isolated, ex vivo preparation. Retinal function in vivo, however, remains elusive, especially in awake animals. Here, we performed single-unit extracellular recordings in the optic tract of head-fixed mice to compare the output of awake, anesthetized, and ex vivo retinas. While the visual response properties were overall similar across conditions, we found that awake retinal output had in general (1) faster kinetics with less variability in the response latencies; (2) a larger dynamic range; and (3) higher firing activity, by ~20 Hz on average, for both baseline and visually evoked responses. Our modeling analyses further showed that such awake response patterns convey comparable total information but less efficiently, and allow for a linear population decoder to perform significantly better than the anesthetized or ex vivo responses. These results highlight distinct retinal behavior in awake states, in particular suggesting that the retina employs dense coding in vivo, rather than sparse efficient coding as has been often assumed from ex vivo studies.

A circuit suppressing retinal drive to the optokinetic system during fast image motion

Optokinetic nystagmus (OKN) assists stabilization of the retinal image during head rotation. OKN is driven by ON direction selective retinal ganglion cells (ON DSGCs), which encode both the direction and speed of global retinal slip. The synaptic circuits responsible for the direction selectivity of ON DSGCs are well understood, but those sculpting their slow-speed preference remain enigmatic. Here, we probe this mechanism in mouse retina through patch clamp recordings, functional imaging, genetic manipulation, and electron microscopic reconstructions. We confirm earlier evidence that feedforward glycinergic inhibition is the main suppressor of ON DSGC responses to fast motion, and reveal the source for this inhibition-the VGluT3 amacrine cell, a dual neurotransmitter, excitatory/inhibitory interneuron. Together, our results identify a role for VGluT3 cells in limiting the speed range of OKN. More broadly, they suggest VGluT3 cells shape the response of many retinal cell types to fast motion, suppressing it in some while enhancing it in others.

Using optogenetics to dissect rod inputs to OFF ganglion cells in the mouse retina

Introduction

Light responses of rod photoreceptor cells traverse the retina through three pathways. The primary pathway involves synapses from rods to ON-type rod bipolar cells with OFF signals reaching retinal ganglion cells (RGCs) via sign-inverting glycinergic synapses. Secondly, rod signals can enter cones through gap junctions. Finally, rods can synapse directly onto cone OFF bipolar cells.

Methods

To analyze these pathways, we obtained whole cell recordings from OFF-type α RGCs in mouse retinas while expressing channelrhodopsin-2 in rods and/or cones.

Results

Optogenetic stimulation of rods or cones evoked large fast currents in OFF RGCs. Blocking the primary rod pathway with L-AP4 and/or strychnine reduced rod-driven optogenetic currents in OFF RGCs by ~1/3. Blocking kainate receptors of OFF cone bipolar cells suppressed both rod- and cone-driven optogenetic currents in OFF RGCs. Inhibiting gap junctions between rods and cones with mecloflenamic acid or quinpirole reduced rod-driven responses in OFF RGCs. Eliminating the exocytotic Ca²⁺ sensor, synaptotagmin 1 (Syt1), from cones abolished cone-driven optogenetic responses in RGCs. Rod-driven currents were not significantly reduced after isolating the secondary pathway by eliminating Syt1 and synaptotagmin 7 (Syt7) to block synaptic release from rods. Eliminating Syt1 from both rods and cones abolished responses to optogenetic stimulation. In Cx36 KO retinas lacking rod-cone gap junctions, optogenetic activation of rods evoked small and slow responses in most OFF RGCs suggesting rod signals reached them through an indirect pathway. Two OFF cells showed faster responses consistent with more direct input from cone OFF bipolar cells.

Discussion

These data show that the secondary rod pathway supports robust inputs into OFF α RGCs and suggests the tertiary pathway recruits both direct and indirect inputs.

Brainstem serotonin neurons selectively gate retinal information flow to thalamus

Retinal ganglion cell (RGC) types relay parallel streams of visual feature information. We hypothesized that neuromodulators might efficiently control which visual information streams reach the cortex by selectively gating transmission from specific RGC axons in the thalamus. Using fiber photometry recordings, we found that optogenetic stimulation of serotonergic axons in primary visual thalamus of awake mice suppressed ongoing and visually evoked calcium activity and glutamate release from RGC boutons. Two-photon calcium imaging revealed that serotonin axon stimulation suppressed RGC boutons that responded strongly to global changes in luminance more than those responding only to local visual stimuli, while the converse was true for suppression induced by increases in arousal. Converging evidence suggests that differential expression of the 5-HT1B receptor on RGC presynaptic terminals, but not differential density of nearby serotonin axons, may contribute to the selective serotonergic gating of specific visual information streams before they can activate thalamocortical neurons.

Layer-Specific Developmentally Precise Axon Targeting of Transient Suppressed-by-Contrast Retinal Ganglion Cells

The mouse retina encodes diverse visual features in the spike trains of >40 retinal ganglion cell (RGC) types. Each RGC type innervates a specific subset of the >50 retinorecipient brain areas. Our catalog of RGC types and feature representations is nearing completion. Yet, we know little about where specific RGC types send their information. Furthermore, the developmental strategies by which RGC axons choose their targets and pattern their terminal arbors remain obscure. Here, we identify a genetic intersection (Cck-Cre and Brn3cCKOAP ) that selectively labels transient Suppressed-by-Contrast (tSbC) RGCs, a member of an evolutionarily conserved functionally mysterious RGC subclass. We find that tSbC RGCs selectively innervate the dorsolateral geniculate nucleus (dLGN) and ventrolateral geniculate nucleus (vLGN) of the thalamus, the superior colliculus (SC), and the nucleus of the optic tract (NOT) in mice of either sex. They binocularly innervate dLGN and vLGN but project only contralaterally to SC and NOT. In each target, tSbC RGC axons occupy a specific sublayer, suggesting that they restrict their input to specific circuits. The tSbC RGC axons span the length of the optic tract by birth and remain poised there until they simultaneously innervate their four targets around postnatal day 3. The tSbC RGC axons choose the right targets and establish mature stratification patterns from the outset. This precision is maintained in the absence of Brn3c. Our results provide the first map of SbC inputs to the brain, revealing a narrow target set, unexpected laminar organization, target-specific binocularity, and developmental precision.SIGNIFICANCE STATEMENT In recent years, we have learned a lot about the visual features encoded by RGCs, the output neurons of the eye. In contrast, we know little about where RGCs send their information and how RGC axons, which carry this information, target specific brain areas during development. Here, we develop an intersectional strategy to label a unique RGC type, the tSbC RGC, and map its projections. We find that tSbC RGC axons are highly selective. They innervate few retinal targets and restrict their arbors to specific sublayers within these targets. The selective tSbC RGC projection patterns develop synchronously and without trial and error, suggesting molecular determinism and coordination.

Unified classification of mouse retinal ganglion cells using function, morphology, and gene expression

Classification and characterization of neuronal types are critical for understanding their function and dysfunction. Neuronal classification schemes typically rely on measurements of electrophysiological, morphological, and molecular features, but aligning such datasets has been challenging. Here, we present a unified classification of mouse retinal ganglion cells (RGCs), the sole retinal output neurons. We use visually evoked responses to classify 1,859 mouse RGCs into 42 types. We also obtain morphological or transcriptomic data from subsets and use these measurements to align the functional classification to publicly available morphological and transcriptomic datasets. We create an online database that allows users to browse or download the data and to classify RGCs from their light responses using a machine learning algorithm. This work provides a resource for studies of RGCs, their upstream circuits in the retina, and their projections in the brain, and establishes a framework for future efforts in neuronal classification and open data distribution.

Differences in spike generation instead of synaptic inputs determine the feature selectivity of two retinal cell types

Retinal ganglion cells (RGCs) are the spiking projection neurons of the eye that encode different features of the visual environment. The circuits providing synaptic input to different RGC types to drive feature selectivity have been studied extensively, but there has been less research aimed at understanding the intrinsic properties and how they impact feature selectivity. We introduce an RGC type in the mouse, the Bursty Suppressed-by-Contrast (bSbC) RGC, and compared it to the OFF sustained alpha (OFFsA). Differences in their contrast response functions arose from differences not in synaptic inputs but in their intrinsic properties. Spike generation was the key intrinsic property behind this functional difference; the bSbC RGC undergoes depolarization block while the OFFsA RGC maintains a high spike rate. Our results demonstrate that differences in intrinsic properties allow these two RGC types to detect and relay distinct features of an identical visual stimulus to the brain.

Feature Detection by Retinal Ganglion Cells

Retinal circuits transform the pixel representation of photoreceptors into the feature representations of ganglion cells, whose axons transmit these representations to the brain. Functional, morphological, and transcriptomic surveys have identified more than 40 retinal ganglion cell (RGC) types in mice. RGCs extract features of varying complexity; some simply signal local differences in brightness (i.e., luminance contrast), whereas others detect specific motion trajectories. To understand the retina, we need to know how retinal circuits give rise to the diverse RGC feature representations. A catalog of the RGC feature set, in turn, is fundamental to understanding visual processing in the brain. Anterograde tracing indicates that RGCs innervate more than 50 areas in the mouse brain. Current maps connecting RGC types to brain areas are rudimentary, as is our understanding of how retinal signals are transformed downstream to guide behavior. In this article, I review the feature selectivities of mouse RGCs, how they arise, and how they are utilized downstream. Not only is knowledge of the behavioral purpose of RGC signals critical for understanding the retinal contributions to vision; it can also guide us to the most relevant areas of visual feature space. Expected final online publication date for the Annual Review of Vision Science, Volume 8 is September 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Selective glycinergic input from vGluT3 amacrine cells confers a suppressed-by-contrast trigger feature in a subtype of M1 ipRGCs in the mouse retina

KEY POINTS

M1 intrinsically photosensitive retinal ganglion cells (ipRGCs) are known to encode absolute light intensity (irradiance) for non-image-forming visual functions (subconscious vision), such as circadian photoentrainment and the pupillary light reflex. It remains unclear how M1 cells respond to relative light intensity (contrast) and patterned visual signals. The present study identified a special form of contrast sensitivity (suppressed-by-contrast) in M1 cells, suggesting a role of patterned visual signals in regulating non-image-forming vision and a potential role of M1 ipRGCs in encoding image-forming visual cues. The study also uncovered a synaptic mechanism and a retinal circuit mediated by vesicular glutamate transporter 3 (vGluT3) amacrine cells that underlie the suppressed-by-contrast response of M1 cells. M1 ipRGC subtypes (M1a and M1b) were revealed that are distinguishable based on synaptic connectivity with vGluT3 amacrine cells, receptive field properties, intrinsic photo sensitivity and membrane excitability, and morphological features, suggesting a division of visual tasks among discrete M1 subpopulations.

ABSTRACT

The M1 type ipRGC (intrinsically photosensitive retinal ganglion cell) is known to encode ambient light signals for non-image-forming visual functions such as circadian photo-entrainment and the pupillary light reflex. Here, we report that a subpopulation of M1 cells (M1a) in the mouse retina possess the suppressed-by-contrast (sbc) trigger feature that is a receptive field property previously found only in ganglion cells mediating image-forming vision. Using optogenetics and the dual patch clamp technique, we found that vesicular glutamate transporter 3 (vGluT3) (vGluT3) amacrine cells make glycinergic, but not glutamatergic, synapses specifically onto M1a cells. The spatiotemporal and pharmacological properties of visually evoked responses of M1a cells closely matched the receptive field characteristics of vGluT3 cells, suggesting a major role of the vGluT3 amacrine cell input in shaping the sbc trigger feature of M1a cells. We found that the other subpopulation of M1 cells (M1b), which did not receive a direct vGluT3 cell input, lacked the sbc trigger feature, being distinctively different from M1a cells in intrinsic photo responses, membrane excitability, receptive-field characteristics and morphological features. Together, the results reveal a retinal circuit that uses the sbc trigger feature to regulate irradiance coding and potentially send image-forming cues to non-image-forming visual centres in the brain.

A High-Density Narrow-Field Inhibitory Retinal Interneuron with Direct Coupling to Müller Glia

Amacrine cells are interneurons composing the most diverse cell class in the mammalian retina. They help encode visual features, such as edges or directed motion, by mediating excitatory and inhibitory interactions between input (i.e., bipolar) and output (i.e., ganglion) neurons in the inner plexiform layer (IPL). Like other brain regions, the retina also contains glial cells that contribute to neurotransmitter uptake, metabolic regulation, and neurovascular control. Here, we report that, in mouse retina (of either sex), an abundant, though previously unstudied inhibitory amacrine cell is coupled directly to Müller glia. Electron microscopic reconstructions of this amacrine type revealed chemical synapses with known retinal cell types and extensive associations with Müller glia, the processes of which often completely ensheathe the neurites of this amacrine cell. Microinjecting small tracer molecules into the somas of these amacrine cells led to selective labeling of nearby Müller glia, leading us to suggest the name "Müller glia-coupled amacrine cell," or MAC. Our data also indicate that MACs release glycine at conventional chemical synapses, and viral retrograde transsynaptic tracing from the dorsal lateral geniculate nucleus showed selective connections between MACs and a subpopulation of retinal ganglion cell types. Visually evoked responses revealed a strong preference for light increments; these "ON" responses were primarily mediated by excitatory chemical synaptic input and direct electrical coupling with other cells. This initial characterization of the MAC provides the first evidence for neuron-glia coupling in the mammalian retina and identifies the MAC as a potential link between inhibitory processing and glial function.SIGNIFICANCE STATEMENT Gap junctions between pairs of neurons or glial cells are commonly found throughout the nervous system and play multiple roles, including electrical coupling and metabolic exchange. In contrast, gap junctions between neurons and glia cells have rarely been reported and are poorly understood. Here we report the first evidence for neuron-glia coupling in the mammalian retina, specifically between an abundant (but previously unstudied) inhibitory interneuron and Müller glia. Moreover, viral tracing, optogenetics, and serial electron microscopy provide new information about the neuron's synaptic partners and physiological responses.

Nonlinear Spatial Integration Underlies the Diversity of Retinal Ganglion Cell Responses to Natural Images

How neurons encode natural stimuli is a fundamental question for sensory neuroscience. In the early visual system, standard encoding models assume that neurons linearly filter incoming stimuli through their receptive fields, but artificial stimuli, such as contrast-reversing gratings, often reveal nonlinear spatial processing. We investigated to what extent such nonlinear processing is relevant for the encoding of natural images in retinal ganglion cells in mice of either sex.

How neurons encode natural stimuli is a fundamental question for sensory neuroscience. In the early visual system, standard encoding models assume that neurons linearly filter incoming stimuli through their receptive fields, but artificial stimuli, such as contrast-reversing gratings, often reveal nonlinear spatial processing. We investigated to what extent such nonlinear processing is relevant for the encoding of natural images in retinal ganglion cells in mice of either sex. We found that standard linear receptive field models yielded good predictions of responses to flashed natural images for a subset of cells but failed to capture the spiking activity for many others. Cells with poor model performance displayed pronounced sensitivity to fine spatial contrast and local signal rectification as the dominant nonlinearity. By contrast, sensitivity to high-frequency contrast-reversing gratings, a classical test for nonlinear spatial integration, was not a good predictor of model performance and thus did not capture the variability of nonlinear spatial integration under natural images. In addition, we also observed a class of nonlinear ganglion cells with inverse tuning for spatial contrast, responding more strongly to spatially homogeneous than to spatially structured stimuli. These findings highlight the diversity of receptive field nonlinearities as a crucial component for understanding early sensory encoding in the context of natural stimuli.

SIGNIFICANCE STATEMENT Experiments with artificial visual stimuli have revealed that many types of retinal ganglion cells pool spatial input signals nonlinearly. However, it is still unclear how relevant this nonlinear spatial integration is when the input signals are natural images. Here we analyze retinal responses to natural scenes in large populations of mouse ganglion cells. We show that nonlinear spatial integration strongly influences responses to natural images for some ganglion cells, but not for others. Cells with nonlinear spatial integration were sensitive to spatial structure inside their receptive fields, and a small group of cells displayed a surprising sensitivity to spatially homogeneous stimuli. Traditional analyses with contrast-reversing gratings did not predict this variability of nonlinear spatial integration under natural images.

Gbx2 Identifies Two Amacrine Cell Subtypes with Distinct Molecular, Morphological, and Physiological Properties

Our understanding of nervous system function is limited by our ability to identify and manipulate neuronal subtypes within intact circuits. We show that the Gbx2^{CreERT2-IRES-EGFP} mouse line labels two amacrine cell (AC) subtypes in the mouse retina that have distinct morphological, physiological, and molecular properties. Using a combination of RNA-seq, genetic labeling, and patch clamp recordings, we show that one subtype is GABAergic that receives excitatory input from On bipolar cells. The other population is a non-GABAergic, non-glycinergic (nGnG) AC subtype that lacks the expression of standard neurotransmitter markers. Gbx2⁺ nGnG ACs have smaller, asymmetric dendritic arbors that receive excitatory input from both On and Off bipolar cells. Gbx2⁺ nGnG ACs also exhibit spatially restricted tracer coupling to bipolar cells (BCs) through gap junctions. This study identifies a genetic tool for investigating the two distinct AC subtypes, and it provides a model for studying synaptic communication and visual circuit function.

Investigations into neural circuit development and function are limited by the lack of genetic tools to label and perturb individual neuronal subtypes. Using the Gbx2^CreERT2 mouse line, Kerstein et al. identify two amacrine cell subtypes in the mouse retina and explore their distinct molecular, morphological, and physiological characteristics.

Complementary Ca2+ Activity of Sensory Activated and Suppressed Layer 6 Corticothalamic Neurons Reflects Behavioral State

Layer 6 (L6) corticothalamic neurons project to thalamus, where they are thought to regulate sensory information transmission to cortex. However, the activity of these neurons during different behavioral states has not been described. Here, we imaged calcium changes in visual cortex L6 primary corticothalamic neurons with two-photon microscopy in head-fixed mice in response to passive viewing during a range of behavioral states, from locomotion to sleep. In addition to a substantial fraction of quiet neurons, we found sensory-activated and suppressed neurons, comprising two functionally distinct L6 feedback channels. Quiet neurons could be dynamically recruited to one or another functional channel, and the opposite, functional neurons could become quiet under different stimulation conditions or behavior states. The state dependence of neuronal activity was heterogeneous with respect to locomotion or level of alertness, although the average activity was largest during highest vigilance within populations of functional neurons. Interestingly, complementary activity of these distinct populations kept the overall corticothalamic feedback relatively constant during any given behavioral state. Thereby, in addition to sensory and non-sensory information, a constant activity level characteristic of behavioral state is conveyed to thalamus, where it can regulate signal transmission from the periphery to cortex.

Arousal Modulates Retinal Output

At various stages of the visual system, visual responses are modulated by arousal. Here, we find that in mice this modulation operates as early as in the first synapse from the retina and even in retinal axons. To measure retinal activity in the awake, intact brain, we imaged the synaptic boutons of retinal axons in the superior colliculus. Their activity depended not only on vision but also on running speed and pupil size, regardless of retinal illumination. Arousal typically reduced their visual responses and selectivity for direction and orientation. Recordings from retinal axons in the optic tract revealed that arousal modulates the firing of some retinal ganglion cells. Arousal had similar effects postsynaptically in colliculus neurons, independent of activity in the other main source of visual inputs to the colliculus, the primary visual cortex. These results indicate that arousal modulates activity at every stage of the mouse visual system.

A retinal circuit for the suppressed-by-contrast receptive field of a polyaxonal amacrine cell

Amacrine cells are a diverse population of interneurons in the retina that play a critical role in extracting complex features of the visual world and shaping the receptive fields of retinal output neurons (ganglion cells). While much of the computational power of amacrine cells is believed to arise from the immense mutual interactions among amacrine cells themselves, the intricate circuitry and functions of amacrine-amacrine interactions are poorly understood in general. Here we report a specific interamacrine pathway from a small-field, glutamate-glycine dual-transmitter amacrine cell (vGluT3) to a wide-field polyaxonal amacrine cell (PAS4/5). Distal tips of vGluT3 cell dendrites made selective glycinergic (but not glutamatergic) synapses onto PAS4/5 dendrites to provide a center-inhibitory, surround-disinhibitory drive that helps PAS4/5 cells build a suppressed-by-contrast (sbc) receptive field, which is a unique and fundamental trigger feature previously found only in a small population of ganglion cells. The finding of this trigger feature in a circuit upstream to ganglion cells suggests that the sbc form of visual computation occurs more widely in the retina than previously believed and shapes visual processing in multiple downstream circuits in multiple ways. We also identified two different subpopulations of PAS4/5 cells based on their differential connectivity with vGluT3 cells and their distinct receptive-field and luminance-encoding characteristics. Moreover, our results revealed a form of crosstalk between small-field and large-field amacrine cell circuits, which provides a mechanism for feature-specific local (<150 µm) control of global (>1 mm) retinal activity.

Understanding the retinal basis of vision across species

The vertebrate retina first evolved some 500 million years ago in ancestral marine chordates. Since then, the eyes of different species have been tuned to best support their unique visuoecological lifestyles. Visual specializations in eye designs, large-scale inhomogeneities across the retinal surface and local circuit motifs mean that all species' retinas are unique. Computational theories, such as the efficient coding hypothesis, have come a long way towards an explanation of the basic features of retinal organization and function; however, they cannot explain the full extent of retinal diversity within and across species. To build a truly general understanding of vertebrate vision and the retina's computational purpose, it is therefore important to more quantitatively relate different species' retinal functions to their specific natural environments and behavioural requirements. Ultimately, the goal of such efforts should be to build up to a more general theory of vision.

Molecular and Cellular Mechanisms Underlying Somatostatin-Based Signaling in Two Model Neural Networks, the Retina and the Hippocampus

Neural inhibition plays a key role in determining the specific computational tasks of different brain circuitries. This functional "braking" activity is provided by inhibitory interneurons that use different neurochemicals for signaling. One of these substances, somatostatin, is found in several neural networks, raising questions about the significance of its widespread occurrence and usage. Here, we address this issue by analyzing the somatostatinergic system in two regions of the central nervous system: the retina and the hippocampus. By comparing the available information on these structures, we identify common motifs in the action of somatostatin that may explain its involvement in such diverse circuitries. The emerging concept is that somatostatin-based signaling, through conserved molecular and cellular mechanisms, allows neural networks to operate correctly.

Mouse dLGN Receives Functional Input from a Diverse Population of Retinal Ganglion Cells with Limited Convergence

Mouse vision is based on the parallel output of more than 30 functional types of retinal ganglion cells (RGCs). Little is known about how representations of visual information change between retina and dorsolateral geniculate nucleus (dLGN) of the thalamus, the main relay between retina and cortex. Here, we functionally characterized responses of retrogradely labeled dLGN-projecting RGCs and dLGN neurons to the same set of visual stimuli. We found that many of the previously identified functional RGC types innervate dLGN, which maintained a high degree of functional diversity. Using a linear model to assess functional connectivity between RGC types and dLGN neurons, we found that responses of dLGN neurons could be predicted as linear combination of inputs from on average five RGC types, but only two of those had the strongest functional impact. Thus, mouse dLGN receives functional input from a diverse population of RGC types with limited convergence.