Fovea-like Photoreceptor Specializations Underlie Single UV Cone Driven Prey-Capture Behavior in Zebrafish

1In vision, photoreceptors drive the retinal network through continuous modulations in synaptic release. However, how changes in incoming photon flux lead to changes in the rate of vesicle fusion at the synapse varies dramatically between photoreceptor designs. For example, in the vertebrate retina, the slow rod photoreceptors typically have large outer segments and high-gain intracellular signaling cascades to deliver single-photon sensitivity critical for vision at low light. In contrast, cone photoreceptors are faster and have smaller outer segments and lower-gain cascades to take over where rods saturate. Clearly, matching the properties of a given photoreceptor type to a specific set of sensory tasks critically underpins vision. However, these visual requirements can differ dramatically across the retinal surface and the corresponding position in visual space. For efficient sampling, even cones of a single type must therefore be functionally tuned depending on their retinal location.

2Indeed, photoreceptor tuning, even within type, is a fundamental property of vision in both invertebrates and vertebrates. Even primates make use of this trick; foveal cones have longer integration times than their peripheral counterparts, likely to boost their signal to noise ratio, as in the foveal center, retinal ganglion cells (RGCs) do not spatially pool their inputs. Understanding the mechanisms that underlie such functional tuning will be important for understanding how sensory systems can operate in the natural sensory world and how they might have evolved to suit new sensory-ecological niches.

3Here, we show that UV cones in the area temporalis (“strike zone” [SZ]; ) of larval zebrafish are selectively tuned to detect microorganisms that these animals feed on (e.g., paramecia).

4Larval zebrafish prey capture is elicited by a bright spot of light, in line with the natural appearance of their prey items (e.g., paramecia) in the upper water column of shallow water when illuminated by the sun (; Figure 1A). To the human observer with comparatively long-wavelength vision, these organisms are largely transparent when viewed against a back light. However, previous work suggests that zooplankton like paramecia scatter light in the UV band (320–390 nm) and thus appear as UV-bright spots.

5To explicitly test this idea, we custom-built a camera system with a UV and a “yellow” channel aligned with the zebrafish UV- and red/green-opsin absorption spectra, respectively. We used this system to film free-swimming paramecia in a naturalistic tank placed outdoors under the midday sun (Figures 1B–1E; Video S1; STAR Methods). While the yellow image provided good spatial detail of the scene’s background and surface water movements, paramecia were difficult to detect among the background clutter (Figure 1D, left). In contrast, the UV channel was dominated by a vertical brightness gradient of scattered light, which almost completely masked the background. Superimposed on this gradient, the upper water column readily highlighted individual paramecia as bright moving spots (Figure 1D, right, and 1E). In agreement, zebrafish use their upper-frontal visual field to detect and capture prey, and inner retinal circuits that process this part of visual space exhibit a strong, regionally specific bias for UV-bright contrasts. This confirmed that vastly different, and largely nonoverlapping types of information are obtainable from these two wavebands available to the zebrafish larvae. Any differences between the UV and yellow waveband (Figures 1D and 1E; Video S1) are likely to be further exacerbated by the fish’s self-movements relative to the scene. These would add major brightness transitions in the yellow, but not the UV, channel. Accordingly, under natural (rather than laboratory-controlled) viewing conditions, paramecia are likely hard to detect in the yellow waveband but readily stand out in the UV. This strongly suggests that larval zebrafish must capitalize on UV vision rather than achromatic or long-wavelength vision to support visual prey detection in nature

6Indeed, UV illumination strongly facilitated behavioral performance: Head-mounted 7–8 days post-fertilization (dpf) larvae in the presence of free-swimming paramecia exhibited significantly more prey-capture attempts when illuminated with UV light (374 nm) compared to yellow light (507 nm) (Figures 1F–1H). This difference was abolished after genetic ablation of UV cones (Figure 1H, bottom; STAR Methods). Together, these results strongly suggest that UV cones provide the dominant input to visual prey-capture circuits in larval zebrafish.

7The ∼300-μm-diameter eyes of larval zebrafish necessarily offer limited spatial resolution, meaning that visually detecting their even smaller prey presents a substantial challenge. We therefore set out to determine the maximal number of UV cones the fish can use for this task. At 8 dpf, larval zebrafish have ∼2,400 UV cones per eye. These are unevenly distributed and exhibit a 3-fold elevation in the center of the SZ, which in visual space is located at 38° azimuth and 27° elevation relative to the center of the monocular field (Figure S1A). At rest and during hunting, larval zebrafish converge their 169° ± 4.9° (n = 4) field-of-view eyes by ∼36° and ∼76° to afford a frontal binocular overlap of 26° and 66°, respectively (Figures S1B–S1D). Based on these numbers, we computed the spatial detection limits of the UV detector array across both eyes (Figures 2A–2D and S1A–S1D).

8Before initiating the actual strike, and prior to converging their eyes, zebrafish must first detect their prey. This mostly occurs within the upper visual field (∼30° elevation; ), where the UV signal from paramecia is particularly prominent (Figures 1D and 1E). Within this region, prey-detection performance is highest when the target is laterally displaced from the center of the binocular visual field by ∼23°. This same region was surveyed by each eye’s SZ (Figures 2A, S1A, and S1B), confirming that zebrafish indeed capitalize on the elevated UV cone density in this part of the eye for prey detection. However, with a mean SZ UV-cone spacing of 0.19 cones/°2 and a UV cone-receptive field diameter of ∼0.76°, even at its peak, this UV-detector array nevertheless dramatically undersamples visual space for this critical behavioral task (Figure 2B): larval zebrafish can detect <100-μm prey at up to 3.25 mm distance, where it subtends a visual angle of only 1.8°. This is more than two times smaller than required for reliable detection at the Nyquist limit. It therefore follows that zebrafish are unlikely to use more than a single UV cone at a time to trigger the initial behavioral response.

9Once this prey is detected, zebrafish orient toward it and converge their eyes. This brings both SZs into near-perfect alignment directly in front of the fish, thus enabling stereoptic estimation of exact prey position for subsequent capture (; Figures 2D, S1C, and S1D). The actual strike is then initiated at a distance of ∼1 mm, when a 100-μm paramecium subtends a visual angle of ∼5.7° (Figure 2D). At this angular size, it reliably covers two or three UV cones per eye yet rarely substantially more. Taken together, single UV cones in the SZ therefore likely underlie initial prey detection, triggering prey-orientation behavior. Further, the actual strike is then likely supported by at most a handful of UV photoreceptors per eye.

10As single cones may suffice for prey detection, and in view of the relatively low UV signal in natural light, UV cones in the SZ must be able to absorb photons with high efficiency to support hunting behavior. In contrast, UV cones outside the SZ might be able to afford lower photon catch probability and thus conserve space and energy, as it is possible to pool the coincident signals from multiple UV cones (e.g., for UV-dark silhouette-based predator detection). A simple way to increase a vertebrate photoreceptor’s photon catch probability is to enlarge its outer segment, which houses the phototransduction machinery. To test this, we genetically labeled all UV cones (green), stained outer segments of all cones using the membrane dye BODIPY (magenta), and assessed their morphology using confocal imaging (Figures 2E–2G). This revealed more than 10-fold variations in outer segment lengths. SZ UV cones had the longest outer segments (9.0 ± 0.4 μm), while the immediately neighboring ventral UV cones had the shortest (0.6 ± 0.8 μm). A secondary peak occurred in nasal UV cones (7.0 ± 0.5 μm), which survey the outward horizon, possibly to also support the UV-driven chromatic circuits in this part of the eye. In cyprinid photoreceptors, photon catch probability (F) scales as a function of outer segment length (l) aswhere k is the photoreceptor-type-specific absorption coefficient of 0.03−μm. Accordingly, the observed variation in outer segment length from 0.6 to 9.0 μm should lead to a ∼14-fold boost in photon catch probability for SZ cones. Together, the combination of UV cone density across the eye (factor 3), outer segment length (factor 14), and binocular superposition of the two eyes’ SZs during hunting (factor 2) should therefore lead to a 42- (monocular) to 84-fold (binocular with eyes converged) variation in UV sensitivity across the visual field.

11Finally, located just beneath each outer segment, SZ UV cones also had consistently enlarged ellipsoid bodies (Figures 2F and S1E–S1G). These structures house the mitochondria that power phototransduction. Mitochondrial pockets might further act as micro-lenses to focus additional light onto outer segments. With a more than 5-fold variation in ellipsoid body 2D area across the eye (Figure S1G), any such focusing effect would further boost UV-detection capacity of the SZ. We next asked how these anatomical differences might be reflected at the level of UV-light responses across the in vivo eye.

12To measure UV cone light responses in vivo, we expressed the synaptically tagged fluorescent calcium biosensor SyGCaMP6f in all UV cone pedicles. We co-expressed mCherry under the same opn1sw1 promotor without synaptic tagging to reveal each cone’s full morphology and to confirm that SyGCaMP6f expression was restricted to the pedicles (Figure 3A). 7–8 dpf larvae were imaged under two photon at 64 × 16 pixel resolution (62.5 Hz), capturing one to five UV cone pedicles at a time. This allowed imaging light-driven cone-pedicle calcium in any part of the in vivo eye.

13We first presented light and dark flashes from a constant UV background (STAR Methods). Prey-capture behavior can be initiated by the presentation of a bright spot as small as 2°, moving at a speed of 90°/s. Such a moving stimulus activates a single UV cone for at most 30 ms if perfectly centered. At times, paramecia will however move somewhat slower (cf. Videos S1 and S2), meaning that also slightly longer stimulus durations are meaningful for prey detection. Accordingly, we presented light and dark flashes at varying durations. In an example recording, we observed that a SZ UV cone indeed responded to 20-ms and 50-ms UV-light flashes, while a dorsal cone failed to exhibit a detectable response (Figure 3B; Video S3). However, compared to the SZ UV cone, the dorsal UV cone responded much more strongly to a 200-ms dark flash.

14Across multiple such recordings, SZ cones consistently responded strongly to light flashes (Figures 3C–3F), including to the 20-ms condition (Figures 3C and 3E), suggesting that SZ UV cones are indeed well suited to detect the presence of UV-bright prey. In contrast, dorsal and nasal UV cones were dark biased (Figures 3D and 3F), as would be useful to signal the presence of a UV-dark predator.

15Next, we tested if variations in UV cone-outer segment lengths (cf. Figure 2G) could be linked to corresponding differences in the amplitudes of UV-light-evoked synaptic calcium signals. For this, we presented varying amplitude light flashes from darkness (Figure 3G). This confirmed that both SZ and nasal UV cones, which had the longest outer segments, also exhibited the largest synaptic calcium signals. To quantify these differences, we fitted a Hill function to each region’s stimulus-response curve (Figure 3H). We then determined the half-maximum response amplitude of ventral cones, which exhibited the smallest responses, and used this number to determine stimulus amplitudes that evoked equivalent-amplitude responses in the other UV cones (Figure 3H). Under this criterion, dorsal UV cones were ∼10-fold more responsive compared to ventral cones, followed by SZ UV cones (∼20-fold) and finally nasal UV cones (∼40 fold). Though qualitatively in line with anatomy, these effective gain changes at the level of synaptic calcium were generally larger than predicted and moreover did not directly scale with the relative distributions of outer segment lengths across the eye. This suggested that additional mechanisms may be at work. To test to what extent outer retinal inhibition may play a key role in defining the gain of UV cone synapses, we next pharmacologically blocked horizontal cells (HCs) using cyanquixaline (CNQX; STAR Methods). However, this circuit manipulation had no effect on the relative order of UV cone-response curves across zones and overall only resulted in minor amplitude variations (Figure 3J). Nevertheless, SZ and nasal UV cones now exhibited more similar response amplitudes, in line with their similar outer segment lengths. Remaining differences between the experimentally determined sensitivity of synaptic calcium responses (Figure 3J) and predictions from outer segment anatomy (Figure 2G) may be linked to a combination of non-linearities in the calcium biosensor, possible differences in synaptic calcium handling, and/or variations in phototransduction (see below).

16Next, also calcium kinetics varied between UV cones. Specifically, SZ UV cones were particularly slow to recover back to baseline following a light flash (Figures 4A and 4B). This prolonged response might aid temporal signal integration across multiple SZ UV cones by postsynaptic circuits as the image of prey traverses the photoreceptor array. In contrast, recovery from dark-flash responses was either similar or even slightly faster compared to the rest of the eye (Figures 4C and 4D). To explore possible mechanisms underlying the slow recovery kinetics of SZ UV cones, we again blocked HCs. This revealed that unlike for UV cone amplitudes (cf. Figure 3J), UV cone kinetics were markedly affected by this manipulation and in a region-specific manner (Figures 4E and 4F). Without feedback from HCs, the recovery kinetics of SZ UV cones was markedly sped up, while other UV cones were not significantly affected.

17Combining our data from the UV cone distributions and in vivo response properties, we set up a simple linear model to estimate how different types of UV stimuli can be detected by the larval zebrafish’s monocular UV-detector array (STAR Methods). For this, we first recorded the position of every UV cone in a single eye and projected their 0.76° receptive fields into visual space (Figure 5A; cf. Figures 2A, 2C, S1A, and S1C). We next computed a series of random-walk stimulus paths across this array by an assumed bright 2° target moving at an average speed of 100°/s and with approximately naturalistic turning behavior. This simulation confirmed our previous calculation that a single such target almost never (<0.1% of the time) covers two UV cones at a time (Figure 5B). In fact, most of the time (>60%), it covers zero UV cones, as it slips through gaps in the detector array. Even when adding all non-UV cones (STAR Methods), the maximal number of cones of any type covered at a time was three, with a single cone being the most likely incidence (∼40%; Figure 5B, bottom).

18We then assigned response amplitudes and decay time constants for both light and dark flashes based on our calcium imaging results to each UV cone receptive field based on their position in the eye (Figure 5C, cf. Figure 3D; STAR Methods). For this, we also computed how an identically moving but larger (5°) dark target, meant to mimic a small or distant predator, activated UV cones. In the model responses shown, single example cones from different parts of the retina responded sparsely to either object as it traversed their receptive fields. The model clearly predicted that the light object would be most detectable in the SZ (Figure 5D; Video S4). Adding even small amounts of noise would rapidly make all but SZ-based UV-light detection of this nature almost impossible. Any detectability difference would be further enhanced by a population of postsynaptic bipolar cells (BCs), here modeled to simply sum the signals from all UV cones within a fixed radius. By integrating across more than one UV cone, BCs also capitalize on the slower light recovery times of SZ UV cones (Figure 5E; cf. Figures 4A and 4B; STAR Methods). In contrast, the large dark object moving along the same path was detectable across the entire array (Figure 5F). Here, the somewhat larger response amplitudes of dorsal UV cones were approximately compensated for by the relatively greater number of UV cones in the ventral half of the retina. This yielded an approximately homogeneous dark response at the level of BCs across the entire visual field (Figure 5G).

19Taken together, the combination of differences in UV cone density (Figure 2A), outer segment size (Figures 2E–2G), and in vivo response properties at the level of presynaptic calcium driving release (Figures 3 and 4) therefore strongly suggests that detection of paramecia using the UV-detector array will be strongly and specifically facilitated in the SZ and perhaps all but impossible in most other parts of the visual field.

20We next explored the mechanisms underlying the dramatic shift in response preference toward light stimuli by SZ UV cones. For this, we returned to in vivo recordings of light-driven calcium across the eye.

21To simultaneously record from all ∼120 UV cone pedicles in the sagittal plane at single-synapse resolution, we turned to higher-spatial-resolution scans of the full eye (STAR Methods). In this configuration, the basal brightness of the SyGCaMP6f signal under a constant UV background was consistently elevated in the SZ (Figure 6A). This brightness gradient was not related to differential SyGCaMP6f expression levels. When the same animal was fixed following live imaging and stained against the GFP fraction of SyGCaMP6f, the regional brightness differences were gone (Figure 6B). This suggests that the SyGCaMP6f signal elevations in the live eye were linked to constitutive variations in UV cone pedicle calcium baseline (Figure 6C). We therefore further explored how calcium baseline varies between UV cones and how this in turn might affect their ability to encode light and dark stimuli.

22To explore this idea, we presented a simple step stimulus with UV light varying from 0% to 100% contrast around a mean background of 50% contrast (Figure 6D; Video S5). On every other repetition, this UV stimulus was superimposed on a naturalistic red-green-blue (RGB) background based on previous measurements of the spectrum of light in the zebrafish natural habitat (STAR Methods). Finally, at the end of n = 5 complete cycles, we presented a single, very bright UV-light flash to drive calcium to its light-evoked minimum. From here, we computed each UV cone’s full dynamic range as the SyGCaMP6f-signal difference between the periods when all lights are off (maximal calcium) and when all lights are on (minimal calcium). Relative to this full dynamic range, we then computed each cone’s baseline during naturalistic stimulation when UV light was held at 50% contrast. The resultant estimate of the calcium baseline across the eye recapitulated the previously observed brightness differences in the unstimulated eye. Signal baseline was maximal in the SZ, followed by a second, shallower peak around the nasal horizon (Figure 6E; cf. Figure 6C).

23Next, we specifically compared response amplitudes to the 0% and 100% UV-contrast flashes during naturalistic background illumination in different zones. Like calcium baselines, this clearly showed that light and dark responses on average were most balanced in the SZ, followed by the nasal horizon, while both dorsal and ventral UV cones were strongly dark biased (Figure 6F).

24To quantify this light-dark preference behavior, we calculated a dark-light index (DLi) from each cone (Figure 6G; see STAR Methods), where a DLi of −1 indicates that a cone exclusively responds to the dark step, while a DLi of 1 corresponds to a fully light-biased response. A DLi of 0 denotes equal responsiveness to dark and light steps. This revealed that DLi varied with eye position, with the most balanced responses observed in the SZ and near the nasal horizon, recapitulating the previously observed gradual variations in calcium baseline (Figure 6G; cf. Figures 6C and 6E) and response properties (cf. Figure 3F). In contrast, ventral and dorsal regions had a consistently negative DLi.

25When compared directly, calcium baseline and DLi were strongly correlated (ρ = 0.85): a higher calcium baseline predicted a higher DLi (Figures S2A–S2D). UV cones from different eye regions simply occupied different ranges of what appeared to be one continuum linking DLi and baseline. Taken together, our whole-eye imaging data therefore strongly suggest that systematic variations in calcium baseline are closely linked to a UV cone’s preference for light or dark contrasts.

26Differences in calcium baseline across UV cones might be driven by differences in cone-intrinsic properties or differential interactions with HCs. In the latter case, possible variations in the strength of a tonic inhibitory input from HCs (cf. Figures 3H, 3J, 4E, and 4F) might drive variations in cone baseline and thus DLi. If this were the case, then blockage of HC feedback should specifically elevate the low DLi of the dorsal and ventral retina. However, if anything, the opposite was observed. Pharmacological blockage of HCs did not elevate dorsal or ventral DLi, but instead slightly elevated DLi near the SZ and decreased it at the nasal horizon (Figures S2E and S2F; STAR Methods). Accordingly, unlike for response kinetics (cf. Figures 4E and 4F), it is unlikely that HCs strongly contribute to the observed shift in DLi among UV cones. Instead, intrinsic differences in the properties of each UV cone are likely dominant. What are these differences?

27To pinpoint intrinsic differences between UV cones that might underlie the observed regional differences among UV cone functions, we used a transcriptomics approach. For this, we dissected entire retinas expressing GFP in all UV cones and surgically separated the SZ from the remainder of the retina (non-SZ). We then dissociated and FACS-sorted UV cones for subsequent transcriptomic profiling (Figure 7A; STAR Methods). Genes involved in phototransduction dominated the transcriptome of both SZ and non-SZ batches, with UV-opsin being the most strongly expressed protein-coding gene (Figures 7B and 7C). Phototransduction genes were generally more highly expressed in SZ batches (Figure 7D), consistent with their larger outer segment sizes (cf. Figure 2). Accordingly, to compare the relative expression of key phototransduction genes, we normalized the expression level of each gene by the respective UV opsin expression level in each sample (Figure 7E). This revealed that some key phototransduction genes had relatively higher expression in the SZ (e.g., gc3), while others were downregulated (e.g., cnga3 or gngt2b). Building on our exquisite understanding on phototransduction in general, each of these regulatory changes can be linked to a specific functional effect.

28To quantitatively explore how the sum of all relative gene expression changes might affect the interplay of activators and repressors of the phototransduction cascade, we used a computational model of phototransduction in ciliary photoreceptors (,; Figure 7F). We kept all preset parameters of the model constant and only adjusted the relative levels of phototransduction elements according to the observed expression differences between SZ and non-SZ batches. In this way, we tested if we could turn a non-SZ cone (default model) into a SZ cone through specific regulatory manipulations. Indeed, altering only the top four most differentially expressed targets (transducin, GC3, recoverin 2 [Rec2], and CNG) phenomenologically reproduced the elevation in constitutive baseline and corresponding increase in the amplitude of the light response in SZ cones (Figure 7G). The modulation of single gene products’ relative expression levels could have major effects, most notably in the case of transducin and GC3 and to a lesser extent also for Rec2 (,; Figure 7H). Interestingly, transducin expression is also systematically adjusted between peripheral and foveal L/M cones in the primate, indicating that this might constitute a regulatory hotspot for tuning cone function.

29We next used the same phototransduction model to also explore any possible effects of molecular tuning on the kinetics (Figures 7I and 7J) and gains (Figures 7K–7M) of UV cone responses. Here, our phototransduction model predicted slightly faster recovery kinetics of SZ compared to non-SZ batches (Figure 7J; cf. Figure 7I). Finally, our phototransduction model also predicted a ∼3-fold gain change in SZ UV cones compared to the remainder of the eye (Figures 7K and 7M), which qualitatively captured a small gain increase when comparing calcium-imaging data from SZ UV cones to the mean of gains across nasal, ventral, and dorsal UV cones (Figure 7L). This tentatively suggests that phototransduction tuning might be one additional factor that ultimately leads to the substantial UV cone gain differences observed across at the level of presynaptic calcium (Figures 3G and 3H).

30Taken together, our transcriptomics data and phototransduction model therefore strongly suggest that diverse aspects of the eye-wide functional heterogeneity among UV cones (cf. Figures 3, 4, and 6) can be linked to differential regulation of phototransduction (Figure 7).

31We next asked if and how the observed variations in UV cone synaptic calcium are translated into rates of light-driven synaptic vesicle release in the live eye.

32To address this question, we established optical glutamate recordings from single cones in the live eye by expressing the fluorescent glutamate biosensor SFiGluSnFR in postsynaptic HCs. HCs contact cones at specialized invaginations that provide a partial diffusion barrier against the extracellular matrix, meaning that their dendrites can act as specific and spatially restricted glutamate antennas. As a population, HCs contact all four types of cones in the zebrafish eye, meaning that only a subset of HC dendritic signals correspond to synaptic release from UV cones. To identify these contacts, we co-expressed mCherry in UV cones (Figures 8A–8C). In an example recording from the nasal retina, we presented a 12.8-Hz tetrachromatic binary noise stimulus (; STAR Methods) and recorded the glutamate signals from the HC dendrites that innervate a row of neighboring cones (Figure 8D; Video S6). Among eight example regions of interest (ROIs), each covering a presumed single cone’s output site, two were identified as UV cones based on mCherry co-expression (ROIs 3 and 7). Across glutamate responses within all eight ROIs (Figure 8E), example sections of traces extracted for the two UV cones were very similar to each other but distinct from all other traces (Figures 8E and 8F). Moreover, reverse correlation of each ROI’s response to the noise stimulus revealed a pronounced UV component for the two UV cones but diverse non-UV components in all other sites (Figure 8G). This strongly indicated that there was no glutamate spillover between neighboring ROIs (see also discussions in,, ). Our approach therefore allowed recording UV cone-driven glutamate in the live eye at single-pedicle resolution. We next used this approach to compare UV cone calcium-to-glutamate transfer functions in different parts of the eye.

33In nature, photoreceptors are constantly exposed to a rapidly changing stream of light- and dark-events as animals explore their visual environment. To explore how UV cones in different parts of the eye differentially encode complex light-dark sequences, we recorded calcium and glutamate responses to the tetrachromatic binary noise-stimulus. Superimposition of the average calcium (top) and glutamate (bottom) responses to this stimulus from SZ and dorsal UV cones revealed a marked difference in the synaptic transfer between these zones (Figure 8H; Video S7): Despite relatively similar responses at the level of calcium (top), only SZ UV cones responded strongly to the most rapid of stimulus reversals (bottom, arrowheads). These differences, which could not be explained by differences in the kinetics of GCaMP6f and SFiGluSnFR (,; Figure S3A), were subtly visible at the level of calcium, but they were strongly accentuated at the level of glutamate. Qualitatively similar effects were observed across all four zones (Figures S3B–S3E).

34To determine how these differences can be linked to the amount of information which can be linearly decoded from the responses in each case, we computed the information rate based on the signal-to-noise ratio (SNR) in the Fourier domain (Figures 8I and 8J; ). Both at the level of calcium and glutamate, the most linearly decodable information was found in SZ cones, followed by nasal, dorsal, and finally ventral UV cones. Moreover, the more pronounced glutamate responses of the SZ to rapid stimulus changes led to a higher SNR, especially in the higher frequency domain. Accordingly, our glutamate imaging experiments suggest that the differential tuning of UV cones at the level of anatomy (Figure 2), phototransduction (Figure 7), inputs from surrounding inhibitory networks (Figures 3 and 4), and synaptic calcium (Figures 3, 4, 5, and 6) is preserved and possibly even enhanced at the level of synaptic release (Figure 8).

35We have shown that larval zebrafish may use single UV cones at a time to detect the UV-bright microorganisms they feed on (Figures 1 and 5). For this, UV cones in the retina’s SZ are particularly dense and exhibit grossly enlarged outer segments (Figures 2 and S1) to boost local UV-photon detection probability. This is complemented by an elevation in these UV cones’ synaptic calcium baseline (Figures 3 and 6) that likely stems from molecular retuning of the phototransduction machinery (Figure 7). In addition, HCs selectively slow down SZ UV cone recovery kinetics following a flash of light (Figure 4). Together, this leads to an increased dynamic range for encoding UV-bright events (Figure 3) and sets of the capacity for increased information transfer across the synapse at the level of vesicle release driving retinal circuits (Figure 8). UV cones in the SZ are therefore exquisitely tuned to support the visual detection of prey. In contrast, the remainder of the UV-detector array is less dense and uses smaller outer segments and a lower calcium baseline to detect large UV-dark objects, such as predators. In doing so, non-SZ UV cones signal more sparsely and presumably conserve energy.

36For all we know, all sighted vertebrates have at least a mild form of an area temporalis or area centralis, and in some species, such as many primates as well as birds of prey and species of reptiles and fish, these specialized regions have further evolved into a fovea. However, data on the possibility of regional tuning of photoreceptor function across most of these species remain outstanding with the notable exception of primates, mice, and now zebrafish. In each of these latter three, cone function has been found to be regionally tuned.

37In many ways, both the “purpose” of functional tuning of SZ UV cones and the underlying cellular and molecular mechanisms are reminiscent of differences between peripheral and foveal cones of the primate retina. For example, in both zebrafish SZ UV cones and primate foveal cones, outer segments are elongated and light-response kinetics are slowed. In the primate fovea, expression of rod-transducin gamma subunit has been discussed as one determinant of the slowed kinetics, which conceptually links with our finding of reduced levels of cone-transducin gamma subunit in zebrafish SZ cones. In each case, these structural and functional alterations can be linked to an increased capacity for the detection of low numbers of photons and subsequent signal processing. In the primate fovea, they are critical to keep noise at bay to supply a low-convergence postsynaptic retinal network. Establishing to what extent the postsynaptic networks in the zebrafish’s SZ resemble those of the primate fovea will be an important area of research in the future. Nevertheless, already now it seems clear that noise reduction will be an asset also for SZ UV cones. In contrast to primates and zebrafish, mice have only a very mild area centralis aligned with visual space above the nose. However, they feature a pronounced opsin expression gradient across the retina’s dorsal-ventral axis, which has been linked to differential processing of light and dark contrasts, much in line with observed differences in zebrafish UV cones. However, unlike in zebrafish, ventral short-wavelength vision in mice is dark biased, which rather hints at the flexibility in how photoreceptors can be tuned to support specific visual tasks.

38For the most part, the detailed cellular and molecular mechanisms that lead to differential cone-tuning across the retinal surface in mice and primates remain to be established. Building on our work, we anticipate that the possibility to perform high-throughput in vivo experiments in genetically modified larval zebrafish will be a major asset for studying mechanisms of photoreceptor tuning in general.

39In the intact eye, photoreceptors rarely signal in isolation. Instead, in both invertebrates and vertebrates, they tend to be intricately interconnected with neighboring photoreceptors and/or local feedback circuits. Locally adjusting how surrounding circuits interact with individual photoreceptors therefore presents another potential mechanism for regional tuning. Here, we have shown in larval zebrafish, HCs differentially interact with UV cones in different parts of the eye (Figures 3G–3J, 4E, 4F, S2E, and S2F). In general, blocking HC circuits had little or no effect on UV cone functions in the dorsal and ventral retina (and generally only weak effects in the nasal retina), while in SZ UV cones, both recovery kinetics and response amplitudes were markedly modulated by HCs. In line, previous imaging work on mouse cones reported a general speeding up of cones in the absence of HCs. However, conversely, electrophysiological recordings from goldfish and primate cones reported slowed responses in the absence of HC feedback. Notably, beside HCs, the zebrafish outer retina is also innervated by interplexiform cells, which may play an additional role in shaping UV cone functions. To what extent regional effects of outer retinal decoupling in zebrafish generalize across other cone types or the dendrites of BCs, and if they can be linked to a putative difference in the functional distribution of HC circuits, will be important to assess in the future.

40Next, if and how other cone photoreceptors may regionally interact with UV cones remains an open question. However, it seems unlikely that interactions with rod photoreceptors contribute strongly to UV cone tuning. First, at 7 dpf, zebrafish rod photoreceptors remain restricted to the dorsal and ventral poles of the eye, precisely opposite to the distribution of HC influences on UV cones. Second, at this age, rods are thought to be immature. Third, across vertebrates, including in adult zebrafish, rod functions tend to be more closely interlinked with the circuits and functions of red and green cones.

41Beyond altering the morphological and biochemical properties of the outer segment, our results further suggest that the pedicle is functionally adjusted to support distinct modes of calcium-dependent vesicle release in UV cones in different parts of the eye. Cones use ribbon-type synapses, which have been a key focus for investigating the functional tuning of neural circuits. For example, electrosensory ribbon synapses in rays and sharks are differentially tuned at the level of both synaptic ion channels and ribbon morphology to support the encoding distinct signal frequency bands required by these two groups of animals. Indeed, ribbon synapses across species and modalities support a vast range of functional properties, and generally, the structure and function of each group of synapses can be closely linked to specific signaling requirements. While therefore ribbon synapses do strongly vary across distinct sets of neurons that support diverse functional tasks, to our knowledge, this type of tuning has not been studied across a single neuron type. Accordingly, in the future it will be important to establish if and how the observed differences in synaptic transfer functions across zebrafish UV cones can be linked to structural and molecular differences in the synapse itself.

42As discussed, the image of UV-bright prey falling onto the back of the retina when zebrafish first detect it barely covers the size of a single UV cone’s outer segment. Yet, behaviorally, zebrafish only respond if the prey moves, which ought to require at least two receptors to decode the angled space-time trajectory in the stimulus. This raises two key questions. How can the bright signal from a single UV cone be reliably propagated to postsynaptic circuits, and how can its motion be encoded?

43Vertebrate photoreceptors are “off” cells, meaning that transmitting brief on events through their positively rectifying synapse can be challenging. Instead, cone ribbon synapses are particularly good at signaling sudden off events by way of a transient burst of near-instantaneous vesicle release following a period of refilling during the preceding on event. Perhaps counterintuitively, rather than attenuating this ribbon rebound, the slowed calcium light-recovery kinetics in SZ UV cones (Figures 4A–4D) might in fact serve to help to reduce calcium levels during the on event so as to facilitate refilling of the ribbon in this time. Once the prey target moves out of the UV cone’s receptive field, calcium can then rush back in and trigger a transient burst of vesicle release as an off event. In support, our glutamate imaging experiments (Figures 8 and S3) show that UV SZ cones are particularly good at encoding the transition from on to off events (e.g., Figure 8H). From here, it is tempting to speculate that the same mechanism might also serve to detect a nondirectional form of motion that does not categorically require a second photoreceptor. Instead, perhaps it is enough for the target to simply enter a UV cone’s receptive field and then to leave again, thus generating a transient and positive glutamate signal for postsynaptic circuits to process. Such a process could potentially even explain the velocity dependence of prey-capture behavior; a too-fast target may not suffice to cause a sufficient drop in calcium, while a too-slow target would likely hamper the transience of the off signal, as release from on suppression may be too gradual. Similarly, it might go partway to explaining why prey-capture behavior can be elicited by darker than background targets. In the future, it will be important to explore if and how this “rebound trick” is meaningfully used to drive inner retinal prey detection and circuits. For example, flashing a suitably sized and positioned stationary UV-bright spot on and off should in this case suffice to trigger prey-orientation behavior.

44Larval zebrafish prey-capture behavior has been extensively studied in the lab, though never specifically under UV. Here, our behavioral experiments (Figure 1H) strongly suggest that it is the UV cones, rather than UV light per se, that provide the dominant inputs to larval zebrafish prey-capture circuits; even though the spectrum of our UV LED overlapped with the alpha band of blue opsin as well as the beta bands of red and green opsins, UV illumination in the absence of UV cones did not rescue the behavioral phenotype. This also rules out a major contribution of a possible chromophore shift from A1 to A2 in any-type cones, since unlike for long-wavelength opsins, UV-opsin action spectra for A1 and A2 are virtually identical.

45Nevertheless, even under low-UV conditions or in the absence of UV cones, zebrafish continue to display some prey-capture behavior. This suggests that non-UV cones can, if required, feed into prey-capture circuits, perhaps to boost signal power in the absence of systematic background clutter, as is also the case under typical lab conditions. In support, the strong UV dominance in SZ BCs and RGCs is complemented by smaller signals elicited also at other wavelengths. In parallel, it is important to consider the specific absorption spectrum of the zebrafish UV opsin relative to the spectrum of any illuminating light. For safety reasons, commercially available thin-film transistor (TFT) monitors, projectors, and organic LED (OLED) screens used in behavioral experiments tend to restrict short wavelengths to <1% signal power below 420 nm. In contrast, zebrafish UV-opsin absorption peaks at 365 nm, meaning that the short-wavelength signal of most of these light sources will activate the UV opsin with <1% efficiency. Nevertheless, owing to the extremely high photon-catch efficiency of SZ UV cones, this might still generate a useful signal, provided the screen is sufficiently bright. Moreover, different projection setups or live paramecia illuminated by indoor lighting or indeed a fluorescence microscope’s excitation light might afford higher spectral overlap. In the future, it will therefore be critical to establish in more detail how the addition of UV light affects behavioral performance.

46The region-specific differences in UV cone function present the first pre-processing steps to detect prey and predators already at the visual system’s first synapse. However, how these signals are used by retinal and brain networks for robust extraction of such behaviorally crucial information remains an open question. Ultimately, light patterns picked up by distinct regions of the UV detector array must lead to the differential activation of brain circuits that control distinct behavioral programs. For this, the signal must first travel to the feature extracting circuits of the inner retina via the diverse set of retinal BCs. Previous work highlighted a strong dominance of inner retinal UV-on circuits specifically in the SZ, suggesting that the signal from SZ UV cones is indeed selectively picked up by a subset of local UV-on BCs for further processing. Next, the UV signal must be selectively sent to the specific relevant processing centers of the brain. Here, recent work showed that also RGCs, whose axons form the optic nerve, are regionally tuned for prey capture in the SZ. Like BCs, the vast majority of RGCs in the SZ are UV-on circuits, in line with the boost of lighter-than-background signals in SZ UV cones (Figures 3 and 6). Moreover, their UV-signals are markedly slowed compared to the remainder of the eye, tentatively suggesting that also kinetic differences first set up in UV cones (Figure 4) reliably propagate through the retinal network. Finally, pretectal arborization field 7 (AF7), which underpins prey-capture behavior, is mainly innervated by temporal, but not nasal, RGCs, strongly hinting that AF7 may be predominately driven by SZ circuits. Clearly, circuits for prey capture in larval zebrafish are both anatomically and functionally regionalized to drive a regionally biased behavioral repertoire. To what extent this can be supported through regional tuning of neuron types alone, as in case of UV cones, or in addition requires the specific positioning of unique neuron types in different parts of the eye and brain will be important to address in the future. Indeed, transcriptomic analysis recently highlighted the putative presence of one “extra” BC type specifically in the primate fovea, with yet-unknown morphology and function.

47Further information and requests for resources and reagents should be directed to and will be fulfilled by the Lead Contact, Tom Baden (t.baden@sussex.ac.uk).

48Plasmids pTo2pA-opn1sw1-SyGCaMP6f-pA, pBH-tUAS-SFiGluSnFR-pA, pME-SyGCaMP6f, pME-SFiGluSnFR, and transgenic lines Tg(opn1sw1:GFP:SyGCaMP6f), Tg(cx55.5:nlsTrpR), and Tg(tUAS:SFiGluSnFR) lines, generated in this study, are available upon request to the lead contact.

49Pre-processed functional 2-photon imaging data, cone-density counts, natural imaging data, transcriptome data, and associated summary statistics will be made freely available from DataDryad via the relevant links on https://datadryad.org/stash/landing/show?id=doi%3A10.5061%2Fdryad.w0vt4b8n3 and at https://www.badenlab.org/resources and http:/retinal-functomics.net/. Any remaining data will be provided upon reasonable request to the Lead Contact.

50All procedures were performed in accordance with the UK Animals (Scientific Procedures) act 1986 and approved by the animal welfare committee of the University of Sussex. For all experiments, we used 6-8 days post fertilization (dpf) zebrafish (Danio rerio) larvae. The following previously published transgenic lines were used: Tg(opn1sw1:nfsBmCherry), Tg(opn1sw1:GFP). In addition, Tg(opn1sw1:GFP:SyGCaMP6f), Tg(cx55.5:nlsTrpR), and Tg(tUAS:SFiGluSnFR) lines were generated by injecting pBH-opn1sw1-SyGCaMP6f-pA, pBH-cx55.5-nlsTrpR-pA, or pBH-tUAS-SFiGluSnFR-pA plasmids into single-cell stage eggs. Injected fish were out-crossed with wild-type fish to screen for founders. Positive progenies were raised to establish transgenic lines.

51All plasmids were made using the Gateway system (ThermoFisher, 12538120) with combinations of entry and destination plasmids as follows: pBH-opn1sw1-SyGCaMP6f-pA: pBH and p5E-opn1sw1, pME-SyGCaMP6f, p3E-pA; pBH-tUAS-SFiGluSnFR-pA: pBH, p5E-tUAS, pME-SFiGluSnFR, p3E-pA. Plasmid pME-SyGCaMP6f was generated by inserting a polymerase chain reaction (PCR)-amplified GCaMP6f into pME plasmid and subsequently inserting a PCR amplified zebrafish synaptophysin without stop codon at the 5′ end of GCaMP6f. pME-SFiGluSnFR was made by inserting a PCR amplified SFiGluSnFR fragment in pME plasmid.

52Animals were housed under a standard 14:10 day/night rhythm and fed three times a day. Animals were grown in 0.1 mM 1-phenyl-2-thiourea (Sigma, P7629) from 1 dpf to prevent melanogenesis. For 2-photon in-vivo imaging, zebrafish larvae were immobilised in 2% low melting point agarose (Fisher Scientific, BP1360-100), placed on a glass coverslip and submerged in fish water. Eye movements were prevented by injection of a-bungarotoxin (1 nL of 2 mg/ml; Tocris, Cat: 2133) into the ocular muscles behind the eye. For some experiments, CNQX (∼0.5 pl, 2 mM, Tocris, Cat: 1045) in artificial cerebro-spinal fluid (aCSF) was injected into the eye.

53Paramecium caudatum (Sciento, P320) were placed in a container filled with fish water and pebbles, to approximately mimic a zebrafish natural habitat. Images were taken outdoors under the sun (typical sunny day in UK, Brighton in May, no cloud at around 1 pm) with a CCD camera (Thorlabs DCU223M) fitted with a lens (Thorlabs ACL1815L), a constitutive glass filter (Thorlabs FGB37) as well as switchable glass filters (UV: FGUV11-UV, Yellow: FGV9; both Thorlabs) on a filter-wheel. Videos were acquired at 10 Hz, with single frame exposure times of 1 and 70 ms for yellow and UV, respectively. The focal distance of the camera was ∼2.5 cm, and it was positioned against the wall of the tank from the outside. The effective recording spectra were computed by multiplying the spectral sensitivity of the camera chip itself with all optical components in the path.

54Individual 7-8 dpf zebrafish larva were head-mounted in 2% low-melting-point agarose (Fisher Scientific, BP1360-100) in a 35 mm Petri dish with the eyes and tail free to move and filmed under infrared illumination (940 nm) using a Raspberry Pi camera at 30 Hz based on a previous design. An Arduino-microcontroller was used to iteratively switch top-illumination of the dish between UV (374 ± 15 nm) or yellow (507 ± 10 nm) LED light in periods of 1 minute. The peak power of both LEDs was set to be equal at 0.12 W m-2. The same fish was filmed continuously for three such cycles (total of 12 minutes per n = 12 fish wild-type and another n = 12 fish with UV cones ablated), and behavioral performance was manually annotated offline as either a “full prey capture bout” (eye convergence plus tail movement) or “tracking” (single or bilateral eye movements in the absence of tail movements). To ablate UV-cones, Tg(opn1sw1:nfsBmCherry) larval zebrafish were treated with 10 mM Metronidazole (Sigma, M3761) for 2 hours and thereafter transferred to fresh fish water without Metronidazole. Behavioral assays were performed one day after the Metronidazole treatment to ensure that UV-cone ablation was complete.

55The UV-cone distribution across the eye was first established from confocal image stacks of Tg(opn1sw1:GFP) eyes from 7 dpf larvae where all UV-cones are labeled. Fish were mounted with one eye facing the objective lens. As in previous work the locations of all UV-cones in the 3D eye were detected using a custom script in Igor Pro 6.3 (Wavemetrics). To project the resultant UV-cone distribution into visual space, we first measured the eye size as being 300 μm on average. In addition, we determined that both the eyeball and the lens follow a nearly perfect spherical curvature with a common point of origin. From this, we assumed that any given UV-cone collects light from a point in the space that aligns with a straight line connecting the UV-cone to the outside world through the center of the lens. From here, we mapped UV-cone receptive field locations across the full monocular visual field.

56Larval zebrafish (7-8 dpf) were euthanised by tricane overdose and then fixed in 4% paraformaldehyde (PFA, Agar Scientific, AGR1026) in PBS for 30 min at room temperature. After three washes in PBS, whole eyes were enucleated and the cornea was removed by hand using the tip of a 30 G needle. Dissected and fixed samples were treated with PBS containing 0.5% Triton X-100 (Sigma, X100) for at least 10 mins and up to 1 day, followed by the addition of primary antibodies. After 3-5 days incubation at 4°C, samples were washed three times with PBS 0.5% Triton X-100 solution and treated with secondary antibodies and/or BODIPY (Invitrogen, C34556) dye. After one day incubation, samples were mounted in 1% agar in PBS on a coverslip and subsequently PBS was replaced with mounting media (VectaShield, H-1000) for imaging. Primary antibodies used were anti-GFP (abcom, chicken, ab13970) and anti-CoxIV (abcom, rabbit, ab209727). Secondary antibodies were Donkey CF488A dye anti-chick (Sigma, SAB4600031) and Goat Alexa647 dye anti-rabbit (ThermoFisher, A-21244). Confocal image stacks were taken on a TSC SP8 (Leica) with 40x water immersion objective (C PL APO CS2, Leica), a 63x oil immersion objective (HC PL APO CS2, Leica) or a 20x dry objective (HC PL APO Dry CS2, Leica). Typical voxel size was 150 nm and 1 μm in xy and z, respectively. Contrast, brightness and pseudo-color were adjusted for display in Fiji (NIH). Quantification of outer segment lengths and anti-GFP staining intensity was performed using custom scripts in Igor Pro 6.3 (Wavemetrics) after manually marking outer segment outer and inner locations.

57All 2-photon imaging was performed on a MOM-type 2-photon microscope (designed by W. Denk, MPI, Martinsried; purchased through Sutter Instruments/Science Products) equipped with a mode-locked Ti:Sapphire laser (Chameleon Vision-S, Coherent) tuned to 927 or 960 nm for SyGCaMP6f and SFiGluSnFR imaging and 960 nm for mCherry and SFiGluSnFR double imaging. We used two fluorescence detection channels for SyGCaMP6f/iGluRSnFR (F48x573, AHF/Chroma) and mCherry (F39x628, AHF/Chroma), and a water immersion objective (W Plan-Apochromat 20x/1,0 DIC M27, Zeiss). For image acquisition, we used custom-written software (ScanM, by M. Mueller, MPI, Martinsried and T. Euler, CIN, Tuebingen) running under IGOR pro 6.3 for Windows (Wavemetrics). Recording configurations were as follows: SyGCaMP6f UV flashes Figures 3 and 4: 128x16 pixels (1 ms per line, 62.5 Hz); SyGCaMP6f whole-eye Figure 6: 512x512 pixels (2 ms per line, 0.97 Hz), SFiGluSnFR noise recording Figures 8D–8G: 128x32 pixels (1 ms per line, 31.25 Hz), SFiGluSnFR and SyGCaMP6f noise recordings Figure 8H: 64x4 pixels (2 ms per line, 125 Hz). Light stimulation was setup-up as described previously. In brief, light stimuli were delivered through the objective, by band-pass filtered light emitting diodes (LEDs) (‘red’ 588 nm, B5B-434-TY, 13.5cd, 20 mA; ‘green’ 477 nm, RLS-5B475-S; 3-4 cd, 20mA; ‘blue’ 415 nm, VL415-5-15; 10-16 mW, 20 mA; ‘ultraviolet, UV’ 365 nm, LED365-06Z; 5.5 mW, 20 mA, Roithner, Germany). LEDs were filtered and combined using FF01-370/36, T450/pxr, ET420/40 m, T400LP, ET480/40x, H560LPXR (AHF/Chroma) and synchronized with the scan retrace at 500 (2 ms lines) or 1,000 Hz (1 ms lines) using a microcontroller and custom scripts (available at https://github.com/BadenLab/Zebrafish-visual-space-model). The ratio of LED intensities was calibrated (in photons per s per cone) such that each LED would relatively stimulate its respective cone-type as it would be activated under natural spectrum light in the zebrafish habitat: 34, 18, 4.7 and 2.1 x105 photons per cone per s for red-, green-, blue-, and UV-cones, respectively. We used these “natural spectrum” LED intensities as a background light and modulated contrasts depends on experiments. LED contrasts were 0% for dark and 2,500% for bright flashes (Figures 3B–3F), 0% background and 2,500% flash (Figures 3G and 3H), 2,500% background and 0% dark flash (Figures 4A and 4C), 0% dark and 200% bright (Figure 6). For tetrachromatic noise (Figures 7 and 8), each of 4 LEDs was simultaneously but independently presented at 100% contrast in a known sequence at 12.8 Hz. Short 5 ms UV flashes with intensities spanning from 67 to 104 photons/cone were delivered to measure UV-cone sensitivities (Figures 3I and 3L) and light-recovery kinetics (Figures 4E and 4F). For all experiments, the animal was kept at constant background illumination for at least 5 s at the beginning of each recording to allow for adaptation to the laser.

58Cone distributions were taken from published data. UV- and blue-cones were taken from the same representative eye and aligned with red- and green-cones from a second eye and projected into visual space. The full array was cropped at ± 60°. Model BCs were randomly spaced at a minimum radius of 10°. BCs summed the activity from all cones within this same fixed radius. Target trajectory was computed as a random walk on an infinite plane (canonical diffeomorphism), such as the left/right and top/bottom borders are continuous with each other. At each 1° step-size iteration (equivalent to 10 ms), the target advanced at a constant speed of 100°/s with a random change of angle (α) that satisfied −15° < α < 15°. Cone activation by the moving target was computed as follows: At each time-point, the distance between the centers of the target and each cone was determined. If this distance was smaller than the sum of the target radius (1° and 2.5° for light and dark target, respectively) and a cone’s receptive field radius (0.38°), the cone was activated to yield a binary activation sequence over time for each cone. This sequence was then convolved with the cone’s impulse response. Here, the peak amplitude and recovery time constant was assigned based on a cone’s position, drawing on the four measurement points established from calcium imaging (dorsal, nasal i.e., horizon, ventral and SZ, cf. Figure 3). Along the dorsal-ventral axis, values were chosen based on the relative distance between the horizon and the dorsal or ventral edge. For example, a cone positioned 75% toward the dorsal edge from the horizon would be assigned values weighted as 0.75:0.25:0 from dorsal, nasal and ventral measurements, respectively. In addition, if a cone was within 30° of the SZ center (−30°,-30°), it was in addition weighted based on values from the SZ in the same way. In each run, all activation values were normalized to the peak activation across the entire array.

59Whole 7 dpf Tg(opn1sw1:GFP) larval zebrafish retinas were dissected in carboxygenated aCSF (CaCl 0.1275 g/L, MgSO4 0.1488 g/L, KCl 0.231 g/L, KH2PO4 0.068 g/L, NaCl 7.01 g/L, D-Glucose 1.081 g/L, and NaHCO3 1.9 g/L) while keeping track of each retina’s orientation. Each retina was then cut into two pieces: SZ, and non-SZ. Typically tissues from ∼10 fish (20 eyes) were batched into one tube and dissociated using a papain dissociation system (Worthingtonm LK003176, LK003170, LK003182) with the following modification in the protocol: Incubation in papain for 10 min at room temperature. During dissociation, tissues were gently pipetted every 3 min to facilitate dissociation using glass pipette with rounded tip. After 10 min incubation, DNase and ovomucoid were added and the tissues were further mechanically dissociated by gentle pipetting. Dissociated cells were immediately sorted for GFP expression by FACSMelody (BD Biosciences). Approximately 100 cells were sorted in one tube, flash frozen in liquid nitrogen and stored at −80 degree until further use. Libraries were prepared using Ultra-low input RNA kit (Takara, 634888) and subjected to next generation sequencing at GENEWIZ (NZ, US). Sequencing data was quality checked and trimmed to remove adaptors using Trim Galore!([CSL STYLE ERROR: reference with no printed form.]), aligned on the zebrafish genome (GRCz11.9) in HISAT2, and counted for gene expression in featureCounts using the public server at the usergalaxy.org online platform. In total, four repeats each were performed for SZ and non-SZ samples.

60For the analysis of differential gene expression of the SZ versus non-SZ we used the DESeq2 package in R/Bioconductor. We only included genes which had a count of at least 5 sequence fragments in at least 2 of the 8 samples (4 SZ + 4 non-SZ). Since we wanted to measure the effect between zones, controlling for differences in the individual eyes, we included the eye as an additional latent variable (design = ∼eye+zone). The DESeq2 package then uses a generalized linear model with a logarithmic link to infer a negative-binomial distribution for gene counts. The inferred means via the poscount estimator, which calculates a modified geometric mean by taking the nth root of the product of non-zero counts, are shown in Figures 7B and 7C. The log-fold changes (Figure 7D) were then also estimated in DESeq2.

61For determining differential expression normalized by UV-opsin (Figure 7E) we instead calculated using the raw count data, normalized by the count of the UV-opsin gene. From here, mean fold changes were calculated by taking fold changes of individual SZ and non-SZ sample pairs.

62We used a previously described and verified computational model of phototransduction in vertebrate ciliary photoreceptors. We simulated the photo-response to 100% dark or 100% bright contrasts (Figures 7G and 7H) or to 5 ms flashes from dark of various intensities (Figures 7J and 7M) using default parameters provided by the model for non-SZ simulation. For simulating the SZ, we then scaled all according to the relative gene expression change between SZ and nSZ conditions. Transducin was scaled by taking the lowest value among components (gngt2b, gnb3b, gnat2) because all components are necessary for transducin function. Similarly, we scaled CNG based on the CNGa3 expression level. Parameters changed for each condition are listed in Table S4.

63Data analysis was performed using IGOR Pro 6.3 (Wavemetrics), Fiji (NIH), Python 3.5 (Anaconda distribution, scikit-learn 0.18.1, scipy 0.19.0 and pandas 0.20.1) and R 3.5.1.

64Regions of interest (ROIs), corresponding to individual presynaptic terminals of UV-cones were defined automatically based on local thresholding of the recording stack’s s.d. projection over time (s.d. typically > 25), followed by filtering for size and shape using custom written software on IGOR Pro 6.3 (Wavemetrics). Specifically, only round ROIs (< 150% elongation) of size 2-5 μm2 were further analyzed. For glutamate recording, ROIs were manually placed as the shape of HC dendritic terminals at cone terminals are often skewed. Calcium or glutamate traces for each ROI were extracted and z-normalized based on the time interval 1-6 s at the beginning of recordings prior to presentation of systematic light stimulation. A stimulus time marker embedded in the recording data served to align the traces relative to the visual stimulus with a temporal precision of 1 or 2 ms (depending on line-scan speed). The Dark-Light-index (DLi) was calculated as:where L and D are the mode of response amplitudes to UV- and dark-flash with RGB background, respectively.

65To calculate information rates, we first filtered recorded traces for quality: We calculated the linear response kernel to UV-light stimulation for each trace and took only the traces where the response amplitude of the kernel, measured as its standard deviation, was at least 70% of the kernel with maximal response amplitude of the same zone.

66We then followed the procedure as described in ref using the bias correction method for finite data. For this, we assumed that the noise between repetitions of the experiment was statistically independent. For independent Gaussian statistics, the information rate R can be computed as:Since photoreceptors are best driven by low frequency signals we chose a cut-off frequency of 12 Hz. We then calculated a bias corrected signal to noise ratio (SNR) as:where Xi is an individual trial, n is the number of trials and and are the Fourier transform of S and N, respectively. We used Welch’s method to reduce noise in the estimated power spectra.

67No statistical methods were used to predetermine sample size. P values were calculated using non-parametric Mann-Whitney U tests in Figures 1H and S1G, and using a paired t test in Figure 1G. For Figures 4B and 4D–4F, p values were calculated using ANOVA with factors treatment and area interaction, and posthoc tests with tukey correction for multiple testing. The posthoc tables are provided in Tables S1–S3 and only stars for relevant comparisons are added to the figures. Owing to the exploratory nature of our study, we did not use randomization or blinding.

68We used Generalized Additive Models (GAMs) to analyze the relationships between eye position and outer segment size, baseline, and dark-light index (Figures 2G, 6C, 6E, 6G, and S2E). GAMs can be seen as an extension to the generalized linear model by allowing linear predictors, which depend on smooth functions of the underlying variables. We used the mgcv-package (version 1.8-28) on a Windows 10 workstation (8 Xeon E3-1270 v5 3.6 GHz; 64 GB RAM) with default parameters. We modeled the dependence of the variable of interest as a smooth term with 20 degrees of freedom. In addition, we incorporated the fish id as a random effect. The models explained ∼40%–80% of the deviance. For plotting, we generated the predicted mean response with approximate 95% confidence intervals excluding fish id (this leads to a slight perceived offset between the raw data points and the mean response). Statistical significance for differences between the dependence of DLi in baseline and HC block conditions were obtained using the plot_diff function of the itsadug-package for R (version 2.3).