Distinct synaptic transfer functions in same-type photoreceptors

  1. Cornelius Schröder
  2. Jonathan Oesterle
  3. Philipp Berens
  4. Takeshi Yoshimatsu
  5. Tom Baden

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    Evaluation Summary:

    This paper compares the properties of UV cone output synapses in different regions of the zebrafish retina using a combination of electron microscopy, quantitative imaging and computational modeling. They relate these differences to ultrastructural differences in synaptic ribbons and evaluate them using a previously-developed biophysical model for the operation of the synapse. The finding of regional differences in ribbon behavior is novel and suggests an under-appreciated degree of control of release by ribbon structure and behavior. The presentation of some of the results, particularly the model, could be strengthened.

    (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #1 and Reviewer #3 agreed to share their names with the authors.)

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Abstract

Many sensory systems use ribbon-type synapses to transmit their signals to downstream circuits. The properties of this synaptic transfer fundamentally dictate which aspects in the original stimulus will be accentuated or suppressed, thereby partially defining the detection limits of the circuit. Accordingly, sensory neurons have evolved a wide variety of ribbon geometries and vesicle pool properties to best support their diverse functional requirements. However, the need for diverse synaptic functions does not only arise across neuron types, but also within . Here we show that UV-cones, a single type of photoreceptor of the larval zebrafish eye, exhibit striking differences in their synaptic ultrastructure and consequent calcium to glutamate transfer function depending on their location in the eye. We arrive at this conclusion by combining serial section electron microscopy and simultaneous ‘dual-colour’ two-photon imaging of calcium and glutamate signals from the same synapse in vivo. We further use the functional dataset to fit a cascade-like model of the ribbon synapse with different vesicle pool sizes, transfer rates, and other synaptic properties. Exploiting recent developments in simulation-based inference, we obtain full posterior estimates for the parameters and compare these across different retinal regions. The model enables us to extrapolate to new stimuli and to systematically investigate different response behaviours of various ribbon configurations. We also provide an interactive, easy-to-use version of this model as an online tool. Overall, we show that already on the synaptic level of single-neuron types there exist highly specialised mechanisms which are advantageous for the encoding of different visual features.

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  1. Version published to 10.7554/elife.67851 on eLife
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    Author Response:

    Evaluation Summary:

    This paper compares the properties of UV cone output synapses in different regions of the zebrafish retina using a combination of electron microscopy, quantitative imaging and computational modeling. They relate these differences to ultrastructural differences in synaptic ribbons and evaluate them using a previously-developed biophysical model for the operation of the synapse. The finding of regional differences in ribbon behavior is novel and suggests an under-appreciated degree of control of release by ribbon structure and behavior. The presentation of some of the results, particularly the model, could be strengthened.

    We thank the reviewers for their valuable inputs. In response, we have substantially extended and restructured the description of preprocessing steps and modelling to aid clarity. Moreover, we include new analysis of “old” GCaMP6f data to show the similarity of calcium dynamics across retinal regions. Additionally, we worked on the description of the simulation-based inference method and provided more intuitive explanations. Finally, we updated the discussion of the model results. We hope to have addressed the helpful critique of the reviewers and strengthened our conclusions and the whole manuscript.

    Reviewer #1 (Public Review):

    Preprocessing of glutamate traces. The bulk of the analysis in the paper uses "scaled and denoised" traces. It is important to verify that this process did not either introduce or obscure any differences across regions. This should include some validation of the assumptions that go into the scaling process (such as whether a sufficiently low calcium level is achieved to use that as a standard). An example of a how this concern could impact the conclusions is that the AZ glutamate traces look less rectified than the others, perhaps due to an elevated baseline, as suggested in the text. But the conclusion about the elevated baseline relies on the scaling process creating a proper alignment such that it is accurate to superimpose the traces as in Figure 3a.

    Thank you for giving us the opportunity to clarify this point. AZ UV-cones indeed have an elevated baseline, as explicitly shown in our previous publication (Yoshimatsu et al. 2020 Neuron). The scaling process recapitulates this baseline shift, as expected. In this previous work we also show how the lower rectification of AZ cones is directly linked to this baseline shift, and it includes experiments specifically designed to find the “true” minimum calcium levels achievable in UV-cones in different parts of the eye, as suggested by the reviewer.

    However, we fully agree that the scaling/denoising process could be described more clearly, and we expanded the explanation in the method section and added a figure (Fig. S3) to visualize all steps explicitly.

    Model fitting. Some key aspects of the model fitting were difficult to evaluate and follow. For example, is the loss function the same as the discrepancy defined in the methods (I assumed that is the case - if not the loss function needs to be defined)? The definition of the discrepancy could be clearer (e.g. be careful about using x here and as the offset of the calcium trace). Related, the results would benefit from a more intuitive description of the fitting, rather than just a reference to the methods (which is a bit dense to go through for that intuitive-level explanation of the model development).

    We added an overview of the simulation-based inference method to the main section of the manuscript. Additionally, we updated the definition of the loss function and tried to give more intuitive explanations. We hope that these changes will help the reader to better understand the computational methods used.

    Some statements seem too strong given the state of current knowledge. E.g. lines 79-80 I think goes too far about the functional role of the ribbon. Similarly lines 97-98 are quite explicit about the connection to prey capture. Lines 276-279 are a particularly important example; I would argue that the statement there requires showing uniqueness of the model.

    We agree that the mentioned statements were perhaps quite strong and we have toned them down in the revised manuscript.

    Could fixation of the retina for EM change the distribution of vesicles in different compartments? I realize this may not be answerable, but a caution about that possibility might be warranted.

    We are not aware of such an effect in previous works. As the reviewer notes it may not be answerable. However, in a way we have an “internal control” for such a possibility, since the different eye regions were treated equally for fixation, yet vesicle distributions differ across eye regions. It seems unlikely that the fixation would have disproportionately distorted vesicle distributions in one eye region without also affecting the others. This is now noted when first discussing the EM approach.

    Line 159: it is not clear how similar the calcium signals are. Specifically, could differences in calcium signal get amplified when passed through simple nonlinearity (e.g. due to the calcium dependence of transmitter release) to account for the differences in glutamate output? Maybe rewording here to leave open that possibility unless you have reason to reject it.

    We agree that this statement was perhaps too strong at this point of the manuscript. We softened it and included a detailed analysis of additional calcium data later to investigate the regional differences of the calcium signal (Fig. 3k-n)

    Can you quantify the fits in Figure 4f,g? For example, can you give a probability of a particular experimental trace or summary parameters for that experimental trace given the parameter probability distributions from the same area and from a different area?

    A quantification of the fits is shown in Fig. S4b,c (previously S3b,c). As we perform “likelihood-free inference”, we cannot give probabilities for the model traces, but we show two different loss functions for the model fits as well as for the linear model: the relevant loss, on which the models are optimized (which is based on the summary statistics) and for comparison the MSE to the experimental traces. We apologize if this was not clearly mentioned in the manuscript. We added it more prominently in the revised version.

    Reviewer #2 (Public Review):

    This study images synaptic calcium and glutamate release from larval zebrafish UV-sensitive cones in vivo. They also study the ultrastructure of ribbon synapses from UV cones in different regions of the retina. They find differences in ribbon dimension and light-evoked glutamate release from cones in different regions of the retina. Cones from dorsal retina show a more pronounced transient component of glutamate release than those from nasal retina. Those in the acute zone in the center of the retina showed intermediate kinetics. Ultrastructural reconstructions of UV-sensitive cones from those regions showed fewer and small ribbons in dorsal cones vs. those in the nasal region or acute zone zone. Light-evoked changes in the kinetics of synaptic calcium were not significantly different suggesting that differences in release kinetics may be related to differences in ribbon behavior in cones from different regions. To relate these different measurements to one another, the authors modified an existing model of cone release to incorporate a simulation-based Bayesian inference approach for estimating best-fit parameters. The model suggested that the differences in glutamate release kinetics could be explained by differences in the rates of transfer between vesicle pools on and off the ribbon. By fixing different parameters, the authors then used the model to explore the parameter space and general properties of ribbon tuning. They also provide a link to the model for others to use.

    The main new experimental finding is that glutamate release properties differ among cones in different regions. The finding that kinetics of glutamate release and ribbon ultrastructure vary systematically in different regions of the retina is interesting. They relate these data using a model of ribbon release. While the model is not novel in its general design, the incorporation of Bayesian inference is new. The most interesting finding from the model is that the kinetic differences in release between cones are not due to calcium kinetics but arise primarily from differences in transitions between vesicle pools. Nevertheless, using the model, the authors show that calcium levels and kinetics matter, since if they hold other parameters fixed, calcium levels and kinetics are the most important factors in shaping response detectability and response kinetics. This is consistent with a lot of earlier work that calcium kinetics are important for shaping response kinetics at ribbon synapses.

    1. The measured changes in glutamate and calcium are small and noisy and there is considerable overlap in the data from cones in different regions. While the example waveforms show considerable differences, the scatter in the data is less persuasive. If I understand correctly, the imaging data comes from 30 AZ, 16 dorsal, and 9 nasal UV cones. With such noisy data, 9 cones seems like particularly small sample. With imaging data, it should be possible to record from dozens or hundreds of cells and a larger sample would strengthen the conclusions.

    We agree that the sample size is quite small, however the dual color experiments are technically extremely challenging. This is part-related to the laser wavelength compromise that needs to be reached for concurrent excitation of red and green fluorescent probes, and the fact that red probes generally give comparatively poor SNR. Notably, to our knowledge concurrent 2P imaging of presynaptic calcium and consequent glutamate release in an in vivo scenario is quite novel, and still very much on the edge of experimental possibilities.

    The green glutamate recordings based on iGluSnFR which are particularly central to our work do have a reasonably high SNR, rather the “problem” is more obviously linked to the calcium recordings. For a better understanding of the calcium handling, we therefore now reanalysed an “old” dataset from Yoshimatsu et al., 2020, Neuron (see Fig. 3k-n) that was recorded with SyGCaMP6f, which provides much higher SNR (and is a little faster albeit also more nonlinear). Notably, the SyGCaMP6f calcium dynamics were also analysed in some detail in Yoshimatsu et al., 2020, Neuron, and we built on these conclusions.

    We hope that the analysis of the additional calcium dataset which is now included in the manuscript adds to more persuasive conclusions.

    1. Calcium and iGluSnfr measurements are both single wavelength measurements and thus sensitive to differences in expression of the indicator. In Fig. 3, the authors show that dorsal cones exhibit larger calcium responses than nasal cones (3c) and that AZ cones show larger glutamate responses than nasal cones (3d). Please address the potential impact of differences in expression on these measurements.

    Thank you for this comment. In Yoshimatsu et. al, 2020, Neuron we compared “live 2p” and “fixed confocal” data of the same sample to show that biosensor expression in UV-cones was uniform across regions, and that the different brightness levels were rather a result of variations in calcium levels. We extrapolated this knowledge to the used biosensors in the new experiments. We now note this explicitly in the revised manuscript.

    1. Please describe controls performed to assess the potential for spectral overlap between the red and green channels. Is there any bleed-through of one dye into the other channel?

    The expression profile of the two indicators is very different, the red fluorescence signal appears in cones, the green in HCs. We illustrated this separation in an additional figure (Fig. S2a,b) which shows that there was no obvious spectral mixing of the two fluorescence channels. We clarified this now in the revised manuscript.

    1. I am not a modeler and while I understand the general approach used for the model, I am not competent to critique specific details of the implementation, particularly the Bayesian inference. However, the fact that the linear statistical model seems to perform just as well as the more ornate model is comforting since it says that the Bayesian inference approach didn't lead the model into an unrealistic parameter space. However, while to my eye the linear model appears to perform just as well as the fancier model, the text says otherwise (Figure 4, lines 270-273). Please clarify.

    Indeed, the linear model captures the general shape of the glutamate response. However, it fails to recover adaptational processes, more precisely the transient components and adaptation over several steps. The model performances are quantified in Fig. S4 (previously S3), and especially with respect to the relevant loss, which is measuring the relevant features, the biophysical model outperforms the linear model. We expanded the discussion on these points in the manuscript and made a more prominent reference to the quantification figure.

    1. Adding a diagram to show where the different regions (dorsal, nasal, acute zone) are located in the eye would be helpful. Is there a difference in the number or size of UV cones from different regions of the retina in larval zebrafish?

    A diagram has been added to Figure 1 as requested. Regarding UV-cone numbers, indeed they do vary across the eye to specifically peak in the acute zone, and to a lesser extent also nasally. This relationship was explored in some detail in

    Zimmermann et al. 2018 Curr Biol, and also touched upon in Yoshimatsu 2020 Neuron. This known density difference is now noted in the introduction.

    1. Are differences in ribbon morphology, glutamate responses or calcium changes retained in adult zebrafish retina? While it may not be feasible to perform similar experiments in adult, some discussion of possible differences and similarities with adult retina would be helpful for putting the results in a more general context.

    The reviewer raises an interesting point. Adult zebrafish display a much broader array of visual behaviours than larvae, and moreover have a rather different diet (meaning that the UV-dependence of prey capture - see Yoshimatsu et al., 2020 Neuron - may be different). Unfortunately, the visual ecology of adult zebrafish remains poorly explored so at this point we can only speculate. Notably, unlike larvae, adults also feature a crystalline mosaic of all cones, meaning that at least numerical anisotropies in cones as they occur in larvae (Zimmermann et al. 2018) are not expected. However, this does not preclude the possibility that UV-cones have different properties across the retina, perhaps it would be the most straightforward way to regionally tune outer retinal outputs in adults. Accordingly, we fully agree that this topic would be exciting to explore, however it would go beyond what could be achieved within a reasonable revision cycle.

    We now added a summarising note of the above into the discussion section.

    Reviewer #3 (Public Review):

    The strengths of the manuscript: It contains a thorough characterization of the anatomical and physiological differences of UV cone ribbons at different locations using the state-of-art techniques including Serial-blockface scanning EM reconstruction and dual-color, simultaneous calcium and glutamate imaging. The Bayesian simulation-based inference model captured the key features of the calcium responses and glutamate release dynamics and provided distributions for each biophysical parameters, which gave insights of their interactions and their impacts on ribbon function. The online tool for ribbon synapse modeling is quite useful. Overall, it is a great effort to understand the function of ribbon synapse with a suitable system that allows multi-facet data collection and a new modeling approach.

    The weaknesses of the manuscript: 1) Overall the writing/formatting of the manuscript can be much improved - there are many imprecise, hard to understand descriptions in the manuscript; figure legends/descriptions are often inadequate for easy understanding; inconsistencies between description in the main text and methods; and above all, the descriptions of model itself and the results from the model are not communicated in a way that facilitates the understanding of process and implications. In contrast, the previous papers from the same group employing similar modeling approaches are much better explained. 2) Based on the intuitions from the modeling, there has not been a strong connection established between the anatomical data and the functional data to which the model is built to fit. More clearly identifying the consistencies and discrepancies between the data and the model will help the readers to understand the pros and cons of the model and the limitations of the generalizations from the model.

    Specific questions and recommendations for the authors:

    1. It will be helpful to have a retina diagram indicating the locations of three different regions.

    The requested diagram has been added to Figure 1.

    1. Fig 1d,e,f (and other figure panels in general) there is no need to mark n.s. On the other hand, in the Statistical Analysis section, GAMs models are mentioned only for Fig 1g, but not other results - needs a clarification.

    We find the “n.s.” labels useful, in part because in some panels none of the differences were significant and the label makes this quite explicit. Accordingly, we have opted to retain them. GAMs were indeed only used for Figure 1g - this is motivated by the difference in data structure of this panel compared to others (i.e. a comparison between continuous rather than discrete distributions). We now clarified this in the methods and added a short paragraph on the used testing procedure.

    1. Fig 1h is quite confusing, with a mixture of 3D and 2D plot, schematic drawing and statistical marks. What comparisons are these marks for? The legend is not specific and the Suppl Fig S1 doesn't clarify much.

    The asterisks are meant to indicate a statistically significant difference in the indicated property (e.g. ribbon size/number) relative to the acute zone. We apologise for not making this clear in the previous version, it is now directly noted in the panel. Regarding the 2D/3D representation, we agree that it may be a little confusing, but we cannot think of a “better” way of summarising all properties analysed by EM in a single panel, so we opted to keep it. We did however expand on the related explanation in the legend to further clarify what is shown.

    1. It will be good to discuss the properties of the calcium sensor. Deconvolution of the calcium signal (lines 617-619) notwithstanding, presumably, the sensor has neither the temporal nor spatial resolution to catch the nano-domain calcium peak near the vesicles in RRP, which is critical for the release of RRP.

    This point seems to link to the ongoing debate on to what extent release from ribbons is driven by micro- and/or nano-domain calcium signalling. It is our understanding that this debate remains unresolved in a truly general sense. Rather, it seems to be non- mutually exclusive (i.e. both micro and nano-domain signals working together), and moreover quite specific to each ribbon synapse in question. In larval zebrafish cones, the pedicle has a rather small cytoplasmic volume, there is only one invagination from postsynaptic processes, and all ribbons inside the cone are opposed to this single invagination. Accordingly, on a possible “sliding scale” of micro- vs nano-domain dominance, we think it is likely that in larval zebrafish cones microdomains will have a notable impact on release. While we are not aware of any data directly looking at this question in zebrafish larval UV-cones, there is good data available from systems that are perhaps quite similar, such as mammalian rods (which also have a single invagination site). For example, from Thoreson et al., 2004, Neuron, Figure 3.

    Already at low micromolar concentrations of calcium that are readily achieved at the level of bulk calcium in the terminal (e.g. 1-2 microM), release is driven to a substantial degree.

    However, we fully agree that we cannot detect possible nano-domain calcium signalling with our imaging method (in fact we are unsure that with currently available technology it is technically possible in an in-vivo preparation). We therefore now further emphasise the possibility of nanodomains acting on release in the discussion.

    Notably, we do already allow exploring the possible influence of nanodomain-type calcium kinetics in the online model, and we think this usefully adds to our exploration of links between calcium signalling and glutamate release.

    1. Likewise, the kinetics of iGluSnFR and of glutamate concentration in the cleft. Admittedly, figs 2a, 3c etc. show that the glutamate signal drops rapidly following the transition from dark to light, however, the rates of vesicle pool replenishment are a topic in the field-some discussion of how glutamate clearance from the cleft and the kinetics of the sensor will influence your estimates of replenishment rates would help future readers better interpret your findings in the context of their own observations.

    We agree that there are technical limitations as to what the iGluSnFR signal can tell us about the exact dynamics of glutamate in an unperturbed situation. Likely this will never be fully addressable. Rather, we use the iGluSnFR signals in a comparative fashion across eye regions, where presumably any distortion of the signals as alluded to by the reviewer would be approximately equal. Following the reviewer’s suggestion, we now explain this more directly in the main text.

    1. In Fig 2d, the rising phase kinetics of the Glu for that nasal cone is strikingly different from that of the acute zone cone. However, such difference is not seen in Fig 3. Therefore, the one in Fig 2d may not be a good representation?

    Thanks, we agree. We have replaced the nasal example with a more representative trace.

    1. In Fig 3a, c.u. and v.u. (only defined in Fig 4 in the context of the model) were used here but not S.D. as in Fig 2, any explanation?

    After scaling, SD adopts arbitrary units. For consistency with the model later we decided to use c.u. and v.u. Here (i.e. “calcium units”, and “vesicle units”). We agree that this could be explained better, and have now rephrased as follows: “We show the rescaled traces in c.u. (calcium units) and v.u. (vesicle units) respectively, to be consistent with the used units in the model later.”

    1. Lines 186-188, how were traces "normalized with respect to the UV-bright stimulus periods"?

    The traces were rescaled such that the UV-bright stimulus periods had a mean of zero and a standard deviation of one. We included this missing piece of information and expanded additionally the explanation of the pre-processing.

    1. Lines 194-195, "In addition, the glutamate release baseline of AZ UV-cones was increased during 50% contrast at the start of the stimulus" - it is unclear whether higher glutamate baseline occurred during the adaptation step (i.e. it increased during that period) or said increase was the level during adaptation compared to that during bright periods?

    Thank you, we meant the former (i.e. glutamate release “is” higher during the adaptation step). This is now clarified in the text.

    1. Lines 219-220, "a sigmoidal non-linearity with slope k and offset x0 which drives the final release" - this sentence is not clear, needs to clarify that it is referring to the relationship between calcium and release.

    Thanks, this is now clarified in the manuscript.

    1. Lines 230-232, "x0 can be understood as the inverted calcium baseline (see Methods)" - Methods don't cover this point, though it is described in the f(Ca) equation, but it isn't obvious how x0 should be the inverted baseline, as if Ca=x0, f(Ca) = 0.5 (i.e., the point of half-release probability). Please clarify this. In general, there are places where explanations of model found in methods don't match those described in the main text (also see some of the points below). Please go over carefully to ensure consistency.

    x0 can be seen as an inverted baseline as it shifts the whole linearity to a different operating point: the smaller x0 the less additional calcium is needed to trigger vesicle release. If we assume a fixed calcium affinity this implies an increased baseline level. We apologise for having omitted these explanations in the initial manuscript, we have expanded the explanation in the Methods of the revised manuscript.

    1. Fig 4e suggests a 5-10 times difference in RRP size between acute zone and nasal UV cones, which is not in line with the anatomical data (Fig 1h). Some discussions and clarifications will be helpful. As we note in the manuscript, it is difficult to quantitatively link anatomical structures to functional data. However, the small RRP size in the nasal zone inferred by the model (Fig. 4e) matches very well to the low vesicle densities at a small distance from the ribbon in the nasal zone in Fig. 1. Our model thus picks up the right trends for an anatomical structure from pure functional recordings, which is in our opinion already remarkable given the experimental noise and fine-grained differences. We commented on this point in the revised manuscript.
    1. From Fig 4h, and Fig S3b,c, the linear model doesn't look too bad (unless I misunderstand the figure panels, which are not explained in great detail). The explanation in lines 272-274 needs some work to make it clearer.

    Compared to the “best model”, the linear model clearly lacks in accuracy, perhaps most intuitively visible when looking at adaptation kinetics. This is especially the case for the relevant loss, which is based on the summary statistics. We extended the mentioned lines and hope to clarify it now in the manuscript.

    1. Sobol indices and their explanation are lacking. Are they computed using Ca2+ and glutamate signals, or just glutamate? It is hard to parse their relative "contributions" to model behavior as described in the text, when the methods caution against interpreting this analysis as determining the "importance" of parameters (lines 805-806).

    The first order Sobol indices measure the direct effect of each parameter on the variance of the model output. More specifically, it tells us the expected reduction in relative variance of the output if we fix one parameter. For the computation, broadly speaking, many parameters were drawn from the posterior distribution and the model was evaluated on these parameters. Afterwards the reduction in variance of the model evaluations was computed if one dimension of the parameter space was fixed. We agree that they are non-intuitive to interpret for a single time point, however its temporal changes give us insight into the time dependent influence on the model output. Often Sobol indices are computed by drawing random samples from a uniform distribution on a high dimensional cuboid [r1,s1] x … x [rn,sn] where each interval [ri,si] is simply defined by the mean+-10% of the parameter fit, where the definition of 10% leaves much room for interpretation and could not be meaningful in the same way for all parameters. We believe that the inferred posterior distributions are a much better suited probability distributions as they encode all parameter combinations which agree with the experimental data.

    We expanded our explanation on this point in the manuscript.

    1. The sensitivity analysis suggests that vesicle transitions are more important than pool sizes or their calcium dependence. Thus, it appears that one intuition from the model is that ribbon size - the main anatomical difference of the UV cone ribbons from different regions - is not very important for the functional difference observed (also see discussion in lines 438-439). Although, it has been discussed that ribbon size does not necessarily correlate with IP or RRP size, but this appears to be the hallmark of the acute zone.

    As the reviewer notes, one potentially interesting hint from our work is that ribbon size does not necessarily translate 1:1 to vesicle pool sizes, or their relative transition rates. One particularly clear example of this might come from comparing Figs. 1d-f and Fig. 1h, between nasal and acute zone. Both have similar ribbon geometry (Fig. 1d-f), but nasal ribbons nevertheless appear to pack fewer vesicles (Fig. 1h). Linking with our functional data and modelling, it then appears that perhaps on top of that, vesicles simply move at different rates between the pools, a property that is impossible to pick up from a static EM reconstruction.

    More generally, as mentioned in the manuscript and discussed in the previous point, it is difficult to judge the overall importance of a parameter from the sensitivity analysis. However, we clearly see time dependent effects of the different parameters and especially the RRP size matters for the transient component, which can be seen in Fig. 5. Indeed, the pattern for IP size seems to be different and it may be that case that the used stimulus is not optimal to infer this parameter from functional recordings.

    How the ribbon size relates to different vesicle densities and how these densities could potentially influence the changing is however still an open question and cannot be answered in the scope of this manuscript.

    1. Lines 460-461, intuitively, a slower RRP refill rate will result in more transient response - after the depletion of RRP, less refilled vesicles to give the sustained component of the response. This is the opposite of what model predicted (a faster RRP). Some explanation and discussion will be helpful.

    The RRP refill rate indeed influences the transience in the mentioned way. However, its influence already starts earlier and is also influencing the overall amplitude (if some minimal background activation is assumed). It is therefore especially influencing the sustained component. However, for the nasal model already the inferred RRP size is the smallest and it seems that a small RRP refill rate is sufficient to produce the sustained response behaviour which we see in Fig. 4f. We thank the reviewer for this thoughtful comment and mentioned this behaviour in the discussion.

    1. Also, the model simplifies vesicle transition rates by removing their calcium dependence. The Methods section indicates that this choice resulted from early fitting results that essentially "dialed out" the calcium dependence. Given the relative freedom that the model seems to have in finding suitable solutions, how is the lack of calcium dependence justified, and what potential impact might it have on the modeling results?

    Identifying model (mis-)specification is a non-trivial task in general. The presented model is complex enough to replicate the recorded data but can easily be extended to more complex dynamics (e.g. more complex calcium handling) in future studies, as it is publicly available online. Further added components could even act as “distractors” to compare the other parameters across zones and we thus decided to use an “as simple as possible” model. Interestingly our previous study (Schröder et al., 2019, Approximate bayesian inference for a mechanistic model of vesicle release at a ribbon synapse, NeurIPS.) showed that even at a temporal resolution of single released glutamate vesicles, it was not necessary to include calcium dependency for the refilling of the vesicle pools. This study thus supports our model choice.

    1. Lines 503-508, "In combination with the approximately equal and opposite effects of calcium baseline on the detectability of On- and Off-events (Fig. 7b,f), this suggest(s) that the calcium baseline may present a key variable that enables ribbons to trade-off the transmission of high frequency stimuli against providing an approximately balanced On- and Off- response behaviour." - what will be the physiological relevance for such conditions, perhaps the level of adaptation? Any existing data or predictions?

    The reviewer raises an interesting but ultimately perhaps unanswerable point, given the scarcity of available data on temporal natural image statistics in the UV band across the larval zebrafish visual field. It is of course tempting to speculate that the ecological need to tune kinetics and On/Off preferences might be linked (e.g. detecting a “dark looming predator” might disproportionately benefit from a rapid Off response). However, to truly understand this idea at a useful level of detail would likely be a rather involved study in its own right. Accordingly, we here prefer to simply point at the possibility to “tune” the ribbon using calcium baseline, and what effects this might have on kinetics if all else was kept equal.

    1. I am slightly skeptical of the predictions that the model might make about the ribbon's frequency tuning (Fig. 7) in light of the fact that the AZ model in particular seems unable to reliably capture the fast transient response to dark flashes (Fig. 4c,f).

    The noted effect in the fast transient components in Fig. 4c,f is partially due to the slow calcium recordings which act as an input for the model in Fig. 4. As mentioned, and discussed above, there is an ongoing discussion to what extent nanodomain or more global calcium concentration drives the release. For this reason, we added a simple calcium model for the simulations for Fig. 7 which includes a variable time constant for calcium (nanodomains would presumably have much faster calcium transients than used for the model default). This allows us to explore the influence of different possible calcium handlings. Although this extrapolation to new stimuli is based on the fitted model, it allows for varying all essential parameters. In the online simulation it can be observed that for fast calcium handlings the ribbon is able to also follow higher frequency stimuli. However, we agree that experimentally testing the influence of different ribbon configurations on frequency tuning is an interesting research direction but goes beyond the scope of this manuscript.

  3. eLife

    Evaluation Summary:

    This paper compares the properties of UV cone output synapses in different regions of the zebrafish retina using a combination of electron microscopy, quantitative imaging and computational modeling. They relate these differences to ultrastructural differences in synaptic ribbons and evaluate them using a previously-developed biophysical model for the operation of the synapse. The finding of regional differences in ribbon behavior is novel and suggests an under-appreciated degree of control of release by ribbon structure and behavior. The presentation of some of the results, particularly the model, could be strengthened.

    (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #1 and Reviewer #3 agreed to share their names with the authors.)

  4. eLife

    Reviewer #1 (Public Review):

    Preprocessing of glutamate traces. The bulk of the analysis in the paper uses "scaled and denoised" traces. It is important to verify that this process did not either introduce or obscure any differences across regions. This should include some validation of the assumptions that go into the scaling process (such as whether a sufficiently low calcium level is achieved to use that as a standard). An example of a how this concern could impact the conclusions is that the AZ glutamate traces look less rectified than the others, perhaps due to an elevated baseline, as suggested in the text. But the conclusion about the elevated baseline relies on the scaling process creating a proper alignment such that it is accurate to superimpose the traces as in Figure 3a.

    Model fitting. Some key aspects of the model fitting were difficult to evaluate and follow. For example, is the loss function the same as the discrepancy defined in the methods (I assumed that is the case - if not the loss function needs to be defined)? The definition of the discrepancy could be clearer (e.g. be careful about using x here and as the offset of the calcium trace). Related, the results would benefit from a more intuitive description of the fitting, rather than just a reference to the methods (which is a bit dense to go through for that intuitive-level explanation of the model development).

    Some statements seem too strong given the state of current knowledge. E.g. lines 79-80 I think goes too far about the functional role of the ribbon. Similarly lines 97-98 are quite explicit about the connection to prey capture. Lines 276-279 are a particularly important example; I would argue that the statement there requires showing uniqueness of the model.

    Could fixation of the retina for EM change the distribution of vesicles in different compartments? I realize this may not be answerable, but a caution about that possibility might be warranted.

    Line 159: it is not clear how similar the calcium signals are. Specifically, could differences in calcium signal get amplified when passed through simple nonlinearity (e.g. due to the calcium dependence of transmitter release) to account for the differences in glutamate output? Maybe rewording here to leave open that possibility unless you have reason to reject it.

    Can you quantify the fits in Figure 4f,g? For example, can you give a probability of a particular experimental trace or summary parameters for that experimental trace given the parameter probability distributions from the same area and from a different area?

  5. eLife

    Reviewer #2 (Public Review):

    This study images synaptic calcium and glutamate release from larval zebrafish UV-sensitive cones in vivo. They also study the ultrastructure of ribbon synapses from UV cones in different regions of the retina. They find differences in ribbon dimension and light-evoked glutamate release from cones in different regions of the retina. Cones from dorsal retina show a more pronounced transient component of glutamate release than those from nasal retina. Those in the acute zone in the center of the retina showed intermediate kinetics. Ultrastructural reconstructions of UV-sensitive cones from those regions showed fewer and small ribbons in dorsal cones vs. those in the nasal region or acute zone zone. Light-evoked changes in the kinetics of synaptic calcium were not significantly different suggesting that differences in release kinetics may be related to differences in ribbon behavior in cones from different regions. To relate these different measurements to one another, the authors modified an existing model of cone release to incorporate a simulation-based Bayesian inference approach for estimating best-fit parameters. The model suggested that the differences in glutamate release kinetics could be explained by differences in the rates of transfer between vesicle pools on and off the ribbon. By fixing different parameters, the authors then used the model to explore the parameter space and general properties of ribbon tuning. They also provide a link to the model for others to use.

    The main new experimental finding is that glutamate release properties differ among cones in different regions. The finding that kinetics of glutamate release and ribbon ultrastructure vary systematically in different regions of the retina is interesting. They relate these data using a model of ribbon release. While the model is not novel in its general design, the incorporation of Bayesian inference is new. The most interesting finding from the model is that the kinetic differences in release between cones are not due to calcium kinetics but arise primarily from differences in transitions between vesicle pools. Nevertheless, using the model, the authors show that calcium levels and kinetics matter, since if they hold other parameters fixed, calcium levels and kinetics are the most important factors in shaping response detectability and response kinetics. This is consistent with a lot of earlier work that calcium kinetics are important for shaping response kinetics at ribbon synapses.

    1. The measured changes in glutamate and calcium are small and noisy and there is considerable overlap in the data from cones in different regions. While the example waveforms show considerable differences, the scatter in the data is less persuasive. If I understand correctly, the imaging data comes from 30 AZ, 16 dorsal, and 9 nasal UV cones. With such noisy data, 9 cones seems like particularly small sample. With imaging data, it should be possible to record from dozens or hundreds of cells and a larger sample would strengthen the conclusions.

    2. Calcium and iGluSnfr measurements are both single wavelength measurements and thus sensitive to differences in expression of the indicator. In Fig. 3, the authors show that dorsal cones exhibit larger calcium responses than nasal cones (3c) and that AZ cones show larger glutamate responses than nasal cones (3d). Please address the potential impact of differences in expression on these measurements.

    3. Please describe controls performed to assess the potential for spectral overlap between the red and green channels. Is there any bleed-through of one dye into the other channel?

    4. I am not a modeler and while I understand the general approach used for the model, I am not competent to critique specific details of the implementation, particularly the Bayesian inference. However, the fact that the linear statistical model seems to perform just as well as the more ornate model is comforting since it says that the Bayesian inference approach didn't lead the model into an unrealistic parameter space. However, while to my eye the linear model appears to perform just as well as the fancier model, the text says otherwise (Figure 4, lines 270-273). Please clarify.

    5. Adding a diagram to show where the different regions (dorsal, nasal, acute zone) are located in the eye would be helpful. Is there a difference in the number or size of UV cones from different regions of the retina in larval zebrafish?

    6. Are differences in ribbon morphology, glutamate responses or calcium changes retained in adult zebrafish retina? While it may not be feasible to perform similar experiments in adult, some discussion of possible differences and similarities with adult retina would be helpful for putting the results in a more general context.

  6. eLife

    Reviewer #3 (Public Review):

    The strengths of the manuscript: It contains a thorough characterization of the anatomical and physiological differences of UV cone ribbons at different locations using the state-of-art techniques including Serial-blockface scanning EM reconstruction and dual-color, simultaneous calcium and glutamate imaging. The Bayesian simulation-based inference model captured the key features of the calcium responses and glutamate release dynamics and provided distributions for each biophysical parameters, which gave insights of their interactions and their impacts on ribbon function. The online tool for ribbon synapse modeling is quite useful. Overall, it is a great effort to understand the function of ribbon synapse with a suitable system that allows multi-facet data collection and a new modeling approach.

    The weaknesses of the manuscript: 1) Overall the writing/formatting of the manuscript can be much improved - there are many imprecise, hard to understand descriptions in the manuscript; figure legends/descriptions are often inadequate for easy understanding; inconsistencies between description in the main text and methods; and above all, the descriptions of model itself and the results from the model are not communicated in a way that facilitates the understanding of process and implications. In contrast, the previous papers from the same group employing similar modeling approaches are much better explained. 2) Based on the intuitions from the modeling, there has not been a strong connection established between the anatomical data and the functional data to which the model is built to fit. More clearly identifying the consistencies and discrepancies between the data and the model will help the readers to understand the pros and cons of the model and the limitations of the generalizations from the model.

    Specific questions and recommendations for the authors:

    1. It will be helpful to have a retina diagram indicating the locations of three different regions.

    2. Fig 1d,e,f (and other figure panels in general) there is no need to mark n.s. On the other hand, in the Statistical Analysis section, GAMs models are mentioned only for Fig 1g, but not other results - needs a clarification.

    3. Fig 1h is quite confusing, with a mixture of 3D and 2D plot, schematic drawing and statistical marks. What comparisons are these marks for? The legend is not specific and the Suppl Fig S1 doesn't clarify much.

    4. It will be good to discuss the properties of the calcium sensor. Deconvolution of the calcium signal (lines 617-619) notwithstanding, presumably, the sensor has neither the temporal nor spatial resolution to catch the nano-domain calcium peak near the vesicles in RRP, which is critical for the release of RRP.

    5. Likewise, the kinetics of iGluSnFR and of glutamate concentration in the cleft. Admittedly, figs 2a, 3c etc. show that the glutamate signal drops rapidly following the transition from dark to light, however, the rates of vesicle pool replenishment are a topic in the field-some discussion of how glutamate clearance from the cleft and the kinetics of the sensor will influence your estimates of replenishment rates would help future readers better interpret your findings in the context of their own observations.

    6. In Fig 2d, the rising phase kinetics of the Glu for that nasal cone is strikingly different from that of the acute zone cone. However, such difference is not seen in Fig 3. Therefore, the one in Fig 2d may not be a good representation?

    7. In Fig 3a, c.u. and v.u. (only defined in Fig 4 in the context of the model) were used here but not S.D. as in Fig 2, any explanation?

    8. Lines 186-188, how were traces "normalized with respect to the UV-bright stimulus periods"?

    9. Lines 194-195, "In addition, the glutamate release baseline of AZ UV-cones was increased during 50% contrast at the start of the stimulus" - it is unclear whether higher glutamate baseline occurred during the adaptation step (i.e. it increased during that period) or said increase was the level during adaptation compared to that during bright periods?

    10. Lines 219-220, "a sigmoidal non-linearity with slope k and offset x0 which drives the final release" - this sentence is not clear, needs to clarify that it is referring to the relationship between calcium and release.

    11. Lines 230-232, "x0 can be understood as the inverted calcium baseline (see Methods)" - Methods don't cover this point, though it is described in the f(Ca) equation, but it isn't obvious how x0 should be the inverted baseline, as if Ca=x0, f(Ca) = 0.5 (i.e., the point of half-release probability). Please clarify this. In general, there are places where explanations of model found in methods don't match those described in the main text (also see some of the points below). Please go over carefully to ensure consistency.

    12. Fig 4e suggests a 5-10 times difference in RRP size between acute zone and nasal UV cones, which is not in line with the anatomical data (Fig 1h). Some discussions and clarifications will be helpful.

    13. From Fig 4h, and Fig S3b,c, the linear model doesn't look too bad (unless I misunderstand the figure panels, which are not explained in great detail). The explanation in lines 272-274 needs some work to make it clearer.

    14. Sobol indices and their explanation are lacking. Are they computed using Ca2+ and glutamate signals, or just glutamate? It is hard to parse their relative "contributions" to model behavior as described in the text, when the methods caution against interpreting this analysis as determining the "importance" of parameters (lines 805-806).

    15. The sensitivity analysis suggests that vesicle transitions are more important than pool sizes or their calcium dependence. Thus, it appears that one intuition from the model is that ribbon size - the main anatomical difference of the UV cone ribbons from different regions - is not very important for the functional difference observed (also see discussion in lines 438-439). Although, it has been discussed that ribbon size does not necessarily correlate with IP or RRP size, but this appears to be the hallmark of the acute zone.

    16. Lines 460-461, intuitively, a slower RRP refill rate will result in more transient response - after the depletion of RRP, less refilled vesicles to give the sustained component of the response. This is the opposite of what model predicted (a faster RRP). Some explanation and discussion will be helpful.

    17. Also, the model simplifies vesicle transition rates by removing their calcium dependence. The Methods section indicates that this choice resulted from early fitting results that essentially "dialed out" the calcium dependence. Given the relative freedom that the model seems to have in finding suitable solutions, how is the lack of calcium dependence justified, and what potential impact might it have on the modeling results?

    18. Lines 503-508, "In combination with the approximately equal and opposite effects of calcium baseline on the detectability of On- and Off-events (Fig. 7b,f), this suggest(s) that the calcium baseline may present a key variable that enables ribbons to trade-off the transmission of high frequency stimuli against providing an approximately balanced On- and Off- response behaviour." - what will be the physiological relevance for such conditions, perhaps the level of adaptation? Any existing data or predictions?

    19. I am slightly skeptical of the predictions that the model might make about the ribbon's frequency tuning (Fig. 7) in light of the fact that the AZ model in particular seems unable to reliably capture the fast transient response to dark flashes (Fig. 4c,f).

  7. Version published to 10.1101/2021.02.24.432674v2 on bioRxiv
  8. Version published to 10.1101/2021.02.24.432674v1 on bioRxiv