This is a powerful and thorough study that uses genetic perturbation to identify human disease genes that control neuronal activity. It's an excellent starting point for untangling the underpinnings of neurological disorders and I'm excited to see how this platform can be used for identifying new therapeutic targets and drug discovery!

I love that you used a second mechanism of activation, and optimized it for the best signal in your assay. It seems powerful and gives me confidence in your results!

This result is so interesting, it made me rethink what "excitability" means in your screen. I was thinking of CaMPARI2 as the integral of action potentials, but these results suggest to me that there may be several mechanisms which could cause a high CaMPARI signal. For instance, if protein knockdown impaired calcium transport into organelles could this also cause a high CaMPARI signal? If so, how do you interpret the results of the screen, their relationship to neuronal excitability, and to disease?

I was a little surprised here to see HTT listed as a Parkinson's disease gene, I think of it as associated with Huntington's disease! Could you share a little more about it's relationship to Parkinson's, particularly in this case where you have a loss of function?

It's awesome you could validate your approach identifying known excitability genes. Did your library include common ion channels like potassium leak channels or voltage-gated sodium channels? It would be a nice additional proof of principle and many of these channels have disease implications.

This is such a cool screen, and I'm really excited by the hits you found! It seems like there may be a relationship between your top hits (e.g. voltage gated calcium channels) and your calcium sensor as a readout. Do you think this could introduce bias? Is there a way to compensate for this?

Having more and better tools for monitoring in vivo processes is so important for research, it's awesome you've validated your FRET-based gamma-secrtase sensor in vivo now. It would be great to learn more about the challenges associated with using this sensor, and the best ways you learned to implement it to get these nice results.

How cool! If it's oxygen linked does that suggest these changes might also be linked to changes in local neuronal firing rates (which drive ganges in oxygenation)? Does the spatial scale of oxygenation match the spatial scale you saw for gamma-secretase clustering?

These are really interesting ideas! Do you see any subcellular heterogeneity in gamma-secretase activity? Also do you see any change in gamma-secretase activity on a per cell basis over time?

This finding is so cool, and I appreciate the multiple analysis and experimental steps you took to validate it!

This dose seems really high! Is this generally how much DAPT is needed to see an effect? How long did you wait before imaging?

rather than simply comparing to some known number of closest neurons, or a fixed (20um) distance, could you look at the change in 720/760 ratio as a function of distance for each neuron? I'd be curious what the pattern looks like across cells and over what spatial scale this clustering occurs!

Wo! What do you think these objects are? Do you think they are due to the craniotomy? Could you show what the distribution of 720/670 ratios look like for these objects in your control animals versus your viral injection animals?

sp. latter

It's great you can see cells using this methodology! Do you have any idea if these are particular types of neurons? Do you think the pattern would change in deeper layers of cortex as the cellular composition changes?

Could you describe this procedure in more depth? What volume did you inject, at what depth, and over what time period? Did you dilute your virus at all before injection? How long did you wait before the crainiotomy, and what was the approximate total time between injection and first imaging? How does fluorescence of the indicator change over time, does it end up saturating or causing cell damage at some point?

This is amazing! Have you tried with other types of microscopy? For instance is this sensor amenable to 2-photon imaging?

I'm really excited by this hypothesis! Do you know how well conserved these mismatch repair proteins are? We recently used comparative genomics to find repeat expanded proteins across diverse organisms, but didn't look into mismatch repair proteins at all. If there's an evolutionary link between the presence/variant of mismatch proteins and repeat expansions that would provide strong evidence for your hypothesis!

It's very compelling that no matter which mismatch repair protein you knock-out you can basically stop expansion in these cells compared to the original culture! Rather than just comparing to the original culture, could you show the repeat length for an unedited culture to the knock out culture over time? It might be particularly nice if the unedited culture also got CRISPR treatment but with a scrambled guide RNA as a negative control!

Wow, how interesting! Do you have data for the in vivo expansion rate for patient P2 to compare this in vitro rate to? Or alternatively, how does this rate compare with the estimated rate of somatic mutation for GDPAG patients in general?

Do you think using optogenetics would have changed your results? I'm wondering if the duration / pattern of claustral activity dramatically impact how it alters cortical activity. It also makes me curious how different claustral pathways might be differently sensitive to optogenetic vs chemogenetic manipulation. Very cool work!

It seems like claustrum is regulating both activity levels and connectivity, how related are those changes in this case? Do you think correlations are increasing because of increased activity, or do you think they are separate processes?

I'm excited by this finding and would love to know if there's a parallel control pathway or if this is due to indirect effects of claustral inhibition. Could you use tracers, or in some other way experimentally determine the answer?

This is a cool finding! Do you think the return to baseline is due to metabolism of CNO, or because of circuit-level adaptation? Do you think there's any significance to the regions that didn't return to baseline?

This is so cool! It's valuable to highlight these changes, it helps me better interpret how changes in protein expression might be related to physiological and developmental changes.

This hypothesis is really cool, and seems testable! Are there any available datasets from slice or in vivo elecrophysiology/calcium imaging where you could look for this? Alternatively, could you do histology with tissue you have from these experiments to look for aberrant synapse growth?

I really appreciate you explaining the experimental design and how you drew your conclusions. However, I'm not sure I agree that the effects of circadian rhythm, sleep, wake, and sleep deprivation are dissociable based on your experiments. It seems to me that multiple mechanisms might result in the same change in protein expression (e.g. sleep deprivation and sleep both independently cause reduced protein expression) that would be interpreted as coming from a single source (e.g. circadian rhythm). It also seems to me that different mechanisms might interact with each other (e.g. protein expression is changed by the combination of wakefulness and circadian rhythm), which might remain true even for proteins you identify as influenced by a single regulation group. Finally, it seems to me that some of the comparisons using sleep deprivation treat it as a wakeful state when controlling for sleep/wake changes and circadian changes, while also identifying it as having separate physiological and molecular effects. Overall, I think the wealth of data you've generated is deeply valuable, but i'm not sure it can be used to isolate single regulatory mechanisms underlying synaptic changes as you suggest.

This is great developmental data, and I'd love to be able to compare across different ages easily. Could you also show the expression data without normalizing?

It seems like Homer1a expression shows much lower changes in expression during wake (as well as during sleep deprivation) in juvenile mice compared to older mice. Does this affect the interpretation of its role and response to sleep deprivation in juvenile mice?

This is such an interesting observation! Is it possible that juveniles have another compensatory mechanism? For instance, it seems from the example traces that juveniles and adolescents showed some increased sleep in the light phase. Could you quantify this and compare it to the adults? Do light phase sleep increases explain the (modest) deficit recovery for juveniles you see in (Fig. 1D)?

I appreciate the context and transparency in this section! I wonder if comparing and clustering the previous results together with the results generated here could highlight both areas of methodological improvement and increase confidence in the biological effects of your compounds of interest.

The way you list out these compounds really helps me understand and interpret the results! It's such a diverse list, are there any compounds that surprise you? Conversely, it seems to me that some of the compounds you found are to pathways with strong behavioral effects generally (e.g. the neuromodulatory pathways involving dopamine, norepinephrine, and serotonin). Are there ways to account for general motor effects when identify CsA-like compounds?

This seems like an important caveat. Would it be possible to remove compounds without an identifiable change in behavior before clustering? It's good that they are a minority (~10%) but but seems odd to call compounds CsA-like based on behavioral effects if it's not clear they have a behavioral effect

This is a nice way to visualize the data, it really helps me get a sense of which compounds are similar to Cyclosporin A and which aren't. Is it possible to do this for all the compounds rather than just the ones found by clustering? Similarly, how to you interpret compounds identified by clustering but with low (e.g. 0.04) correlation to CsA?

These behaviors seem interesting but somewhat arbitrary. Is there a way to analyze the data you collected in an unbiased way to extract the most relevant features?

Could you specifically point out where CsA lies here, I'm having trouble understanding the results without that reference point. Similarly, could you point out which point(s) are DMSO, is it all the unlabeled data points? Knowing where the control points lie would be very helpful!

It would be awesome to see the curve you used here and some examples of other clustering results. Particularly because clustering is fundamental to the downstream analysis some more information on the effects of clustering differently would give the results more context

This is an impressive screen! Particularly since you have so many controls, could you show more analysis of baseline variability in larvae behavior and how it changes with DMSO? It would be helpful to understand the typical range of larval responses to interpret the experimental compound-induced changes.

This is very impressive and useful! Well done!

I'm surprised and intrigued that such a small portion (<10%) of the FOV showed changes in dopamine.It seem challenging to understand heterogeneity patterns with such a small proportion of responses. Do you think this is due to the expression level or sensitivity of the indicator? Perhaps a cortical region (like prefrontal cortex) or different stimulus (perhaps a rewarding paradigm) would allow for a larger dopaminergic response and clearer analysis?

The timing here is really interesting! Particularly from panels i-k it appears the dopamine sensor fluorescence remains elevated above pre-footshock levels. Is this a property of the sensors or do you believe this to be biological in nature? How does it effect the calculation of Toff in this paradigm? Does it inform your interpretation of the "fast" dopamine vs "slow" endocannabinoid singals?

This is an awesome result! The correlation is so strong, how much of the correlation do you think is directly due to dopamine signaling vs other effects? Do you think there is any cross-talk between the channels or other effects increasing the observed correlation?

Do these results have implications for using the Rdlight1 or rDA3 for experiments involving dual-color imaging and/or optogenetics?

The specificity of this dopamine sensor is great overall, and it's good to see higher sensitivity to dopamine compared with norepinephrine! Could you also talk about the variability between individual cells and show the individual data points for this graph rather than just the mean +/- SEM? It would help me understand over what range and in what contexts I might expect some fluorescence due to norepinephrine.

This finding is very exciting, is it also true for female mice? Is it possible to test the female sexual reflex in mice?

What leads to the different physiological responses of optogenetic stimulation between stimulation paradigms and between constructs? How does optogenetic stimulation compare with responses to mechanical stimulation?

An example video of DRG calcium responses to mechanical stimuli would be so cool to see here!

The identify of the neurons that make up these corpuscles is very interesting! Could you explain more how you excluded other sensory fibers from involvement in these corpuscles? For instance it is not obvious to me that fibers that pass through a corpuscle are not involved in its formation. Some quantification of the location of other sensory neurons in the glans/clitoris and their overlap with Krause corpuscles would be very helpful for me here.

Typo: Tdtomato

This is a really important and useful finding! Sharing the data for the specificity of this genetic strategy and quantification of which other fibers are labeled (and in what proportion) would be helpful

It would be helpful to have the scale bar in J show the density counts that correspond to "low" and "high"

This is compelling and seems intuitive! Is there a way to determine whether changes in synchrony strength are based on increased firing rates of the same L6CT population or if you are activating greater numbers of L6CT cells as you increase LED intensity?

This contribution to bidirectional influence is very interesting! Is there a way to experimentally test this hypothesis, perhaps with chemical or optogenetic inactivation of TRN while stimulating L6CT and recording from VPm?

I'm fascinated by the specific effects you see during ramp (e.g. suppression in Fig. 1e and 2e) as well as potential VPM ramping during hold in Fig. 2e and adaptation with stimulation in Fig. 2c with repeated trials. Do you think these effects are particular to the duration and intensity pattern of the stimulation in the same way you show other stimulation parameters (i.e. intensity and frequency content) dramatically change the effects of L6CT stimulation? Could you explain a little more about why you chose ramp-and-hold stimulation and the durations for your optogenetic stimulation?

It's great you have a way to measure cilia in a straightforward way! Could you include here the resolution of your microscope and how that compares to the size of cilia?

This is a remarkable observation! Is it possible there is some aspect of the imaging, registration, or data analysis that is leading to the strong alignment along cartesian axes? Perhaps in the way the bounding rectangle is drawn?

Based on the long right tail for the cilia length distributions, it might be more helpful to report and compare median lengths for the whole brain and each brain region.

The wide range in cilia you see between these regions is interesting! Could you also report these numbers normalized to the amount of data you included so it is easier to interpret? For instance to the area of the brain region analyzed, the number of samples included per region, the number of cells in the brain region, etc.

This is an impressive amount of data! It makes sense that you would need an automated way to quantify cilia from this many mice and brain regions. It would helpful to assess how this program works, is it possible it includes specific types of noise or misses some types of cilia? Could you take a subset of your data and compare the output of your automatic software to a manual analysis?

This is a beautiful and very detailed paper using multiple approaches to carefully dissect the role of an unknown channel. The thermosensitivity of the TRPA5 channel is clear and compelling, and although its role in noxious heat sensing is not yet validated, it is a strong hypothesis. Overall the use of multiple lines of evidence, and particularly matching evolutionary biology, structural modelling, and cellular physiology is extremely impressive and a strong addition to the field.

How interesting! Is this unique to Rhodnius or true of all species that express TRPA52?

This paper seems to show preferential nervous system expression of Pyrexia, is that true of TRPA52?

Based on the unique thermal sensitivity of TRP5A2 relative to Pyrexia and the other thermosensitive channels, in what ways is it redundant/convergent and in what ways is it innovative? Behaviorally, does Rhodnius have higher thresholds for noxious heat responses?

The temperature-evoked current changes with TRPA52 seems substantially smaller than the other profiled thermosensitive channels. Could you directly compare the current changes between these channels? How does the reduced current (if that is accurate) from TRPA52 change its role?

I got a little confused here by the role of the open patch pipette and the calibration that was used, and how it related to the expression of the ion channels referenced in the previous sentence. It seemed to me at first the calibration was somehow specific to the transient expression of the known thermoTRPs (which was clarified by reading the great methods section!). Referencing the methods and a little more information on the calibration in the text could help.

This expression pattern is interesting, but it's not immediately obvious to me how the magnitude of tissue expression relates to heat sensing. For example, is it known that Rhodnius preferentially sense heat with their head far more than their antennae? Profiling TRPA52 expression in tissues unrelated to sensation, showing specific localization or enrichment to sensory neurons, or linking the highly expressed tissues with known behavior might all help clarify how this suggestive tissue expression pattern is linked to heat sensing!

Overall, this is a very clear and thorough investigation which uncovers an interesting biological circuit. These findings help explain the variable vocal capacity of parrots, and perhaps even the same capacity in humans. Well done!

I agree, this would be fascinating!

What an interesting result! Does this suggest that the individual-distinguishing features are not, for the most part, lost, but instead shift after TTX? Could you discuss more about the implications of this?

This is very cool! Can you tell if the acoustic changes identified in Figure 4 are responsible for this convergence, or if is due to other features?

This seems like an effective strategy that clearly had a strong behavioral result! My understanding is that ttx can also effect fibers of passage, is that relevant in this case? By using ttx can you also infer other brain regions with fibers that go through or near MO/NAO that might be important for call variability?

Is the number of inputs related to the number of experimental subjects used in this study, or seperate? More generally, can you identify the biological features that allow for discrimination of call identity? It was interesting to me, for instance, that sex did not seem to strongly impact clustering or identity on the UMAP. What spectral or other features do determine clustering?

This is an impressive volume of calls! Did you observe variation in the calls, within an individual over trials or days of recordings, as well as the reported differences across weeks? What aspects of contact calls do you think are preserved in this experimental setup and which are altered in this paradigm?

This indeed is a surprising finding! The hypothesis presented for a sort of "competitive attention" is interesting, but it's hard without a clear presentation of the data (both in the text and additional figures) for me to tell whether it is the only, or best supported hypothesis. Would you be able to present alternative hypothesis and show which are supported or refuted by the data? This is particularly useful in cases like this one, where there is a well-established literature (as stated in the introduction) but the findings do not fit into the prevailing theory

For all the results, I see the results from GLMM analysis, but don't see as much description of the underlying data clearly stated (e.g. probability of "saccade" per subject, per session, per condition etc.). Clearly presenting this information would be extremely helpful for understanding the outcomes of these experiments.

I'm interested in this particular stimulation. What was the rationale for having a "flash" that alternates rather than flashing by increasing luminance and keeping that value steady? Similarly, for alternating why was 30Hz used? Do you think the nature of the cue here will alter the results?

This section is very detailed! I'm finding it hard to visualize the placement of the spider, the field of view for the AME and secondary eyes, and the location of the visual stimuli. Could you include a schematic to clarify the experimental setup?

As previously mentioned the distinction between dopaminergic and catecholaminergic projections has large implications here. For instance, dopaminergic projections to the sensory cortex are rare, while noradrenergic projections to the striatum are rare. Accounting for these known projection differences when interpreting this data could be very helpful and clarify the effects of ketamine specifically on the dopaminergic system

This finding is amazing! How cool.

It's really great this validation was done! Could you directly compare the validation results to the suiteWB results? It might help convey the confidence of different results and give a sense of the advantages of suiteWB.

I'm having a hard time understanding the differences between the effects of 30mg/kg and 100mg/kg across brain areas, and which effects were chosen to be schematized in Fig 1b. For instance, the DMH has reduced TH+ neurons at 30mg/kg but not at 100mg/kg, and Fig1b summarizes this as a reduction, but it's not clear to me why that's the interpretation. Overall it would be really helpful if there was more discussion of where the effects across doses are similar and different, how that matches or defies expectations, and what conclusions are drawn which lead to the schematic in Fig 1b.

I'm not totally sure how to understand the findings at higher vs. lower level annotation. Including how ROI size/number influences detection in this methodology, and a direct comparison of the findings when using different annotation levels would be very clarifying

Even though there weren't significant differences detected, this is a huge amount of potentially useful data! Could the data and analysis done to find non-significance be displayed? Also, it would be great to know whether the interpretation here is that there is no true change at 1 and 5 days, or that there was not sufficient information (e.g. statistical power) to tell the effect at these time points

Wow, this is great! It seems like it could be a really useful tool for the community. More description of the uses, limitations, and accuracy of this tool could really help others to use this great innovation.

What an interesting observation! Based on the effects of locomotion on brain circuits and plasticity, how do you think this difference might affect the observed changes in TH+ neurons/axons?

This seems like an important distinction and I'm glad it was mentioned both here and in the discussion. It wasn't clear to me, however, how this impacts the interpretation of further results, particularly for Figure 5. It would be very helpful to further comment on the nature of the observed TH+ axons, particularly in relation to known projection differences between dopaminergic and noradrenergic neurons.

These images are an impressive proof of principle! I'm wondering, why is the color of the 1 month post-op tissue different than that at the time of surgery? Is this a difference in lighting, discoloration of the tissue, from fluorescent protein expression, or something else? It also looks to me like the large vasculature observed during surgery does not appear the same post-operatively, why is this the case?

I'm very impressed by the technical challenges surmounted in order to develop and implement this challenging preparation and imaging. I think this demonstration would be even stronger if there was a quantification of success rate and post-operative tissue health.

This is fantastic! A quantitative comparison of the efficacy of transduction vectors would be very useful. In addition, histology with some marker(s) of cortical damage could help demonstrate the (lack of) tissue damage using this methodology.

This is fantastic! A quantitative comparison of the efficacy of transduction vectors would be very useful. In addition, histology with some marker(s) of cortical damage could help demonstrate the (lack of) tissue damage using this methodology.

I'm very impressed by the technical challenges surmounted in order to develop and implement this challenging preparation and imaging. I think this demonstration would be even stronger if there was a quantification of success rate and post-operative tissue health.

These images are an impressive proof of principle! I'm wondering, why is the color of the 1 month post-op tissue different than that at the time of surgery? Is this a difference in lighting, discoloration of the tissue, from fluorescent protein expression, or something else? It also looks to me like the large vasculature observed during surgery does not appear the same post-operatively, why is this the case?