Author response:
The following is the authors’ response to the original reviews.
Public Reviews:
Reviewer #1 (Public Review):
Summary:
The emergence of Drosophila EM connectomes has revealed numerous neurons within the associative learning circuit. However, these neurons are inaccessible for functional assessment or genetic manipulation in the absence of cell-type-specific drivers. Addressing this knowledge gap, Shuai et al. have screened over 4000 split-GAL4 drivers and correlated them with identified neuron types from the "Hemibrain" EM connectome by matching light microscopy images to neuronal shapes defined by EM. They successfully generated over 800 split-GAL4 drivers and 22 split-LexA drivers covering a substantial number of neuron types across layers of the mushroom body associative learning circuit. They provide new labeling tools for olfactory and non-olfactory sensory inputs to the mushroom body; interneurons connected with dopaminergic neurons and/or mushroom body output neurons; potential reinforcement sensory neurons; and expanded coverage of intrinsic mushroom body neurons. Furthermore, the authors have optimized the GR64f-GAL4 driver into a sugar sensory neuron-specific split-GAL4 driver and functionally validated it as providing a robust optogenetic substitute for sugar reward. Additionally, a driver for putative nociceptive ascending neurons, potentially serving as optogenetic negative reinforcement, is characterized by optogenetic avoidance behavior. The authors also use their very large dataset of neuronal anatomies, covering many example neurons from many brains, to identify neuron instances with atypical morphology. They find many examples of mushroom body neurons with altered neuronal numbers or mistargeting of dendrites or axons and estimate that 1-3% of neurons in each brain may have anatomic peculiarities or malformations. Significantly, the study systematically assesses the individualized existence of MBON08 for the first time. This neuron is a variant shape that sometimes occurs instead of one of two copies of MBON09, and this variation is more common than that in other neuronal classes: 75% of hemispheres have two MBON09's, and 25% have one MBON09 and one MBON08. These newly developed drivers not only expand the repertoire for genetic manipulation of mushroom body-related neurons but also empower researchers to investigate the functions of circuit motifs identified from the connectomes. The authors generously make these flies available to the public. In the foreseeable future, the tools generated in this study will allow important advances in the understanding of learning and memory in Drosophila.
Strengths:
(1) After decades of dedicated research on the mushroom body, a consensus has been established that the release of dopamine from DANs modulates the weights of connections between KCs and MBONs. This process updates the association between sensory information and behavioral responses. However, understanding how the unconditioned stimulus is conveyed from sensory neurons to DANs, and the interactions of MBON outputs with innate responses to sensory context remains less clear due to the developmental and anatomic diversity of MBONs and DANs. Additionally, the recurrent connections between MBONs and DANs are reported to be critical for learning. The characterization of split-GAL4 drivers for 30 major interneurons connected with DANs and/or MBONs in this study will significantly contribute to our understanding of recurrent connections in mushroom body function.
(2) Optogenetic substitutes for real unconditioned stimuli (such as sugar taste or electric shock) are sometimes easier to implement in behavioral assays due to the spatial and temporal specificity with which optogenetic activation can be induced. GR64f-GAL4 has been widely used in the field to activate sugar sensory neurons and mimic sugar reward. However, the authors demonstrate that GR64f-GAL4 drives expression in other neurons not necessary for sugar reward, and the potential activation of these neurons could introduce confounds into training, impairing training efficiency. To address this issue, the authors have elaborated on a series of intersectional drivers with GR64f-GAL4 to dissect subsets of labeled neurons. This approach successfully identified a more specific sugar sensory neuron driver, SS87269, which consistently exhibited optimal training performance and triggered ethologically relevant local searching behaviors. This newly characterized line could serve as an optimized optogenetic tool for sugar reward in future studies.
(3) MBON08 was first reported by Aso et al. 2014, exhibiting dendritic arborization into both ipsilateral and contralateral γ3 compartments. However, this neuron could not be identified in the previously published Drosophila brain connectomes. In the present study, the existence of MBON08 is confirmed, occurring in one hemisphere of 35% of imaged flies. In brains where MBON08 is present, its dendrite arborization disjointly shares contralateral γ3 compartments with MBON09. This remarkable phenotype potentially serves as a valuable resource for understanding the stochasticity of neurodevelopment and the molecular mechanisms underlying mushroom body lobe compartment formation.
Weaknesses:
There are some minor weaknesses in the paper that can be clarified:
(1) In Figure 8, the authors trained flies with a 20s, weak optogenetic conditioning first, followed by a 60s, strong optogenetic conditioning. The rationale for using this training paradigm is not explicitly provided.
These experiments were designed to test if flies could maintain consistent performance with repetitive and intense LED activation, which is essential for experiments involving long training protocols or coactivation of other neurons inside a brain.
In Figure 8E, if data for training with GR64f-GAL4 using the same paradigm is available, it would be beneficial for readers to compare the learning performance using newly generated split-GAL4 lines with the original GR64f-GAL4, which has been used in many previous research studies. It is noteworthy that in previously published work, repeating training test sessions typically leads to an increase in learning performance in discrimination assays. However, this augmentation is not observed in any of the split-GAL4 lines presented in Figure 8E. The authors may need to discuss possible reasons for this.
As the reviewer pointed out, many previous studies including ours used the original Gr64f-GAL4 in olfactory conditioning. Figure 1H of Yamada et al., 2023 (https://doi.org/10.7554/eLife.79042) showed such a result, where the first and second-order olfactory conditioning were assayed. Indeed, the first-order conditioning scores were gradually augmented over repeated training. In this experiment, we used low red LED intensity for the optogenetic activation. In the Figure 8E of the present paper, the first memory test was after 3x pairing of 20s odor with five 1s red LED without intermediate tests. Therefore, flies were already sufficiently trained to show a plateau memory level in “Test1”. In the revision of another recent report (Figure 1C-F of Aso et al., 2023; https://doi.org/10.7554/eLife.85756), we included the learning curve data of our best Gr64f-split-GAL4, SS87269. Under a less saturated training conditioning, SS87269 did show learning augmentation over repeated training.
(2) In line 327, the authors state that in all samples, the β'1 compartment is arborized by MBON09. However, in Figure 11J, the probability of having at least one β'1 compartment not arborized is inferred to be 2%. The authors should address and clarify this conflict in the text to avoid misunderstanding.
The chance of visualizing MBON08 in MCFO images was 21/209 in total (Figure 11I). If we assume that each of four cells adopt MBON08 development fate at this chance, we can calculate the probability for each case of MBON08/09 cell type composition. From this calculation, we inferred approximately 2% of flies would lack innervations to β'1 compartment in at least one hemisphere. However, we didn't observe a lack of β'1 arborizations in 169 sample flies. If these MBONs independently develop into MBON08 at 21/209 odds, the chance of never observing two MBON08s in either hemisphere of all 169 samples is 3.29%. Therefore, some developmental mechanisms may prevent the emergence of two MBON08 in the same hemisphere.
In the revised manuscript, we displayed these estimated probability for each case separately, and annotated actual observation on the right side.
(3) In general, are the samples presented male or female? This sample metadata will be shown when the images are deposited in FlyLight, but it would be useful in the context of this manuscript to describe in the methods whether animals are all one sex or mixed sex, and in some example images (e.g. mAL3A) to note whether the sample is male or female.
The samples presented in this study are mixed sex, except for Figure 11I, where genders are specified. We provided metadata information of the presented images in Supplemental File 7, and we added a paragraph in the in the method section:
“Most samples were collected from females, though typically at least one male fly was examined for each driver line. While we noticed certain lines such as SS48900, exhibited distinct expression patterns in females and males, we did not particularly focus on sexual dimorphism, which is analyzed elsewhere (Meissner et al. 2024). Therefore, unless stated otherwise, the presented samples are of mixed gender.
Detailed metadata, including gender information and the reporter used, can be found in Supplementary File 7.”
Reviewer #2 (Public Review):
Summary:
The article by Shuai et al. describes a comprehensive collection of over 800 split-GAL4 and split-LexA drivers, covering approximately 300 cell types in Drosophila, aimed at advancing the understanding of associative learning. The mushroom body (MB) in the insect brain is central to associative learning, with Kenyon cells (KCs) as primary intrinsic neurons and dopaminergic neurons (DANs) and MB output neurons (MBONs) forming compartmental zones for memory storage and behavior modulation. This study focuses on characterizing sensory input as well as direct upstream connections to the MB both anatomically and, to some extent, behaviorally. Genetic access to specific, sparsely expressed cell types is crucial for investigating the impact of single cells on computational and functional aspects within the circuitry. As such, this new and extensive collection significantly extends the range of targeted cell types related to the MB and will be an outstanding resource to elucidate MB-related processes in the future.
Strengths:
The work by Shuai et al. provides novel and essential resources to study MB-related processes and beyond. The resulting tools are publicly available and, together with the linked information, will be foundational for many future studies. The importance and impact of this tool development approach, along with previous ones, for the field cannot be overstated. One of many interesting aspects arises from the anatomical analysis of cell types that are less stereotypical across flies. These discoveries might open new avenues for future investigations into how such asymmetry and individuality arise from development and other factors, and how it impacts the computations performed by the circuitry that contains these elements.
Weaknesses:
Providing such an array of tools leaves little to complain about. However, despite the comprehensive genetic access to diverse sensory pathways and MB-connected cell types, the manuscript could be improved by discussing its limitations. For example, the projection neurons from the visual system seem to be underrepresented in the tools produced (or almost absent). A discussion of these omissions could help prevent misunderstandings.
We internally distributed efforts to produce split-GAL4 lines at Janelia Research Campus. The recent preprint (Nern et al., 2024; doi: https://doi.org/10.1101/2024.04.16.589741) described the full collection of split-GAL4 driver lines in the optic lobe including the visual projection neurons to the mushroom body. We cited this preprint in the revised manuscript by adding a short paragraph of discussion.
“Although less abundant than the olfactory input, the MB also receives visual information from the visual projection neurons (VPNs) that originate in the medulla and lobula and are targeted to the accessory calyx (Vogt et al. 2016; Li et al. 2020). A recent preprint described the full collection of split-GAL4 driver lines in the optic lobe, which includes the VPNs to the MB (Nern et al. 2024).”
Additionally, more details on the screening process, particularly the selection of candidate split halves and stable split-GAL4 lines, would provide valuable insights into the methodology and the collection's completeness.
The details of our split-GAL4 design and screening procedures were described in previous studies (Aso et al., 2014; Dolan et al., 2019). Available data and tools to design split-GAL4 changed over time, and we took different approaches accordingly. Many of split-GAL4 lines presented in this study were designed and screened in parallel to the lines for MBONs and DANs in 2010-2014 when MCFO images of GAL4 drivers and EM connectome were not yet available. With knowledge of where MBONs and DANs project, I (Y.A.) manually examined and annotated thousands of confocal stacks (Jenett et al., 2012; https://doi.org/10.1016/j.celrep.2012.09.011) to find candidate cell types that may concat with them.
Later I used more advanced computational tools (Otsuna et al., 2018; doi: https://doi.org/10.1101/318006) and MCFO images aligned to the standard brain volume (Meissner et al., 2023; DOI: 10.7554/eLife.80660.). Now, if one needs to further generate split-GAL4 lines for cell type identified in EM connectome data, neuron bridge website (https://neuronbridge.janelia.org/) can be very helpful to provide a list of GAL4 drivers that may label the neuron of interest.
Reviewer #3 (Public Review):
Summary:
Previous research on the Drosophila mushroom body (MB) has made this structure the best-understood example of an associative memory center in the animal kingdom. This is in no small part due to the generation of cell-type specific driver lines that have allowed consistent and reproducible genetic access to many of the MB's component neurons. The manuscript by Shuai et al. now vastly extends the number of driver lines available to researchers interested in studying learning and memory circuits in the fly. It is an 800-plus collection of new cell-type specific drivers target neurons that either provide input (direct or indirect) to MB neurons or that receive output from them. Many of the new drivers target neurons in sensory pathways that convey conditioned and unconditioned stimuli to the MB. Most drivers are exquisitely selective, and researchers will benefit from the fact that whenever possible, the authors have identified the targeted cell types within the Drosophila connectome. Driver expression patterns are beautifully documented and are publicly available through the Janelia Research Campus's Flylight database where full imaging results can be accessed. Overall, the manuscript significantly augments the number of cell type-specific driver lines available to the Drosophila research community for investigating the cellular mechanisms underlying learning and memory in the fly. Many of the lines will also be useful in dissecting the function of the neural circuits that mediate sensorimotor circuits.
Strengths:
The manuscript represents a huge amount of careful work and leverages numerous important developments from the last several years. These include the thousands of recently generated split-Gal4 lines at Janelia and the computational tools for pairing them to make exquisitely specific targeting reagents. In addition, the manuscript takes full advantage of the recently released Drosophila connectomes. Driver expression patterns are beautifully illustrated side-by-side with corresponding skeletonized neurons reconstructed by EM. A comprehensive table of the new lines, their split-Gal4 components, their neuronal targets, and other valuable information will make this collection eminently useful to end-users. In addition to the anatomical characterization, the manuscript also illustrates the functional utility of the new lines in optogenetic experiments. In one example, the authors identify a specific subset of sugar reward neurons that robustly promotes associative learning.
Weaknesses:
While the manuscript succeeds in making a mass of descriptive detail quite accessible to the reader, the way the collection is initially described - and the new lines categorized - in the text is sometimes confusing. Most of the details can be found elsewhere, but it would be useful to know how many of the lines are being presented for the first time and have not been previously introduced in other publications/contexts.
We revised the text as below.
“Among the 828 lines, a subset of 355 lines, collectively labeling at least 319 different cell types, exhibit highly specific and non-redundant expression patterns are likely to be particularly valuable for behavioral experiments. Detailed information, including genotype, expression specificity, matched EM cell type(s), and recommended driver for each cell type, can be found in Supplementary File 1. A small subset of 40 lines from this collection have been previously used in studies (Aso et al., 2023; Dolan et al., 2019; Gao et al., 2019; Scaplen et al., 2021; Schretter et al., 2020; Takagi et al., 2017; Xie et al., 2021; Yamada et al., 2023). All transgenic lines newly generated in this study are listed in Supplementary File 2 (Aso et al., 2023; Dolan et al., 2019; Gao et al., 2019; Scaplen et al., 2021; Schretter et al., 2020; Takagi et al., 2017; Xie et al., 2021; Yamada et al., 2023).”
And where can the lines be found at Flylight? Are they listed as one collection or as many?
They are listed as one collection - “Aso 2021” release. It is named “2021” because we released the images and started sharing lines in December of 2021 without a descriptive paper. We added a sentence in the Methods section.
“All splitGAL4 lines can be found at flylight database under “Aso 2021” release, and fly strains can be requested from Janelia or the Bloomington stock center.”
Also, the authors say that some of the lines were included in the collection despite not necessarily targeting the intended type of neuron (presumably one that is involved in learning and memory). What percentage of the collection falls into this category?
We do not have a good record of split-GAL4 screening to calculate the chance to intersect unintended cell types, but it was rather rare. Those unintended cell types can still be a part of circuits for associative learning (e.g. olfactory projection neurons) or totally unrelated cell types. For instance, among a new collection of split-LexA lines using Gr43a-LexADBD hemidriver (Figure 7-figure supplement 2), one line specifically intersected T1 neurons in the optic lobe despite that the AD line was selected to intersect sugar sensory neurons. We suspect that this is due to ectopic expression of Gr43a-LexADBD. Nonetheless, we included it in the paper because cell-type-specific Split-LexA driver for T1 will be useful irrespective of whether the expression of Gr43a gene is expressed in T1 or not.
And what about the lines that the authors say they included in the collection despite a lack of specificity? How many lines does this represent?
For a short answer, there are about 100 lines in the collection that lack the specificity for behavioral experiments.
We ranked specificity of split-GAL4 drivers in the Supplementary File 1. Rank 2 are the ideal lines, Rank 1 are less ideal but acceptable, and Rank 0 is not suitable for activation screening in behavioral experiments. Out of the 828 split-GAL4 lines reported here, there are 413, 305 and 103 lines in rank2, rank1 and rank0 categories respectively. 7 lines are not ranked for specificity because only flipout expression data are available.
Recommendations for the authors:
Reviewer #2 (Recommendations For The Authors):
As mentioned elsewhere and in addition to the minor points below, it is advisable for the authors to elaborate on the details of the screening process. Furthermore, a discussion about the circuits not targeted by their research, such as the visual projection neurons, would be beneficial.
See the response above to Reviewer #2’s public review.
Line 32-33: The citations are very fly-centric. the authors might want to consider reviews on the MB of other insect species regarding learning and memory.
We additionally cited Rybak and Menzel 2017’s book chapter on honey bee mushroom body.
Line 43-44: Citations should be added, e.g. Séjourné et al. (2011), Pai et al. (2013), Plaçais et al. (2013).
Citation added
Line 50-52: Citation Hulse et al. (2021) should be added.
Citation added
Line 162: In this part, it might be valuable for the reader to understand which of these PNs are actually connecting with KCs.
A full list of cell types within the MB were provided in Supplementary File 4 of the revised manuscript. See also response to Reviewer 3, Lines 150-1.
Line 179: Citation Burke et al. (2012) should be mentioned.
Citation added
Line 181: Thermogenic might be thermogenetic.
Corrected
Line 189: Citations add Otto et al. (2020) and Felsenberg et al. (2018).
Citations added
Line 208ff: The authors should consider discussing why they did not use other GR and IR promoters. For example, Gr5a is prominent in sugar-sensing, while Ir76b could be a reinforcement signal related to yeast food (Steck et al., 2018; Ganguly et al., 2017; see also Corfas et al., 2019 for local search).
We focused on the Gr64f promoter because of its relatively broad expression and successful use of Gr64f-GAL4 for fictive reward experiment. We added the Split-LexA lines with Gr43a and Gr66a promoters (Figure 7-figure supplement 2). Other gustatory sensory neurons also have the potential to be reinforcement signals, but we just did not have the bandwidth to cover them all.
Line 319: Consider citing Linneweber et al. (2020) for a neurodevelopmental account of such individuality.
We added a sentence and cited this reference.
“On the other hand, the neurodevelopmental origin of neuronal morphology appeared to have functional significance on behavioral individuality (Linneweber et al. 2020).”
Line 352: Citation add Hulse et al. (2021).
Citations added
Line 356ff: The utility and value of Split-LexA may not be apparent to non-expert readers. Moreover, how were LexADBDs chosen for creating these lines?
We have added an introductory sentence at the beginning of the paragraph and explained that these split-LexA lines were a conversion of split-GAL4 lines that were published in 2014 and frequently used in studying the mushroom body circuit.
“Split-GAL4 lines enable cell-type-specific manipulation, but some experiments require independent manipulation of two cell types. Split-GAL4 lines can be converted into split-LexA lines by replacing the GAL4 DNA binding domain with that of LexA (Ting et al., 2011). To broaden the utility of the split-GAL4 lines that have been frequently used since the publication in 2014 (Aso et al., 2014a), we have generated over 20 LexADBD lines to test the conversions of split-GAL4 to split-LexA. The majority (22 out of 34) of the resulting split-LexA lines exhibited very similar expression patterns to their corresponding original split-GAL4 lines (Figure 12).”
Line 374: Italicize Drosophila melanogaster.
Revised as suggested.
Reviewer #3 (Recommendations For The Authors):
Major Comments:
As mentioned in the Public Review, the drivers are nicely classified in the various subsections of the manuscript, but the statements in the text summarizing how many lines there are in specific categories are often confusing. For example, line 129 refers to "drivers encompassing 111 cell types that connect with the DANs and MBONs", but Figure 1E indicates that 46 new cell types downstream of MBONs and upstream of DANs have been generated. This seems like a discrepancy.
The 46 cell types in Figure 1E consider only the CRE/SMP/SIP/SLP area, where MBON downstreams and DAN upstreams are highly enriched, while the 111 cell types include all. To avoid confusion, we removed the “MBON downstream and DAN upstream” counting in Figure 1E in the revised manuscript.
Also, at line 75 the MBON lines previously generated by Rubin and Aso (2023) are referred to as though they are separate from the 828 described "In this report." Supplementary file 1 suggests, however, that they are included as part of this report.
Twenty five lines generated in Rubin and Aso (2023) were initially included in Supplementary file 1 for the convenience of users, but they were not counted towards the 828 new lines described in this report. To avoid confusion, we removed these 25 lines in the revised manuscript. Now all lines listed in Supplementary file 1 were generated in this study (“Aso 2021” release), and if a line has been used in earlier studies, or introduced in other contexts, for example the accompanying omnibus preprint (Meissener 2024, doi: 10.1101/2024.01.09.574419), the citations are listed in the reference column.
More generally, in lines 94-102 "828 useful lines based on their specificity, intensity and non-redundancy" are referred to, but they are subsequently subdivided into categories of lines with lower specificity (i.e. with off-target expression) and lines that did not target intended cell types (presumably ones unlikely to be involved in learning and memory). It would be useful to know how many lines (at least roughly) fall into these subcategories.
See the response above to Reviewer #3’s public review.
Finally, Figures 3B & C indicate cell types connected to DANs and MBONs and the number for which Split-Gal4 lines are available. The text (lines 136-7) states that the new collection covers 30 of these major cell types (Figure 3C)," but Figure 3C clearly has more than 30 dots showing the drivers available. Presumably existing and new driver lines are being pooled, but this should either be explained or the two should be distinguished.
“(Figure 3C)” was replaced with “(Supplementaryl File 3)” in the revised manuscript to correct the reference. Figure 3B & C are plots of all MB interneurons, not just the major cell types.
Minor Comments:
Although the paper is generally well written there are minor grammatical errors throughout (e.g. dropped articles, odd constructions, etc.) that somewhat detract from an otherwise smooth and enjoyable reading experience. A quick editing pass by a native speaker (i.e. any of several of the authors) could clean up these and numerous other small mistakes. A few examples: line 138 "presented" should be present; line 204: "contain off-targeted expressions" should be "have off-target expression;" line 219: "usage to substitute reward" is awkward at best and could be something like "use in generating fictive rewards"; line 326 "arborize[s]"; l. 331 "Based on the likelihood" should be something like "based on these observations"'; line 349 "[is] likely to appear"; l. 352 "extensive connection[s]"; line 353 "has [a] strong influence;" l. 963 "Projections" should be singular; etc.
All the mentioned examples have been corrected, and we have asked a native speaker to edit through the revised manuscript.
Lines 81-3: Is the lookup table referred to Suppl. File 1? A reference is desirable.
Yes, the lookup table referred to “Supplementary File 1” and a reference was added.
Lines 111-2: what is a "non-redundant set of...cell types?" Cell types that are represented by a single cell (or bilateral pair)? Or does this sentence mean that of the 828 lines, 355 are specific to a single cell type, and in total 319 cell types are targeted? The statement is confusing.
We revised the text as below.
“Figure 1E provides an overview of the categories of covered cell types. Among the 828 lines, a subset of 355 lines, collectively labeling at least 319 different cell types, exhibit highly specific and non-redundant expression patterns are likely to be particularly valuable for behavioral experiments. Detailed information, including genotype, expression specificity, matched EM cell type(s), and recommended driver for each cell type, can be found in Supplementary File 1. A small subset of 40 lines from this collection have been previously used in studies (Aso et al.,
2023; Dolan et al., 2019; Gao et al., 2019; Scaplen et al., 2021; Schretter et al., 2020; Takagi et al., 2017; Xie et al., 2021; Yamada et al., 2023). All transgenic lines newly generated in this study are listed in Supplementary File 2 (Aso et al., 2023; Dolan et al., 2019; Gao et al., 2019; Scaplen et al., 2021; Schretter et al., 2020; Takagi et al., 2017; Xie et al., 2021; Yamada et al., 2023).”
Line 148: "MB major interneurons" is a confusing descriptor for postsynaptic partners of MBONs.
We added a sentence to clarify the definition of the “MB major interneurons”.
“In the hemibrain EM connectome, there are about 400 interneuron cell types that have over 100 total synaptic inputs from MBONs and/or synaptic outputs to DANs. Our newly developed collection of split-GAL4 drivers covers 30 types of these ‘major interneurons’ of the MB (Supplementary File 3).”
Lines 150-1: Not sure what is meant by "have innervations within the MB." Sounds like cells are presynaptic to KCs, DANS, and MBONs, but Figure 3 Figure Supplement 1 indicates they include neurons that both provide and receive innervation to/from MB neurons. Please clarify.
For clarification, in the revised manuscript we have included a full list of cell types within the MB in Supplementary File 4. Included are all neurons with >= 50 pre-synaptic connections or with >=250 post-synaptic connections in the MB roi in the hemibrain (excluding the accessory calyx). The cell types include KCs, MBONs, DANs, PNs, and a few other cell types. The coverage ratio was updated based on this list.
Also, in line 152, what does it mean that they "may have been overlooked previously?" this seems unnecessarily ambiguous. Were they overlooked or weren't they?
Changed the text to “These lines offer valuable tools to study cell types that previously are not genetically accessible. Notably, SS85572 enables the functional study of LHMB1, which forms a rare direct pathway from the calyx and the lateral horn (LH) to the MB lobes (Bates et al., 2020). ”
Line 158 refers to PN cells within the MB, which are not mentioned in any place else as MB components.
What are these PNs and how do they differ from MBONs?
See responses to Lines 150-1 for clarification of cell types within the MB.
Line 188: not clear what is meant by "more continual learning tasks".
We rephrase it as “more complex learning tasks” to avoid jargon.
Line 235: Not clear why "extended training with high LED intensity" wouldn't promote the formation of robust memories. Is this for some reason unexpected based on previous experiments? Please explain.
See responses to weakness #1 of the same reviewer
Lines 317-9: It would be useful to state here that MB0N08 and MB0N09 are the two neurons labeled by MB083C.
Revised as suggested.
Line 368: Presumably the "lookup table" referred to is Supplementary File 1, but a reference here would be useful.
Yes, Supplementary File 1 and a reference was added.
Comments on Figures:
Figure 1C The "Dopamine Neurons" label position doesn't align with the Punishment and Reward labels, which is a bit confusing.
They are intentionally not aligned, because dopamine neurons are not reward/punishment per se. We intend to use the schematic to show that the punishment and reward are conveyed to the MB through the dopamine neuron layer, just as the output from the MB output neuron layer is used to guide further integration and actions. To keep the labels of “Dopamine neurons” and “MB Output Neurons” in a symmetrical position, we decide to keep the original figure unchanged. But we thank the reviewer for the kind suggestion.
Figure 1F and Figure 1 - Figure Supplement 1: the light gray labels presumably indicate the (EM-identified) neuron labeled by each line, but this should be explicitly stated in the figure legends. It would also be useful in the legends to direct the reader to the key (Supplementary File 1) for decoding neuronal identities.
Revised as suggested.
Figure 2: For clarity, I'd recommend titling this figure "LM-EM Match of the CRE011-specific driver SS45245". This reduces the confusion of mixing and matching the driver and cell-type names. Also, it would be helpful to indicate (e.g. with labels above the figure parts) that A & B represent the MCFO characterization step and C & D represent the LM-EM matching step of the pipeline. Revised as suggested.
Figure 6: For clarity, it would be useful to separately label the PN and sensory neuron groups. Also, for the sensory neurons at the bottom, what is the distinction between the cell names in gray and black font?
Figure 6 was updated to separate the non-olfactory PN and sensory neuron groups. The gray was intended for olfactory receptor neuron cell types that are additionally labeled in the driver lines. To avoid confusion, the gray cell types were removed in the revised figure, and a clarification sentence was added to the legend.
“Other than thermo-/hygro-sensory receptor neurons (TRNs and HRNs), SS00560 and MB408B also label olfactory receptor neurons (ORNs): ORN_VL2p and ORN_VC5 for SS00560, ORN_VL1 and ORN_VC5 for MB408B.”
Figure 7A: It's unclear why the creation of 6 Gr64f-LexADBD lines is reported. Aren't all these lines the same? If not, an explanation would be useful.
These six Gr64f-LexADBD lines are with different insertion sites, and with the presence or absence of the p10 translational enhancer. Explanation was added to legend. Enhanced expression level with p10 can be helpful to compensate for the general tendency that split-LexA is weaker than split-GAL4. Different insertions will be useful to avoid transvections with split-GAL4s, which are mostly in attP40 and attP2.
Figure 8F: It would help to include in the legend a brief description of each parameter being measured-essentially defining the y-axis label on the graphs as in Figure Supplement 2. Also, how is the probability of return calculated and what behavioral parameter does the change of curvature refer to?
We added a brief description to the behavioral parameters in the legend of Figure 8F.
“Return behavior was assessed within a 15-second time window. The probability of return (P return) is the percentage of flies that made an excursion (>10 mm) and then returned to within 3 mm of their initial position. Curvature is the ratio of angular velocity to walking speed.”
Figure 9E: What are the parenthetical labels for lines SS49267, SS49300, and SS35008?
They are EM bodyIDs. Figure legend was revised.
