Many experimental approaches to understanding the nervous system require the ability to repeatedly target-specific neurons in order to efficiently explore their anatomy, physiology, gene expression, or function. In Drosophila melanogaster, the dominant approaches to targeting cells have been GAL4/UAS and related binary systems (Brand and Perrimon, 1993; Lai and Lee, 2006; Pfeiffer et al., 2010; Potter et al., 2010). The GAL4 protein, expressed from one transgene, binds upstream activation sequence (UAS) elements inserted in a separate transgene and activates the expression and translation of an adjacent functional protein. An extensive toolkit of UAS transgenes has been developed (reviewed in Guo et al., 2019). Large collections of GAL4 driver lines have been created, including collections (referred to here as ‘Generation 1’ or ‘Gen1’ GAL4 lines) in which GAL4 expression is typically controlled by 2–4 kilobase fragments of enhancer and promoter regions (Pfeiffer et al., 2008; Jenett et al., 2012; Tirian and Dickson, 2017). Published image libraries of the expression patterns of these GAL4 lines are available and provide a basis for visual or computational searches for driver lines with expression in cell populations of interest.

Despite these extensive resources, obtaining precise experimental access to individual neuronal cell types remains challenging. A GAL4 driver line from one of the above collections typically expresses in tens or more neuronal cell types and even more individual neurons, which is not sufficiently specific for many experiments. Several intersectional approaches have been designed to improve targeting specificity (reviewed in Guo et al., 2019), the most widely used of which is the split-GAL4 system (Luan et al., 2006; Pfeiffer et al., 2010). In brief, to create a split-GAL4 driver, the activation domain (AD) and DNA-binding domain (DBD) of GAL4 are individually placed under control of separate enhancer fragments. The AD and DBD are attached to leucine zipper motifs that further stabilize binding. Only in those neurons where both enhancer fragments are active is a functional GAL4 reassembled to activate the UAS, resulting in a positive intersection between enhancer expression patterns. The split-GAL4 system provides the required targeting specificity and has been used at an increasingly large scale (e.g., Gao et al., 2008; Tuthill et al., 2013; Aso et al., 2014a; Wu et al., 2016; Namiki et al., 2018; Wolff and Rubin, 2018; Dolan et al., 2019; Davis et al., 2020; Sterne et al., 2021), but good split combinations remain challenging to predict.

Split-GAL4 construction typically begins with the identification of GAL4 driver lines with expression in the cell type of interest. While the stereotyped shape of fly neurons can sometimes be directly distinguished by visual inspection, the specific features of a neuron are often obscured by other cells in a GAL4 expression pattern. Several stochastic labeling methods that reveal single cells present in broader expression patterns have been developed (reviewed in Germani et al., 2018). While large libraries of single-cell images exist (Chiang et al., 2011), these were mainly generated using a few widely expressed GAL4 lines. MultiColor FlpOut (MCFO; Nern et al., 2015) enables the labeling of stochastic subsets of neurons within a GAL4 or split-GAL4 pattern in multiple colors. In brief, MCFO can use several UAS reporters that are independently stochastically activated by low levels of Flp recombinase. Flp levels can be adjusted to tailor MCFO labeling density for different GAL4 lines or purposes. Labeling a GAL4 pattern using MCFO allows for the efficient determination of a significant fraction of the neurons present within it.

The need for resources to identify single cells of interest using genetic tools (GAL4 lines) has become more urgent due to recent advances in connectomics. Comprehensive electron microscopy (EM) mapping of specific brain regions or whole nervous systems is transforming neuroscience (e.g., Zheng et al., 2018; Maniates-Selvin et al., 2020; Scheffer et al., 2020) by providing anatomy at unparalleled resolution, near complete cell type coverage, and connectivity information. Leveraging these new datasets to understand more than pure anatomy will be greatly facilitated by the ability to genetically target-specific neurons and circuits. Light microscopy (LM) data also complement EM datasets by revealing features outside a reconstructed EM volume or by providing independent validation of cell shapes with a greater sample size. To integrate these formats requires datasets and methods for matching EM neurons with LM-derived GAL4/split-GAL4 data.

Recently developed techniques allow searching for neuron shapes (including neuron fragments, whole neurons, or overlapping groups of neurons) in coregistered LM and EM data. Two leading approaches are NBLAST (Costa et al., 2016), which performs comparisons between segmented neurons, and Color Depth Maximum intensity projection (CDM) search (Otsuna et al., 2018), which efficiently compares bitmap images using color to represent depth within the samples. NBLAST was recently expanded upon with the combination of PatchPerPix neuron segmentation (Hirsch et al., 2020) and PatchPerPixMatch search (PPPM; Mais et al., 2021). PPPM identifies neuron segments with similar color and high NBLAST scores that best cover a target neuron of interest, allowing the use of partial segments from densely labeled MCFO samples. Overlapping neurons remain challenging to segment manually or algorithmically, making this an area of rapid development. Advanced anatomical templates such as JRC2018 improve point-to-point mapping between samples and modalities (Bogovic et al., 2020). These search tools and templates bridge the EM/LM gap but require single-cell-level image collections that cover many neurons present within Gen1 GAL4 patterns to reach their maximum utility. In particular, to identify multiple Gen1 GAL4s that can be combined to make a split-GAL4 driver, the morphologies of individual neurons within many GAL4 lines must be available.

Here, we used MCFO to dissect Gen1 GAL4 line patterns at scale to create a resource for linking EM-reconstructed neurons to GAL4 lines, and to improve the process of making split-GAL4 reporters to target neurons, whether they were first identified in EM or LM. We therefore focused on 5155 Gen1 GAL4 lines, most of which have been converted into split-GAL4 hemidrivers, performing three rounds of MCFO labeling to improve coverage of neurons. The resource includes images of 74,337 fly samples, with an average of 14 brain and 7 ventral nerve cord (VNC) images per line. We have released the image data and made it searchable on the NeuronBridge website together with data from the FlyEM Hemibrain and published split-GAL4 lines.

We used the MCFO approach on Generation 1 GAL4 lines (Figure 1A) to visualize individual neurons (Figure 1B) making up the GAL4 expression pattern. These neurons can be matched to EM neurons (Figure 1C, D) in order to predict split-GAL4 combinations for an EM neuron of interest (Figure 1E). We generated two collections of Gen1 MCFO images (Table 1). The collection imaged with 20× and 63× microscope objectives targeted particular neurons of interest to collaborators annotating regions primarily in the brain and optic lobes. The collection imaged with 40× objectives broadly canvassed neurons in the central brain and VNC.

Figure 1 with 1 supplement see all

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A challenge with any stochastic neuron labeling approach is to optimize the number of identifiable neurons in each sample: too sparse and samples are empty or have few labeled neurons; too dense and the neurons overlap, making it difficult to fully isolate individual neurons even if they are labeled in different colors. MCFO allows for control of labeling density by optimizing the amount of Flp activity, either by selecting different Flp drivers, or altering heat shock duration for hs-Flp (Nern et al., 2015). GAL4 lines with broader expression typically require lower Flp activity to yield isolated neurons. In the 20×/63× MCFO collection, labeling density was customized for collaborators focused on annotating particular central nervous system (CNS) regions, iterating on prior results (Nern et al., 2015). In the 40× MCFO collection, labeling density was initially standardized (Phase 1), then optimized based on overall GAL4 expression density (Phase 2; Figure 1—figure supplement 1A). For many lines, there is no globally ideal level of Flp activity, as they have varying levels of expression density in different CNS regions.

The 20×/63× and 40× datasets differed in several other respects (Table 1). The 20×/63× collection was imaged with 20× objectives, followed by 63× imaging of specific regions of interest, whereas the 40× collection was uniformly imaged at 40×. The 20×/63× collection was focused on a smaller set of lines visualized primarily in female brains (94.2%), whereas the 40× collection covered more lines (4575 vs. 2463), a mixture of male and female samples (44.9% female), and both brains and VNCs (7.1 VNCs per line vs. 0.9 in the 20×/63× dataset).

Finally, as the 20×/63× dataset and existing publications (e.g., Fischbach and Dittrich, 1989; Morante and Desplan, 2008; Takemura et al., 2013; Nern et al., 2015; Takemura et al., 2015) effectively documented the largely repetitive structure of the optic lobes, the 40× dataset excluded them. Collections of split-GAL4 driver lines for many optic lobe cell types are already available (Tuthill et al., 2013; Wu et al., 2016; Davis et al., 2020). Many neurons that connect the optic lobe with the central brain can still be identified in the 40× dataset based on their central brain arborizations. The optic lobe anatomy of such cells could be further characterized in follow-up experiments with the identified GAL4 lines.

40× Gen1 MCFO collection

After performing extensive MCFO labeling for the 20×/63× dataset, we performed comprehensive MCFO mapping of Gen1 GAL4 lines across most of the CNS. MCFO labeling of Drosophila neurons was performed with a pan-neuronal Flp recombinase (R57C10-Flp) on 4562 Generation 1 GAL4 lines in Phase 1. We generated images of 27,226 central brains and 26,512 VNCs from 27,729 flies. The CNS was typically dissected from six flies per line. A medium-strength Flp transgene (R57C10-Flp2::PEST in attP18; Nern et al., 2015) was used for almost all lines, yielding a wide range of neuronal labeling in each MCFO sample. 238 of the sparser lines were crossed to an MCFO reporter with a stronger Flp transgene (R57C10-FlpL in su(Hw)attP8), and 71 lines were crossed to both reporters.

GAL4 lines were qualitatively categorized into rough groups by density of expression within the central brain and VNC, ranging from Category 1 yielding no unique neurons per sample, to Category 5 being so dense that it overwhelmed our immunohistochemical approach, leaving a shell of partially labeled neurons around the outside of each sample (Figure 1—figure supplement 1A). Category 2 lines were characterized by sparse, easily separable neurons, whereas Category 3 yielded denser but identifiable neurons. Category 4 displayed densely labeled neurons that were challenging to distinguish. Most lines ranged between Categories 2 and 4 (Figure 1—figure supplement 1B).

In order to increase the number of identifiable neurons, a subset of lines was re-examined with altered parameters. Phase 2 of the 40× pipeline generated images of an additional 18,894 central brains and 6235 VNCs from 19,062 flies. Phase 2 GAL4 expression density was optimized by (1) selecting lines with expression most likely useful for split halves, (2) adjusting MCFO parameters to maximize separable neurons obtained per sample, and (3) limiting brains and VNCs processed per line to minimize the diminishing returns associated with oversampling. Phase 2 focused on Category 2 and 3 lines as most likely to be useful for split-GAL4 creation. Category 1 and 5 lines were outside our effective labeling range and were therefore excluded from further work. High neuron density within Category 4 means that although the theoretical neuron yield from each sample is high, our ability to distinguish individual neurons is low (although future improvements to neuron segmentation approaches are expected to improve yields).

Heat-shock Flp (hs-Flp) was used in Phase 2 rather than R57C10-Flp (Figure 2). While both R57C10-Flp and hs-Flp are theoretically expected to label all neurons, in practice each is likely to have subtle biases as previously proposed (Nern et al., 2015; see also below). By switching Flp enhancers in Phase 2, we attempted to mitigate the impact of these biases. The 37°C heat shock duration for hs-Flp was optimized for each density category. Prior results reported by Nern et al., 2015 indicated that heat shock effectiveness is nonlinear: limited to background activity up to ~10 min, a somewhat linear range between 10 and 20 min, and gradually diminishing returns up to ~40 min; heat shocks longer than an hour begin to harm fly survival. We chose a heat shock duration of 40 min for Category 2 lines to yield as many neurons as possible per sample. For Category 3 a 13 min heat shock provided the desired labeling density similar to Category 3 in Phase 1. To increase the chance of obtaining sex-specific neurons and neuronal morphology, we randomly choose one sex for each half of the lines in Phase 1 and then in Phase 2 switched them to the opposite sex.

Figure 2

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As the number of MCFO samples for a given GAL4 line increases, the probability of labeling additional unique neurons diminishes until every neuron labeled by that GAL4 line is represented within the MCFO dataset. Sparser lines approach saturation more rapidly, especially because we can use higher Flp activity to label a greater fraction of available GAL4 neurons per sample without overwhelming detection. Thus, in Phase 2 we processed fewer samples for Category 2 GAL4 lines than for Category 3. In addition to diminishing returns within each GAL4 line, there are diminishing returns within each region of the CNS. Although recent estimates vary (37–100k neurons for the central brain including subesophageal ganglion but not the optic lobes, 15–20k for the VNC; Bates et al., 2019; Godfrey et al., 2021; Mu et al., 2021; Raji and Potter, 2021), the adult Drosophila central brain has many more neurons than the VNC, suggesting earlier diminishing returns in the VNC. Thus, we focused Phase 2 more heavily on the brain than the VNC, which together with the above density adjustment led to imaging on average 6.0 brains in Category 2 or 9.1 brains in Category 3, and 2.5 VNCs per line across both categories.

Search approach evaluation

We performed limited evaluations of CDM and PPPM search performance between the EM Hemibrain (Scheffer et al., 2020) and the Gen1 MCFO dataset in the context of making split-GAL4 lines specifically targeting EM bodies of interest (Figure 3).

Figure 3

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Search performance can be evaluated in several ways depending on the application (Costa et al., 2016; Otsuna et al., 2018; Mais et al., 2021). We refer here to ‘forward’ and ‘reverse’ analysis in the context of split-GAL4 creation. Forward analysis consisted of direct qualitative evaluation of EM to LM search results, determining whether top LM results appeared to contain the searched for EM body. Forward analysis is best performed with detailed knowledge of the examined neurons to avoid false positives, and we restricted our analyzed set of neurons accordingly. Reverse analysis made use of previously documented associations between split-GAL4 lines and EM bodies. If a split-GAL4 line labels a neuron, its constituent split hemidrivers should as well, as should some MCFO of Gen1 GAL4 lines with the same enhancers. We thus evaluated whether known EM/LM matches were highly ranked within the search results. Due to the stochastic nature of MCFO, not every sample of a valid matching GAL4 line will contain the target neuron.

Evaluation of the search approaches also addressed neuron coverage of the Gen1 MCFO dataset. For both search directions the total number of correct matching samples and GAL4 lines gave a measure of how completely the Gen1 MCFO dataset labels each queried neuron.

We performed forward analysis on the top 100 CDM and PPPM Phase 1 Gen1 MCFO search results for 10 Hemibrain bodies (Figure 4). Both CDM and PPPM correctly identified many highly ranked matches in the dataset for each examined EM body. CDM identified 17.6 ± 8.3 (average ± standard deviation) correct lines per Hemibrain body, whereas PPPM identified 20.1 ± 10.6.

Figure 4 with 1 supplement see all

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Figure 4—source data 1

Table of forward analysis results by cell type.

Table shows all individual scores and the following metrics: the number of lines independently identified by CDM and PPPM, number only identified by one approach (XOR), number identified by both approaches (AND), and total number identified (OR).

https://cdn.elifesciences.org/articles/80660/elife-80660-fig4-data1-v2.xlsx

Download elife-80660-fig4-data1-v2.xlsx

For cell type LC18, PPPM outperformed CDM, with 24 and 13 correct matches in the top 100, respectively (Figure 4B). For cell type CT1, on the other hand, CDM correctly found 8 results in the top 100, compared to 3 for PPPM (Figure 4C). More generally, CDM and PPPM each identified many lines in the top 100 results that were not identified by the other search approach (Figure 4—source data 1). CDM uniquely identified 8.2 ± 6.1 and PPPM uniquely identified 10.7 ± 8.7 lines, respectively.

Thus, at least for this limited set of neurons, the Gen1 MCFO collection isolates enough examples of each neuron to likely create a split-GAL4 combination. CDM and PPPM successfully identify these correct matches, although they are interspersed with a larger number of false matches. Both approaches varied widely by neuron, without obvious correlation to neuron morphology (Figure 4—figure supplement 1). Although all 10 neurons examined here yielded at least 9 matching lines, we do not expect this to hold for every neuron. It remains likely that expanding the MCFO collection with more samples or more drivers would improve the chances of obtaining a good set of matches.

We extended the PPPM reverse analysis in Mais et al., 2021 with a comparison to CDM (Table 2). We examined nine Hemibrain bodies, each with 2–13 published split-GAL4 associations (Schretter et al., 2020; Wang et al., 2020b). The best rank of each known-matching line was recorded, with Table 2 showing the median line rank and the percentage of lines with ranks in the top 50 results. PPPM and CDM both had median line ranks under 100 for most EM bodies. PPPM was somewhat more consistent, with 33–80% of known matches in the top 50 results, compared to 0–100% for CDM. As with the forward analysis, each approach performed better on some neurons than the other approach.

Table 2

Name	Hemibrain body ID	Known-matching lines	PPPM median line rank	CDM median line rank	PPPM % in top 50	CDM % in top 50
pC1e	514850616	13	14	58	69%	46%
pC1d	5813063587	12	28	41	58%	50%
aIPg	645456880	5	6	3	80%	100%
oviDN	550655668	4	70	42	50%	75%
oviDN	519949044	4	95	41	50%	50%
SAG	517587356	2	49	78	50%	0%
SAG	5812981862	2	44	118	50%	0%
vpoDN	5813057864	4	NA	95	50%	50%
DNp13	887195902	3	84	53	33%	33%

We have described an extensive MCFO image resource from Generation 1 GAL4 lines, providing single-cell-level resolution of the neurons labeled by each line. The NeuronBridge website allows rapid searching of this resource from published EM datasets or uploaded images. CDM and PPPM search approaches both find valid EM/LM matches for several tested neurons, supporting their effectiveness and the good coverage of the brain by the Gen1 MCFO collection. NeuronBridge has already seen frequent usage (Bidaye et al., 2020; Morimoto et al., 2020; Nojima et al., 2021; Sareen et al., 2021; Zolin et al., 2021; Israel et al., 2022; Tanaka and Clark, 2022; Laturney et al., 2022). Together these tools allow for the rapid determination of likely split-GAL4 lines and other enhancer-based approaches to target most neurons initially found in the FlyEM Hemibrain and eventually in the full Drosophila CNS.

While performing these analyses and practically applying the tools to screen split-GAL4 combinations, we made some qualitative observations: (1) In general, both CDM and PPPM are complimentary and best used in combination, although PPPM tended to bring good matches closer to the top of search results. (2) CDM occasionally struggled with occluded neurons and benefited from examination of full 3D stacks of matching MCFO samples. (3) PPPM correspondingly showed the most improvement in samples with occluded neurons. (4) Both techniques return some highly ranked false positives with clear flaws, such that rankings alone are insufficient for algorithmic association of EM and LM neurons. (5) We estimate the image collection and search techniques can lead to good split combinations for 50–80% of cell types, depending on how clean a combination is needed. More split hemidrivers would likely be needed to increase this rate. The search techniques do not significantly change which cell types can be targeted, but greatly simplify identifying candidate split combinations without requiring as much anatomical expertise.

There are several caveats for why close EM/LM matches do not always lead to successful split-GAL4 combinations: (1) Many CNS cell types contain multiple neurons that are indistinguishable based on morphology. Thus, two matches for a cell type may label different neurons within the cell type and fail as a split combination. Information from connectomic approaches and other modalities are also continuing to refine cell type definitions. (2) Although split-GAL4 hemidrivers are made with the same enhancer fragments as Gen1 GAL4 lines, they can differ in vector sequence and genomic insertion site. These differences can alter expression patterns and hence split-GAL4 effectiveness. (3) UAS reporters can vary in genomic insertion site, number of UAS elements, and other factors that affect how well they label particular cell types. MCFO reporters in particular can tend to brightly label neurons that are weakly labeled by reporters for the full GAL4 pattern. An examination of the full Gen1 GAL4 patterns (if not too dense) can help predict likely effectiveness of a split combination. (4) GAL4 driver expression can vary temporally, so there could be spatial but not temporal overlap between two split hemidrivers.

In creating the image resource, we have optimized driver line selection, sample preparation, and imaging to yield the maximum identifiable neurons per sample, per line, and across the central brain and VNC. For the search resource, we have implemented two complementary search approaches that effectively identify neuron matches in an easy to use interface. The image resource should be amenable to analysis with future search approaches as they continue to develop.

While our focus has been on the EM to split-GAL4 use case, we described other uses, including guiding EM proofreading and extending EM analyses beyond limited regions or sample sizes currently available. We anticipate other uses will be found for this resource.

The footprint of this image resource (~105 TB) exceeds our known current practical limits on standard public data repositories. Thus, we have made all the primary data (and a variety of processed outputs) used in this study freely available under a CC BY 4.0 license at https://doi.org/10.25378/janelia.21266625.v1 and through the publicly accessible website https://gen1mcfo.janelia.org. The images are made searchable with the same permissions on the user-friendly NeuronBridge website https://neuronbridge.janelia.org. NeuronBridge code is available at Clements et al., 2021 and the application and implementation are discussed further in Clements et al., 2022.All other data generated or analyzed during this study are included in the manuscript and supporting files.

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Author details

1. Geoffrey W Meissner

   Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States

   Contribution

   Conceptualization, Data curation, Formal analysis, Supervision, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing – original draft, Project administration, Writing – review and editing

   For correspondence

   meissnerg@janelia.hhmi.org

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-0369-9788

2. Aljoscha Nern

   Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States

   Contribution

   Conceptualization, Data curation, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing – original draft, Writing – review and editing

   For correspondence

   nerna@janelia.hhmi.org

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-3822-489X

3. Zachary Dorman

   Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States

   Contribution

   Data curation, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing – original draft, Project administration, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-9933-7217

4. Gina M DePasquale

   Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States

   Contribution

   Validation, Investigation

   Competing interests

   No competing interests declared

5. Kaitlyn Forster

   Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States

   Contribution

   Validation, Investigation

   Competing interests

   No competing interests declared

6. Theresa Gibney

   Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States

   Contribution

   Validation, Investigation

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-5461-724X

7. Joanna H Hausenfluck

   Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States

   Contribution

   Investigation

   Competing interests

   No competing interests declared

8. Yisheng He

   Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States

   Contribution

   Validation, Investigation

   Competing interests

   No competing interests declared

9. Nirmala A Iyer

   Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States

   Contribution

   Validation, Investigation, Methodology

   Competing interests

   No competing interests declared

10. Jennifer Jeter

    Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States

    Contribution

    Data curation, Validation, Investigation, Visualization, Methodology

    Competing interests

    No competing interests declared

11. Lauren Johnson

    Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States

    Contribution

    Validation, Investigation

    Competing interests

    No competing interests declared

12. Rebecca M Johnston

    Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States

    Contribution

    Data curation, Supervision, Validation, Investigation, Methodology, Project administration

    Competing interests

    No competing interests declared

13. Kelley Lee

    Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States

    Contribution

    Data curation, Validation, Investigation, Project administration

    Competing interests

    No competing interests declared

14. Brian Melton

    Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States

    Contribution

    Data curation, Validation, Investigation, Methodology

    Competing interests

    No competing interests declared

15. Brianna Yarbrough

    Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States

    Contribution

    Validation, Investigation

    Competing interests

    No competing interests declared

16. Christopher T Zugates

    Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States

    Contribution

    Supervision, Funding acquisition, Project administration

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-1882-3665

17. Jody Clements

    Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States

    Contribution

    Software, Visualization

    Competing interests

    No competing interests declared

18. Cristian Goina

    Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States

    Contribution

    Software, Visualization

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-2835-7602

19. Hideo Otsuna

    Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States

    Contribution

    Software, Formal analysis, Visualization, Methodology

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-2107-8881

20. Konrad Rokicki

    Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States

    Contribution

    Data curation, Software, Supervision, Funding acquisition, Validation, Visualization, Methodology, Writing – original draft, Project administration, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-2799-9833

21. Robert R Svirskas

    Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States

    Contribution

    Data curation, Software, Validation

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0001-8374-6008

22. Yoshinori Aso

    Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States

    Contribution

    Conceptualization, Investigation, Methodology, Writing – review and editing

    For correspondence

    asoy@janelia.hhmi.org

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-2939-1688

23. Gwyneth M Card

    Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States

    Contribution

    Conceptualization, Supervision

    For correspondence

    cardg@janelia.hhmi.org

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-7679-3639

24. Barry J Dickson

    Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States

    Contribution

    Conceptualization, Supervision, Writing – review and editing

    For correspondence

    dicksonb@janelia.hhmi.org

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-0715-892X

25. Erica Ehrhardt

    Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States

    Contribution

    Investigation

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-9252-1414

26. Jens Goldammer

    Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States

    Contribution

    Investigation

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-5623-8339

27. Masayoshi Ito

    Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States

    Contribution

    Investigation

    Competing interests

    No competing interests declared

28. Dagmar Kainmueller

    Max-Delbrueck-Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany

    Contribution

    Conceptualization, Software, Formal analysis, Supervision, Validation, Visualization, Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-9830-2415

29. Wyatt Korff

    Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States

    Contribution

    Conceptualization, Supervision, Funding acquisition, Methodology, Project administration

    For correspondence

    korffw@janelia.hhmi.org

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0001-8396-1533

30. Lisa Mais

    Max-Delbrueck-Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany

    Contribution

    Software, Formal analysis, Validation, Visualization, Methodology

    Competing interests

    No competing interests declared

31. Ryo Minegishi

    Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States

    Contribution

    Investigation

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-2895-9438

32. Shigehiro Namiki

    Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States

    Contribution

    Investigation

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-1559-799X

33. Gerald M Rubin

    Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States

    Contribution

    Conceptualization, Supervision, Funding acquisition, Methodology, Writing – review and editing

    For correspondence

    rubing@janelia.hhmi.org

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0001-8762-8703

34. Gabriella R Sterne

    Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States

    Contribution

    Investigation, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-7221-648X

35. Tanya Wolff

    Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States

    Contribution

    Investigation, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-8681-1749

36. Oz Malkesman

    Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States

    Contribution

    Conceptualization, Supervision, Funding acquisition, Methodology, Project administration

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-2219-7476

37. FlyLight Project Team

    Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States

    Contribution

    Conceptualization, Data curation, Funding acquisition, Investigation, Methodology, Project administration, Supervision, Validation, Visualization, Writing – original draft, Writing – review and editing

    Competing interests

    No competing interests declared

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