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Brain-wide cellular resolution imaging of Cre transgenic zebrafish lines for functional circuit-mapping

  1. Kathryn M Tabor
  2. Gregory D Marquart
  3. Christopher Hurt
  4. Trevor S Smith
  5. Alexandra K Geoca
  6. Ashwin A Bhandiwad
  7. Abhignya Subedi
  8. Jennifer L Sinclair
  9. Hannah M Rose
  10. Nicholas F Polys
  11. Harold A Burgess  Is a corresponding author
  1. Eunice Kennedy Shriver National Institute of Child Health and Human Development, United States
  2. University of Maryland, United States
  3. Virginia Polytechnic Institute and State University, United States
  4. National Institute of General Medical Sciences, United States
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Cite this article as: eLife 2019;8:e42687 doi: 10.7554/eLife.42687

Abstract

Decoding the functional connectivity of the nervous system is facilitated by transgenic methods that express a genetically encoded reporter or effector in specific neurons; however, most transgenic lines show broad spatiotemporal and cell-type expression. Increased specificity can be achieved using intersectional genetic methods which restrict reporter expression to cells that co-express multiple drivers, such as Gal4 and Cre. To facilitate intersectional targeting in zebrafish, we have generated more than 50 new Cre lines, and co-registered brain expression images with the Zebrafish Brain Browser, a cellular resolution atlas of 264 transgenic lines. Lines labeling neurons of interest can be identified using a web-browser to perform a 3D spatial search (zbbrowser.com). This resource facilitates the design of intersectional genetic experiments and will advance a wide range of precision circuit-mapping studies.

https://doi.org/10.7554/eLife.42687.001

Introduction

Elucidating the functional circuitry of the brain requires methods to visualize neuronal cell types and to reproducibly control and record activity from identified neurons. Genetically encoded reporters and effectors enable non-invasive manipulations in neurons but are limited by the precision with which they can be targeted. While gene regulatory elements are often exploited to direct transgene expression, very few transgenic lines strongly express reporter genes in a single cell type within a spatially restricted domain. Precise targeting of optogenetic reagents can be achieved using spatially restricted illumination in immobilized or optic-fiber implanted animals (Aravanis et al., 2007; Arrenberg et al., 2009; Wyart et al., 2009; Zhu et al., 2012). However, non-invasive genetic methods that confine expression to small groups of neurons enable analysis of behavior in freely moving animals. Such methods include intersectional genetic strategies, where reporter expression is controlled by multiple independently-expressed activators (Dymecki et al., 2010; Gohl et al., 2011).

In zebrafish, intersectional control of transgene expression has been achieved by combining the Gal4/UAS and Cre/lox systems (Förster et al., 2017; Satou et al., 2013; Tabor et al., 2018). Gal4-Cre intersectional systems take advantage of hundreds of existing Gal4 lines which are already widely used in zebrafish circuit neuroscience (Asakawa et al., 2008; Bergeron et al., 2012; Scott et al., 2007), but are limited by the relatively poor repertoire of existing Cre lines, and difficulty in identifying pairs of driver lines that co-express in neurons of interest. The first version of the Zebrafish Brain Browser (ZBB) atlas provided a partial solution to these problems by co-aligning high resolution image stacks of more than 100 transgenic lines, with an accuracy approaching the limit of biological variability (Marquart et al., 2017; Marquart et al., 2015). ZBB enabled users to conduct a 3D spatial search for lines with reporter expression in areas of interest and predict the area of intersection between Gal4 and Cre lines, aiding the design of intersectional genetic experiments. However, ZBB software required local installation and only included 9 Cre lines, limiting opportunities for intersectional targeting.

Here, we describe the ZBB2 atlas, which comprises whole-brain expression patterns for 264 transgenic lines, including 65 Cre lines, and 158 Gal4 lines. We generated more than 100 new enhancer trap lines that express Cre or Gal4 in diverse subsets of neurons, then registered a high resolution image of each to the original ZBB atlas. For 3D visualization of expression patterns and to facilitate spatial searches for experimentally useful lines, we now provide an online interface to the atlas. Collectively, ZBB2 labels almost all cellular regions within the brain and will facilitate the reproducible targeting of neuronal subsets for circuit-mapping studies.

Results

To accelerate discovery of functional circuits we generated a library of transgenic lines that express Cre in restricted patterns within the brain and built an online 3D atlas (Figure 1). New Cre lines were generated through an enhancer trap screen: we injected embryos with a Cre vector containing a basal promoter that includes a neuronal-restrictive silencing element to suppress expression outside the nervous system, and tol2 transposon arms for high efficiency transgenesis (REx2-SCP1:BGi-Cre-2a-Cer) (Bergeron et al., 2012; Kawakami, 2007; Marquart et al., 2015). In injected G0 embryos, the reporter randomly integrates into the genome such that Cre expression is directed by local enhancer elements. To isolate lines with robust brain expression in relatively restricted domains, we visually screened progeny of G0 adults crossed to the βactin:Switch transgenic line that expresses red fluorescent protein (RFP) in cells with Cre (Horstick et al., 2015). We retained 52 new lines that express Cre in restricted brain regions at 6 days post-fertilization (dpf), the stage most frequently used for behavioral experiments and circuit-mapping. We then used a confocal microscope to scan brain-wide GFP and RFP fluorescence from et-Cre, βactin:Switch larvae at high resolution, aligned the merged signal to the ZBB βactin:GFP pattern and applied the resulting transformation matrix to the RFP signal alone. To obtain a representative image of Cre expression, we averaged registered brain scans from 3 to 10 larvae and masked expression outside the brain. Cre lines reported here for the first time are shown in Figure 2, and an overview of all 65 Cre lines that can be searched using ZBB2 is summarized in Supplementary file 1.

Procedure for imaging and co-registering new Cre lines.

Inset schematics: neuronal-restrictive silencing element (NRSE) sites in the enhancer trap construct are targets for the REST protein, which suppresses Cre expression outside the brain.

https://doi.org/10.7554/eLife.42687.002
Cre enhancer trap lines.

Horizontal maximum projection of 52 new Cre enhancer trap lines, with color indicating depth along the dorsal-ventral dimension (huC counter-label, grey).

https://doi.org/10.7554/eLife.42687.003

We previously isolated more than 200 Gal4 lines with expression in subregions of the brain; however, the original ZBB atlas represented only those that showed expression restricted to the nervous system. The subsequent development of a synthetic untranslated region (UTR.zb3) that suppresses non-neuronal expression has mitigated issues associated with Gal4 driving effector genes in non-neuronal tissues (Marquart et al., 2015). We therefore imaged 45 additional Gal4 lines in which robust brain expression is accompanied by expression in non-neural tissues. We visualized Gal4 expression using the UAS:Kaede transgenic line and registered patterns to ZBB by co-imaging vglut2a:DsRed expression. New Gal4 lines reported here are shown in Figure 3. We also aligned 20 high resolution brain scans of Gal4 lines performed by either the Dorsky or Baier laboratories by adapting a method for multi-channel registration of the Z-Brain and ZBB atlases (Förster et al., 2017; Marquart et al., 2017; Otsuna et al., 2015). In total, ZBB2 includes the spatial pattern of expression for 158 Gal4 lines. Supplementary file 2 summarizes all Gal4 enhancer traps generated by our laboratory that can be searched in ZBB2. The relative expression of each transgenic line within 20 µm-side bins is reported in Supplementary file 3. In total, ZBB2 describes the expression pattern for 264 transgenic lines, more than doubling the number in the original atlas (Table 1).

Gal4 enhancer trap lines.

Horizontal maximum projection of 45 new Gal4 enhancer trap lines (depth coded; huC counter-label, grey).

https://doi.org/10.7554/eLife.42687.004
Table 1
Summary of transgenic lines in ZBB2.

Total numbers of enhancer trap lines, and transgenic lines (made using promoter fragments from genes, or through BAC recombination), broken down by type: Gal4, Cre or fluorescent protein (FP). Right columns total the number of lines where genomic information driving the expression pattern is available. This information inherently exists for transgenic lines, and was derived through integration site mapping for enhancer trap lines.

https://doi.org/10.7554/eLife.42687.005
All lines (n = 264)Mapped (n = 171)
Gal4CreFPGal4CreFP
Enhancer trap13865596154
Transgenic2003620036
Total15865411161540

Enhancer traps randomly integrate into the genome and it is not usually possible to determine the identity of the cells labeled by their spatial pattern of expression alone. However, cell-type information for enhancer trap lines can be inferred from co-localization with reporters whose expression is directed by a defined promoter, or through integration into a bacterial artificial chromosome. ZBB2 contains expression data for 56 such transgenic lines, including reporters for most major neurotransmitters. The relative mean expression intensity for nine major cell-type markers within neuroanatomic. Additional cell-type information in enhancer trap lines may be revealed by integration site mapping, because enhancer traps often recapitulate, at least in part, the expression pattern of genes close to the site of transgene integration. We therefore developed a new method to efficiently map integration sites, using an oligonucleotide to hybridize with the enhancer trap tol2 arms and capture flanking genomic DNA fragments for sequencing (see Materials and methods for detail). We recovered the integration site for 55 Gal4 and Cre enhancer trap lines (detailed in ). Altogether 171 of the lines in ZBB2 either use a defined promoter, or have a known genomic integration site, providing molecular genetic information on cell-type identity (Table 1).

To assess how useful the lines represented in ZBB2 will be for circuit-mapping studies, we calculated the selectivity of each line. For this, we first estimated the volume of the brain that includes cell bodies — using transgenic markers of cell bodies and neuropil (Figure 4A) — then calculated the percent of the cell body volume labeled by each line. For Cre lines, median coverage of the cell body volume was 6% (range 0.1% to 48%, Figure 4B,D). As we estimate that there are ~92,000 neurons in the six dpf brain (see Materials and methods), this equates to a median of around 5500 neurons per line. In total, 96% of the cellular volume is labeled by at least one Cre line, with an average of 6 lines per voxel. However, Cre lines provide limited access to the midbrain tegmentum, posterior tuberculum and trigeminal ganglion (Figure 4B). Gal4 lines tend to have more restricted expression than Cre lines, with a median coverage of 1% (range 0.02% to 29%,~900 neurons per line). Collectively Gal4 lines label 91% of the cell body volume (Figure 4C,D). Salient areas that are not labeled include a rostro-dorsal domain of the optic tectum, the caudal lobe of the cerebellum, and a medial area within rhombomeres 3–4 of the medulla oblongata. Despite Gal4 lines having more restricted expression, few show tightly confined expression, highlighting the importance of intersectional approaches for precise targeting.

Spatial coverage of Cre and Gal4 enhancer trap lines.

(A) Horizontal (left), coronal (top right), sagittal (bottom right) sections showing huC:nls-mCar (cell bodies, red) and huC:Gal4,UAS:syp-RFP (neuropil, cyan) from ZBB (huC counter-label, gray), illustrating the separation of cell bodies and neuropil in six dpf brains. (B–C) Horizontal sections (at the levels indicated in A) of heat-maps showing the number of et-Cre (B) and et-Gal4 (C) lines that label each voxel within the cellular area of the brain (scale bar, right). Voxels that lack coverage indicated in green. Coverage for Cre lines is highest in the pallium (Pa). Gal4 lines conspicuously lack coverage in the anterior optic tectum (TeO) and in a medial zone of the medulla oblongata (MO). (D) Histogram of the cellular-region coverage for et-Cre (red) and et-Gal4 (green) lines.

https://doi.org/10.7554/eLife.42687.006

We envisaged using Cre lines to select experimentally useful subdomains of Gal4 expression patterns. To test this, we selected pairs of Cre and Gal4 lines with overlapping expression in spatially restricted regions, then analyzed reporter expression in triple transgenic Gal4/Cre/UAS:Switch larvae. As any given brain region likely contains multiple intermingled cell types, Gal4 and Cre lines that appear to overlap, may actually label distinct neurons. Nevertheless, intersectional patterns observed closely matched predicted intersects, labeling relatively small clusters of neurons (Figure 5). In general, intersectional expression in individual larvae is more restricted than predicted. In part, this may be because UAS transgenics are susceptible to silencing, reducing the extent of expression.

Spatially restricted reporter expression in Gal4/Cre intersectional domains.

Maximum projections for et-Gal4 and et-Cre lines predicted to show co-expression in small domains of neurons, and of the resulting pattern of expression in a triple transgenic et-Gal4, et-Cre, UAS:KillSwitch larva. Scale bar 100 µm. (A) Horizontal (top) and sagittal (bottom) maximum projections for (left to right): y318-Gal4 expression, y481-Cre expression, predicted reporter distribution in cells that co-express y318 and y481, actual RFP expression in an individual y318, y481, UAS:KillSwitch larva. Closed arrowhead indicates the cerebellar eminentia granularis (Eg), which is labeled in both lines and the intersect. Open arrowhead indicates descending fiber tract from Eg neurons. (B) As for (A) with y393-Gal4 and y528-Cre lines. Closed arrowheads indicate the lateral habenula (black) and trigeminal ganglion (grey). Open arrowhead indicates central projections from the trigeminal ganglion.

https://doi.org/10.7554/eLife.42687.007

For the first release of the ZBB atlas, we provided downloadable Brain Browser software that allowed users to conduct a 3D spatial search for transgenic lines that label neurons within a specific Z-Brain defined neuroanatomic region (Randlett et al., 2015) or selected volume. For ZBB2, we have imported all the new lines into the original Brain Browser. However, recognizing that requiring a locally installed IDL runtime platform posed a limit to accessibility, we implemented an online version that can be accessed using a web-browser (http://zbbrowser.com). The online version includes key features of the original Brain Browser, including 3D spatial search, prediction of the area of intersectional expression between selected lines, partial/maximal/3D projections, information about the neuroanatomical identity of any selected voxel and ability to load user-generated image data (Figure 6). Additionally, the online version features an integrated virtual reality viewer for Google cardboard. We used X3DOM libraries to achieve rapid volume rendering and enable users to select data resolution that best matches their connection speed, so that the browser-based implementation remains highly responsive (Arbelaiz et al., 2017b; Arbelaiz et al., 2017a). Both local and web-based versions include hyper-links to PubMed, UCSC Genome Browser and Zfin, so that users can quickly retrieve publications describing each line, its integration site, and ordering information, respectively.

Web-browser based edition of the Zebrafish Brain Browser with key functions annotated.

Panel size can be adjusted to best fit screen dimensions with the Settings menu.

https://doi.org/10.7554/eLife.42687.008

Discussion

The ZBB2 atlas provides cellular resolution imaging data for 65 Cre lines, and 158 Gal4 lines, enabling small clusters of neurons to be genetically addressed through intersectional targeting. To aid in the design of such experiments, we have implemented a web-based interface that allows users to perform a spatial search for lines that express Cre or Gal4 in any selected 3D volume, and thereby identify transgenic lines for intersectional visualization or manipulation of selected neurons (Tabor et al., 2018). We anticipate that the ZBB2 lines will also facilitate structural mapping of the zebrafish brain because many strongly label discrete neuroanatomical entities.

In Drosophila, intersectional genetic targeting is often achieved using 'split' systems (Gal4, lexA and Q) in which the DNA-binding domain and transactivation domain of a transcription factor are separately expressed ensuring that activity is reconstituted only in neurons that express both protein domains during the same time interval (Pfeiffer et al., 2010; Ting et al., 2011; Wei et al., 2012). We did not pursue this approach because of the investment needed to create and maintain numerous 'half' lines which can not be used alone. In contrast, Gal4 and Cre lines are independently useful and Cre lines can be used intersectionally with hundreds of existing zebrafish Gal4 lines. Several intersectional reporter lines have already been generated to visualize cells that co-express Cre and Gal4 (Förster et al., 2017; Satou et al., 2013). In addition, the UAS:KillSwitch line enables selective ablation of neurons that co-express Gal4 and Cre, and the UAS:DoubleSwitch line sparsely labels neurons within a Gal4/Cre co-expression domain for morphological reconstruction (Tabor et al., 2018).

New Gal4 and Cre lines described here were generated through enhancer trap screening, a high throughput method that facilitates the isolation of strongly expressing transgenic reporters in diverse anatomical regions and cell types. An alternative is to use CRISPR or TALEN based methods to insert transgenes into genes known to mark useful cell types or anatomical regions (Auer et al., 2014; Kimura et al., 2014). These methods are moderate throughput, and have the advantage of providing more reliable cell type information on the resulting transgene expression patterns. Limitations are that expression from an endogenous locus may not be sufficient to drive experimentally useful levels of Gal4 or Cre, and that an insertion may depress expression of the endogenous protein from the targeted allele. Given that many neurological disorders are associated with haploinsufficiency, reporter lines generated in this way may therefore perturb brain development or function. BAC transgenesis is a third method that has been widely used to make new Gal4 and Cre lines, that also exploits known gene expression patterns, while avoiding potentially disruptive insertions (Förster et al., 2017). However BAC transgenesis is low throughput, and as for targeted insertions, can not guarantee robust reporter expression. Thus future efforts at generating new transgenic reporters will likely be best served through enhancer traps and CRISPR-mediated transgene insertion.

A limitation of the Cre system is that insufficient expression may lead to stochastic activity at target loxP sites and consequent mosaic reporter expression (Förster et al., 2017; Schmidt-Supprian and Rajewsky, 2007). We therefore only retained lines with strong expression across multiple animals. Conversely Cre is toxic at high levels — in some cases, mosaic expression may be due to the death of a subset of cells (Bersell et al., 2013). While we cannot readily assess the toxicity of our lines, confounding effects can be experimentally addressed with the proper controls. Our enhancer trap Cre lines tend to have broader expression than Gal4 lines, likely because (1) Cre expression in progenitor cells labels all offspring, expanding the domain of expression, and (2) switch reporters remain active after transient Cre expression, whereas Gal4 must be continually expressed to drive UAS reporters. Occasionally we have observed UAS:Switch expression in neurons outside the domain of Cre expression in the ZBB2 atlas. This may be because the 14xUAS-E1b promoter is stronger than the β-actin promoter present in the switch reporter used to build the atlas. Additionally, most brain regions contain multiple cell types with biological variability in their precise position. Thus predicted overlapping expression based on co-registered brain scans must be experimentally verified.

The ZBB2 atlas advances mapping of the zebrafish brain at single cell resolution by comprehensively describing the cellular-resolution pattern of brain expression for 264 transgenic lines and providing a user-friendly web-browser based interface for searching and visualizing the expression in each. New Cre and Gal4 enhancer trap lines are freely available and we anticipate will advance circuit-mapping studies by providing essential reagents for intersectional targeting of neurons. We expect that this database and the associated transgenic lines will drive exploration of structure/function relations in the vertebrate brain.

Materials and methods

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y515This paperZFIN:ZDB-ALT-180717–64ZFIN symbol:y515Et; Et(REx2-cfos:kGal4ff)y515
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y519This paperZFIN:ZDB-ALT-180717–65ZFIN symbol:y519Et; Et(REx2-SCP1:BGi-Cre-2a-Cer)y519
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y520This paperZFIN:ZDB-ALT-180717–66ZFIN symbol:y520Et; Et(REx2-SCP1:BGi-Cre-2a-Cer)y520
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y521This paperZFIN:ZDB-ALT-180717–67ZFIN symbol:y521Et; Et(REx2-SCP1:BGi-Cre-2a-Cer.zf3)y521
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y523This paperZFIN:ZDB-ALT-180717–68ZFIN symbol:y523Et; Et(REx2-SCP1:BGi-Cre-2a-Cer.zf3)y523
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y524This paperZFIN:ZDB-ALT-180717–69ZFIN symbol:y524Et; Et(REx2-SCP1:BGi-Cre-2a-Cer)y524
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y526This paperZFIN:ZDB-ALT-180717–70ZFIN symbol:y526Et; Et(REx2-SCP1:BGi-Cre-2a-Cer.zf3)y526
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y528This paperZFIN:ZDB-ALT-180717–71ZFIN symbol:y528Et; Et(REx2-SCP1:BGi-Cre-2a-Cer.zf3)y528
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y532This paperZFIN:ZDB-ALT-180717–72ZFIN symbol:y532Et; Et(cfos:Gal4ff)y532
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y533This paperZFIN:ZDB-ALT-180717–73ZFIN symbol:y533Et; Et(SCP1:Gal4ff)y533
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y541This paperZFIN:ZDB-ALT-180717–74ZFIN symbol:y541Et; Et(REx2-SCP1:BGi-Cre-2a-Cer.zf3)y541
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y542This paperZFIN:ZDB-ALT-180717–75ZFIN symbol:y542Et; Et(REx2-SCP1:BGi-Cre-2a-Cer.zf3)y542
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y543This paperZFIN:ZDB-ALT-180717–76ZFIN symbol:y543Et; Et(REx2-SCP1:BGi-Cre-2a-Cer.zf3)y543
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y544This paperZFIN:ZDB-ALT-180717–77ZFIN symbol:y544Et; Et(REx2-SCP1:BGi-Cre-2a-Cer.zf3)y544
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y545This paperZFIN:ZDB-ALT-180717–78ZFIN symbol:y545Et; Et(REx2-SCP1:BGi-Cre-2a-Cer.zf3)y545
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y546This paperZFIN:ZDB-ALT-180717–79ZFIN symbol:y546Et; Et(REx2-SCP1:BGi-Cre-2a-Cer.zf3)y546
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y547This paperZFIN:ZDB-ALT-180717–80ZFIN symbol:y547Et; Et(REx2-SCP1:BGi-Cre)y547
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y548This paperZFIN:ZDB-ALT-180717–81ZFIN symbol:y548Et; Et(REx2-SCP1:BGi-Cre)y548
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y549This paperZFIN:ZDB-ALT-180717–82ZFIN symbol:y549Et; Et(REx2-SCP1:BGi-Cre)y549
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y550This paperZFIN:ZDB-ALT-180717–83ZFIN symbol:y550Et; Et(REx2-SCP1:BGi-Cre)y550
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y551This paperZFIN:ZDB-ALT-180717–84ZFIN symbol:y551Et; Et(REx2-SCP1:BGi-Cre-2a-Cer.zf3)y551
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y552This paperZFIN:ZDB-ALT-180717–85ZFIN symbol:y552Et; Et(REx2-SCP1:BGi-Cre-2a-Cer.zf3)y552
Genetic reagent
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y554This paperZFIN:ZDB-ALT-180717–87ZFIN symbol:y554Et; Et(REx2-SCP1:BGi-Cre-2a-Cer.zf3)y554
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y555This paperZFIN:ZDB-ALT-180717–88ZFIN symbol:y555Et; Et(REx2-SCP1:BGi-Cre)y555
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y556This paperZFIN:ZDB-ALT-180717–89ZFIN symbol:y556Et; Et(REx2-SCP1:BGi-Cre)y556
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y557This paperZFIN:ZDB-ALT-180717–90ZFIN symbol:y557Et; Et(REx2-SCP1:BGi-Cre)y557
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y558This paperZFIN:ZDB-ALT-180717–91ZFIN symbol:y558Et; Et(REx2-SCP1:BGi-Cre-2a-Cer.zf3)y558
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y559This paperZFIN:ZDB-ALT-180717–92ZFIN symbol:y559Et; Et(REx2-SCP1:BGi-Cre)y559
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y564This paperZFIN:ZDB-ALT-180717–93ZFIN symbol:y564Et; Et(SCP1:Gal4ff)y564
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y565This paperZFIN:ZDB-ALT-180717–94ZFIN symbol:y565Et; Et(REx2-SCP1:Gal4ff)y565
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y566This paperZFIN:ZDB-ALT-180717–95ZFIN symbol:y566Et; Et(REx2-cfos:Gal4ff)y566
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y567This paperZFIN:ZDB-ALT-180717–96ZFIN symbol:y567Et; Et(REx2-cfos:Gal4ff)y567
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y568This paperZFIN:ZDB-ALT-180717–97ZFIN symbol:y568Et; Et(REx2-SCP1:BGi-Cre-2a-Cer.zf3)y568
Genetic reagent
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y569This paperZFIN:ZDB-ALT-180717–98ZFIN symbol:y569Et; Et(REx2-SCP1:BGi-Cre)y569
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y570This paperZFIN:ZDB-ALT-180717–99ZFIN symbol:y570Et; Et(REx2-SCP1:BGi-Cre)y570
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y571This paperZFIN:ZDB-ALT-180717–100ZFIN symbol:y571Et; Et(R2R6-hoxa2-CNE-SCP1:BGi-Cre-2a-Cer)y571
Genetic reagent
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y574This paperZFIN:ZDB-ALT-180717–101ZFIN symbol:y574Et; Et(REx2-SCP1:BGi-Cre)y574
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y575This paperZFIN:ZDB-ALT-180717–102ZFIN symbol:y575Et; Et(cfos:Gal4)y575
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y576This paperZFIN:ZDB-ALT-180717–103ZFIN symbol:y576Et; Et(cfos:Gal4)y576
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βactin:Switch; y272PMID:25628360ZFIN:ZDB-ALT-150721–8
Genetic reagent
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vglut2a:DsRed; nns14PMID:22302816ZFIN:ZDB-ALT-110413–5
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UAS:KillSwitch; y518PMID:30078569ZFIN:ZDB-ALT-181218–5
Genetic reagent
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UAS:Kaede; s1999tPMID:17335798ZFIN:ZDB-ALT-070314–1
Genetic reagent
(D. rerio)
elavl3:h2b-GCaMP6; jf5PMID:25068735ZFIN:ZDB-ALT-141023–2
Recombinant
DNA reagent
REx2-SCP1:BGi-Cre-2a-Cer (plasmid)PMID:26635538ZFIN:ZDB-ETCONSTRCT-151102–1
Recombinant
DNA reagent
REx2-SCP1:BGi-CreThis paperZFIN:ZDB-ETCONSTRCT-180514–2Progenitors:REx2-SCP1:BGi-Cre-2a-Cer (plasmid)
Recombinant
DNA reagent
REx2-SCP1:BGi-Cre-2a-Cer.zf3 (plasmid)This paperZFIN:ZDB-ETCONSTRCT-180518–1Progenitors:REx2-SCP1:BGi-Cre-2a-Cer (plasmid)
Recombinant
DNA reagent
attP-REx2-SCP1:BGi-Cre-2a-Cer-attPThis paperZFIN:ZDB-ETCONSTRCT-151102–2Progenitors:REx2-SCP1:BGi-Cre-2a-Cer (plasmid)
Recombinant
DNA reagent
R2R6-hoxa2-CNE-SCP1:BGi-Cre-2a-CerThis paperZFIN:ZDB-ETCONSTRCT-180514–1Progenitors:REx2-SCP1:BGi-Cre-2a-Cer (plasmid)
Sequence-based
reagent
tol2-arrm pulldown oligonucleotideThis paper5-CTCAAGTGAAAGTACAAGTACTTAGGGAAAATTTTACTCAATTAAAAGTAAAAGTATCTGGCTAGAATCTTACTTGAGTAAAAGTAAAAAAGTACTCCATTAAAATTGTACTTGAGTATT
Sequence-based
reagent
tol2-arrm pulldown oligonucleotideThis paper5-TGTAATTAAGTAAAAGTAAAAGTATTGATTTTTAATTGTACTCAAGTAAAGTAAAAATCCCCAAAAATAATACTTAAGTACAGTAATCAAGTAAAATTACTCAAGTACTTTACACCTCTG
SoftwareAdvanced Normalization ToolsPMID:17659998
SoftwareZebrafish Brain Browser (desktop)PMID:26635538Download at https://science.nichd.nih.gov/confluence/display/burgess/Brain+Browser
SoftwareZebrafish Brain Browser (online)This paperGitHub:BurgessLab/ZebrafishBrainBrowserzbbrowser.com; Githhub hosts javascript code using X3DOM to render image files
SoftwareImageJPMID:22930834
OtherConfocal images for ZBB2 linesThis paperDryad:doi:10.5061/dryad.tk467n8Compressed archives containing 16-bit NIFTI format scans of individual larvae

Husbandry

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Zebrafish (Danio rerio) were maintained on a Tubingen long fin strain background. Larval zebrafish were raised on 14/10 hr light/dark cycle at 28°C in E3h medium (5 mM NaCl, 0.17 mM KCl, 0.33 mM CaCl2, 0.33 mM MgSO4, 1.5 mM HEPES, pH 7.3) with 300 µM N-Phenylthiourea (PTU, Sigma) to suppress melanogenesis for imaging. Experiments were conducted with larvae at 6 days post fertilization (dpf), before sex differentiation. Experimental procedures were approved by the NICHD animal care and use committee.

Zebrafish lines

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Enhancer trap lines that express Cre (et-Cre lines) were initially isolated through enhancer trap screening using a tol2 vector containing a REx2-SCP1:BGi-Cre-2a-Cer cassette (286 adult fish screened) (Marquart et al., 2015). Although the fluorescent protein Cerulean is co-expressed with Cre in this vector, it was rarely strong enough to visualize directly, and we instead screened using the βactin:Switch transgenic line (Horstick et al., 2015). Thus subsequently, we removed the 2a-Cer cassette from the enhancer trap vector for generating new lines and injected a vector with a REx2-SCP1:BGi-Cre cassette (75 adult fish screened). At least 50 (usually over 100) offspring of injected fish were visually screened for RFP fluorescence from the βactin:Switch (Tg(actb2:loxP-eGFP-loxP-ly-TagRFPT)y272) reporter line (Horstick et al., 2015). As around 10% of injected animals transmitted more than a single expression pattern, likely reflecting several integration loci, we bred each line for multiple generations to isolate a single heritable transgene. Gal4 lines newly described here have been maintained for at least seven generations, in all cases with the UAS:Kaede reporter for visualizing expression. New Cre lines have been maintained for at least three generations, with the βactin:Switch reporter. Because we only retained lines with restricted areas of brain expression, we ultimately kept lines from around 20% of injected fish. For maintenance, Cre lines were crossed to fish heterozygous for the βactin:Switch transgene. Outcrossing to βactin:Switch was necessary because, as in other systems, leaky Cre expression recombines lox sites that are transmitted through the same gamete (Schmidt-Supprian and Rajewsky, 2007). Consequently, in clutches from et-Cre;βactin:Switch crossed to βactin:Switch, we discarded ~25% of embryos that showed ubiquitous RFP expression due to complete recombination of the Switch reporter in gametes also containing the Cre transgene. We also imaged previously described Cre lines with rhombomere-specific expression (Tabor et al., 2018). Gal4 enhancer trap lines were isolated as previously described (Bergeron et al., 2012). UAS reporter transgenes are susceptible to silencing leading to variegated expression. To minimize silencing, we raise single insertion UAS reporters with a broadly expressed Gal4 transgene. In each generation, we outcross to wildtype stock, and raise only double transgenic individuals with the brightest and most complete expression (typically around 20% of fluorescent protein positive embryos).

Other zebrafish lines in this study were: UAS:Kaede (Tg(UAS-E1b:Kaede)s1999t (Davison et al., 2007), UAS:KillSwitch (Tg(14xUAS-E1b:BGi-lox-GFP-sv40-loxepNTR-TagRFPT)y518) (Tabor et al., 2018), y379-Cre and y484-Cre (Marquart et al., 2017), vglut2a:DsRed (TgBAC(slc17a6b:loxP-DsRed-loxP-GFP)nns9) (Satou et al., 2013), Tg(gata1:dsRed)sd2 (Traver et al., 2003), Tg(−4.9sox10:EGFP)ba2 (Wada et al., 2005), Tg(−8.4neurog1:GFP)sb1 (Blader et al., 2003), Tg(kctd15a:GFP)y534 (Heffer et al., 2017), Tg(pou4f3:gap43-GFP)s356t (Xiao et al., 2005), Et(−1.5hsp70l:Gal4-VP16)s1156t and Et(fos:Gal4-VP16)s1181t (Scott and Baier, 2009), TgBAC(neurod:EGFP)nl1 (Obholzer et al., 2008), Tg(mnx1:GFP)ml2 (Flanagan-Steet et al., 2005), and y271-Gal4 (Horstick et al., 2015). For counting neurons in the brain, we used huC:h2b-GCaMP6 (Tg(elavl3:h2b-GCaMP6)jf5) (Vladimirov et al., 2014), which has multiple transgene integrations, minimizing effects of variable expression and silencing.

Brain imaging and processing

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For imaging, six dpf larvae were embedded in 1.5–3.5% agarose in E3h and oriented dorsal to the objective. Each larval brain was scanned in two image stacks (anterior and posterior halves, 1 × 1×2 µm resolution) with an inverted Leica TCS-SP5 II confocal with a 25X, 0.95 NA objective, while adjusting laser power during scans to compensate for intensity loss with depth. Gal4 expression was visualized using UAS:Kaede and Cre expression using RFP expression from βactin:Switch. Color channels were usually acquired simultaneously and crosstalk removed in post-processing using a Leica dye separation algorithm. Substacks were connected using the pairwise stitching plugin in ImageJ (Preibisch et al., 2009; Schneider et al., 2012).

Image registration was performed using affine and diffeomorphic algorithms in ANTs (Avants et al., 2011) with parameters optimized for live embryonic zebrafish brain scans that produce alignments with an accuracy of approximately one cell diameter (8 µm) (Marquart et al., 2017). For registration, each image stack required a reference image previously registered to the ZBB coordinate system. Reference channels were vglut2a:DsRed for Gal4 lines and βactin:Switch GFP for Cre lines. Other transgenic lines and patterns were registered using either vglut2a:dsRed or vglut2a:GFP where appropriate. For Cre lines, we merged the βactin:Switch GFP and RFP signals into a combined pattern to provide a channel for registration. We then applied the resulting transformation matrix to the RFP channel alone. Next, we averaged registered brain images from at least three larvae per line, using the ANTs AverageImages command, to create a representative image of each line. Mean images were masked to remove expression outside the brain, except where inner ear hair cells or neuromasts were labeled. Next, we normalized intensity to saturate the top 0.01% of pixels, and downsampled to 8-bit to reduce file size and facilitate distribution. We manually defined fluorescent intensity thresholds for each line that best distinguished cellular expression from neuropil or background to facilitate spatial search for lines that express in selected cell populations. Mean images were also aligned to Z-Brain using a previously described bridging transformation matrix (Marquart et al., 2017).

Because registration using the βactin:Switch bridging channel proved more accurate than our previous bridging registration with HuC:Cer, we re-imaged and registered the Cre lines recovered in our pilot screen. Gal4 lines generated and imaged by the Dorsky lab (Otsuna et al., 2015) were registered using two channels: the nuclear counter-stain channel (TO-PRO−3) and immunolabeling for myosin heavy chain, aligned to HuC:nls-mCar and tERK in ZBB respectively. We also used multichannel registration to align brain scans performed by the Baier lab (Förster et al., 2017), taking advantage of three expression patterns present in both datasets: vglut2a:dsRed, isl2b:GFP and gad1b:GFP.

Integration site mapping

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To efficiently map enhancer trap integration sites we extracted genomic DNA from embryos from each line (Qiagen DNeasy Blood and Tissue Kit) and generated a barcoded library. We hybridized the library to biotinylated 120 bp primers (IDT ultramers) designed against the tol2 sequence arms and enriched for genomic integration sites using avidin-pulldown. Enriched libraries were combined into 15 pools such that each pool contained a unique combination of five transgenic lines and each line was exclusively represented in two pools. Pooled libraries were sequenced using an Illumina MiSeq (Illumina) which produced 250 bp paired-end reads. Reads were aligned against the biotinylated primer sequence, then unique sequences within each read subsequently aligned to a zebrafish reference genome (danRer10). Sequences common to all pools were assumed to be off-target and removed from analysis. Remaining reads from each pool were cross-referenced to the combination of embryos in each pool. Regions that had high and specific enrichment in both pools containing DNA from a particular sample were assigned as candidate insertion sites for that sample. To validate this procedure, we confirmed the map position for four lines through direct PCR genotyping.

Expression analysis

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To assess the selectivity of transgene expression, we manually set an intensity threshold for each line to distinguish cell body labeling from background, and calculated the proportion of voxels in the total cell body volume with a super-threshold signal. In assessing total brain coverage by the Cre library, we excluded y457-Cre which has extremely broad (possibly pan-neuronal) expression. To quantitatively describe transgene expression patterns, we created 20 × 20 × 20 µm cubic bins (total 1804 bins) that were each entirely within the left hemisphere of the brain. For each line, we measured the mean expression within each bin and its corresponding volume on the right hemisphere. The resulting 1804 element vector was re-scaled in the range 0 to 1.0 to describe the relative intensity of transgene expression in each bin. Supplementary file 3 reports these values together with the location of center voxel for each bin (Horizontal: dorsal to ventral; Transverse: Anterior to Posterior; Sagittal: Left to Right). In the downloadable version of ZBB2, this position can be recalled by using View → Jump to Frame, then entering the coordinates provided in first three columns in Supplementary file 3 (e.g. 230, 90, 210 to access the center position for the first volume in the Table).

To estimate the total number of cells at six dpf, we dissected brains and counted dissociated cells using a cell sorter, yielding a total of 124700 ± 2200 cells per brain (mean and standard error, N = 9), including mature and immature neurons, glia and non-neural cells (e.g. connective tissue and vasculature).

For this procedure, brains were dissected at room temperature in 1x PBS (K-D Medical), dissociated in papain (Papain Dissociation System, Worthington Biochemical) by incubation in 20 U/mL papain for 20 min then triturated and 0.005% DNAse added. The final volume was adjusted to 200 µL in DMEM fluorobrite (Gibco) and immediately counted using a BD FACSCalibur (BD Biosciences), using a custom gate to include single cells in the forward and side scattered area. To minimize cell death, the entire process was completed within 45 min per brain.

We estimated the number of differentiated neurons by manually counting fluorescently labeled post-mitotic neurons in brain images. For this, we first imaged huC:h2b-GCaMP6 transgenic brains at high-resolution (0.5 × 0.5 × 0.5 μm per voxel). In this line, nuclear-localized GCaMP fluorescence provides a discrete signal that facilitates identification of single neurons even in dense regions. We assigned all voxels in the brain to five neuronal-density bins based on the ratio of huC:nls-mCar (somas), and huC:Gal4, UAS:syp-RFP (synapses; see Figure 4A) and identified five representative volumes (30 × 30 × 30 μm each) for each of the five bins. We then counted neurons in huC:h2b-GCaMP6 brain scans in each of the 25 volumes. Finally, we scaled the mean number of neurons in each bin by the relative fraction of the brain that each bin covers to obtain an estimate of the total neuron number. Using this procedure, we estimated that there are 92,000 ± 3000 mature neurons in the six dpf brain (N = 3 larvae).

To analyze the regional distribution of cell types based on molecular marker expression, we calculated the mean transgene expression intensity for 72 manually annotated anatomical structures (Z-Brain, Supplementary file 4) and for 168 computational defined brain regions (subset of Pajevic 180 regions, excluding narrow regions on the perimeter of the brain, Supplementary file 5) (Gupta et al., 2018; Randlett et al., 2015). For each line analyzed, voxel expression values were scaled 0–1, and means thus reflect the relative intensity of expression for regions within each maker and can not be compared between markers.

Zebrafish Brain Browser software

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The lines scanned and registered here were incorporated into the locally run Zebrafish Brain Browser, which requires downloading and installing the free IDL runtime environment. ZBB2 (including software and full resolution datasets) can be downloaded from our website (https://science.nichd.nih.gov/confluence/display/burgess/Brain+Browser).

To increase accessibility we also implemented an online version of ZBB2 that does not require downloading, and runs in any javascript-enabled web-browser (http://zbbrowser.com). We used Bootstrap (http://getbootstrap.com/) for interface design and jQuery for event-handling (https://jquery.com/). For rendering of 2D slices and 3D projections, we used X3DOM, a powerful set of open-source 3D graphics libraries for web development which integrates the X3D file format into the HTML5 DOM (Behr et al., 2009; Congote, 2012; John et al., 2008; Polys and Wood, 2012). ZBB2 uses X3DOM’s built in MPRVolumeStyle and BoundaryEnhancementVolumeStyle functions to render 2D image files (texture atlases) in 3D space. The MPRVolumeStyle is used for the X, Y and Z slicer views to display a single slice from a 3D volume along a defined axis. We modified X3DOM source code for this volume style to support additional features including color selection, contrast and brightness controls, rendering of crosshairs, spatial search boxes and intersections between selected lines. The BoundaryEnhancementVolumeStyle renders the 3D projection. We also modified this function's source code, including additions of color, contrast, and brightness values. Other minor changes were made to the X3DOM libraries including a hardcoded override to allow additive blending of line colors. The online ZBB2 loads images of each line as a single 2D texture atlas. Image volumes for each line were converted to a montage, downsampling by taking every 4th plane in the z-dimension, and to 0.25, 0.5, and 0.75 their original size for low, medium, and high resolutions respectively to ensure rapid loading time. Texture atlas images were then referenced using X3DOM’s ‘ImageTextureAtlas’ node, and its ‘numberOfSlices’, ‘slicesOverX’, and ‘slicesOverY’ attributes were specified as 100, 10, and 10, respectively. These atlases were then referenced by ‘VolumeData’ nodes, along with an MPRVolumeStyle or BoundaryEnhancementVolumeStyle node, to build the volumes visible on the screen.

To implement the 3D spatial search in the online edition of ZBB2, we first binarized and 4x-downsampled the resolution of each line. The data for each line was then parsed into a single array (width, height, depth). We compressed adjacent binary values into a single byte using bit shifting operators, downsampling the data once again by eight times. While greatly downsized, the entire dataset was still much too large to quickly download. We therefore fragmented the array for each line into 8 × 8×8 blocks of 64 bytes each, and concatenated blocks for every line, creating a single array of around 17 kb for a specific sub-volume of the brain. After the user defines the search volume, relevant volume fragments are downloaded and searched. Data from each fragment file is passed to a JavaScript Web Worker, allowing each file to be searched in a separate thread. This procedure facilitates minimal search times, with the main limitation being that thousands of binary files must be regenerated whenever a new line is added to the library.

Quantification and statistical analysis

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Analysis was performed with IDL (http://www.harrisgeospatial.com/SoftwareTechnology/IDL.aspx), Gnumeric (http://projects.gnome.org/gnumeric/) and Matlab (Mathworks).

Resource sharing

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Most enhancer trap lines are available from Zebrafish International Resource Center (https://zebrafish.org), with all others available from the authors upon request. Registered individual confocal brain scans can be downloaded from Dryad (https://doi.org/10.5061/dryad.tk467n8). Brain browser javascript code can be downloaded from GitHub (Hurt et al., 2018; copy archived at https://github.com/elifesciences-publications/ZebrafishBrainBrowser).

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Decision letter

  1. Indira M Raman
    Reviewing Editor; Northwestern University, United States
  2. Didier Y Stainier
    Senior Editor; Max Planck Institute for Heart and Lung Research, Germany
  3. Claire Wyart
    Reviewer; Hôpital Pitié-Salpêtrière, Sorbonne Universités, UPMC Univ Paris 06, Inserm, CNRS, France

In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.

Thank you for submitting your article "Brain-wide cellular resolution imaging of Cre transgenic zebrafish lines for functional circuit-mapping" for consideration by eLife. Your article has been reviewed by three peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Didier Stainier as the Senior Editor. The following individuals involved in review of your submission have agreed to reveal their identity: Claire Wyart (Reviewer #1). The other reviewers remain anonymous.

The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.

Summary:

This tool/resource provides a large set of zebrafish Cre and Gal4 lines for use by the zebrafish research community for the generation of specific lines through intersectional genetics. The data are organized into an online platform that makes the content readily accessible to investigators.

Essential revisions:

The reviewers expressed enthusiasm about the platform as well as the care that went into the work. These comments are listed below under "general assessments" from each reviewer. Two points of concern/interest were raised.

1) Reviewer 3 brought up the point that it is not clear whether the intersectional approach will work well or not with fish in which both Gal4 and cCre are expressed in only a small number of neurons. The idea here is that it should, but evidence is not provided that it does. (Reasons for questioning whether it does are given in the reviewer's comments, but include stochastic activity at target loxP sites for Cre, and variable expression and silencing for Gal4.) In the consultation, the other reviewers agreed with reviewer 3's concern, and therefore suggest that an example or two be provided. We realize this will require some breeding time if no such data is already in hand; please let us know if this is feasible.

2) Reviewer 1 suggested that the utility of the resource could be greatly increased by a somewhat deeper analysis of the existing data, e.g., by providing information on neurotransmitter type by quantifying overlap with transgenic lines targeting specific neurotransmitter types. This would not require new data, but additional information on the existing data.

These points are more fully spelled out in the collected major comments from the reviewers, which are listed below, with editorial indications in square brackets.

General assessments:

Reviewer #1:

The manuscript of Tabor et al. provide a very important set of cre and gal4 lines that will be extremely useful for intersectional genetics in the field of zebrafish neuronal circuits, together with a novel online platform, which is extremely convenient to analyse the profile of expression online using ZBB2. I believe that this effort is determinant for the expansion of the field of the investigation of neuronal circuits underlying behavior in zebrafish larvae.

Reviewer #2:

This paper is a resources and tools contribution that presents many (100ish) new zebrafish CRE and gal4 lines to facilitate intersectional genetic strategies in fish. Importantly, the lines are presented in a beautiful online (or downloadable) browser that makes it very easy to explore them to decide which ones are useful, while learning about the larval fish brain at the same time. Accessibility and easy evaluation of the lines is as important as the lines themselves. The combination of lines with the ability to easily explore them and their relationships and intersection with other lines makes for a strong contribution. I have no substantive issues with a well presented and very useful tool. All the materials and fish are made accessible.

Reviewer #3:

In this manuscript Tabor et al. have generated more than 50 enhancer trap lines that express Cre in defined population of cells in the CNS. The authors obtained high-resolution images of these new lines along with 45 Gal4 enhancer trap lines that have not yet been reported. The authors then registered these images with the Zebrafish Brain Browser (ZBB) atlas, software that was created by the authors in a previous study (Marquart et al., 2015). Together with the images that have already been reported (109 lines), the new database (ZBB2) now include 65 Cre lines and 158 Gal4 lines. Additionally, the authors provide an online interface to the atlas. There is no doubt that these resources will be very useful for the zebrafish neuroscience community.

Major comments [concatenated and reordered, from all reviewers]:

1) [Related to Essential revision 1] Throughout the manuscript, the words, "cellular resolution", "single cell resolution", and "intersectional targeting" are used multiple times. With these words, readers would expect the following experiments: searching for Cre and Gal4 lines in the database, finding small numbers of neurons in which Cre and Gal4 expression overlap, and expressing reporter/effecter genes in these neurons. Nowhere in the manuscript, however, are examples of such experiments are provided. Thus, it is not clear whether targeting expression of reporter genes with cellular resolution is possible with the combination of Cre and Gal4. There are several potential concerns for the intersectional targeting of small numbers of neurons. These include stochastic activity at target loxP sites for Cre, and variable expression and silencing for Gal4. The authors cite the Current Biology paper (Tabor et al., 2018) as an example of intersectional targeting, but the Cre lines used in that study are broad expression lines (Cre expression in particular rhombomeres). So, the study does not serve as an example of intersectional targeting at cellular resolution. There is one place in the paper where the authors mention UAS:Switch expression experiments (in Discussion). However, it is written in a negative context: "Occasionally, we have observed UAS:Switch expression in neurons outside the domain of Cre expression in the ZBB2 atlas". In any case, the authors need to show clear examples of intersectional targeting in small numbers of neurons where Cre and Gal4 expression overlap in the ZBB2database.

2) [Related to Essential revision 2] While I do not request more experiments to the authors, I think an in-depth analysis of the distribution of interneurons types based on neurotransmitter and neuromodulator expressions per brain area, which seems amenable to the authors based on the data acquired, would provide more relevant information to the reader and the field. In particular, it would be useful to take it a step further and analyze out of the ~92 000 neurons in the brain the putative distribution as a function of neurotransmitter, neuromodulator and/or neuropeptide types. This effort would constitute a critical resource for the field and seems possible to estimate with ZBB2 and the collection of transgenic lines mapped in the 6 dpf zebrafish larval brain.

3) [Related to Essential revision 2] Related to Figure 3: Seeing images of the lines or 3D views online is very nice qualitatively. Naive question here: I wonder if we could take it one step further to reach a quantitative representation of the expression over the entire brain space of the larva. Do the authors have a simple way to converge on a physical 3D map indexed in a matrix with absolute coordinates that we could refer to in publications from different labs?

4) [Related to Essential revision 2] In order to locate with absolute coordinates the expression of transgenes better than by eye on the atlas: could the authors come up with a matrix of ~1000 bins (containing on average 10 neurons) for which each index corresponds to a graded value of expression on a 0-1 scale?

https://doi.org/10.7554/eLife.42687.018

Author response

Major comments [concatenated and reordered, from all reviewers]:

1) [Related to Essential revision 1] Throughout the manuscript, the words, "cellular resolution", "single cell resolution", and "intersectional targeting" are used multiple times. With these words, readers would expect the following experiments: searching for Cre and Gal4 lines in the database, finding small numbers of neurons in which Cre and Gal4 expression overlap, and expressing reporter/effecter genes in these neurons. Nowhere in the manuscript, however, are examples of such experiments are provided. Thus, it is not clear whether targeting expression of reporter genes with cellular resolution is possible with the combination of Cre and Gal4. There are several potential concerns for the intersectional targeting of small numbers of neurons. These include stochastic activity at target loxP sites for Cre, and variable expression and silencing for Gal4. The authors cite the Current Biology paper (Tabor et al., 2018) as an example of intersectional targeting, but the Cre lines used in that study are broad expression lines (Cre expression in particular rhombomeres). So, the study does not serve as an example of intersectional targeting at cellular resolution. There is one place in the paper where the authors mention UAS:Switch expression experiments (in Discussion). However, it is written in a negative context: "Occasionally, we have observed UAS:Switch expression in neurons outside the domain of Cre expression in the ZBB2 atlas". In any case, the authors need to show clear examples of intersectional targeting in small numbers of neurons where Cre and Gal4 expression overlap in the ZBB2database.

In the manuscript we have generally used the phrases like 'cellular resolution' to refer to the level of detail observable in the database rather than the level of resolution that we think can be usually obtained by intersectional targeting with transgenic reagents. Rather, our vision is that Cre lines with relatively broad expression can be used to select out sub-domains within Gal4 expression patterns. To achieve true single cell resolution, a third intersectional reagent is generally necessary (for example, the B3 recombinase that we used in Tabor et al., 2018).

However, the reviewers correctly observe, in Tabor et al., we performed intersectional targeting using Gal4 and Cre lines with relatively broad expression. We also agree that UAS lines (including the UAS:Switch) tend to silence and show variable expression. Therefore in the revised manuscript we have added:

A) A new Figure 5 which illustrates examples where Gal4 and Cre intersectionally drive expression in a small groups of neurons. In the accompanying text, we explicitly acknowledge the confound of variable expression.

B) A short description to the Materials and methods section describing the protocol that we use to minimize transgene silencing.

2) [Related to Essential revision 2] While I do not request more experiments to the authors, I think an in-depth analysis of the distribution of interneurons types based on neurotransmitter and neuromodulator expressions per brain area, which seems amenable to the authors based on the data acquired, would provide more relevant information to the reader and the field. In particular, it would be useful to take it a step further and analyze out of the ~92 000 neurons in the brain the putative distribution as a function of neurotransmitter, neuromodulator and/or neuropeptide types. This effort would constitute a critical resource for the field and seems possible to estimate with ZBB2 and the collection of transgenic lines mapped in the 6 dpf zebrafish larval brain.

As requested, we provide new supplementary information (Supplementary files 4 and 5) that describe the density of neurotransmitter/neuromodulator expression in different brain regions. For this, we have used both manually curated anatomical masks from Z-Brain, and second set of computational defined spatially smaller masks recently described by our group (Gupta et al., 2018). However, we cannot reliably extend this to a neuron-level description (if we understand the idea correctly) due to biological variability. Almost all brain regions contain multiple cell types that are intermingled, and – with the possible exception of the Mauthner neuron – specific cells are not precisely located at the same coordinates in different individuals.

3) [Related to Essential revision 2] Related to Figure 3: Seeing images of the lines or 3D views online is very nice qualitatively. Naive question here: I wonder if we could take it one step further to reach a quantitative representation of the expression over the entire brain space of the larva. Do the authors have a simple way to converge on a physical 3D map indexed in a matrix with absolute coordinates that we could refer to in publications from different labs?

The ZBB reference volume constitutes a 3D map with an absolute coordinate system in microns. We modified the code for the downloadable version so that irrespective of user-selected downscaling values, slices are now labeled by their position in ZBB reference space. The online version already displays the absolute coordinates of the selected voxel. This position can be reported in publications.

4) [Related to Essential revision 2] In order to locate with absolute coordinates the expression of transgenes better than by eye on the atlas: could the authors come up with a matrix of ~1000 bins (containing on average 10 neurons) for which each index corresponds to a graded value of expression on a 0-1 scale?

This is a great idea – thank you. We have added new Supplementary file 3 that provides coordinates for 1804 bins (20 µm side volumes) and the density of transgene expression in each. Using the bin coordinates, users can quickly navigate to the selected region in the atlas.

https://doi.org/10.7554/eLife.42687.019

Article and author information

Author details

  1. Kathryn M Tabor

    Division of Developmental Biology, Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, United States
    Contribution
    Conceptualization, Data curation, Formal analysis, Validation, Investigation, Methodology, Writing—original draft, Writing—review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3696-4584
  2. Gregory D Marquart

    1. Division of Developmental Biology, Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, United States
    2. Neuroscience and Cognitive Science Program, University of Maryland, College Park, United States
    Contribution
    Data curation, Investigation, Visualization, Methodology
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9811-5372
  3. Christopher Hurt

    1. Division of Developmental Biology, Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, United States
    2. Advanced Research Computing, Department of Computer Science, Virginia Polytechnic Institute and State University, Blacksburg, United States
    Contribution
    Software, Writing—original draft
    Competing interests
    No competing interests declared
  4. Trevor S Smith

    Division of Developmental Biology, Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, United States
    Contribution
    Data curation, Formal analysis, Investigation, Writing—review and editing
    Competing interests
    No competing interests declared
  5. Alexandra K Geoca

    Division of Developmental Biology, Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, United States
    Contribution
    Data curation, Investigation
    Competing interests
    No competing interests declared
  6. Ashwin A Bhandiwad

    Division of Developmental Biology, Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, United States
    Contribution
    Formal analysis, Investigation, Methodology
    Competing interests
    No competing interests declared
  7. Abhignya Subedi

    Postdoctoral Research Associate Training Program, National Institute of General Medical Sciences, Bethesda, United States
    Contribution
    Formal analysis, Investigation
    Competing interests
    No competing interests declared
  8. Jennifer L Sinclair

    Division of Developmental Biology, Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, United States
    Contribution
    Investigation
    Competing interests
    No competing interests declared
  9. Hannah M Rose

    Division of Developmental Biology, Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, United States
    Contribution
    Investigation
    Competing interests
    No competing interests declared
  10. Nicholas F Polys

    Advanced Research Computing, Department of Computer Science, Virginia Polytechnic Institute and State University, Blacksburg, United States
    Contribution
    Conceptualization, Resources, Software, Supervision
    Competing interests
    No competing interests declared
  11. Harold A Burgess

    Division of Developmental Biology, Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, United States
    Contribution
    Conceptualization, Data curation, Software, Formal analysis, Supervision, Funding acquisition, Validation, Investigation, Methodology, Writing—original draft, Writing—review and editing
    For correspondence
    burgessha@mail.nih.gov
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1966-7801

Funding

Eunice Kennedy Shriver National Institute of Child Health and Human Development (1ZIAHD008884-04)

  • Harold A Burgess

Virginia Tech (Advanced Research Computing)

  • Nicholas F Polys

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

We thank Steven Coon and James Iben from the NICHD Molecular Genomics Core and Lisa Williams-Simons from the NICHD FACS core for vital assistance. We are grateful to Jeremy Swan for help with interface design. This study utilized the high-performance computational capabilities of the Biowulf Linux cluster at the National Institutes of Health, Bethesda, MD (https://hpc.nih.gov/). The authors acknowledge Advanced Research Computing at Virginia Tech for providing computational resources and technical support that have contributed to the results reported within this paper (http://www.arc.vt.edu). This work was supported by the Intramural Research Program of the Eunice Kennedy Shriver National Institute for Child Health and Human Development.

Ethics

Animal experimentation: This study was performed in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health. All of the animals were handled according to approved institutional animal care and use committee (IACUC) protocols (#15-039) of the Eunice Kennedy Shriver National Institute of Child Health and Human Development.

Senior Editor

  1. Didier Y Stainier, Max Planck Institute for Heart and Lung Research, Germany

Reviewing Editor

  1. Indira M Raman, Northwestern University, United States

Reviewer

  1. Claire Wyart, Hôpital Pitié-Salpêtrière, Sorbonne Universités, UPMC Univ Paris 06, Inserm, CNRS, France

Publication history

  1. Received: October 8, 2018
  2. Accepted: February 7, 2019
  3. Accepted Manuscript published: February 8, 2019 (version 1)
  4. Version of Record published: February 27, 2019 (version 2)

Copyright

This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.

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