Transparent Danionella translucida as a genetically tractable vertebrate brain model

A Publisher Correction to this article was published on 05 November 2018

Abstract

Understanding how distributed neuronal circuits integrate sensory information and generate behavior is a central goal of neuroscience. However, it has been difficult to study neuronal networks at single-cell resolution across the entire adult brain in vertebrates because of their size and opacity. We address this challenge here by introducing the fish Danionella translucida to neuroscience as a potential model organism. This teleost remains small and transparent even in adulthood, when neural circuits and behavior have matured. Despite having the smallest known adult vertebrate brain, D. translucida displays a rich set of complex behaviors, including courtship, shoaling, schooling, and acoustic communication. In order to carry out optical measurements and perturbations of neural activity with genetically encoded tools, we established CRISPR–Cas9 genome editing and Tol2 transgenesis techniques. These features make D. translucida a promising model organism for the study of adult vertebrate brain function at single-cell resolution.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: D. translucida body size, transparency and miniature brain.
Fig. 2: Behavioral repertoire of D. translucida.
Fig. 3: Tol2-mediated transgenesis and CRISPR–Cas9-mediated gene editing.
Fig. 4: Functional imaging of neural activity in GCaMP6f-expressing D. translucida.

Data availability

Anatomical datasets are available at a GIN repository provided by the German Neuroinformatics Node22 at https://doid.gin.g-node.org/230a07ec2dd35f087f152e6e83a81e4b. Sequence alignment data have been deposited in the NCBI SRA database with the accession number SRP136594.

References

  1. 1.

    Betzig, E. et al. Imaging intracellular fluorescent proteins at nanometer resolution. Science 313, 1642–1645 (2006).

    CAS  Google Scholar 

  2. 2.

    Bouchard, M. B. et al. Swept confocally-aligned planar excitation (SCAPE) microscopy for high speed volumetric imaging of behaving organisms. Nat. Photonics 9, 113–119 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  3. 3.

    Tian, L. et al. Imaging neural activity in worms, flies and mice with improved GCaMP calcium indicators. Nat. Methods 6, 875–881 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  4. 4.

    Chen, T.-W. et al. Ultrasensitive fluorescent proteins for imaging neuronal activity. Nature 499, 295–300 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. 5.

    Packer, A. M., Roska, B. & Häusser, M. Targeting neurons and photons for optogenetics. Nat. Neurosci. 16, 805–815 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  6. 6.

    Ji, N., Freeman, J. & Smith, S. L. Technologies for imaging neural activity in large volumes. Nat. Neurosci. 19, 1154–1164 (2016).

    PubMed  PubMed Central  Google Scholar 

  7. 7.

    Chan, K. Y. et al. Engineered AAVs for efficient noninvasive gene delivery to the central and peripheral nervous systems. Nat. Neurosci. 20, 1172–1179 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. 8.

    Friedrich, R. W., Jacobson, G. A. & Zhu, P. Circuit neuroscience in zebrafish. Curr. Biol. 20, R371–R381 (2010).

    CAS  PubMed  Google Scholar 

  9. 9.

    O’Malley, D. M., Kao, Y. H. & Fetcho, J. R. Imaging the functional organization of zebrafish hindbrain segments during escape behaviors. Neuron 17, 1145–1155 (1996).

    PubMed  Google Scholar 

  10. 10.

    Orger, M. B., Kampff, A. R., Severi, K. E., Bollmann, J. H. & Engert, F. Control of visually guided behavior by distinct populations of spinal projection neurons. Nat. Neurosci. 11, 327–333 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. 11.

    Ahrens, M. B. et al. Brain-wide neuronal dynamics during motor adaptation in zebrafish. Nature 485, 471–477 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. 12.

    Panier, T. et al. Fast functional imaging of multiple brain regions in intact zebrafish larvae using selective plane illumination microscopy. Front. Neural Circuits 7, 65 (2013).

    PubMed  PubMed Central  Google Scholar 

  13. 13.

    Portugues, R., Feierstein, C. E., Engert, F. & Orger, M. B. Whole-brain activity maps reveal stereotyped, distributed networks for visuomotor behavior. Neuron 81, 1328–1343 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. 14.

    Dunn, T. W. et al. Brain-wide mapping of neural activity controlling zebrafish exploratory locomotion. eLife 5, e12741 (2016).

    PubMed  PubMed Central  Google Scholar 

  15. 15.

    Naumann, E. A. et al. From whole-brain data to functional circuit models: the zebrafish optomotor response. Cell 167, 947–960 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. 16.

    Hildebrand, D. G. C. et al. Whole-brain serial-section electron microscopy in larval zebrafish. Nature 545, 345–349 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. 17.

    Roberts, T. R. Danionella translucida, a new genus and species of cyprinid fish from Burma, one of the smallest living vertebrates. Environ. Biol. Fishes 16, 231–241 (1986).

    Google Scholar 

  18. 18.

    Britz, R. Danionella mirifica, a new species of miniature fish from the upper Myanmar (Ostariophysi: Cyprinidae). Ichthyol. Explor. Freshw. 14, 217–222 (2003).

    Google Scholar 

  19. 19.

    Britz, R. Danionella priapus, a new species of miniature cyprinid fish from West Bengal, India (Teleostei: Cypriniformes: Cyprinidae). Zootaxa 2277, 53–60 (2009).

    Google Scholar 

  20. 20.

    Britz, R., Conway, K. W. & Rüber, L. Spectacular morphological novelty in a miniature cyprinid fish, Danionella dracula n. sp. Proc. Biol. Sci. 276, 2179–2186 (2009).

    PubMed  PubMed Central  Google Scholar 

  21. 21.

    Britz, R. & Conway, K. W. Danionella dracula, an escape from the cypriniform Bauplan via developmental truncation? J. Morphol. 277, 147–166 (2015).

    PubMed  Google Scholar 

  22. 22.

    Schulze, L. et al. Dataset: transparent Danionella translucida as a genetically tractable vertebrate brain model. G-Node https://doid.gin.g-node.org/230a07ec2dd35f087f152e6e83a81e4b/ (2018).

  23. 23.

    Hinsch, K. & Zupanc, G. K. H. Generation and long-term persistence of new neurons in the adult zebrafish brain: a quantitative analysis. Neuroscience 146, 679–696 (2007).

    CAS  PubMed  Google Scholar 

  24. 24.

    Bauchot, M. L., Ridet, J. M. & Diagne, M. Encephalization in Gobioidei (Teleostei). Gyoruigaku Zasshi 36, 63–74 (1989).

    Google Scholar 

  25. 25.

    van Dongen, P. A. M. in The Central Nervous System of Vertebrates (eds Nieuwenhuys, R., ten Donkelaar, H. J. & Nicholson, C.) 2099–2134 (Springer, Berlin, Heidelberg, 1998).

  26. 26.

    Pitcher, T. J. The Behaviour of Teleost Fishes (Springer Science & Business Media, Berlin, Heidelberg, 2012).

  27. 27.

    Kawakami, K. Tol2: a versatile gene transfer vector in vertebrates. Genome. Biol. 8, S7 (2007).

    PubMed  PubMed Central  Google Scholar 

  28. 28.

    Rupprecht, P., Prendergast, A., Wyart, C. & Friedrich, R. W. Remote z-scanning with a macroscopic voice coil motor for fast 3D multiphoton laser scanning microscopy. Biomed. Opt. Express 7, 1656–1671 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. 29.

    Jao, L.-E., Wente, S. R. & Chen, W. Efficient multiplex biallelic zebrafish genome editing using a CRISPR nuclease system. Proc. Natl Acad. Sci. USA 110, 13904–13909 (2013).

    CAS  PubMed  Google Scholar 

  30. 30.

    Di Donato, V. et al. 2C-Cas9: a versatile tool for clonal analysis of gene function. Genome Res. 26, 681–692 (2016).

    PubMed  PubMed Central  Google Scholar 

  31. 31.

    Bradbury, J. W. & Vehrencamp, S. L. Principles of Animal Communication (Sinauer Associates, Sunderland, MA, 2011).

    Google Scholar 

  32. 32.

    Parmentier, E. & Fine, M. L. in Vertebrate Sound Production and Acoustic Communication (eds Suthers, R. A. et al.) 19–49 (Springer, Cham, 2016).

  33. 33.

    Grothe, B., Carr, C. E., Casseday, J. H., Fritzsch, B. & Köppl, C. in Evolution of the Vertebrate Auditory System (eds Manley, G. A., Popper, A. N. & Fay, R. R.) 289–359 (Springer, New York, 2004).

  34. 34.

    Mueller, T. What is the thalamus in zebrafish? Front. Neurosci. 6, 64 (2012).

    PubMed  PubMed Central  Google Scholar 

  35. 35.

    McCormick, C. A. & Wallace, A. C. Otolith end organ projections to auditory neurons in the descending octaval nucleus of the goldfish, Carassius auratus: a confocal analysis. Brain Behav. Evol. 80, 41–63 (2012).

    PubMed  Google Scholar 

  36. 36.

    Chagnaud, B. P., Engelmann, J., Fritzsch, B., Glover, J. C. & Straka, H. Sensing external and self-motion with hair cells: a comparison of the lateral line and vestibular systems from a developmental and evolutionary perspective. Brain Behav. Evol. 90, 98–116 (2017).

    PubMed  PubMed Central  Google Scholar 

  37. 37.

    Straka, H. & Baker, R. Vestibular blueprint in early vertebrates. Front. Neural Circuits 7, 182 (2013).

    PubMed  PubMed Central  Google Scholar 

  38. 38.

    Duncan, J. S. & Fritzsch, B. Evolution of sound and balance perception: innovations that aggregate single hair cells into the ear and transform a gravistatic sensor into the organ of corti. Anat. Rec. (Hoboken) 295, 1760–1774 (2012).

  39. 39.

    Ji, N., Milkie, D. E. & Betzig, E. Adaptive optics via pupil segmentation for high-resolution imaging in biological tissues. Nat. Methods 7, 141–147 (2010).

    CAS  PubMed  Google Scholar 

  40. 40.

    Wang, C. et al. Multiplexed aberration measurement for deep tissue imaging in vivo. Nat. Methods 11, 1037–1040 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. 41.

    Wang, K. et al. Direct wavefront sensing for high-resolution in vivo imaging in scattering tissue. Nat. Commun. 6, 7276 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. 42.

    Papadopoulos, I. N., Jouhanneau, J.-S., Poulet, J. F. A. & Judkewitz, B. Scattering compensation by focus scanning holographic aberration probing (F-SHARP). Nat. Photonics 11, 116–123 (2017).

    CAS  Google Scholar 

  43. 43.

    Horton, N. G. et al. In vivo three-photon microscopy of subcortical structures within an intact mouse brain. Nat. Photonics 7, 205–209 (2013).

    CAS  PubMed Central  Google Scholar 

  44. 44.

    Ouzounov, D. G. et al. In vivo three-photon imaging of activity of GCaMP6-labeled neurons deep in intact mouse brain. Nat. Methods 14, 388–390 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. 45.

    Denk, W. & Horstmann, H. Serial block-face scanning electron microscopy to reconstruct three-dimensional tissue nanostructure. PLoS Biol. 2, e329 (2004).

    PubMed  PubMed Central  Google Scholar 

  46. 46.

    Denk, W., Briggman, K. L. & Helmstaedter, M. Structural neurobiology: missing link to a mechanistic understanding of neural computation. Nat. Rev. Neurosci. 13, 351–358 (2012).

    CAS  PubMed  Google Scholar 

  47. 47.

    Rein, K., Zöckler, M., Mader, M. T., Grübel, C. & Heisenberg, M. The Drosophila standard brain. Curr. Biol. 12, 227–231 (2002).

    CAS  PubMed  Google Scholar 

  48. 48.

    Zheng, Z. et al. A complete electron microscopy volume of the brain of adult Drosophila melanogaster. bioRxiv Preprint at https://www.biorxiv.org/content/early/2017/06/13/140905 (2017).

  49. 49.

    Smith, T. E. Notebook on spatial data analysis. University of Pennsylvania School of Engineering and Applied Science https://www.seas.upenn.edu/~ese502/#notebook (2016).

  50. 50.

    Todd, B. S. & Andrews, D. C. The identification of peaks in physiological signals. Comput. Biomed. Res. 32, 322–335 (1999).

    CAS  PubMed  Google Scholar 

  51. 51.

    Martins, S. et al. Toward an integrated zebrafish health management program supporting cancer and neuroscience research. Zebrafish 13, S47–S55 (2016).

    Google Scholar 

  52. 52.

    Aronesty, E. Comparison of sequencing utility programs. Open Bioinforma. J. 2013, 1–8 (2013).

    Google Scholar 

  53. 53.

    Yates, A. et al. Ensembl 2016. Nucleic Acids Res. 44, D710–D716 (2016).

    CAS  PubMed  Google Scholar 

  54. 54.

    Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    PubMed  PubMed Central  Google Scholar 

  55. 55.

    Robinson, J. T. et al. Integrative Genomics Viewer. Nat. Biotechnol. 29, 24–26 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  56. 56.

    Labun, K., Montague, T. G., Gagnon, J. A., Thyme, S. B. & Valen, E. CHOPCHOPv2: a web tool for the next generation of CRISPR genome engineering. Nucleic Acids Res. 44, W272–W276 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank M. Brecht, C. Wyart, R. Britz, E. Naumann, M. Hoffmann and E. Bobrov for helpful discussions and critical reading of this manuscript. We are grateful to P. Liptrot, P. Dixon, O. Deters and other members of the Danionin aquarist community for advice on Danionella husbandry. A. Prendergast and C. Wyart (ICM, Paris, France) advised us on the Tol2 strategy and kindly provided the NeuroD:GCaMP6f DNA construct before publication. S. Luna and R. Froese (FishBase Project) kindly shared a digital copy of the FishBase brain data. We thank S. Mueller and the MRI core facility at the Charité for providing their services and expertise. We acknowledge funding by the Einstein Foundation Berlin, the DFG (EXC 257 NeuroCure), and the Human Frontiers Science Program. B.J. is a recipient of a Starting Grant from the European Research Council (ERC-2016-StG-714560) and the Alfried Krupp Prize for Young University Teachers, awarded by the Alfried Krupp von Bohlen und Halbach-Stiftung.

Author information

Affiliations

Authors

Contributions

B.J., L.S. and J.H. conceived the project; L.S., J.H., T.C., M.K., N.H., A.I.F., F.D.B. and B.J. developed the methodology; J.H., M.K., T.C. and B.J. developed software; J.H., T.C., M.K., L.S., S.A., L.M. and B.J. conducted analyses; L.S., J.H., A.I.F., M.K., S.A. and N.H. conducted experiments; N.H. provided resources; B.J. and J.H. wrote the manuscript, with help from M.K., L.S., A.I.F. and T.C.; M.S., L.M. and F.D.B. supervised specific aspects of the study; and B.J. provided overall supervision for the entire study.

Corresponding author

Correspondence to Benjamin Judkewitz.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Integrated supplementary information

Supplementary Figure 1 Shoaling and schooling in the dark.

A) Nearest-neighbor distances of fish swimming in the dark (blue). Bootstrapped distribution is shown in red. The mean distance under the assumption of spatial randomness (see Methods) is shown as a gray line; n = 12 fish. B) Distribution of fish orientations relative to the mean orientation of all fish for each frame during schooling, n = 12 fish. Note that subtraction of the mean direction causes a general bias towards zero that is also evident in the shuffled data curves.

Supplementary Figure 2 Temporal structure of Danionella translucida vocalizations.

A) Histogram of burst durations. B) Histogram of inter-burst intervals. C) Histogram of durations of vocalization clusters. D) Histogram of the number of pulses per burst cluster. Red dotted lines indicate 95-percentiles. Data extracted from 24 h of recording of a community tank with about 30 fish, n = 2.2 million pulses.

Supplementary Figure 3 Link between vocalization and male–male aggression.

To test whether vocalizations may be linked to male aggression-driven behaviors, we monitored a group of four males in a small, shallow tank (24 × 24 cm) using audio and video recordings during a period of frequent vocalization (10 am-1 pm). (A) Still image of two males engaged in fighting behavior. Scale bar: 1 cm. (B) Detected periods of vocalization and fighting over the course of 3 h. Fighting episodes were labeled manually without listening to the audio track and vocalization episodes were detected using software. (C) Analysis of the co-occurrence of fighting and vocalizations. We quantified the conditional probabilities P(vocalization|fighting) and P(fighting|vocalization), indicated by the dashed lines in red and blue, which were significantly higher than in temporally shuffled data (p « 1e-5, one-sided one-sample Student’s t-test, n = 20000, distributions indicated by continuous lines in the respective colors). This analysis was repeated for two more groups of males (1 h each) with similar results.

Supplementary Figure 4 Danionella translucida short-read alignment.

Screenshot of Danionella translucida short read alignment to the zebrafish reference genome (GRCz10), as viewed with the freely available program IGV. The underlying data is being published alongside this publication.

Supplementary Figure 5 Confirmation of successful genome editing.

Left panel: Alignment of wild-type DT tyr sequence (WT, yellow highlight, top) and mutated sequences derived from the co-injection of the tyr gRNA 1 and 2 at their respective target sites. gRNA sequences and PAM are underlined in purple for reference. Right panel: PCR analysis of a pool of 3 dfp non-injected (WT) and injected embryos with gRNA pairs. A wild-type band could be detected for all conditions at the expected size and shorter bands around 500 base pairs (bp) could also be observed, indicative of large deletions (large Δ) generated by the co-injection of the gRNA pairs. gRNAs 1 and 2 were used for the quantification of knock-out efficiency and for the line generation. Additional injections of gRNAs A (5’-GCTCTGAAGAGCTTCTTGAG-3’), B (ACGATGGCACAGATGGGCAA), C (TGTGGGGTCCAATCAGGTCG) and D (AGCTTTCCTCCCCTGGCACC) also led to genomic deletions. PCR analysis was repeated 3 independent times.

Supplementary Information

Supplementary Text and Figures

Supplementary Figures 1–5

Reporting Summary

Supplementary Video 1

Compressed histology video. Registered series of Nissl-stained transverse sections (8 µm thickness) of the Danionella translucida head.

Supplementary Video 2

3D MRI video. Rotating maximum intensity projection of high-resolution 35 µm MRI of Danionella translucida.

Supplementary Video 3

Courtship video. Danionella translucida courtship at 0.2× the original speed.

Supplementary Video 4

Fighting behavior between males. Movie of 4 Danionella translucida males displaying fighting behavior as quantified in Supplementary Fig. 3.

Supplementary Audio 1

Sound recordings shown in Fig. 2e. Short sequence of Danionella translucida vocalizations.

Supplementary Audio 2

Sound recordings shown in Fig. 2f. Sequence of Danionella translucida vocalizations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Schulze, L., Henninger, J., Kadobianskyi, M. et al. Transparent Danionella translucida as a genetically tractable vertebrate brain model. Nat Methods 15, 977–983 (2018). https://doi.org/10.1038/s41592-018-0144-6

Download citation

Further reading

Search

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing