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.
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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.
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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.
Integrated supplementary information
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.
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.
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.
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.
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 Figures 1–5
Compressed histology video. Registered series of Nissl-stained transverse sections (8 µm thickness) of the Danionella translucida head.
3D MRI video. Rotating maximum intensity projection of high-resolution 35 µm MRI of Danionella translucida.
Courtship video. Danionella translucida courtship at 0.2× the original speed.
Fighting behavior between males. Movie of 4 Danionella translucida males displaying fighting behavior as quantified in Supplementary Fig. 3.
Sound recordings shown in Fig. 2e. Short sequence of Danionella translucida vocalizations.
Sound recordings shown in Fig. 2f. Sequence of Danionella translucida vocalizations.
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