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Behavioral neuroscience and the comparative method
Larval ZF has become a very popular model organism in behavioral neuroscience. The popularity can be ascribed to their small size, transparency and development of tools over the years to perform genetic manipulations. One of the major advantages is the ability to image whole brain activity at single cell resolution using genetically encoded calcium indicators (GECI) (Ahrens et al., 2013). By virtue of the close evolutionary relationship between ZF and DT, a lot of these genetic tools are easily transferable from the former to the latter. To create a mutant lacking any pigmentation, including in the eye — based on the fact that tyr gene encoding tyrosinase is involved in melanin synthesis pathway in ZF — Schulze, Henninger et al., (2018) successfully used CRISPR-Cas9 genome editing technique to target tyr gene in DT. They have also been successful in generating a stable Tg(NeuroD:GCaMP6f) transgenic line using Tol2 mediated transgenesis technique. Moreover, Kadobianskyi et al. (2019) have recently published an assembled and annotated genome sequence of DT. This will aid all the future work on targeted genetic manipulations in DT.
In ZF, most studies have focused on simple behavioral questions pertaining to perception, locomotion or sensorimotor transformations of reflexive nature. This is mainly due to the fact that most complex behaviors appear in the later stages of larval development. ZF larvae start to show reliable learning at 3 weeks (Valente et al., 2012). Similarly, social preference starts to appear in 3 weeks old larvae (Dreosti et al., 2015). This is where the advantages of DT become more obvious. While the adults of DT are small in size and transparent, they also show a rich repertoire of behaviors. They perform visually mediated shoaling and schooling. The males also exhibit vocalization which is likely related to male-male aggression (Schulze, Henninger et al., 2008). Penalva et al. (2018) also showed socially reinforced learning in adult DT. As shown in Figure 1.3, imaging of adult fish can be carried out non-invasively in moderately sedated fish embedded in agar where the gills are still free to move. This rich behavioral repertoire in adult DT combined with its small size, lack of pigmentation and the ease of transfer of genetic tools from ZF to DT makes adult DT a very favorable system to understand neuronal underpinnings of complex behaviors.
Locomotor signatures of larval zebrafish
A lot has been characterized about behaviors and underlying stereotypical locomotion in larval zebrafish (ZF). ZF exhibits swimming in temporally distinct events called bouts. Each bout consists of a tail burst followed by a glide. This distinct pattern has helped in characterizing the behaviors in terms of the underlying pattern of the bouts. Larval ZF show a variety of behaviors. The emergence of these behaviors during development is shown in Figure 1.4. During the thesis work, I focused on spontaneous exploratory swimming and acoustic startle behaviors in DT and ZF. Additionally, I also looked at prey capture (see Annex #1), opto-motor response and loom-induced escape (unpublished collaborative work) behaviors in ZF. In this section, I will briefly review some of the widely described ZF locomotor patterns underlying some of the behaviors outlined in Figure 1.4. For a thorough review of ZF behaviors, see Fero et al. (2011) and Orger & De Polavieja (2017). Kinematic analysis of swim bouts has produced information on different movement patterns produced by larval ZF. During exploration, forward propulsion is achieved by low-angle slow-swim movements or scoots, along with faster swims called burst swims where the large tail amplitudes are used (Budick & O’Malley, 2000). Change in orientation is achieved by what is called a ‘routine turn’ and produces a change in orientation of around 45 degrees.
Clustering of free-swim half tail beats
A total of over 200,000 half beats from 14 DT: ~18,000 half beats per fish were used and the three variables – mean speed, half beat frequency and maximum tail angle – were considered as the predictors for performing k-means clustering. Outliers and missing values were identified and the respective data entries were discarded. An outlier is defined as a value that is more than three scaled median absolute deviations (MAD) away.
I tested cluster sizes of 2 to 5 and optimal clustering was detected at k=2 with a silhouette coefficient >0.50. The silhouette coefficient gives a measure of the similarity between the points of the same cluster by measuring how close the points of a given cluster lie when compared to their distance to the points in the other clusters. It ranges from -1 to 1. A higher silhouette value indicates a better clustering. Once we identified the cluster size to be 2, the clustering was then performed with k=2 clusters and n=100 repetitions to obtain a reliable clustering.
The representative trajectory with slow/fast tracks was plotted by first filtering the raw velocity traces from 700 Hz acquisition with a 100 ms moving mean filter and then down sampling the signal into 70 Hz. The velocity was normalized between 0 and 1. Every step is 100 ms in time.
Head-embedded swimming behavior set-up
I used a high-speed camera (MC4082, Mikrotron-GmbH, Germany) with a Navitar Zoom 7000 macro lens to carry out the head-embedded acquisitions. The resolution of the images were 400 x 400 pixels with 75 pixels /mm and the temporal resolution of the acquisition was 250 Hz. The fish were illuminated with an infrared LED array placed below the swimming arena. An 850 nm infrared bandpass filter (BP850-35.5, Midwest Optical Systems, Inc.) was used on the objective to block all the visible light.
6 dpf DT and ZF were embedded in 0.5ml of 1.5% agarose. For ZF, nacre incross were used. The agarose covered the head up to the pectoral fins. Each fish was acclimatized for at least 90 minutes before acquisition. Recordings lasted for 10-20 mins per fish. 4 DT / 5 ZF were used in this experiment.
Analysis pipeline for head-embedded data
The head-embedded videos were primarily used to determine the amount of time spent in swimming. I wrote a custom program on MATLAB to achieve this. Briefly, the following approach was used. An ROI was drawn around the tail. A threshold was selected empirically for every video to binarize the image to segment the tail. The image was divided into 12 vertical bocks perpendicular the tail segment. A mask was used to run through the image block-by-block to determine the center of mass of the largest blob (tail segment) per block. This center of mass information was used to calculate the angle between two consecutive tail spots. Of the 11 tail segment angles, the first nine were used to determine a curvature of the tail throughout the video. A cut-off value on the curvature was used to determine regions of active swimming. The results from the obtained from script were manually verified. 6 dpf ZF (n=6) and DT (n=5) were used for the experiment. The duration of swimming was normalized to the total length of the acquisition and reported as a percentage of the total duration of acquisition.
Assay to study tap-induced escape behavior
The same set-up as described in 2.2.1 was used for this assay. In addition to the free-swimming acquisition system, I added an Arduino controlled solenoid to the behavioral arena. The Arduino was triggered from the aforementioned acquisition program written in C# (Microsoft, USA). When triggered, the solenoid would hit the surface of the arena from the bottom and cause the fish to escape in response to this stimulus. The trigger was only initiated if the fish was not at the edges of the Petri dish and there was an inter-stimulus interval of at least 50 seconds between the trials. The delay between the trigger onset and the delivery of the solenoid on the arena was estimated and incorporated in the analysis to calculate an accurate reaction time. 19 DT (n=141 trials) and 15 ZF (n=159 trials) were tested in the assay.
Analysis pipeline for tap-induced escape data
To analyze the escape kinematics, the peak escape velocities were identified in a window of approx. 450 ms after the stimulus delivery. A peak speed was considered as at least 2 times the peak speed during free-swimming (9.25mm/s and 42.5 mm/s for DT and ZF respectively). In case of multiple peak escape velocities in the window, only the first one was considered. Now, I selected a 140 ms region of interest around the peak speed to consider 40 ms before the peak and 100 ms after the peak as shown in Figure 2.3 for a zebrafish. The region of interest was empirically decided after exploring many trials across both the fish species. Mean and maximum velocities, total distance covered and the delay to reach the peak speed after the stimulus delivery – these parameters were computed for all the trials in each fish.
Table of contents :
List of Figures
List of Tables
Abbreviations
Acknowledgements
Chapter 1: Introduction
1.1 The model systems: Danionella translucida and Danio rerio
1.2 Behavioral neuroscience and the comparative method
1.3 Evolution of locomotor circuits
1.4 Locomotor signatures of larval zebrafish
1.5 Supraspinal neuronal correlates of locomotion
1.6 Summary and objectives of the current study
Chapter 2: Materials and Methods
2.1 Animal maintenance
2.1.1 Danio rerio (zebrafish)
2.1.2 Danionella translucida
2.2 Behavior
2.2.1 Free-swimming behavioral acquisition, fish tracking and tail segmentation
2.2.2 Analysis pipeline for free-swim data
2.2.3 Clustering of free-swim half tail beats
2.2.4 Head-embedded swimming behavior set-up
2.2.5 Analysis pipeline for head-embedded data
2.2.6 Assay to study tap-induced escape behavior
2.2.7 Analysis pipeline for tap-induced escape data
2.2.8 Mean squared displacement (MSD) and reorientation analysis
2.2.9 Energetics and swimming speed
2.2.10 Quantification of depth preference
2.2.11 Quantification of body length and swim bladder inflation
2.3 Anatomy
2.3.1 Fluorescence In-situ hybridization and Immuno-histochemistry
2.3.2 Confocal imaging of the whole brain FISH/IHC samples
2.3.3 Retrograde labelling of reticulospinal (RS) neurons
2.4 Physiology
2.4.1 Generation of pan-neuronal calcium sensor Tg(HuC-H2B:GCaMP6s) line
2.4.2 Light-sheet imaging
2.4.3 Image processing and analysis pipeline for whole-brain light-sheet data .
2.5 Statistical methods
Chapter 3: Characterization of exploratory locomotion in Danionella translucida (DT) and Danio rerio (ZF)
3.1 Length of larval DT and ZF is in a similar range
3.2 DT execute swim events with a continuous tail-burst activity
3.3 Duration of swim events are much longer in DT
3.4 DT swim slower with a lower half beat frequency and tail angle
3.5 DT has lower mean escape velocity than ZF but tends to show a lower latency to achieve peak escape velocity
3.6 Exploratory swimming in DT has a longer ballistic phase
3.7 The continuous swimming in DT can be divided into at least two types, slow and fast swims
3.8 DT’s instantaneous energy requirement during activity appears lower than ZF
3.9 A lower oxygen availability and delayed swim bladder inflation might have contributed to the differences in swimming style at the micro scale
Chapter 4: Anatomy and physiology underlying locomotion in Danionella translucida and Danio rerio
4.1 Anatomy
4.1.1. Distribution of glutamatergic, glycinergic and GABAergic neurons in the hindbrain of DT
4.1.2. Reticulospinal (RS) neurons in DT and ZF
4.2 Physiology
4.2.1 Generation of pan-neuronal Tg(HuC:H2B-GCaMP6s) DT fish and whole-brain
imaging 4.2.2 Similar brain nuclei are correlated with swimming in DT and ZF
4.2.3 Neurons in the identified nuclei of DT constitute a functionally heterogenous population
4.2.4 Onset neurons form a continuum between onset and swim maintenance components
Chapter 5: Discussion
5.1 General experimental approach
5.2 Kinematics of spontaneous swimming and escapes
5.3 Exploration and organismal biology
5.4 Anatomy
5.5 Physiology
5.6 Conclusions and future directions
Chapter 6: Appendix
Bibliography
