TITLE: Z-Loc 4: The Standardized 4 dpf Zebrafish (Danio rerio) Larval Light/Dark Locomotion Protocol
AUTHORS: Courtney Hillman, James Kearn, Matthew Parker

[DESCRIPTION]
Zebrafish (Danio rerio) are a dynamic model organism, the larval stage (≤ 4 days post-fertilization (dpf)) of which offers a unique avenue for high-throughput in vivo investigation in a vertebrate that aligns strongly with the 3Rs of in vivo research. The light/dark locomotor assay is widely employed for studying larval behavior; however, there remains a lack of experimental consistency. Here, we provide step-by-step experimental and
analytical protocols for a standardized behavioral assay that exploits locomotion differences during light/dark phases. 
 
For details on the use and execution of this protocol, please refer to Hillman (2024).

[BEFORE_START]
Breeding and Rearing of Zebrafish
 
Timing: 4 days
 
This section describes the method for breeding and rearing the zebrafish larvae in preparation for the light/dark behavioral assay. 5 days prior to running the larval behavioral assay the adult zebrafish (6-12 months) should be prepared for breeding. 
 
Breeding is performed through the addition of substrate (typically marbles) to the home tank and/or pair breeding. For marbling, the night before embryo collection place a container of marbles into the tank of adult fish. The following day, at 1100 - mid-day remove the container and collect the embryos (Figure 1A and B). For pair breeding, select either one male and one female, or two females and one male, or two males and one female and place into a small tank with a middle divider and a slope. Split the sexes the night before and the next morning remove the divider when the lights in the facility turn on and allow 30-mins for breeding before returning to their home tank and collecting the embryos (Figure 1C). 


 

2. Transfer ~80 0 hour post-fertilization (hpf) embryos into labelled 250 mL Petri dishes, filled with system water (or the water you use in your lab) and place the dishes into a temperature controlled incubator with a standard 14h:10h light/dark cycle (Figure 1D). Each morning post breeding perform a complete water change of the Petri dishes and remove any dead/unfertilized larvae. 

 


Uploading Tracking Script to Behavioral Unit

Timing: 5-mins

This section describes the setup and use of the zantiks (Zantiks Ltd., Cambridge, UK) automated tracking script for running the light/dark assay using MWP behavioural units. 

3. Download the tracking script from the attached file    or from the OSF linked to this protocol.
4. Connect to the Zantiks Wi-Fi specific for your unit(s)
5. Open the zanscript section and upload the downloaded script

CRITICAL: If you are using a different automated system ensure the tracking settings are identical (record in 1 second time-bins, 5-minute light (350 lx) to dark transitions repeated three times for a total of 30-minutes. Output as movement per mm.)


  


 

[STEPS]
SECTION: Habituation to New Environment
1. At 8:30 am, transfer the Petri dishes of 4 dpf larvae from the fish facility holding room to the experimental room and place in a benchtop incubator set at 28 °C  for 30 min of acclimating in the light.

 

SECTION: Habituation to New Environment
2. Immediately after starting habituation, switch on the Zantiks MWP behavioral unit(s) and allow the temperature to get to 28 °C . 

 


SECTION: Plating Larvae (Timing: 8-mins/plate)
3. Using either a wide bore pipette tip, or a standard p1000 with the tip cut at approximately 2 mm from the end, transfer 225 µL  of facility water and a randomly selected larvae into each well of a 48-well plate. 
SECTION: Acclimation to the Zantiks MWP Behavioural Unit
4. Trasfer the full well plate with the lid on into the behavioral unit with the lights on for 30 min of untracked acclimation 

 

SECTION: Baseline Recording and Chemical Exposure Preparation
5. Run a baseline recording using the Zanscript tracking script (see before start: Uploading Tracking Script to Behavioral Unit). 

SECTION: Baseline Recording and Chemical Exposure Preparation
6. Immediately after starting the baseline recording, prepare your 10X solutions for drug exposure in a 96-well plate (Figure 2 and Table 1).

 


 
Figure 2: Example 96-well plate setup with increasing 10X concentrations from A to H. The vehicle control should also be a 10X concentration (e.g., for a final concentration of 0.5% DMSO, 5% DMSO should be made up in the 96-well plate). Rows C and F are kept empty.

 

SECTION: Dosing the Larvae and Recording Exposure Response
7. Immediately after the baseline recording finishes remove the plate from the behavioral unit. 

 

SECTION: Dosing the Larvae and Recording Exposure Response
8. Using a multi-channel pipette, add 25 µL of the 10X dilutions from the 96-well plate to the corresponding well of the 48-well plate (Figure 3). 

 

 


SECTION: Dosing the Larvae and Recording Exposure Response
9. Once all the wells have been exposed, return the well plate with the lid on to the behavioral unit and restart the light/dark tracking script for a 0-30-min post-exposure recording.

 

SECTION: Dosing the Larvae and Recording Exposure Response
10. Once complete, remove the fish and dispose of as appropriate.

 

SECTION: Dosing the Larvae and Recording Exposure Response
11. Download all the datafiles generated by the tracking script for analysis.
SECTION: Analytical Process
12. Save and rename the baseline recordings as 'baseline_x'.xlsx where x is an increasing number from 1 depending how many runs were performed.
SECTION: Analytical Process
13. Create a new Microsoft Excel document for the exposed analysis with the concentration in row 1 and the 1800 sec movement data in the following rows.
SECTION: Analytical Process
14. Open a new R project and upload the custom R script    or download from the OSF file.
SECTION: Analytical Process
15. Change the group sizes to the same sizes used within your experiment in lines 38, 299, 532, 715, 736 and 835.

 

NOTE: We strongly encourage a sample size of n = 16 per concentration for preliminary power calculations.
SECTION: Analytical Process
16. Change the file path to your own file path where the saved datafiles are in lines 137 and 483.

 

SECTION: Analytical Process
17. Change the file path to your own file path where the saved baseline files are for lines 245, 248, 478 and 481.

 

NOTE: If this is a preliminary run your will have two baselines. Alternatively, if you have a large sample size and numerous different baselines, add more lines.
SECTION: Analytical Process
18. Change the lines for calculating the mean and standard error of the mean (SEM) for producing the line graphs in lines 359-364 and 376-381).

 

NOTE: The example uses the sample size of 16 per concentration, change accordingly.
SECTION: Analytical Process
19. Change the labels for the line graph to match your tested concentration/exposure solutions (line 456).

 

SECTION: Analytical Process
20. Change the lines corresponding to the sample size for vehicle-controls in the swim phenotypes analysis (lines 672 and 675).

 

NOTE: The example uses the sample size of 16 per concentration, change accordingly.
SECTION: Analytical Process
21. Change to match your concentrations/exposure solutions and the sample sizes for generating the dataframe to save the swim phenotype data (lines 712 and 715).


 

SECTION: Analytical Process
22. Prepare to generate the heat maps by changing the group sizes and concentration labels accordingly (lines 736, 740, 748, 764, 835, 862).

 

SECTION: Analytical Process
23. To create the radar charts, the concentrations must be changed and you must input your data from the files generated named "average_responses_df" and "average_responses_df_light" (lines 928-33, 937, 972-78, 980). Below is an example of the dark phase data.

 

SECTION: Analytical Process
24. To prepare the data for statistical analysis, the dataset has to be restructured, change this section depending on your sample sizes. (Lines 1033-1045).


 

SECTION: Analytical Process
25. Input your sample sizes in the 1458 to calculate observed power.

 

SECTION: Expected Outcomes
26. 
 

SECTION: Quantification and Statistical Analysis
27. We perform a normalization of the data by calculating the percentage of movement per minute per fish to the overall baseline response. This step is essential to ensure reproducible and robust responses (Hillman et al., 2024). Tracking errors are common and are eliminated using a regularly used method for outlier identification and removal (median absolute deviation) (Leys et al., 2013).
 
An important step in the statistical analysis is the calculation of observed power and sample size. We have provided a flow chart for how to deal with 4 dpf larval behavior sample size and power analyses (Figure 6). A preliminary behavioral experiment must be conducted with a sample size of n = 16 per concentration. The R script can be run and observed power calculated for dark phase responses. Where power is below 0.8 (80%), sample size is calculated. If the observed power is above or equal to 0.8 (80%) then the sample size can remain as n =16.


 

The first analysis performed is a linear mixed model (LMM) analysis with movement as the dependent variable and lights as the independent variable with fish ID as the random effect. This is done for group and time in place of lights as the independent variables. An example of the output is below:


 

Next, a full model is run (this includes all three of the independent variables of interest) and a null model, which is only including the dependent variable and the random effect. This allows for likelihood ratio tests to be performed with the null model and all the tested models. For example:


 

These calculations are performed with dark phase data as well. 

A large amount of data can be obtained from these calculations. Table 2 describes the different outcomes:

 
Next, linear regressions for the different tested concentrations in the dark phase are run with time as the independent variable. This allows investigation of the impact of time on dark phase response. For example:

 

The findings suggest an increase in dark phase locomotion as time progresses (time estimate). Despite a significant positive effect, the low R-squared value suggests that time alone does not account for the variability in the movement. 

The swim phenotypes are analyzed individually using ANOVAs comparing the group (concentration) locomotion in either light or dark conditions. For example:


 

The df represents the degrees of freedom and residuals refers to the degrees of freedom associated with the residuals of the model. The sum of squares represents the variability explained by the group (concentration) and the residuals represents unexplained variability after accounting for the group factor. The F-value is the test statistic for ANOVA’s which assesses whether the mean differences among the groups are statistically significant. Here the output indicates a significant difference among the group factor in terms of the effect on total movement.
SECTION: Troubleshooting
28. Problem 1: Issues with tracking larvae  
Potential Solution: Tracking errors are typically seen with poor tracking settings on the MWP unit. The script we have provided should ensure this doesn’t occur. However, there may be an issue with the asset set up on your unit. Each unit should have a uniquely set up asset matching to the 48-well plate you are using. Zantiks provide a step-by-step guide for adding your own assets to the MWP unit