Drug Screening to Treat Early-Onset Eye Diseases: Can Zebrafish Expedite the Discovery?

Normalization of large-scale behavioural data collected from zebrafish

Many contemporary neuroscience experiments utilize high-throughput approaches to simultaneously collect behavioural data from many animals. The resulting data are often complex in structure and are subjected to systematic biases, which require new approaches for analysis and normalization. This study addressed the normalization need by establishing an approach based on linear-regression modeling. The model was established using a dataset of visual motor response (VMR) obtained from several strains of wild-type (WT) zebrafish collected at multiple stages of development. The VMR is a locomotor response triggered by drastic light change, and is commonly measured repeatedly from multiple larvae arrayed in 96-well plates. This assay is subjected to several systematic variations. For example, the light emitted by the machine varies slightly from well to well. In addition to the light-intensity variation, biological replication also created batch-batch variation. These systematic variations may result in differences in the VMR and must be normalized. Our normalization approach explicitly modeled the effect of these systematic variations on VMR. It also normalized the activity profiles of different conditions to a common baseline. Our approach is versatile, as it can incorporate different normalization needs as separate factors. The versatility was demonstrated by an integrated normalization of three factors: light-intensity variation, batch-batch variation and baseline. After normalization, new biological insights were revealed from the data. For example, we found larvae of TL strain at 6 days post-fertilization (dpf) responded to light onset much stronger than the 9-dpf larvae, whereas previous analysis without normalization shows that their responses were relatively comparable. By removing systematic variations, our model-based normalization can facilitate downstream statistical comparisons and aid detecting true biological differences in high-throughput studies of neurobehaviour.

Statistical Analysis of Zebrafish Locomotor Behaviour by Generalized Linear Mixed Models

Upon a drastic change in environmental illumination, zebrafish larvae display a rapid locomotor response. This response can be simultaneously tracked from larvae arranged in multi-well plates. The resulting data have provided new insights into neuro-behaviour. The features of these data, however, present a challenge to traditional statistical tests. For example, many larvae display little or no movement. Thus, the larval responses have many zero values and are imbalanced. These responses are also measured repeatedly from the same well, which results in correlated observations. These analytical issues were addressed in this study by the generalized linear mixed model (GLMM). This approach deals with binary responses and characterizes the correlation of observations in the same group. It was used to analyze a previously reported dataset. Before applying the GLMM, the activity values were transformed to binary responses (movement vs. no movement) to reduce data imbalance. Moreover, the GLMM estimated the variations among the effects of different well locations, which would eliminate the location effects when two biological groups or conditions were compared. By addressing the data-imbalance and location-correlation issues, the GLMM effectively quantified true biological effects on zebrafish locomotor response.

Utilizing Zebrafish Visual Behaviors in Drug Screening for Retinal Degeneration

Zebrafish are a popular vertebrate model in drug discovery. They produce a large number of small and rapidly-developing embryos. These embryos display rich visual-behaviors that can be used to screen drugs for treating retinal degeneration (RD). RD comprises blinding diseases such as Retinitis Pigmentosa, which affects 1 in 4000 people. This disease has no definitive cure, emphasizing an urgency to identify new drugs. In this review, we will discuss advantages, challenges, and research developments in using zebrafish behaviors to screen drugs in vivo. We will specifically discuss a visual-motor response that can potentially expedite discovery of new RD drugs.

Automated, high-throughput, in vivo analysis of visual function using the zebrafish

BACKGROUND

Modern genomics has enabled the identification of an unprecedented number of genetic variants, which in many cases are extremely rare, associated with blinding disorders. A significant challenge will be determining the pathophysiology of each new variant. The Zebrafish is an excellent model for the study of inherited diseases of the eye. By 5 days post-fertilization (dpf), they have quantifiable behavioral responses to visual stimuli. However, visual behavior assays can take several hours to perform or can only be assessed one fish at a time.

RESULTS

To increase the throughput for vision assays, we used the Viewpoint Zebrabox to automate the visual startle response and created software, Visual Interrogation of Zebrafish Manipulations (VIZN), to automate data analysis. This process allows 96 Zebrafish larvae to be tested and resultant data to be analyzed in less than 35 minutes. We validated this system by disrupting function of a gene necessary for photoreceptor differentiation and observing decreased response to visual stimuli.

CONCLUSIONS

This automated method along with VIZN allows rapid, high-throughput, in vivo testing of Zebrafish's ability to respond to light/dark stimuli. This allows the rapid analysis of novel genes involved in visual function by morpholino, CRISPRS, or small-molecule drug screens. Developmental Dynamics 245:605-613, 2016. © 2016 Wiley Periodicals, Inc.

A Naturally-Derived Compound Schisandrin B Enhanced Light Sensation in the pde6c Zebrafish Model of Retinal Degeneration

Retinal degeneration is often progressive. This feature has provided a therapeutic window for intervention that may extend functional vision in patients. Even though this approach is feasible, few promising drug candidates are available. The scarcity of new drugs has motivated research to discover novel compounds through different sources. One such example is Schisandrin B (SchB), an active component isolated from the five-flavor fruit (Fructus Schisandrae) that is postulated in traditional Chinese medicines to exert prophylactic visual benefit. This SchB benefit was investigated in this study in pde6c^w59, a zebrafish retinal-degeneration model. In this model, the pde6c gene (phosphodiesterase 6C, cGMP-specific, cone, alpha prime) carried a mutation which caused cone degeneration. This altered the local environment and caused the bystander rods to degenerate too. To test SchB on the pde6c^w59 mutants, a treatment concentration was first determined that would not cause morphological defects, and would initiate known physiological response. Then, the mutants were treated with the optimized SchB concentration before the appearance of retinal degeneration at 3 days postfertilization (dpf). The light sensation of animals was evaluated at 6 dpf by the visual motor response (VMR), a visual startle that could be initiated by drastic light onset and offset. The results show that the VMR of pde6c^w59 mutants towards light onset was enhanced by the SchB treatment, and that the initial phase of the enhancement was primarily mediated through the mutants’ eyes. Further immunostaining analysis indicates that the treatment specifically reduced the size of the abnormally large rods. These observations implicate an interesting hypothesis: that the morphologically-improved rods drive the observed VMR enhancement. Together, these investigations have identified a possible visual benefit of SchB on retinal degeneration, a benefit that can potentially be further developed to extend functional vision in patients.

Computational classification of different wild-type zebrafish strains based on their variation in light-induced locomotor response

Zebrafish larvae display a rapid and characteristic swimming behaviour after abrupt light onset or offset. This light-induced locomotor response (LLR) has been widely used for behavioural research and drug screening. However, the locomotor responses have long been shown to be different between different wild-type (WT) strains. Thus, it is critical to define the differences in the WT LLR to facilitate accurate interpretation of behavioural data. In this investigation, we used support vector machine (SVM) models to classify LLR data collected from three WT strains: AB, TL and TLAB (a hybrid of AB and TL), during early embryogenesis, from 3 to 9 days post-fertilisation (dpf). We analysed both the complete dataset and a subset of the data during the first 30after light change. This initial period of activity is substantially driven by vision, and is also known as the visual motor response (VMR). The analyses have resulted in three major conclusions: First, the LLR is different between the three WT strains, and at different developmental stages. Second, the distinguishable information in the VMR is comparable to, if not better than, the full dataset for classification purposes. Third, the distinguishable information of WT strains in the light-onset response differs from that in the light-offset response. While the classification accuracies were higher for the light-offset than light-onset response when using the complete LLR dataset, a reverse trend was observed when using a shorter VMR dataset. Together, our results indicate that one should use caution when extrapolating interpretations of LLR/VMR obtained from one WT strain to another.

Statistical Analysis of Zebrafish Locomotor Response

Zebrafish larvae display rich locomotor behaviour upon external stimulation. The movement can be simultaneously tracked from many larvae arranged in multi-well plates. The resulting time-series locomotor data have been used to reveal new insights into neurobiology and pharmacology. However, the data are of large scale, and the corresponding locomotor behavior is affected by multiple factors. These issues pose a statistical challenge for comparing larval activities. To address this gap, this study has analyzed a visually-driven locomotor behaviour named the visual motor response (VMR) by the Hotelling's T-squared test. This test is congruent with comparing locomotor profiles from a time period. Different wild-type (WT) strains were compared using the test, which shows that they responded differently to light change at different developmental stages. The performance of this test was evaluated by a power analysis, which shows that the test was sensitive for detecting differences between experimental groups with sample numbers that were commonly used in various studies. In addition, this study investigated the effects of various factors that might affect the VMR by multivariate analysis of variance (MANOVA). The results indicate that the larval activity was generally affected by stage, light stimulus, their interaction, and location in the plate. Nonetheless, different factors affected larval activity differently over time, as indicated by a dynamical analysis of the activity at each second. Intriguingly, this analysis also shows that biological and technical repeats had negligible effect on larval activity. This finding is consistent with that from the Hotelling's T-squared test, and suggests that experimental repeats can be combined to enhance statistical power. Together, these investigations have established a statistical framework for analyzing VMR data, a framework that should be generally applicable to other locomotor data with similar structure.

A High-Throughput Zebrafish Screening Method for Visual Mutants by Light-Induced Locomotor Response

Normal and visually-impaired zebrafish larvae have differentiable light-induced locomotor response (LLR), which is composed of visual and non-visual components. It is recently demonstrated that differences in the acute phase of the LLR, also known as the visual motor response (VMR), can be utilized to evaluate new eye drugs. However, most of the previous studies focused on the average LLR activity of a particular genotype, which left information that could address differences in individual zebrafish development unattended. In this study, machine learning techniques were employed to distinguish not only zebrafish larvae of different genotypes, but also different batches, based on their response to light stimuli. This approach allows us to perform efficient high-throughput zebrafish screening with relatively simple preparations. Following the general machine learning framework, some discriminative features were first extracted from the behavioral data. Both unsupervised and supervised learning algorithms were implemented for the classification of zebrafish of different genotypes and batches. The accuracy of the classification in genotype was over 80 percent and could achieve up to 95 percent in some cases. The results obtained shed light on the potential of using machine learning techniques for analyzing behavioral data of zebrafish, which may enhance the reliability of high-throughput drug screening.