Rüdiger Alshut

Nonvisual photosensation enables animals to sense light without sight. However, the cellular and molecular mechanisms of nonvisual photobehaviors are poorly understood, especially in vertebrate animals. Here, we describe the photomotor response (PMR), a robust and reproducible series of motor behaviors in zebrafish that is elicited by visual wavelengths of light but does not require the eyes, pineal gland, or other canonical deep-brain photoreceptive organs. Unlike the relatively slow effects of canonical nonvisual pathways, motor circuits are strongly and quickly (seconds) recruited during the PMR behavior. We find that the hindbrain is both necessary and sufficient to drive these behaviors. Using in vivo calcium imaging, we identify a discrete set of neurons within the hindbrain whose responses to light mirror the PMR behavior. Pharmacological inhibition of the visual cycle blocks PMR behaviors, suggesting that opsin-based photoreceptors control this behavior. These data represent the first known light-sensing circuit in the vertebrate hindbrain.

Over the past decade, the zebrafish has become a key model organism in genetic screenings and drug discovery. A number of genes have been identified to affect the development of the shape and functioning of the heart, leading to zebrafish mutants with heart defects. The development of semiautomated microscopy systems has allowed for the investigation of drugs that reverse a disease phenotype on a larger scale. However, there is a lack of automated feature detection, and commercially available computer-aided microscopes are expensive. Screening of the zebrafish heart for drug discovery typically includes the identification of heart parameters, such as the frequency or fractional shortening. Until now, screening processes have been characterized by manual handling of the larvae and manual microscopy. Here, an intelligent robotic microscope is presented, which automatically identifies the orientation of a zebrafish in a micro well. A predefined region of interest, such as the heart, is detected automatically, and a video with higher magnification is recorded. Screening of a 96-well plate takes 35 to 55 min, depending on the length of the videos. Of the zebrafish hearts, 75% are recorded accurately without any user interaction. A description of the system, including the graphical user interface, is given.

A fully automatic detection and analysis method of heartbeats in videos of nonfixed and nonanesthetized zebrafish embryos is presented. This method reduces the manual workload and time needed for preparation and imaging of the zebrafish embryos, as well as for evaluating heartbeat parameters such as frequency, beat-to-beat intervals, and arrhythmicity. The method is validated by a comparison of the results from automatic and manual detection of the heart rates of wild-type zebrafish embryos 36-120 h postfertilization and of embryonic hearts with bradycardia and pauses in the cardiac contraction.

Automated imaging has become a commonplace and widespread technique for researchers aiming to increase both biological and medical knowledge. Systematic high-throughput screening approaches produce a vast amount of data that needs to be quantified automatically. To address this problem, we present an extended version of the open-source MATLAB toolbox Gait-CAD providing integrated tools for automated image analysis, video object tracking and data mining. Gait-CAD offers a convenient graphical user interface (GUI) and is shipped with a great selection of predefined, customizable plugins for both image analysis and data mining. The plugin-based architecture and templates for customized tools provide easy expandability in order to develop comprehensive data-analysis pipelines. Process automation via batch-files and macro recording functionality enables the handling of large datasets like multi-dimensional 2D or 3D images and videos. The scope of the presented tools ranges from automated high-throughput toxicity testing in zebrafish embryos to cellular analysis tasks in developmental biology. In both examples, the toolbox is successfully applied for pre-processing, normalization, segmentation and tracking of spatio-temporal microscopy images, as well as for subsequent data mining and report generation. As automatically acquired images tend to differ in each recording, researchers can significantly accelerate parameter adjustments, process automation and result visualization by using the presented software. The toolbox is not limited to these applications, but they already reveal the great potential of the extended Gait-CAD release. The presented toolbox is a powerful instrument for data analysis in life sciences. A user-friendly GUI provides functionality to create sophisticated approaches even for users with limited programming knowledge.