Automated processing of zebrafish imaging data: a survey

Bringing Artificial Intelligence (AI) into Environmental Toxicology Studies: A Perspective of AI-Enabled Zebrafish High-Throughput Screening

The booming development of artificial intelligence (AI) has brought excitement to many research fields that could benefit from its big data analysis capability for causative relationship establishment and knowledge generation. In toxicology studies using zebrafish, the microscopic images and videos that illustrate the developmental stages, phenotypic morphologies, and animal behaviors possess great potential to facilitate rapid hazard assessment and dissection of the toxicity mechanism of environmental pollutants. However, the traditional manual observation approach is both labor-intensive and time-consuming. In this Perspective, we aim to summarize the current AI-enabled image and video analysis tools to realize the full potential of AI. For image analysis, AI-based tools allow fast and objective determination of morphological features and extraction of quantitative information from images of various sorts. The advantages of providing accurate and reproducible results while avoiding human intervention play a critical role in speeding up the screening process. For video analysis, AI-based tools enable the tracking of dynamic changes in both microscopic cellular events and macroscopic animal behaviors. The subtle changes revealed by video analysis could serve as sensitive indicators of adverse outcomes. With AI-based toxicity analysis in its infancy, exciting developments and applications are expected to appear in the years to come.

Recent Advances in Bioimage Analysis Methods for Detecting Skeletal Deformities in Biomedical and Aquaculture Fish Species

Detecting skeletal or bone-related deformities in model and aquaculture fish is vital for numerous biomedical studies. In biomedical research, model fish with bone-related disorders are potential indicators of various chemically induced toxins in their environment or poor dietary conditions. In aquaculture, skeletal deformities are affecting fish health, and economic losses are incurred by fish farmers. This survey paper focuses on showcasing the cutting-edge image analysis tools and techniques based on artificial intelligence that are currently applied in the analysis of bone-related deformities in aquaculture and model fish. These methods and tools play a significant role in improving research by automating various aspects of the analysis. This paper also sheds light on some of the hurdles faced when dealing with high-content bioimages and explores potential solutions to overcome these challenges.

Unlocking the Potential of Zebrafish Research with Artificial Intelligence: Advancements in Tracking, Processing, and Visualization

Zebrafish have become a widely accepted model organism for biomedical research due to their strong cortisol stress response, behavioral strain differences, and sensitivity to both drug treatments and predators. However, experimental zebrafish studies generate substantial data that must be analyzed through objective, accurate, and repeatable analysis methods. Recently, advancements in artificial intelligence (AI) have enabled automated tracking, image recognition, and data analysis, leading to more efficient and insightful investigations. In this review, we examine key AI applications in zebrafish research, including behavior analysis, genomics, and neuroscience. With the development of deep learning technology, AI algorithms have been used to precisely analyze and identify images of zebrafish, enabling automated testing and analysis. By applying AI algorithms in genomics research, researchers have elucidated the relationship between genes and biology, providing a better basis for the development of disease treatments and gene therapies. Additionally, the development of more effective neuroscience tools could help researchers better understand the complex neural networks in the zebrafish brain. In the future, further advancements in AI technology are expected to enable more extensive and in-depth medical research applications in zebrafish, improving our understanding of this important animal model. This review highlights the potential of AI technology in achieving the full potential of zebrafish research by enabling researchers to efficiently track, process, and visualize the outcomes of their experiments.

A new software tool for computer assisted in vivo high-content analysis of transplanted fluorescent cells in intact zebrafish larvae

Acute myeloid leukemia and myelodysplastic syndromes are cancers of the bone marrow with poor prognosis in frail and older patients. To investigate cancer pathophysiology and therapies, confocal imaging of fluorescent cancer cells and their response to treatments in zebrafish larvae yields valuable information. While zebrafish larvae are well suited for confocal imaging, the lack of efficient processing of large datasets remains a severe bottleneck. To alleviate this problem, we present a software tool that segments cells from confocal images and track characteristics such as volume, location in the larva and fluorescent intensity on a single-cell basis. Using this software tool, we were able to characterise the responses of the cancer cell lines Molm-13 and MDS-L to established treatments. By utilizing the computer-assisted processing of confocal images as presented here, more information can be obtained while being less time-consuming and reducing the demand of manual data handling, when compared to a manual approach, thereby accelerating the pursuit of novel anti-cancer treatments. The presented software tool is available as an ImageJ java-plugin at https://zenodo.org/10.5281/zenodo.7383160 and the source code at https://github.com/Jfo004/ConfocalCellSegmentation.

High-throughput screening paradigms in ecotoxicity testing: Emerging prospects and ongoing challenges

The rapidly increasing number of new production chemicals coupled with stringent implementation of global chemical management programs necessities a paradigm shift towards boarder uses of low-cost and high-throughput ecotoxicity testing strategies as well as deeper understanding of cellular and sub-cellular mechanisms of ecotoxicity that can be used in effective risk assessment. The latter will require automated acquisition of biological data, new capabilities for big data analysis as well as computational simulations capable of translating new data into in vivo relevance. However, very few efforts have been so far devoted into the development of automated bioanalytical systems in ecotoxicology. This is in stark contrast to standardized and high-throughput chemical screening and prioritization routines found in modern drug discovery pipelines. As a result, the high-throughput and high-content data acquisition in ecotoxicology is still in its infancy with limited examples focused on cell-free and cell-based assays. In this work we outline recent developments and emerging prospects of high-throughput bioanalytical approaches in ecotoxicology that reach beyond in vitro biotests. We discuss future importance of automated quantitative data acquisition for cell-free, cell-based as well as developments in phytotoxicity and in vivo biotests utilizing small aquatic model organisms. We also discuss recent innovations such as organs-on-a-chip technologies and existing challenges for emerging high-throughput ecotoxicity testing strategies. Lastly, we provide seminal examples of the small number of successful high-throughput implementations that have been employed in prioritization of chemicals and accelerated environmental risk assessment.

Screening of Mineralogenic and Osteogenic Compounds in Zebrafish—Tools to Improve Assay Throughput and Data Accuracy

Bone disorders affect millions of people worldwide and treatments currently available often produce undesirable secondary effects or have limited efficacy. It is therefore of the utmost interest for patients to develop more efficient drugs with reduced off-target activities. In the long process of drug development, screening and preclinical validation have recently gained momentum with the increased use of zebrafish as a model organism to study pathological processes related to human bone disorders, and the development of zebrafish high-throughput screening assays to identify bone anabolic compounds. In this review, we provided a comprehensive overview of the literature on zebrafish bone-related assays and evaluated their performance towards an integration into screening pipelines for the discovery of mineralogenic/osteogenic compounds. Tools available to standardize fish housing and feeding procedures, synchronize embryo production, and automatize specimen sorting and image acquisition/analysis toward faster and more accurate screening outputs were also presented.

Technologies bringing young Zebrafish from a niche field to the limelight

Fundamental life science and pharmaceutical research are continually striving to provide physiologically relevant context for their biological studies. Zebrafish present an opportunity for high-content screening (HCS) to bring a true in vivo model system to screening studies. Zebrafish embryos and young larvae are an economical, human-relevant model organism that are amenable to both genetic engineering and modification, and direct inspection via microscopy. The use of these organisms entails unique challenges that new technologies are overcoming, including artificial intelligence (AI). In this perspective article, we describe the state-of-the-art in terms of automated sample handling, imaging, and data analysis with zebrafish during early developmental stages. We highlight advances in orienting the embryos, including the use of robots, microfluidics, and creative multi-well plate solutions. Analyzing the micrographs in a fast, reliable fashion that maintains the anatomical context of the fluorescently labeled cells is a crucial step. Existing software solutions range from AI-driven commercial solutions to bespoke analysis algorithms. Deep learning appears to be a critical tool that researchers are only beginning to apply, but already facilitates many automated steps in the experimental workflow. Currently, such work has permitted the cellular quantification of multiple cell types in vivo, including stem cell responses to stress and drugs, neuronal myelination and macrophage behavior during inflammation and infection. We evaluate pro and cons of proprietary versus open-source methodologies for combining technologies into fully automated workflows of zebrafish studies. Zebrafish are poised to charge into HCS with ever-greater presence, bringing a new level of physiological context.

Light sheet fluorescence microscopy for the investigation of blood-sucking arthropods dyed via artificial membrane feeding

Physical methods to control pest arthropods are increasing in importance, but detailed knowledge of the effects of some of these methods on the target organisms is lacking. The aim of this study was to use light sheet fluorescence microscopy (LSFM) in anatomical studies of blood-sucking arthropods in vivo to assess the suitability of this method to investigate the morphological structures of arthropods and changes in these structures over time, using the human louse Pediculus humanus (Phthiraptera: Pediculidae) as sample organism. Plasma treatment was used as an example of a procedure employed to control arthropods. The lice were prepared using an artificial membrane feeding method involving the ingestion of human blood alone and human blood with an added fluorescent dye in vitro. It was shown that such staining leads to a notable enhancement of the imaging contrast with respect to unstained whole lice and internal organs that can normally not be viewed by transmission microscopy but which become visible by this approach. Some lice were subjected to plasma treatment to inflict damage to the organisms, which were then compared to untreated lice. Using LSFM, a change in morphology due to plasma treatment was observed.

These results demonstrate that fluorescence staining coupled with LSFM represents a powerful and straightforward method enabling the investigation of the morphology—including anatomy—of blood-sucking lice and other arthropods.

Artificial intelligence-driven phenotyping of zebrafish psychoactive drug responses

Zebrafish (Danio rerio) are rapidly emerging in biomedicine as promising tools for disease modelling and drug discovery. The use of zebrafish for neuroscience research is also growing rapidly, necessitating novel reliable and unbiased methods of neurophenotypic data collection and analyses. Here, we applied the artificial intelligence (AI) neural network-based algorithms to a large dataset of adult zebrafish locomotor tracks collected previously in a series of in vivo experiments with multiple established psychotropic drugs. We first trained AI to recognize various drugs from a wide range of psychotropic agents tested, and then confirmed prediction accuracy of trained AI by comparing several agents with known similar behavioral and pharmacological profiles. Presenting a framework for innovative neurophenotyping, this proof-of-concept study aims to improve AI-driven movement pattern classification in zebrafish, thereby fostering drug discovery and development utilizing this key model organism.

A Workflow to Track and Analyze Endothelial Migration During Vascular Development in Zebrafish Embryos Using Lightsheet Microscopy

Zebrafish allow unrivalled in vivo imaging of vascular development due to their optical translucency and the availability of transgenic lines which fluorescently label cells and tissues of interest. Advances in light sheet fluorescence microscopy allow longer and faster imaging of live embryos at higher resolutions than previously possible, which facilitates study of dynamic cellular and molecular mechanisms underlying vessel formation and function. Here we describe a workflow using lightsheet microscopy to quantify endothelial cell (EC) migration dynamics during vascular development. Tracking movement of EC nuclei and analyzing the properties of EC migration trajectories permit detailed studies of angiogenesis and vascular remodeling in different contexts.

Quantitative measurements of zebrafish heartrate and heart rate variability: A survey between 1990-2020

Zebrafish is an essential model organism for studying cardiovascular diseases, given its advantages of fast proliferation and high gene homology with humans. Zebrafish embryos/larvae are valuable experimental models used in toxicology studies to analyze drug toxicity, including hepatoxicity, nephrotoxicity and cardiotoxicity, as well as for drug discovery and drug safety screening in the preclinical stage. Heart rate (HR) serves as a functional endpoint in studies of cardiotoxicity, while heart rate variability (HRV) serves as an indicator of cardiac arrhythmia. Cardiotoxicity is a major cause of early and late termination of drug trials, so a more comprehensive understanding of zebrafish HR and HRV is important. This review summarized HR and HRV in a specific range of applications and fields, focusing on zebrafish heartbeat detection procedures, signal analysis technology and well-established commercial software, such as LabVIEW, Rvlpulse, and ZebraLab. We also compared HR detection algorithms and electrocardiography (ECG)-based methods of heart signal extraction. The relationship between HR and HRV was also systematically analyzed; HR was shown to have an inverse correlation with HRV. Applications to drug testing are also highlighted in this review. Furthermore, HR and HRV were shown to be regulated by the automatic nervous system; their connections with ECG measurements are also summarized herein.

Minimum reporting standards based on a comprehensive review of the zebrafish embryo teratogenicity assay

Reproductive toxicity chemical safety assessment involves extensive use of vertebrate animals for regulatory testing purposes. Although alternative methods such as the zebrafish embryo teratogenicity assay (identified in the present manuscript by the acronym ZETA) are promising for replacing tests with mammals, challenges to regulatory application involve lack of standardization and incomplete validation. To identify key protocol aspects and ultimately support improving this situation, a comprehensive review of the literature on the level of harmonization/standardization and validation status of the ZETA has been conducted. The gaps and needed advances of the available ZETA protocols were evaluated and discussed with respect to its applicability as an alternative approach for teratogenicity assessment. Based on the review outcomes, a set of minimum reporting standards for the experimental protocol is proposed. Together with other initiatives towards implementation of alternative approaches at the screening and regulatory levels, the application of minimum reporting requirements is anticipated to support future method standardization and validation, as well as identifying potential improvement aspects. Present findings are expected to ultimately support advancing the ongoing validation initiatives towards the regulatory acceptance of the ZETA.

ZFTool: A Software for Automatic Quantification of Cancer Cell Mass Evolution in Zebrafish

Background: Zebrafish (Danio rerio) is a model organism for the study of human cancer. Compared with the murine model, the zebrafish model has several properties ideal for personalized therapies. The transparency of the zebrafish embryos and the development of the pigment-deficient ”casper“ zebrafish line give the capacity to directly observe cancer formation and progression in the living animal. Automatic quantification of cellular proliferation in vivo is critical to the development of personalized medicine. Methods: A new methodology was defined to automatically quantify the cancer cellular evolution. ZFTool was developed to establish a base threshold that eliminates the embryo autofluorescence, automatically measures the area and intensity of GFP (green-fluorescent protein) marked cells, and defines a proliferation index. Results: The proliferation index automatically computed on different targets demonstrates the efficiency of ZFTool to provide a good automatic quantification of cancer cell evolution and dissemination. Conclusion: Our results demonstrate that ZFTool is a reliable tool for the automatic quantification of the proliferation index as a measure of cancer mass evolution in zebrafish, eliminating the influence of its autofluorescence.

Watching the embryo: Evolution of the microscope for the study of embryogenesis

Embryos and microscopes share a long, remarkable history and biologists have always been intrigued to watch how embryos develop under the microscope. Here we discuss the advances in microscopy which have greatly influenced our current understanding of embryogenesis. We highlight the evolution of microscopes and the optical technologies that have been instrumental in studying various developmental processes. These imaging modalities provide mechanistic insights into the dynamic cellular and molecular events which drive lineage commitment and morphogenetic changes in the developing embryo. We begin the journey with a brief history of microscopy to study embryos. First, we review the principles and optics of light, fluorescence, confocal, and electron microscopy which have been key techniques for imaging cellular and molecular events during embryonic development. Next, we discuss recent key imaging modalities such as light-sheet microscopy, which are suitable for whole embryo imaging. Further, we highlight imaging techniques like multiphoton and super resolution microscopy for beyond light diffraction limit, high resolution imaging. Lastly, we review some of the scattering-based imaging methods and techniques used for imaging human embryos.

Toward High-Throughput Fish Embryo Toxicity Tests in Aquatic Toxicology

Addressing the shift from classical animal testing to high-throughput in vitro and/or simplified in vivo proxy models has been defined as one of the upcoming challenges in aquatic toxicology. In this regard, the fish embryo toxicity test (FET) has gained significant popularity and wide standardization as one of the sensitive alternative approaches to acute fish toxicity tests in chemical risk assessment and water quality evaluation. Nevertheless, despite the growing regulatory acceptance, the actual manipulation, dispensing, and analysis of living fish embryos remains very labor intensive. Moreover, the FET is commonly performed in plastic multiwell plates under static or semistatic conditions, potentially inadequate for toxicity assessment of some organic, easily degradable or highly adsorptive toxicants. Recent technological advances in the field of mechatronics, fluidics and digital vision systems demonstrate promising future opportunities for automation of many analytical stages in embryo toxicity testing. In this review, we highlight emerging advances in fluidic and laboratory automation systems that can prospectively enable high-throughput FET testing (HT-FET) akin to pipelines commonly found in in vitro drug discovery pipelines. We also outline the existing challenges, barriers to future development and provide an outlook of ground-breaking fluidic technologies in embryo toxicity testing.

Developmental neurotoxicity and toxic mechanisms induced by olaquindox in zebrafish

Olaquindox (OLA) has been widely used as an animal feed additive in China for decades; however, its toxicity and toxic mechanisms have not been well investigated. In this study, the developmental neurotoxicity and toxic mechanisms of OLA were evaluated in zebrafish. Zebrafish embryos were exposed to different concentrations of OLA (25-1,000 mg/L) from 6 to 120 hours post fertilization (hpf). OLA exposure resulted in many abnormal phenotypes in zebrafish, including shortened body length, notochord degeneration, spinal curvature, brain apoptosis, damage of axon and peripheral motor neuron, and hepatotoxicity. Interestingly, OLA increased zebrafish spontaneous tail coiling, while reduced locomotor capacity. Quantitative polymerase chain reaction (Q-PCR) showed that the expression levels of nine marker genes for nervous system functions or development, namely, α1-tubulin, glial fibrillary acidic protein (gfap), myelin basic protein (mbp), synapsinII a (syn2a), sonic hedgehog a (shha), encoding HuC (elavl3), mesencephalic astrocyte-derived neurotrophic factor (manf) growth associated protein 43 (gap43), and acetylcholinesterase (ache) were all down-regulated significantly in zebrafish after treated with OLA. Besides, the anti-apoptotic and pro-apoptotic genes bcl-2/bax ratio was reduced. These results show that OLA exposure could cause severe developmental neurotoxicity in the early stages of zebrafish life and OLA might induce neurotoxicity by inhibiting the expression of neuro-developmental genes and promoting apoptosis.

Zebrafish Larvae Phenotype Classification from Bright-field Microscopic Images Using a Two-Tier Deep-Learning Pipeline

Classification of different zebrafish larvae phenotypes is useful for studying the environmental influence on embryo development. However, the scarcity of well-annotated training images and fuzzy inter-phenotype differences hamper the application of machine-learning methods in phenotype classification. This study develops a deep-learning approach to address these challenging problems. A convolutional network model with compressed separable convolution kernels is adopted to address the overfitting issue caused by insufficient training data. A two-tier classification pipeline is designed to improve the classification accuracy based on fuzzy phenotype features. Our method achieved an averaged accuracy of 91% for all the phenotypes and maximum accuracy of 100% for some phenotypes (e.g., dead and chorion). We also compared our method with the state-of-the-art methods based on the same dataset. Our method obtained dramatic accuracy improvement up to 22% against the existing method. This study offers an effective deep-learning solution for classifying difficult zebrafish larvae phenotypes based on very limited training data.

ZF-AutoML: An Easy Machine-Learning-Based Method to Detect Anomalies in Fluorescent-Labelled Zebrafish

Background: Zebrafish are efficient animal models for conducting whole organism drug testing and toxicological evaluation of chemicals. They are frequently used for high-throughput screening owing to their high fecundity. Peripheral experimental equipment and analytical software are required for zebrafish screening, which need to be further developed. Machine learning has emerged as a powerful tool for large-scale image analysis and has been applied in zebrafish research as well. However, its use by individual researchers is restricted due to the cost and the procedure of machine learning for specific research purposes. Methods: We developed a simple and easy method for zebrafish image analysis, particularly fluorescent labelled ones, using the free machine learning program Google AutoML. We performed machine learning using vascular- and macrophage-Enhanced Green Fluorescent Protein (EGFP) fishes under normal and abnormal conditions (treated with anti-angiogenesis drugs or by wounding the caudal fin). Then, we tested the system using a new set of zebrafish images. Results: While machine learning can detect abnormalities in the fish in both strains with more than 95% accuracy, the learning procedure needs image pre-processing for the images of the macrophage-EGFP fishes. In addition, we developed a batch uploading software, ZF-ImageR, for Windows (.exe) and MacOS (.app) to enable high-throughput analysis using AutoML. Conclusions: We established a protocol to utilize conventional machine learning platforms for analyzing zebrafish phenotypes, which enables fluorescence-based, phenotype-driven zebrafish screening.

Performance of computational methods for the evaluation of pericentriolar material 1 missense variants in CAGI-5

The CAGI-5 pericentriolar material 1 (PCM1) challenge aimed to predict the effect of 38 transgenic human missense mutations in the PCM1 protein implicated in schizophrenia. Participants were provided with 16 benign variants (negative controls), 10 hypomorphic, and 12 loss of function variants. Six groups participated and were asked to predict the probability of effect and standard deviation associated to each mutation. Here, we present the challenge assessment. Prediction performance was evaluated using different measures to conclude in a final ranking which highlights the strengths and weaknesses of each group. The results show a great variety of predictions where some methods performed significantly better than others. Benign variants played an important role as negative controls, highlighting predictors biased to identify disease phenotypes. The best predictor, Bromberg lab, used a neural-network-based method able to discriminate between neutral and non-neutral single nucleotide polymorphisms. The CAGI-5 PCM1 challenge allowed us to evaluate the state of the art techniques for interpreting the effect of novel variants for a difficult target protein.

A Smart Imaging Workflow for Organ-Specific Screening in a Cystic Kidney Zebrafish Disease Model

The zebrafish is being increasingly used in biomedical research and drug discovery to conduct large-scale compound screening. However, there is a lack of accessible methodologies to enable automated imaging and scoring of tissue-specific phenotypes at enhanced resolution. Here, we present the development of an automated imaging pipeline to identify chemical modifiers of glomerular cyst formation in a zebrafish model for human cystic kidney disease. Morpholino-mediated knockdown of intraflagellar transport protein Ift172 in Tg(wt1b:EGFP) embryos was used to induce large glomerular cysts representing a robustly scorable phenotypic readout. Compound-treated embryos were consistently aligned within the cavities of agarose-filled microplates. By interfacing feature detection algorithms with automated microscopy, a smart imaging workflow for detection, centring and zooming in on regions of interests was established, which enabled the automated capturing of standardised higher resolution datasets of pronephric areas. High-content screening datasets were processed and analysed using custom-developed heuristic algorithms implemented in common open-source image analysis software. The workflow enables highly efficient profiling of entire compound libraries and scoring of kidney-specific morphological phenotypes in thousands of zebrafish embryos. The demonstrated toolset covers all the aspects of a complex whole organism screening assay and can be adapted to other organs, specimens or applications.

Fishing forward and reverse: Advances in zebrafish phenomics

Understanding how the genome instructs the phenotypic characteristics of an organism is one of the major scientific endeavors of our time. Advances in genetics have progressively deciphered the inheritance, identity and biological relevance of genetically encoded information, contributing to the rise of several, complementary omic disciplines. One of them is phenomics, an emergent area of biology dedicated to the systematic multi-scale analysis of phenotypic traits. This discipline provides valuable gene function information to the rapidly evolving field of genetics. Current molecular tools enable genome-wide analyses that link gene sequence to function in multi-cellular organisms, illuminating the genome-phenome relationship. Among vertebrates, zebrafish has emerged as an outstanding model organism for high-throughput phenotyping and modeling of human disorders. Advances in both systematic mutagenesis and phenotypic analyses of embryonic and post-embryonic stages in zebrafish have revealed the function of a valuable collection of genes and the general structure of several complex traits. In this review, we summarize multiple large-scale genetic efforts addressing parental, embryonic, and adult phenotyping in the zebrafish. The genetic and quantitative tools available in the zebrafish model, coupled with the broad spectrum of phenotypes that can be assayed, make it a powerful model for phenomics, well suited for the dissection of genotype-phenotype associations in development, physiology, health and disease.

Automated Versatile DIY Microscope Platform

A versatile robot platform is presented that can be used to design low-cost custom made microscopes in do-ityourself construction. All components like the framework, the linear drives, robot controller and driver, the illumination and the camera are described as well as optional features like fluorescence microscopy and auto-focus. Finally, an application for automated imaging of 3D-cell cultures in 96-well microplates is presented.

An automated quantitative image analysis pipeline of in vivo oxidative stress and macrophage kinetics

Macrophage behavior is of great interest in response to tissue injury and promotion of regeneration. With increasing numbers of zebrafish reporter-based assays, new capabilities now exist to characterize macrophage migration, and their responses to biochemical cues, such as reactive oxygen species. Real time detection of macrophage behavior in response to oxidative stress using quantitative measures is currently beyond the scope of commercially available software solutions, presenting a gap in understanding macrophage behavior. To address this gap, we developed an image analysis pipeline solution to provide real time quantitative measures of cellular kinetics and reactive oxygen species content in vivo after tissue injury. This approach, termed Zirmi, differs from current software solutions that may only provide qualitative, single image analysis, or cell tracking solutions. Zirmi is equipped with user-defined algorithm parameters to customize quantitative data measures with visualization checks for an analysis pipeline of time-based changes. Moreover, this pipeline leverages open-source PhagoSight, as an automated keyhole cell tracking solution, to avoid parallel developments and build upon readily available tools. This approach demonstrated standardized space- and time-based quantitative measures of (1) fluorescent probe based oxidative stress and (2) macrophage recruitment kinetic based changes after tissue injury. Zirmi image analysis pipeline performed at execution speeds up to 10-times faster than manual image-based approaches. Automated segmentation methods were comparable to manual methods with a DICE Similarity coefficient > 0.70. Zirmi provides an open-source, quantitative, and non-generic image analysis pipeline. This strategy complements current wide-spread zebrafish strategies, for automated standardizations of analysis and data measures.

Zebrafish: an emerging real-time model system to study Alzheimer's disease and neurospecific drug discovery

Zebrafish (Danio rerio) is emerging as an increasingly successful model for translational research on human neurological disorders. In this review, we appraise the high degree of neurological and behavioural resemblance of zebrafish with humans. It is highly validated as a powerful vertebrate model for investigating human neurodegenerative diseases. The neuroanatomic and neurochemical pathways of zebrafish brain exhibit a profound resemblance with the human brain. Physiological, emotional and social behavioural pattern similarities between them have also been well established. Interestingly, zebrafish models have been used successfully to simulate the pathology of Alzheimer’s disease (AD) as well as Tauopathy. Their relatively simple nervous system and the optical transparency of the embryos permit real-time neurological imaging. Here, we further elaborate on the use of recent real-time imaging techniques to obtain vital insights into the neurodegeneration that occurs in AD. Zebrafish is adeptly suitable for Ca²⁺ imaging, which provides a better understanding of neuronal activity and axonal dystrophy in a non-invasive manner. Three-dimensional imaging in zebrafish is a rapidly evolving technique, which allows the visualisation of the whole organism for an elaborate in vivo functional and neurophysiological analysis in disease condition. Suitability to high-throughput screening and similarity with humans makes zebrafish an excellent model for screening neurospecific compounds. Thus, the zebrafish model can be pivotal in bridging the gap from the bench to the bedside. This fish is becoming an increasingly successful model to understand AD with further scope for investigation in neurodevelopment and neurodegeneration, which promises exciting research opportunities in the future.

Zebrafish as a Model for Drug Screening in Genetic Kidney Diseases

Genetic disorders account for a wide range of renal diseases emerging during childhood and adolescence. Due to the utilization of modern biochemical and biomedical techniques, the number of identified disease-associated genes is increasing rapidly. Modeling of congenital human disease in animals is key to our understanding of the biological mechanism underlying pathological processes and thus developing novel potential treatment options. The zebrafish (Danio rerio) has been established as a versatile small vertebrate organism that is widely used for studying human inherited diseases. Genetic accessibility in combination with elegant experimental methods in zebrafish permit modeling of human genetic diseases and dissecting the perturbation of underlying cellular networks and physiological processes. Beyond its utility for genetic analysis and pathophysiological and mechanistic studies, zebrafish embryos, and larvae are amenable for phenotypic screening approaches employing high-content and high-throughput experiments using automated microscopy. This includes large-scale chemical screening experiments using genetic models for searching for disease-modulating compounds. Phenotype-based approaches of drug discovery have been successfully performed in diverse zebrafish-based screening applications with various phenotypic readouts. As a result, these can lead to the identification of candidate substances that are further examined in preclinical and clinical trials. In this review, we discuss zebrafish models for inherited kidney disease as well as requirements and considerations for the technical realization of drug screening experiments in zebrafish.

Automatic multiple zebrafish larvae tracking in unconstrained microscopic video conditions

The accurate tracking of zebrafish larvae movement is fundamental to research in many biomedical, pharmaceutical, and behavioral science applications. However, the locomotive characteristics of zebrafish larvae are significantly different from adult zebrafish, where existing adult zebrafish tracking systems cannot reliably track zebrafish larvae. Further, the far smaller size differentiation between larvae and the container render the detection of water impurities inevitable, which further affects the tracking of zebrafish larvae or require very strict video imaging conditions that typically result in unreliable tracking results for realistic experimental conditions. This paper investigates the adaptation of advanced computer vision segmentation techniques and multiple object tracking algorithms to develop an accurate, efficient and reliable multiple zebrafish larvae tracking system. The proposed system has been tested on a set of single and multiple adult and larvae zebrafish videos in a wide variety of (complex) video conditions, including shadowing, labels, water bubbles and background artifacts. Compared with existing state-of-the-art and commercial multiple organism tracking systems, the proposed system improves the tracking accuracy by up to 31.57% in unconstrained video imaging conditions. To facilitate the evaluation on zebrafish segmentation and tracking research, a dataset with annotated ground truth is also presented. The software is also publicly accessible.

Fully Automated Pipetting Sorting System for Different Morphological Phenotypes of Zebrafish Embryos

Systems biology methods, such as transcriptomics and metabolomics, require large numbers of small model organisms, such as zebrafish embryos. Manual separation of mutant embryos from wild-type embryos is a tedious and time-consuming task that is prone to errors, especially if there are variable phenotypes of a mutant. Here we describe a zebrafish embryo sorting system with two cameras and image processing based on template-matching algorithms. In order to evaluate the system, zebrafish rx3 mutants that lack eyes due to a patterning defect in brain development were separated from their wild-type siblings. These mutants show glucocorticoid deficiency due to pituitary defects and serve as a model for human secondary adrenal insufficiencies. We show that the variable phenotypes of the mutant embryos can be safely distinguished from phenotypic wild-type zebrafish embryos and sorted from one petri dish into another petri dish or into a 96-well microtiter plate. On average, classification of a zebrafish embryo takes approximately 1 s, with a sensitivity and specificity of 87% to 95%, respectively. Other morphological phenotypes may be classified and sorted using similar techniques.

Automatic determination of heart rates from microscopy videos of early life stages of fish

Toxic effects of organic hydrophobic contaminants include impacts on fish heart rate (HR) and cardiac functioning. Thus, in ecotoxicology as well as aquaculture and even medicine, fish heart functioning plays an important role in application areas. The aim of this study was to assemble a pipeline of image processing and statistical techniques to extract HR information from microscopy videos of the embryo and larval stages of three species of fish (Atlantic cod, haddock, and Atlantic bluefin tuna). The method enables automatic processing for a large number of individuals, saving a significant amount of time compared with manual processing, while simultaneously eliminating the type of errors such a manual process might incur.

Midbrain-Hindbrain Boundary Morphogenesis: At the Intersection of Wnt and Fgf Signaling

A constriction in the neural tube at the junction of the midbrain and hindbrain is a conserved feature of vertebrate embryos. The constriction is a defining feature of the midbrain-hindbrain boundary (MHB), a signaling center that patterns the adjacent midbrain and rostral hindbrain and forms at the junction of two gene expression domains in the early neural plate: an anterior otx2/wnt1 positive domain and a posterior gbx/fgf8 positive domain. otx2 and gbx genes encode mutually repressive transcription factors that create a lineage restriction boundary at their expression interface. Wnt and Fgf genes form a mutually dependent feedback system that maintains their expression domains on the otx2 or gbx side of the boundary, respectively. Constriction morphogenesis occurs after these conserved gene expression domains are established and while their mutual interactions maintain their expression pattern; consequently, mutant studies in zebrafish have led to the suggestion that constriction morphogenesis should be considered a unique phase of MHB development. We analyzed MHB morphogenesis in fgf8 loss of function zebrafish embryos using a reporter driven by the conserved wnt1 enhancer to visualize anterior boundary cells. We found that fgf8 loss of function results in a re-activation of wnt1 reporter expression posterior to the boundary simultaneous with an inactivation of the wnt1 reporter in the anterior boundary cells, and that these events correlate with relaxation of the boundary constriction. In consideration of other results that correlate the boundary constriction with Wnt and Fgf expression, we propose that the maintenance of an active Wnt-Fgf feedback loop is a key factor in driving the morphogenesis of the MHB constriction.

The rise of photoresponsive protein technologies applications in vivo: a spotlight on zebrafish developmental and cell biology

The zebrafish ( Danio rerio) is a powerful vertebrate model to study cellular and developmental processes in vivo. The optical clarity and their amenability to genetic manipulation make zebrafish a model of choice when it comes to applying optical techniques involving genetically encoded photoresponsive protein technologies. In recent years, a number of fluorescent protein and optogenetic technologies have emerged that allow new ways to visualize, quantify, and perturb developmental dynamics. Here, we explain the principles of these new tools and describe some of their representative applications in zebrafish.

Reproducible Large-Scale Neuroimaging Studies with the OpenMOLE Workflow Management System

OpenMOLE is a scientific workflow engine with a strong emphasis on workload distribution. Workflows are designed using a high level Domain Specific Language (DSL) built on top of Scala. It exposes natural parallelism constructs to easily delegate the workload resulting from a workflow to a wide range of distributed computing environments. OpenMOLE hides the complexity of designing complex experiments thanks to its DSL. Users can embed their own applications and scale their pipelines from a small prototype running on their desktop computer to a large-scale study harnessing distributed computing infrastructures, simply by changing a single line in the pipeline definition. The construction of the pipeline itself is decoupled from the execution context. The high-level DSL abstracts the underlying execution environment, contrary to classic shell-script based pipelines. These two aspects allow pipelines to be shared and studies to be replicated across different computing environments. Workflows can be run as traditional batch pipelines or coupled with OpenMOLE's advanced exploration methods in order to study the behavior of an application, or perform automatic parameter tuning. In this work, we briefly present the strong assets of OpenMOLE and detail recent improvements targeting re-executability of workflows across various Linux platforms. We have tightly coupled OpenMOLE with CARE, a standalone containerization solution that allows re-executing on a Linux host any application that has been packaged on another Linux host previously. The solution is evaluated against a Python-based pipeline involving packages such as scikit-learn as well as binary dependencies. All were packaged and re-executed successfully on various HPC environments, with identical numerical results (here prediction scores) obtained on each environment. Our results show that the pair formed by OpenMOLE and CARE is a reliable solution to generate reproducible results and re-executable pipelines. A demonstration of the flexibility of our solution showcases three neuroimaging pipelines harnessing distributed computing environments as heterogeneous as local clusters or the European Grid Infrastructure (EGI).

Automated phenotype pattern recognition of zebrafish for high-throughput screening

Over the last years, the zebrafish (Danio rerio) has become a key model organism in genetic and chemical screenings. A growing number of experiments and an expanding interest in zebrafish research makes it increasingly essential to automatize the distribution of embryos and larvae into standard microtiter plates or other sample holders for screening, often according to phenotypical features. Until now, such sorting processes have been carried out by manually handling the larvae and manual feature detection. Here, a prototype platform for image acquisition together with a classification software is presented. Zebrafish embryos and larvae and their features such as pigmentation are detected automatically from the image. Zebrafish of 4 different phenotypes can be classified through pattern recognition at 72 h post fertilization (hpf), allowing the software to classify an embryo into 2 distinct phenotypic classes: wild-type versus variant. The zebrafish phenotypes are classified with an accuracy of 79-99% without any user interaction. A description of the prototype platform and of the algorithms for image processing and pattern recognition is presented.

Systems Biology to Support Nanomaterial Grouping

The assessment of potential health risks of engineered nanomaterials (ENMs) is a challenging task due to the high number and great variety of already existing and newly emerging ENMs. Reliable grouping or categorization of ENMs with respect to hazards could help to facilitate prioritization and decision making for regulatory purposes. The development of grouping criteria, however, requires a broad and comprehensive data basis. A promising platform addressing this challenge is the systems biology approach. The different areas of systems biology, most prominently transcriptomics, proteomics and metabolomics, each of which provide a wealth of data that can be used to reveal novel biomarkers and biological pathways involved in the mode-of-action of ENMs. Combining such data with classical toxicological data would enable a more comprehensive understanding and hence might lead to more powerful and reliable prediction models. Physico-chemical data provide crucial information on the ENMs and need to be integrated, too. Overall statistical analysis should reveal robust grouping and categorization criteria and may ultimately help to identify meaningful biomarkers and biological pathways that sufficiently characterize the corresponding ENM subgroups. This chapter aims to give an overview on the different systems biology technologies and their current applications in the field of nanotoxicology, as well as to identify the existing challenges.

Zebrafish in Toxicology and Environmental Health

As manufacturing processes and development of new synthetic compounds increase to keep pace with the expanding global demand, environmental health, and the effects of toxicant exposure are emerging as critical public health concerns. Additionally, chemicals that naturally occur in the environment, such as metals, have profound effects on human and animal health. Many of these compounds are in the news: lead, arsenic, and endocrine disruptors such as bisphenol A have all been widely publicized as causing disease or damage to humans and wildlife in recent years. Despite the widespread appreciation that environmental toxins can be harmful, there is limited understanding of how many toxins cause disease. Zebrafish are at the forefront of toxicology research; this system has been widely used as a tool to detect toxins in water samples and to investigate the mechanisms of action of environmental toxins and their related diseases. The benefits of zebrafish for studying vertebrate development are equally useful for studying teratogens. Here, we review how zebrafish are being used both to detect the presence of some toxins as well as to identify how environmental exposures affect human health and disease. We focus on areas where zebrafish have been most effectively used in ecotoxicology and in environmental health, including investigation of exposures to endocrine disruptors, industrial waste byproducts, and arsenic.

Extensive Regulation of Diurnal Transcription and Metabolism by Glucocorticoids

Altered daily patterns of hormone action are suspected to contribute to metabolic disease. It is poorly understood how the adrenal glucocorticoid hormones contribute to the coordination of daily global patterns of transcription and metabolism. Here, we examined diurnal metabolite and transcriptome patterns in a zebrafish glucocorticoid deficiency model by RNA-Seq, NMR spectroscopy and liquid chromatography-based methods. We observed dysregulation of metabolic pathways including glutaminolysis, the citrate and urea cycles and glyoxylate detoxification. Constant, non-rhythmic glucocorticoid treatment rescued many of these changes, with some notable exceptions among the amino acid related pathways. Surprisingly, the non-rhythmic glucocorticoid treatment rescued almost half of the entire dysregulated diurnal transcriptome patterns. A combination of E-box and glucocorticoid response elements is enriched in the rescued genes. This simple enhancer element combination is sufficient to drive rhythmic circadian reporter gene expression under non-rhythmic glucocorticoid exposure, revealing a permissive function for the hormones in glucocorticoid-dependent circadian transcription. Our work highlights metabolic pathways potentially contributing to morbidity in patients with glucocorticoid deficiency, even under glucocorticoid replacement therapy. Moreover, we provide mechanistic insight into the interaction between the circadian clock and glucocorticoids in the transcriptional regulation of metabolism.

Zebrafish response to a robotic replica in three dimensions

As zebrafish emerge as a species of choice for the investigation of biological processes, a number of experimental protocols are being developed to study their social behaviour. While live stimuli may elicit varying response in focal subjects owing to idiosyncrasies, tiredness and circadian rhythms, video stimuli suffer from the absence of physical input and rely only on two-dimensional projections. Robotics has been recently proposed as an alternative approach to generate physical, customizable, effective and consistent stimuli for behavioural phenotyping. Here, we contribute to this field of investigation through a novel four-degree-of-freedom robotics-based platform to manoeuvre a biologically inspired three-dimensionally printed replica. The platform enables three-dimensional motions as well as body oscillations to mimic zebrafish locomotion. In a series of experiments, we demonstrate the differential role of the visual stimuli associated with the biologically inspired replica and its three-dimensional motion. Three-dimensional tracking and information-theoretic tools are complemented to quantify the interaction between zebrafish and the robotic stimulus. Live subjects displayed a robust attraction towards the moving replica, and such attraction was lost when controlling for its visual appearance or motion. This effort is expected to aid zebrafish behavioural phenotyping, by offering a novel approach to generate physical stimuli moving in three dimensions.

Semi-automated detection of fractional shortening in zebrafish embryo heart videos

Quantifying cardiac functions in model organisms like embryonic zebrafish is of high importance in small molecule screens for new therapeutic compounds. One relevant cardiac parameter is the fractional shortening (FS). A method for semi-automatic quantification of FS in video recordings of zebrafish embryo hearts is presented. The software provides automated visual information about the end-systolic and end-diastolic stages of the heart by displaying corresponding colored lines into a Motion-mode display. After manually marking the ventricle diameters in frames of end-systolic and end-diastolic stages, the FS is calculated. The software was evaluated by comparing the results of the determination of FS with results obtained from another established method. Correlations of 0.96 < r < 0.99 between the two methods were found indicating that the new software provides comparable results for the determination of the FS.

A framework for feedback-based segmentation of 3D image stacks

3D segmentation has become a widely used technique. However, automatic segmentation does not deliver high accuracy in optically dense images and manual segmentation lowers the throughput drastically. Therefore, we present a workflow for 3D segmentation being able to forecast segments based on a user-given ground truth. We provide the possibility to correct wrong forecasts and to repeatedly insert ground truth in the process. Our aim is to combine automated and manual segmentation and therefore to improve accuracy by a tunable amount of manual input.

Collaborative analysis of multi-gigapixel imaging data using Cytomine

MOTIVATION

Collaborative analysis of massive imaging datasets is essential to enable scientific discoveries.

RESULTS

We developed Cytomine to foster active and distributed collaboration of multidisciplinary teams for large-scale image-based studies. It uses web development methodologies and machine learning in order to readily organize, explore, share and analyze (semantically and quantitatively) multi-gigapixel imaging data over the internet. We illustrate how it has been used in several biomedical applications.

AVAILABILITY AND IMPLEMENTATION

Cytomine (http://www.cytomine.be/) is freely available under an open-source license from http://github.com/cytomine/ A documentation wiki (http://doc.cytomine.be) and a demo server (http://demo.cytomine.be) are also available.

CONTACT

info@cytomine.be

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Reconstruction and Visualization of Coordinated 3D Cell Migration Based on Optical Flow

Animal development is marked by the repeated reorganization of cells and cell populations, which ultimately determine form and shape of the growing organism. One of the central questions in developmental biology is to understand precisely how cells reorganize, as well as how and to what extent this reorganization is coordinated. While modern microscopes can record video data for every cell during animal development in 3D+t, analyzing these videos remains a major challenge: reconstruction of comprehensive cell tracks turned out to be very demanding especially with decreasing data quality and increasing cell densities. In this paper, we present an analysis pipeline for coordinated cellular motions in developing embryos based on the optical flow of a series of 3D images. We use numerical integration to reconstruct cellular long-term motions in the optical flow of the video, we take care of data validation, and we derive a LIC-based, dense flow visualization for the resulting pathlines. This approach allows us to handle low video quality such as noisy data or poorly separated cells, and it allows the biologists to get a comprehensive understanding of their data by capturing dynamic growth processes in stills. We validate our methods using three videos of growing fruit fly embryos.

Using zebrafish in systems toxicology for developmental toxicity testing

With the high cost and the long-term assessment of developmental toxicity testing in mammals, the vertebrate zebrafish has become a useful alternative model organism for high-throughput developmental toxicity testing. Zebrafish is also very favorable for the 3R perspective in toxicology; however, the methodologies used by research groups vary greatly, posing considerable challenges to integrative analysis. In this review, we discuss zebrafish developmental toxicity testing, focusing on the methods of chemical exposure, the assessment of morphological abnormalities, housing conditions and their effects on the production of healthy embryos, and future directions. Zebrafish as a systems toxicology model has the potential to elucidate developmental toxicity pathways, and to provide a sound basis for human health risk assessments.

Bioimage Informatics for Big Data

Bioimage informatics is a field wherein high-throughput image informatics methods are used to solve challenging scientific problems related to biology and medicine. When the image datasets become larger and more complicated, many conventional image analysis approaches are no longer applicable. Here, we discuss two critical challenges of large-scale bioimage informatics applications, namely, data accessibility and adaptive data analysis. We highlight case studies to show that these challenges can be tackled based on distributed image computing as well as machine learning of image examples in a multidimensional environment.

XPIWIT--an XML pipeline wrapper for the Insight Toolkit

The Insight Toolkit offers plenty of features for multidimensional image analysis. Current implementations, however, often suffer either from a lack of flexibility due to hard-coded C++ pipelines for a certain task or by slow execution times, e.g. caused by inefficient implementations or multiple read/write operations for separate filter execution. We present an XML-based wrapper application for the Insight Toolkit that combines the performance of a pure C++ implementation with an easy-to-use graphical setup of dynamic image analysis pipelines. Created XML pipelines can be interpreted and executed by XPIWIT in console mode either locally or on large clusters. We successfully applied the software tool for the automated analysis of terabyte-scale, time-resolved 3D image data of zebrafish embryos.

AVAILABILITY AND IMPLEMENTATION

XPIWIT is implemented in C++ using the Insight Toolkit and the Qt SDK. It has been successfully compiled and tested under Windows and Unix-based systems. Software and documentation are distributed under Apache 2.0 license and are publicly available for download at https://bitbucket.org/jstegmaier/xpiwit/downloads/.

CONTACT

johannes.stegmaier@kit.edu

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Zebrafish as a systems toxicology model for developmental neurotoxicity testing

The developing brain is extremely sensitive to many chemicals. Exposure to neurotoxicants during development has been implicated in various neuropsychiatric and neurological disorders, including autism spectrum disorder, attention deficit hyperactive disorder, schizophrenia, Parkinson's disease, and Alzheimer's disease. Although rodents have been widely used for developmental neurotoxicity testing, experiments using large numbers of rodents are time-consuming, expensive, and raise ethical concerns. Using alternative non-mammalian animal models may relieve some of these pressures by allowing testing of large numbers of subjects while reducing expenses and minimizing the use of mammalian subjects. In this review, we discuss some of the advantages of using zebrafish in developmental neurotoxicity testing, focusing on central nervous system development, neurobehavior, toxicokinetics, and toxicodynamics in this species. We also describe some important examples of developmental neurotoxicity testing using zebrafish combined with gene expression profiling, neuroimaging, or neurobehavioral assessment. Zebrafish may be a systems toxicology model that has the potential to reveal the pathways of developmental neurotoxicity and to provide a sound basis for human risk assessments.

Phenotype classification of zebrafish embryos by supervised learning

Zebrafish is increasingly used to assess biological properties of chemical substances and thus is becoming a specific tool for toxicological and pharmacological studies. The effects of chemical substances on embryo survival and development are generally evaluated manually through microscopic observation by an expert and documented by several typical photographs. Here, we present a methodology to automatically classify brightfield images of wildtype zebrafish embryos according to their defects by using an image analysis approach based on supervised machine learning. We show that, compared to manual classification, automatic classification results in 90 to 100% agreement with consensus voting of biological experts in nine out of eleven considered defects in 3 days old zebrafish larvae. Automation of the analysis and classification of zebrafish embryo pictures reduces the workload and time required for the biological expert and increases the reproducibility and objectivity of this classification.

Myotonia congenita-associated mutations in chloride channel-1 affect zebrafish body wave swimming kinematics

Myotonia congenita is a human muscle disorder caused by mutations in CLCN1, which encodes human chloride channel 1 (CLCN1). Zebrafish is becoming an increasingly useful model for human diseases, including muscle disorders. In this study, we generated transgenic zebrafish expressing, under the control of a muscle specific promoter, human CLCN1 carrying mutations that have been identified in human patients suffering from myotonia congenita. We developed video analytic tools that are able to provide precise quantitative measurements of movement abnormalities in order to analyse the effect of these CLCN1 mutations on adult transgenic zebrafish swimming. Two new parameters for body-wave kinematics of swimming reveal changes in body curvature and tail offset in transgenic zebrafish expressing the disease-associated CLCN1 mutants, presumably due to their effect on muscle function. The capability of the developed video analytic tool to distinguish wild-type from transgenic zebrafish could provide a useful asset to screen for compounds that reverse the disease phenotype, and may be applicable to other movement disorders besides myotonia congenita.

Label free cell-tracking and division detection based on 2D time-lapse images for lineage analysis of early embryo development

In this paper we report a database and a series of techniques related to the problem of tracking cells, and detecting their divisions, in time-lapse movies of mammalian embryos. Our contributions are (1) a method for counting embryos in a well, and cropping each individual embryo across frames, to create individual movies for cell tracking; (2) a semi-automated method for cell tracking that works up to the 8-cell stage, along with a software implementation available to the public (this software was used to build the reported database); (3) an algorithm for automatic tracking up to the 4-cell stage, based on histograms of mirror symmetry coefficients captured using wavelets; (4) a cell-tracking database containing 100 annotated examples of mammalian embryos up to the 8-cell stage; and (5) statistical analysis of various timing distributions obtained from those examples.

Automatic zebrafish heartbeat detection and analysis for zebrafish embryos

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.