Automated phenotype recognition for zebrafish embryo based in vivo high throughput toxicity screening of engineered nano-materials

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.

Zebrafish xenograft as a tool for the study of colorectal cancer: a review

Colorectal cancer (CRC) is the second leading cause of cancer-related death, mostly due to metastatic disease and the fact that many patients already show signs of metastasis at the time of first diagnosis. Current CRC therapies negatively impact patients' quality of life and have little to no effect on combating the tumor once the dissemination has started. Danio rerio (zebrafish) is a popular animal model utilized in cancer research. One of its main advantages is the ease of xenograft transplantation due to the fact that zebrafish larvae lack the adaptative immune system, guaranteeing the impossibility of rejection. In this review, we have presented the many works that choose zebrafish xenograft as a tool for the study of CRC, highlighting the methods used as well as the promising new therapeutic molecules that have been identified due to this animal model.

Automated staging of zebrafish embryos with deep learning

The zebrafish (Danio rerio) is an important biomedical model organism used in many disciplines. The phenomenon of developmental delay in zebrafish embryos has been widely reported as part of a mutant or treatment-induced phenotype. However, the detection and quantification of these delays is often achieved through manual observation, which is both time-consuming and subjective. We present KimmelNet, a deep learning model trained to predict embryo age (hours post fertilisation) from 2D brightfield images. KimmelNet's predictions agree closely with established staging methods and can detect developmental delays between populations with high confidence using as few as 100 images. Moreover, KimmelNet generalises to previously unseen data, with transfer learning enhancing its performance. With the ability to analyse tens of thousands of standard brightfield microscopy images on a timescale of minutes, we envisage that KimmelNet will be a valuable resource for the developmental biology community. Furthermore, the approach we have used could easily be adapted to generate models for other organisms.

Deep Learning-Enabled Morphometric Analysis for Toxicity Screening Using Zebrafish Larvae

Toxicology studies heavily rely on morphometric analysis to detect abnormalities and diagnose disease processes. The emergence of ever-increasing varieties of environmental pollutants makes it difficult to perform timely assessments, especially using in vivo models. Herein, we propose a deep learning-based morphometric analysis (DLMA) to quantitatively identify eight abnormal phenotypes (head hemorrhage, jaw malformation, uninflated swim bladder, pericardial edema, yolk edema, bent spine, dead, unhatched) and eight vital organ features (eye, head, jaw, heart, yolk, swim bladder, body length, and curvature) of zebrafish larvae. A data set composed of 2532 bright-field micrographs of zebrafish larvae at 120 h post fertilization was generated from toxicity screening of three categories of chemicals, i.e., endocrine disruptors (perfluorooctanesulfonate and bisphenol A), heavy metals (CdCl2 and PbI2), and emerging organic pollutants (acetaminophen, 2,7-dibromocarbazole, 3-monobromocarbazo, 3,6-dibromocarbazole, and 1,3,6,8-tetrabromocarbazo). Two typical deep learning models, one-stage and two-stage models (TensorMask, Mask R-CNN), were trained to implement phenotypic feature classification and segmentation. The accuracy was statistically validated with a mean average precision >0.93 in unlabeled data sets and a mean accuracy >0.86 in previously published data sets. Such a method effectively enables subjective morphometric analysis of zebrafish larvae to achieve efficient hazard identification of both chemicals and environmental pollutants.

Converting Nanotoxicity Data to Information Using Artificial Intelligence and Simulation

Decades of nanotoxicology research have generated extensive and diverse data sets. However, data is not equal to information. The question is how to extract critical information buried in vast data streams. Here we show that artificial intelligence (AI) and molecular simulation play key roles in transforming nanotoxicity data into critical information, i.e., constructing the quantitative nanostructure (physicochemical properties)-toxicity relationships, and elucidating the toxicity-related molecular mechanisms. For AI and molecular simulation to realize their full impacts in this mission, several obstacles must be overcome. These include the paucity of high-quality nanomaterials (NMs) and standardized nanotoxicity data, the lack of model-friendly databases, the scarcity of specific and universal nanodescriptors, and the inability to simulate NMs at realistic spatial and temporal scales. This review provides a comprehensive and representative, but not exhaustive, summary of the current capability gaps and tools required to fill these formidable gaps. Specifically, we discuss the applications of AI and molecular simulation, which can address the large-scale data challenge for nanotoxicology research. The need for model-friendly nanotoxicity databases, powerful nanodescriptors, new modeling approaches, molecular mechanism analysis, and design of the next-generation NMs are also critically discussed. Finally, we provide a perspective on future trends and challenges.

Recent Advancement in Breast Cancer Research: Insights from Model Organisms-Mouse Models to Zebrafish

Animal models have been utilized for decades to investigate the causes of human diseases and provide platforms for testing novel therapies. Indeed, breakthrough advances in genetically engineered mouse (GEM) models and xenograft transplantation technologies have dramatically benefited in elucidating the mechanisms underlying the pathogenesis of multiple diseases, including cancer. The currently available GEM models have been employed to assess specific genetic changes that underlay many features of carcinogenesis, including variations in tumor cell proliferation, apoptosis, invasion, metastasis, angiogenesis, and drug resistance. In addition, mice models render it easier to locate tumor biomarkers for the recognition, prognosis, and surveillance of cancer progression and recurrence. Furthermore, the patient-derived xenograft (PDX) model, which involves the direct surgical transfer of fresh human tumor samples to immunodeficient mice, has contributed significantly to advancing the field of drug discovery and therapeutics. Here, we provide a synopsis of mouse and zebrafish models used in cancer research as well as an interdisciplinary 'Team Medicine' approach that has not only accelerated our understanding of varied aspects of carcinogenesis but has also been instrumental in developing novel therapeutic strategies.

Usefulness of Vaccine Adverse Event Reporting System for Machine-Learning Based Vaccine Research: A Case Study for COVID-19 Vaccines

Usefulness of Vaccine-Adverse Event-Reporting System (VAERS) data and protocols required for statistical analyses were pinpointed with a set of recommendations for the application of machine learning modeling or exploratory analyses on VAERS data with a case study of COVID-19 vaccines (Pfizer-BioNTech, Moderna, Janssen). A total of 262,454 duplicate reports (29%) from 905,976 reports were identified, which were merged into a total of 643,522 distinct reports. A customized online survey was also conducted providing 211 reports. A total of 20 highest reported adverse events were first identified. Differences in results after applying various machine learning algorithms (association rule mining, self-organizing maps, hierarchical clustering, bipartite graphs) on VAERS data were noticed. Moderna reports showed injection-site-related AEs of higher frequencies by 15.2%, consistent with the online survey (12% higher reporting rate for pain in the muscle for Moderna compared to Pfizer-BioNTech). AEs {headache, pyrexia, fatigue, chills, pain, dizziness} constituted >50% of the total reports. Chest pain in male children reports was 295% higher than in female children reports. Penicillin and sulfa were of the highest frequencies (22%, and 19%, respectively). Analysis of uncleaned VAERS data demonstrated major differences from the above (7% variations). Spelling/grammatical mistakes in allergies were discovered (e.g., ~14% reports with incorrect spellings for penicillin).

The Zebrafish Xenograft Models for Investigating Cancer and Cancer Therapeutics

Simple Summary

The identification and development of new anti-cancer drugs requires extensive testing in animal models to establish safety and efficacy of drug candidates. The transplantation of human tumor tissue into mouse (tumor xenografts) is commonly used to study cancer progression and to test potential drugs for their anti-cancer activity. Mouse models do not afford the ability to test a large number of drug candidates quickly as it takes several weeks to conduct these experiments. In contrast, tumor xenograft studies in zebrafish provide an efficient platform for rapid testing of safety and efficacy in less than two weeks.

Abstract

In order to develop new cancer therapeutics, rapid, reliable, and relevant biological models are required to screen and validate drug candidates for both efficacy and safety. In recent years, the zebrafish (Danio rerio) has emerged as an excellent model organism suited for these goals. Larval fish or immunocompromised adult fish are used to engraft human cancer cells and serve as a platform for screening potential drug candidates. With zebrafish sharing ~80% of disease-related orthologous genes with humans, they provide a low cost, high-throughput alternative to mouse xenografts that is relevant to human biology. In this review, we provide background on the methods and utility of zebrafish xenograft models in cancer research.

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.

Rough sets and social ski-driver optimization for drug toxicity analysis

BACKGROUND AND OBJECTIVES

Toxicity testing is an important step for developing new drugs, and animals are widely used in this step by exposing them to the toxicants. Zebrafishes are widely used for measuring and detecting the toxicity. However, measuring and testing toxicity manually is not feasible due to the large number of embryos. This work presents an automated model to investigate the toxicity of two toxicants (3, 4-Dichloroaniline (34DCA) and p-Tert-Butylphenol (PTBP)).

METHODS

The proposed model consists of two steps. In the first step, a set of features is extracted from microscopic images of zebrafish embryos using the Segmentation-Based Fractal Texture Analysis (SFTA) technique. Secondly, a novel rough set-based model using Social ski-driver (SSD) is used to find a global minimal subset of features that preserves important information of the original features. In the third step, the AdaBoost classifier is used to classify an unknown sample to alive or coagulant after exposing the embryo to a toxic compound.

RESULTS

For detecting the toxicity, the proposed model is compared with (i) three deterministic rough set reduction algorithms and (ii) the PSO-based algorithm. The classification performance rate of our model was ranged from 97.1% to 99.5% and it outperformed the other algorithms.

CONCLUSIONS

The results of our experiments proved that the proposed drug toxicity model is efficient for rough set-based feature selection and it obtains a high classification performance.

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.

Automatic Zebrafish Egg Phenotype Recognition from Bright-Field Microscopic Images Using Deep Convolutional Neural Network

Zebrafish eggs are widely used in biological experiments to study the environmental and genetic influence on embryo development. Due to the high throughput of microscopic imaging, automated analysis of zebrafish egg microscopic images is highly demanded. However, machine learning algorithms for zebrafish egg image analysis suffer from the problems of small imbalanced training dataset and subtle inter-class differences. In this study, we developed an automated zebrafish egg microscopic image analysis algorithm based on deep convolutional neural network (CNN). To tackle the problem of insufficient training data, the strategies of transfer learning and data augmentation were used. We also adopted the global averaged pooling technique to overcome the subtle phenotype differences between the fertilized and unfertilized eggs. Experimental results of a five-fold cross-validation test showed that the proposed method yielded a mean classification accuracy of 95.0% and a maximum accuracy of 98.8%. The network also demonstrated higher classification accuracy and better convergence performance than conventional CNN methods. This study extends the deep learning technique to zebrafish egg phenotype classification and paves the way for automatic bright-field microscopic image analysis.

Automated Morphological Feature Assessment for Zebrafish Embryo Developmental Toxicity Screens

Detection of developmental phenotypes in zebrafish embryos typically involves a visual assessment and scoring of morphological features by an individual researcher. Subjective scoring could impact results and be of particular concern when phenotypic effect patterns are also used as a diagnostic tool to classify compounds. Here we introduce a quantitative morphometric approach based on image analysis of zebrafish embryos. A software called FishInspector was developed to detect morphological features from images collected using an automated system to position zebrafish embryos. The analysis was verified and compared with visual assessments of 3 participating laboratories using 3 known developmental toxicants (methotrexate, dexamethasone, and topiramate) and 2 negative compounds (loratadine and glibenclamide). The quantitative approach exhibited higher sensitivity and made it possible to compare patterns of effects with the potential to establish a grouping and classification of developmental toxicants. Our approach improves the robustness of phenotype scoring and reliability of assay performance and, hence, is anticipated to improve the predictivity of developmental toxicity screening using the zebrafish embryo.

Morphometric characteristics and time to hatch as efficacious indicators for potential nanotoxicity assay in zebrafish

Although the effects of nano-sized titania (nTiO₂ ) on hatching events (change in hatching time and total hatching) in zebrafish have been reported, additional consequences of nTiO₂ exposure (i.e., the effects of nTiO₂ -induced changes in hatching events and morphometric parameters on embryo-larvae development and survivability) have not been reported. To address this knowledge gap, embryos 4 h postfertilization were exposed to nTiO₂ (0, 0.01, 10, and 1000 μg/mL) for 220 h. Hatching rate (58, 82, and 106 h postexposure [hpe]), survival rate (8 times from 34 to 202 hpe), and 21 morphometric characteristics (8 times from 34 to 202 hpe) were recorded. Total hatching (rate at 106 hpe) was significantly and positively correlated to survival rate, but there was no direct association between nTiO₂ -induced change in hatching time (hatching rate at 58 and 82 hpe) and survival rate. At 58, 82, and 106 hpe, morphometric characteristics were significantly correlated to hatching rate, suggesting that the nTiO₂ -induced change in hatching time can affect larval development. The morphometric characteristics that were associated with change in hatching time were also significantly correlated to survival rate, suggesting an indirect significant influence of the nTiO₂ -induced change in hatching time on survivability. These results show a significant influence of nTiO₂ -induced change in hatching events on zebrafish embryo-larvae development and survivability. They also show that morphometric maldevelopments can predict later-in-life consequences (survivability) of an embryonic exposure to nTiO₂ . This suggests that zebrafish can be sensitive biological predictors of nTiO₂ acute toxicity. Environ Toxicol Chem 2018;37:3063-3076. © 2018 SETAC.

Advanced tools for the safety assessment of nanomaterials

Engineered nanomaterials (ENMs) have tremendous potential to produce beneficial technological impact in numerous sectors in society. Safety assessment is, of course, of paramount importance. However, the myriad variations of ENM properties makes the identification of specific features driving toxicity challenging. At the same time, reducing animal tests by introducing alternative and/or predictive in vitro and in silico methods has become a priority. It is important to embrace these new advances in the safety assessment of ENMs. Indeed, remarkable progress has been made in recent years with respect to mechanism-based hazard assessment of ENMs, including systems biology approaches as well as high-throughput screening platforms, and new tools are also emerging in risk assessment and risk management for humans and the environment across the whole life-cycle of nano-enabled products. Here, we highlight some of the key advances in the hazard and risk assessment of ENMs.

A Systematic Review on Non-mammalian Models in Epilepsy Research

Epilepsy is a common neurological disorder characterized by seizures which result in distinctive neurobiological and behavioral impairments. Not much is known about the causes of epilepsy, making it difficult to devise an effective cure for epilepsy. Moreover, clinical studies involving epileptogenesis and ictogenesis cannot be conducted in humans due to ethical reasons. As a result, animal models play a crucial role in the replication of epileptic seizures. In recent years, non-mammalian models have been given a primary focus in epilepsy research due to their advantages. This systematic review aims to summarize the importance of non-mammalian models in epilepsy research, such as in the screening of anti-convulsive compounds. The reason for this review is to integrate currently available information on the use and importance of non-mammalian models in epilepsy testing to aid in the planning of future studies as well as to provide an overview of the current state of this field. A PRISMA model was utilized and PubMed, Springer, ScienceDirect and SCOPUS were searched for articles published between January 2007 and November 2017. Fifty-one articles were finalized based on the inclusion/exclusion criteria and were discussed in this review. The results of this review demonstrated the current use of non-mammalian models in epilepsy research and reaffirmed their potential to supplement the typical rodent models of epilepsy in future research into both epileptogenesis and the treatment of epilepsy. This review also revealed a preference for zebrafish and fruit flies in lieu of other non-mammalian models, which is a shortcoming that should be corrected in future studies due to the great potential of these underutilized animal models.

A 3-dimensional (3D)-printed Template for High Throughput Zebrafish Embryo Arraying

The zebrafish is a globally recognized fresh water organism frequently used in developmental biology, environmental toxicology, and human disease related research fields. Thanks to its unique features, including large fecundity, embryo translucency, rapid and simultaneous development, etc., zebrafish embryos are often used for large scale toxicity assessment of chemicals and drug/compound screening. A typical screening procedure involves adult zebrafish spawning, embryos selection, and arraying the embryos into multi-well plates. From there, embryos are subjected to exposure and the toxicity of chemical, or the effectiveness of the drugs/compounds can be evaluated relatively quickly based on phenotypic observations. Among these processes, embryos arraying is one of the most time-consuming and labor-intensive steps that limits the throughput level. In this protocol, we present an innovative approach that makes use of a 3D-printed arraying template coupled with vacuum manipulation to speed up this laborious step. The protocol herein describes the overall design of the arraying template, a detailed experimental setup and step-by-step procedure, followed by representative results. When implemented, this approach should prove beneficial in a variety of research applications using zebrafish embryos as testing subjects.

The Potential of Zebrafish as a Model Organism for Improving the Translation of Genetic Anticancer Nanomedicines

In the last few decades, the field of nanomedicine applied to cancer has revolutionized cancer treatment: several nanoformulations have already reached the market and are routinely being used in the clinical practice. In the case of genetic nanomedicines, i.e., designed to deliver gene therapies to cancer cells for therapeutic purposes, advances have been less impressive. This is because of the many barriers that limit the access of the therapeutic nucleic acids to their target site, and the lack of models that would allow for an improvement in the understanding of how nanocarriers can be tailored to overcome them. Zebrafish has important advantages as a model species for the study of anticancer therapies, and have a lot to offer regarding the rational development of efficient delivery of genetic nanomedicines, and hence increasing the chances of their successful translation. This review aims to provide an overview of the recent advances in the development of genetic anticancer nanomedicines, and of the zebrafish models that stand as promising tools to shed light on their mechanisms of action and overall potential in oncology.

Nanomaterial libraries and model organisms for rapid high-content analysis of nanosafety

Safety analysis of engineered nanomaterials (ENMs) presents a formidable challenge regarding environmental health and safety, due to their complicated and diverse physicochemical properties. Although large amounts of data have been published regarding the potential hazards of these materials, we still lack a comprehensive strategy for their safety assessment, which generates a huge workload in decision-making. Thus, an integrated approach is urgently required by government, industry, academia and all others who deal with the safe implementation of nanomaterials on their way to the marketplace. The rapid emergence and sheer number of new nanomaterials with novel properties demands rapid and high-content screening (HCS), which could be performed on multiple materials to assess their safety and generate large data sets for integrated decision-making. With this approach, we have to consider reducing and replacing the commonly used rodent models, which are expensive, time-consuming, and not amenable to high-throughput screening and analysis. In this review, we present a ‘Library Integration Approach’ for high-content safety analysis relevant to the ENMs. We propose the integration of compositional and property-based ENM libraries for HCS of cells and biologically relevant organisms to be screened for mechanistic biomarkers that can be used to generate data for HCS and decision analysis. This systematic approach integrates the use of material and biological libraries, automated HCS and high-content data analysis to provide predictions about the environmental impact of large numbers of ENMs in various categories. This integrated approach also allows the safer design of ENMs, which is relevant to the implementation of nanotechnology solutions in the pharmaceutical industry.

Enhancement of scutellarin oral delivery efficacy by vitamin B12-modified amphiphilic chitosan derivatives to treat type II diabetes induced-retinopathy

BACKGROUND

Diabetic retinopathy is the most common complication in diabetic patients relates to high expression of VEGF and microaneurysms. Scutellarin (Scu) turned out to be effective against diabetes related vascular endothelial cell dysfunction. However, its clinical applications have been limited by its low bioavailability. In this study, we formulated and characterized a novel intestinal target nanoparticle carrier based on amphiphilic chitosan derivatives (Chit-DC-VB12) loaded with scutellarin to enhance its bioavailability and then evaluated its therapeutic effect in experimental diabetic retinopathy model.

RESULTS

Chit-DC-VB12 nanoparticles showed low toxicity toward the human colon adenocarcinoma (Caco-2) cells and zebra fish within concentration of 250 μg/ml, owing to good biocompatibility of chitosan. The scutellarin-loaded Chit-DC-VB12 nanoparticles (Chit-DC-VB12-Scu) were then prepared by self-assembly in aqueous solution. Scanning electron microscopy and dynamic light scattering analysis indicated that the Chit-DC-VB12-Scu nanoparticles were spherical particles in the sizes ranging from 150 to 250 nm. The Chit-DC-VB12-Scu nanoparticles exhibited high permeation in Caco-2 cell, indicated it could be beneficial to be absorbed in humans. We also found that Chit-DC-VB12 nanoparticles had a high cellular uptake. Bioavailability studies were performed in Sprague-Dawley rats, which present the area under the curve of scutellarin of Chit-DC-VB12-Scu was two to threefolds greater than that of free scutellarin alone. Further to assess the therapeutic efficacy of diabetic retinopathy, we showed Chit-DC-VB12-Scu down-regulated central retinal artery resistivity index and the expression of angiogenesis proteins (VEGF, VEGFR2, and vWF) of retinas in type II diabetic rats.

CONCLUSIONS

Chit-DC-VB12 nanoparticles loaded with scutellarin have better bioavailability and cellular uptake efficiency than Scu, while Chit-DC-VB12-Scu nanoparticles alleviated the structural disorder of intraretinal neovessels in the retina induced by diabetes, and it also inhibited the retinal neovascularization via down-regulated the expression of angiogenesis proteins. In conclusion, the Chit-DC-VB12 nanoparticles enhanced scutellarin oral delivery efficacy and exhibited potential as small intestinal target promising nano-carriers for treatment of type II diabetes induced-retinopathy.

Development of a quantitative morphological assessment of toxicant-treated zebrafish larvae using brightfield imaging and high-content analysis

One of the rate-limiting procedures in a developmental zebrafish screen is the morphological assessment of each larva. Most researchers opt for a time-consuming, structured visual assessment by trained human observer(s). The present studies were designed to develop a more objective, accurate and rapid method for screening zebrafish for dysmorphology. Instead of the very detailed human assessment, we have developed the computational malformation index, which combines the use of high-content imaging with a very brief human visual assessment. Each larva was quickly assessed by a human observer (basic visual assessment), killed, fixed and assessed for dysmorphology with the Zebratox V4 BioApplication using the Cellomics® ArrayScan® V(TI) high-content image analysis platform. The basic visual assessment adds in-life parameters, and the high-content analysis assesses each individual larva for various features (total area, width, spine length, head-tail length, length-width ratio, perimeter-area ratio). In developing the computational malformation index, a training set of hundreds of embryos treated with hundreds of chemicals were visually assessed using the basic or detailed method. In the second phase, we assessed both the stability of these high-content measurements and its performance using a test set of zebrafish treated with a dose range of two reference chemicals (trans-retinoic acid or cadmium). We found the measures were stable for at least 1 week and comparison of these automated measures to detailed visual inspection of the larvae showed excellent congruence. Our computational malformation index provides an objective manner for rapid phenotypic brightfield assessment of individual larva in a developmental zebrafish assay. Copyright © 2016 John Wiley & Sons, Ltd.

Effects of embryonic exposure to polychlorinated biphenyls (PCBs) on anxiety-related behaviors in larval zebrafish

The zebrafish (Danio rerio) is an excellent model system for assessing the effects of toxicant exposure on behavior and neurodevelopment. In the present study, we examined the effects of sub-chronic embryonic exposure to polychlorinated biphenyls (PCBs), a ubiquitous anthropogenic pollutant, on anxiety-related behaviors. We found that exposure to the PCB mixture, Aroclor (A) 1254, from 2 to 26h post-fertilization (hpf) induced two statistically significant behavioral defects in larvae at 7 days post-fertilization (dpf). First, during 135min of free swimming, larvae that had been exposed to 2ppm, 5ppm or 10ppm A1254 exhibited enhanced thigmotaxis (edge preference) relative to control larvae. Second, during the immediately ensuing 15-min visual startle assay, the 5ppm and 10ppm PCB-exposed larvae reacted differently to a visual threat, a red 'bouncing' disk, relative to control larvae. These results are consistent with the anxiogenic and attention-disrupting effects of PCB exposure documented in children, monkeys and rodents and merit further investigation.

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.

Understanding the transformation, speciation, and hazard potential of copper particles in a model septic tank system using zebrafish to monitor the effluent

Although copper-containing nanoparticles are used in commercial products such as fungicides and bactericides, we presently do not understand the environmental impact on other organisms that may be inadvertently exposed. In this study, we used the zebrafish embryo as a screening tool to study the potential impact of two nano Cu-based materials, CuPRO and Kocide, in comparison to nanosized and micron-sized Cu and CuO particles in their pristine form (0-10 ppm) as well as following their transformation in an experimental wastewater treatment system. This was accomplished by construction of a modeled domestic septic tank system from which effluents could be retrieved at different stages following particle introduction (10 ppm). The Cu speciation in the effluent was identified as nondissolvable inorganic Cu(H2PO2)2 and nondiffusible organic Cu by X-ray diffraction, inductively coupled plasma mass spectrometry (ICP-MS), diffusive gradients in thin-films (DGT), and Visual MINTEQ software. While the nanoscale materials, including the commercial particles, were clearly more potent (showing 50% hatching interference above 0.5 ppm) than the micron-scale particulates with no effect on hatching up to 10 ppm, the Cu released from the particles in the septic tank underwent transformation into nonbioavailable species that failed to interfere with the function of the zebrafish embryo hatching enzyme. Moreover, we demonstrate that the addition of humic acid, as an organic carbon component, could lead to a dose-dependent decrease in Cu toxicity in our high content zebrafish embryo screening assay. Thus, the use of zebrafish embryo screening, in combination with the effluents obtained from a modeled exposure environment, enables a bioassay approach to follow the change in the speciation and hazard potential of Cu particles instead of difficult-to-perform direct particle tracking.

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.

Promoting cell proliferation using water dispersible germanium nanowires

Group IV Nanowires have strong potential for several biomedical applications. However, to date their use remains limited because many are synthesised using heavy metal seeds and functionalised using organic ligands to make the materials water dispersible. This can result in unpredicted toxic side effects for mammalian cells cultured on the wires. Here, we describe an approach to make seedless and ligand free Germanium nanowires water dispersible using glutamic acid, a natural occurring amino acid that alleviates the environmental and health hazards associated with traditional functionalisation materials. We analysed the treated material extensively using Transmission electron microscopy (TEM), High resolution-TEM, and scanning electron microscope (SEM). Using a series of state of the art biochemical and morphological assays, together with a series of complimentary and synergistic cellular and molecular approaches, we show that the water dispersible germanium nanowires are non-toxic and are biocompatible. We monitored the behaviour of the cells growing on the treated germanium nanowires using a real time impedance based platform (xCELLigence) which revealed that the treated germanium nanowires promote cell adhesion and cell proliferation which we believe is as a result of the presence of an etched surface giving rise to a collagen like structure and an oxide layer. Furthermore this study is the first to evaluate the associated effect of Germanium nanowires on mammalian cells. Our studies highlight the potential use of water dispersible Germanium Nanowires in biological platforms that encourage anchorage-dependent cell growth.

Automated prior knowledge-based quantification of neuronal patterns in the spinal cord of zebrafish

MOTIVATION

To reliably assess the effects of unknown chemicals on the development of fluorescently labeled sensory-, moto- and interneuron populations in the spinal cord of zebrafish, automated data analysis is essential.

RESULTS

For the evaluation of a high-throughput screen of a large chemical library, we developed a new method for the automated extraction of quantitative information from green fluorescent protein (eGFP) and red fluorescent protein (RFP) labeled spinal cord neurons in double-transgenic zebrafish embryos. The methodology comprises region of interest detection, intensity profiling with reference comparison and neuron distribution histograms. All methods were validated on a manually evaluated pilot study using a Notch inhibitor dose-response experiment. The automated evaluation showed superior performance to manual investigation regarding time consumption, information detail and reproducibility.

AVAILABILITY AND IMPLEMENTATION

Being part of GNU General Public Licence (GNU-GPL) licensed open-source MATLAB toolbox Gait-CAD, an implementation of the presented methods is publicly available for download at http://sourceforge.net/projects/zebrafishimage/.

Automated processing of zebrafish imaging data: a survey

Due to the relative transparency of its embryos and larvae, the zebrafish is an ideal model organism for bioimaging approaches in vertebrates. Novel microscope technologies allow the imaging of developmental processes in unprecedented detail, and they enable the use of complex image-based read-outs for high-throughput/high-content screening. Such applications can easily generate Terabytes of image data, the handling and analysis of which becomes a major bottleneck in extracting the targeted information. Here, we describe the current state of the art in computational image analysis in the zebrafish system. We discuss the challenges encountered when handling high-content image data, especially with regard to data quality, annotation, and storage. We survey methods for preprocessing image data for further analysis, and describe selected examples of automated image analysis, including the tracking of cells during embryogenesis, heartbeat detection, identification of dead embryos, recognition of tissues and anatomical landmarks, and quantification of behavioral patterns of adult fish. We review recent examples for applications using such methods, such as the comprehensive analysis of cell lineages during early development, the generation of a three-dimensional brain atlas of zebrafish larvae, and high-throughput drug screens based on movement patterns. Finally, we identify future challenges for the zebrafish image analysis community, notably those concerning the compatibility of algorithms and data formats for the assembly of modular analysis pipelines.

Zebrafish high-throughput screening to study the impact of dissolvable metal oxide nanoparticles on the hatching enzyme, ZHE1

The zebrafish is emerging as a model organism for the safety assessment and hazard ranking of engineered nanomaterials. In this Communication, the implementation of a roboticized high-throughput screening (HTS) platform with automated image analysis is demonstrated to assess the impact of dissolvable oxide nanoparticles on embryo hatching. It is further demonstrated that this hatching interference is mechanistically linked to an effect on the metalloprotease, ZHE 1, which is responsible for degradation of the chorionic membrane. The data indicate that 4 of 24 metal oxide nanoparticles (CuO, ZnO, Cr2 O3 , and NiO) could interfere with embryo hatching by a chelator-sensitive mechanism that involves ligation of critical histidines in the ZHE1 center by the shed metal ions. A recombinant ZHE1 enzymatic assay is established to demonstrate that the dialysates from the same materials responsible for hatching interference also inhibit ZHE1 activity in a dose-dependent fashion. A peptide-based BLAST search identifies several additional aquatic species that express enzymes with homologous histidine-based catalytic centers, suggesting that the ZHE1 mechanistic paradigm could be used to predict the toxicity of a large number of oxide nanoparticles that pose a hazard to aquatic species.

Emerging in vitro models for safety screening of high-volume production nanomaterials under environmentally relevant exposure conditions

The rising production of nanomaterial-based consumer products has raised safety concerns. Testing these with animal and other direct models is neither ethically nor economically viable, nor quick enough. This review aims to discuss the strength of in vitro testing, including the use of 2D and 3D cultures, stem cells, and tissue constructs, etc., which would give fast and repeatable answers of a highly specific nature, while remaining relevant to in vivo outcomes. These results can then be combined and the overall toxicity predicted with relative accuracy. Such in vitro models can screen potentially toxic nanomaterials which, if required, can undergo further stringent studies in animals. The cyto- and phototoxicity of some high-volume production nanomaterials, using in vitro models, is also reviewed.

Zebrafish: an in vivo model for nano EHS studies

To assure a responsible and sustainable growth of nanotechnology, the environmental health and safety (EHS) aspect of engineered nanomaterials and nano-related products needs to be addressed at a rate commensurate with the expansion of nanotechnology. Zebrafish has been demonstrated as a correlative in vivo vertebrate model for such task, and the current advances of using zebrafish for nano EHS studies are summarized here. In addition to morphological and histopathological observations, the accessibility of gene manipulation would greatly empower such a model for detailed mechanistic studies of any nanoparticles of interest. The potential for establishing high-throughput screening platforms to facilitate the nano EHS studies is highlighted, and a discussion is presented on how toxicogenomics approaches represent a future direction to guide the identification of toxicity pathways.

Implementation of a multidisciplinary approach to solve complex nano EHS problems by the UC Center for the Environmental Implications of Nanotechnology

UC CEIN was established with funding from the US National Science Foundation and the US Environmental Protection Agency in 2008 with the mission to study the impact of nanotechnology on the environment, including the identification of hazard and exposure scenarios that take into consideration the unique physicochemical properties of engineered nanomaterials (ENMs). Since its inception, the Center has made great progress in assembling a multidisciplinary team to develop the scientific underpinnings, research, knowledge acquisition, education and outreach that is required for assessing the safe implementation of nanotechnology in the environment. In this essay, the development of the infrastructure, protocols, and decision-making tools that are required to effectively integrate complementary scientific disciplines allowing knowledge gathering in a complex study area that goes beyond the traditional safety and risk assessment protocols of the 20th century is outlined. UC CEIN's streamlined approach, premised on predictive hazard and exposure assessment methods, high-throughput discovery platforms and environmental decision-making tools that consider a wide range of nano/bio interfaces in terrestrial and aquatic ecosystems, demonstrates the implementation of a 21st-century approach to the safe implementation of nanotechnology in the environment.

Nanomaterial toxicity testing in the 21st century: use of a predictive toxicological approach and high-throughput screening

The production of engineered nanomaterials (ENMs) is a scientific breakthrough in material design and the development of new consumer products. While the successful implementation of nanotechnology is important for the growth of the global economy, we also need to consider the possible environmental health and safety (EHS) impact as a result of the novel physicochemical properties that could generate hazardous biological outcomes. In order to assess ENM hazard, reliable and reproducible screening approaches are needed to test the basic materials as well as nanoenabled products. A platform is required to investigate the potentially endless number of biophysicochemical interactions at the nano/bio interface, in response to which we have developed a predictive toxicological approach. We define a predictive toxicological approach as the use of mechanisms-based high-throughput screening in vitro to make predictions about the physicochemical properties of ENMs that may lead to the generation of pathology or disease outcomes in vivo. The in vivo results are used to validate and improve the in vitro high-throughput screening (HTS) and to establish structure-activity relationships (SARs) that allow hazard ranking and modeling by an appropriate combination of in vitro and in vivo testing. This notion is in agreement with the landmark 2007 report from the US National Academy of Sciences, "Toxicity Testing in the 21st Century: A Vision and a Strategy" (http://www.nap.edu/catalog.php?record_id=11970), which advocates increased efficiency of toxicity testing by transitioning from qualitative, descriptive animal testing to quantitative, mechanistic, and pathway-based toxicity testing in human cells or cell lines using high-throughput approaches. Accordingly, we have implemented HTS approaches to screen compositional and combinatorial ENM libraries to develop hazard ranking and structure-activity relationships that can be used for predicting in vivo injury outcomes. This predictive approach allows the bulk of the screening analysis and high-volume data generation to be carried out in vitro, following which limited, but critical, validation studies are carried out in animals or whole organisms. Risk reduction in the exposed human or environmental populations can then focus on limiting or avoiding exposures that trigger these toxicological responses as well as implementing safer design of potentially hazardous ENMs. In this Account, we review the tools required for establishing predictive toxicology paradigms to assess inhalation and environmental toxicological scenarios through the use of compositional and combinatorial ENM libraries, mechanism-based HTS assays, hazard ranking, and development of nano-SARs. We will discuss the major injury paradigms that have emerged based on specific ENM properties, as well as describing the safer design of ZnO nanoparticles based on characterization of dissolution chemistry as a major predictor of toxicity.

Advances in zebrafish chemical screening technologies

Due to several inherent advantages, zebrafish are being utilized in increasingly sophisticated screens to assess the physiological effects of chemical compounds directly in living vertebrate organisms. Diverse screening platforms showcase these advantages. Morphological assays encompassing basic qualitative observations to automated imaging, manipulation, and data-processing systems provide whole organism to subcellular levels of detail. Behavioral screens extend chemical screening to the level of complex systems. In addition, zebrafish-based disease models provide a means of identifying new potential therapeutic strategies. Automated systems for handling/sorting, high-resolution imaging and quantitative data collection have significantly increased throughput in recent years. These advances will make it easier to capture multiple streams of information from a given sample and facilitate integration of zebrafish at the earliest stages of the drug-discovery process, providing potential solutions to current drug-development bottlenecks. Here we outline advances that have been made within the growing field of zebrafish chemical screening.