A versatile, automated and high-throughput drug screening platform for zebrafish embryos

Lipid conjugate dissociation analysis improves the in vivo understanding of lipid-based nanomedicine

Lipid conjugates have advanced the field of lipid-based nanomedicine by promoting active-targeting (ligand, peptide, antibody), stability (PEGylation), controlled release (lipoid prodrug), and probe-based tracking (fluorophore). Recent findings indicate lipid conjugates dissociating from nanomedicine upon encountering a biological environment. Yet, implications for (pre)clinical outcomes remain unclear. In this study, using the zebrafish model (Danio rerio), we investigated the fate of liposome-incorporated lipid fluorophore conjugates (LFCs) after intravenous (IV) administration. LFCs having a bilayer mismatch and relatively polar fluorophore revealed counter-predictive outcomes for Caelyx/Doxil (clearance vs. circulating) and AmBisome-like liposomes (scavenger endothelial cell vs. macrophage uptake). Findings on LFC (mis)match for Caelyx/Doxil-like liposomes were supported by translational intravital imaging studies in mice. Importantly, contradicting observations suggest to originate from LFC dissociation in vivo, which was investigated by Asymmetric Flow Field-Flow Fractionation (AF4) upon liposome-serum incubation in situ. Our data suggests that LFCs matching with the liposome bilayer composition - that did not dissociate upon serum incubation - revealed improved predictive outcomes for liposome biodistribution profiles. Altogether, this study highlights the critical importance of fatty acid tail length and headgroup moiety when selecting lipid conjugates for lipid-based nanomedicine.

Zebrafish as Versatile Model for Assessing Animal Venoms and Toxins: Current Applications and Future Prospects

Animal venoms and toxins hold promise as sources of novel drug candidates, therapeutic agents, and biomolecules. To fully harness their potential, it is crucial to develop reliable testing methods that provide a comprehensive understanding of their effects and mechanisms of action. However, traditional rodent assays encounter difficulties in mimicking venom-induced effects in human due to the impractical venom dosage levels. The search for reliable testing methods has led to the emergence of zebrafish (Danio rerio) as a versatile model organism for evaluating animal venoms and toxins. Zebrafish possess genetic similarities to humans, rapid development, transparency, and amenability to high-throughput assays, making it ideal for assessing the effects of animal venoms and toxins. This review highlights unique attributes of zebrafish and explores their applications in studying venom- and toxin-induced effects from various species, including snakes, jellyfish, cuttlefish, anemones, spiders, and cone snails. Through zebrafish-based research, intricate physiological responses, developmental alterations, and potential therapeutic interventions induced by venoms are revealed. Novel techniques such as CRISPR/Cas9 gene editing, optogenetics, and high-throughput screening hold great promise for advancing venom research. As zebrafish-based insights converge with findings from other models, the comprehensive understanding of venom-induced effects continues to expand, guiding the development of targeted interventions and promoting both scientific knowledge and practical applications.

New Insights into Antioxidant Peptides: An Overview of Efficient Screening, Evaluation Models, Molecular Mechanisms, and Applications

Antioxidant peptides are currently a hotspot in food science, pharmaceuticals, and cosmetics. In different fields, the screening, activity evaluation, mechanisms, and applications of antioxidant peptides are the pivotal areas of research. Among these topics, the efficient screening of antioxidant peptides stands at the forefront of cutting-edge research. To this end, efficient screening with novel technologies has significantly accelerated the research process, gradually replacing the traditional approach. After the novel antioxidant peptides are screened and identified, a time-consuming activity evaluation is another indispensable procedure, especially in in vivo models. Cellular and rodent models have been widely used for activity evaluation, whilst non-rodent models provide an efficient solution, even with the potential for high-throughput screening. Meanwhile, further research of molecular mechanisms can elucidate the essence underlying the activity, which is related to several signaling pathways, including Keap1-Nrf2/ARE, mitochondria-dependent apoptosis, TGF-β/SMAD, AMPK/SIRT1/PGC-1α, PI3K/Akt/mTOR, and NF-κB. Last but not least, antioxidant peptides have broad applications in food manufacture, therapy, and the cosmetics industry, which requires a systematic review. This review introduces novel technologies for the efficient screening of antioxidant peptides, categorized with a new vision. A wide range of activity evaluation assays, encompassing cellular models, as well as rodent and non-rodent models, are provided in a comprehensive manner. In addition, recent advances in molecular mechanisms are analyzed with specific cases. Finally, the applications of antioxidant peptides in food production, therapy, and cosmetics are systematically reviewed.

Role of trafficking protein particle complex 2 in medaka development

The skeletal dysplasia spondyloepiphyseal dysplasia tarda (SEDT) is caused by mutations in the TRAPPC2 gene, which encodes Sedlin, a component of the trafficking protein particle (TRAPP) complex that we have shown previously to be required for the export of type II collagen (Col2) from the endoplasmic reticulum. No vertebrate model for SEDT has been generated thus far. To address this gap, we generated a Sedlin knockout animal by mutating the orthologous TRAPPC2 gene (olSedl) of Oryzias latipes (medaka) fish. OlSedl deficiency leads to embryonic defects, short size, diminished skeletal ossification and altered Col2 production and secretion, resembling human defects observed in SEDT patients. Moreover, SEDT knock-out animals display photoreceptor degeneration and gut morphogenesis defects, suggesting a key role for Sedlin in the development of these organs. Thus, by studying Sedlin function in vivo, we provide evidence for a mechanistic link between TRAPPC2-mediated membrane trafficking, Col2 export, and developmental disorders.

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.

Automated, high-throughput quantification of EGFP-expressing neutrophils in zebrafish by machine learning and a highly-parallelized microscope

Normal development of the immune system is essential for overall health and disease resistance. Bony fish, such as the zebrafish (Danio rerio), possess all the major immune cell lineages as mammals and can be employed to model human host response to immune challenge. Zebrafish neutrophils, for example, are present in the transparent larvae as early as 48 hours post fertilization and have been examined in numerous infection and immunotoxicology reports. One significant advantage of the zebrafish model is the ability to affordably generate high numbers of individual larvae that can be arrayed in multi-well plates for high throughput genetic and chemical exposure screens. However, traditional workflows for imaging individual larvae have been limited to low-throughput studies using traditional microscopes and manual analyses. Using a newly developed, parallelized microscope, the Multi-Camera Array Microscope (MCAM™), we have optimized a rapid, high-resolution algorithmic method to count fluorescently labeled cells in zebrafish larvae in vivo. Using transgenic zebrafish larvae, in which neutrophils express EGFP, we captured 18 gigapixels of images across a full 96-well plate, in 75 seconds, and processed the resulting datastream, counting individual fluorescent neutrophils in all individual larvae in 5 minutes. This automation is facilitated by a machine learning segmentation algorithm that defines the most in-focus view of each larva in each well after which pixel intensity thresholding and blob detection are employed to locate and count fluorescent cells. We validated this method by comparing algorithmic neutrophil counts to manual counts in larvae subjected to changes in neutrophil numbers, demonstrating the utility of this approach for high-throughput genetic and chemical screens where a change in neutrophil number is an endpoint metric. Using the MCAM™ we have been able to, within minutes, acquire both enough data to create an automated algorithm and execute a biological experiment with statistical significance. Finally, we present this open-source software package which allows the user to train and evaluate a custom machine learning segmentation model and use it to localize zebrafish and analyze cell counts within the segmented region of interest. This software can be modified as needed for studies involving other zebrafish cell lineages using different transgenic reporter lines and can also be adapted for studies using other amenable model species.

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.

Automated, high-throughput quantification of EGFP-expressing neutrophils in zebrafish by machine learning and a highly-parallelized microscope

Normal development of the immune system is essential for overall health and disease resistance. Bony fish, such as the zebrafish (Danio rerio), possess all the major immune cell lineages as mammals and can be employed to model human host response to immune challenge. Zebrafish neutrophils, for example, are present in the transparent larvae as early as 48 hours post fertilization and have been examined in numerous infection and immunotoxicology reports. One significant advantage of the zebrafish model is the ability to affordably generate high numbers of individual larvae that can be arrayed in multi-well plates for high throughput genetic and chemical exposure screens. However, traditional workflows for imaging individual larvae have been limited to low-throughput studies using traditional microscopes and manual analyses. Using a newly developed, parallelized microscope, the Multi-Camera Array Microscope (MCAM™), we have optimized a rapid, high-resolution algorithmic method to count fluorescently labeled cells in zebrafish larvae in vivo. Using transgenic zebrafish larvae, in which neutrophils express EGFP, we captured 18 gigapixels of images across a full 96-well plate, in 75 seconds, and processed the resulting datastream, counting individual fluorescent neutrophils in all individual larvae in 5 minutes. This automation is facilitated by a machine learning segmentation algorithm that defines the most in-focus view of each larva in each well after which pixel intensity thresholding and blob detection are employed to locate and count fluorescent cells. We validated this method by comparing algorithmic neutrophil counts to manual counts in larvae subjected to changes in neutrophil numbers, demonstrating the utility of this approach for high-throughput genetic and chemical screens where a change in neutrophil number is an endpoint metric. Using the MCAM™ we have been able to, within minutes, acquire both enough data to create an automated algorithm and execute a biological experiment with statistical significance. Finally, we present this open-source software package which allows the user to train and evaluate a custom machine learning segmentation model and use it to localize zebrafish and analyze cell counts within the segmented region of interest. This software can be modified as needed for studies involving other zebrafish cell lineages using different transgenic reporter lines and can also be adapted for studies using other amenable model species.

Flight for fish in drug discovery: a review of zebrafish-based screening of molecules

Human disease and biological practices are modelled in zebrafish (Danio rerio) at various phases of drug development as well as toxicity evaluation. The zebrafish is ideal for in vivo pathological research and high-resolution investigation of disease progress. Zebrafish has an advantage over other mammalian models, it is cost-effective, it has external development and embryo transparency, easy to apply genetic manipulations, and open to both forward and reverse genetic techniques. Drug screening in zebrafish is suitable for target identification, illness modelling, high-throughput screening of compounds for inhibition or prevention of disease phenotypes and developing new drugs. Several drugs that have recently entered the clinic or clinical trials have their origins in zebrafish. The sophisticated screening methods used in zebrafish models are expected to play a significant role in advancing drug development programmes. This review highlights the current developments in drug discovery processes, including understanding the action of drugs in the context of disease and screening novel candidates in neurological diseases, cardiovascular diseases, glomerulopathies and cancer. Additionally, it summarizes the current techniques and approaches for the selection of small molecules and current technical limitations on the execution of zebrafish drug screening tests.

Application of Zebrafish as a Model for Anti-Cancer Activity Evaluation and Toxicity Testing of Natural Products

Developing natural product-based anti-cancer drugs/agents is a promising way to overcome the serious side effects and toxicity of traditional chemotherapeutics for cancer treatment. However, rapid assessment of the in vivo anti-cancer activities of natural products is a challenge. Alternatively, zebrafish are useful model organisms and perform well in addressing this challenging issue. Nowadays, a growing number of studies have utilized zebrafish models to evaluate the in vivo activities of natural compounds. Herein, we reviewed the application of zebrafish models for evaluating the anti-cancer activity and toxicity of natural products over the past years, summarized its process and benefits, and provided future outlooks for the development of natural product-based anti-cancer drugs.

Coupling Environmental Whole Mixture Toxicity Screening with Unbiased RNA-Seq Reveals Site-Specific Biological Responses in Zebrafish

Passive sampling device (PSD) extracts paired with developmental toxicity assays in Danio Rerio (zebrafish) are excellent sensors for whole mixture toxicity associated with the bioavailable non-polar organics at environmental sites. We expand this concept by incorporating RNA-Seq in 48-h post fertilization zebrafish statically exposed to PSD extracts from two Portland Harbor Superfund Site locations: river mile 6.5W (RM 6.5W) and river mile 7W (RM 7W). RM 6.5W contained higher concentrations of polycyclic aromatic hydrocarbons (PAHs), but the diagnostic ratios of both extracts indicated similar PAH sourcing and composition. Developmental screens determined RM 6.5W to be more toxic with the most sensitive endpoint being a "wavy" notochord malformation. Differential gene expression from exposure to both extracts was largely parallel, although more pronounced for RM 6.5W. When compared to the gene expression associated with individual chemical exposures, PSD extracts produced some gene signatures parallel to PAHs but were more closely matched by oxygenated-PAHs. Additionally, differential expression, reminiscent of the wavy notochord phenotype, was not accounted for by either class of chemical, indicating the potential of other contaminants driving mixture toxicity. These techniques offer a compelling method for non-targeted hazard characterization of whole mixtures in an in vivo vertebrate system without requiring complete chemical characterization.

High-throughput detection of craniofacial defects in fluorescent zebrafish

Losses and malformations of cranial neural crest cell (cNCC) derivatives are a hallmark of several common brain and face malformations. Nevertheless, the etiology of these cNCC defects remains unknown for many cases, suggesting a complex basis involving interactions between genetic and/or environmental factors. However, the sheer number of possible factors (thousands of genes and hundreds of thousands of toxicants) has hindered identification of specific interactions. Here, we develop a high-throughput analysis that will enable faster identification of multifactorial interactions in the genesis of craniofacial defects. Zebrafish embryos expressing a fluorescent marker of cNCCs (fli1:EGFP) were exposed to a pathway inhibitor standard or environmental toxicant, and resulting changes in fluorescence were measured in high-throughput using a fluorescent microplate reader to approximate cNCC losses. Embryos exposed to the environmental Hedgehog pathway inhibitor piperonyl butoxide (PBO), a Hedgehog pathway inhibitor standard, or alcohol (ethanol) exhibited reduced fli1:EGFP fluorescence at one day post fertilization, which corresponded with craniofacial defects at five days post fertilization. Combining PBO and alcohol in a co-exposure paradigm synergistically reduced fluorescence, demonstrating a multifactorial interaction. Using pathway reporter transgenics, we show that the plate reader assay is sensitive at detecting alterations in Hedgehog signaling, a critical regulator of craniofacial development. We go on to demonstrate that this technique readily detects defects in other important cell types, namely neurons. Together, these findings demonstrate this novel in vivo platform can predict developmental abnormalities and multifactorial interactions in high-throughput.

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.

Zebrafish Models of Cardiac Disease: From Fortuitous Mutants to Precision Medicine

Heart disease is the leading cause of death worldwide. Despite decades of research, most heart pathologies have limited treatments, and often the only curative approach is heart transplantation. Thus, there is an urgent need to develop new therapeutic approaches for treating cardiac diseases. Animal models that reproduce the human pathophysiology are essential to uncovering the biology of diseases and discovering therapies. Traditionally, mammals have been used as models of cardiac disease, but the cost of generating and maintaining new models is exorbitant, and the studies have very low throughput. In the last decade, the zebrafish has emerged as a tractable model for cardiac diseases, owing to several characteristics that made this animal popular among developmental biologists. Zebrafish fertilization and development are external; embryos can be obtained in high numbers, are cheap and easy to maintain, and can be manipulated to create new genetic models. Moreover, zebrafish exhibit an exceptional ability to regenerate their heart after injury. This review summarizes 25 years of research using the zebrafish to study the heart, from the classical forward screenings to the contemporary methods to model mutations found in patients with cardiac disease. We discuss the advantages and limitations of this model organism and introduce the experimental approaches exploited in zebrafish, including forward and reverse genetics and chemical screenings. Last, we review the models used to induce cardiac injury and essential ideas derived from studying natural regeneration. Studies using zebrafish have the potential to accelerate the discovery of new strategies to treat cardiac diseases.

Patient-Derived Xenograft: A More Standard "Avatar" Model in Preclinical Studies of Gastric Cancer

Despite advances in diagnosis and treatment, gastric cancer remains the third most common cause of cancer-related death in humans. The establishment of relevant animal models of gastric cancer is critical for further research. Due to the complexity of the tumor microenvironment and the genetic heterogeneity of gastric cancer, the commonly used preclinical animal models fail to adequately represent clinically relevant models of gastric cancer. However, patient-derived models are able to replicate as much of the original inter-tumoral and intra-tumoral heterogeneity of gastric cancer as possible, reflecting the cellular interactions of the tumor microenvironment. In addition to implanting patient tissues or primary cells into immunodeficient mouse hosts for culture, the advent of alternative hosts such as humanized mouse hosts, zebrafish hosts, and in vitro culture modalities has also facilitated the advancement of gastric cancer research. This review highlights the current status, characteristics, interfering factors, and applications of patient-derived models that have emerged as more valuable preclinical tools for studying the progression and metastasis of gastric cancer.

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.

First person – Alexandra Lubin

First Person is a series of interviews with the first authors of a selection of papers published in Biology Open, helping early-career researchers promote themselves alongside their papers. Alexandra Lubin is first author on ‘ A versatile, automated and high-throughput drug screening platform for zebrafish embryos’, published in BiO. Alexandra is a postdoctoral research associate in the lab of Elspeth Payne at University College London (UCL), London, UK, investigating high-throughput automated drug screening in live zebrafish to discover novel therapeutics for myelodysplastic syndrome (MDS) and acute myeloid leukaemia (AML).