Computerized image analysis for quantitative neuronal phenotyping in zebrafish

Advanced Techniques for the Intelligent Diagnosis of Fish Diseases: A Review

Aquatic products, as essential sources of protein, have attracted considerable concern by producers and consumers. Precise fish disease prevention and treatment may provide not only healthy fish protein but also ecological and economic benefits. However, unlike intelligent two-dimensional diagnoses of plants and crops, one of the most serious challenges confronted in intelligent aquaculture diagnosis is its three-dimensional space. Expert systems have been applied to diagnose fish diseases in recent decades, allowing for restricted diagnosis of certain aquaculture. However, this method needs aquaculture professionals and specialists. In addition, diagnosis speed and efficiency are limited. Therefore, developing a new quick, automatic, and real-time diagnosis approach is very critical. The integration of image-processing and computer vision technology intelligently allows the diagnosis of fish diseases. This study comprehensively reviews image-processing technology and image-based fish disease detection methods, and analyzes the benefits and drawbacks of each diagnostic approach in different environments. Although it is widely acknowledged that there are many approaches for disease diagnosis and pathogen identification, some improvements in detection accuracy and speed are still needed. Constructing AR 3D images of fish diseases, standard and shared datasets, deep learning, and data fusion techniques will be helpful in improving the accuracy and speed of fish disease diagnosis.

Automated analysis of zebrafish images for screening toxicants

An important factor facilitating the application of zebrafish in biomedical research is high throughput screening of vertebrate animal models. For example, being able to model the growth of blood vessel in the vasculature system is interesting for understanding both the circulatory system in humans, and for facilitating large scale screening of the influence of various chemicals on vascular development. Compared to other models, the zebrafish embryo is an attractive alternative for environmental risk assessment of chemicals since it offers the possibility to perform high-throughput analyses in vivo. However the lack of an automated image analysis framework restricts high throughput screening. In this paper, we provide a method for quantitative measurements of zebrafish blood vessel morphology since it is difficult to assess changes in vessel structure by visual inspection. The method presented is generalized, i.e. it is not restricted to any specific chemically treated zebrafish, and can be used with wide variety of chemicals.

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.

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.

Computational techniques in zebrafish image processing and analysis

The zebrafish (Danio rerio) has been widely used as a vertebrate animal model in neurobiological. The zebrafish has several unique advantages that make it well suited for live microscopic imaging, including its fast development, large transparent embryos that develop outside the mother, and the availability of a large selection of mutant strains. As the genome of zebrafish has been fully sequenced it is comparatively easier to carry out large scale forward genetic screening in zebrafish to investigate relevant human diseases, from neurological disorders like epilepsy, Alzheimer's disease, and Parkinson's disease to other conditions, such as polycystic kidney disease and cancer. All of these factors contribute to an increasing number of microscopic images of zebrafish that require advanced image processing methods to objectively, quantitatively, and quickly analyze the image dataset. In this review, we discuss the development of image analysis and quantification techniques as applied to zebrafish images, with the emphasis on phenotype evaluation, neuronal structure quantification, vascular structure reconstruction, and behavioral monitoring. Zebrafish image analysis is continually developing, and new types of images generated from a wide variety of biological experiments provide the dataset and foundation for the future development of image processing algorithms.

Automated quantification of zebrafish somites based on PDE method

With the availability of high-throughput imaging machines and a large number of zebrafish embryos, zebrafish are clearly among the most cost-effective vertebrate systems for high-throughput or high-content screens with applications in drug discovery and biological pathway analysis. With the tremendous volume of images generated from large numbers of zebrafish screens, computerized image analysis for accurate and efficient data interpretation becomes essential. This paper presents an automated algorithm for a high-throughput screening pipeline for quantification of zebrafish somite. First, the main body is segmented using the level set method; then the head is removed; after that, the body is aligned and a coherence-enhancing filter is carried out so as to facilitate the somite detection. Finally, the somites can be easily extracted. Preliminary evaluation results are reported to demonstrate the good performance of the algorithm.

Automated gene oscillation phase classification for zebrafish presomitic mesoderm cells

Zebrafish somitogenesis is governed by a segmentation clock that generates oscillations of gene expression in the zebrafish presomitic mesoderm (PSM) cells. The segmentation clock causes cells to undergo repeated cycles of transcriptional activation and repression, which can be divided into eight phases based on their distinct mRNA co-localizations. Recognizing different gene oscillation phases of cells is important in zebrafish research, but manual analysis is time-consuming and difficult. In this article, an effective automated gene oscillation phase classification framework is established for zebrafish PSM cell images. The framework consists of three major steps: (1) identify the individual cells by a two-stage segmentation procedure; (2) extract multiple features on each cell patch to measure the subcellular mRNA distribution; (3) employ a support vector machine (SVM) with a combined kernel to complete feature fusion and classification. To evaluate the effectiveness of this framework, a dataset containing 2,227 cell samples is constructed. Experimental results on this dataset indicate that our approach can achieve reasonably good performance for this gene oscillation classification problem. The feature sets NF9 and SPIN introduced in this article have proved to be superior to other cell features in this problem. Besides, the kernel fusion method used in the third step provides a way to combine heterogeneous features together, i.e., numerical feature set and histogram-based feature set, and classification performance with the combined kernel is better than single feature.

Phenotypic analysis of images of zebrafish treated with Alzheimer's gamma-secretase inhibitors

Background

Several γ-secretase inhibitors (GSI) are in clinical trials for the treatment of Alzheimer's disease (AD). This enzyme mediates the proteolytic cleavage of amyloid precursor protein (APP) to generate amyloid β protein, Aβ, the pathogenic protein in AD. The γ-secretase also cleaves Notch to generate Notch Intracellular domain (NICD), the signaling molecule that is implicated in tumorigenesis.

Results

We have developed a method to examine live zebrafish that were each treated with γ-secretase inhibitors (GSI), DAPT {N- [N-(3,5-Difluorophenacetyl-L-alanyl)]-S-phenylglycine t-Butyl Ester}, Gleevec, or fragments of Gleevec. These compounds were first tested in a cell-based assay and the effective concentrations of these compounds that blocked Aβ generation were quantitated. The mortality of zebrafish, as a result of exposure to different doses of compound, was assessed, and any apoptotic processes were examined by TUNEL staining. We then used conventional and automatic microscopes to acquire images of zebrafish and applied algorithms to automate image composition and processing. Zebrafish were treated in 96- or 384-well plates, and the phenotypes were analyzed at 2, 3 and 5 days post fertilization (dpf). We identified that AD95, a fragment of Gleevec, effectively blocks Aβ production and causes specific phenotypes that were different from those treated with DAPT. Finally, we validated the specificity of two Notch phenotypes (pigmentation and the curvature of tail/trunk) induced by DAPT in a dose-dependent manner. These phenotypes were examined in embryos treated with GSIs or AD95 at increasing concentrations. The expression levels of Notch target gene her6 were also measured by in situ hybridization and the co-relationship between the levels of Notch inhibition by DAPT and AD95 and the severity of phenotypes were determined.

Conclusion

The results reported here of the effects on zebrafish suggest that this newly developed method may be used to screen novel GSIs and other leads for a variety of therapeutic indications.

Zebrafish embryos as models for embryotoxic and teratological effects of chemicals

The experimental virtues of the zebrafish embryo such as small size, development outside of the mother, cheap maintenance of the adult made the zebrafish an excellent model for phenotypic genetic and more recently also chemical screens. The availability of a genome sequence and several thousand mutants and transgenic lines together with gene arrays and a broad spectrum of techniques to manipulate gene functions add further to the experimental strength of this model. Pioneering studies suggest that chemicals can have in many cases very similar toxicological and teratological effects in zebrafish embryos and humans. In certain areas such as cardiotoxicity, the zebrafish appears to outplay the traditional rodent models of toxicity testing. Several pilot projects used zebrafish embryos to identify new chemical entities with specific biological functions. In combination with the establishment of transgenic sensor lines and the further development of existing and new automated imaging systems, the zebrafish embryos could therefore be used as cost-effective and ethically acceptable animal models for drug screening as well as toxicity testing.

An automated method for cell detection in zebrafish

Quantification of cells is a critical step towards the assessment of cell fate in neurological disease or developmental models. Here, we present a novel cell detection method for the automatic quantification of zebrafish neuronal cells, including primary motor neurons, Rohon-Beard neurons, and retinal cells. Our method consists of four steps. First, a diffused gradient vector field is produced. Subsequently, the orientations and magnitude information of diffused gradients are accumulated, and a response image is computed. In the third step, we perform non-maximum suppression on the response image and identify the detection candidates. In the fourth and final step the detected objects are grouped into clusters based on their color information. Using five different datasets depicting zebrafish cells, we show that our method consistently displays high sensitivity and specificity of over 95%. Our results demonstrate the general applicability of this method to different data samples, including nuclear staining, immunohistochemistry, and cell death detection.

ZFIQ: a software package for zebrafish biology

Rapid development, transparency and small size are the outstanding features of zebrafish that make it as an increasingly important vertebrate system for developmental biology, functional genomics, disease modeling and drug discovery. Zebrafish has been regarded as ideal animal specie for studying the relationship between genotype and phenotype, for pathway analysis and systems biology. However, the tremendous amount of data generated from large numbers of embryos has led to the bottleneck of data analysis and modeling. The zebrafish image quantitator (ZFIQ) software provides streamlined data processing and analysis capability for developmental biology and disease modeling using zebrafish model.

AVAILABILITY

ZFIQ is available for download at http://www.cbi-platform.net.

Detection of blob objects in microscopic zebrafish images based on gradient vector diffusion

The zebrafish has become an important vertebrate animal model for the study of developmental biology, functional genomics, and disease mechanisms. It is also being used for drug discovery. Computerized detection of blob objects has been one of the important tasks in quantitative phenotyping of zebrafish. We present a new automated method that is able to detect blob objects, such as nuclei or cells in microscopic zebrafish images. This method is composed of three key steps. The first step is to produce a diffused gradient vector field by a physical elastic deformable model. In the second step, the flux image is computed on the diffused gradient vector field. The third step performs thresholding and nonmaximum suppression based on the flux image. We report the validation and experimental results of this method using zebrafish image datasets from three independent research labs. Both sensitivity and specificity of this method are over 90%. This method is able to differentiate closely juxtaposed or connected blob objects, with high sensitivity and specificity in different situations. It is characterized by a good, consistent performance in blob object detection.

Computational approaches to phenotyping: high-throughput phenomics

The recent completion of the Human Genome Project has made possible a high-throughput "systems approach" for accelerating the elucidation of molecular underpinnings of human diseases, and subsequent derivation of molecular-based strategies to more effectively prevent, diagnose, and treat these diseases. Although altered phenotypes are among the most reliable manifestations of altered gene functions, research using systematic analysis of phenotype relationships to study human biology is still in its infancy. This article focuses on the emerging field of high-throughput phenotyping (HTP) phenomics research, which aims to capitalize on novel high-throughput computation and informatics technology developments to derive genomewide molecular networks of genotype-phenotype associations, or "phenomic associations." The HTP phenomics research field faces the challenge of technological research and development to generate novel tools in computation and informatics that will allow researchers to amass, access, integrate, organize, and manage phenotypic databases across species and enable genomewide analysis to associate phenotypic information with genomic data at different scales of biology. Key state-of-the-art technological advancements critical for HTP phenomics research are covered in this review. In particular, we highlight the power of computational approaches to conduct large-scale phenomics studies.