Dataset Information

Classification and prediction of toxicity of chemicals using an automated phenotypic profiling of Caenorhabditis elegans.

ABSTRACT: BACKGROUND:Traditional toxicological studies have relied heavily on various animal models to understand the effect of various compounds in a biological context. Considering the great cost, complexity and time involved in experiments using higher order organisms. Researchers have been exploring alternative models that avoid these disadvantages. One example of such a model is the nematode Caenorhabditis elegans. There are some advantages of C. elegans, such as small size, short life cycle, well defined genome, ease of maintenance and efficient reproduction. METHODS:As these benefits allow large scale studies to be initiated with relative ease, the problem of how to efficiently capture, organize and analyze the resulting large volumes of data must be addressed. We have developed a new method for quantitative screening of chemicals using C. elegans. 33 features were identified for each chemical treatment. RESULTS:The compounds with different toxicities were shown to alter the phenotypes of C. elegans in distinct and detectable patterns. We found that phenotypic profiling revealed conserved functions to classify and predict the toxicity of different chemicals. CONCLUSIONS:Our results demonstrate the power of phenotypic profiling in C. elegans under different chemical environments.

SUBMITTER: Gao S

PROVIDER: S-EPMC5907177 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature

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Classification and prediction of toxicity of chemicals using an automated phenotypic profiling of Caenorhabditis elegans.

Gao Shan S Chen Weiyang W Zeng Yingxin Y Jing Haiming H Zhang Nan N Flavel Matthew M Jois Markandeya M Han Jing-Dong J JJ Xian Bo B Li Guojun G

BMC pharmacology & toxicology 20180418 1

<h4>Background</h4>Traditional toxicological studies have relied heavily on various animal models to understand the effect of various compounds in a biological context. Considering the great cost, complexity and time involved in experiments using higher order organisms. Researchers have been exploring alternative models that avoid these disadvantages. One example of such a model is the nematode Caenorhabditis elegans. There are some advantages of C. elegans, such as small size, short life cycle, ...[more]

PMID: 29669598

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Project description:Quantitative imaging has become a vital technique in biological discovery and clinical diagnostics; a plethora of tools have recently been developed to enable new and accelerated forms of biological investigation. Increasingly, the capacity for high-throughput experimentation provided by new imaging modalities, contrast techniques, microscopy tools, microfluidics and computer controlled systems shifts the experimental bottleneck from the level of physical manipulation and raw data collection to automated recognition and data processing. Yet, despite their broad importance, image analysis solutions to address these needs have been narrowly tailored. Here, we present a generalizable formulation for autonomous identification of specific biological structures that is applicable for many problems. The process flow architecture we present here utilizes standard image processing techniques and the multi-tiered application of classification models such as support vector machines (SVM). These low-level functions are readily available in a large array of image processing software packages and programming languages. Our framework is thus both easy to implement at the modular level and provides specific high-level architecture to guide the solution of more complicated image-processing problems. We demonstrate the utility of the classification routine by developing two specific classifiers as a toolset for automation and cell identification in the model organism Caenorhabditis elegans. To serve a common need for automated high-resolution imaging and behavior applications in the C. elegans research community, we contribute a ready-to-use classifier for the identification of the head of the animal under bright field imaging. Furthermore, we extend our framework to address the pervasive problem of cell-specific identification under fluorescent imaging, which is critical for biological investigation in multicellular organisms or tissues. Using these examples as a guide, we envision the broad utility of the framework for diverse problems across different length scales and imaging methods.

Dataset Information

Classification and prediction of toxicity of chemicals using an automated phenotypic profiling of Caenorhabditis elegans.

Publications

Classification and prediction of toxicity of chemicals using an automated phenotypic profiling of Caenorhabditis elegans.

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