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. 2018 Apr 18;19(1):18.
doi: 10.1186/s40360-018-0208-3.

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

Shan Gao  1   2 , Weiyang Chen  3 , Yingxin Zeng  1 , Haiming Jing  1 , Nan Zhang  1 , Matthew Flavel  4 , Markandeya Jois  4 , Jing-Dong J Han  5 , Bo Xian  6 , Guojun Li  7   8
Affiliations

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

Shan Gao et al. BMC Pharmacol Toxicol. .

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.

Keywords: C. elegans; Chemicals; Image analysis; Phenotype; Toxicity.

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The authors declare that they have no competing interests.

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Figures

Fig. 1
Fig. 1
Phenotypes of lactic acid under different concentrations. (a) Major axis length. (b) Minor axis length. (c) Minor major axis length ratio. (d) Eccentricity. (e) Motility (the moved area). (f) Motility (the moved area/worm size). * and ** denote unpaired two-sided Student’s t-Test p-value < 0.05 and 0.01, respectively. Bar plots shows the average quantification for each phenotype on single worms. Error bars denote +/− standard deviation (SD). Concentration unit: mg/ml
Fig. 2
Fig. 2
Cluster on all phenotypes of all experiments (3 time points). Each column is one feature (See Methods for a detailed name and description of each feature), each row is an experiment. The proportions of each time point’s experiments (in each sub-cluster) are listed in the right of heat map
Fig. 3
Fig. 3
a Cluster on the mean profile of 8 repeats, one time point (12 h). b Cluster on the mean profile of 8 repeats, one time point (24 h). Each column is one feature (we show the detailed name of each feature in Methods). The proportions of different concentrations’ experiments (in each sub-cluster) are listed in the right of heat map. LC50 is 50% lethal concentration
Fig. 4
Fig. 4
a PCA on the mean profile of 8 repeats (3 time points). b PCA on the mean profile of 8 repeats (12 h). c PCA on the mean profile of single chemical compound (KCL and Lactic acid of 12 h), the value with different color is the concentration of different experiment
Fig. 5
Fig. 5
Method design. Mainly there are three steps in our method: (1) culture worms in 384-well plates with different chemicals and take video for each well; (2) process these videos, get each frame, reduce the uneven illumination, segment the image, and then quantify phenotypes for worms under each chemical treatment; (3) characterize different chemicals and their toxicities based on these quantified phenotypes
See this image and copyright information in PMC

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