Multi-parametric profiling network based on gene expression and phenotype data: a novel approach to developmental neurotoxicity testing

Abstract

The establishment of more efficient approaches for developmental neurotoxicity testing (DNT) has been an emerging issue for children’s environmental health. This study showed a systematic approach for DNT using the neuronal differentiation of mouse or human embryonic stem cells (mESCs or hESCs) as a model of fetal programming. During embryoid body (EB) formation, mESCs were exposed to 12 chemicals for 24 h and hESCs were exposed to 3 chemicals for 96 h. Then global gene expression profiling was performed using whole genome microarray analysis. Gene expression signatures for gene sets related to neuronal development and neuronal diseases were selected for further analysis. At the later stages of neuronal cell differentiation from EBs, neuronal phenotypic parameters were determined using a high-content image analyzer. Bayesian network analysis was then performed based on global gene expression and neuronal phenotypic data to generate comprehensive networks with a linkage between early events and later effects. Furthermore, the probability distribution values for the strength of the linkage between parameters in each network was calculated and then used in principal component analysis. The characterization of chemicals according to their neurotoxic potential reveals that the multi-parametric analysis based on phenotype and gene expression profiling during neuronal differentiation of mESCs or hESCs can provide a useful tool to monitor fetal programming and to predict developmentally neurotoxic compounds.