Solving Issues Hpsc-based Neural Differentiation Models

In the last twenty years, great progress has been made in our understanding of hPSC neural differentiation. Several differentiation protocols have been established and perfected, such as the ones to generate mature neurons, astrocytes and oligodendrocytes with multiple biochemical and functional traits (Table 1). Based on these differentiation strategies, a battery of models targeting various neural developmental stages has been implemented to predict DNT (Table 2). However, many challenges remain to be overcome for these assays to become in vitro gold standards.

It generally takes several weeks or even months to achieve functional and mature neural cell types from hPSCs, which may be impracticable for quick toxicity evaluations.

To shorten the induction time, several groups have tried gene-editing strategies to artificially express key transcription factors regulating the development of targeted neural cell types (Ehrlich et al., 2017; Garcia-Leon et al., 2018; Sun et al., 2016; Yang et al., 2017). Nevertheless, the sensitivity of this kind of differentiation models in DNT assays should be carefully verified, since the overexpressed transcription factors may cover subtle toxicity effects of tested chemicals.

Additional co-culture systems should be developed for more comprehensive DNT assessments. Perturbations in one single cell type may not represent all the effects on the onset and/or progression of neural disorders by industrial and environmental chemicals. For example, abnormal interactions between neurons and glial cells are one of the major causes of neural disorders (Meyer and Kaspar, 2017). Moreover, during the CNS developmental process, it is not uncommon that one type of cells would secret molecules to regulate the development of another cell type.

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In fact, FGFs secreted by motor neurons regulate astrocytes’ development. Glia also secret a set of neural trophic factors (like BDNF and GDNF) which supports neuron growth (Roybon et al., 2013). Therefore, the roles of industrial compounds during the communication between neural cell types should be accounted for in DNT analyses. To better recapitulate neural cell interactions and functions in vivo, besides the already implemented systems (Pamies et al., 2017; Pamies et al., 2018; Sandstrom et al., 2017; Schwartz et al., 2015), additional 3D models mimicking the complexity of the human brain and spinal cord are needed to further develop in vitro toxicology.

A great challenge is to validate the links between toxicants and neural disorders. Since the dysfunction of neural cells is involved in a wide range of neural disorders, the objectives of DNT examinations should be not only the confident identification of toxicants, but also to figure out which kinds of diseases these chemicals may cause or exacerbate. For example, in one hPSC-based neural differentiation assay, nanoparticles were found to cause a set of adverse effects typically observed in Alzheimer’s disease (Begum et al., 2016), suggesting a potential risk-correlation between nanoparticles’ exposure and Alzheimer’s disease. Although the concepts of adverse outcome pathways (AOPs) and toxicity endophenotypes (TEP) have been introduced to facilitate the dissection of the connection between molecular events and adverse outcomes and neuronal connectivity changes due to toxicants, respectively (Ankley et al., 2010; Aschner et al., 2017; Bal-Price et al., 2015; Bal-Price et al., 2017; Kadereit et al., 2012), it remains difficult to predict how industrial compounds are linked to neural disorders in vivo.

Standardization of hPSC-based DNT assays will help obtain meaningful and comparable data among different laboratories. For example, in the EST described above, toxicants are classified in three categories (non-embryotoxic, weakly embryotoxic and strongly embryotoxic) according to defined endpoints collected in specific cell lines during specific processes, and using empirical mathematical functions obtained from data from a number of toxic and non-toxic compounds (Genschow et al., 2004). However, up to now, there are no universal criteria to define DNT in hPSC differentiation-based models. Thus, there is no consensus on which cell line(s) to use, which detailed protocol(s) to follow, which endpoints should be measured, and how to define toxicity based on those values. This may be partly due to the complexity of the CNS development, which includes cell proliferation, migration, differentiation, interactions, etc. In addition, the number of chemicals already assessed in hPSC-based neural differentiation assays is still very limited, which may also restrict the establishment of general evaluation criteria. Consequently, standardized platforms for DNT assessments should include a set of different differentiation protocols to represent different stages of neural development, and should be verified by an appropriate number of well-known neural toxic and non-toxic chemicals.

In conclusion, even though there are still many issues to be solved, hPSC-based neural differentiation models that mimic key developmental processes in vivo have been proved to be promising systems for DNT examinations in vitro. Therefore, more efforts should be devoted to improve these platforms to obtain a better knowledge of industrial chemicals’ impact on human health.