No abstract

Neurosphere cultures consisting of primary human neural stem/progenitor cells (hNPC) are used for studying the effects of substances on early neurodevelopmental processes in vitro. Differentiating hNPCs migrate and differentiate into radial glia, neurons, astrocytes, and oligodendrocytes upon plating on a suitable extracellular matrix and thus model processes of early neural development. In order to characterize alterations in hNPC development, it is thus an essential task to reliably identify the cell type of each migrated cell in the migration area of a neurosphere. To this end, we introduce and validate a deep learning approach for identifying and quantifying cell types in microscopic images of differentiated hNPC. As we demonstrate, our approach performs with high accuracy and is robust against typical potential confounders. We demonstrate that our deep learning approach reproduces the dose responses of well‐established developmental neurotoxic compounds and controls, indicating its potential in medium or high throughput in vitro screening studies. Hence, our approach can be used for studying compound effects on neural differentiation processes in an automated and unbiased process.

Adverse outcome pathways (AOPs) are organized sequences of key events (KEs) that are triggered by a xenobiotic-induced molecular initiating event (MIE) and summit in an adverse outcome (AO) relevant to human or ecological health. The AOP framework causally connects toxicological mechanistic information with apical endpoints for application in regulatory sciences. AOPs are very useful to link endophenotypic, cellular endpoints in vitro to adverse health effects in vivo. In the field of in vitro developmental neurotoxicity (DNT), such cellular endpoints can be assessed using the human “Neurosphere Assay,” which depicts different endophenotypes for a broad variety of neurodevelopmental KEs. Combining this model with large-scale transcriptomics, we evaluated DNT hazards of two selected Chinese herbal medicines (CHMs) Lei Gong Teng (LGT) and Tian Ma (TM), and provided further insight into their modes-of-action (MoA). LGT disrupted hNPC migration eliciting an exceptional migration endophenotype. Time-lapse microscopy and intervention studies indicated that LGT disturbs laminin-dependent cell adhesion. TM impaired oligodendrocyte differentiation in human but not rat NPCs and activated a gene expression network related to oxidative stress. The LGT results supported a previously published AOP on radial glia cell adhesion due to interference with integrin-laminin binding, while the results of TM exposure were incorporated into a novel putative, stressor-based AOP. This study demonstrates that the combination of phenotypic and transcriptomic analyses is a powerful tool to elucidate compounds’ MoA and incorporate the results into novel or existing AOPs for a better perception of the DNT hazard in a regulatory context. Graphical abstract