Traditional toxicity risk assessment approaches have until recently focussed mainly on histochemical readouts for cell death. Modern toxicology methods attempt to deduce a mechanistic understanding of pathways involved in the development of toxicity, by using transcriptomics and other big data-driven methods such as high-content screening. Here, we used a recently described optimised method to differentiate human induced pluripotent stem cells (hiPSCs) to hepatocyte-like cells (HLCs), to assess their potential to classify hepatotoxic and non-hepatotoxic chemicals and their use in mechanistic toxicity studies. The iPSC-HLCs could accurately classify chemicals causing acute hepatocellular injury, and the transcriptomics data on treated HLCs obtained by TempO-Seq technology linked the cytotoxicity to cellular stress pathways, including oxidative stress and unfolded protein response (UPR). Induction of these stress pathways in response to amiodarone, diclofenac, and ibuprofen, was demonstrated to be concentration and time dependent. The transcriptomics data on diclofenac-treated HLCs were found to be more sensitive in detecting differentially expressed genes in response to treatment, as compared to existing datasets of other diclofenac-treated in vitro hepatocyte models. Hence iPSC-HLCs generated by transcription factor overexpression and in metabolically optimised medium appear suitable for chemical toxicity detection as well as mechanistic toxicity studies.

To date there is no representative in vitro model for liver sinusoidal endothelial cells (LSECs), as primary LSECs dedifferentiate very fast in culture and no combination of cytokines or growth factors can induce an LSEC fate in (pluripotent stem cell-derived) endothelial cells (ECs). Furthermore, the transcriptional programs driving an LSEC fate have not yet been described. Here, we first present a computational workflow ( CenTFinder ) that can identify transcription factors (TFs) that are crucial for modulating pathways involved in cell lineage specification. Using CenTFinder , we identified several novel LSEC-specific protein markers such as FCN2 and FCN3, which were validated by analysis of previously published single-cell RNAseq data. We also identified PU.1 (encoded by the SPI1 gene) as a major regulator of LSEC-specific immune functions. We show that SPI1 overexpression (combined with the general EC transcription factor ETV2 ) in human pluripotent stem cells (PSCs) induces ECs with an LSEC-like phenotype. The ETV2-SPI1-ECs display increased expression of LSEC markers such as CD32B and MRC1 as well as several of the proposed novel markers. More importantly, ETV2-SPI1-ECs acquire LSEC functions, including uptake of FSA-FITC as well as labelled IgG. In conclusion, we present the CenTFinder computational tool to identify key regulatory TFs within specific pathways, in this work pathways of lineage specification, and we demonstrate its use by the identification and validation of PU.1 as a master regulator for LSEC fating.