High-Fidelity Drug-Induced Liver Injury Screen Using Human Pluripotent Stem Cell-Derived Organoids.

Gastroenterology(2020)

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摘要
BACKGROUND & AIMS:Preclinical identification of compounds at risk of causing drug induced liver injury (DILI) remains a significant challenge in drug development, highlighting a need for a predictive human system to study complicated DILI mechanism and susceptibility to individual drug. Here, we established a human liver organoid (HLO)-based screening model for analyzing DILI pathology at organoid resolution. METHODS:We first developed a reproducible method to generate HLO from storable foregut progenitors from pluripotent stem cell (PSC) lines with reproducible bile transport function. The qRT-PCR and single cell RNA-seq determined hepatocyte transcriptomic state in cells of HLO relative to primary hepatocytes. Histological and ultrastructural analyses were performed to evaluate micro-anatomical architecture. HLO based drug-induced liver injury assays were transformed into a 384 well based high-speed live imaging platform. RESULTS:HLO, generated from 10 different pluripotent stem cell lines, contain polarized immature hepatocytes with bile canaliculi-like architecture, establishing the unidirectional bile acid transport pathway. Single cell RNA-seq profiling identified diverse and zonal hepatocytic populations that in part emulate primary adult hepatocytes. The accumulation of fluorescent bile acid into organoid was impaired by CRISPR-Cas9-based gene editing and transporter inhibitor treatment with BSEP. Furthermore, we successfully developed an organoid based assay with multiplexed readouts measuring viability, cholestatic and/or mitochondrial toxicity with high predictive values for 238 marketed drugs at 4 different concentrations (Sensitivity: 88.7%, Specificity: 88.9%). LoT positively predicts genomic predisposition (CYP2C9∗2) for Bosentan-induced cholestasis. CONCLUSIONS:Liver organoid-based Toxicity screen (LoT) is a potential assay system for liver toxicology studies, facilitating compound optimization, mechanistic study, and precision medicine as well as drug screening applications.
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