A comprehensive transcriptomic comparison of hepatocyte model systems improves selection of models for experimental use

Abstract

The myriad of available hepatocyte in vitro models provides researchers the possibility to select hepatocyte-like cells (HLCs) for specific research goals. However, direct comparison of hepatocyte models is currently challenging. We systematically searched the literature and compared different HLCs, but reported functions were limited to a small subset of hepatic functions. To enable a more comprehensive comparison, we developed an algorithm to compare transcriptomic data across studies that tested HLCs derived from hepatocytes, biliary cells, fibroblasts, and pluripotent stem cells, alongside primary human hepatocytes (PHHs). This revealed that no HLC covered the complete hepatic transcriptome, highlighting the importance of HLC selection. HLCs derived from hepatocytes had the highest transcriptional resemblance to PHHs regardless of the protocol, whereas the quality of fibroblasts and PSC derived HLCs varied depending on the protocol used. Finally, we developed and validated a web application (HLCompR) enabling comparison for specific pathways and addition of new HLCs. In conclusion, our comprehensive transcriptomic comparison of HLCs allows selection of HLCs for specific research questions and can guide improvements in culturing conditions.

A systematic comparison of transcriptomes across different hepatocyte models provides a valuable resource to determine the most suitable model for a particular application or research question.