bioRxivCell BiologyPreprint
Comparison of hiPSC-derived hepatic organoids and liver-on-a-chip systems reveal microenvironment-driven maturation
Isabel Tamargo-RubioiD, Tim KrempeliD, Victoria E.J.M. PalasantzasiD, Bella Green, Gwen Weijer, Renée MoerkensiD, Channen van der Woude, Sven CD van IJzendoorn, Daan Johannes TouwiD, Joanne A. HoogerlandiD, Sebo WithoffiD, Jingyuan FuiD
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Abstract
Human liver organoids (HLOs) and liver-on-a-chip (LoC) systems are emerging as physiologically relevant human models for studying liver function, disease, and drug metabolism, often in combination with human induced pluripotent stem cell (hiPSC)-derived tissues. However, hiPSC-derived models often display batch-to-batch variation and incomplete maturation, and the contribution of microfluidic flow to hepatic maturation remains insufficiently characterized. Here, we developed a cryopreservable and scalable workflow to generate hiPSC-derived hepatic organoids that can be directly matured to either static HLOs or LoC systems, enabling matched comparison of both platforms. Transcriptomic and functional characterization revealed progressive hepatic maturation during organoid differentiation, including increased expression of liver-specific metabolic pathways, enhanced albumin secretion, and increased CYP3A4 activity. Compared to mature HLOs, LoCs exposed to continuous microfluidic flow exhibited transcriptomic profiles suggesting further maturation, with increased enrichment of pathways related to lipid metabolism, xenobiotic metabolism, transport, and tissue organization. These findings demonstrate that microfluidic perfusion promotes hepatic metabolic specialization compared to static organoid culture while maintaining donor-specific characteristics. Together, this study establishes a robust hiPSC-derived LoC platform and highlights the potential of flow-based systems for improved modeling of human liver physiology, disease mechanisms, and drug responses.
- organism: human
- assay: scRNA-seq
- assay: bulk RNA-seq
- research focus: cell type annotation
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: deep learning
- approach: statistical model
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