Genetically Engineered Human Pluripotent Stem Cells Utilizing Multi-Step Automated Differentiation Networks for Development of Three-Dimensional Liver Organoids | AIChE

Genetically Engineered Human Pluripotent Stem Cells Utilizing Multi-Step Automated Differentiation Networks for Development of Three-Dimensional Liver Organoids

Authors 

Weiss, R., Massachusetts Institute of Technology
Wang, L., Institution of Process Engineering, Chinese Academy of Sciences, Beijing
Perry, E., MIT
Baudy, A., Merck & Co., Inc.
Roesner, J., Merck & Co., Inc.
Pacchione, S., Merck & Co., Inc.
Su, M., Merck & Co., Inc.
Gibson, C., Merck & Co., Inc.
Three-dimensional (3D) human stem cell-derived systems allow recapitulation of architecture, composition, and physiology of organs. Organoids may be formed by combining individually differentiated cell populations or via introduction of external cues to human induced pluripotent stem cells (hiPSCs) to mimic stages of human development. However, such approaches may limit intercellular interactions or faithful mimicry of a mature organ’s structure. Here, we present an approach to genetically engineer hiPSCs with transcriptional and post-transcriptional regulatory networks to achieve multi-step differentiation in a cell-specific, semi-automated manner to form 3D liver organoids. The system relies on two independently operating networks, the first is small-molecule inducible heterogeneous expression of GATA-binding protein 6 (Gata6) to initiate germ layer formation and is capable of directing formation of 3D vascularized liver organoids. The second network operates post-transcriptionally, coupling miRNA sensing of miR-122a-5p and restriction endoribonucleases (ERNs) to autonomously express protein payloads in a hepatic-lineage cell-conditional manner. These engineered iPSCs were grown for thirty days to form 3D liver organoids and when compared to a single-stage network, exhibited 60% increase in urea production, 36% increase in albumin production, and an order of magnitude increase of Cyp3A4 enzyme function while maintaining vasculature and cell compositional diversity. Collectively, our two-stage genetic network approach enabled selective differentiation, maturation, and conditional payload expression for hepatocyte development. We anticipate systems such as ours utilizing cell-specific miRNA sensors and ERN controlled payloads will allow selective differentiation of germ layers and progenitors as well as cell maturation across many existing organoid models.