An Artificial Intelligence Optimized Hepatic Differentiation Unveils NR5A2 and AP-1 Transcriptional Regulation in Hepatic Maturation

Authors

• Zijun Huo
• Jian Tu
• Wei-Lei Yang
• Mo-Fan Huang
• Ruoyu Wang
• Chih-Wei Chu
• An Xu
• Yao Yu
• Tara N Tavakol
• Mikal Kizilbash
• Megan E Fisher
• Yu-Wen Huang
• Dandan Zhu
• Trinh T T Phan
• Rachel Shoemaker
• Ya-Wen Chen
• Yang Zhang
• Chad D Huff
• Shih-Yu Chen
• Tien-Jen Liu
• Haipeng Xiao
• Dung-Fang Lee
• Ruiying Zhao

Language

English

Publication Date

4-8-2026

Journal

Journal of Biological Chemistry

DOI

10.1016/j.jbc.2026.111435

PMID

41962865

Abstract

The generation of hepatocyte-like cells (HLCs) from human pluripotent stem cells (hPSCs) holds great promise for drug discovery and cell-based therapy for liver disease. However, current differentiation protocols are complicated and unstable, and the underlying gene regulatory mechanisms of hepatic differentiation remain incompletely defined. Here, we developed a machine learning-based artificial intelligence (AI) tool using phase-contrast images of hepatic progenitor cells (HPCs), which are essential for generating HLCs. The AI tool significantly improves the success rate of hepatic differentiation without the need for immunostaining or lineage tracing. By optimizing the methodology, we achieved an impressive purity of 90-95% for HLCs derived from hPSCs, aided by the AI algorithm. Through further investigating transcriptomes and epigenomic changes, we discovered the pivotal roles of NR5A2 and AP-1 transcription factors in regulating the maturation of hepatocytes. Single-cell RNA sequencing (scRNA-seq) demonstrated the upregulation of NR5A2 and AP-1 during hepatic differentiation. Importantly, mutagenesis and tumorigenesis assays confirmed the safety of this modified hepatic differentiation protocol. This work highlights the potential of combining AI algorithm and computational genomics to facilitate development of lineage differentiation and molecular mechanism study.

Keywords

AP-1, NR5A2, artificial intelligence, hepatic differentiation, multi-omics study

Published Open-Access

yes

Recommended Citation

Huo, Zijun; Tu, Jian; Yang, Wei-Lei; et al., "An Artificial Intelligence Optimized Hepatic Differentiation Unveils NR5A2 and AP-1 Transcriptional Regulation in Hepatic Maturation" (2026). Faculty, Staff and Student Publications. 3488.
https://digitalcommons.library.tmc.edu/uthmed_docs/3488