A spatiotemporal and machine-learning platform facilitates the manufacturing of hPSC-derived esophageal mucosa

Ying Yang; Carmel Grace McCullough; Lucas Seninge; Lihao Guo; Woo-Joo Kwon; Yongchun Zhang; Nancy Yanzhe Li; Sadhana Gaddam; Cory Pan; Hanson Zhen; Jessica Torkelson; Ian A Glass; Birth Defects Research Laboratory; Gregory W Charville; Jianwen Que; Joshua M Stuart; Hongxu Ding; Anthony E Oro

doi:10.1016/j.devcel.2024.12.030

A spatiotemporal and machine-learning platform facilitates the manufacturing of hPSC-derived esophageal mucosa

Dev Cell. 2025 Jan 7:S1534-5807(24)00769-X. doi: 10.1016/j.devcel.2024.12.030. Online ahead of print.

Authors

Ying Yang¹, Carmel Grace McCullough¹, Lucas Seninge², Lihao Guo³, Woo-Joo Kwon¹, Yongchun Zhang⁴, Nancy Yanzhe Li¹, Sadhana Gaddam¹, Cory Pan¹, Hanson Zhen¹, Jessica Torkelson¹, Ian A Glass⁵; Birth Defects Research Laboratory⁵; Gregory W Charville⁶, Jianwen Que⁷, Joshua M Stuart², Hongxu Ding⁸, Anthony E Oro⁹

Affiliations

¹ Program in Epithelial Biology and Center for Definitive and Curative Medicine, Stanford University, Stanford, CA, USA.
² Department of Biomolecular Engineering and Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, USA.
³ Department of Pharmacy Practice and Science, University of Arizona, Tucson, AZ, USA.
⁴ State Key Laboratory of Microbial Metabolism & Joint International Research Laboratory of Metabolic and Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.
⁵ Birth Defect Research Laboratory Department of Pediatrics, University of Washington, Seattle, WA, USA.
⁶ Department of Pathology, Stanford University, Stanford, CA, USA.
⁷ Department of Medicine, Columbia University, New York, NY, USA.
⁸ Department of Pharmacy Practice and Science, University of Arizona, Tucson, AZ, USA. Electronic address: [email protected].
⁹ Program in Epithelial Biology and Center for Definitive and Curative Medicine, Stanford University, Stanford, CA, USA. Electronic address: [email protected].

PMID: 39798574
DOI: 10.1016/j.devcel.2024.12.030

Abstract

Human pluripotent stem cell-derived tissue engineering offers great promise for designer cell-based personalized therapeutics, but harnessing such potential requires a deeper understanding of tissue-level interactions. We previously developed a cell replacement manufacturing method for ectoderm-derived skin epithelium. However, it remains challenging to manufacture the endoderm-derived esophageal epithelium despite possessing a similar stratified epithelial structure. Here, we employ single-cell and spatial technologies to generate a spatiotemporal multi-omics cell census for human esophageal development. We identify the cellular diversity, dynamics, and signal communications for the developing esophageal epithelium and stroma. Using Manatee, a machine-learning algorithm, we prioritize the combinations of candidate human developmental signals for in vitro derivation of esophageal basal cells. Functional validation of Manatee predictions leads to a clinically compatible system for manufacturing human esophageal mucosa.

Keywords: epidermolysis bullosa; esophagus; gastrointestinal development; human pluripotent cell; machine learning; single-cell transcriptomics; spatial transcriptomics; stratified squamous epithelium; tissue engineering.