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

A spatiotemporal and machine-learning platform facilitates the manufacturing of hPSC-derived esophageal mucosa. Developmental cell Yang, Y., McCullough, C. G., Seninge, L., Guo, L., Kwon, W. J., Zhang, Y., Li, N. Y., Gaddam, S., Pan, C., Zhen, H., Torkelson, J., Glass, I. A., Charville, G. W., Que, J., Stuart, J. M., Ding, H., Oro, A. E. 2025

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.

View details for DOI 10.1016/j.devcel.2024.12.030

View details for PubMedID 39798574