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Quantifying the fidelity of in vitro human cell culture systems using a biomedical foundation model

GSE319586 Homo sapiens Expression profiling by high throughput sequencing 6 samples Submitted 2026/02/28 Platform GPL24676
Summary
Primary cell culture is fast becoming a dominant method for discovery work regarding human disease. Currently there are no methods to quantitatively benchmark these systems. Here we apply a uniform in vitro culture system of human intestinal epithelial cells to achieve this goal. We previously established methods for long-term 2-dimensional (2D) cultivation of mouse intestinal epithelial cells using an air-liquid interface (ALI) technique. Here, we further refined these methods for long-term 2D cultivation of human IECs, with histological and molecular features of differentiated intestinal epithelia. Leveraging the power and scalability of a biomedical foundation model (BMFM) trained on single cell RNA sequencing data (BMFM-RNA), we performed classification tasks to identify cell types across sample sources and to quantitatively benchmark our in vitro differentiated cells against cells collected from patient biopsies. We observed a striking concordance between our in vitro differentiated cells and the corresponding cell types in vivo for multiple differentiated secretory cell types. This novel approach using BMFM-RNA holds promise to expand our understanding of the regulatory mechanisms, including gene-gene regulation underlying homeostasis and regeneration, as well as the functions of rare and poorly understood lineages within the human intestinal epithelia. Moreover, these methods may be applicable to other organs, model systems, and experimental modalities. We propose that the framework used here can be deployed as a standard benchmarking methodology, ultimately improving the fidelity of primary human culture systems.
Published in
Quantifying the fidelity of in vitro human cell culture systems using a biomedical foundation model
Fujii S, Espenschied ST, Anand V et al. · Proceedings of the National Academy of Sciences of the United States of America 2026 · PMID 41860964 · doi:10.1073/pnas.2520482123
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Also filed as BioProject PRJNA1423791 and SRA study SRP677179. Searching any of these in the dataset finder brings you back here.

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