@misc{376,
  author = {Quan Xu and Lennard Halle and Soroor Hediyeh-zadeh and Merel Kuijs and Umut Kilik and Qianhui Yu and Tristan Frum and Lukas Adam and Shrey Parikh and Manuel Gander and Raphael Kfuri-Rubens and Dominik Klein and Zhisong He and Jonas Simon Fleck and Koen Oost and Maurice Kahnwald and Silvia Barbiero and Olga Mitrofanova and Grzegorz Maciag and Kim B. Jensen and Matthias Lutolf and Prisca Liberali and Joep Beumer and Jason R. Spence and Barbara Treutlein and Fabian J. Theis and J. Gray Camp},
  title = {An integrated transcriptomic cell atlas of human endoderm-derived organoids},
  abstract = {Human stem cells can generate complex, multicellular epithelial tissues of endodermal origin in vitro that recapitulate aspects of developing and adult human physiology. These tissues, also called organoids, can be derived from pluripotent stem cells or tissue-resident fetal and adult stem cells. However, it has remained difficult to understand the precision and accuracy of organoid cell states through comparison with primary counterparts, and to comprehensively assess the similarity and differences between organoid protocols. Advances in computational single-cell biology now allow the integration of datasets with high technical variability. Here, we integrate single-cell transcriptomes from 218 samples covering organoids of diverse endoderm-derived tissues including lung, pancreas, intestine, liver, biliary system, stomach, and prostate to establish an initial version of a human endoderm organoid cell atlas (HEOCA). The integration includes nearly one million cells across diverse conditions, data sources and protocols. We align and compare cell types and states between organoid models, and harmonize cell type annotations by mapping the atlas to primary tissue counterparts. To demonstrate utility of the atlas, we focus on intestine and lung, and clarify ontogenic cell states that can be modeled in vitro. We further provide examples of mapping novel data from new organoid protocols to expand the atlas, and showcase how integrating organoid models of disease into the HEOCA identifies altered cell proportions and states between healthy and disease conditions. The atlas makes diverse datasets centrally available, and will be valuable to assess organoid fidelity, characterize perturbed and diseased states, and streamline protocol development.},
  year = {2023},
  month = {2023-12-04},
  url = {https://www.biorxiv.org/content/10.1101/2023.11.20.567825v2},
  doi = {10.1101/2023.11.20.567825},
  language = {en},
}