02905nas a2200433   4500000000100000000000100001008004100002260001500043100001200058700001800070700002500088700001600113700001500129700001500144700001700159700001500176700001700191700001800208700002500226700001800251700001500269700002200284700001400306700002100320700002000341700002100361700002000382700001800402700002000420700002000440700001600460700002000476700002200496700002000518700001700538245008000555856006400635520177200699       2023                            d  c2023-12-041 aQuan Xu1 aLennard Halle1 aSoroor Hediyeh-zadeh1 aMerel Kuijs1 aUmut Kilik1 aQianhui Yu1 aTristan Frum1 aLukas Adam1 aShrey Parikh1 aManuel Gander1 aRaphael Kfuri-Rubens1 aDominik Klein1 aZhisong He1 aJonas Simon Fleck1 aKoen Oost1 aMaurice Kahnwald1 aSilvia Barbiero1 aOlga Mitrofanova1 aGrzegorz Maciag1 aKim B. Jensen1 aMatthias Lutolf1 aPrisca Liberali1 aJoep Beumer1 aJason R. Spence1 aBarbara Treutlein1 aFabian J. Theis1 aJ. Gray Camp00aAn integrated transcriptomic cell atlas of human endoderm-derived organoids  uhttps://www.biorxiv.org/content/10.1101/2023.11.20.567825v23 aHuman 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.