an integrated transcriptomic cell atlas of human endod erm - derived organoids source paper: pmc12081310 human pluripotent stem cells and tissue - resident fetal and adult stem cells can generate epithelial tissues of endodermal origin in vitro that recapitulate aspects of developing and adult human physiology. here, we integrate single - cell transcriptomes from 218 samples covering organoids and other models of diverse endod erm - derived tissues to establish an initial version of a human endod erm - derived organoid cell atlas. the integration includes nearly one million cells across diverse conditions, data sources and protocols. we compare cell types and states between organoid models and harmonize cell annotations through mapping to primary tissue counterparts. focusing on the intestine and lung, we provide examples of mapping data from new protocols and show how the atlas can be used as a diverse cohort to assess perturbations and disease models. the human endod erm - derived organoid cell atlas makes diverse datasets centrally available and will be valuable to assess fidelity, characterize perturbed and diseased states, and streamline protocol development. in vitro human biosystems that model complex aspects of human tissues in controlled conditions can be used as inroads into human - specific biology and disease, as well as ac curate alternatives to animal models. the term organoid is a current nomenclature to describe three - dimensional ( 3d ) cell cultures derived from pluripotent, fetal or adult stem cells ( pscs, fscs, ascs ) that recapitulate important aspects of cell composition, cytoarchitecture and functional properties of the tissue counterpart. however, variations in protocols, culture conditions and stem cell sources make it challenging to assess how well organoid - derived cell states and interactions reflect those in vivo. in addition, the lack of centralized datasets and inconsistent protocol reporting complicate comparisons across studies, making it difficult to evaluate organoid fidelity, identify off - target or missing cell types, and predict genetic drivers of differentiation. overcoming these obstacles could help to better understand how human cell types and states develop, as well as support opportunities for translational research. advances in technology have led to the growth of single - cell transcriptome datasets, both in terms of dataset size and quantity. this has prompted collaborations to create extensive reference atlases for adult and developing human organs. organoids offer the opportunity to deepen our understanding of health and disease, by providing avatars of diverse developmental stages, genetic variation and disease states that will complement primary tissue atlases. however, the scale of generating a comprehensive organoid atlas in individual research groups is currently impractical. therefore, the integration of datasets generated by the wider research community becomes crucial. the endod erm contributes to the development of the epithelial lining of a variety of different organs including thyroid, esophagus, lung, pancreas, liver, biliary system, stomach, small intestine and colon. complex endodermal 3d organoids can be differentiated from ipscs, fscs and ascs in media supplemented with growth factors that promote stem cell proliferation and differentiation, potentially enabling exploration of human ontogenetic processes of each tissue. here, we present an integrated single - cell transcriptomic atlas of human endod erm - derived organoids encompassing nine different tissues, combining newly generated data and data from 55 publications. we applied the atlas as a diverse cohort to assess organoid protocols, perturbations and disease models. to create an endod erm - derived organoid cell atlas, we assembled single - cell rna sequencing ( scrna - seq ) and single-nucleus rna sequencing data from 54 published datasets and a newly generated dataset ( 45, 281 cells, 11 samples, small and large intestine, stomach and liver organoids ) ( fig. 1a and supplementary table 1 ). together, these datasets include samples from 218 experiments conducted on organoid models of 9 different organs ( lung, liver, biliary system, stomach, pancreas, small and large intestine, prostate, salivary glands ) ( fig. 1a, b ). data were obtained using multiple sequencing protocols, including plate - based methods such as smart - seq, cel - seq and sort - seq, as well as commercialized droplet - based methods ( for example, 10x genomics ) ( fig. 1c ). based on availability, we incorporated organoid datasets that model healthy states primarily of human endod erm - derived tissues, with source material from pscs ( embryonic stem cells and induced pscs ), fscs or ascs ( fig. 1d ). notably, we obtained data of each stem cell source from intestine, lung, liver and biliary system organoid models ( fig. 1b, d ). in total, we collected 806, 646 cells to be utilized for downstream integration and analysis ( fig. 1a – d ). we clustered cells at high resolution in each dataset and assigned cell annotations based on known marker gene expression and differential expression between clusters ( supplementary table 2 ). to assist with label - aware integration, we established a three - level hierarchical cell - type annotation: class ( level 1 ), type ( level 2 ) and subtype ( level 3 ) ( extended data fig. 1 ). to address batch effects and achieve a robust atlas integration, we assessed 12 different data - integration methods using single - cell integration benchmarking ( extended data fig. 1a – d ), and selected scpoli to generate an integrated embedding of all organoid cells, enabling a cohesive representation of the diverse data ( fig. 1e and extended data fig. 1e ). the integrated atlas was re annotate d based on the most frequent cell type in each cluster, resulting in 5 cell classes at level 1, 48 cell types at level 2 and 51 cell subtypes at level 3 ( fig. 1f – h and extended data fig. 1f ). comparing annotations before and after integration with annotations in the original manuscripts showed a high consistency across most cell - type labels ( extended data fig. 2a – c ). inconsistencies were related to states on continuous differentiation trajectories and nomenclature granularity between publications ( supplementary table 3 ). integration performance was unaffected by stem cell source, single - cell method or tissue type, but dataset origin substantially influenced integration outcomes ( extended data fig. 3 and supplementary table 4 ). pseudo - bulk analysis using both raw and scpoli embedding on all organoid single - cell datasets revealed stem cell source and tissue type as primary drivers of variance ( extended data fig. 4 ). overall, epithelial cells from different organs clustered together in the integrated atlas and clusters were compose d of cells from different stem cell sources ( fig. 1e, i ). however, we also identified cell types with contributions from multiple organoid models. for example, goblet cells were found in both intestine ( 68. 08 % ) and lung ( 31. 84 % ), with a minor presence in other organs ( 0. 08 % ). basal cells were observed in the lung ( 71. 29 % ), salivary gland ( 16. 28 % ), intestine ( 10. 41 % ) and thyroid ( 1. 32 % ) models ( fig. 1j ). these results suggest the existence of cell types that exhibit partial or shared characteristics across different organ models, and also may indicate off - target cells in organoids. we identified consistent markers for each integrated cell type across datasets and protocols, such as olfm4 for stem cells and tp63 for basal cells ( fig. 1j and supplementary table 5 ). we note instances in which cells derived from organoid models of a certain organ clustered with cells annotate d as being from a different organ. given the difficulty in precisely controlling organoid development, especially psc - derived organoids, off - target cells in organoids are a known issue.