heoca mapping analysis showed that crc samples exhibited a lower percentage of mature colonocytes, and a higher proportion of stem cells ( fig. 7b, c ). interestingly, we also observed the emergence of mesothelial cells in the crc samples, consistent with the published findings that crc can lead to an increase in mesenchymal cells ( fig. 7b, c ). distance - to - atlas analysis disting uished cancer from normal cells, with stem cells and colonocytes showing the greatest deviation, while goblet cells remained closer to normal states ( fig. 7d, e ). subsetting and integrating colonocytes from both normal and cancer organoids identified two distinc t groups: a mixed normal – cancer cluster and a cancer - specific cluster with markedly higher atlas distances ( fig. 7f – h ). deg analysis revealed higher expression levels of crc markers such as ceacam6, spink1, tgfbi and rspo3 in the cancer cell group ( fig. 7i ). notably, recurrent r - spondin gene fusions have been described in certain patients with crc and this event potentiates wnt signaling and tumorigenesis. go enrichment analysis highlighted immunity and cytotoxicity genes in cancer cells ( extended data fig. 9c ). these analyses show the utility of distance measures to the heoca as a strategy to elucidate cell states that deviate healthy or otherwise normal states. in a second assessment, we used a publicly available dataset of two different organoid types generated from cells of patients with chronic obstructive pulmonary disease ( copd ) ( fig. 7j ). these were derived from nasopharyngeal and bronchial stem cells of these patients respectively. both nasopharyngeal and bronchial copd organoids mapped to lung populations in the heoca, but whereas nasopharyngeal organoids resembled healthy samples, bronchial organoids exhibited an increased proportion of club cells and fewer basal cells ( fig. 7k, l ). distance - to - atlas analysis effectively disting uished normal from copd conditions, with the bronchial copd organoids showing notable deviations ( fig. 7m ). these results matched with the original publication, which showed similar differences in cell - type composition and reported differences in resistance to viral infection between the bronchial and nasopharyngeal copd organoids. based on atlas similarity, we observed that the nasopharyngeal normal and copd samples showed relatively minor differences across all cell types, whereas basal cells in the bronchial copd samples displayed a bimodal distribution ( fig. 7n ). distance to heoca states identified one basal cell population indicative of a disease state, which was further clarified in a heterogeneity analysis of basal cells from healthy and bronchial organoids ( fig. 7n – q ). deg analysis revealed decreased krt5 and krt15 expression, and high expression of genes known to be upregulated in copd such as psca and bpifb1 ( fig. 7r ). go enrichment analysis of the degs shows that disease cells have enriched expression of cilium and axoneme genes ( extended data fig. 9d ). together these data show that the heoca can be used to place cell states observed in organoid disease models in a larger context, which helps to better understand holistic effects on cell composition and gene expression patterns. finally, we provide an assessment of the viability of organoids derived from different source types ( psc, fsc, asc ) for drug target screening. we used drug2cell ( d2c ) to score the expression levels of 2, 395 drug target signatures from the chembl database in single cells from heoca, and used scdecaf to select drug target signatures that exhibited global covariation in two or more cell types to identify multicellular drug signatures ( extended data fig. 10a and supplementary table 6 ). comparison between asc -, fsc - and psc - derived intestine and lung organoid models showed substantial differences in drug target pathway activities ( extended data fig. 10b, c ). druggable targets in categories including alimentary tract metabolism, systemic hormones, anti - infectives, antiparasitics, and antineoplastic and immunomodulating agents were implicated in signatures that varied between cell types and stem cell sources. comparison between lung and intestine organoid models across all cell types suggested that many druggable targets are distinc t between the two tissues and cell types common to both intestine and lung ( for example, stem and goblet cells ) have unique features ( extended data fig. 10d – f ). in summary, our analyses demonstrate that heoca is a technically and biologically diverse cohort that can be leveraged to evaluate organoid models, identify pathways impacted by perturbations, and, more broadly, explore the ontogeny of human biology. single - cell transcriptome sequencing technologies have advanced organoid research by offering a powerful set of experimental and computational tools to investigate cell types present in these complex 3d models. despite immense progress, it remains a challenge to understand and quantify organoid fidelity and to place variation between organoid datasets into a larger context. to begin to address these challenges, we have built an integrated cell atlas of organoids that model endod erm - derived tissues, incorporating organoid datasets that have been generated from multiple different types of stem cells and protocols. we have established a framework for integration and harmonized cell - type annotation, which makes interpreting cell heterogeneity between organoid datasets tractable. harmonization of cell - type annotation and nomenclature is challenging, and we envision that comprehensive integrated reference atlases across the human lifespan will enhance the robustness of cell annotation in organoid datasets. with regard to atlas building, single - cell transcriptome data from diverse experimental designs can introduce strong technical noise because of batch effects, protocol variation, genomic method and other technical biases, making data integration challenging. to overcome this, we evaluated existing integration methods and identified a suitable model based on bioconservation and integration metrics. this integration method, scpoli, is structured to incorporate additional data, enabling rapid comparison of datasets through the sc2heoca package or archmap website ( https: / / www. archmap. bio ). we find that there is notable variation in organoid cell composition, prevalence of off - target cells and overall cell state similarity. this variation, and comparison with available reference atlases, revealed that current organoid technologies cover a large diversity of human cell types and states, and particularly that organoids can model both early stages of fetal development as well as stages of adulthood. this result helps to clarify the use of human organoid technologies to explore development, model disease and test therapeutics. through cross - organ, multiorganoid integration, it was possible to identify off - target cells, a particular problem in psc - derived organoids because of incomplete specification, as well as to disting uish cell states that markedly differed from states present in the atlas. this ability to disting uish nonpresent states is helpful to assess protocols, as well as to identify features of disease models that are absent in normal, healthy organoids. indeed, the integrated heoca presents an opportunity to place an endodermal organoid dataset into a relationship with datasets generated from a technically and biologically diverse cohort. there is still a major challenge with organoid fidelity quantification, particularly with rare cell types or transient ontogenetic states, because there is not yet a complete and integrated atlas of human cell - type diversity during development and adulthood from primary tissues.