for example, organoid neurons from the dorsal parts of most brain regions showed higher similarity to their primary counterparts across organoid datasets than cell types derived from the ventral parts of most brain regions ( fig. 3i ). to identify molecular features other than metabolic state that decreased organoid fidelity, we incorporated dorsal telencephal ic glutamatergic neurons from four different primary developing human brain atlases as an integrated primary reference, and identified neuron subtype and maturation state heterogeneity ( extended data fig. 8 ). projection of dorsal telencephal ic neurons in the hnoca to the primary atlases revealed the corresponding heterogeneity in neural organoids. considering metabolic state, maturation state and cell subtype as covariates during de analysis significantly reduced the number of degs, supporting the idea that these are the major factors differentiating organoid and primary brain cells ( extended data fig. 8 and supplementary table 6 ). we observed enriched biological processes that included synaptic vesicle cycle and negative regulation of high voltage - gated calcium channel activity ( extended data fig. 8 ), suggesting that organoids are deficient in these processes. of note, these differences are observed across organoid protocols, and highlight areas of consistent transcriptomic divergence between in vitro and primary counterparts. the hnoca, as well as the analytical pipeline we established, provides a framework to query new neural organoid scrna - seq datasets not included in the hnoca. to showcase this application, we retrieved scrna - seq data from a recently published multiplexed neural organoid morphogen screen and projected them to the hnoca and primary reference latent spaces ( fig. 4a, extended data fig. 9 and supplementary table 7 ). we transferred regional labels and found high consistency with the provided regional annotation, but with higher resolution within each of the broad brain sections of forebrain, midbrain and hindbrain ( fig. 4b ). our transferred annotation therefore allowed a more comprehensive assessment of the effects of different morphogen conditions on generating neurons of different brain regions ( fig. 4c ). we further calculated presence scores for reference cells in each screen condition and compared the data of the different screen conditions with the 36 hnoca datasets. using hierarchical clustering on average presence scores revealed distinc t presence score profiles for many screen conditions ( fig. 4d ), suggesting regional cell type composition distinc t from the hnoca datasets. next, we summarized the max presence scores for the whole morphogen screen data ( fig. 4e ), and compared them to those for the hnoca data to identify primary reference cell types with increased presence in the screen ( fig. 4f ). this analysis highlighted several reference cell clusters with significant abundance increase under certain screen conditions ( fig. 4g ) such as lhx6 / ackr3 / mpped1 tripl e - positive gabaergic neurons in the ventral telencephalon and dopaminergic neurons in ventral midbrain. in summary, the projection of the morphogen screen query data to hnoca and primary reference allowed a refined annotation of the morphogen screen data, as well as a comprehensive and quantitative evaluation of the value of new differentiation protocols to generate neuronal cell types previously under - represented or lacking in neural organoids. we next tested whether the integrated hnoca can serve as a control cohort for assessing organoid models of neural disease. we collected 11 scrna - seq datasets from 10 neural organoid disease models and their respective controls ( microcephaly, amyot rophic lateral sclerosis, alzheimer ’ s disease, autism, fragile - x syndrome ( fxs ), schizophrenia, neuronal heterotopia, pitt – hopkins syndrome, myotonic dystrophy and glioblastoma ) ( fig. 5a, extended data fig. 10 and supplementary table 8 ). we projected the data to the hnoca and the primary reference atlas to transfer annotations ( fig. 5b – f ). we found differences in cell type and brain regional composition between disease model organoids and their respective, study - specific control organoids for most studies ( fig. 5g, h ). these differences might represent disease phenotypes, but could also be the consequence of cell line variability. it is therefore important to properly annotate the cell type and regional composition of disease and control organoids to identify disease phenotypes, particularly when analysing disease-associated transcriptomic alterations in a given cell type. we developed a wknn - based strategy to generate matched hnoca metacells for every cell in each disease model organoid scrna - seq dataset ( fig. 5i ), and quantified their transcriptomic similarity ( fig. 5j ). the dataset of glioblastoma organoids showed substantially lower similarity to their primary counterpart than the other disease models ( fig. 5k ). to assess these transcriptomic differences, we performed de analysis between glioblastoma and matched control metacells. focusing on the aqp4 population ( extended data fig. 10 ), we identified 1, 951 degs in glioblastoma cells compared to matched hnoca metacells ( supplementary table 9 ) and found increased expression of genes such as rbm25 ( ref. ) hnrnpu and sparc ( fig. 5l ), all of which have been reported to be relevant to glioblastoma. next, we focused on the organoid model of fxs, in which npcs and neurons in the control organoids were of non - telencephal ic identities whereas the disease model organoids mainly contained telencephal ic cells ( fig. 5h and extended data fig. 10 ). the integrated hnoca provides the opportunity to perform de analysis for fxs neocortical neurons with matched hnoca metacells, which identified 444 degs. degs higher expressed in fxs cells ( 122 genes ) were enriched for autism - associated genes annotate d in the simons foundation autism research initiative ( sfari ) database. one such gene, chd2, was reported in the original publication as a key regulator of fxs with increased protein level, but its expression change on messenger rna ( mrna ) level change could not be detected in a bulk rna - seq experiment. we also detected decreased expression of fmr1, whose loss - of - function mutation causes fxs. new scrna - seq datasets of human neural organoids continue to be generated, and it will be important to continuously extend and update the hnoca with this extra data. we therefore established a computational toolkit to project new scrna - seq data to the hnoca ( fig. 6a ). we demonstrate the use of the toolkit by incorporating scrna - seq data from six more studies into the hnoca ( hnoca - extended; fig. 6b and supplementary table 10 ), using query - to - reference mapping. we harmonized cell type annotations using wknn - based label transfer, and placed the cells in the context of the existing organoid single - cell transcriptomic landscape as represented by the hnoca ( fig. 6c – e ). mapping further datasets to the hnoca using our approach enhances the atlas by increasing its coverage over existing neural organoid protocols and neural cell types generated in organoids. to enable researchers to use the hnoca in their own analysis, we provide various options for exploration and interaction with the atlas ( fig. 6f ). the hnoca can be browsed through an online portal, enabling visualization of gene expression and discovery of marker genes. we also provide the hnoca through an online interface ( http: / / www. archmap. bio / ) for the interactive mapping of new datasets, enabling label transfer, presence score computation and metabolic scoring of cell states. finally, we have developed hnoca - tools, a python package implementing all central analysis approaches presented in this paper, such as annotation, reference mapping, label transfer and de testing methods.