an integrated transcriptomic cell atlas of human neural organoids source paper: pmc11578878 human neural organoids, generated from pluripotent stem cells in vitro, are useful tools to study human brain development, evolution and disease. however, it is unclear which parts of the human brain are covered by existing protocols, and it has been difficult to quantitatively assess organoid variation and fidelity. here we integrate 36 single - cell transcriptomic datasets spanning 26 protocols into one integrated human neural organoid cell atlas totalling more than 1. 7 million cells. mapping to developing human brain references shows primary cell types and states that have been generated in vitro, and estimates transcriptomic similarity between primary and organoid counterparts across protocols. we provide a programmatic interface to browse the atlas and query new datasets, and showcase the power of the atlas to annotate organoid cell types and evaluate new organoid protocols. finally, we show that the atlas can be used as a diverse control cohort to annotate and compare organoid models of neural disease, identifying genes and pathways that may underlie pathological mechanisms with the neural models. the human neural organoid cell atlas will be useful to assess organoid fidelity, characterize perturbed and diseased states and facilitate protocol development. human neural organoids, self - organizing three - dimensional human neural tissues grown in vitro, are becoming powerful tools for studying the mechanisms of human brain development, evolution and disease. they can be generated using external patterning factors ( for example, morphogens ) to guide their development towards certain brain regions or to drive the emergence of specific cell types ( guided protocols ). conversely, unguided protocols rely on the self - patterning capacity of organoids to generate diverse cell types and states. single - cell rna sequencing ( scrna - seq ) is a powerful technology to characterize cell type heterogeneity in complex tissues, and has illuminated a remarkable heterogeneity of diverse progenitor, neuronal and glial cell types that can develop within neural organoids, as well as differentiation trajectories of certain neural lineages. the data also enable the comparison of human neural organoid cells to those in the primary human brain, and most analyses have revealed strong similarity in molecular signatures. substantial differences have also been reported, including differential gene expression linked to media components and perturbed metabolic signatures associated with glyco lysis. nevertheless, analysis of organoid tissues supports a useful recapitulation of early brain development, and scrna - seq methods have been applied to study the molecular basis of neural cell type fate determination, evolutionary differences in primates and pathological changes in neural disor ders. however, it is unclear which portions of the developing central nervous system can be generated with existing protocols and which ones are still lacking. it has also remained challenging to systematically quantify the transcriptomic fidelity of neural organoid cells compared to their primary counterparts. in this study, we address these challenges by combining 36 scrna - seq datasets covering numerous human neural organoid protocols into an integrated transcriptomic cell atlas. we establish an analytical pipeline that allows for the comprehensive and quantitative comparison of the organoid atlas to reference atlases of the developing human brain. we harmonize annotations of cell populations in the primary and organoid systems, estimate the capacity and precision of different neural organoid protocols to generate different brain regions, and identify primary cell populations that are under - represented in neural organoids. we estimate transcriptomic fidelity of neurons in neural organoids, and identify previously described cell stress as a universal factor disting uishing metabolic states of in vitro neurons from primary neurons without strongly affecting core identities of neuronal cell types. we map the data of a neural organoid morphogen screen to the integrated atlas to assess regional specificity and generation of new states. we also collect 11 scrna - seq datasets modelling 10 different neural diseases, and map the integrated data to the neural organoid atlas for cell type annotation and differential expression ( de ) analysis. finally, we show that the atlas can be expanded by projecting new data to the current atlas. together, our work provides a rich resource and a new framework to assess the fidelity of neural organoids, characterize perturbed and diseased states and streamline protocol development. to build a transcriptomic human neural organoid cell atlas ( hnoca ), we collected scrna - seq data and detailed, harmonized technical and biological metadata from 36 datasets, including 34 published and two as yet unpublished ones ( supplementary table 1 ), accounting for 1. 77 million cells after consistent preprocessing and quality control ( fig. 1a ). the hnoca represents cell types and states generated with 26 distinc t neural organoid differentiation protocols, including three unguided and 23 guided ones, at time points ranging from 7 to 450 days ( fig. 1b ). to remove batch effects, we implemented a three - step integration pipeline. first, we projected the hnoca to a single - cell atlas of the developing human brain using reference similarity spectrum ( rss ). then, we developed snapseed ( methods ) to perform preliminary marker - based hierarchical cell type annotation. last, we used scpoli for label - aware data integration based on the snapseed annotations. evaluation of different integration approaches using a previously established benchmarking pipeline showed that scpoli had the best performance for these datasets ( extended data fig. 1 ). we performed clustering on the basis of the scpoli representation and annotate d clusters on the basis of canonical marker gene expression, organoid sample age and the auto - generated cell type labels. a uniform manifold approximation and projection ( umap ) embedding highlighted three neuronal differentiation trajectories corresponding to dorsal telencephal ic, ventral telencephal ic and non - telencephal ic populations as well as trajectories leading from progenitors to glial cell types such as astrocytes and oligodendrocytes precursors ( fig. 1c – e and extended data fig. 2 ). cells from both unguided and guided protocols were distributed across all trajectories ( fig. 1f ). to elucidate the dynamics and transitions of cell states and types, we reconstructed a real - age - informed pseudotime of hnoca cells on the basis of neural optimal transport using moscot ( fig. 1h ). focusing on the dorsal telencephal ic neural trajectory, we observed consistent pseudotemporal expression profiles of marker genes such as sox2 ( neural progenitor cells ( npcs ) ), bcl11b ( deeper layer cortical neurons ) and satb2 ( upper layer cortical neurons ) ( fig. 1i ). to further resolve heterogeneity among non - telencephal ic neurons, we performed subclustering of this population, which revealed numerous neuronal populations characterized by distinc t marker gene expression ( fig. 1j, k ). hnoca projection to a human developing brain atlas source paper: pmc11578878 to assess our cell type annotation, and more precisely annotate the heterogeneous non - telencephal ic neuronal populations, we compared the hnoca to a recently published single - cell transcriptomic atlas of the developing human brain ( fig. 2a ). we applied scvi and scanvi to the primary reference atlas, and used scarches to project the hnoca to the same latent space.