data dict | annotations list |
|---|---|
{
"text": "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... | [] |
{
"text": "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 ... | [] |
{
"text": "Discussion\nSource paper: PMC11578878"
} | [] |
{
"text": "In this study, we built a large-scale integrated cell atlas of human neural organoids, the HNOCA, by integrating 1.8 million cells spanning 36 scRNA-seq datasets generated by 15 different laboratories worldwide using 26 different differentiation protocols as well as diverse scRNA-seq technologies. The resu... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 523,
"end": 528,
"text": "brain",
"labels": [
"Tissue"
]
}
},
{
"from_name": "label",
... |
{
"text": "We performed DE analysis between organoid neuron types and their primary counterparts to evaluate transcriptomic fidelity, and identified metabolic changes related to the glycolysis pathway as a main factor that distinguishes organoid and primary cell states, consistent with previous reports. Despite the n... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 42,
"end": 48,
"text": "neuron",
"labels": [
"Cell"
]
}
},
{
"from_name": "label",
... |
{
"text": "We showcased the mapping of query data, a recently published single-cell transcriptomic neural organoid morphogen screen, to the HNOCA and the primary reference, which enabled a refined cell type annotation, as well as a compositional comparison with existing neural organoid datasets. Our powerful framewor... | [] |
{
"text": "Consistent with earlier reports , we find that unguided protocols generate neural cells with high brain regional variability, which is useful when studying broader fate determination during neurodevelopment. Guided protocols resulted in a strong enrichment of the targeted brain regions. We also note that s... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 98,
"end": 103,
"text": "brain",
"labels": [
"Tissue"
]
}
},
{
"from_name": "label",
... |
{
"text": "The integrated HNOCA is also an excellent resource for analysis of disease-modelling neural organoid data. It facilitates cell type annotation and provides a large control cohort of single-cell transcriptomes for comparison. For example, we observed discrepancy of cell type and regional composition between... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 502,
"end": 507,
"text": "scale",
"labels": [
"Tissue"
]
}
}
]
}
] |
{
"text": "We demonstrate how the HNOCA can be extended and updated by projecting extra single-cell transcriptomic data of neural organoids to the atlas. Further, we have developed a computational toolkit, HNOCA-tools, which will enable other researchers to recapitulate the analytic framework applied in our study. To... | [] |
{
"text": "Methods\nSource paper: PMC11578878"
} | [] |
{
"text": "We included 33 human neural organoid data from a total of 25 publications plus three unpublished datasets in our atlas (Supplementary Table 1 ). We curated all neural organoid datasets used in this study through the sfaira framework (GitHub dev branch, 18 April 2023). For this, we obtained scRNA-seq count ... | [] |
{
"text": "We next removed any non-applicable subsets of the published datasets: diseased samples or samples expressing disease-associated mutations (refs. ), fused organoids (ref. ), primary fetal data (refs. ), hormone-treated samples (ref. ), data collected before neural induction (refs. ) and share-seq data (ref.... | [
{
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{
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"to_name": "text",
"type": "labels",
"value": {
"start": 139,
"end": 143,
"text": "refs",
"labels": [
"Cell"
]
}
},
{
"from_name": "label",
... |
{
"text": "All processing and analyses were carried out using scanpy (v.1.9.3) unless indicated otherwise. For quality control and filtering of HNOCA, we removed any cells with fewer than 200 genes expressed. We next removed outlier cells in terms of two quality control metrics: the number of expressed genes and perc... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 971,
"end": 974,
"text": "raw",
"labels": [
"Cell"
]
}
},
{
"from_name": "label",
... |
{
"text": "As a next step we generated a log-normalized expression matrix by first dividing the counts for each cell by the total counts in that cell and multiplying by a factor of 1,000,000 before taking the natural logarithm of each count + 1. We computed 3,000 highly variable features in a batch-aware manner using... | [] |
{
"text": "Snapseed is a scalable auto-annotation strategy, which annotates cells on the basis of a provided hierarchy of cell types and the corresponding cell type markers. It is based on enrichment of marker gene expression in cell clusters (high-resolution clustering is preferred), and data integration is not nece... | [] |
{
"text": "In this study, we used snapseed to obtain initial annotations for label-aware integration. First, we constructed a hierarchy of cell types including progenitor, neuron and non-neural types, each defined by a set of marker genes (Supplementary Data 1 ). Next, we represented the data by the RSS to average ex... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 161,
"end": 167,
"text": "neuron",
"labels": [
"Cell"
]
}
},
{
"from_name": "label",
... |
{
"text": "For all cell type marker genes on a given level in the hierarchy, we computed the area under the receiver operating characteristic curve (AUROC) as well as the detection rate across clusters. For each cell type, a score was computed by multiplying the maximum AUROC with the maximum detection rate among its... | [] |
{
"text": "This auto-annotation strategy was implemented in the snapseed Python package and is available on GitHub ( https://github.com/devsystemslab/snapseed ). Snapseed is a light-weight package to enable scalable marker-based annotation for atlas-level datasets in which manual annotation is not readily feasible. T... | [] |
{
"text": "We performed integration of the organoid datasets for HNOCA using the scPoli model from the scArches package. We defined the batch covariate for integration as a concatenation of the dataset identifier (annotation column ‘id’), the annotation of biological replicates (annotation column ‘bio_sample’) as wel... | [] |
{
"text": "To quantitatively compare the organoid atlas integration results from several tools, we used the GPU-accelerated scib-metrics Python package (v.0.3.3) and used the embedding with the highest overall performance for all downstream analyses. We compared the data integration performance across the following l... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 1047,
"end": 1050,
"text": "ref",
"labels": [
"Cell"
]
}
},
{
"from_name": "label",
... |
{
"text": "To infer a global ordering of differentiation state, we sought to infer a real-time-informed pseudotime on the basis of neural optimal transport in the scPoli latent space. We first grouped organoid age in days into seven bins ((0, 15], (15, 30], (30,60], (60, 90], (90, 120], (120, 150], (150, 450]). Next,... | [] |
{
"text": "The optimal transport problem was solved using the following parameters: iterations = 25,000, compute_wasserstein_baseline = False, batch_size = 1,024, patience = 100, pretrain = True, train_size = 1. To compute displacement vectors for each cell in age bin i , we used the subproblem corresponding to the [... | [] |
{
"text": "The cell ranger-processed scRNA-seq data for the primary atlas were obtained from the link provided on its GitHub page ( https://storage.googleapis.com/linnarsson-lab-human/human_dev_GRCh38-3.0.0.h5ad ). For further quality control, cells with fewer than 300 detected genes were filtered out. Transcript cou... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 388,
"end": 395,
"text": "scaling",
"labels": [
"Tissue"
]
}
}
]
}
] |
{
"text": "To compare our organoid atlas with data from the primary developing human brain, we used scArches to project it to the above mentioned primary human brain scRNA-seq atlas . We first pretrained a scVI model on the primary atlas with ‘Donor’ as the batch key. The model was constructed with following paramete... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 74,
"end": 79,
"text": "brain",
"labels": [
"Tissue"
]
}
},
{
"from_name": "label",
... |
{
"text": "With the primary reference and query (HNOCA) data projected to the same latent space, an unweighted bipartite kNN graph was constructed by identifying 100 nearest neighbours of each query cell in the reference data with either PyNNDescent or RAPIDS-cuML ( https://github.com/rapidsai/cuml ) in Python, depen... | [] |
{
"text": "The square of Jaccard index was then assigned as the weight of the edge, to get the bipartite weighted kNN graph between the reference and query datasets.\nSource paper: PMC11578878"
} | [] |
{
"text": "Given the wkNN estimated between primary reference and query (HNOCA), any categorical metadata label of reference can be transferred to query cells by means of majority voting. In brief, for each category, its support was calculated for each query cell as the sum of weights of edges that link to reference ... | [] |
{
"text": "To get the final regional labels for the non-telencephalic NPCs and neurons, as well as the NTT labels for non-telencephalic neurons, constraints were added to the transfer procedure. For regional labels, only the non-telencephalic regions, namely diencephalon, hypothalamus, thalamus, midbrain, midbrain do... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 68,
"end": 75,
"text": "neurons",
"labels": [
"Cell"
]
}
},
{
"from_name": "label",
... |
{
"text": "For NTT labels, we first identified valid region-NTT label pairs in the reference on the basis of the provided NTT labels in the reference neuroblast and neuron clusters and their most common regions. Here, the most common regions were re-estimated in a hierarchical manner to the finest resolution mentione... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 154,
"end": 160,
"text": "neuron",
"labels": [
"Cell"
]
}
},
{
"from_name": "label",
... |
{
"text": "To match telencephalic NPCs and neurons in HNOCA to developmental stages, we used the recently published human neocortical development atlas as the reference. The processed single nucleus RNA-seq data were obtained from its data portal ( https://cell.ucsf.edu/snMultiome/ ). Given the ‘class’, ‘subclass’ an... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 32,
"end": 39,
"text": "neurons",
"labels": [
"Cell"
]
}
},
{
"from_name": "label",
... |
{
"text": "To extend the analysis to other neuronal cell types, the second-trimester multiple-region human brain atlas was also introduced. The processed count matrices and metadata were obtained from the NeMO data portal ( https://data.nemoarchive.org/biccn/grant/u01_devhu/kriegstein/transcriptome/scell/10x_v2/human... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 1003,
"end": 1009,
"text": "neuron",
"labels": [
"Cell"
]
}
},
{
"from_name": "label",
... |
{
"text": "Given a reference dataset and a query dataset, the presence score is a score assigned to each cell in the reference, which describes the frequency or likelihood of the cell type or state of that reference cell appearing in the query data. In this study, we calculated the presence scores of primary atlas ce... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 408,
"end": 420,
"text": "primary cell",
"labels": [
"Cell"
]
}
}
]
}
] |
{
"text": "Specifically, for each HNOCA dataset, we first subset the wkNN graph to only HNOCA cells in that dataset. Next, the raw weighted degree was calculated for each cell in the primary atlas, as the sum of weights of the remaining edges linked to the cell. A random-walk-with-restart procedure was then applied t... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 116,
"end": 119,
"text": "raw",
"labels": [
"Cell"
]
}
},
{
"from_name": "label",
... |
{
"text": "This procedure was performed 100 times to get the smooth presence scores that were subsequently log transformed. Scores lower than the 5th percentile or higher than the 95th percentile were trimmed. The trimmed scores were normalized into the range of as the final presence scores in the HNOCA dataset.\nSou... | [] |
{
"text": "Given the final presence scores in each of the HNOCA datasets, the max presence scores in the whole HNOCA data were then easily calculated as the maximum of all the presence scores for each cell in the primary atlas. A large (close to one) max presence score indicates a high frequency of appearance for the... | [] |
{
"text": "To test the cell type compositional changes on admission of certain morphogens from different organoid differentiation protocols, we used the pertpy implementation of the scCODA algorithm . scCODA is a Bayesian model for detecting compositional changes in scRNA-seq data. For this, we have extracted the inf... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 785,
"end": 789,
"text": "stem",
"labels": [
"Tissue"
]
}
},
{
"from_name": "label",
... |
{
"text": "To complement the composition analysis conducted with scCODA, we devised an alternative approach to test for differential composition using regularized linear regression. We fit a generalized linear model with the region composition matrix as the response Y and molecule usage as independent variables X : [... | [] |
{
"text": "The model was fit with lasso regularization (alpha = 1) using Gaussian noise and an identity link function. The regularization parameter lambda was automatically determined through cross-validation as implemented in the function cv.glmnet() from the glmnet R package. All non-zero coefficients β were consid... | [] |
{
"text": "To study the transcriptomic differences between organoid and primary cells, we subset HNOCA using the final level 1 annotation to cells labelled ‘Neuron’. We furthermore subset the human developing brain atlas to cells that had been assigned a valid label in the neuron_ntt_label annotation column. We added... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 146,
"end": 152,
"text": "Neuron",
"labels": [
"Cell"
]
}
},
{
"from_name": "label",
... |
{
"text": "To compute DE between HNOCA cells and their primary counterparts, we first aggregated cells of the same regional neural cell type into pseudobulk samples by summing the counts for every sample (annotation columns, ‘batch’ for HNOCA; ‘SampleID’ for the human developing brain atlas; ‘sample’ for ref. and ‘in... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 662,
"end": 669,
"text": "neurons",
"labels": [
"Cell"
]
}
},
{
"from_name": "label",
... |
{
"text": "To evaluate the effect of different primary datasets on the DE results, we computed the DE between Dorsal Telencephalic Neuron NT-VGLUT from the HNOCA subset generated with the protocol from ref. and the matching cell type from the Braun et al. primary dataset as well as the data from ref. . To prevent tec... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 120,
"end": 126,
"text": "Neuron",
"labels": [
"Cell"
]
}
},
{
"from_name": "label",
... |
{
"text": "To evaluate the effect of different organoid datasets on the protocol-based DE analysis, we computed DE between Dorsal Telencephalic Neuron NT-VGLUT of every organoid publication (further split by protocol, where more than one protocol was used in a publication) and the matching cell type in the dataset fr... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 133,
"end": 139,
"text": "Neuron",
"labels": [
"Cell"
]
}
},
{
"from_name": "label",
... |
{
"text": "To estimate the transcriptomic similarity between neurons in HNOCA and the human developing brain atlas , we first summarized the average expression of each neural cell type in the primary reference, as well as in each dataset of HNOCA. For each HNOCA dataset, only neural cell types with at least 20 cells ... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 50,
"end": 57,
"text": "neurons",
"labels": [
"Cell"
]
}
},
{
"from_name": "label",
... |
{
"text": "To estimate the similarity of the core transcriptomic identity, which is defined by the coexpression of transcription factors, the highly variable genes were subset to only transcription factorsfor calculating Spearman correlations. The list of transcription factors was retrieved from the AnimalTFDB v.4.0 ... | [] |
{
"text": "To identify metabolically stressed cells in the datasets, we used the scanpy score_genes function with default parameters to score the ‘canonical glycolysis’ gene set obtained from the enrichR GO_Biological_Process_2021 database across all neuronal cells from HNOCA and refs. .\nSource paper: PMC11578878"
} | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 270,
"end": 274,
"text": "refs",
"labels": [
"Cell"
]
}
},
{
"from_name": "label",
... |
{
"text": "To estimate the significance of the difference between the correlation of glycolysis scores and whole transcriptomic similarities, and the correlation of glycolysis scores and core transcriptomic identity similarities, we generated 100 subsets of highly variable genes, each with the same size as the highly... | [] |
{
"text": "To characterize heterogeneity of telencephalic NPCs and neurons in HNOCA, we first transferred the cell type labels (as indicated as the ‘type’ label in the given metadata) from the human neocortical development atlas to the HNOCA telencephalic NPCs, intermediate progenitor cells and neurons, on the basis ... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 56,
"end": 63,
"text": "neurons",
"labels": [
"Cell"
]
}
},
{
"from_name": "label",
... |
{
"text": "To benchmark how well different integration strategies recover the neuron subcell type heterogeneity, we generated four different clustering labels: (1) Louvain clustering (resolution, 2) with the original scPoli latent representation; (2) Louvain clustering (resolution, 2) with the updated scPoli represen... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 67,
"end": 73,
"text": "neuron",
"labels": [
"Cell"
]
}
},
{
"from_name": "label",
... |
{
"text": "To create a comprehensive primary atlas of dorsal telencephalic neurons for DE analysis between neural organoids and primary tissues, we subset dorsal telencephalic neurons or neocortical neurons from four different primary atlases . For ref. , cells in five author-defined clusters (60, 57, 79, 45, 65) wit... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 64,
"end": 71,
"text": "neurons",
"labels": [
"Cell"
]
}
},
{
"from_name": "label",
... |
{
"text": "Next, cells in the joint neocortical neuron atlas were correlated with the average expression profile of each excitatory neuron cluster of the Wang et al. atlas . The cluster label of the best-correlated cluster was assigned to each cell in the joined neocortical neuron atlas, so that cell cluster labels w... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 37,
"end": 43,
"text": "neuron",
"labels": [
"Cell"
]
}
},
{
"from_name": "label",
... |
{
"text": "We used scArches to map scRNA-seq data from the neural organoid morphogen screen to both the scANVI model of the human developing brain atlas and the scPoli model of the HNOCA. In both cases, the ‘dataset’ field of the screen data was used as the batch covariate, which indicates belonging to one of the thr... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 130,
"end": 135,
"text": "brain",
"labels": [
"Tissue"
]
}
},
{
"from_name": "label",
... |
{
"text": "We included 11 scRNA-seq datasets of neural organoids, which were designed to model 10 different neural diseases including microcephaly , amyotrophic lateral sclerosis , Alzheimer’s disease , autism , FXS , schizophrenia , neuronal heterotopia , Pitt–Hopkins syndrome , myotonic dystrophy and glioblastoma .... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 943,
"end": 953,
"text": "cell lines",
"labels": [
"Cell"
]
}
},
{
"from_name": "label",
... |
{
"text": "To compare the disease-modelling atlas with the integrated HNOCA, we used scArches to project it to the HNOCA as well as the first-trimester primary human brain scRNA-seq atlas . For projecting to the primary atlas, the same implementation as mentioned above to map HNOCA to the atlas was used. For projecti... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 155,
"end": 160,
"text": "brain",
"labels": [
"Tissue"
]
}
}
]
}
] |
{
"text": "Next, for both HNOCA and the disease-modelling atlas, cells were represented by the concatenated representation of HNOCA-scPoli and primary-scANVI models. A bipartite wkNN graph was then reconstructed as mentioned above, by identifying 50 nearest neighbours in HNOCA for each disease-modelling atlas cell. O... | [] |
{
"text": "For each cell in the disease-modelling atlas, a matched HNOCA metacell was reconstructed on the basis of the above mentioned bipartite wkNN. In brief, for a query cell i and a gene j measured in HNOCA, its matched metacell expression of j , denoted as , is calculated as: [12pt] $$_}^ }=__}_}_}}__}_}}$$ e i... | [] |
{
"text": "Given the matched HNOCA metacell transcriptomic profile, the similarity between a query cell and its matched cell state in HNOCA is then calculated as the Spearman correlation between the query cell transcriptomic profile and its matched HNOCA metacell transcriptomic profile.\nSource paper: PMC11578878"
} | [] |
{
"text": "To analyse the glioblastoma organoid dataset (GBM-2019), cells from the publication were subset from the integrated disease-modelling atlas. Using scanpy, highly variable genes were identified with default parameters. The log-normalized expression values of the highly variable genes were then scaled across... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 294,
"end": 300,
"text": "scaled",
"labels": [
"Tissue"
]
}
}
]
}
] |
{
"text": "To analyse the FXS dataset (FXS-2021), cells from the publication were subset from the integrated disease-modelling atlas. The same procedure of highly variable gene identification, data scaling and PCA as the GBM-2019 dataset was applied. Next, the nearest neighbour graph was created directly on the basis... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 616,
"end": 623,
"text": "neurons",
"labels": [
"Cell"
]
}
}
]
}
] |
{
"text": "To compare expression levels of two groups of paired cells, the expression difference per gene of each cell pair is first calculated on the basis of the log-normalized expression values. Next, for each gene to test for DE, its variance over the calculated expression difference per cell pair ( σ ) is compar... | [] |
{
"text": "To construct the HNOCA-CE, we first collected raw count matrices and associated metadata of five more neural organoid studies. For two publications , we obtained them from the sources listed in the ‘Data availability’ section of the paper. For the remaining three publications , count matrices and associate... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 46,
"end": 49,
"text": "raw",
"labels": [
"Cell"
]
}
}
]
}
] |
{
"text": "Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.\nSource paper: PMC11578878"
} | [] |
{
"text": "Online content\nSource paper: PMC11578878"
} | [] |
{
"text": "Any methods, additional references, Nature Portfolio reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at 10.1038/s41586-0... | [] |
{
"text": "PMC12081310\nSource paper: PMC12081310"
} | [] |
{
"text": "An integrated transcriptomic cell atlas of human endoderm-derived organoids\nSource paper: PMC12081310"
} | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 49,
"end": 57,
"text": "endoderm",
"labels": [
"Tissue"
]
}
}
]
}
] |
{
"text": "Abstract\nSource paper: PMC12081310"
} | [] |
{
"text": "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... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 679,
"end": 688,
"text": "intestine",
"labels": [
"Tissue"
]
}
},
{
"from_name": "label",... |
{
"text": "Main\nSource paper: PMC12081310"
} | [] |
{
"text": "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 accurate alternatives to animal models . The term organoid is a current nomenclature to describe three-dimensional (3D) cell cultures der... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 1343,
"end": 1349,
"text": "organs",
"labels": [
"Tissue"
]
}
},
{
"from_name": "label",
... |
{
"text": "The endoderm 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 supplement... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 267,
"end": 272,
"text": "IPSCs",
"labels": [
"Cell"
]
}
},
{
"from_name": "label",
... |
{
"text": "Results\nSource paper: PMC12081310"
} | [] |
{
"text": "To create an endoderm-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 Supplementar... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 236,
"end": 251,
"text": "large intestine",
"labels": [
"Tissue"
]
}
},
{
"from_name": "l... |
{
"text": "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 (lev... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 1542,
"end": 1545,
"text": "raw",
"labels": [
"Cell"
]
}
},
{
"from_name": "label",
... |
{
"text": "Overall, epithelial cells from different organs clustered together in the integrated atlas and clusters were composed 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... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 9,
"end": 25,
"text": "epithelial cells",
"labels": [
"Cell"
]
}
},
{
"from_name": "label... |
{
"text": "To evaluate the fidelity of cell states observed in the human endoderm-derived organoid cell atlas (HEOCA), we obtained published scRNA-seq data on human endoderm-derived organs from adult (small and large intestine, lung, liver, pancreas, prostate, salivary gland) (Fig. 2a ) and fetal (small and large int... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 200,
"end": 215,
"text": "large intestine",
"labels": [
"Tissue"
]
}
},
{
"from_name": "l... |
{
"text": "We identified major cell types from each adult and fetal tissue (Fig. 2a,b ), and compared organoid cell types and states with primary counterparts using neighborhood graph correlation . We quantified the proportion of cell types in each organoid sample and compared the similarity of each cell type with co... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 726,
"end": 730,
"text": "stem",
"labels": [
"Tissue"
]
}
}
]
}
] |
{
"text": "To explore organoid cell states of different stem cell origin, we focused on intestinal organoid models in which there is substantial coverage from PSC-, FSC- and ASC-derived organoid cells. This subset consisted of 98 samples from 23 different publications representing 353,140 single-cell transcriptomes (... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 641,
"end": 646,
"text": "colon",
"labels": [
"Tissue"
]
}
},
{
"from_name": "label",
... |
{
"text": "To assess intestinal organoid fidelity and maturation, we integrated time series scRNA-seq data from duodenal development (59 to 132 days post fertilization) with adult intestinal epithelium (Fig. 3e ). These data revealed distinct fetal and adult stem cell-to-enterocyte differentiation trajectories, while... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 314,
"end": 329,
"text": "epithelial cell",
"labels": [
"Cell"
]
}
},
{
"from_name": "lab... |
{
"text": "We performed a detailed analysis of lung organoid cells, consisting of 221,425 cells obtained from 52 samples and 13 publications, comprising PSC-, FSC- and ASC-derived sources (Fig. 4a ). We integrated, clustered and annotated these data to generate a human lung organoid cell atlas (HLOCA) (Fig. 4b,c ). T... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 636,
"end": 644,
"text": "endoderm",
"labels": [
"Tissue"
]
}
},
{
"from_name": "label",
... |
{
"text": "To gain insights into how the lung organoid datasets correspond with primary tissue, we integrated a unified reference of primary adult and fetal lung tissues (Fig. 4e–k ). The query to reference mapping of the lung organoid data showed that PSC-derived organoid cells preferentially integrated with fetal c... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 1043,
"end": 1059,
"text": "epithelial cells",
"labels": [
"Cell"
]
}
},
{
"from_name": "... |
{
"text": "We developed a toolkit to incorporate organoid datasets and compare data with cell states in the integrated HEOCA (Fig. 5a ). This toolkit (sc2heoca) offers functions to compare samples with tissue references and assess ‘on or off’ target status and cell state maturation. In addition, it enables sample pro... | [] |
{
"text": "We provide several examples of how the HEOCA can be used to evaluate single-cell transcriptome datasets from recent organoid protocols (Fig. 5b–e ). First, we validate a finding that modulation of the TNF pathway promotes M cell abundance in intestinal organoids (Extended Data Fig. 6m ). We generated ASC-d... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 222,
"end": 228,
"text": "M cell",
"labels": [
"Cell"
]
}
},
{
"from_name": "label",
... |
{
"text": "Second, we assessed colonic epithelial tissue generated by seeding human colon ASC-derived organoids on a scaffolded hydrogel in a fluidic chip (Fig. 5c ). Projection analysis demonstrated that this protocol led to colonocyte differentiation and maturation, as indicated by a substantially higher proportion... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 577,
"end": 587,
"text": "epithelium",
"labels": [
"Tissue"
]
}
},
{
"from_name": "label"... |
{
"text": "Third, we analyzed two lung datasets consisting of time courses of lung progenitor organoids differentiated into alveolar or airway organoids (Fig. 5d,e ). In the alveolar dataset, cells showed increased mapping to alveolar epithelial identities (AT1 and AT2) over the course of differentiation (Fig. 5d ). ... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 23,
"end": 27,
"text": "lung",
"labels": [
"Tissue"
]
}
},
{
"from_name": "label",
... |
{
"text": "Finally, we incorporated four additional ASC-derived intestinal organoid datasets (two published and two unpublished) including condition versus control ileum organoids treated with IL-4 and IL-13 and colon organoids treated with IL-22, and time course data of ileum and colon organoids in a medium to promo... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 201,
"end": 206,
"text": "colon",
"labels": [
"Tissue"
]
}
},
{
"from_name": "label",
... |
{
"text": "We next sought to use the HEOCA as a cohort to assess organoid perturbations. We conducted two perturbation experiments aimed at modeling response to viral infection (interferon (IFN)α, IFNβ and IFNɣ) and acute pathogenic inflammation (TNF, Oncostatin M (OSM), IFNɣ, stem cell factor (SCF), IL-6, IL-17A and... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 1676,
"end": 1691,
"text": "epithelial cell",
"labels": [
"Cell"
]
}
},
{
"from_name": "l... |
{
"text": "We next assessed the utility of the integrated atlas to understand organoid models of disease. Through comparison with the HEOCA we assess cell proportion, identify disease-associated states and perform differential expression analysis against the atlas data (Fig. 5a ). We first explored colorectal cancer ... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 568,
"end": 572,
"text": "stem",
"labels": [
"Tissue"
]
}
},
{
"from_name": "label",
... |
{
"text": "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 an... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 344,
"end": 348,
"text": "lung",
"labels": [
"Tissue"
]
}
},
{
"from_name": "label",
... |
{
"text": "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 sele... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 534,
"end": 543,
"text": "intestine",
"labels": [
"Tissue"
]
}
},
{
"from_name": "label",... |
{
"text": "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.\nSource paper: PMC12081310"
} | [] |
{
"text": "Discussion\nSource paper: PMC12081310"
} | [] |
{
"text": "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 an... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 478,
"end": 486,
"text": "endoderm",
"labels": [
"Tissue"
]
}
},
{
"from_name": "label",
... |
{
"text": "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 distinguish cell states that markedly differed from states present in the atlas. This ability to distinguish nonpres... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 14,
"end": 19,
"text": "organ",
"labels": [
"Tissue"
]
}
}
]
}
] |
{
"text": "Methods\nSource paper: PMC12081310"
} | [] |
{
"text": "The experiments conducted in this study did not require approval from a specific ethics board.\nSource paper: PMC12081310"
} | [] |
{
"text": "To integrate the atlas, all available datasets were included, with no sample exclusion. For integration method comparisons and sample variance effect analyses, random samples were selected from the full dataset. Reproducibility codes for the analyses are available online via GitHub, as detailed in the ‘Cod... | [] |
{
"text": "Human intestinal tissue samples were obtained and experimental procedures performed within the framework of the nonprofit foundation HTCR (Munich, Germany) including informed patient consent. Ileal organoids were derived and maintained according to previously published culture conditions . For the cytokine... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 1442,
"end": 1447,
"text": "tubes",
"labels": [
"Tissue"
]
}
},
{
"from_name": "label",
... |
{
"text": "The scRNA-seq data used in this study were obtained from the original papers (Supplementary Table 1 ). If the raw fastq files were available, they were downloaded. The seq2science (v.1.2.2) method was used to download the raw fastq files from the Gene Expression Omnibus database ( https://www.ncbi.nlm.nih.... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 110,
"end": 113,
"text": "raw",
"labels": [
"Cell"
]
}
},
{
"from_name": "label",
... |
{
"text": "To integrate the data, we combined the count data from all the samples into a unified dataset. For subsequent analysis, we retained only the genes classified as protein-coding genes and long noncoding RNA genes. The low-quality cells in each sample were filed. The raw counts were then normalized to a total... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 265,
"end": 268,
"text": "raw",
"labels": [
"Cell"
]
}
}
]
}
] |
{
"text": "Cell-type annotation was performed using the snapseed method ( https://github.com/devsystemslab/snapseed ). For each sample, the raw counts were normalized to a total count of 10,000 and then log-transformed. From these normalized counts, the top 3,000 highly variable genes were identified using the defaul... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 129,
"end": 132,
"text": "raw",
"labels": [
"Cell"
]
}
}
]
}
] |
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