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An integrated transcriptomic cell atlas of human neural organoids
Source paper: PMC11578878 | [] | Single_Cell |
Human neural organoids, generated from pluripotent stem cells in vitro, are useful tools to study human brain development, evolution and disease. | [
{
"end": 61,
"label": "CellType",
"start": 39,
"text": "pluripotent stem cells"
}
] | Single_Cell |
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. | [
{
"end": 53,
"label": "Tissue",
"start": 42,
"text": "human brain"
}
] | Single_Cell |
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 . | [] | Single_Cell |
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. | [] | Single_Cell |
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. | [] | Single_Cell |
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. | [] | Single_Cell |
The human neural organoid cell atlas will be useful to assess organoid fidelity, characterize perturbed and diseased states and facilitate protocol development. | [] | Single_Cell |
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 . | [
{
"end": 78,
"label": "Tissue",
"start": 58,
"text": "human neural tissues"
}
] | Single_Cell |
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) . | [] | Single_Cell |
Conversely, unguided protocols rely on the self-patterning capacity of organoids to generate diverse cell types and states . | [] | Single_Cell |
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 ne... | [
{
"end": 192,
"label": "CellType",
"start": 182,
"text": "progenitor"
},
{
"end": 202,
"label": "CellType",
"start": 194,
"text": "neuronal"
},
{
"end": 223,
"label": "CellType",
"start": 207,
"text": "glial cell types"
},
{
"end": 264,
"label": "T... | Single_Cell |
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 . | [
{
"end": 66,
"label": "CellType",
"start": 39,
"text": "human neural organoid cells"
},
{
"end": 102,
"label": "Tissue",
"start": 91,
"text": "human brain"
}
] | Single_Cell |
Substantial differences have also been reported, including differential gene expression linked to media components and perturbed metabolic signatures associated with glycolysis . | [] | Single_Cell |
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 disorders . | [
{
"end": 42,
"label": "Tissue",
"start": 26,
"text": "organoid tissues"
}
] | Single_Cell |
However, it is unclear which portions of the developing central nervous system can be generated with existing protocols and which ones are still lacking. | [
{
"end": 78,
"label": "Tissue",
"start": 56,
"text": "central nervous system"
}
] | Single_Cell |
It has also remained challenging to systematically quantify the transcriptomic fidelity of neural organoid cells compared to their primary counterparts. | [
{
"end": 112,
"label": "CellType",
"start": 91,
"text": "neural organoid cells"
}
] | Single_Cell |
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. | [] | Single_Cell |
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 . | [
{
"end": 170,
"label": "Tissue",
"start": 159,
"text": "human brain"
}
] | Single_Cell |
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. | [
{
"end": 44,
"label": "CellType",
"start": 28,
"text": "cell populations"
},
{
"end": 59,
"label": "Tissue",
"start": 52,
"text": "primary"
},
{
"end": 80,
"label": "Tissue",
"start": 64,
"text": "organoid systems"
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{
"end": 192,
"label": "Tissue... | Single_Cell |
We estimate transcriptomic fidelity of neurons in neural organoids, and identify previously described cell stress as a universal factor distinguishing metabolic states of in vitro neurons from primary neurons without strongly affecting core identities of neuronal cell types. | [
{
"end": 46,
"label": "Tissue",
"start": 39,
"text": "neurons"
},
{
"end": 66,
"label": "Tissue",
"start": 50,
"text": "neural organoids"
},
{
"end": 187,
"label": "CellType",
"start": 180,
"text": "neurons"
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{
"end": 208,
"label": "CellType",
... | Single_Cell |
We map the data of a neural organoid morphogen screen to the integrated atlas to assess regional specificity and generation of new states. | [] | Single_Cell |
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. | [] | Single_Cell |
Finally, we show that the atlas can be expanded by projecting new data to the current atlas. | [] | Single_Cell |
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. | [
{
"end": 106,
"label": "Tissue",
"start": 90,
"text": "neural organoids"
}
] | Single_Cell |
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 a... | [] | Single_Cell |
The HNOCA represents cell types and states generated with 26 distinct neural organoid differentiation protocols, including three unguided and 23 guided ones, at time points ranging from 7 to 450 days (Fig. 1b ). | [] | Single_Cell |
To remove batch effects, we implemented a three-step integration pipeline. | [] | Single_Cell |
First, we projected the HNOCA to a single-cell atlas of the developing human brain using reference similarity spectrum (RSS) . | [
{
"end": 82,
"label": "Tissue",
"start": 71,
"text": "human brain"
}
] | Single_Cell |
Then, we developed snapseed ( Methods ) to perform preliminary marker-based hierarchical cell type annotation. | [] | Single_Cell |
Last, we used scPoli for label-aware data integration based on the snapseed annotations. | [] | Single_Cell |
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 ). | [] | Single_Cell |
We performed clustering on the basis of the scPoli representation and annotated clusters on the basis of canonical marker gene expression, organoid sample age and the auto-generated cell type labels. | [] | Single_Cell |
A uniform manifold approximation and projection (UMAP) embedding highlighted three neuronal differentiation trajectories corresponding to dorsal telencephalic, ventral telencephalic and non-telencephalic populations as well as trajectories leading from progenitors to glial cell types such as astrocytes and oligodendroc... | [
{
"end": 158,
"label": "CellType",
"start": 138,
"text": "dorsal telencephalic"
},
{
"end": 181,
"label": "CellType",
"start": 160,
"text": "ventral telencephalic"
},
{
"end": 215,
"label": "CellType",
"start": 186,
"text": "non-telencephalic populations"
},... | Single_Cell |
Cells from both unguided and guided protocols were distributed across all trajectories (Fig. 1f ). | [] | Single_Cell |
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 ). | [] | Single_Cell |
Focusing on the dorsal telencephalic 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 ). | [
{
"end": 174,
"label": "CellType",
"start": 144,
"text": "neural progenitor cells (NPCs)"
},
{
"end": 214,
"label": "CellType",
"start": 198,
"text": "cortical neurons"
},
{
"end": 255,
"label": "CellType",
"start": 239,
"text": "cortical neurons"
}
] | Single_Cell |
To further resolve heterogeneity among non-telencephalic neurons, we performed subclustering of this population, which revealed numerous neuronal populations characterized by distinct marker gene expression (Fig. 1j,k ). | [
{
"end": 64,
"label": "CellType",
"start": 39,
"text": "non-telencephalic neurons"
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{
"end": 157,
"label": "CellType",
"start": 137,
"text": "neuronal populations"
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] | Single_Cell |
HNOCA projection to a human developing brain atlas
Source paper: PMC11578878 | [] | Single_Cell |
To assess our cell type annotation, and more precisely annotate the heterogeneous non-telencephalic neuronal populations, we compared the HNOCA to a recently published single-cell transcriptomic atlas of the developing human brain (Fig. 2a ). | [
{
"end": 120,
"label": "CellType",
"start": 82,
"text": "non-telencephalic neuronal populations"
},
{
"end": 230,
"label": "Tissue",
"start": 219,
"text": "human brain"
}
] | Single_Cell |
We applied scVI and scANVI to the primary reference atlas, and used scArches to project the HNOCA to the same latent space. | [] | Single_Cell |
The shared latent space allowed us to reconstruct a bipartite weighted k -nearest-neighbour (wkNN) graph between cells in the HNOCA and the primary reference atlas, which was used to transfer the ‘CellClass’ and ‘Subregion’ labels, as well as the neurotransmitter transporter (NTT) information of neuroblasts and neurons... | [
{
"end": 308,
"label": "CellType",
"start": 297,
"text": "neuroblasts"
},
{
"end": 320,
"label": "CellType",
"start": 313,
"text": "neurons"
}
] | Single_Cell |
The transferred labels are strongly consistent with our assigned labels (Extended Data Fig. 3 ) and allowed us to refine the regional annotation of HNOCA non-telencephalic NPCs and neurons, as well as the NTT annotation of the non-telencephalic neurons (Fig. 2b ), resulting in the final hierarchical HNOCA cell type ann... | [
{
"end": 176,
"label": "CellType",
"start": 148,
"text": "HNOCA non-telencephalic NPCs"
},
{
"end": 188,
"label": "CellType",
"start": 181,
"text": "neurons"
},
{
"end": 252,
"label": "CellType",
"start": 227,
"text": "non-telencephalic neurons"
}
] | Single_Cell |
We also sought to compare organoid cells to stages of human brain development beyond the first trimester. | [
{
"end": 40,
"label": "CellType",
"start": 26,
"text": "organoid cells"
}
] | Single_Cell |
Focusing on dorsal telencephalon, we compared the transcriptomic profile of HNOCA NPCs and neurons with cells in a primary atlas of human cortex development spanning the first trimester to adolescence . | [
{
"end": 32,
"label": "Tissue",
"start": 12,
"text": "dorsal telencephalon"
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{
"end": 86,
"label": "CellType",
"start": 76,
"text": "HNOCA NPCs"
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{
"end": 98,
"label": "CellType",
"start": 91,
"text": "neurons"
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{
"end": 144,
"label": "Tissue... | Single_Cell |
We observed a transition from cell states observed in the first trimester to more mature states observed in the second-trimester cortex (Fig. 2c ), and did not detect substantial matching to later stages. | [
{
"end": 135,
"label": "Tissue",
"start": 129,
"text": "cortex"
}
] | Single_Cell |
We extended the comparison to other brain regions using two primary atlases representing the first and second trimester, respectively. | [
{
"end": 41,
"label": "Tissue",
"start": 36,
"text": "brain"
}
] | Single_Cell |
We confirmed increased similarity to second-trimester cell states in older organoids for other brain regions (Extended Data Fig. 3 ). | [
{
"end": 100,
"label": "Tissue",
"start": 95,
"text": "brain"
}
] | Single_Cell |
We evaluated the capacity of each neural organoid protocol to generate neural cells of different brain regions (Fig. 2d , Extended Data Figs. 3 and 4 and Supplementary Table 2 ). | [
{
"end": 83,
"label": "CellType",
"start": 71,
"text": "neural cells"
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{
"end": 102,
"label": "Tissue",
"start": 97,
"text": "brain"
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] | Single_Cell |
Datasets of unguided neural organoids contain cells across all brain regions with proportions varying across datasets, indicating the capacity of unguided protocols to generate many brain regions but with high variability. | [
{
"end": 37,
"label": "Tissue",
"start": 21,
"text": "neural organoids"
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{
"end": 68,
"label": "Tissue",
"start": 63,
"text": "brain"
},
{
"end": 187,
"label": "Tissue",
"start": 182,
"text": "brain"
}
] | Single_Cell |
By contrast, datasets derived from guided organoid protocols are strongly enriched for cells of the targeted brain region, but often show an increased proportion of cells of the brain regions neighbouring the targeted regions. | [
{
"end": 114,
"label": "Tissue",
"start": 109,
"text": "brain"
},
{
"end": 183,
"label": "Tissue",
"start": 178,
"text": "brain"
}
] | Single_Cell |
For example, several datasets derived from midbrain organoid protocols also show high proportions of hindbrain neurons, indicating an imprecision of morphogen guidance. | [
{
"end": 118,
"label": "CellType",
"start": 101,
"text": "hindbrain neurons"
}
] | Single_Cell |
To comprehensively evaluate how well organoid protocols represented by the HNOCA generate primary brain cell types, we estimated presence scores for every primary cell type in each HNOCA dataset ( Methods ). | [
{
"end": 114,
"label": "CellType",
"start": 98,
"text": "brain cell types"
}
] | Single_Cell |
A large presence score indicates high frequency and likelihood that cells of a similar type are observed in the HNOCA dataset. | [] | Single_Cell |
By normalizing the scores per organoid dataset (Extended Data Fig. 5 and Supplementary Table 3 ), we obtained a metric to describe how well each primary cell type is represented in at least one HNOCA dataset (Fig. 2d ). | [] | Single_Cell |
This analysis confirmed the absence of erythrocytes, immune cells and vascular endothelial cells in the HNOCA, all of which are derived from non-neuroectodermal germ layers (Fig. 2e ). | [
{
"end": 51,
"label": "CellType",
"start": 39,
"text": "erythrocytes"
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{
"end": 65,
"label": "CellType",
"start": 53,
"text": "immune cells"
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{
"end": 96,
"label": "CellType",
"start": 70,
"text": "vascular endothelial cells"
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{
"end": 172,
"... | Single_Cell |
As expected, telencephalic cell types are most strongly represented in HNOCA. | [
{
"end": 37,
"label": "CellType",
"start": 13,
"text": "telencephalic cell types"
}
] | Single_Cell |
By contrast, cell types of the thalamus, midbrain and cerebellum are least represented, including thalamic reticular nucleus GABAergic neurons, dorsal midbrain m1-derived GABAergic neurons and m1/m2-derived glutamatergic neurons, and cerebellar Purkinje cells (Fig. 2f,g ). | [
{
"end": 39,
"label": "Tissue",
"start": 31,
"text": "thalamus"
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{
"end": 49,
"label": "CellType",
"start": 41,
"text": "midbrain"
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{
"end": 64,
"label": "CellType",
"start": 54,
"text": "cerebellum"
},
{
"end": 142,
"label": "CellType",
"s... | Single_Cell |
It is worth noting that, even though these cell types are less abundant in HNOCA datasets than in the primary atlas, certain organoid protocols can generate some of these under-represented cell types (for example, Purkinje cells in posterior brain organoid protocols). | [
{
"end": 228,
"label": "CellType",
"start": 214,
"text": "Purkinje cells"
}
] | Single_Cell |
We next aimed to understand the transcriptomic similarities and differences between organoids generated by distinct differentiation protocols as well as between organoids and primary brain tissue. | [
{
"end": 195,
"label": "Tissue",
"start": 183,
"text": "brain tissue"
}
] | Single_Cell |
We identified differentially expressed genes (DEGs), comparing neural cell types in the HNOCA with their primary counterparts (Fig. 3a and Supplementary Table 4 ). | [
{
"end": 80,
"label": "CellType",
"start": 63,
"text": "neural cell types"
}
] | Single_Cell |
We found that for most neural cell types, more than one-third (mean 34.4%, standard deviation 12.1%) of DEGs were shared across at least half of the protocols (protocol-common DEGs), suggesting that many transcriptomic differences between organoid and primary cells were independent of organoid protocol (Fig. 3b ). | [
{
"end": 40,
"label": "CellType",
"start": 23,
"text": "neural cell types"
},
{
"end": 247,
"label": "CellType",
"start": 239,
"text": "organoid"
}
] | Single_Cell |
We verified our results using an extra primary human cortex scRNA-seq dataset (Extended Data Fig. 6 and Supplementary Table 5 ). | [] | Single_Cell |
We next assessed differential transcriptomic programmes that were shared across regional neural cell types, and identified a total of 920 ubiquitous, protocol-common DEGs (uDEGs) that were differentially expressed in at least 14 out of the 16 neural cell types (Fig. 3c ). | [
{
"end": 106,
"label": "CellType",
"start": 89,
"text": "neural cell types"
},
{
"end": 260,
"label": "CellType",
"start": 243,
"text": "neural cell types"
}
] | Single_Cell |
These uDEGs showed consistent fold changes ( r > 0.8) across neuron types and protocols (Fig. 3d ), and represent consistent molecular differences between neurons in organoids and those in primary tissues regardless of protocol or neuronal cell type. | [
{
"end": 67,
"label": "CellType",
"start": 61,
"text": "neuron"
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{
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"label": "CellType",
"start": 155,
"text": "neurons"
},
{
"end": 249,
"label": "CellType",
"start": 231,
"text": "neuronal cell type"
}
] | Single_Cell |
Out of all 920 uDEGs, 363 genes were consistently upregulated and 673 genes were consistently downregulated, with only 59 genes (6%) inconsistently differentially expressed across subtypes or protocols (Fig. 3e ). | [] | Single_Cell |
Using gene ontology enrichment analysis on the uDEGs, we found downregulated uDEGs enriched in neurodevelopmental processes including neuron cell–cell adhesion and synapse organization (Fig. 3f ). | [] | Single_Cell |
Upregulated uDEGs were enriched in many metabolism-associated terms including mitochondrial ATP synthesis-coupled electron transport (electron transport in short) and canonical glycolysis (Fig. 3f ). | [] | Single_Cell |
An enrichment of energy-associated pathways has previously been associated with metabolic changes caused by the limitations of current culture conditions . | [] | Single_Cell |
Also, the Molecular Signatures Database gene set hallmark glycolysis has previously been used to define metabolic states in neural organoids . | [
{
"end": 140,
"label": "Tissue",
"start": 124,
"text": "neural organoids"
}
] | Single_Cell |
Scoring mitochondrial electron transport, canonical glycolysis and hallmark glycolysis gene sets across the HNOCA and the primary reference atlas , we found that all three terms showed significant separation of organoid and primary cells (Fig. 3g,h ). | [
{
"end": 219,
"label": "CellType",
"start": 211,
"text": "organoid"
}
] | Single_Cell |
Using the datasets from refs. and as representative examples, we identified a similar distribution of glycolysis scores across all neural cell types with an overall increased score in organoid cells (Extended Data Fig. 7 ). | [
{
"end": 148,
"label": "CellType",
"start": 131,
"text": "neural cell types"
},
{
"end": 198,
"label": "CellType",
"start": 184,
"text": "organoid cells"
}
] | Single_Cell |
Focusing on dorsal telencephalic neurons, we compared the distribution of glycolysis scores across organoid differentiation protocols and identified several protocol features that correlated with metabolic cell stress. | [
{
"end": 40,
"label": "CellType",
"start": 12,
"text": "dorsal telencephalic neurons"
}
] | Single_Cell |
For instance, the usage of maturation media, slicing or cutting of organoids and, to a lesser extent, shaking or spinning of organoids led to overall lower glycolysis scores (Fig. 3h ). | [] | Single_Cell |
Mean glycolysis score and transcriptomic similarity of organoid and primary reference cell types across differentiation protocols were negatively correlated . | [
{
"end": 63,
"label": "CellType",
"start": 55,
"text": "organoid"
}
] | Single_Cell |
The correlation was significantly reduced when considering only variable transcription factors, indicating that the metabolic changes in organoids have limited impact on the core molecular identity of neuronal cell types (Extended Data Fig. 7 ). | [
{
"end": 220,
"label": "CellType",
"start": 201,
"text": "neuronal cell types"
}
] | Single_Cell |
This observation is consistent with previous studies of distinct metabolic states of cells in neural organoids relative to the primary tissue, which were shown to not affect neuron fate specification and maturation. | [
{
"end": 110,
"label": "Tissue",
"start": 94,
"text": "neural organoids"
}
] | Single_Cell |
Next, we focused on the expression of 366 variable transcription factors to calculate the correlation between corresponding neuronal cell types in the HNOCA datasets and the primary reference atlas . | [
{
"end": 143,
"label": "CellType",
"start": 124,
"text": "neuronal cell types"
}
] | Single_Cell |
We found that both guided and unguided organoid differentiation protocols generated neuronal cell types with comparable similarity to the corresponding primary reference cell types. | [
{
"end": 103,
"label": "CellType",
"start": 84,
"text": "neuronal cell types"
}
] | Single_Cell |
However, we observed brain region-dependent differences in transcriptomic similarity. | [] | Single_Cell |
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 ). | [
{
"end": 29,
"label": "CellType",
"start": 13,
"text": "organoid neurons"
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{
"end": 65,
"label": "Tissue",
"start": 60,
"text": "brain"
},
{
"end": 214,
"label": "Tissue",
"start": 209,
"text": "brain"
}
] | Single_Cell |
To identify molecular features other than metabolic state that decreased organoid fidelity, we incorporated dorsal telencephalic glutamatergic neurons from four different primary developing human brain atlases as an integrated primary reference, and identified neuron subtype and maturation state heterogeneity (Extended... | [
{
"end": 150,
"label": "CellType",
"start": 108,
"text": "dorsal telencephalic glutamatergic neurons"
}
] | Single_Cell |
Projection of dorsal telencephalic neurons in the HNOCA to the primary atlases revealed the corresponding heterogeneity in neural organoids. | [
{
"end": 42,
"label": "CellType",
"start": 14,
"text": "dorsal telencephalic neurons"
},
{
"end": 139,
"label": "Tissue",
"start": 123,
"text": "neural organoids"
}
] | Single_Cell |
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 ). | [
{
"end": 215,
"label": "CellType",
"start": 207,
"text": "organoid"
},
{
"end": 239,
"label": "CellType",
"start": 228,
"text": "brain cells"
}
] | Single_Cell |
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. | [] | Single_Cell |
Of note, these differences are observed across organoid protocols, and highlight areas of consistent transcriptomic divergence between in vitro and primary counterparts. | [] | Single_Cell |
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. | [] | Single_Cell |
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 ). | [] | Single_Cell |
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 ). | [
{
"end": 154,
"label": "Tissue",
"start": 149,
"text": "brain"
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{
"end": 176,
"label": "Tissue",
"start": 167,
"text": "forebrain"
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{
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"label": "Tissue",
"start": 178,
"text": "midbrain"
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{
"end": 200,
"label": "Tissue",
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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 ). | [
{
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"label": "CellType",
"start": 140,
"text": "neurons"
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{
"end": 166,
"label": "Tissue",
"start": 161,
"text": "brain"
}
] | Single_Cell |
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. | [] | Single_Cell |
Using hierarchical clustering on average presence scores revealed distinct presence score profiles for many screen conditions (Fig. 4d ), suggesting regional cell type composition distinct from the HNOCA datasets. | [] | Single_Cell |
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 ). | [] | Single_Cell |
This analysis highlighted several reference cell clusters with significant abundance increase under certain screen conditions (Fig. 4g ) such as LHX6 / ACKR3 / MPPED1 triple-positive GABAergic neurons in the ventral telencephalon and dopaminergic neurons in ventral midbrain. | [
{
"end": 200,
"label": "CellType",
"start": 183,
"text": "GABAergic neurons"
},
{
"end": 229,
"label": "Tissue",
"start": 208,
"text": "ventral telencephalon"
},
{
"end": 254,
"label": "CellType",
"start": 234,
"text": "dopaminergic neurons"
},
{
"end"... | Single_Cell |
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 lac... | [
{
"end": 284,
"label": "CellType",
"start": 265,
"text": "neuronal cell types"
},
{
"end": 344,
"label": "Tissue",
"start": 328,
"text": "neural organoids"
}
] | Single_Cell |
We next tested whether the integrated HNOCA can serve as a control cohort for assessing organoid models of neural disease. | [] | Single_Cell |
We collected 11 scRNA-seq datasets from 10 neural organoid disease models and their respective controls (microcephaly , amyotrophic lateral sclerosis , Alzheimer’s disease , autism , fragile-X syndrome (FXS) , schizophrenia , neuronal heterotopia , Pitt–Hopkins syndrome , myotonic dystrophy and glioblastoma ) (Fig. 5a ... | [] | Single_Cell |
We projected the data to the HNOCA and the primary reference atlas to transfer annotations (Fig. 5b–f ). | [] | Single_Cell |
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 ). | [] | Single_Cell |
These differences might represent disease phenotypes, but could also be the consequence of cell line variability. | [] | Single_Cell |
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