Mardiyyah/TAPT_data_V2_split · Datasets at Hugging Face

PMCID stringclasses 24 values	Title stringclasses 24 values	Sentences stringlengths 2 40.7k
PMC11578878	An integrated transcriptomic cell atlas of human neural organoids.	In brief, we first represented the primary atlas kNN graph as its adjacency matrix (A), followed by row normalization to convert it into a transition probability matrix (P).
PMC11578878	An integrated transcriptomic cell atlas of human neural organoids.	With the raw scores represented as a vector s0, in each iteration t, we generated st as[12pt] $$_= }}_+(1- )^_$$=αs0+(1−α)PTst−1 This procedure was performed 100 times to get the smooth presence scores that were subsequently log transformed.
PMC11578878	An integrated transcriptomic cell atlas of human neural organoids.	Scores lower than the 5th percentile or higher than the 95th percentile were trimmed.
PMC11578878	An integrated transcriptomic cell atlas of human neural organoids.	The trimmed scores were normalized into the range of as the final presence scores in the HNOCA dataset.
PMC11578878	An integrated transcriptomic cell atlas of human neural organoids.	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.
PMC11578878	An integrated transcriptomic cell atlas of human neural organoids.	A large (close to one) max presence score indicates a high frequency of appearance for the cell type or state in at least one HNOCA dataset whereas a small (close to zero) max presence score suggests under-representation in all the HNOCA datasets.
PMC11578878	An integrated transcriptomic cell atlas of human neural organoids.	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.
PMC11578878	An integrated transcriptomic cell atlas of human neural organoids.	scCODA is a Bayesian model for detecting compositional changes in scRNA-seq data.
PMC11578878	An integrated transcriptomic cell atlas of human neural organoids.	For this, we have extracted the information about the added morphogens from each differentiation protocol and grouped them into 15 broad molecule groups on the basis of their role in neural differentiation (Supplementary Table 1).
PMC11578878	An integrated transcriptomic cell atlas of human neural organoids.	These molecule groups were used as a covariate in the model.
PMC11578878	An integrated transcriptomic cell atlas of human neural organoids.	The region labels transferred from the primary atlas were used as labels in the analysis (cell_type_identifier).
PMC11578878	An integrated transcriptomic cell atlas of human neural organoids.	For cell types without regional identity, the cell type labels presented in Fig. 1c were used.
PMC11578878	An integrated transcriptomic cell atlas of human neural organoids.	Pluripotent stem cells and neuroepithelium cells were removed from the analysis because they are mainly present in the early organoid stages.
PMC11578878	An integrated transcriptomic cell atlas of human neural organoids.	We used bio_sample as the sample_identifier.
PMC11578878	An integrated transcriptomic cell atlas of human neural organoids.	We ran scCODA sequentially with default parameters, using No-U-turn sampling (run_nuts function) and selecting each cell type once as a reference.
PMC11578878	An integrated transcriptomic cell atlas of human neural organoids.	We used a majority vote-based system to find the cell types that were credibly changing in more than half of the iterations.
PMC11578878	An integrated transcriptomic cell atlas of human neural organoids.	To complement the composition analysis conducted with scCODA, we devised an alternative approach to test for differential composition using regularized linear regression.
PMC11578878	An integrated transcriptomic cell atlas of human neural organoids.	We fit a generalized linear model with the region composition matrix as the response Y and molecule usage as independent variables X:[12pt] $$Y X}$$~Xβ The model was fit with lasso regularization (alpha = 1) using Gaussian noise and an identity link function.
PMC11578878	An integrated transcriptomic cell atlas of human neural organoids.	The regularization parameter lambda was automatically determined through cross-validation as implemented in the function cv.glmnet() from the glmnet R package.
PMC11578878	An integrated transcriptomic cell atlas of human neural organoids.	All non-zero coefficients β were considered as indications of enrichment and depletion.
PMC11578878	An integrated transcriptomic cell atlas of human neural organoids.	To study the transcriptomic differences between organoid and primary cells, we subset HNOCA using the final level 1 annotation to cells labelled ‘Neuron’.
PMC11578878	An integrated transcriptomic cell atlas of human neural organoids.	We furthermore subset the human developing brain atlas to cells that had been assigned a valid label in the neuron_ntt_label annotation column.
PMC11578878	An integrated transcriptomic cell atlas of human neural organoids.	We added an extra two datasets of fetal cortical cells from ref.
PMC11578878	An integrated transcriptomic cell atlas of human neural organoids.	and ref. .
PMC11578878	An integrated transcriptomic cell atlas of human neural organoids.	For the data from ref. ,
PMC11578878	An integrated transcriptomic cell atlas of human neural organoids.	we subset the data to cells labelled ‘fetal’ and estimated transcripts per million reads for each gene in each cell using RSEM given the STAR mapping results.
PMC11578878	An integrated transcriptomic cell atlas of human neural organoids.	We then computed a PCA, a kNN graph, UMAP and Leiden clustering (resolution 0.2) using scanpy.
PMC11578878	An integrated transcriptomic cell atlas of human neural organoids.	We then selected the cluster with the highest STMN2 and NEUROD6 expression as the cortical neuron cluster and used only those cells.
PMC11578878	An integrated transcriptomic cell atlas of human neural organoids.	For the data from ref.
PMC11578878	An integrated transcriptomic cell atlas of human neural organoids.	we subset the datasets to cells annotated as ‘Neuronal’ in Supplementary Table 5 (‘Cortex annotations’) of their publication and computed a PCA, neighbourhood graph and UMAP to visualize the dataset.
PMC11578878	An integrated transcriptomic cell atlas of human neural organoids.	We found that only samples from the individuals CS14_3, CS20, CS22 and CS20 contained detectable expression of STMN2 and NEUROD6 so we subset the dataset further to only cells from those individuals.
PMC11578878	An integrated transcriptomic cell atlas of human neural organoids.	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.
PMC11578878	An integrated transcriptomic cell atlas of human neural organoids.	and ‘individual’ for ref. )
PMC11578878	An integrated transcriptomic cell atlas of human neural organoids.	using the Python implementation of decoupler (v.1.4.0) while discarding any samples with fewer than ten cells or 1,000 total counts.
PMC11578878	An integrated transcriptomic cell atlas of human neural organoids.	We then subsetted the feature space to the intersection of features of all datasets and removed any cells with fewer than 200 genes expressed.
PMC11578878	An integrated transcriptomic cell atlas of human neural organoids.	We further removed any genes expressed in less than 1% of neurons in HNOCA and any genes located on the X and Y chromosomes.
PMC11578878	An integrated transcriptomic cell atlas of human neural organoids.	Out of the remaining 11,636 genes, on average, 99% were reported in each of the constituent HNOCA datasets.
PMC11578878	An integrated transcriptomic cell atlas of human neural organoids.	For each regional neural cell type, we removed any sample from the pseudobulk data that was associated with an organoid differentiation assay with fewer than two total samples or fewer than 100 total cells.
PMC11578878	An integrated transcriptomic cell atlas of human neural organoids.	We next used edgeR to iteratively compute DE genes between each organoid differentiation protocol and primary cells of the matching regional neural cell types for every regional neural cell type while correcting for organoid age in days, number of cells per pseudobulk sample, median and standard deviation of the number of detected genes per pseudobulk sample.
PMC11578878	An integrated transcriptomic cell atlas of human neural organoids.	We used the data from ref. (
PMC11578878	An integrated transcriptomic cell atlas of human neural organoids.	the human developing brain atlas mentioned above), ref.
PMC11578878	An integrated transcriptomic cell atlas of human neural organoids.	and ref.
PMC11578878	An integrated transcriptomic cell atlas of human neural organoids.	as primary data for the DE comparison in the cell type ‘Dorsal Telencephalic Neuron NT-VGLUT’, whereas for all other cell types we used the human developing brain atlas as the fetal dataset.
PMC11578878	An integrated transcriptomic cell atlas of human neural organoids.	We used the edgeR genewise negative binomial generalized linear model with quasi-likelihood F-tests.
PMC11578878	An integrated transcriptomic cell atlas of human neural organoids.	We deemed a gene significantly DE if its false-discovery rate (Benjamini–Hochberg) corrected P value was smaller than 0.05 and it had an absolute log2-fold change above 0.5.
PMC11578878	An integrated transcriptomic cell atlas of human neural organoids.	We used the GSEApy Python package to carry out functional enrichment analysis in our DE results using the ‘GO_Biological_Process_2021’ gene set.
PMC11578878	An integrated transcriptomic cell atlas of human neural organoids.	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.
PMC11578878	An integrated transcriptomic cell atlas of human neural organoids.	and the matching cell type from the Braun et al. primary dataset as well as the data from ref. .
PMC11578878	An integrated transcriptomic cell atlas of human neural organoids.	To prevent technology effects to affect this analysis, we only used cells generated with the 10X Genomics 3′ v.2 protocol in this comparison.
PMC11578878	An integrated transcriptomic cell atlas of human neural organoids.	We generate pseudobulk samples as described above and corrected organoid age in days and number of cells per pseudobulk sample in the DE comparison.
PMC11578878	An integrated transcriptomic cell atlas of human neural organoids.	We used the same edgeR-based procedure and cut-offs as described above.
PMC11578878	An integrated transcriptomic cell atlas of human neural organoids.	We used the scipy fcluster method to cluster genes on the basis of their log-fold changes in the two primary datasets.
PMC11578878	An integrated transcriptomic cell atlas of human neural organoids.	We grouped clusters to represent consistently upregulated, consistently downregulated and three different inconsistently regulated groups of genes.
PMC11578878	An integrated transcriptomic cell atlas of human neural organoids.	We computed functional enrichment of each gene group as described above.
PMC11578878	An integrated transcriptomic cell atlas of human neural organoids.	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 from ref. .
PMC11578878	An integrated transcriptomic cell atlas of human neural organoids.	We computed pseudobulk samples and carried out the DE analysis using the same procedure and cut-offs as in the protocol-based DE analysis.
PMC11578878	An integrated transcriptomic cell atlas of human neural organoids.	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.
PMC11578878	An integrated transcriptomic cell atlas of human neural organoids.	For each HNOCA dataset, only neural cell types with at least 20 cells were considered.
PMC11578878	An integrated transcriptomic cell atlas of human neural organoids.	Highly variable genes were identified across the neural cell types in the primary reference using a Chi-squared test-based variance ratio test on the generalized linear model with Gamma distribution (identity link), given coefficient of variance of transcript counts across neural cell types as the response and the reciprocal of average transcript count across neural cell types as the independent variable.
PMC11578878	An integrated transcriptomic cell atlas of human neural organoids.	Genes with Benjamini–Hochberg adjusted P values less than 0.01 were considered as highly variable genes.
PMC11578878	An integrated transcriptomic cell atlas of human neural organoids.	Similarity between one neural cell type in the primary atlas and its counterpart in each HNOCA dataset was then calculated as the Spearman correlation coefficient across the identified highly variable genes.
PMC11578878	An integrated transcriptomic cell atlas of human neural organoids.	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.
PMC11578878	An integrated transcriptomic cell atlas of human neural organoids.	The list of transcription factors was retrieved from the AnimalTFDB v.4.0 database.
PMC11578878	An integrated transcriptomic cell atlas of human neural organoids.	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. .
PMC11578878	An integrated transcriptomic cell atlas of human neural organoids.	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 variable transcription factor.
PMC11578878	An integrated transcriptomic cell atlas of human neural organoids.	Transcriptomic similarities were calculated on the basis of those subsets, and then correlated with the glycolysis scores.
PMC11578878	An integrated transcriptomic cell atlas of human neural organoids.	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 of transcriptomic correlation.
PMC11578878	An integrated transcriptomic cell atlas of human neural organoids.	In brief, each primary atlas cluster we obtained as mentioned above was assigned to a cell type as the most abundant cell type among cells in the cluster.
PMC11578878	An integrated transcriptomic cell atlas of human neural organoids.	The label of the best-correlated primary cluster was then transferred to every query cell.
PMC11578878	An integrated transcriptomic cell atlas of human neural organoids.	Given the transferred label, together with the level 2 cell type annotation shown in Fig. 1c, as the annotation label, scPoli was applied to the telencephalic subset of HNOCA for data integration.
PMC11578878	An integrated transcriptomic cell atlas of human neural organoids.	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 representation; (3) Louvain clustering (resolution, 2) with PCA of HNOCA telencephalic subset (based on scaled expression of 3,000 highly variable genes of the telencephalic subset with flavor = ‘seurat’) and (4) Louvain clustering (resolution, 1) for each sample separately (each with 3,000 highly variable genes identified with flavor = ‘seurat’, followed by data scale and PCA).
PMC11578878	An integrated transcriptomic cell atlas of human neural organoids.	Next, for each sample with at least 500 dorsal telencephalic neurons, the adjusted mutual information scores were calculated between each of those four clustering labels with the transferred cell type label mentioned above as the gold standard, across the dorsal telencephalic neurons as annotated as the level 2 annotation.
PMC11578878	An integrated transcriptomic cell atlas of human neural organoids.	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.
PMC11578878	An integrated transcriptomic cell atlas of human neural organoids.	For ref. ,
PMC11578878	An integrated transcriptomic cell atlas of human neural organoids.	cells in five author-defined clusters (60, 57, 79, 45, 65) with high expression of MAP2, DCX and NEUROD6 were selected.
PMC11578878	An integrated transcriptomic cell atlas of human neural organoids.	For ref. ,
PMC11578878	An integrated transcriptomic cell atlas of human neural organoids.	cells with the following ‘clusterv2 - final’ labels were selected: ‘Neuron_28’, ‘Neuron_34’, ‘GW19_2_29NeuronNeuron’, ‘Neuron_30’, ‘Neuron_66Neuron’, ‘GW18_2_42NeuronNeuron’, ‘Neuron_33’, ‘Neuron_39Neuron’, ‘Neuron_35’, ‘Neuron_63Neuron’, ‘Neuron_9’, ‘Neuron_11’, ‘Neuron_20’, ‘Neuron_22’, ‘Neuron_5Neuron’, ‘Neuron_21’, ‘Neuron_18’, ‘Neuron_101Neuron’, ‘Neuron_17’, ‘Neuron_19’, ‘Neuron_16’, ‘Neuron_50Neuron’, ‘Neuron_12’, ‘Neuron_13’, ‘Neuron_68Neuron’, ‘Neuron_100Neuron’, ‘Neuron_25’, ‘Neuron_27’, ‘Neuron_53Neuron’, ‘Neuron_23’, ‘Neuron_26’, ‘Neuron_24’, ‘Neuron_102Neuron’, ‘Neuron_72Neuron’, ‘Neuron_15’, ‘Neuron_29’ and ‘Neuron_35Neuron’ on the basis of their high expression of NEUROD6 and FOXG1.
PMC11578878	An integrated transcriptomic cell atlas of human neural organoids.	For ref. ,
PMC11578878	An integrated transcriptomic cell atlas of human neural organoids.	cells dissected from dorsal telencephalon that were annotated as neurons with and only with the VGLUT NTT label were selected.
PMC11578878	An integrated transcriptomic cell atlas of human neural organoids.	For ref. ,
PMC11578878	An integrated transcriptomic cell atlas of human neural organoids.	cells annotated as excitatory neurons were selected.
PMC11578878	An integrated transcriptomic cell atlas of human neural organoids.	The curated clusters of the Wang et al. primary atlas, as described earlier, were also subset to those with excitatory neuron labels.
PMC11578878	An integrated transcriptomic cell atlas of human neural organoids.	The selected dorsal telencephalic neuron subsets of the atlases were merged into the joint neocortical neuron atlas.
PMC11578878	An integrated transcriptomic cell atlas of human neural organoids.	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.
PMC11578878	An integrated transcriptomic cell atlas of human neural organoids.	The cluster label of the best-correlated cluster was assigned to each cell in the joined neocortical neuron atlas, so that cell cluster labels were harmonized for all cells in the atlas.
PMC11578878	An integrated transcriptomic cell atlas of human neural organoids.	Label-aware data integration was then performed using scPoli.
PMC11578878	An integrated transcriptomic cell atlas of human neural organoids.	On the basis of the scPoli latent representation, Louvain clustering was performed on the joint neocortical neuron atlas (resolution, 1).
PMC11578878	An integrated transcriptomic cell atlas of human neural organoids.	This cluster label was transferred to the dorsal telencephalic neurons in HNOCA with max-correlation manner across highly variable genes defined on average transcriptomic profiles of clusters in the joint neocortical neuron atlas.
PMC11578878	An integrated transcriptomic cell atlas of human neural organoids.	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.
PMC11578878	An integrated transcriptomic cell atlas of human neural organoids.	In both cases, the ‘dataset’ field of the screen data was used as the batch covariate, which indicates belonging to one of the three categories: ‘organoid screen’, ‘secondary organoid screen’ or ‘fetal striatum 21pcw’.
PMC11578878	An integrated transcriptomic cell atlas of human neural organoids.	For mapping to the primary reference, we used the scvi-tools implementation of scArches without the use of cell type annotations and trained the model for 500 epochs with weight_decay of 0 and otherwise default parameters.
PMC11578878	An integrated transcriptomic cell atlas of human neural organoids.	For mapping to HNOCA we used scArches through scPoli and trained the model for 500 epochs without unlabelled prototype training.
PMC11578878	An integrated transcriptomic cell atlas of human neural organoids.	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.
PMC11578878	An integrated transcriptomic cell atlas of human neural organoids.	Count matrices and metadata were directly downloaded for the ten datasets with processed data provided in the Gene Expression Omnibus or ArrayExpress.
PMC11578878	An integrated transcriptomic cell atlas of human neural organoids.	For the dataset with only FASTQ files available, we downloaded the FASTQ files and used Cell Ranger (v.4.0) to map reads to the human reference genome and transcriptome retrieved from Cell Ranger website (GRCh38 v.3.0.0) for gene expression quantification.
PMC11578878	An integrated transcriptomic cell atlas of human neural organoids.	All datasets were concatenated together with anndata in Python (join = ‘inner’).
PMC11578878	An integrated transcriptomic cell atlas of human neural organoids.	For each dataset, samples were grouped into either ‘disease’ or ‘control’ as their disease status, with ‘disease’ representing data from patient cell lines, mutant cell lines with disease-related alleles, cells carrying targeting guide RNAs (gRNAs) in CRISPR-based screen and tumour-derived organoids.
PMC11578878	An integrated transcriptomic cell atlas of human neural organoids.	and ‘control’ representing data from healthy cell lines, mutation-corrected cell lines and cells carrying only non-targeting gRNAs in a CRISPR-based screen.
PMC11578878	An integrated transcriptomic cell atlas of human neural organoids.	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.
PMC11578878	An integrated transcriptomic cell atlas of human neural organoids.	For projecting to the primary atlas, the same implementation as mentioned above to map HNOCA to the atlas was used.

PMC11578878

An integrated transcriptomic cell atlas of human neural organoids.

In brief, we first represented the primary atlas kNN graph as its adjacency matrix (A), followed by row normalization to convert it into a transition probability matrix (P).

PMC11578878

An integrated transcriptomic cell atlas of human neural organoids.

With the raw scores represented as a vector s0, in each iteration t, we generated st as[12pt] $$_= }}_+(1- )^_$$=αs0+(1−α)PTst−1 This procedure was performed 100 times to get the smooth presence scores that were subsequently log transformed.

PMC11578878

An integrated transcriptomic cell atlas of human neural organoids.

Scores lower than the 5th percentile or higher than the 95th percentile were trimmed.

PMC11578878

An integrated transcriptomic cell atlas of human neural organoids.

The trimmed scores were normalized into the range of as the final presence scores in the HNOCA dataset.

PMC11578878

An integrated transcriptomic cell atlas of human neural organoids.

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.

PMC11578878

An integrated transcriptomic cell atlas of human neural organoids.

A large (close to one) max presence score indicates a high frequency of appearance for the cell type or state in at least one HNOCA dataset whereas a small (close to zero) max presence score suggests under-representation in all the HNOCA datasets.

PMC11578878

An integrated transcriptomic cell atlas of human neural organoids.

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.

PMC11578878

An integrated transcriptomic cell atlas of human neural organoids.

scCODA is a Bayesian model for detecting compositional changes in scRNA-seq data.

PMC11578878

An integrated transcriptomic cell atlas of human neural organoids.

For this, we have extracted the information about the added morphogens from each differentiation protocol and grouped them into 15 broad molecule groups on the basis of their role in neural differentiation (Supplementary Table 1).

PMC11578878

An integrated transcriptomic cell atlas of human neural organoids.

These molecule groups were used as a covariate in the model.

PMC11578878

An integrated transcriptomic cell atlas of human neural organoids.

The region labels transferred from the primary atlas were used as labels in the analysis (cell_type_identifier).

PMC11578878

An integrated transcriptomic cell atlas of human neural organoids.

For cell types without regional identity, the cell type labels presented in Fig. 1c were used.

PMC11578878

An integrated transcriptomic cell atlas of human neural organoids.

Pluripotent stem cells and neuroepithelium cells were removed from the analysis because they are mainly present in the early organoid stages.

PMC11578878

An integrated transcriptomic cell atlas of human neural organoids.

We used bio_sample as the sample_identifier.

PMC11578878

An integrated transcriptomic cell atlas of human neural organoids.

We ran scCODA sequentially with default parameters, using No-U-turn sampling (run_nuts function) and selecting each cell type once as a reference.

PMC11578878

An integrated transcriptomic cell atlas of human neural organoids.

We used a majority vote-based system to find the cell types that were credibly changing in more than half of the iterations.

PMC11578878

An integrated transcriptomic cell atlas of human neural organoids.

To complement the composition analysis conducted with scCODA, we devised an alternative approach to test for differential composition using regularized linear regression.

PMC11578878

An integrated transcriptomic cell atlas of human neural organoids.

We fit a generalized linear model with the region composition matrix as the response Y and molecule usage as independent variables X:[12pt] $$Y X}$$~Xβ The model was fit with lasso regularization (alpha = 1) using Gaussian noise and an identity link function.

PMC11578878

An integrated transcriptomic cell atlas of human neural organoids.

The regularization parameter lambda was automatically determined through cross-validation as implemented in the function cv.glmnet() from the glmnet R package.

PMC11578878

An integrated transcriptomic cell atlas of human neural organoids.

All non-zero coefficients β were considered as indications of enrichment and depletion.

PMC11578878

An integrated transcriptomic cell atlas of human neural organoids.

To study the transcriptomic differences between organoid and primary cells, we subset HNOCA using the final level 1 annotation to cells labelled ‘Neuron’.

PMC11578878

An integrated transcriptomic cell atlas of human neural organoids.

We furthermore subset the human developing brain atlas to cells that had been assigned a valid label in the neuron_ntt_label annotation column.

PMC11578878

An integrated transcriptomic cell atlas of human neural organoids.

We added an extra two datasets of fetal cortical cells from ref.

PMC11578878

An integrated transcriptomic cell atlas of human neural organoids.

and ref. .

PMC11578878

An integrated transcriptomic cell atlas of human neural organoids.

For the data from ref. ,

PMC11578878

An integrated transcriptomic cell atlas of human neural organoids.

we subset the data to cells labelled ‘fetal’ and estimated transcripts per million reads for each gene in each cell using RSEM given the STAR mapping results.

PMC11578878

An integrated transcriptomic cell atlas of human neural organoids.

We then computed a PCA, a kNN graph, UMAP and Leiden clustering (resolution 0.2) using scanpy.

PMC11578878

An integrated transcriptomic cell atlas of human neural organoids.

We then selected the cluster with the highest STMN2 and NEUROD6 expression as the cortical neuron cluster and used only those cells.

PMC11578878

An integrated transcriptomic cell atlas of human neural organoids.

For the data from ref.

PMC11578878

An integrated transcriptomic cell atlas of human neural organoids.

we subset the datasets to cells annotated as ‘Neuronal’ in Supplementary Table 5 (‘Cortex annotations’) of their publication and computed a PCA, neighbourhood graph and UMAP to visualize the dataset.

PMC11578878

An integrated transcriptomic cell atlas of human neural organoids.

We found that only samples from the individuals CS14_3, CS20, CS22 and CS20 contained detectable expression of STMN2 and NEUROD6 so we subset the dataset further to only cells from those individuals.

PMC11578878

An integrated transcriptomic cell atlas of human neural organoids.

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.

PMC11578878

An integrated transcriptomic cell atlas of human neural organoids.

and ‘individual’ for ref. )

PMC11578878

An integrated transcriptomic cell atlas of human neural organoids.

using the Python implementation of decoupler (v.1.4.0) while discarding any samples with fewer than ten cells or 1,000 total counts.

PMC11578878

An integrated transcriptomic cell atlas of human neural organoids.

We then subsetted the feature space to the intersection of features of all datasets and removed any cells with fewer than 200 genes expressed.

PMC11578878

An integrated transcriptomic cell atlas of human neural organoids.

We further removed any genes expressed in less than 1% of neurons in HNOCA and any genes located on the X and Y chromosomes.

PMC11578878

An integrated transcriptomic cell atlas of human neural organoids.

Out of the remaining 11,636 genes, on average, 99% were reported in each of the constituent HNOCA datasets.

PMC11578878

An integrated transcriptomic cell atlas of human neural organoids.

For each regional neural cell type, we removed any sample from the pseudobulk data that was associated with an organoid differentiation assay with fewer than two total samples or fewer than 100 total cells.

PMC11578878

An integrated transcriptomic cell atlas of human neural organoids.

We next used edgeR to iteratively compute DE genes between each organoid differentiation protocol and primary cells of the matching regional neural cell types for every regional neural cell type while correcting for organoid age in days, number of cells per pseudobulk sample, median and standard deviation of the number of detected genes per pseudobulk sample.

PMC11578878

An integrated transcriptomic cell atlas of human neural organoids.

We used the data from ref. (

PMC11578878

An integrated transcriptomic cell atlas of human neural organoids.

the human developing brain atlas mentioned above), ref.

PMC11578878

An integrated transcriptomic cell atlas of human neural organoids.

and ref.

PMC11578878

An integrated transcriptomic cell atlas of human neural organoids.

as primary data for the DE comparison in the cell type ‘Dorsal Telencephalic Neuron NT-VGLUT’, whereas for all other cell types we used the human developing brain atlas as the fetal dataset.

PMC11578878

An integrated transcriptomic cell atlas of human neural organoids.

We used the edgeR genewise negative binomial generalized linear model with quasi-likelihood F-tests.

PMC11578878

An integrated transcriptomic cell atlas of human neural organoids.

We deemed a gene significantly DE if its false-discovery rate (Benjamini–Hochberg) corrected P value was smaller than 0.05 and it had an absolute log2-fold change above 0.5.

PMC11578878

An integrated transcriptomic cell atlas of human neural organoids.

We used the GSEApy Python package to carry out functional enrichment analysis in our DE results using the ‘GO_Biological_Process_2021’ gene set.

PMC11578878

An integrated transcriptomic cell atlas of human neural organoids.

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.

PMC11578878

An integrated transcriptomic cell atlas of human neural organoids.

and the matching cell type from the Braun et al. primary dataset as well as the data from ref. .

PMC11578878

An integrated transcriptomic cell atlas of human neural organoids.

To prevent technology effects to affect this analysis, we only used cells generated with the 10X Genomics 3′ v.2 protocol in this comparison.

PMC11578878

An integrated transcriptomic cell atlas of human neural organoids.

We generate pseudobulk samples as described above and corrected organoid age in days and number of cells per pseudobulk sample in the DE comparison.

PMC11578878

An integrated transcriptomic cell atlas of human neural organoids.

We used the same edgeR-based procedure and cut-offs as described above.

PMC11578878

An integrated transcriptomic cell atlas of human neural organoids.

We used the scipy fcluster method to cluster genes on the basis of their log-fold changes in the two primary datasets.

PMC11578878

An integrated transcriptomic cell atlas of human neural organoids.

We grouped clusters to represent consistently upregulated, consistently downregulated and three different inconsistently regulated groups of genes.

PMC11578878

An integrated transcriptomic cell atlas of human neural organoids.

We computed functional enrichment of each gene group as described above.

PMC11578878

An integrated transcriptomic cell atlas of human neural organoids.

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 from ref. .

PMC11578878

An integrated transcriptomic cell atlas of human neural organoids.

We computed pseudobulk samples and carried out the DE analysis using the same procedure and cut-offs as in the protocol-based DE analysis.

PMC11578878

An integrated transcriptomic cell atlas of human neural organoids.

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.

PMC11578878

An integrated transcriptomic cell atlas of human neural organoids.

For each HNOCA dataset, only neural cell types with at least 20 cells were considered.

PMC11578878

An integrated transcriptomic cell atlas of human neural organoids.

Highly variable genes were identified across the neural cell types in the primary reference using a Chi-squared test-based variance ratio test on the generalized linear model with Gamma distribution (identity link), given coefficient of variance of transcript counts across neural cell types as the response and the reciprocal of average transcript count across neural cell types as the independent variable.

PMC11578878

An integrated transcriptomic cell atlas of human neural organoids.

Genes with Benjamini–Hochberg adjusted P values less than 0.01 were considered as highly variable genes.

PMC11578878

An integrated transcriptomic cell atlas of human neural organoids.

Similarity between one neural cell type in the primary atlas and its counterpart in each HNOCA dataset was then calculated as the Spearman correlation coefficient across the identified highly variable genes.

PMC11578878

An integrated transcriptomic cell atlas of human neural organoids.

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.

PMC11578878

An integrated transcriptomic cell atlas of human neural organoids.

The list of transcription factors was retrieved from the AnimalTFDB v.4.0 database.

PMC11578878

An integrated transcriptomic cell atlas of human neural organoids.

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. .

PMC11578878

An integrated transcriptomic cell atlas of human neural organoids.

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 variable transcription factor.

PMC11578878

An integrated transcriptomic cell atlas of human neural organoids.

Transcriptomic similarities were calculated on the basis of those subsets, and then correlated with the glycolysis scores.

PMC11578878

An integrated transcriptomic cell atlas of human neural organoids.

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 of transcriptomic correlation.

PMC11578878

An integrated transcriptomic cell atlas of human neural organoids.

In brief, each primary atlas cluster we obtained as mentioned above was assigned to a cell type as the most abundant cell type among cells in the cluster.

PMC11578878

An integrated transcriptomic cell atlas of human neural organoids.

The label of the best-correlated primary cluster was then transferred to every query cell.

PMC11578878

An integrated transcriptomic cell atlas of human neural organoids.

Given the transferred label, together with the level 2 cell type annotation shown in Fig. 1c, as the annotation label, scPoli was applied to the telencephalic subset of HNOCA for data integration.

PMC11578878

An integrated transcriptomic cell atlas of human neural organoids.

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 representation; (3) Louvain clustering (resolution, 2) with PCA of HNOCA telencephalic subset (based on scaled expression of 3,000 highly variable genes of the telencephalic subset with flavor = ‘seurat’) and (4) Louvain clustering (resolution, 1) for each sample separately (each with 3,000 highly variable genes identified with flavor = ‘seurat’, followed by data scale and PCA).

PMC11578878

An integrated transcriptomic cell atlas of human neural organoids.

Next, for each sample with at least 500 dorsal telencephalic neurons, the adjusted mutual information scores were calculated between each of those four clustering labels with the transferred cell type label mentioned above as the gold standard, across the dorsal telencephalic neurons as annotated as the level 2 annotation.

PMC11578878

An integrated transcriptomic cell atlas of human neural organoids.

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.

PMC11578878

An integrated transcriptomic cell atlas of human neural organoids.

For ref. ,

PMC11578878

An integrated transcriptomic cell atlas of human neural organoids.

cells in five author-defined clusters (60, 57, 79, 45, 65) with high expression of MAP2, DCX and NEUROD6 were selected.

PMC11578878

An integrated transcriptomic cell atlas of human neural organoids.

For ref. ,

PMC11578878

An integrated transcriptomic cell atlas of human neural organoids.

cells with the following ‘clusterv2 - final’ labels were selected: ‘Neuron_28’, ‘Neuron_34’, ‘GW19_2_29NeuronNeuron’, ‘Neuron_30’, ‘Neuron_66Neuron’, ‘GW18_2_42NeuronNeuron’, ‘Neuron_33’, ‘Neuron_39Neuron’, ‘Neuron_35’, ‘Neuron_63Neuron’, ‘Neuron_9’, ‘Neuron_11’, ‘Neuron_20’, ‘Neuron_22’, ‘Neuron_5Neuron’, ‘Neuron_21’, ‘Neuron_18’, ‘Neuron_101Neuron’, ‘Neuron_17’, ‘Neuron_19’, ‘Neuron_16’, ‘Neuron_50Neuron’, ‘Neuron_12’, ‘Neuron_13’, ‘Neuron_68Neuron’, ‘Neuron_100Neuron’, ‘Neuron_25’, ‘Neuron_27’, ‘Neuron_53Neuron’, ‘Neuron_23’, ‘Neuron_26’, ‘Neuron_24’, ‘Neuron_102Neuron’, ‘Neuron_72Neuron’, ‘Neuron_15’, ‘Neuron_29’ and ‘Neuron_35Neuron’ on the basis of their high expression of NEUROD6 and FOXG1.

PMC11578878

An integrated transcriptomic cell atlas of human neural organoids.

For ref. ,

PMC11578878

An integrated transcriptomic cell atlas of human neural organoids.

cells dissected from dorsal telencephalon that were annotated as neurons with and only with the VGLUT NTT label were selected.

PMC11578878

An integrated transcriptomic cell atlas of human neural organoids.

For ref. ,

PMC11578878

An integrated transcriptomic cell atlas of human neural organoids.

cells annotated as excitatory neurons were selected.

PMC11578878

An integrated transcriptomic cell atlas of human neural organoids.

The curated clusters of the Wang et al. primary atlas, as described earlier, were also subset to those with excitatory neuron labels.

PMC11578878

An integrated transcriptomic cell atlas of human neural organoids.

The selected dorsal telencephalic neuron subsets of the atlases were merged into the joint neocortical neuron atlas.

PMC11578878

An integrated transcriptomic cell atlas of human neural organoids.

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.

PMC11578878

An integrated transcriptomic cell atlas of human neural organoids.

The cluster label of the best-correlated cluster was assigned to each cell in the joined neocortical neuron atlas, so that cell cluster labels were harmonized for all cells in the atlas.

PMC11578878

An integrated transcriptomic cell atlas of human neural organoids.

Label-aware data integration was then performed using scPoli.

PMC11578878

An integrated transcriptomic cell atlas of human neural organoids.

On the basis of the scPoli latent representation, Louvain clustering was performed on the joint neocortical neuron atlas (resolution, 1).

PMC11578878

An integrated transcriptomic cell atlas of human neural organoids.

This cluster label was transferred to the dorsal telencephalic neurons in HNOCA with max-correlation manner across highly variable genes defined on average transcriptomic profiles of clusters in the joint neocortical neuron atlas.

PMC11578878

An integrated transcriptomic cell atlas of human neural organoids.

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.

PMC11578878

An integrated transcriptomic cell atlas of human neural organoids.

In both cases, the ‘dataset’ field of the screen data was used as the batch covariate, which indicates belonging to one of the three categories: ‘organoid screen’, ‘secondary organoid screen’ or ‘fetal striatum 21pcw’.

PMC11578878

An integrated transcriptomic cell atlas of human neural organoids.

For mapping to the primary reference, we used the scvi-tools implementation of scArches without the use of cell type annotations and trained the model for 500 epochs with weight_decay of 0 and otherwise default parameters.

PMC11578878

An integrated transcriptomic cell atlas of human neural organoids.

For mapping to HNOCA we used scArches through scPoli and trained the model for 500 epochs without unlabelled prototype training.

PMC11578878

An integrated transcriptomic cell atlas of human neural organoids.

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.

PMC11578878

An integrated transcriptomic cell atlas of human neural organoids.

Count matrices and metadata were directly downloaded for the ten datasets with processed data provided in the Gene Expression Omnibus or ArrayExpress.

PMC11578878

An integrated transcriptomic cell atlas of human neural organoids.

For the dataset with only FASTQ files available, we downloaded the FASTQ files and used Cell Ranger (v.4.0) to map reads to the human reference genome and transcriptome retrieved from Cell Ranger website (GRCh38 v.3.0.0) for gene expression quantification.

PMC11578878

An integrated transcriptomic cell atlas of human neural organoids.

All datasets were concatenated together with anndata in Python (join = ‘inner’).

PMC11578878

An integrated transcriptomic cell atlas of human neural organoids.

For each dataset, samples were grouped into either ‘disease’ or ‘control’ as their disease status, with ‘disease’ representing data from patient cell lines, mutant cell lines with disease-related alleles, cells carrying targeting guide RNAs (gRNAs) in CRISPR-based screen and tumour-derived organoids.

PMC11578878

An integrated transcriptomic cell atlas of human neural organoids.

and ‘control’ representing data from healthy cell lines, mutation-corrected cell lines and cells carrying only non-targeting gRNAs in a CRISPR-based screen.

PMC11578878

An integrated transcriptomic cell atlas of human neural organoids.

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.

PMC11578878

An integrated transcriptomic cell atlas of human neural organoids.

For projecting to the primary atlas, the same implementation as mentioned above to map HNOCA to the atlas was used.