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and the matching cell type from the Braun et al. primary dataset as well as the data from ref. .
|
[] |
Single_Cell
|
To prevent technology effects to affect this analysis, we only used cells generated with the 10X Genomics 3′ v.2 protocol in this comparison.
|
[] |
Single_Cell
|
We generate pseudobulk samples as described above and corrected organoid age in days and number of cells per pseudobulk sample in the DE comparison.
|
[] |
Single_Cell
|
We used the same edgeR-based procedure and cut-offs as described above.
|
[] |
Single_Cell
|
We used the scipy fcluster method to cluster genes on the basis of their log-fold changes in the two primary datasets.
|
[] |
Single_Cell
|
We grouped clusters to represent consistently upregulated, consistently downregulated and three different inconsistently regulated groups of genes.
|
[] |
Single_Cell
|
We computed functional enrichment of each gene group as described above.
|
[] |
Single_Cell
|
Source paper: PMC11578878
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. .
|
[
{
"end": 176,
"label": "CellType",
"start": 140,
"text": "Dorsal Telencephalic Neuron NT-VGLUT"
},
{
"end": 317,
"label": "CellType",
"start": 299,
"text": "matching cell type"
}
] |
Single_Cell
|
We computed pseudobulk samples and carried out the DE analysis using the same procedure and cut-offs as in the protocol-based DE analysis.
|
[] |
Single_Cell
|
Source paper: PMC11578878
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.
|
[
{
"end": 85,
"label": "CellType",
"start": 78,
"text": "neurons"
},
{
"end": 201,
"label": "CellType",
"start": 185,
"text": "neural cell type"
}
] |
Single_Cell
|
For each HNOCA dataset, only neural cell types with at least 20 cells were considered.
|
[
{
"end": 46,
"label": "CellType",
"start": 29,
"text": "neural cell types"
},
{
"end": 69,
"label": "CellType",
"start": 64,
"text": "cells"
}
] |
Single_Cell
|
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.
|
[
{
"end": 66,
"label": "CellType",
"start": 49,
"text": "neural cell types"
},
{
"end": 291,
"label": "CellType",
"start": 274,
"text": "neural cell types"
},
{
"end": 379,
"label": "CellType",
"start": 362,
"text": "neural cell types"
}
] |
Single_Cell
|
Genes with Benjamini–Hochberg adjusted P values less than 0.01 were considered as highly variable genes.
|
[] |
Single_Cell
|
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.
|
[
{
"end": 39,
"label": "CellType",
"start": 23,
"text": "neural cell type"
}
] |
Single_Cell
|
Source paper: PMC11578878
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. .
|
[
{
"end": 282,
"label": "CellType",
"start": 268,
"text": "neuronal cells"
},
{
"end": 68,
"label": "CellType",
"start": 40,
"text": "metabolically stressed cells"
}
] |
Single_Cell
|
Source paper: PMC11578878
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.
|
[
{
"end": 91,
"label": "CellType",
"start": 84,
"text": "neurons"
},
{
"end": 320,
"label": "CellType",
"start": 313,
"text": "neurons"
},
{
"end": 79,
"label": "CellType",
"start": 61,
"text": "telencephalic NPCs"
},
{
"end": 277,
"label": "CellType",
"start": 259,
"text": "telencephalic NPCs"
},
{
"end": 308,
"label": "CellType",
"start": 279,
"text": "intermediate progenitor cells"
}
] |
Single_Cell
|
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.
|
[
{
"end": 95,
"label": "CellType",
"start": 86,
"text": "cell type"
},
{
"end": 126,
"label": "CellType",
"start": 117,
"text": "cell type"
},
{
"end": 138,
"label": "CellType",
"start": 133,
"text": "cells"
}
] |
Single_Cell
|
The label of the best-correlated primary cluster was then transferred to every query cell.
|
[
{
"end": 89,
"label": "CellType",
"start": 85,
"text": "cell"
}
] |
Single_Cell
|
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.
|
[] |
Single_Cell
|
Source paper: PMC11578878
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).
|
[] |
Single_Cell
|
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.
|
[
{
"end": 68,
"label": "CellType",
"start": 40,
"text": "dorsal telencephalic neurons"
},
{
"end": 284,
"label": "CellType",
"start": 256,
"text": "dorsal telencephalic neurons"
}
] |
Single_Cell
|
Source paper: PMC11578878
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 .
|
[
{
"end": 99,
"label": "CellType",
"start": 71,
"text": "dorsal telencephalic neurons"
},
{
"end": 140,
"label": "Tissue",
"start": 124,
"text": "neural organoids"
},
{
"end": 160,
"label": "Tissue",
"start": 145,
"text": "primary tissues"
},
{
"end": 200,
"label": "CellType",
"start": 172,
"text": "dorsal telencephalic neurons"
},
{
"end": 223,
"label": "CellType",
"start": 204,
"text": "neocortical neurons"
}
] |
Single_Cell
|
For ref. ,
|
[] |
Single_Cell
|
cells in five author-defined clusters (60, 57, 79, 45, 65) with high expression of MAP2 , DCX and NEUROD6 were selected.
|
[] |
Single_Cell
|
For ref. ,
|
[] |
Single_Cell
|
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 .
|
[] |
Single_Cell
|
For ref. ,
|
[] |
Single_Cell
|
cells dissected from dorsal telencephalon that were annotated as neurons with and only with the VGLUT NTT label were selected.
|
[
{
"end": 72,
"label": "CellType",
"start": 65,
"text": "neurons"
},
{
"end": 41,
"label": "CellType",
"start": 0,
"text": "cells dissected from dorsal telencephalon"
}
] |
Single_Cell
|
For ref. ,
|
[] |
Single_Cell
|
cells annotated as excitatory neurons were selected.
|
[
{
"end": 37,
"label": "CellType",
"start": 19,
"text": "excitatory neurons"
},
{
"end": 5,
"label": "CellType",
"start": 0,
"text": "cells"
}
] |
Single_Cell
|
The curated clusters of the Wang et al. primary atlas, as described earlier, were also subset to those with excitatory neuron labels.
|
[] |
Single_Cell
|
The selected dorsal telencephalic neuron subsets of the atlases were merged into the joint neocortical neuron atlas.
|
[
{
"end": 48,
"label": "CellType",
"start": 13,
"text": "dorsal telencephalic neuron subsets"
}
] |
Single_Cell
|
Source paper: PMC11578878
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 .
|
[
{
"end": 39,
"label": "CellType",
"start": 34,
"text": "cells"
}
] |
Single_Cell
|
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.
|
[
{
"end": 74,
"label": "CellType",
"start": 70,
"text": "cell"
}
] |
Single_Cell
|
Label-aware data integration was then performed using scPoli .
|
[] |
Single_Cell
|
On the basis of the scPoli latent representation, Louvain clustering was performed on the joint neocortical neuron atlas (resolution, 1).
|
[] |
Single_Cell
|
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.
|
[
{
"end": 70,
"label": "CellType",
"start": 42,
"text": "dorsal telencephalic neurons"
}
] |
Single_Cell
|
Source paper: PMC11578878
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 .
|
[
{
"end": 81,
"label": "Tissue",
"start": 65,
"text": "neural organoids"
}
] |
Single_Cell
|
Count matrices and metadata were directly downloaded for the ten datasets with processed data provided in the Gene Expression Omnibus or ArrayExpress.
|
[] |
Single_Cell
|
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.
|
[] |
Single_Cell
|
All datasets were concatenated together with anndata in Python (join = ‘inner’).
|
[] |
Single_Cell
|
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.
|
[
{
"end": 155,
"label": "CellLine",
"start": 137,
"text": "patient cell lines"
},
{
"end": 203,
"label": "CellLine",
"start": 157,
"text": "mutant cell lines with disease-related alleles"
},
{
"end": 271,
"label": "CellType",
"start": 205,
"text": "cells carrying targeting guide RNAs (gRNAs) in CRISPR-based screen"
},
{
"end": 300,
"label": "Tissue",
"start": 276,
"text": "tumour-derived organoids"
}
] |
Single_Cell
|
and ‘control’ representing data from healthy cell lines, mutation-corrected cell lines and cells carrying only non-targeting gRNAs in a CRISPR-based screen.
|
[
{
"end": 55,
"label": "CellLine",
"start": 37,
"text": "healthy cell lines"
},
{
"end": 86,
"label": "CellLine",
"start": 57,
"text": "mutation-corrected cell lines"
},
{
"end": 155,
"label": "CellType",
"start": 91,
"text": "cells carrying only non-targeting gRNAs in a CRISPR-based screen"
}
] |
Single_Cell
|
Source paper: PMC11578878
Next, for both HNOCA and the disease-modelling atlas, cells were represented by the concatenated representation of HNOCA-scPoli and primary-scANVI models.
|
[
{
"end": 87,
"label": "CellType",
"start": 82,
"text": "cells"
}
] |
Single_Cell
|
A bipartite wkNN graph was then reconstructed as mentioned above, by identifying 50 nearest neighbours in HNOCA for each disease-modelling atlas cell.
|
[
{
"end": 149,
"label": "CellType",
"start": 139,
"text": "atlas cell"
}
] |
Single_Cell
|
On the basis of the bipartite wkNN, the majority voting-based label transfer was applied to transfer the four levels of hierarchical cell type annotation and regional identity to the disease-modelling atlas.
|
[] |
Single_Cell
|
Source paper: PMC11578878
To analyse the glioblastoma organoid dataset (GBM-2019), cells from the publication were subset from the integrated disease-modelling atlas.
|
[
{
"end": 90,
"label": "CellType",
"start": 85,
"text": "cells"
}
] |
Single_Cell
|
Using scanpy, highly variable genes were identified with default parameters.
|
[] |
Single_Cell
|
The log-normalized expression values of the highly variable genes were then scaled across cells, the truncated PCA was performed with the top 20 principal components used for the following analysis.
|
[
{
"end": 95,
"label": "CellType",
"start": 90,
"text": "cells"
}
] |
Single_Cell
|
Next, harmonypy, the Python implementation of harmony , was applied to integrate cells from different samples.
|
[
{
"end": 86,
"label": "CellType",
"start": 81,
"text": "cells"
}
] |
Single_Cell
|
On the basis of the harmony-integrated embeddings, the neighbour graph was reconstructed.
|
[] |
Single_Cell
|
UMAP embeddings and Louvain clusters (resolution, 0.5) were created on the basis of the nearest neighbour graph.
|
[] |
Single_Cell
|
Among the 12 identified clusters, cluster-7 and cluster-0, the two clusters with the highest AQP4 expression, were selected for the following DE analysis.
|
[] |
Single_Cell
|
Source paper: PMC11578878
To analyse the FXS dataset (FXS-2021), cells from the publication were subset from the integrated disease-modelling atlas.
|
[
{
"end": 72,
"label": "CellType",
"start": 67,
"text": "cells"
}
] |
Single_Cell
|
The same procedure of highly variable gene identification, data scaling and PCA as the GBM-2019 dataset was applied.
|
[] |
Single_Cell
|
Next, the nearest neighbour graph was created directly on the basis of the top 20 principal components.
|
[] |
Single_Cell
|
UMAP embeddings and Louvain clusters (resolution, 1) were then created on the basis of the reconstructed nearest neighbour graph.
|
[] |
Single_Cell
|
Among the 30 clusters, cluster-17 and cluster-23, which express EMX1 and FOXG1 and were largely predicted to be dorsal telencephalic NPCs and neurons according to the transferred labels from HNOCA, were selected for the following DE analysis.
|
[
{
"end": 149,
"label": "CellType",
"start": 142,
"text": "neurons"
},
{
"end": 137,
"label": "CellType",
"start": 112,
"text": "dorsal telencephalic NPCs"
}
] |
Single_Cell
|
Source paper: PMC11578878
To construct the HNOCA-CE, we first collected raw count matrices and associated metadata of five more neural organoid studies.
|
[] |
Single_Cell
|
For two publications , we obtained them from the sources listed in the ‘Data availability’ section of the paper.
|
[] |
Single_Cell
|
For the remaining three publications , count matrices and associated metadata were provided directly by the authors.
|
[] |
Single_Cell
|
We subset each dataset to the healthy control cells and removed any cells with fewer than 200 genes expressed.
|
[
{
"end": 51,
"label": "CellType",
"start": 30,
"text": "healthy control cells"
},
{
"end": 73,
"label": "CellType",
"start": 68,
"text": "cells"
}
] |
Single_Cell
|
We subset the gene space of every dataset to the 3,000 HVGs of HNOCA while filling the expression of missing genes in the community datasets with zeros.
|
[] |
Single_Cell
|
On average, 23% of genes with zero expression were added per dataset.
|
[] |
Single_Cell
|
We instantiated a mapping object from the HNOCA-tools package (at commit fe38c52) using the saved scPoli model weights from the HNOCA integration.
|
[] |
Single_Cell
|
Using the map_query method of the mapper instance, we projected the community datasets to HNOCA.
|
[] |
Single_Cell
|
We used the following training hyperparameters: retrain = ‘partial’, batch_size = 256, unlabeled_prototype_training = False, n_epochs = 10, pretraining_epochs = 9, early_stopping_kwargs = early_stopping_kwargs, eta = 10, alpha_epoch_anneal = 10.
|
[] |
Single_Cell
|
We computed the wkNN graph using the compute_wknn method of the mapper instance with k = 100.
|
[] |
Single_Cell
|
We transferred the final level_2 cell type labels from HNOCA to the community datasets using this neighbour graph.
|
[] |
Single_Cell
|
To obtain the combined representation of HNOCA-CE, we projected HNOCA together with the added community datasets through the trained model and computed a neighbour graph and UMAP from the resulting latent representation.
|
[] |
Single_Cell
|
Source paper: PMC11578878
Multimodal profiling reveals tissue-directed signatures of human immune cells altered with age
Source paper: PMC12396968
The immune system comprises multiple cell lineages and subsets maintained in tissues throughout the lifespan, with unknown effects of tissue and age on immune cell function.
|
[
{
"end": 105,
"label": "CellType",
"start": 87,
"text": "human immune cells"
},
{
"end": 167,
"label": "Tissue",
"start": 154,
"text": "immune system"
},
{
"end": 290,
"label": "Tissue",
"start": 284,
"text": "tissue"
},
{
"end": 200,
"label": "CellType",
"start": 187,
"text": "cell lineages"
}
] |
Single_Cell
|
Here we comprehensively profiled RNA and surface protein expression of over 1.25 million immune cells from blood and lymphoid and mucosal tissues from 24 organ donors aged 20–75 years.
|
[
{
"end": 112,
"label": "Tissue",
"start": 107,
"text": "blood"
},
{
"end": 101,
"label": "CellType",
"start": 89,
"text": "immune cells"
},
{
"end": 125,
"label": "Tissue",
"start": 117,
"text": "lymphoid"
},
{
"end": 145,
"label": "Tissue",
"start": 130,
"text": "mucosal tissues"
}
] |
Single_Cell
|
We annotated major lineages (T cells, B cells, innate lymphoid cells and myeloid cells) and corresponding subsets using a multimodal classifier and probabilistic modeling for comparison across tissue sites and age.
|
[
{
"end": 45,
"label": "CellType",
"start": 38,
"text": "B cells"
},
{
"end": 36,
"label": "CellType",
"start": 29,
"text": "T cells"
},
{
"end": 68,
"label": "CellType",
"start": 47,
"text": "innate lymphoid cells"
},
{
"end": 86,
"label": "CellType",
"start": 73,
"text": "myeloid cells"
},
{
"end": 205,
"label": "Tissue",
"start": 193,
"text": "tissue sites"
}
] |
Single_Cell
|
We identified dominant site-specific effects on immune cell composition and function across lineages; age-associated effects were manifested by site and lineage for macrophages in mucosal sites, B cells in lymphoid organs, and circulating T cells and natural killer cells across blood and tissues.
|
[
{
"end": 271,
"label": "CellType",
"start": 251,
"text": "natural killer cells"
},
{
"end": 284,
"label": "Tissue",
"start": 279,
"text": "blood"
},
{
"end": 100,
"label": "CellType",
"start": 92,
"text": "lineages"
},
{
"end": 176,
"label": "CellType",
"start": 165,
"text": "macrophages"
},
{
"end": 193,
"label": "Tissue",
"start": 180,
"text": "mucosal sites"
},
{
"end": 202,
"label": "CellType",
"start": 195,
"text": "B cells"
},
{
"end": 221,
"label": "Tissue",
"start": 206,
"text": "lymphoid organs"
},
{
"end": 246,
"label": "CellType",
"start": 227,
"text": "circulating T cells"
},
{
"end": 296,
"label": "Tissue",
"start": 289,
"text": "tissues"
},
{
"end": 59,
"label": "CellType",
"start": 48,
"text": "immune cell"
}
] |
Single_Cell
|
Our results reveal tissue-specific signatures of immune homeostasis throughout the body, from which to define immune pathologies across the human lifespan.
|
[] |
Single_Cell
|
Source paper: PMC12396968
The immune system leverages a dynamic network of specialized cells spread across the body to defend against infections and cancer, regulate inflammation and repair tissue damage.
|
[
{
"end": 45,
"label": "Tissue",
"start": 32,
"text": "immune system"
},
{
"end": 94,
"label": "CellType",
"start": 77,
"text": "specialized cells"
}
] |
Single_Cell
|
Myeloid cells—macrophages, monocytes and dendritic cells (DCs)—initiate innate immunity at mucosal and barrier sites, while adaptive immunity is mediated by antigen-specific T and B lymphocytes in lymphoid organs.
|
[
{
"end": 25,
"label": "CellType",
"start": 14,
"text": "macrophages"
},
{
"end": 36,
"label": "CellType",
"start": 27,
"text": "monocytes"
},
{
"end": 56,
"label": "CellType",
"start": 41,
"text": "dendritic cells"
},
{
"end": 193,
"label": "CellType",
"start": 180,
"text": "B lymphocytes"
},
{
"end": 13,
"label": "CellType",
"start": 0,
"text": "Myeloid cells"
},
{
"end": 61,
"label": "CellType",
"start": 58,
"text": "DCs"
},
{
"end": 98,
"label": "Tissue",
"start": 91,
"text": "mucosal"
},
{
"end": 116,
"label": "Tissue",
"start": 103,
"text": "barrier sites"
},
{
"end": 176,
"label": "CellType",
"start": 157,
"text": "antigen-specific T "
},
{
"end": 212,
"label": "Tissue",
"start": 197,
"text": "lymphoid organs"
}
] |
Single_Cell
|
Immune memory is established following antigen-driven activation and differentiation of T cells and B cells, resulting in heterogeneous subsets of circulating and tissue resident memory T cells (T RM ) and B cells that persist in diverse tissues .
|
[
{
"end": 107,
"label": "CellType",
"start": 100,
"text": "B cells"
},
{
"end": 213,
"label": "CellType",
"start": 206,
"text": "B cells"
},
{
"end": 95,
"label": "CellType",
"start": 88,
"text": "T cells"
},
{
"end": 158,
"label": "CellType",
"start": 147,
"text": "circulating"
},
{
"end": 193,
"label": "CellType",
"start": 163,
"text": "tissue resident memory T cells"
},
{
"end": 199,
"label": "CellType",
"start": 195,
"text": "T RM"
},
{
"end": 245,
"label": "Tissue",
"start": 238,
"text": "tissues"
}
] |
Single_Cell
|
With age, immune memory accumulates, although responses can become dysregulated, increasing susceptibility to infections, cancer and autoimmunity.
|
[] |
Single_Cell
|
As human immune cells and the effect of age are mostly studied in blood , we lack a comprehensive understanding of the effect of age on the majority of innate and adaptive immune cells that are maintained in tissues.
|
[
{
"end": 71,
"label": "Tissue",
"start": 66,
"text": "blood"
},
{
"end": 21,
"label": "CellType",
"start": 3,
"text": "human immune cells"
},
{
"end": 158,
"label": "CellType",
"start": 152,
"text": "innate"
},
{
"end": 184,
"label": "CellType",
"start": 163,
"text": "adaptive immune cells"
},
{
"end": 215,
"label": "Tissue",
"start": 208,
"text": "tissues"
}
] |
Single_Cell
|
Source paper: PMC12396968
Investigating human tissue immunity across diverse ages has been difficult to achieve.
|
[] |
Single_Cell
|
Obtaining tissues from organ donors enables the acquisition of blood and multiple tissues from individual donors and the isolation of viable immune cells across lineages for phenotypic, functional and multimodal single-cell profiling.
|
[
{
"end": 68,
"label": "Tissue",
"start": 63,
"text": "blood"
},
{
"end": 17,
"label": "Tissue",
"start": 10,
"text": "tissues"
},
{
"end": 89,
"label": "CellType",
"start": 82,
"text": "tissues"
},
{
"end": 153,
"label": "CellType",
"start": 134,
"text": "viable immune cells"
}
] |
Single_Cell
|
Previous studies of lymphocytes from organ donors showed that T lymphocyte, natural killer (NK) cell and innate lymphoid cell (ILC) subset composition, tissue residence and certain functional attributes are specific to the tissue , indicating that localization has a dominant role in determining the maintenance and functional responses of lymphocytes.
|
[
{
"end": 31,
"label": "CellType",
"start": 20,
"text": "lymphocytes"
},
{
"end": 351,
"label": "CellType",
"start": 340,
"text": "lymphocytes"
},
{
"end": 74,
"label": "CellType",
"start": 62,
"text": "T lymphocyte"
},
{
"end": 100,
"label": "CellType",
"start": 76,
"text": "natural killer (NK) cell"
},
{
"end": 125,
"label": "CellType",
"start": 105,
"text": "innate lymphoid cell"
},
{
"end": 130,
"label": "CellType",
"start": 127,
"text": "ILC"
},
{
"end": 229,
"label": "Tissue",
"start": 223,
"text": "tissue"
}
] |
Single_Cell
|
Whether these tissue-specific effects on human T cells are exhibited by other immune cell lineages, such as B cells and myeloid cells, and whether aging exerts general or tissue-specific effects have not been established.
|
[
{
"end": 115,
"label": "CellType",
"start": 108,
"text": "B cells"
},
{
"end": 54,
"label": "CellType",
"start": 41,
"text": "human T cells"
},
{
"end": 98,
"label": "CellType",
"start": 78,
"text": "immune cell lineages"
},
{
"end": 133,
"label": "CellType",
"start": 120,
"text": "myeloid cells"
}
] |
Single_Cell
|
Source paper: PMC12396968
Here, we present a comprehensive analysis of human immune cells using cellular indexing of transcriptomes and epitopes (CITE-seq) to simultaneously profile transcriptomes and >125 surface proteins of myeloid and lymphoid-lineage cells in 14 tissue sites of 24 organ donors aged 20–75 years.
|
[
{
"end": 91,
"label": "CellType",
"start": 73,
"text": "human immune cells"
},
{
"end": 235,
"label": "CellType",
"start": 228,
"text": "myeloid"
},
{
"end": 262,
"label": "CellType",
"start": 240,
"text": "lymphoid-lineage cells"
},
{
"end": 281,
"label": "Tissue",
"start": 269,
"text": "tissue sites"
}
] |
Single_Cell
|
We identified a crucial role of tissue on immune cell composition, function, homing and differentiation across myeloid and lymphocyte lineages, including signatures specific for the gut, lungs and lymphoid organs.
|
[
{
"end": 192,
"label": "Tissue",
"start": 187,
"text": "lungs"
},
{
"end": 185,
"label": "Tissue",
"start": 182,
"text": "gut"
},
{
"end": 38,
"label": "Tissue",
"start": 32,
"text": "tissue"
},
{
"end": 118,
"label": "CellType",
"start": 111,
"text": "myeloid"
},
{
"end": 142,
"label": "CellType",
"start": 123,
"text": "lymphocyte lineages"
},
{
"end": 212,
"label": "Tissue",
"start": 197,
"text": "lymphoid organs"
},
{
"end": 53,
"label": "CellType",
"start": 41,
"text": " immune cell"
}
] |
Single_Cell
|
Across age, site-specific immune cell composition was largely maintained, although age-associated changes in function, signaling and metabolism were identified in certain subsets and sites, including macrophages in the lung, B cells in lymphoid organs and CD8 T cells across sites.
|
[
{
"end": 211,
"label": "CellType",
"start": 200,
"text": "macrophages"
},
{
"end": 232,
"label": "CellType",
"start": 225,
"text": "B cells"
},
{
"end": 223,
"label": "Tissue",
"start": 219,
"text": "lung"
},
{
"end": 251,
"label": "Tissue",
"start": 236,
"text": "lymphoid organs"
},
{
"end": 267,
"label": "CellType",
"start": 256,
"text": "CD8 T cells"
}
] |
Single_Cell
|
Together, our findings reveal the complex interplay between tissue, lineage, subset and age in immune homeostasis that is important for defining immune dysfunctions in disease.
|
[
{
"end": 66,
"label": "Tissue",
"start": 60,
"text": "tissue"
}
] |
Single_Cell
|
Source paper: PMC12396968
We isolated mononuclear cells (MNCs) from blood, multiple lymphoid organs, lungs, airways, intestines and other sites using established protocols from 24 donors (10 females and 14 males, aged 20–75 years) (Fig. 1a ).
|
[
{
"end": 129,
"label": "Tissue",
"start": 119,
"text": "intestines"
},
{
"end": 75,
"label": "Tissue",
"start": 70,
"text": "blood"
},
{
"end": 108,
"label": "Tissue",
"start": 103,
"text": "lungs"
},
{
"end": 57,
"label": "CellType",
"start": 40,
"text": "mononuclear cells"
},
{
"end": 63,
"label": "CellType",
"start": 59,
"text": "MNCs"
},
{
"end": 101,
"label": "Tissue",
"start": 77,
"text": "multiple lymphoid organs"
},
{
"end": 117,
"label": "Tissue",
"start": 110,
"text": "airways"
}
] |
Single_Cell
|
Organ donors originated from New York City (USA) and Cambridge (UK) and were free of chronic infection, cancer and overt disease (Supplementary Table 1 ).
|
[] |
Single_Cell
|
We performed single-cell RNA sequencing (scRNA-seq) on tissues from all donors, including CITE-seq with 127 proteins from 22 donors (Supplementary Table 2 ).
|
[
{
"end": 62,
"label": "Tissue",
"start": 55,
"text": "tissues"
}
] |
Single_Cell
|
We obtained 1.28 million immune cell events from 10 sites with >75,000 cells per site, including blood, bone marrow (BM), spleen, different lymph nodes (LNs) including lung-associated LN (LLN), mesenteric LN (MLN) and inguinal LN (ILN), lungs, comprising bronchoalveolar lavage (BAL) and lung parenchyma, and jejunum (JEJ), divided into the intraepithelial layer (JEL) and lamina propria (JLP) (Fig. 1a ).
|
[
{
"end": 151,
"label": "Tissue",
"start": 140,
"text": "lymph nodes"
},
{
"end": 387,
"label": "Tissue",
"start": 373,
"text": "lamina propria"
},
{
"end": 102,
"label": "Tissue",
"start": 97,
"text": "blood"
},
{
"end": 242,
"label": "Tissue",
"start": 237,
"text": "lungs"
},
{
"end": 128,
"label": "Tissue",
"start": 122,
"text": "spleen"
},
{
"end": 316,
"label": "Tissue",
"start": 309,
"text": "jejunum"
},
{
"end": 115,
"label": "Tissue",
"start": 104,
"text": "bone marrow"
},
{
"end": 303,
"label": "Tissue",
"start": 288,
"text": "lung parenchyma"
},
{
"end": 277,
"label": "Tissue",
"start": 255,
"text": "bronchoalveolar lavage"
},
{
"end": 119,
"label": "Tissue",
"start": 117,
"text": "BM"
},
{
"end": 156,
"label": "Tissue",
"start": 153,
"text": "LNs"
},
{
"end": 186,
"label": "Tissue",
"start": 168,
"text": "lung-associated LN"
},
{
"end": 191,
"label": "Tissue",
"start": 188,
"text": "LLN"
},
{
"end": 207,
"label": "Tissue",
"start": 194,
"text": "mesenteric LN"
},
{
"end": 212,
"label": "Tissue",
"start": 209,
"text": "MLN"
},
{
"end": 229,
"label": "Tissue",
"start": 218,
"text": "inguinal LN"
},
{
"end": 234,
"label": "Tissue",
"start": 231,
"text": "ILN"
},
{
"end": 282,
"label": "Tissue",
"start": 279,
"text": "BAL"
},
{
"end": 321,
"label": "Tissue",
"start": 318,
"text": "JEJ"
},
{
"end": 362,
"label": "Tissue",
"start": 341,
"text": "intraepithelial layer"
},
{
"end": 367,
"label": "Tissue",
"start": 364,
"text": "JEL"
},
{
"end": 392,
"label": "Tissue",
"start": 389,
"text": "JLP"
}
] |
Single_Cell
|
We also purified low numbers of immune cells from liver, skin, colon epithelium and colon lamina propria from 9 donors (4 females, aged 25–75 years; 5 males, aged 20–55 years).
|
[
{
"end": 55,
"label": "Tissue",
"start": 50,
"text": "liver"
},
{
"end": 61,
"label": "Tissue",
"start": 57,
"text": "skin"
},
{
"end": 44,
"label": "CellType",
"start": 32,
"text": "immune cells"
},
{
"end": 79,
"label": "Tissue",
"start": 63,
"text": "colon epithelium"
},
{
"end": 104,
"label": "Tissue",
"start": 84,
"text": "colon lamina propria"
}
] |
Single_Cell
|
These sites were therefore not included in the annotated dataset below and are provided as a separate reference (Extended Data Fig. 1a ).
|
[] |
Single_Cell
|
Source paper: PMC12396968
For data integration, we leveraged multi-resolution variational inference (MrVI), which is designed for cohort studies.
|
[] |
Single_Cell
|
MrVI harmonizes variation between cell states (for unified annotation of cell states across samples) and accounts for differences between samples .
|
[] |
Single_Cell
|
Visualization with uniform manifold approximation and projection (UMAP) showed similar results across US and UK donors, sequencing technologies and other donor covariates such as sex, cytomegalovirus (CMV) and Epstein–Barr virus serostatus (Fig. 1b and Extended Data Fig. 1b–g ).
|
[] |
Single_Cell
|
Although cells from blood and lymphoid organs (BM, spleen, LNs) clustered similarly, cells from mucosal sites (lung, JEJ) clustered distinctly (Fig. 1c ).
|
[
{
"end": 115,
"label": "Tissue",
"start": 111,
"text": "lung"
},
{
"end": 57,
"label": "Tissue",
"start": 51,
"text": "spleen"
},
{
"end": 49,
"label": "Tissue",
"start": 47,
"text": "BM"
},
{
"end": 62,
"label": "Tissue",
"start": 59,
"text": "LNs"
},
{
"end": 120,
"label": "Tissue",
"start": 117,
"text": "JEJ"
},
{
"end": 14,
"label": "CellType",
"start": 9,
"text": "cells"
},
{
"end": 25,
"label": "Tissue",
"start": 20,
"text": "blood"
},
{
"end": 45,
"label": "Tissue",
"start": 30,
"text": "lymphoid organs"
},
{
"end": 90,
"label": "CellType",
"start": 85,
"text": "cells"
},
{
"end": 109,
"label": "Tissue",
"start": 96,
"text": "mucosal sites"
}
] |
Single_Cell
|
For annotation, we used MultiModal Classifier Hierarchy (MMoCHi) , which leverages both surface protein and gene expression to hierarchically classify cells into predefined categories (Supplementary Fig. 1 and Supplementary Table 3 ).
|
[
{
"end": 156,
"label": "CellType",
"start": 151,
"text": "cells"
}
] |
Single_Cell
|
MMoCHi defined six major immune lineages found across all tissues (Fig. 1d ) comprising 13 T cell, 5 NK/ILC, 6 B cell and 7 myeloid subsets (Extended Data Fig. 1h ).
|
[
{
"end": 117,
"label": "CellType",
"start": 111,
"text": "B cell"
},
{
"end": 97,
"label": "CellType",
"start": 91,
"text": "T cell"
},
{
"end": 40,
"label": "CellType",
"start": 25,
"text": "immune lineages"
},
{
"end": 65,
"label": "Tissue",
"start": 58,
"text": "tissues"
},
{
"end": 107,
"label": "CellType",
"start": 101,
"text": "NK/ILC"
},
{
"end": 139,
"label": "CellType",
"start": 124,
"text": "myeloid subsets"
}
] |
Single_Cell
|
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