GTEX v9

Single-nucleus cross-tissue molecular reference maps to decipher disease gene function

cellxgene_census

Info

cellxgene_census/gtex_v9
Eraslan et al. (2022)
2.99 GiB
02-02-2024
209126 cells × 28094 genes

Used in

No related benchmarks found.

Description

Understanding the function of genes and their regulation in tissue homeostasis and disease requires knowing the cellular context in which genes are expressed in tissues across the body. Single cell genomics allows the generation of detailed cellular atlases in human tissues, but most efforts are focused on single tissue types. Here, we establish a framework for profiling multiple tissues across the human body at single-cell resolution using single nucleus RNA-Seq (snRNA-seq), and apply it to 8 diverse, archived, frozen tissue types (three donors per tissue). We apply four snRNA-seq methods to each of 25 samples from 16 donors, generating a cross-tissue atlas of 209,126 nuclei profiles, and benchmark them vs. scRNA-seq of comparable fresh tissues. We use a conditional variational autoencoder (cVAE) to integrate an atlas across tissues, donors, and laboratory methods. We highlight shared and tissue-specific features of tissue-resident immune cells, identifying tissue-restricted and non-restricted resident myeloid populations. These include a cross-tissue conserved dichotomy between LYVE1- and HLA class II-expressing macrophages, and the broad presence of LAM-like macrophages across healthy tissues that is also observed in disease. For rare, monogenic muscle diseases, we identify cell types that likely underlie the neuromuscular, metabolic, and immune components of these diseases, and biological processes involved in their pathology. For common complex diseases and traits analyzed by GWAS, we identify the cell types and gene modules that potentially underlie disease mechanisms. The experimental and analytical frameworks we describe will enable the generation of large-scale studies of how cellular and molecular processes vary across individuals and populations.

Preview

dataset is an AnnData object with n_obs × n_vars = 209126 × 28094 with slots:

Reference

Name Description Type Data type Size
obs
assay Type of assay used to generate the cell data, indicating the methodology or technique employed. vector category 209126
assay_ontology_term_id Experimental Factor Ontology (EFO:) term identifier for the assay, providing a standardized reference to the assay type. vector category 209126
batch A batch identifier. This label is very context-dependent and may be a combination of the tissue, assay, donor, etc. vector category 209126
cell_type Classification of the cell type based on its characteristics and function within the tissue or organism. vector category 209126
cell_type_ontology_term_id Cell Ontology (CL:) term identifier for the cell type, offering a standardized reference to the specific cell classification. vector category 209126
dataset_id Identifier for the dataset from which the cell data is derived, useful for tracking and referencing purposes. vector category 209126
development_stage Stage of development of the organism or tissue from which the cell is derived, indicating its maturity or developmental phase. vector category 209126
development_stage_ontology_term_id Ontology term identifier for the developmental stage, providing a standardized reference to the organism’s developmental phase. If the organism is human (organism_ontology_term_id == 'NCBITaxon:9606'), then the Human Developmental Stages (HsapDv:) ontology is used. If the organism is mouse (organism_ontology_term_id == 'NCBITaxon:10090'), then the Mouse Developmental Stages (MmusDv:) ontology is used. Otherwise, the Uberon (UBERON:) ontology is used. vector category 209126
disease Information on any disease or pathological condition associated with the cell or donor. vector category 209126
disease_ontology_term_id Ontology term identifier for the disease, enabling standardized disease classification and referencing. Must be a term from the Mondo Disease Ontology (MONDO:) ontology term, or PATO:0000461 from the Phenotype And Trait Ontology (PATO:). vector category 209126
donor_id Identifier for the donor from whom the cell sample is obtained. vector category 209126
is_primary_data Indicates whether the data is primary (directly obtained from experiments) or has been computationally derived from other primary data. vector bool 209126
self_reported_ethnicity Ethnicity of the donor as self-reported, relevant for studies considering genetic diversity and population-specific traits. vector category 209126
self_reported_ethnicity_ontology_term_id Ontology term identifier for the self-reported ethnicity, providing a standardized reference for ethnic classifications. If the organism is human (organism_ontology_term_id == 'NCBITaxon:9606'), then the Human Ancestry Ontology (HANCESTRO:) is used. vector category 209126
sex Biological sex of the donor or source organism, crucial for studies involving sex-specific traits or conditions. vector category 209126
sex_ontology_term_id Ontology term identifier for the biological sex, ensuring standardized classification of sex. Only PATO:0000383, PATO:0000384 and PATO:0001340 are allowed. vector category 209126
size_factors The size factors created by the normalisation method, if any. vector float32 209126
soma_joinid If the dataset was retrieved from CELLxGENE census, this is a unique identifier for the cell. vector int64 209126
suspension_type Type of suspension or medium in which the cells were stored or processed, important for understanding cell handling and conditions. vector category 209126
tissue Specific tissue from which the cells were derived, key for context and specificity in cell studies. vector category 209126
tissue_general General category or classification of the tissue, useful for broader grouping and comparison of cell data. vector category 209126
tissue_general_ontology_term_id Ontology term identifier for the general tissue category, aiding in standardizing and grouping tissue types. For organoid or tissue samples, the Uber-anatomy ontology (UBERON:) is used. The term ids must be a child term of UBERON:0001062 (anatomical entity). For cell cultures, the Cell Ontology (CL:) is used. The term ids cannot be CL:0000255, CL:0000257 or CL:0000548. vector category 209126
tissue_ontology_term_id Ontology term identifier for the tissue, providing a standardized reference for the tissue type. For organoid or tissue samples, the Uber-anatomy ontology (UBERON:) is used. The term ids must be a child term of UBERON:0001062 (anatomical entity). For cell cultures, the Cell Ontology (CL:) is used. The term ids cannot be CL:0000255, CL:0000257 or CL:0000548. vector category 209126
var
feature_id Unique identifier for the feature, usually a ENSEMBL gene id. vector object 28094
feature_name A human-readable name for the feature, usually a gene symbol. vector object 28094
hvg Whether or not the feature is considered to be a ‘highly variable gene’ vector bool 28094
hvg_score A ranking of the features by hvg. vector float64 28094
soma_joinid If the dataset was retrieved from CELLxGENE census, this is a unique identifier for the feature. vector int64 28094
obsp
knn_connectivities K nearest neighbors connectivities matrix. sparsematrix float32 209126 × 209126
knn_distances K nearest neighbors distance matrix. sparsematrix float64 209126 × 209126
obsm
X_pca The resulting PCA embedding. densematrix float32 209126 × 50
varm
pca_loadings The PCA loadings matrix. densematrix float32 28094 × 50
layers
counts Raw counts sparsematrix float32 209126 × 28094
normalized Normalised expression values sparsematrix float32 209126 × 28094
uns
dataset_description Long description of the dataset. atomic str 1
dataset_id A unique identifier for the dataset. This is different from the obs.dataset_id field, which is the identifier for the dataset from which the cell data is derived. atomic str 1
dataset_name A human-readable name for the dataset. atomic str 1
dataset_organism The organism of the sample in the dataset. atomic str 1
dataset_reference Bibtex reference of the paper in which the dataset was published. atomic str 1
dataset_summary Short description of the dataset. atomic str 1
dataset_url Link to the original source of the dataset. atomic str 1
knn Supplementary K nearest neighbors data. dict 3
normalization_id Which normalization was used atomic str 1
pca_variance The PCA variance objects. dict 2

References

Eraslan, Gökcen, Eugene Drokhlyansky, Shankara Anand, Evgenij Fiskin, Ayshwarya Subramanian, Michal Slyper, Jiali Wang, et al. 2022. “Single-Nucleus Cross-Tissue Molecular Reference Maps Toward Understanding Disease Gene Function.” Science 376 (6594). https://doi.org/10.1126/science.abl4290.