HTCA

HTCA provides an integrated single-cell and multi-omics atlas of healthy human adult and fetal tissues for analysis of cell-type-specific transcriptomic, splicing, chromatin accessibility, spatial, and regulatory signatures.

Key Features:

• Comprehensive dataset: Approximately 2.3 million high-quality cells aggregated from around 3,000 single-cell RNA-sequencing (scRNA-seq) samples.
• Tissue coverage: Phenotype profiles for 19 adult tissues together with their corresponding fetal counterparts.
• Gene signatures: Precomputed and queryable gene signature profiles across cell types and tissues.
• Transcription factor analyses: Computation of transcription factor (TF) activities and TF motif information.
• Cell–cell interactions: Receptor–ligand interaction annotations across cell types.
• Functional enrichment: Enriched Gene Ontology (GO) term annotations tied to cell-type signatures.
• Splicing variants: Single-cell splicing variant data for 16 adult and fetal tissues.
• Spatial transcriptomics: Spatial transcriptomics profiles for 11 tissues.
• Chromatin accessibility: Single-cell ATAC-sequencing (scATAC-seq) profiles for 27 tissues.
• Analysis tooling: Computational tools supporting the major steps of typical scRNA-seq workflows.

Scientific Applications:

• Developmental biology: Comparative analysis of adult and fetal tissue profiles to study developmental trajectories and tissue ontogeny.
• Tissue-specific expression: Characterization of tissue- and cell-type-specific gene expression patterns across 19 adult tissues.
• Regulatory mechanism investigation: Analysis of TF activities, TF motifs, and receptor–ligand interactions to study cellular regulatory networks.
• Multi-omics integration studies: Joint analysis of transcriptomics, splicing variants, spatial transcriptomics, and scATAC-seq to obtain holistic molecular insights.
• Disease modeling and translational research: Use of healthy tissue baselines for comparative studies in disease modeling and personalized medicine research.

Methodology:

Integration of approximately 2.3 million single-cell profiles from ~3,000 scRNA-seq samples with multi-omics layers (single-cell splicing variants, spatial transcriptomics, scATAC-seq) and computation/annotation of gene signatures, TF activities, TF motifs, receptor–ligand interactions, and GO term enrichments; provision of computational tools for major scRNA-seq workflow steps.