SPASCER

SPASCER maps and annotates spatial transcriptomics data at single-cell resolution to analyze tissue architecture heterogeneity, region-specific microenvironments, and intercellular interactions.

Key Features:

• Extensive Dataset Integration: Integrates spatial transcriptomics datasets from 43 studies encompassing 1082 sub-datasets across 16 organ types and four species.
• Single-Cell Resolution Annotation: Leverages single-cell RNA sequencing (scRNA-seq) to deconvolve and map spatial transcriptomic data to cellular identities at single-cell resolution.
• Spatial Heterogeneity Analysis: Analyzes spatial distributions of genes and cells to reveal region-specific heterogeneity within tissues.
• Cell-Cell Communication Mapping: Infers and maps cell-cell communication activities within spatial contexts to characterize intercellular interactions.
• Microenvironment Characterization: Examines cell type composition within defined microenvironments to profile local cellular contexts.
• Pathway Enrichment and Gene Pattern Analysis: Performs pathway enrichment analysis and identifies gene expression patterns within spatial domains.
• Integration with scRNA-seq Data: Combines spatial transcriptomics with scRNA-seq data to enable matched analyses such as cell-cell interaction inference and gene regulatory network construction.

Scientific Applications:

• Tumorigenesis studies: Dissects tumor tissue architecture, tumor microenvironment composition, and spatially resolved intercellular signaling involved in tumorigenesis.
• Embryonic differentiation and developmental biology: Resolves region-specific gene expression and cell-type organization during embryonic differentiation and development.
• Disease progression and microenvironment analysis: Profiles region-specific changes in cell composition and signaling relevant to disease progression.
• Regenerative medicine and therapeutic target discovery: Identifies spatially restricted pathways and cell interactions that inform regenerative strategies and targeted therapeutics.

Methodology:

Integrates spatial transcriptomics datasets (43 studies, 1082 sub-datasets, 16 organs, four species), aligns and integrates scRNA-seq with spatial data, deconvolves and maps spatial transcriptomic spots to cell types, performs cell-cell interaction analyses and constructs gene regulation networks, and conducts pathway enrichment and gene pattern identification.