webSCST

webSCST integrates single-cell RNA-sequencing (scRNA-seq) data with spatial transcriptomic datasets to predict spatial locations of individual cell samples and provide spatial context across species and organs.

Key Features:

• Integration of scRNA-seq and spatial transcriptomics: Combines scRNA-seq profiles with curated spatial transcriptomic datasets to assign predictive spatial coordinates to individual cell samples.
• Species- and organ-specific datasets: Organizes spatial transcriptome sequencing datasets categorized by species and organ to enable context-specific spatial prediction.
• Data harmonization: Harmonizes spatial sequencing datasets presented in disparate formats for integrative analysis.
• Raw scRNA-seq processing for spatial prediction: Processes raw scRNA-seq input to align single-cell expression profiles with spatial coordinates and predict cell locations.

Scientific Applications:

• Developmental biology: Maps cell types to spatial locations to study spatial patterns during development.
• Tissue architecture analysis: Characterizes spatial organization and cellular interactions within tissues.
• Disease modeling: Provides spatially resolved single-cell context to investigate cellular heterogeneity and spatially localized pathology.

Methodology:

Integration of scRNA-seq data with curated spatial transcriptomic datasets and computational alignment of single-cell expression profiles to spatial coordinates, with processing of raw scRNA-seq input for spatial prediction.