SCAN

SCAN integrates multiomic single-cell and spatial transcriptomics data to produce a spatiotemporal atlas of neural cells for comparative, developmental, and disease-focused analyses.

Key Features:

• Multiomic integration: Integration and analysis of single-cell transcriptome (scRNA-seq) and spatial transcriptome (ST) datasets.
• Scale and coverage: A curated collection comprising over 10.6 million cells from the nervous system.
• Taxonomic breadth: Multi-omic information from more than 900 species to support evolutionary data mining and comparative studies.
• Spatiotemporal resolution: Analysis across developmental stages, annotated cell types, and spatial transcriptomics spots.
• Disease-focused analysis: Systematic analysis of neurological diseases, including Alzheimer's disease, Parkinson's disease, and Down syndrome.
• Biomarker screening: High-throughput screening for potential biomarkers associated with neural conditions.
• Differential expression profiling: Discernment of differential expression patterns across parameters such as cell type, space, and developmental stage.

Scientific Applications:

• Comparative evolution: Evolutionary data mining and cross-species comparative studies of neural cell types and expression patterns.
• Developmental neurobiology: Investigation of developmental stage–specific cell-type composition and spatial gene expression.
• Neurological disease research: Identification of disease-associated cell populations, expression signatures, and candidate biomarkers for Alzheimer's disease, Parkinson's disease, and Down syndrome.
• Cell-type and spatial mapping: Mapping cell types and their spatial organization within nervous system tissues.

Methodology:

Integration and analysis of scRNA-seq and spatial transcriptomics (ST) data, systematic analysis of neurological diseases, high-throughput biomarker screening, differential expression analysis, and evolutionary comparative analyses.