A single-cell and spatial RNA-seq database for Alzheimer's disease (ssREAD).

Area of Science:

• Neuroscience
• Genomics
• Bioinformatics

Background:

• Alzheimer's Disease (AD) research increasingly utilizes single-cell and spatial transcriptomics (scRNA-seq, snRNA-seq, ST) to unravel complex pathology.
• A significant challenge is the lack of a centralized, user-friendly repository for this rapidly growing volume of data.

Purpose of the Study:

• To introduce ssREAD, a novel database consolidating diverse single-cell and spatial RNA sequencing datasets for Alzheimer's Disease research.
• To provide an optimized analytical pipeline and enhanced usability for researchers studying AD at a cellular and spatial level.

Main Methods:

• Aggregated 1,053 samples (277 datasets) from 67 scRNA-seq/snRNA-seq studies and 381 ST datasets from 18 human and mouse brain studies.
• Annotated datasets with critical metadata including species, gender, brain region, disease status, age, and AD Braak stages.
• Developed an integrated analysis suite for tasks like cell clustering, differential gene expression, and spatial analysis.

Main Results:

• The ssREAD database contains 7,332,202 cells from scRNA-seq/snRNA-seq studies and numerous spatial transcriptomics datasets.
• Comprehensive annotations facilitate detailed comparative analyses across different studies and conditions.
• The analysis suite enables advanced exploration of cell-type-specific markers, gene regulation, and spatial patterns in AD.

Conclusions:

• ssREAD offers a valuable, freely accessible resource for the Alzheimer's Disease research community.
• The database and its analytical tools empower deeper insights into AD pathogenesis through multi-omic data integration.
• Facilitates discovery of novel biomarkers and therapeutic strategies for Alzheimer's Disease.