The article introduces ssREAD, a comprehensive single-cell and spatial RNA-seq database for Alzheimer's Disease (AD). ssREAD addresses the growing demand for a user-friendly repository of AD-related datasets, offering an extensive collection of 1,053 samples from 67 studies, including 277 integrated datasets of scRNA-seq and snRNA-seq, and 381 ST datasets from 18 human and mouse brain studies. Each dataset is annotated with detailed information such as species, gender, brain region, disease/control status, age, and AD Braak stages. The database provides an optimized analytical pipeline for cell clustering, identification of differentially expressed and spatially variable genes, cell-type-specific marker genes, and regulons, and spot deconvolution for integrative analysis. ssREAD is freely available at <https://bmblix.bmi.osumc.edu/ssread/>. The article highlights the importance of integrating scRNA-seq, snRNA-seq, and ST data to uncover the complex molecular mechanisms underlying AD. It demonstrates the capabilities of ssREAD through various analyses, including spatially informed subpopulation analysis, cell-type deconvolution, and sex-specific differences in AD. The database's user-friendly interface and robust infrastructure ensure efficient data management and analysis, making it a valuable resource for researchers studying AD.The article introduces ssREAD, a comprehensive single-cell and spatial RNA-seq database for Alzheimer's Disease (AD). ssREAD addresses the growing demand for a user-friendly repository of AD-related datasets, offering an extensive collection of 1,053 samples from 67 studies, including 277 integrated datasets of scRNA-seq and snRNA-seq, and 381 ST datasets from 18 human and mouse brain studies. Each dataset is annotated with detailed information such as species, gender, brain region, disease/control status, age, and AD Braak stages. The database provides an optimized analytical pipeline for cell clustering, identification of differentially expressed and spatially variable genes, cell-type-specific marker genes, and regulons, and spot deconvolution for integrative analysis. ssREAD is freely available at <https://bmblix.bmi.osumc.edu/ssread/>. The article highlights the importance of integrating scRNA-seq, snRNA-seq, and ST data to uncover the complex molecular mechanisms underlying AD. It demonstrates the capabilities of ssREAD through various analyses, including spatially informed subpopulation analysis, cell-type deconvolution, and sex-specific differences in AD. The database's user-friendly interface and robust infrastructure ensure efficient data management and analysis, making it a valuable resource for researchers studying AD.