ssREAD is a comprehensive, user-friendly database for Alzheimer's disease (AD) that integrates single-cell and spatial RNA-seq data. It includes 1,053 samples from 67 AD-related scRNA-seq and snRNA-seq studies, totaling 7,332,202 cells, and 381 spatial transcriptomics (ST) datasets from 18 human and mouse brain studies. The database provides detailed annotations for each dataset, including species, gender, brain region, disease/control status, age, and AD Braak stages. ssREAD offers an analysis suite for cell clustering, identification of differentially expressed and spatially variable genes, cell-type-specific marker genes and regulons, and spot deconvolution for integrative analysis. It is freely available at https://bmblx.bmi.osumc.edu/ssread/. AD is a progressive neurodegenerative disorder affecting over 57 million people globally. Despite advances in research, therapeutic interventions remain ineffective. Recent high-throughput sequencing technologies, such as scRNA-seq and snRNA-seq, have provided new insights into AD pathogenesis. ssREAD was developed to address the need for a specialized database that integrates spatial transcriptomics data and offers comprehensive differential analyses under various conditions, such as sex-specific, region-specific, and comparisons between AD and control groups. It includes 381 ST and 277 sc/snRNA-seq AD-related datasets, enabling researchers to investigate transcriptomic alterations in AD compared to the control and their regulatory mechanisms at various resolutions: sub-cellular, cellular, and spatial levels. ssREAD provides a range of functions for sc/snRNA-seq and ST data, including cell clustering, cell type annotation, marker gene expression visualization, and cell proportion analysis. For ST data, it provides visualizations for original spatial H&E image, layer/tissue architecture/spatial domain annotation, marker gene expression on spatial map, and spot deconvolution. DEGs can be identified for cell types in sc/snRNA-seq data or spatial layers in ST data. Cross-data queries and analyses are also facilitated, including comparative studies between male and female subjects across various datasets. Spatially variable genes (SVGs) can be identified via spaGCN from ST data to show marker genes with spatially resolved expression patterns that may be related to tissue functions. Functional enrichment analysis is included to identify pathways or gene ontology enriched by DEGs or SVGs. ssREAD also features cell-type-specific (or layer-specific) regulons for individual datasets and the integrated cell atlas, focusing on cellular and regional vulnerability in AD. The database is freely available and includes a user-friendly web portal that allows AD researchers to access data without requiring extensive programming knowledge. It offers interactive plots for visualizing cells and spatial spots, including scatter plots, bar plots, and violin plots, as well as real-time analyses for DEGs, SVGs, and functional enrichment queries. AllssREAD is a comprehensive, user-friendly database for Alzheimer's disease (AD) that integrates single-cell and spatial RNA-seq data. It includes 1,053 samples from 67 AD-related scRNA-seq and snRNA-seq studies, totaling 7,332,202 cells, and 381 spatial transcriptomics (ST) datasets from 18 human and mouse brain studies. The database provides detailed annotations for each dataset, including species, gender, brain region, disease/control status, age, and AD Braak stages. ssREAD offers an analysis suite for cell clustering, identification of differentially expressed and spatially variable genes, cell-type-specific marker genes and regulons, and spot deconvolution for integrative analysis. It is freely available at https://bmblx.bmi.osumc.edu/ssread/. AD is a progressive neurodegenerative disorder affecting over 57 million people globally. Despite advances in research, therapeutic interventions remain ineffective. Recent high-throughput sequencing technologies, such as scRNA-seq and snRNA-seq, have provided new insights into AD pathogenesis. ssREAD was developed to address the need for a specialized database that integrates spatial transcriptomics data and offers comprehensive differential analyses under various conditions, such as sex-specific, region-specific, and comparisons between AD and control groups. It includes 381 ST and 277 sc/snRNA-seq AD-related datasets, enabling researchers to investigate transcriptomic alterations in AD compared to the control and their regulatory mechanisms at various resolutions: sub-cellular, cellular, and spatial levels. ssREAD provides a range of functions for sc/snRNA-seq and ST data, including cell clustering, cell type annotation, marker gene expression visualization, and cell proportion analysis. For ST data, it provides visualizations for original spatial H&E image, layer/tissue architecture/spatial domain annotation, marker gene expression on spatial map, and spot deconvolution. DEGs can be identified for cell types in sc/snRNA-seq data or spatial layers in ST data. Cross-data queries and analyses are also facilitated, including comparative studies between male and female subjects across various datasets. Spatially variable genes (SVGs) can be identified via spaGCN from ST data to show marker genes with spatially resolved expression patterns that may be related to tissue functions. Functional enrichment analysis is included to identify pathways or gene ontology enriched by DEGs or SVGs. ssREAD also features cell-type-specific (or layer-specific) regulons for individual datasets and the integrated cell atlas, focusing on cellular and regional vulnerability in AD. The database is freely available and includes a user-friendly web portal that allows AD researchers to access data without requiring extensive programming knowledge. It offers interactive plots for visualizing cells and spatial spots, including scatter plots, bar plots, and violin plots, as well as real-time analyses for DEGs, SVGs, and functional enrichment queries. All