Innovative ssREAD Database Provides Rich Alzheimer's Disease RNA-Seq Resources for Researchers

The use of single-cell ribonucleic acid sequencing (scRNAseq), single-nucleus RNA-sequencing (snRNAseq), and transcriptomics to study the pathology of Alzheimer’s Disease has been steadily increasing. Consequently, the substantial volumes of data generated highlight the need for a user-friendly and comprehensive sequence data repository.

In a recent study published in Nature Communications, researchers introduced the Single-cell and Spatial RNA-seq databasE for Alzheimer’s Disease, or ssREAD, containing spatial transcriptomic, scRNAseq, and snRNAseq data on Alzheimer’s disease.

​​​​​​​Study: A single-cell and spatial RNA-seq database for Alzheimer’s disease (ssREAD). Image Credit: mi_viri/Shutterstock.com​​​​​​​Study: A single-cell and spatial RNA-seq database for Alzheimer’s disease (ssREAD). Image Credit: mi_viri/Shutterstock.com

Background

Alzheimer's disease is the most common type of dementia in the world, affecting more than 57 million individuals. It is a progressive form of neurodegenerative disorder for which no effective therapeutic interventions have yet been developed.

Understanding the complex molecular mechanisms that determine the cellular susceptibility associated with the disease might help develop more effective treatment methods.

The recent growth in high-throughput sequencing methods, especially scRNAseq and snRNAseq, has opened up some new avenues to understanding the pathogenesis of Alzheimer’s disease.

The ability to visualize patterns of gene expression spatially through spatial transcriptomics has significantly expanded our understanding of neurobiology in general, and Alzheimer's disease pathogenesis specifically.

Recent studies have also explored spatial transcriptomics to decipher how differentially expressed genes are associated with the pathology of Alzheimer's disease, as well as with biomarkers for Alzheimer's disease.

About the Study

In the present study, the researchers described the database ssREAD, which was created to handle the substantial volumes and complexities of the rapidly growing scRNAseq and snRNAseq data for Alzheimer’s disease.

Other repositories and databases for general scRNAseq data as well as for Alzheimer’s disease specifically, such as the SC2Disease database and The Alzheimer's Cell Atlas (TACA), have provided comprehensive and accurate gene expression profiles and data on drug screening and cell-cell communications.

However, a database offering spatial transcriptomics data for Alzheimer’s disease with region and sex-specific comparisons for Alzheimer's disease patients and controls has been lacking.

To create the ssREAD database, the researchers curated data from 67 studies exploring scRNAseq and snRNAseq data for numerous brain regions, spanning a wide age range and both sexes.

Additionally, the original datasets were reclassified into subsets, further differentiated based on whether the study was conducted on human subjects or murine models.

The spatial transcriptomics data was subjected to quality control and normalization measures, and highly variable genes were identified.

Annotations for cell types included inhibitory and excitatory neurons, and the proportions of cell types were calculated from the gene expression signatures specific to each cell type.

Differentially expressed genes were analyzed to detect the changes in gene expression between disease conditions and control or between males and females. For this analysis, sex and age at death were included as covariates. Additionally, a functional enrichment analysis was conducted on the data.

The researchers also investigated cellular communication patterns from the spatial transcriptomics and single-cell sequence data, identified tissue architecture using a tool to visualize variations in gene expression patterns in a tissue, and included an analysis of the gene regulatory network.

Major Findings

The study showed that the ssREAD repository provided researchers access to large RNA sequencing data for investigating alterations in transcription in Alzheimer’s disease but was also designed to accommodate the rapidly expanding volume of spatial transcriptomic, scRNAseq, and snRNAseq data.

The repository contains RNA and transcriptomic data on Alzheimer's disease from various human and animal model species and a wide range of cell types, tissues, and diseases, allowing researchers to study the intricate biological mechanisms associated with Alzheimer's disease pathology.

The ssREAD repository contains 381 spatial transcriptomics samples and 1,053 samples of scRNAseq and snRNAseq gathered from 16 and 85 studies, respectively. The scRNAseq and snRNAseq samples were consolidated into 277 datasets based on replications.

Compared to the previous scREAD repository developed by the same researchers, ssREAD has 379% more integrated datasets of scRNAseq and snRNAseq.

The data in the repository was annotated meticulously for details such as the species the data was obtained from, the region of the brain studied, the distinction between disease and control used in the study, the gender of the patient, and the Braak stage for Alzheimer’s disease.

Of the 277 RNA datasets, 133 were from murine model-based studies, while the remaining 144 were from human samples. The spatial transcriptomic data, however, largely came from murine samples, with only 62 out of the 381 datasets belonging to human samples.

Conclusions

To conclude, the ssREAD repository provides researchers with a consolidated and comprehensive database containing broad-spectrum, well-annotated spatial transcriptomic and scRNAseq and snRNAseq data on Alzheimer's disease from human and murine samples, and an enhanced analytical pipeline to investigate the molecular mechanisms of Alzheimer's disease pathology.

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