Molecular mechanism decipher of Alzheimer’s disease based on single-cell RNA-seq information
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Background
Alzheimer’s disease (AD) is a most common age-related neurodegenerative disease with no effective treatments, is one of the leading contributions to global disease burden. AD is represented by complicated biological mechanisms and complexity of brain tissues, often manifested histologically by the parenchymal deposition of amyloid-beta (Aβ) plaques, the formation of neurofibrillary tangles and neuroinflammation. The etiology of AD is likely due to complex interactions among different brain cell types leading to interconnected cellular pathologies. Single-cell RNA sequencing (RNA-seq) technologies enable unbiased characterization of cell types and states, transitions from normal to disease and responses to therapies, providing an effective tool to systematically resolve cellular heterogeneity in AD.
Methods
In this study, to investigate the molecular mechanisms of AD, we collected single cell RNA-seq data of disease and control samples and designed an precise computational analysis pipeline for the dataset. We further proposed a consensus non-negative matrix factorization model to extract disease cellular gene expression programs.
Results
According to the computational pipeline, we get the most disease-related sub-clusters, cellular process gene expression programs, and disease-related gene programs. We further investigated the enrichment pathways of disease gene programs.
Discussion
This research might provide a cell type specific gene program targets therapeutic approach to combat the disease.
Highlights
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We designed a consensus non-negative matrix factorization method, which could extract cellular process gene expression programs, and disease-related gene programs.
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We further investigated the enrichment pathways of the most disease-related sub-clusters.
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We provide a cellular foundation for a new perspective on AD molecular mechanism that informs personalized therapeutic development, targeting the disease-related gene program.
