Supervised latent factor modeling isolates cell-type-specific transcriptomic modules that underlie Alzheimer's disease progression.
Liam HodgsonYue LiYasser Iturria-MedinaJo Anne StrattonGuy WolfSmita KrishnaswamyDavid A BennettDanilo BzdokPublished in: Communications biology (2024)
Late onset Alzheimer's disease (AD) is a progressive neurodegenerative disease, with brain changes beginning years before symptoms surface. AD is characterized by neuronal loss, the classic feature of the disease that underlies brain atrophy. However, GWAS reports and recent single-nucleus RNA sequencing (snRNA-seq) efforts have highlighted that glial cells, particularly microglia, claim a central role in AD pathophysiology. Here, we tailor pattern-learning algorithms to explore distinct gene programs by integrating the entire transcriptome, yielding distributed AD-predictive modules within the brain's major cell-types. We show that these learned modules are biologically meaningful through the identification of new and relevant enriched signaling cascades. The predictive nature of our modules, especially in microglia, allows us to infer each subject's progression along a disease pseudo-trajectory, confirmed by post-mortem pathological brain tissue markers. Additionally, we quantify the interplay between pairs of cell-type modules in the AD brain, and localized known AD risk genes to enriched module gene programs. Our collective findings advocate for a transition from cell-type-specificity to gene modules specificity to unlock the potential of unique gene programs, recasting the roles of recently reported genome-wide AD risk loci.
Keyphrases
- genome wide
- dna methylation
- single cell
- white matter
- copy number
- resting state
- late onset
- machine learning
- cerebral ischemia
- network analysis
- public health
- genome wide identification
- rna seq
- multiple sclerosis
- inflammatory response
- early onset
- cognitive decline
- deep learning
- gene expression
- signaling pathway
- climate change
- cell proliferation
- bioinformatics analysis
- electronic health record
- blood brain barrier
- risk assessment
- mild cognitive impairment
- genome wide association study
- cell cycle arrest
- structural basis
- spinal cord injury
- physical activity
- sleep quality