Supervised latent factor modeling isolates cell-type-specific transcriptomic modules that underlie Alzheimer’s disease progression

• Published in: Communications biology 2024-05, Vol.7 (1), p.591-19, Article 591
• Main Authors: Hodgson, Liam, Li, Yue, Iturria-Medina, Yasser, Stratton, Jo Anne, Wolf, Guy, Krishnaswamy, Smita, Bennett, David A., Bzdok, Danilo
• Format: Article
• Language: English
• Subjects: 38/39; 38/43; 38/62; 631/114/2397; 631/208/199; Alzheimer Disease - genetics; Alzheimer Disease - metabolism; Alzheimer Disease - pathology; Alzheimer's disease; Artificial intelligence; Atrophy; Biomedical and Life Sciences; Brain; Brain - metabolism; Brain - pathology; Computer science; Disease; Disease Progression; Gene expression; Gene Expression Profiling; Gene Regulatory Networks; Genomes; Genotype & phenotype; Glial cells; Humans; Life Sciences; Machine learning; Medical imaging; Medicine; Microglia; Microglia - metabolism; Microglia - pathology; Neurodegenerative diseases; Neuroimaging; Pathophysiology; snRNA; Transcriptome; Transcriptomes; Transcriptomics
• ISSN: 2399-3642; 2399-3642
• DOI: 10.1038/s42003-024-06273-8

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. Designing a supervised latent factor framework for snRNA-seq human brain, the authors find distinct Alzheimer’s-predictive gene modules across celltypes, suggesting subcelltype disease progression trajectories.