Brain tissue gene expression from donors with and without
Alzheimer's disease has been used to help inform the molecular changes associated with the development and potential treatment of this disorder. Here, we use a deep learning method to analyse
RNA-seq data from 1114 brain donors from the Accelerating Medicines Project for
Alzheimer's Disease consortium to characterize post-mortem brain transcriptome signatures associated with
amyloid-β plaque, tau neurofibrillary tangles and clinical severity in multiple
Alzheimer's disease dementia populations. Starting from the cross-sectional data in the Religious Orders Study and Memory and Aging Project cohort (n = 634), a deep learning framework was built to obtain a trajectory that mirrors
Alzheimer's disease progression. A severity index was defined to quantitatively measure the progression based on the trajectory. Network analysis was then carried out to identify key gene (index gene) modules present in the model underlying the progression. Within this data set, severity indexes were found to be very closely correlated with all
Alzheimer's disease neuropathology
biomarkers (R ∼ 0.5, P < 1e-11) and global cognitive function (R = -0.68,
P < 2.2e-16). We then applied the model to additional transcriptomic data sets from different brain regions (MAYO, n = 266; Mount Sinai Brain Bank, n = 214), and observed that the model remained significantly predictive (P < 1e-3) of neuropathology and clinical severity. The index genes that significantly contributed to the model were integrated with
Alzheimer's disease co-expression regulatory networks, resolving four discrete gene modules that are implicated in vascular and metabolic dysfunction in different cell types, respectively. Our work demonstrates the generalizability of this signature to frontal and temporal cortex measurements and additional brain donors with
Alzheimer's disease, other age-related
neurological disorders and controls, and revealed that the transcriptomic network modules contribute to neuropathological and clinical disease severity. This study illustrates the promise of using deep learning methods to analyse heterogeneous omics data and discover potentially targetable molecular networks that can inform the development, treatment and prevention of
neurodegenerative diseases like
Alzheimer's disease.