A Multi-Network Approach Identifies Proteins Related to Dendritic Spines in Alzheimer's Disease

Proteomic studies have generated robust assessments of protein abundance changes in Alzheimer's disease (AD); however, identifying how the protein abundance changes affect specific biological processes remains a challenge. To address these hurdles, we used a multi-network computational analysis approach that integrated dendritic spine morphometry data with mass spectrometry-based proteomics from the same individuals. The samples exhibited a range of AD neuropathology and were categorized into three groups: controls, asymptomatic AD, and AD cases. Multiplex tandem mass tag mass spectrometry proteomic data (N = 8,212 proteins) was generated on Brodmann area 46 (BA46) dorsolateral prefrontal cortex (DLPFC) human samples (N = 41, 23 males and 18 females), from which dendritic spine morphometry analysis existed. To integrate the multi-scale data types, two computational network analysis methods were performed, including weighted coexpression network analysis (WGCNA) and SpeakEasy2 (SE2). Both WGCNA and SE2 revealed that the mitochondria protein modules were decreased in AsymAD and AD cases compared with controls, whereas the DNA repair modules were increased in AsymAD and AD compared with controls. Synaptic protein modules that correlated to multiple spine morphology traits were identified in both WGCNA and SE2. Pearson's correlation analyses identified over a dozen individual proteins linked to multiple dendritic spine density and morphology traits. Collectively, these findings demonstrate how integration of spine morphometry data with proteomics can contextualize proteins for functional validation and identify synaptic alterations in AD progression.