AbstractBackgroundSenile plaques (SPs) and neurofibrillary tangles (NFTs) are the main histopathological hallmarks of Alzheimer’s disease (AD). Although these two deposits have main components, like amyloid fibrils in SPs and hyperphosphorylated tau protein in NFTs, the molecular composition underlying these lesions has not been completely unraveled. Hence, this work aims to characterize the SPs and NFTs proteomes’ by applying a bioinformatic analysis, to identify new possible biomarkers candidates for AD.MethodAn extensive literature research was carried out on Pubmed, using keywords that allowed the recovery of information regarding SPs and NFTs proteomes in AD brains. The data obtained was loaded and analysed on Cytoscape v3.8.2. For the Gene Ontology (GO) analysis, the plugins ClueGO v2.5.8 + CluePedia v1.5.8 were employed.ResultMore than 800 proteins were identified for SPs and more than 600 for the NFTs. GO analyses allowed the characterization of both proteomes at the biological process, molecular function, and cellular component levels. The top biological processes for SPs were related to cell development, secretion, and exocytosis, while for NFTs these were related to protein localization, vesicle‐mediated transport in synapse, and response to oxidative stress. Overlap of both proteomes revealed more than 300 proteins that represent interesting targets for AD. GO analyses revealed that these proteins are involved in biological processes like vesicle‐mediated transport in synapse and apoptotic signaling pathway. Additional experiments using antibody‐based methodologies need to be carried out to validate the potential of these candidates for AD.ConclusionThis study allowed the identification of several proteins, present in both SPs and NFTs, that can not only assist in unraveling pathways common to both histopathological hallmarks but also constitute possible biomarker candidates for AD diagnosis and/or therapeutics. To this end research was funded by a grant from the Alzheimer’s Association (2019‐AARG‐644347) and supported by iBiMED UIDB/04501/2020, FCT, COMPETE program, QREN, European Union. T.S.M. is supported by the FCT through the individual PhD grant (SFRH/BD/145979/2019). RV’s Fellowship Grant (IF/00286/2015).