Merrihew, Gennifer E.Park, JeaPlubell, DeannaSearle, Brian C.Keene, C. DirkLarson, Eric B.Bateman, RandallPerrin, Richard J.Chhatwal, Jasmeer P.Farlow, Martin R.McLean, Catriona A.Ghetti, BernardinoNewell, Kathy L.Frosch, Matthew P.Montine, Thomas J.MacCoss, Michael J.2023-12-012023-12-012023-04-14Merrihew GE, Park J, Plubell D, et al. A peptide-centric quantitative proteomics dataset for the phenotypic assessment of Alzheimer's disease. Sci Data. 2023;10(1):206. Published 2023 Apr 14. doi:10.1038/s41597-023-02057-7https://hdl.handle.net/1805/37260Alzheimer's disease (AD) is a looming public health disaster with limited interventions. Alzheimer's is a complex disease that can present with or without causative mutations and can be accompanied by a range of age-related comorbidities. This diverse presentation makes it difficult to study molecular changes specific to AD. To better understand the molecular signatures of disease we constructed a unique human brain sample cohort inclusive of autosomal dominant AD dementia (ADD), sporadic ADD, and those without dementia but with high AD histopathologic burden, and cognitively normal individuals with no/minimal AD histopathologic burden. All samples are clinically well characterized, and brain tissue was preserved postmortem by rapid autopsy. Samples from four brain regions were processed and analyzed by data-independent acquisition LC-MS/MS. Here we present a high-quality quantitative dataset at the peptide and protein level for each brain region. Multiple internal and external control strategies were included in this experiment to ensure data quality. All data are deposited in the ProteomeXchange repositories and available from each step of our processing.en-USAttribution 4.0 InternationalAlzheimer diseaseLiquid chromatographyProteomicsTandem mass spectrometryArticle