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Abstract

Neurodegenerative diseases are a growing burden, and there is an urgent need for better biomarkers for diagnosis, prognosis, and treatment efficacy. Structural and functional brain alterations are reflected in the protein composition of cerebrospinal fluid (CSF). Alzheimer's disease (AD) patients have higherCSFlevels of tau, but we lack knowledge of systems-wide changes ofCSFprotein levels that accompanyAD. Here, we present a highly reproducible mass spectrometry (MS)-based proteomics workflow for the in-depth analysis ofCSFfrom minimal sample amounts. From three independent studies (197 individuals), we characterize differences in proteins byADstatus (> 1,000 proteins,CV < 20%). Proteins with previous links to neurodegeneration such as tau,SOD1, andPARK7 differed most strongly byADstatus, providing strong positive controls for our approach.CSFproteome changes in Alzheimer's disease prove to be widespread and often correlated with tau concentrations. Our unbiased screen also reveals a consistent glycolytic signature across our cohorts and a recent study. Machine learning suggests clinical utility of this proteomic signature.