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 higher CSF levels of tau, but we lack knowledge of systems‐wide changes of CSF protein levels that accompany AD . Here, we present a highly reproducible mass spectrometry (MS )‐based proteomics workflow for the in‐depth analysis of CSF from minimal sample amounts. From three independent studies (197 individuals), we characterize differences in proteins by AD status (> 1,000 proteins, CV < 20%). Proteins with previous links to neurodegeneration such as tau, SOD 1, and PARK 7 differed most strongly by AD status, providing strong positive controls for our approach. CSF proteome 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.

中文翻译：

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脑脊液中的蛋白质组分析揭示了阿尔茨海默病的新生物标志物。

神经退行性疾病是一个日益严重的负担，迫切需要更好的生物标志物用于诊断、预后和治疗效果。脑结构和功能的改变反映在脑脊液（CSF）的蛋白质组成中。阿尔茨海默病 (AD) 患者的脑脊液中 tau 水平较高，但我们缺乏对伴随 AD 的全系统脑脊液蛋白水平变化的了解。在这里，我们提出了一种高度可重复的基于质谱（MS）的蛋白质组学工作流程，用于从最小的样本量中深入分析脑脊液。根据三项独立研究（197 名个体），我们根据 AD 状态描述了蛋白质差异（> 1,000 种蛋白质，CV < 20%）。先前与神经退行性疾病相关的蛋白质（例如 tau、SOD 1 和 PARK 7）在 AD 状态方面差异最大，为我们的方法提供了强有力的阳性对照。阿尔茨海默病中脑脊液蛋白质组的变化被证明是广泛的，并且通常与 tau 蛋白浓度相关。我们的公正筛选还揭示了我们的队列和最近的一项研究中一致的糖酵解特征。机器学习表明了这种蛋白质组学特征的临床实用性。