使用磁网富集细胞外囊泡以分析血浆蛋白质组。
Enrichment of extracellular vesicles using Mag-Net for the analysis of the plasma proteome.
作者信息
Wu Christine C, Tsantilas Kristine A, Park Jea, Plubell Deanna, Sanders Justin A, Naicker Previn, Govender Ireshyn, Buthelezi Sindisiwe, Stoychev Stoyan, Jordaan Justin, Merrihew Gennifer, Huang Eric, Parker Edward D, Riffle Michael, Hoofnagle Andrew N, Noble William S, Poston Kathleen L, Montine Thomas J, MacCoss Michael J
机构信息
Department of Genome Sciences, University of Washington, Seattle, WA, USA.
Department of Computer Science, University of Washington, Seattle, WA, USA.
出版信息
Nat Commun. 2025 Jul 1;16(1):5447. doi: 10.1038/s41467-025-60595-7.
Extracellular vesicles (EVs) in plasma are composed of exosomes, microvesicles, and apoptotic bodies. We report a plasma EV enrichment strategy using magnetic beads called Mag-Net. Proteomic interrogation of this plasma EV fraction enables the detection of proteins that are beyond the dynamic range of liquid chromatography-mass spectrometry of unfractionated plasma. Mag-Net is robust, reproducible, inexpensive, and requires <100 μL plasma input. Coupled to data-independent mass spectrometry, we demonstrate the measurement of >37,000 peptides from >4,000 proteins. Using Mag-Net on a pilot cohort of patients with neurodegenerative disease and healthy controls, we find 204 proteins that differentiate (q-value < 0.05) patients with Alzheimer's disease dementia (ADD) from those without ADD. There are also 310 proteins that differ between individuals with Parkinson's disease and without. Using machine learning we distinguish between individuals with ADD and not ADD with an area under the receiver operating characteristic curve (AUROC) = 0.98 ± 0.06.
血浆中的细胞外囊泡(EVs)由外泌体、微囊泡和凋亡小体组成。我们报告了一种使用名为Mag-Net的磁珠进行血浆EV富集的策略。对该血浆EV组分进行蛋白质组学分析能够检测到未分级血浆液相色谱-质谱动态范围之外的蛋白质。Mag-Net方法可靠、可重复、成本低廉,且血浆输入量小于100μL。结合数据非依赖型质谱分析,我们证明可从4000多种蛋白质中检测到超过37000种肽段。在一组神经退行性疾病患者和健康对照的试点队列中使用Mag-Net,我们发现有204种蛋白质能够区分阿尔茨海默病痴呆(ADD)患者和非ADD患者(q值<0.05)。帕金森病患者和非帕金森病患者之间也有310种蛋白质存在差异。使用机器学习,我们区分ADD患者和非ADD患者的受试者工作特征曲线下面积(AUROC)=0.98±0.06。