Mag-Net:快速富集膜结合颗粒,实现血浆蛋白质组的高覆盖定量分析。

IF 12.5 1区 医学 Q1 ONCOLOGY Cancer research Pub Date : 2024-04-02 DOI:10.1101/2023.06.10.544439
Christine C Wu, Kristine A Tsantilas, Jea Park, Deanna Plubell, Justin A Sanders, Previn Naicker, Ireshyn Govender, Sindisiwe Buthelezi, Stoyan Stoychev, Justin Jordaan, Gennifer Merrihew, Eric Huang, Edward D Parker, Michael Riffle, Andrew N Hoofnagle, William S Noble, Kathleen L Poston, Thomas J Montine, Michael J MacCoss
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引用次数: 0

摘要

血浆中的膜结合颗粒由外泌体、微囊泡和凋亡体组成,占总蛋白质组成的 1-2%。对这部分血浆蛋白质进行蛋白质组学检测,可增强组织特异性蛋白质的代表性,相当于 "液体活检",同时还能检测未分馏血浆液相色谱-串联质谱动态范围之外的蛋白质。我们开发了一种富集策略(Mag-Net),利用超多孔强阴离子交换磁性微粒筛除血浆中的膜结合颗粒。Mag-Net 方法具有稳健性、可重复性和低成本等特点,能从超过 4,000 种血浆蛋白中高精度地筛选出 37,000 个肽段。我们在一小批神经退行性疾病患者和年龄匹配的健康对照组中使用了这一分析管道,发现了 204 种可区分(q 值小于 0.05)阿尔茨海默病痴呆症(ADD)患者和非 ADD 患者的蛋白质。我们的方法还发现了 310 种蛋白质,这些蛋白质在帕金森病患者与痴呆症患者或认知能力正常的健康人之间存在差异。通过机器学习,我们能够以平均 ROC AUC = 0.98 ± 0.06 来区分有无注意力缺失症。
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Mag-Net: Rapid enrichment of membrane-bound particles enables high coverage quantitative analysis of the plasma proteome.

Membrane-bound particles in plasma are composed of exosomes, microvesicles, and apoptotic bodies and represent ~1-2% of the total protein composition. Proteomic interrogation of this subset of plasma proteins augments the representation of tissue-specific proteins, representing a "liquid biopsy," while enabling the detection of proteins that would otherwise be beyond the dynamic range of liquid chromatography-tandem mass spectrometry of unfractionated plasma. We have developed an enrichment strategy (Mag-Net) using hyper-porous strong-anion exchange magnetic microparticles to sieve membrane-bound particles from plasma. The Mag-Net method is robust, reproducible, inexpensive, and requires <100 μL plasma input. Coupled to a quantitative data-independent mass spectrometry analytical strategy, we demonstrate that we can collect results for >37,000 peptides from >4,000 plasma proteins with high precision. Using this analytical pipeline on a small cohort of patients with neurodegenerative disease and healthy age-matched controls, we discovered 204 proteins that differentiate (q-value < 0.05) patients with Alzheimer's disease dementia (ADD) from those without ADD. Our method also discovered 310 proteins that were different between Parkinson's disease and those with either ADD or healthy cognitively normal individuals. Using machine learning we were able to distinguish between ADD and not ADD with a mean ROC AUC = 0.98 ± 0.06.

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来源期刊
Cancer research
Cancer research 医学-肿瘤学
CiteScore
16.10
自引率
0.90%
发文量
7677
审稿时长
2.5 months
期刊介绍: Cancer Research, published by the American Association for Cancer Research (AACR), is a journal that focuses on impactful original studies, reviews, and opinion pieces relevant to the broad cancer research community. Manuscripts that present conceptual or technological advances leading to insights into cancer biology are particularly sought after. The journal also places emphasis on convergence science, which involves bridging multiple distinct areas of cancer research. With primary subsections including Cancer Biology, Cancer Immunology, Cancer Metabolism and Molecular Mechanisms, Translational Cancer Biology, Cancer Landscapes, and Convergence Science, Cancer Research has a comprehensive scope. It is published twice a month and has one volume per year, with a print ISSN of 0008-5472 and an online ISSN of 1538-7445. Cancer Research is abstracted and/or indexed in various databases and platforms, including BIOSIS Previews (R) Database, MEDLINE, Current Contents/Life Sciences, Current Contents/Clinical Medicine, Science Citation Index, Scopus, and Web of Science.
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