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
{"title":"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。","authors":"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","doi":"10.1101/2023.06.10.544439","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":9441,"journal":{"name":"Cancer research","volume":"72 1","pages":""},"PeriodicalIF":12.5000,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11014469/pdf/","citationCount":"0","resultStr":"{\"title\":\"Mag-Net: Rapid enrichment of membrane-bound particles enables high coverage quantitative analysis of the plasma proteome.\",\"authors\":\"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\",\"doi\":\"10.1101/2023.06.10.544439\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>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.</p>\",\"PeriodicalId\":9441,\"journal\":{\"name\":\"Cancer research\",\"volume\":\"72 1\",\"pages\":\"\"},\"PeriodicalIF\":12.5000,\"publicationDate\":\"2024-04-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11014469/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Cancer research\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1101/2023.06.10.544439\",\"RegionNum\":1,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ONCOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cancer research","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1101/2023.06.10.544439","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ONCOLOGY","Score":null,"Total":0}
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.
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.