IP-to-MS: An Unbiased Workflow for Antigen Profiling.

IF 3.8 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS
Stephanie Biedka, Svitlana Yablonska, Xi Peng, Duah Alkam, Mara Hartoyo, Hannah VanEvery, Daniel J Kass, Stephanie D Byrum, Kunhong Xiao, Yingze Zhang, Robyn T Domsic, Robert Lafyatis, Dana P Ascherman, Jonathan S Minden
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引用次数: 0

Abstract

Immunoprecipitation is among the most widely utilized methods in biomedical research, with applications that include the identification of antibody targets and associated proteins. The path to identifying these targets is not straightforward, however, and often requires the use of chemical cross-linking and/or gel electrophoresis to separate targets from an overabundance of immunoglobulin protein. Such experiments are labor intensive and often yield long lists of candidate antibody targets. Here, we describe an unbiased immunoprecipitation-to-mass spectrometry (IP-to-MS) method that relies on a novel protein tag to separate low abundance immunoprecipitated proteins from overwhelmingly abundant immunoglobulins. We demonstrate that the IP-to-MS serotyping workflow is highly reproducible and can be used for the identification of novel, patient-specific antigen targets in multiple disease states. Furthermore, we show that IP-to-MS may outperform conventional methods of antibody detection, including enzyme-linked immunosorbent assay, while also enabling patient stratification beyond what is possible with traditional approaches.

IP-to-MS:抗原分析的无偏工作流程。
免疫沉淀是生物医学研究中应用最广泛的方法之一,其应用包括抗体靶点和相关蛋白的鉴定。然而,确定这些靶标的途径并不简单,通常需要使用化学交联和/或凝胶电泳将靶标从过量的免疫球蛋白中分离出来。这样的实验是劳动密集型的,通常会产生一长串候选抗体目标。在这里,我们描述了一种无偏倚的免疫沉淀-质谱(ip - ms)方法,该方法依赖于一种新的蛋白质标签,从大量丰富的免疫球蛋白中分离出低丰度的免疫沉淀蛋白。我们证明了ip - ms血清分型工作流程具有高度可重复性,可用于鉴定多种疾病状态下的新型患者特异性抗原靶点。此外,我们表明,IP-to-MS可能优于传统的抗体检测方法,包括酶联免疫吸附测定,同时也使患者分层超越了传统方法的可能性。
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来源期刊
Journal of Proteome Research
Journal of Proteome Research 生物-生化研究方法
CiteScore
9.00
自引率
4.50%
发文量
251
审稿时长
3 months
期刊介绍: Journal of Proteome Research publishes content encompassing all aspects of global protein analysis and function, including the dynamic aspects of genomics, spatio-temporal proteomics, metabonomics and metabolomics, clinical and agricultural proteomics, as well as advances in methodology including bioinformatics. The theme and emphasis is on a multidisciplinary approach to the life sciences through the synergy between the different types of "omics".
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