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

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作者:Biedka Stephanie, Yablonska Svitlana, Peng Xi, Alkam Duah, Hartoyo Mara, VanEvery Hannah, Kass Daniel J, Byrum Stephanie D, Xiao Kunhong, Zhang Yingze, Domsic Robyn T, Lafyatis Robert, Ascherman Dana P, Minden Jonathan S
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.

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