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SEC-TMT facilitates quantitative differential analysis of protein interaction networks

The majority of cellular proteins interact with at least one partner or assemble into molecular-complexes to exert their function. This network of protein-protein interactions (PPIs) and the composition of macromolecular machines differ between cell types and physiological conditions. Therefore, cha...

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Autores principales: Doron-Mandel, Ella, Bokor, Benjamin J., Ma, Yanzhe, Street, Lena A., Tang, Lauren C., Abdou, Ahmed A., Shah, Neel H., Rosenberger, George A., Jovanovic, Marko
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cold Spring Harbor Laboratory 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9882152/
https://www.ncbi.nlm.nih.gov/pubmed/36711903
http://dx.doi.org/10.1101/2023.01.12.523793
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author Doron-Mandel, Ella
Bokor, Benjamin J.
Ma, Yanzhe
Street, Lena A.
Tang, Lauren C.
Abdou, Ahmed A.
Shah, Neel H.
Rosenberger, George A.
Jovanovic, Marko
author_facet Doron-Mandel, Ella
Bokor, Benjamin J.
Ma, Yanzhe
Street, Lena A.
Tang, Lauren C.
Abdou, Ahmed A.
Shah, Neel H.
Rosenberger, George A.
Jovanovic, Marko
author_sort Doron-Mandel, Ella
collection PubMed
description The majority of cellular proteins interact with at least one partner or assemble into molecular-complexes to exert their function. This network of protein-protein interactions (PPIs) and the composition of macromolecular machines differ between cell types and physiological conditions. Therefore, characterizing PPI networks and their dynamic changes is vital for discovering novel biological functions and underlying mechanisms of cellular processes. However, producing an in-depth, global snapshot of PPIs from a given specimen requires measuring tens to thousands of LC-MS/MS runs. Consequently, while recent works made seminal contributions by mapping PPIs at great depth, almost all focused on just 1-2 conditions, generating comprehensive but mostly static PPI networks. In this study we report the development of SEC-TMT, a method that enables identifying and measuring PPIs in a quantitative manner from only 4-8 LC-MS/MS runs per biological sample. This was accomplished by incorporating tandem mass tag (TMT) multiplexing with a size exclusion chromatography mass spectrometry (SEC-MS) work-flow. SEC-TMT reduces measurement time by an order of magnitude while maintaining resolution and coverage of thousands of cellular interactions, equivalent to the gold standard in the field. We show that SEC-TMT provides benefits for conducting differential analyses to measure changes in the PPI network between conditions. This development makes it feasible to study dynamic systems at scale and holds the potential to drive more rapid discoveries of PPI impact on cellular processes.
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spelling pubmed-98821522023-01-28 SEC-TMT facilitates quantitative differential analysis of protein interaction networks Doron-Mandel, Ella Bokor, Benjamin J. Ma, Yanzhe Street, Lena A. Tang, Lauren C. Abdou, Ahmed A. Shah, Neel H. Rosenberger, George A. Jovanovic, Marko bioRxiv Article The majority of cellular proteins interact with at least one partner or assemble into molecular-complexes to exert their function. This network of protein-protein interactions (PPIs) and the composition of macromolecular machines differ between cell types and physiological conditions. Therefore, characterizing PPI networks and their dynamic changes is vital for discovering novel biological functions and underlying mechanisms of cellular processes. However, producing an in-depth, global snapshot of PPIs from a given specimen requires measuring tens to thousands of LC-MS/MS runs. Consequently, while recent works made seminal contributions by mapping PPIs at great depth, almost all focused on just 1-2 conditions, generating comprehensive but mostly static PPI networks. In this study we report the development of SEC-TMT, a method that enables identifying and measuring PPIs in a quantitative manner from only 4-8 LC-MS/MS runs per biological sample. This was accomplished by incorporating tandem mass tag (TMT) multiplexing with a size exclusion chromatography mass spectrometry (SEC-MS) work-flow. SEC-TMT reduces measurement time by an order of magnitude while maintaining resolution and coverage of thousands of cellular interactions, equivalent to the gold standard in the field. We show that SEC-TMT provides benefits for conducting differential analyses to measure changes in the PPI network between conditions. This development makes it feasible to study dynamic systems at scale and holds the potential to drive more rapid discoveries of PPI impact on cellular processes. Cold Spring Harbor Laboratory 2023-01-13 /pmc/articles/PMC9882152/ /pubmed/36711903 http://dx.doi.org/10.1101/2023.01.12.523793 Text en https://creativecommons.org/licenses/by-nc-nd/4.0/This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (https://creativecommons.org/licenses/by-nc-nd/4.0/) , which allows reusers to copy and distribute the material in any medium or format in unadapted form only, for noncommercial purposes only, and only so long as attribution is given to the creator.
spellingShingle Article
Doron-Mandel, Ella
Bokor, Benjamin J.
Ma, Yanzhe
Street, Lena A.
Tang, Lauren C.
Abdou, Ahmed A.
Shah, Neel H.
Rosenberger, George A.
Jovanovic, Marko
SEC-TMT facilitates quantitative differential analysis of protein interaction networks
title SEC-TMT facilitates quantitative differential analysis of protein interaction networks
title_full SEC-TMT facilitates quantitative differential analysis of protein interaction networks
title_fullStr SEC-TMT facilitates quantitative differential analysis of protein interaction networks
title_full_unstemmed SEC-TMT facilitates quantitative differential analysis of protein interaction networks
title_short SEC-TMT facilitates quantitative differential analysis of protein interaction networks
title_sort sec-tmt facilitates quantitative differential analysis of protein interaction networks
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9882152/
https://www.ncbi.nlm.nih.gov/pubmed/36711903
http://dx.doi.org/10.1101/2023.01.12.523793
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