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Protocol for high-throughput semi-automated label-free- or TMT-based phosphoproteome profiling

Tandem mass tags data-dependent acquisition (TMT-DDA) as well as data-independent acquisition-based label-free quantification (LFQ-DIA) have become the leading workflows to achieve deep proteome and phosphoproteome profiles. We present a modular pipeline for TMT-DDA and LFQ-DIA that integrates steps...

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Autores principales: Koenig, Claire, Martinez-Val, Ana, Naicker, Previn, Stoychev, Stoyan, Jordaan, Justin, Olsen, Jesper V.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10491724/
https://www.ncbi.nlm.nih.gov/pubmed/37659085
http://dx.doi.org/10.1016/j.xpro.2023.102536
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author Koenig, Claire
Martinez-Val, Ana
Naicker, Previn
Stoychev, Stoyan
Jordaan, Justin
Olsen, Jesper V.
author_facet Koenig, Claire
Martinez-Val, Ana
Naicker, Previn
Stoychev, Stoyan
Jordaan, Justin
Olsen, Jesper V.
author_sort Koenig, Claire
collection PubMed
description Tandem mass tags data-dependent acquisition (TMT-DDA) as well as data-independent acquisition-based label-free quantification (LFQ-DIA) have become the leading workflows to achieve deep proteome and phosphoproteome profiles. We present a modular pipeline for TMT-DDA and LFQ-DIA that integrates steps to perform scalable phosphoproteome profiling, including protein lysate extraction, clean-up, digestion, phosphopeptide enrichment, and TMT-labeling. We also detail peptide and/or phosphopeptide fractionation and pre-mass spectrometry desalting and provide researchers guidance on choosing the best workflow based on sample number and input. For complete details on the use and execution of this protocol, please refer to Koenig et al.(1) and Martínez-Val et al.(2)
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spelling pubmed-104917242023-09-10 Protocol for high-throughput semi-automated label-free- or TMT-based phosphoproteome profiling Koenig, Claire Martinez-Val, Ana Naicker, Previn Stoychev, Stoyan Jordaan, Justin Olsen, Jesper V. STAR Protoc Protocol Tandem mass tags data-dependent acquisition (TMT-DDA) as well as data-independent acquisition-based label-free quantification (LFQ-DIA) have become the leading workflows to achieve deep proteome and phosphoproteome profiles. We present a modular pipeline for TMT-DDA and LFQ-DIA that integrates steps to perform scalable phosphoproteome profiling, including protein lysate extraction, clean-up, digestion, phosphopeptide enrichment, and TMT-labeling. We also detail peptide and/or phosphopeptide fractionation and pre-mass spectrometry desalting and provide researchers guidance on choosing the best workflow based on sample number and input. For complete details on the use and execution of this protocol, please refer to Koenig et al.(1) and Martínez-Val et al.(2) Elsevier 2023-09-01 /pmc/articles/PMC10491724/ /pubmed/37659085 http://dx.doi.org/10.1016/j.xpro.2023.102536 Text en © 2023 The Authors https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Protocol
Koenig, Claire
Martinez-Val, Ana
Naicker, Previn
Stoychev, Stoyan
Jordaan, Justin
Olsen, Jesper V.
Protocol for high-throughput semi-automated label-free- or TMT-based phosphoproteome profiling
title Protocol for high-throughput semi-automated label-free- or TMT-based phosphoproteome profiling
title_full Protocol for high-throughput semi-automated label-free- or TMT-based phosphoproteome profiling
title_fullStr Protocol for high-throughput semi-automated label-free- or TMT-based phosphoproteome profiling
title_full_unstemmed Protocol for high-throughput semi-automated label-free- or TMT-based phosphoproteome profiling
title_short Protocol for high-throughput semi-automated label-free- or TMT-based phosphoproteome profiling
title_sort protocol for high-throughput semi-automated label-free- or tmt-based phosphoproteome profiling
topic Protocol
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10491724/
https://www.ncbi.nlm.nih.gov/pubmed/37659085
http://dx.doi.org/10.1016/j.xpro.2023.102536
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