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高通量半自动非标记或 TMT 基础磷酸化蛋白质组分析协议。

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

机构信息

Novo Nordisk Foundation Center for Protein Research, Copenhagen, Denmark.

Novo Nordisk Foundation Center for Protein Research, Copenhagen, Denmark.

出版信息

STAR Protoc. 2023 Sep 15;4(3):102536. doi: 10.1016/j.xpro.2023.102536. Epub 2023 Sep 1.

Abstract

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. and Martínez-Val et al..

摘要

串联质谱标签数据依赖采集(TMT-DDA)以及基于非标记定量的无依赖数据采集(LFQ-DIA)已经成为实现深度蛋白质组和磷酸化蛋白质组分析的主流方法。我们提出了一个用于 TMT-DDA 和 LFQ-DIA 的模块化流程,集成了可扩展的磷酸化蛋白质组分析步骤,包括蛋白质裂解物提取、净化、消化、磷酸肽富集和 TMT 标记。我们还详细描述了肽和/或磷酸肽的分级分离以及预质谱脱盐,并为研究人员提供了根据样品数量和输入选择最佳工作流程的指导。有关此方案使用和执行的详细信息,请参考 Koenig 等人和 Martínez-Val 等人的研究。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/186e/10491724/af3154a897db/fx1.jpg

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