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气相分级DDA促进深度DIA磷酸化蛋白质组分析。

Gas-phase fractionation DDA promotes in-depth DIA phosphoproteome analysis.

作者信息

Tu Zhiwei, Li Yabin, Ji Shuhui, Wang Shanshan, Zhou Rui, Kramer Gertjan, Cui Yu, Xie Fei

机构信息

State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, 102206, Beijing, China.

College of Pulmonary and Critical Care Medicine, Chinese PLA General Hospital, 100048, Beijing, China.

出版信息

Heliyon. 2025 Jan 14;11(2):e41928. doi: 10.1016/j.heliyon.2025.e41928. eCollection 2025 Jan 30.

Abstract

Data-independent acquisition (DIA) is a promising method for quantitative proteomics. Library-based DIA database searching against project-specific data-dependent acquisition (DDA) spectral libraries is the gold standard. These libraries are constructed using material-consuming pre-fractionation two dimensional DDA analysis. The alternative to this is library-free DIA analysis. Limited sample amounts restrict the use of fractionation to build spectral libraries for post-translational modifications (PTMs) DIA analysis. We present the use of gas-phase fractionation (GPF) DDA data to improve the depth of library-free DIA identification for the phosphoproteome, called GPF-DDA hybrid DIA. This method fully utilizes the remnants of samples post-DIA analysis and leverages both library-based and -free DIA database searching. GPF-DDA hybrid DIA analyzes phosphopeptides surplus sample after DIA analysis using a number of DDA injections with each scanning different mass-to-charge (/) windows, instead of preforming traditional off-line fractionation-based DDA. The GPF-DDA data is integrated into the library-free DIA database search to create a hybrid library, enhancing phosphopeptide identification. Two GPF-DDA injections proved to increase 18 % phosphopeptide and 13 % phosphosite identification in HEK293 cell lines, while five injections resulted in up to 28 % phosphopeptide and 21 % phosphosite increases compared to library-free DIA analysis alone. We used GPF-DDA hybrid DIA phosphoproteomics to characterize lung tissue upon direct (smoke induced) and indirect (sepsis induced) acute lung injury (ALI) in mice. The differentially expressed phosphosites (DEPsites) in direct ALI were found in proteins related to mRNA processing and RNA. DEPsites in indirect ALI were enriched in proteins related to microtubule polymerization, positive regulation of microtubule polymerization and fibroblast migration. This study demonstrates that GPF-DDA hybrid DIA analysis workflow can indeed promote depth of DIA analysis of phosphoproteome and could be extended to DIA analysis of other PTMs.

摘要

数据非依赖采集(DIA)是定量蛋白质组学中一种很有前景的方法。基于文库的DIA数据库搜索特定项目的数据依赖采集(DDA)光谱库是金标准。这些文库是使用消耗材料的预分级二维DDA分析构建的。其替代方法是无文库DIA分析。有限的样本量限制了用于构建翻译后修饰(PTM)DIA分析光谱库的分级方法的使用。我们展示了使用气相分级(GPF)DDA数据来提高无文库DIA鉴定磷酸化蛋白质组的深度,即GPF-DDA混合DIA。该方法充分利用了DIA分析后样本的残余物,并利用了基于文库和无文库的DIA数据库搜索。GPF-DDA混合DIA使用多次DDA进样分析DIA分析后的磷酸肽剩余样本,每次扫描不同的质荷比(/)窗口,而不是进行传统的基于离线分级的DDA。将GPF-DDA数据整合到无文库DIA数据库搜索中以创建混合文库,增强磷酸肽鉴定。在HEK293细胞系中,两次GPF-DDA进样证明可使磷酸肽鉴定增加18%,磷酸位点鉴定增加13%,而与单独的无文库DIA分析相比,五次进样导致磷酸肽增加高达28%,磷酸位点增加21%。我们使用GPF-DDA混合DIA磷酸蛋白质组学来表征小鼠直接(烟雾诱导)和间接(脓毒症诱导)急性肺损伤(ALI)后的肺组织。在直接ALI中差异表达的磷酸位点(DEP位点)存在于与mRNA加工和RNA相关的蛋白质中。间接ALI中的DEP位点富含与微管聚合、微管聚合的正调控和成纤维细胞迁移相关的蛋白质。这项研究表明,GPF-DDA混合DIA分析工作流程确实可以促进磷酸蛋白质组DIA分析的深度,并且可以扩展到其他PTM的DIA分析。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b42f/11787513/e4f0fe18b2ce/gr1.jpg

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