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大尺度 LC-MS 数据集:来自氘水代谢标记的鼠肝蛋白质组时程研究。

A large-scale LC-MS dataset of murine liver proteome from time course of heavy water metabolic labeling.

机构信息

Department of Biochemistry and Molecular Biology, The University of Texas Medical Branch, Galveston, Texas, USA.

Department of Surgery, The University of Texas Medical Branch, Galveston, Texas, USA.

出版信息

Sci Data. 2023 Sep 19;10(1):635. doi: 10.1038/s41597-023-02537-w.

Abstract

Metabolic stable isotope labeling with heavy water followed by liquid chromatography coupled with mass spectrometry (LC-MS) is a powerful tool for in vivo protein turnover studies. Several algorithms and tools have been developed to determine the turnover rates of peptides and proteins from time-course stable isotope labeling experiments. The availability of benchmark mass spectrometry data is crucial to compare and validate the effectiveness of newly developed techniques and algorithms. In this work, we report a heavy water-labeled LC-MS dataset from the murine liver for protein turnover rate analysis. The dataset contains eighteen mass spectral data with their corresponding database search results from nine different labeling durations and quantification outputs from d2ome+ software. The dataset also contains eight mass spectral data from two-dimensional fractionation experiments on unlabeled samples.

摘要

利用重水进行代谢稳定同位素标记,然后结合液相色谱和质谱(LC-MS)分析,是研究体内蛋白质周转率的有力工具。已经开发了几种算法和工具,用于从时间过程稳定同位素标记实验中确定肽和蛋白质的周转率。基准质谱数据的可用性对于比较和验证新开发技术和算法的有效性至关重要。在这项工作中,我们报告了来自小鼠肝脏的重水标记 LC-MS 数据集,用于蛋白质周转率分析。该数据集包含 18 个质谱数据及其相应的数据库搜索结果,来自 9 个不同的标记时间和 d2ome+软件的定量输出。该数据集还包含来自未标记样品二维分级实验的 8 个质谱数据。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0e81/10509199/292fb8cafdba/41597_2023_2537_Fig1_HTML.jpg

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