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

Henock M. Deberneh, Doaa R. Abdelrahman, Sunil Verma, Jennifer J. Linares, Andrew J. Murton, William K. Russell, Muge N. Kuyumcu-Martinez, Benjamin F. Miller, Rovshan G. Sadygov

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

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.

Original languageEnglish (US)
Article number635
JournalScientific Data
Volume10
Issue number1
DOIs
StatePublished - Dec 2023

ASJC Scopus subject areas

  • Statistics and Probability
  • Information Systems
  • Education
  • Computer Science Applications
  • Statistics, Probability and Uncertainty
  • Library and Information Sciences

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