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Deberneh HM, Abdelrahman DR, Verma SK, Linares JJ, Murton AJ, Russell WK, Kuyumcu-Martinez MN, Miller BF, Sadygov RG. A large-scale LC-MS dataset of murine liver proteome from time course of heavy water metabolic labeling. Sci Data 2023; 10:635. [PMID: 37726365 PMCID: PMC10509199 DOI: 10.1038/s41597-023-02537-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Accepted: 09/04/2023] [Indexed: 09/21/2023] Open
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.
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Affiliation(s)
- Henock M Deberneh
- Department of Biochemistry and Molecular Biology, The University of Texas Medical Branch, Galveston, Texas, USA.
| | - Doaa R Abdelrahman
- Department of Surgery, The University of Texas Medical Branch, Galveston, Texas, USA
- Sealy Center of Aging, The University of Texas Medical Branch, Galveston, Texas, USA
| | - Sunil K Verma
- Department of Biochemistry and Molecular Biology, The University of Texas Medical Branch, Galveston, Texas, USA
- Department of Neuroscience, Cell Biology and Anatomy, The University of Texas Medical Branch, Galveston, Texas, USA
- Department of Molecular Physiology and Biological Physics, The University of Virginia, Charlottesville, Virginia, USA
| | - Jennifer J Linares
- Department of Biochemistry and Molecular Biology, The University of Texas Medical Branch, Galveston, Texas, USA
| | - Andrew J Murton
- Department of Surgery, The University of Texas Medical Branch, Galveston, Texas, USA
- Sealy Center of Aging, The University of Texas Medical Branch, Galveston, Texas, USA
| | - William K Russell
- Department of Biochemistry and Molecular Biology, The University of Texas Medical Branch, Galveston, Texas, USA
| | - Muge N Kuyumcu-Martinez
- Department of Biochemistry and Molecular Biology, The University of Texas Medical Branch, Galveston, Texas, USA
- Department of Neuroscience, Cell Biology and Anatomy, The University of Texas Medical Branch, Galveston, Texas, USA
- Department of Molecular Physiology and Biological Physics, The University of Virginia, Charlottesville, Virginia, USA
| | - Benjamin F Miller
- Aging and Metabolism Research Foundation, Oklahoma Medical Research Foundation, Oklahoma City, Oklahoma, USA
- Oklahoma City VA, Oklahoma City, Oklahoma, USA
| | - Rovshan G Sadygov
- Department of Biochemistry and Molecular Biology, The University of Texas Medical Branch, Galveston, Texas, USA.
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Slavov N. Great Gains in Mass Spectrometry Data Interpretation. J Proteome Res 2023; 22:659. [PMID: 36866536 PMCID: PMC10031382 DOI: 10.1021/acs.jproteome.3c00099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/04/2023]
Affiliation(s)
- Nikolai Slavov
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Proteomics Center, and Barnett Institute, Northeastern University, Boston, Massachusetts 02115, United States
- Parallel Squared Technology Institute, Watertown, Massachusetts 02472, United States
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Deberneh HM, Sadygov RG. Retention Time Alignment for Protein Turnover Studies Using Heavy Water Metabolic Labeling. J Proteome Res 2023; 22:410-419. [PMID: 36692003 PMCID: PMC10233748 DOI: 10.1021/acs.jproteome.2c00592] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
Retention time (RT) alignment has been important for robust protein identification and quantification in proteomics. In data-dependent acquisition mode, whereby the precursor ions are semistochastically chosen for fragmentation in MS/MS, the alignment is used in an approach termed matched between runs (MBR). MBR transfers peptides, which were fragmented and identified in one experiment, to a replicate experiment where they were not identified. Before the MBR transfer, the RTs of experiments are aligned to reduce the chance of erroneous transfers. Despite its widespread use in other areas of quantitative proteomics, RT alignment has not been applied in data analyses for protein turnover using an atom-based stable isotope-labeling agent such as metabolic labeling with deuterium oxide, D2O. Deuterium incorporation changes isotope profiles of intact peptides in full scans and their fragment ions in tandem mass spectra. It reduces the peptide identification rates in current database search engines. Therefore, the MBR becomes more important. Here, we report on an approach to incorporate RT alignment with peptide quantification in studies of proteome turnover using heavy water metabolic labeling and LC-MS. The RT alignment uses correlation-optimized time warping. The alignment, followed by the MBR, improves labeling time point coverage, especially for long labeling durations.
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Affiliation(s)
- Henock M. Deberneh
- Department of Biochemistry and Molecular Biology, The University of Texas Medical Branch, 301 University of Blvd, Galveston, TX 77555
| | - Rovshan G. Sadygov
- Department of Biochemistry and Molecular Biology, The University of Texas Medical Branch, 301 University of Blvd, Galveston, TX 77555
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