» Articles » PMID: 26358907

Dynamic Proteomics: In Vivo Proteome-Wide Measurement of Protein Kinetics Using Metabolic Labeling

Overview
Journal Methods Enzymol
Specialty Biochemistry
Date 2015 Sep 12
PMID 26358907
Citations 28
Authors
Affiliations
Soon will be listed here.
Abstract

Control of biosynthetic and catabolic rates of polymers, including proteins, stands at the center of phenotype, physiologic adaptation, and disease pathogenesis. Advances in stable isotope-labeling concepts and mass spectrometric instrumentation now allow accurate in vivo measurement of protein synthesis and turnover rates, both for targeted proteins and for unbiased screening across the proteome. We describe here the underlying principles and operational protocols for measuring protein dynamics, focusing on metabolic labeling with (2)H2O (heavy water) combined with tandem mass spectrometric analysis of mass isotopomer abundances in trypsin-generated peptides. The core principles of combinatorial analysis (mass isotopomer distribution analysis or MIDA) are reviewed in detail, including practical advantages, limitations, and technical procedures to ensure optimal kinetic results. Technical factors include heavy water labeling protocols, optimal duration of labeling, clean up and simplification of sample matrices, accurate quantitation of mass isotopomer abundances in peptides, criteria for adequacy of mass spectrometric abundance measurements, and calculation algorithms. Some applications are described, including the noninvasive "virtual biopsy" strategy for measuring molecular flux rates in tissues through measurements in body fluids. In addition, application of heavy water labeling to measure flux lipidomics is noted. In summary, the combination of stable isotope labeling, particularly from (2)H2O, with tandem mass spectrometric analysis of mass isotopomer abundances in peptides, provides a powerful approach for characterizing the dynamics of proteins across the global proteome. Many applications in research and clinical medicine have been achieved and many others can be envisioned.

Citing Articles

Implications of tissue specific STING protein flux and abundance on inflammation and the development of targeted therapeutics.

Angel T, Chen Z, Moghieb A, Ng S, Beal A, Capriotti C PLoS One. 2025; 20(2):e0319216.

PMID: 39999142 PMC: 11856325. DOI: 10.1371/journal.pone.0319216.


Measuring HSD17β13 protein turnover in mouse liver with DO metabolic labeling and hybrid LC-MS.

Shi Y, Del Rosario A, Wang S, Kang L, Liu H, Rady B Bioanalysis. 2025; 17(3):151-159.

PMID: 39819243 PMC: 11853646. DOI: 10.1080/17576180.2025.2452757.


Changes in protein fluxes in skeletal muscle during sequential stages of muscle regeneration after acute injury in male mice.

Bizieff A, Cheng M, Chang K, Mohammed H, Ziari N, Nyangau E Sci Rep. 2024; 14(1):13172.

PMID: 38849371 PMC: 11161603. DOI: 10.1038/s41598-024-62115-x.


Altered extracellular matrix dynamics is associated with insulin resistance in adolescent children with obesity.

Slusher A, Nouws J, Tokoglu F, Vash-Margita A, Matthews M, Fitch M Obesity (Silver Spring). 2024; 32(3):593-602.

PMID: 38410080 PMC: 11034857. DOI: 10.1002/oby.23974.


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

Deberneh H, Abdelrahman D, Verma S, Linares J, Murton A, Russell W Sci Data. 2023; 10(1):635.

PMID: 37726365 PMC: 10509199. DOI: 10.1038/s41597-023-02537-w.