Metabolic Labeling of Proteins for Proteomics

Beynon, Robert J.; Pratt, Julie M.

doi:10.1074/mcp.r400010-mcp200

Cited by 189 publications

(181 citation statements)

References 65 publications

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“…Recently, metabolic labelling with stable (nonradioactive) isotopes (deuterium, 13 C or 15 N, the most common) have been used to trace protein turnover (for recent reviews see: [179,185].…”

Section: Proteome Turnovermentioning

confidence: 99%

Two‐dimensional gel electrophoresis and mass spectrometry for biomarker discovery

Penque

2009

Proteomics Clinical Apps

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The proteome project, initiated in 1995, was made possible by 2-DE combined with MS. The project main objective was and remains the identification of all proteins expressed by a cell, tissue or organism in a given time and condition. Following this objective, the global profiling of proteins in health versus pathological state by the 2-DE/MS-based proteomic approach has contributed to the elucidation of the basic mechanisms of disease by discovering candidate disease biomarkers and disease targets for new drug development. This review will briefly summarize the historical evolution of 2-DE up to today, and review 2-DE/MS technology and its specific methods of study of immunoresponse (immunoproteomics), PTM of proteins, complex proteinprotein interactions (interactome), the proteome of cell membrane and intracellular proteome turnover in disease biomarker discovery. 2-DE historical evolutionGlobal profiling of proteins in healthy versus pathological states is a strategy to discover biomarkers for early diagnostics, disease progression, treatment response and novel targets for therapeutic drugs development. The idea of making an inventory of all the proteins in a cell, tissue or organism with normal or abnormal physiology, was (re)initiated in the middle of the 1990s with the proteome project [1]. Since then, many technologies to make the analysis of the proteome a reality have been united.The perfect technological 'marriage' that made the proteome project feasible was between high resolution 2-DE for separation of large numbers of proteins and MS for identification and characterization of the proteins displayed on this gel [2]. However, before this happy union occured, science and technology devoted to protein study had a long journey until 2-DE and MS combination was possible (Fig. 1). It started in the last century, in 1937, when Tiselius [3] introduced electrophoresis technique as a modification of Reiner's early concept (1927) [4] to analyse a complex mixture of proteins such as serum proteins. Only 20 years later, in 1959, electrophoresis on a gel support formed by crosslinked polymerization of Cyanogum 41, a commercial name for two organic monomers, acrylamide and the crosslinking agent, N,N1-methylenebisacrylamide, was shown to be useful and was named PAGE by Raymond and Weintraub [5]. The adaptation of polyacrylamide gel to IEF in a stable pH gradient, developed by Svensson in 1962 [6], was possible by the end of the 1960s [7][8][9]. About this time, the 2-D by combining IEF to separate undenatured proteins according to their charge and gradient SDS-PAGE for a second separation, according to their molecular mass, was for the first time introduced by Kenrick and Margolis (1970) [10]. Only in 1975, was IEF in denaturing conditions, as known today, used to follow the 2-D PAGE and it was described in three independent works [11][12][13]. The most powerful demonstraCorrespondence: Dr. Deborah Penque, Laboratório de Proteó-mica, Departamento de Genética, Instituto Nacional de Saúde, Dr. Ricardo Jorge, I.P., A...

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“…Recently, metabolic labelling with stable (nonradioactive) isotopes (deuterium, 13 C or 15 N, the most common) have been used to trace protein turnover (for recent reviews see: [179,185].…”

Section: Proteome Turnovermentioning

confidence: 99%

Two‐dimensional gel electrophoresis and mass spectrometry for biomarker discovery

Penque

2009

Proteomics Clinical Apps

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“…Although an optimized strategy using a smaller ICAT analog was recently reported by Ramus et al (11), the quantification efficiency of this labeling strategy on the proteome scale remains to be evaluated. Alternatively SILAC offers more options for selection of isotopically labeled amino acids such as arginine, leucine, lysine, serine, methionine, and tyrosine (12). Although some successes have been reported in analysis of the membrane proteome (13,14), the major drawback of SILAC is that it is most applicable to cell culture rather than tissue or body fluids.…”

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confidence: 99%

A Multiplexed Quantitative Strategy for Membrane Proteomics

Han

Chien

Chen³

et al. 2008

Molecular & Cellular Proteomics

129

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Toward multiplexed, comprehensive, and robust quantitation of the membrane proteome, we report a strategy combining gel-assisted digestion, iTRAQ (isobaric tags for relative and absolute quantitation) labeling, and LC-MS/MS. Quantitation of four independently purified membrane fractions from HeLa cells gave high accuracy (<8% error) and precision (<12% relative S.D.), demonstrating a high degree of consistency and reproducibility of this quantitation platform. Under stringent identification criteria (false discovery rate ‫؍‬ 0%), the strategy efficiently quantified membrane proteins; as many as 520 proteins (91%) were membrane proteins, each quantified based on an average of 14.1 peptides per integral membrane protein. In addition to significant improvements in signal intensity for most quantified proteins, most remarkably, topological analysis revealed that the biggest improvement was achieved in detection of transmembrane peptides from integral membrane proteins with up to 19 transmembrane helices. To the best of our knowledge, this level of coverage exceeds that achieved previously using MS and provides superior quantitation accuracy compared with other methods. We applied this approach to the first proteomics delineation of phenotypic expression in a mouse model of autosomal dominant polycystic kidney disease (ADPKD). By characterizing kidney cell plasma membrane from wild-type versus PKD1 knock-out mice, 791 proteins were quantified, and 67 and 37 proteins showed >2-fold up-regulation and down-regulation, respectively. Some of these differentially expressed membrane proteins are involved in the mechanisms underlying major abnormalities in ADPKD, including epithelial cell proliferation and apoptosis, cell-cell and cell-matrix interactions, ion and fluid secretion, and membrane protein polarity. Among these proteins, targeting therapeutics to certain transporters/receptors, such as epidermal growth factor receptor, has proven effective in preclinical studies of ADPKD; others are known drug targets in various diseases. Our method demonstrates how comparative membrane proteomics can provide insight into the molecular mechanisms underlying ADPKD and the identification of potential drug targets, which may lead to new therapeutic opportunities to prevent or retard the disease.

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“…For comparison with S calc (m), the experimental spectrum must be scaled according to the charge state z. It is straightforward to determine the charge state from the spacing of the peaks in the experimental isotope distribution, which is 1/z, giving the scaled spectrum (7) Alternatively the calculated spectrum could be scaled, but for least-squares fitting, scaling the experimental spectrum is done just once prior to fitting, while scaling the calculated spectrum would need to be done at each iteration.…”

Section: Least-squares Fitting Of Peak Profilesmentioning

confidence: 99%

“…7 The growth of simple organisms on 15 N-ammonium ions as the nitrogen source can result in fractional atom labeling of the proteins where the nitrogen atoms are composed of a statistical mixture of 14 N and 15 N atoms. The growth of more complex organisms on labeled amino acids can result in fractional residue labeling, where there is a statistical mixture of unlabeled and labeled residues.…”

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confidence: 99%

Quantitative Analysis of Isotope Distributions In Proteomic Mass Spectrometry Using Least-Squares Fourier Transform Convolution

et al. 2008

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Quantitative proteomic mass spectrometry involves comparison of the amplitudes of peaks resulting from different isotope labeling patterns, including fractional atomic labeling and fractional residue labeling. We have developed a general and flexible analytical treatment of the complex isotope distributions that arise in these experiments, using Fourier transform convolution to calculate labeled isotope distributions and least-squares for quantitative comparison with experimental peaks. The degree of fractional atomic and fractional residue labeling can be determined from experimental peaks at the same time as the integrated intensity of all of the isotopomers in the isotope distribution. The approach is illustrated using data with fractional 15 N-labeling and fractional 13 C-isoleucine labeling. The least-squares Fourier transform convolution approach can be applied to many types of quantitive proteomic data, including data from stable isotope labeling by amino acids in cell culture and pulse labeling experiments.Stable isotope labeling in cells coupled with mass spectrometry has many important applications in the analysis of protein expression, modification, turnover, and metabolism. 1 Cells and organisms can be isotopically labeled by supplying labeled precursors in the form of nutrients such as amino acids, glucose, and ammonia. These labeled precursors are incorporated into proteins, whose resulting isotope labeling patterns reflect their abundance and the dynamics of protein synthesis and turnover. The era of quantitative proteomics began with experiments where mixtures of unlabeled control cells and 15 N-labeled or isotope depleted test cells were quantitatively analyzed to determine protein expression and phosphorylation levels. 2,3 More recently, the SILAC technique was developed based on specific amino acid labeling of cells for quantitative analysis of protein expression, modification, and turnover. 4-6The two basic classes of quantitative metabolic labeling approaches are the stable isotope labeling by amino acids in cell culture (SILAC) experiments and pulse labeling experiments, both of which have been implemented in a variety of ways. 7 In the SILAC method, independent samples are prepared with different labeling patterns that are mixed prior to mass spectrometry analysis. For pulse labeling experiments, labels that are added to the growth medium are taken up to metabolically label the proteome, which generates fractionally enriched proteins from a

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Metabolic Labeling of Proteins for Proteomics

Cited by 189 publications

References 65 publications

Two‐dimensional gel electrophoresis and mass spectrometry for biomarker discovery

Two‐dimensional gel electrophoresis and mass spectrometry for biomarker discovery

A Multiplexed Quantitative Strategy for Membrane Proteomics

Quantitative Analysis of Isotope Distributions In Proteomic Mass Spectrometry Using Least-Squares Fourier Transform Convolution

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