Literature DB >> 20845935

Direct comparison of stable isotope labeling by amino acids in cell culture and spectral counting for quantitative proteomics.

Timothy S Collier1, Prasenjit Sarkar, William L Franck, Balaji M Rao, Ralph A Dean, David C Muddiman.   

Abstract

Numerous experimental strategies exist for relative protein quantification, one of the primary objectives of mass spectrometry based proteomics analysis. These strategies mostly involve the incorporation of a stable isotope label via either metabolic incorporation in cell or tissue culture (¹⁵N/¹⁴N metabolic labeling, stable isotope labeling by amino acids in cell culture (SILAC)), chemical derivatization (ICAT, iTRAQ, TMT), or enzymatically catalyzed incorporation (¹⁸O labeling). Also, these techniques can be cost or time prohibitive or not amenable to the biological system of interest (i.e., metabolic labeling of clinical samples, most animals, or fungi). This is the case with the quantification of fungal proteomes, which often require auxotroph mutants to fully metabolically label. Alternatively, label-free strategies for protein quantification such as using integrated ion abundance and spectral counting have been demonstrated for quantification affording over 2 orders of magnitude of dynamic range which is comparable to metabolic labeling strategies. Direct comparisons of these quantitative techniques are largely lacking in the literature but are highly warranted in order to evaluate the capabilities, limitations, and analytical variability of available quantitative strategies. Here, we present the direct comparison of SILAC to label-free quantification by spectral counting of an identical set of data from the bottom-up proteomic analysis of human embryonic stem cells, which are readily able to be quantified using both strategies, finding that both strategies result in a similar number of protein identifications. We also discuss necessary constraints for accurate quantification using spectral counting and assess the potential of this label-free strategy as a viable alternative for quantitative proteomics.

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Year:  2010        PMID: 20845935     DOI: 10.1021/ac101978b

Source DB:  PubMed          Journal:  Anal Chem        ISSN: 0003-2700            Impact factor:   6.986


  31 in total

1.  Evaluation of normalization methods on GeLC-MS/MS label-free spectral counting data to correct for variation during proteomic workflows.

Authors:  Emine Gokce; Christopher M Shuford; William L Franck; Ralph A Dean; David C Muddiman
Journal:  J Am Soc Mass Spectrom       Date:  2011-09-24       Impact factor: 3.109

Review 2.  Phosphoproteomic analysis: an emerging role in deciphering cellular signaling in human embryonic stem cells and their differentiated derivatives.

Authors:  Brian T D Tobe; Junjie Hou; Andrew M Crain; Ilyas Singec; Evan Y Snyder; Laurence M Brill
Journal:  Stem Cell Rev Rep       Date:  2012-03       Impact factor: 5.739

3.  Utilizing spectral counting to quantitatively characterize tandem removal of abundant proteins (TRAP) in human plasma.

Authors:  Christopher M Shuford; Adam M Hawkridge; John C Burnett; David C Muddiman
Journal:  Anal Chem       Date:  2010-11-19       Impact factor: 6.986

4.  Mass Defect-Based N,N-Dimethyl Leucine Labels for Quantitative Proteomics and Amine Metabolomics of Pancreatic Cancer Cells.

Authors:  Ling Hao; Jillian Johnson; Christopher B Lietz; Amanda Buchberger; Dustin Frost; W John Kao; Lingjun Li
Journal:  Anal Chem       Date:  2017-01-04       Impact factor: 6.986

5.  Integrative Metabolic Pathway Analysis Reveals Novel Therapeutic Targets in Osteoarthritis.

Authors:  Beatriz Rocha; Berta Cillero-Pastor; Gert Eijkel; Valentina Calamia; Patricia Fernandez-Puente; Martin R L Paine; Cristina Ruiz-Romero; Ron M A Heeren; Francisco J Blanco
Journal:  Mol Cell Proteomics       Date:  2020-01-24       Impact factor: 5.911

6.  The proteome of mouse brain microvessel membranes and basal lamina.

Authors:  Hyun Bae Chun; Michael Scott; Sherry Niessen; Heather Hoover; Andrew Baird; John Yates; Bruce E Torbett; Brian P Eliceiri
Journal:  J Cereb Blood Flow Metab       Date:  2011-07-27       Impact factor: 6.200

7.  Functional Consequences of Mannose and Asialoglycoprotein Receptor Ablation.

Authors:  Yiling Mi; Marcy Coonce; Dorothy Fiete; Lindsay Steirer; Gabriela Dveksler; R Reid Townsend; Jacques U Baenziger
Journal:  J Biol Chem       Date:  2016-07-12       Impact factor: 5.157

Review 8.  Causes and consequences of aneuploidy in cancer.

Authors:  David J Gordon; Benjamin Resio; David Pellman
Journal:  Nat Rev Genet       Date:  2012-01-24       Impact factor: 53.242

9.  Sources of technical variability in quantitative LC-MS proteomics: human brain tissue sample analysis.

Authors:  Paul D Piehowski; Vladislav A Petyuk; Daniel J Orton; Fang Xie; Ronald J Moore; Manuel Ramirez-Restrepo; Anzhelika Engel; Andrew P Lieberman; Roger L Albin; David G Camp; Richard D Smith; Amanda J Myers
Journal:  J Proteome Res       Date:  2013-04-10       Impact factor: 4.466

Review 10.  Solid-phase capture for the detection and relative quantification of S-nitrosoproteins by mass spectrometry.

Authors:  J Will Thompson; Michael T Forrester; M Arthur Moseley; Matthew W Foster
Journal:  Methods       Date:  2012-10-11       Impact factor: 3.608

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