MS-based proteomics is a uniquely powerful and versatile tool in biology as it allows unbiased, comprehensive and sensitive detection of proteins in complex mixtures. With the ability to identify thousands of proteins in a single experiment, MS-based proteomics makes it easy to generate lengthy protein catalogs, but qualitative comparisons of lists of proteins is less informative. Instead, the ability to measure abundances of specific proteins and observe these changing over time in response to a defined perturbation is extremely powerful. Such information can be obtained with quantitative proteomics, which greatly enhances the power and utility of MS-based methods.

We use chemical labeling methods, like iTRAQ or metabolic labeling with SILAC, to quantify changes in protein abundance, enabling functional assays to compare protein expression levels in perturbed and control cell states. In SILAC, proteins from two cell populations labeled with normal isotope abundance or stable isotope labeled amino acids are observable in the same mass spectrum and distinguishable by their respective "light" and "heavy" peptide signals. This transforms the proteomic experiment into a format akin to a microarray experiment: when samples are mixed in equal proportions, signals from both populations are detectable unless the absence of either the light or heavy member is a direct result of the experimental perturbation. With this method, protein expression changes can be modeled and significant changes called with high confidence. Issues related to stochastic sampling of control-experiment pairs that plague classical proteomic approaches are avoided altogether. Along with dramatic improvements in the speed and sensitivity of MS instruments over the last decade, these quantitative methods have enabled impressive proteomics studies like the comprehensive identification of proteins in sub-cellular organelles like mitochondria and nucleoli, and quantification of subtle changes in whole proteomes induced by microRNA overexpression. 

Selected Publications:

Comparing SILAC and dimethyl labeling approaches for quantitative proteomics.
Lau HT, Suh HW, Golkowski MG, Ong SE. 
J Proteome Res. 2014. Epub 2014/08/01. doi: 10.1021/pr500630a. PubMed PMID: 25077673.

Rapid profiling of protein kinase inhibitors by quantitative proteomics.
Golkowski M, Brigham JL, Perera GK, Romano GE, Maly DJ, Ong SE. 
Medchemcomm. 2014;5(3):363-9. Epub 2014/03/22. doi: 10.1039/C3MD00315A. PubMed PMID: 24648882; PubMed Central PMCID: PMC3955727.

Identification of regulators of the innate immune response to cytosolic DNA and retroviral infection by an integrative approach.
Lee MN, Roy M, Ong SE, Mertins P, Villani AC, Li W, Dotiwala F, Sen J, Doench JG, Orzalli MH, Kramnik I, Knipe DM, Lieberman J, Carr SA, Hacohen N.
Nat Immunol. 2013;14(2):179-85. Epub 2012/12/25. doi: 10.1038/ni.2509. PubMed PMID: 23263557; PubMed Central PMCID: PMC3838897.

FOSL2 promotes leptin gene expression in human and mouse adipocytes.
Wrann CD, Eguchi J, Bozec A, Xu Z, Mikkelsen T, Gimble J, Nave H, Wagner EF, Ong SE, Rosen ED.
J Clin Invest. 2012;122(3):1010-21. Epub 2012/02/14. doi: 10.1172/JCI58431. PubMed PMID: 22326952; PubMed Central PMCID: PMC3322535.

The expanding field of SILAC.
Ong SE
Anal Bioanal Chem. 2012;404(4):967-76. Epub 2012/04/25. doi: 10.1007/s00216-012-5998-3. PubMed PMID: 22526667.