Super-SILAC: current trends and future perspectives Expert Rev. Proteomics 12(1), 13–19 (2014) Anjana Shenoy and Tamar Geiger* Department of Human Molecular Genetics and Biochemistry, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, 69978, Israel *Author for correspondence: geiger@post.tau.ac.il Stable isotope labeling with amino acids in cell culture (SILAC) has risen as a powerful quantification technique in mass spectrometry (MS)–based proteomics in classical and modified forms. Previously, SILAC was limited to cultured cells because of the requirement of active protein synthesis; however, in recent years, it was expanded to model organisms and tissue samples. Specifically, the super-SILAC technique uses a mixture of SILAC-labeled cells as a spike-in standard for accurate quantification of unlabeled samples, thereby enabling quantification of human tissue samples. Here, we highlight the recent developments in super-SILAC and its application to the study of clinical samples, secretomes, post-translational modifications and organelle proteomes. Finally, we propose super-SILAC as a robust and accurate method that can be commercialized and applied to basic and clinical research. KEYWORDS: biomarkers • cancer • clinical proteomics • mass spectrometry • quantitative proteomics • spike-in standard • stable isotope labeling • super-SILAC Mass-spectrometry (MS)–based proteomics, in analogy to the term ‘genomics’, aims at the identification of the assortment of proteins expressed in a biological system, their interac- tions and modifications. However, given the high qualitative similarity between large varie- ties of cell types [1], accurate protein quantifi- cation is a requisite when applying proteomics to the study of biological processes. Inherently, MS data are not quantitative because of the differences in peptide chemical and physical properties. Quantitative comparison between samples can be achieved by the use of stable isotope labels that induce a mass shift, which is resolved by the MS, or by computational algorithms that compare peptide signals in independent LC-MS runs. These ‘label-free’ approaches, such as spectral counting and intensity-based quantification, reduce the experimental costs and simplify the procedure, but compromise the quantification accuracy [2]. However, recent computational advances have dramatically improved the accuracy of intensity-based nonisotope-labeled techniques, which can be readily used when large quantita- tive differences are expected [3]. The develop- ment of multiple techniques that use stable isotope-labeled compounds initiated the field of quantitative proteomics (TABLE 1). Differential isotope labeling of samples creates a mass dif- ference between the peptides originating from the different samples, while it retains identical chemical properties. This mass offset can then be resolved by the mass spectrometer and can enable determination of their quantitative dif- ferences. Isotope Coded Affinity Tag (ICAT) was the first chemical labeling technique, based on cysteine tagging with deuterium-labeled bio- tinylated ICAT reagent [4]. However, the lim- ited number of cysteines per protein resulted in a low number of protein identifications and quantification events. In the following years, alternative chemical labeling and metabolic labeling techniques were developed. The dimethyl labeling technique uses deuterium or heavy carbon formaldehyde to label peptide amino termini and to enable accurate MS-level quantification [5,6]. The nonisobaric mTRAQ reagent relies on N-hydroxysuccinimide ester chemistry. All primary amines are chemically labeled, thus separating peptides by a mass dif- ference of 4 and 8 Da per tag. A recent study demonstrated that the high accuracy and depth can be achieved by this technique [7]. Isobaric tagging techniques, Tandem Mass Tags (TMT) and Isobaric Tag for Relative and Absolute Quantification (iTRAQ) provide MS/MS level separation between fragments of differentially labeled peptides and allow sample multiplex- ing [8,9]. These techniques undergo dynamic- range compression and reduced quantitative accuracy because of near-isobaric precursor informahealthcare.com 10.1586/14789450.2015.982538 Ó 2014 Informa UK Ltd ISSN 1478-9450 13 Special Report Expert Review of Proteomics Downloaded from informahealthcare.com by Nyu Medical Center on 02/12/15 For personal use only.