Super-SILAC mix for quantitative proteomics of human tumor tissue. Nat Methods. 2010;7:383-385. [PMID: 20364148 DOI: 10.1038/nmeth.1446]

The grand challenge to decipher the cancer proteome

The quest to decipher protein alterations in cancer has spanned well over half a century. The vast dynamic range of protein abundance coupled with a plethora of isoforms and disease heterogeneity have been formidable challenges. Progress in cancer proteomics has substantially paralleled technological developments. Advances in analytical techniques and the implementation of strategies to de-complex the proteome into manageable components have allowed proteins across a wide dynamic range to be explored. The massive amounts of data that can currently be collected through proteomics allow the near-complete definition of cancer subproteomes, which reveals the alterations in signalling and developmental pathways. This allows the discovery of predictive biomarkers and the annotation of the cancer genome based on proteomic findings. There remains a considerable need for infrastructure development and the organized collaborative efforts to efficiently mine the cancer proteome.

Whole proteomes as internal standards in quantitative proteomics

As mass-spectrometry-based quantitative proteomics approaches become increasingly powerful, researchers are taking advantage of well established methodologies and improving instrumentation to pioneer new protein expression profiling methods. For example, pooling several proteomes labeled using the stable isotope labeling by amino acids in cell culture (SILAC) method yields a whole-proteome stable isotope-labeled internal standard that can be mixed with a tissue-derived proteome for quantification. By increasing quantitative accuracy in the analysis of tissue proteomes, such methods should improve integration of protein expression profiling data with transcriptomic data and enhance downstream bioinformatic analyses. An accurate and scalable quantitative method to analyze tumor proteomes at the depth of several thousand proteins provides a powerful tool for global protein quantification of tissue samples and promises to redefine our understanding of tumor biology.

A genetic engineering solution to the "arginine conversion problem" in stable isotope labeling by amino acids in cell culture (SILAC)

Stable isotope labeling by amino acids in cell culture (SILAC) provides a straightforward tool for quantitation in proteomics. However, one problem associated with SILAC is the in vivo conversion of labeled arginine to other amino acids, typically proline. We found that arginine conversion in the fission yeast Schizosaccharomyces pombe occurred at extremely high levels, such that labeling cells with heavy arginine led to undesired incorporation of label into essentially all of the proline pool as well as a substantial portion of glutamate, glutamine, and lysine pools. We found that this can be prevented by deleting genes involved in arginine catabolism using methods that are highly robust yet simple to implement. Deletion of both fission yeast arginase genes or of the single ornithine transaminase gene, together with a small modification to growth medium that improves arginine uptake in mutant strains, was sufficient to abolish essentially all arginine conversion. We demonstrated the usefulness of our approach in a large scale quantitative analysis of proteins before and after cell division; both up- and down-regulated proteins, including a novel protein involved in septation, were successfully identified. This strategy for addressing the "arginine conversion problem" may be more broadly applicable to organisms amenable to genetic manipulation.

Decoding signalling networks by mass spectrometry-based proteomics

Signalling networks regulate essentially all of the biology of cells and organisms in normal and disease states. Signalling is often studied using antibody-based techniques such as western blots. Large-scale 'precision proteomics' based on mass spectrometry now enables the system-wide characterization of signalling events at the levels of post-translational modifications, protein-protein interactions and changes in protein expression. This technology delivers accurate and unbiased information about the quantitative changes of thousands of proteins and their modifications in response to any perturbation. Current studies focus on phosphorylation, but acetylation, methylation, glycosylation and ubiquitylation are also becoming amenable to investigation. Large-scale proteomics-based signalling research will fundamentally change our understanding of signalling networks.

Proteomics technologies for the global identification and quantification of proteins

This review provides an introduction for the nonspecialist to proteomics and in particular the major approaches available for global protein identification and quantification. Proteomics technologies offer considerable opportunities for improved biological understanding and biomarker discovery. The central platform for proteomics is tandem mass spectrometry (MS) but a number of other technologies, resources, and expertise are absolutely required to perform meaningful experiments. These include protein separation science (and protein biochemistry in general), genomics, and bioinformatics. There are a range of workflows available for protein (or peptide) separation prior to tandem MS and subsequent bioinformatics analysis to achieve protein identifications. The predominant approaches are 2D electrophoresis (2DE) and subsequent MS, liquid chromatography-MS (LC-MS), and GeLC-MS. Beyond protein identification, there are a number of well-established options available for protein quantification. Difference gel electrophoresis (DIGE) following 2DE is one option but MS-based methods (most commonly iTRAQ-Isobaric Tags for Relative and Absolute Quantification or SILAC-Stable Isotope Labeling by Amino Acids) are now the preferred options. Sample preparation is critical to performing good experiments and subcellular fractionation can additionally provide protein localization information compared with whole cell lysates. Differential detergent solubilization is another valid option. With biological fluids, it is possible to remove the most abundant proteins by immunodepletion. Sample enrichment is also used extensively in certain analyses and most commonly in phosphoproteomics with the initial purification of phosphopeptides. Proteomics produces considerable datasets and resources to facilitate the necessary extended analysis of this data are improving all the time. Beyond the opportunities afforded by proteomics there are definite challenges to achieving full proteomic coverage. Proteomes are highly complex and identifying and quantifying low abundance proteins is a significant issue. Additionally, the analysis of poorly soluble proteins, such as membrane proteins and multiprotein complexes, is difficult. However, it is without doubt that proteomics has already provided significant insights into biological function and this will continue as the technology continues to improve. We also anticipate that the promise of proteomics in terms of biomarker discovery will increasingly be realized.