Research Press Release

Quantifying human tumour proteins

Nature Methods

April 5, 2010

A method for quantifying proteins in human tumour tissue is reported in a paper published online this week in Nature Methods. This approach could lead to new insights in tumour biology.

Proteomics ― the study of cellular proteins on a large scale ― is routinely carried out using mass spectrometry technology. Many approaches have been developed to quantitatively measure protein levels in biological samples by mass spectrometry. One of these methods is known as SILAC, or stable isotope labeling by amino acids in cell culture. Using SILAC, proteins in a biological sample that serves as an internal quantitative standard are marked or 'labeled' with amino acids containing 'heavy' isotopes such that their mass is shifted on the mass spectrum. This is in comparison to the test sample, which is left unlabeled. This allows very accurate quantitative analysis of the test sample by mass spectrometry. However, this method can typically only be applied to cells or organisms that can be fully metabolically labeled with heavy amino acids.

Matthias Mann and colleagues now describe a twist to the SILAC approach that allows them to quantify proteins in primary human tissue, which cannot be metabolically labeled. Human tissues, including tumours, are made up of many different cell types expressing proteins at different levels. To represent the different cell types and proteins found in a particular tumour, the team labeled a mixture of several different immortalized human cancer cell lines with heavy amino acids. They use this mixture as an internal standard for mass spectrometry-based quantification of the tumour tissue proteome. With this 'super-SILAC' method, they were able to accurately quantify a large number of proteins in primary human tumour tissues, including breast and brain tumours.

Besides being useful for the study of tumor biology, the super-SILAC approach could be applied for the discovery of protein biomarkers for the early detection of cancer.

DOI:10.1038/nmeth.1446 | Original article

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