The use of various strategies in quantitative proteomics to measure the relative or absolute expression patterns of proteins in a sample, tissue, or organism or when comparing two different genetic or disease conditions is a common practice. There are various techniques available to the modern researcher, including differential two-dimensional gel electrophoresis (DIGE), stable isotope labeling with amino acids in active culture (SILAC), isotope-coded affinity tag (iCAT), isobaric tag for relative and absolute quantitation (iTRAQ), and absolute protein quantitation.1 Those are all examples of quantitation strategies that rely on various labels to differentiate samples for further analysis, and all but DIGE do not require gel electrophoresis. There are additional label-free protein quantitation strategies that also can be used but will not be covered here.
There are several advantages to protein labeling strategies, such as iTRAQ, when analyzing a disease state compared to the wild type. iTRAQ is an amine-reactive chemical that adds a tag of known mass to peptides that have either an N terminus or a free epsilon amine on the lysine residue exposed.1 The tags all have slightly different molecular weights and therefore can be differentiated in a single mass spectrometry scan without introducing any known ionization bias. This provides a robust technique for the single comparison of samples from two separate populations.
Quantitative mass spectrometry techniques have been used in the past to characterize the proteomic profiles of various tissues, subcellular compartments, or biological fluids, such as human tears.2 While studies in the past have identified over 1,500 proteins in the tears of healthy individuals, Srinivasan et al.3 identified 386 proteins in patients suffering from dry eye, a disease symptomatic of inflammation, increased osmolarity of the tear film, and visual disturbances. Using the iTRAQ labeling system and analyzing the sample on an LTQ Orbitrap mass spectrometer (Thermo Scientific), the researchers were able to identify several proteins that were universally upregulated in patients suffering from dry eye. By examining different subgroups of patients suffering from various different symptoms of this disease, it was possible to classify unique protein signatures for some of the conditions. As in previous studies, proteins such as prolactin inducible protein (PIP), lipocalin-1, lactoferrin, and lysozyme were all downregulated in patients suffering from dry eye.2,3 In Srinivasan et al.,3 there were additional upregulated proteins identified by the iTRAQ data, aldehyde dehydrogenase and haptoglobin. It is unclear at this time what the role of these protein expression changes play in the various physiological states of the disease.
The use of quantitative proteomics strategies, such as iTRAQ and others, is a valuable tool in high-throughput protein identification and quantification of normal or disease state tissues, as is the case with the tear proteomes of patients suffering from dry eye compared to normal healthy patients.2,3 Additional, careful quantitation and proteomic profiling of various disease states and comparative healthy tissue can lead to the discovery of biomarkers or novel treatments for patients suffering from these diseases.
1. Chen, X., et al. (2007) ‘Amino acid-coded tagging approaches in quantitative proteomics‘, Expert Review of Proteomics, 4 (1), (pp. 25–37)
2. Zhou, L., et al. (2012) ‘In-depth analysis of the human tear proteome‘, Journal of Proteomics, 75 (13), (pp. 3877–3885)
3. Srinivasan, S., et al. (2012) ‘iTRAQ quantitative proteomics in the analysis of tears in dry eye patients‘, Investigative Ophthalmology and Visual Science, 53 (8), (pp. 5052–5059)