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Nov 6, 2013

Principles of Proteomics

This primer provides a simple introduction to a ubiquitous technology.

Principles of Proteomics

This figure appears in the second edition of the book Principles of Proteomics. For more information on this book, click here.

  • This book excerpt from the recently published second edition of Principles of Proteomics by Richard Twyman provides an introduction to proteomics and then delves into its diagnostic applications. In this excerpt, Twyman discusses the importance of biomarkers and the methods used to discover them and use them in clinical applications.


  • Proteomics is used to identify biomarkers of disease states

    A biomarker is a biological feature of a cell, tissue, or organism that corresponds to a particular physiological state. In a medical context, the most important biomarkers are those that appear or disappear specifically in the disease state (disease biomarkers), or those that appear or disappear in response to drugs (toxicity biomarkers). There are many different types of disease biomarker, including the presence of particular pathogenic entities disease-specific cytological or histological characteristics, gene or chromosome mutations, the appearance of specific transcripts or proteins, new post-translational variants, or alterations in the level of mRNA or protein expression. Molecular biomarkers, such as mutations, transcripts, and proteins, are the most useful because they tend to appear well before the symptoms of the disease manifest, allowing early detection and prompt treatment. Furthermore, different biomarkers can sometimes be used to monitor the progress of a disease or its treatment.

    Proteins are advantageous biomarkers because the direct analysis of proteins can reveal characteristics, such as post-translational modifications, that cannot be identified by DNA sequencing or mRNA profiling. Perhaps more importantly, protein biomarkers can be assayed in body fluids, among which serum is the most valuable because it is in contact with all parts of the body and its contents are influenced by secretions or leakage from cells that are damaged by disease. Potential serum biomarkers for many types of disease have been discovered using different proteomics methods. The ideal biomarker should highly specific for a certain disease condition, a feature that can only be established by extensive validation in a broad population. Unfortunately, such biomarkers are rare and most candidate biomarkers are found in many different types of disease, perhaps with different expression levels in each case. A combination of relatively nonspecific biomarkers can, in some cases, provide a more specific disease index, and proteomics is useful in this context since it allows the expression profiles of hundreds of proteins to be studied in parallel. Many licensed tests that use proteins for disease diagnosis are ELISA-based systems that exploit protein biomarkers found in easily accessible fluids so that the assay is noninvasive. In most cases, however, these assays have been developed after the fortuitous discovery of individual proteins that are overexpressed or ectopically expressed in the disease state. What proteomics can offer is the opportunity to compare the protein profiles of samples from healthy people and those with a given disease to identify protein biomarkers in a systematic fashion. Thus far, very few biomarkers discovered via proteomics have been introduced into the clinic, but this mainly reflects the long period of testing required for full validation.

  • Biomarkers can be discovered by finding plus/minus or quantitative differences between samples

    Biomarker discovery was probably the first application envisaged for proteomics. As early as 1982, it was suggested that two-dimensional gels could be used to detect quantitative differences in protein profiles between healthy individuals and those suffering from particular diseases, although at the time there was no easy way to identify the differentially expressed proteins that were discovered. This all changed in the early 1990s with the advent of mass spectrometry techniques that allowed proteins to be identified by correlative database searching. The combination of 2DGE and mass spectrometry soon became the standard way to find potential new protein biomarkers. An initial strategy was to compare silver-stained gels by eye or using visual analysis software. Spots that were present on one gel and absent on another, or spots that showed obvious quantitative differences between gels, were picked and analyzed by mass spectrometry. The proteins contained within the spots were thus identified, and their relative abundance in different samples was confirmed using other methods. This led to the discovery of numerous potential disease biomarkers, many of which offered the prospect of diagnosis for different forms of cancer, but also for cardiovascular disease, neurological disease, autoimmune and inflammatory diseases, and infectious diseases such as hepatitis.

    Cancer has been the primary target for proteomic analysis because it is relatively easy to obtain matched samples of disease and healthy tissue from the same patient in sufficient amounts to carry out 2DGE. Good examples of this approach include the pioneering studies of Sam Hanash and colleagues that identified various biomarkers suitable for the diagnosis and classification of different forms of leukemia. One such study identified the protein stathmin (otherwise known as oncoprotein 18), which functions as an intracellular signal relay in the transduction of growth factor signals, as a reliable biomarker for childhood leukemia. The interesting feature of this particular protein is that only the phosphorylated form is implicated in the disease. Pioneering work was also carried out by Julio Celis and colleagues, who initially used 2DGE to study the changes in protein expression that occurred as cultured cells underwent growth transformation. The knowledge gained from this series of investigations was later applied to the analysis of bladder cancer resulting in the discovery of several markers, including different forms of keratin, which can be used to follow the progression of the disease from normal epithelium through the early transitional epithelium stage to the late squamous cell carcinoma. Another protein, called psoriasin, is shed into the urine of squamous cell carcinoma patients and thus has the potential to be developed as a validated biomarker for disease diagnosis. Breast cancer has also received much attention, particularly since proteins can be isolated from nipple aspiration fluid allowing noninvasive diagnosis. Several potential biomarkers have been identified through the comparative 2DGE analysis of bilateral matched samples of fluid taken from women with unilateral breast cancer.

    Despite the many successes that have been reported, 2DGE has a number of disadvantages for biomarker discovery, including its low sensitivity and the requirement for relatively large samples. The information content of 2DGE can be improved through multiplex analysis (difference gel electrophoresis) and the sensitivity can be increased through the use of novel protein stains, or by pre-fractionation of the sample prior to separation. Various strategies for pre-fractionation have also been tested in biomarker discovery projects, including approaches that select a particular component of the proteome for analysis or eliminate a certain fraction of the proteome during analysis. The selection of cell surface proteins on cancer cells by labeling the extracellular portion of cell surface proteins on intact cells with a hydrophilic biotin reagent is an example of the first approach. An example of the second approach is the use of narrow pH range gels or simple chromatographic procedures that select proteins with particular physicochemical properties. In these cases, however, it is beneficial to use even larger amounts of the starting material to provide enough of the protein sample to facilitate the identification of low-abundance proteins. Unfortunately, most clinical samples are small and heterogeneous, and are surrounded by contaminating normal tissue, which makes the detection of useful biomarkers much more difficult. One way to address the problem of contamination is to use laser capture microdissection (LCM), a technique in which particular cell populations can be isolated under direct microscopic visualization.

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