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1 Cardiology Division and Center for Immunology and Inflammatory Diseases, Massachusetts General Hospital, Charlestown, Massachusetts, United States
2 Pediatrics, University of Colorado School of Medicine, Denver, Colorado, United States
3 Allergy, Pulmonary and Critical Care Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee, United States
4 Mass Spectrometry Research Center, Vanderbilt University, Nashville, Tennessee, United States
5 Department of Laboratory Medicine and Pathology, Mayo Clinic and Mayo Foundation, Rochester, Minnesota, United States
6 Cardiovascular Diseases, Mayo Foundation, Rochester, Minnesota, United States
7 Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, Massachusetts, United States
8 Cardiovascular Division, Brigham and Women's Hospital and Department of Medicine, Harvard Medical School, Boston, Massachusetts, United States
9 National Heart Lung and Blood Institute, National Institutes of Health, Bethesda, Maryland, United States
* To whom correspondence should be addressed. E-mail: lorraine.ware{at}vanderbilt.edu.
The emerging scientific field of proteomics encompasses the identification, characterization and quantification of the protein content or proteome of whole cells, tissues or body fluids. The potential for proteomic technologies to identify and quantify novel proteins in the plasma that can function as biomarkers of the presence or severity of clinical disease states holds great promise for clinical use. However, there are many challenges in translating plasma proteomics from bench to bedside and relatively few plasma biomarkers have successfully transitioned from proteomic discovery to routine clinical use. Key barriers to this translation include the need for "orthogonal" biomarkers (ie., uncorrelated with existing markers), the complexity of the proteome in biological samples, the presence of high abundance proteins such as albumin in biological samples that hinder detection of low abundance proteins, false positive associations that occur with analysis of high dimensional datasets and the limited understanding of the effects of growth, development and age on the normal plasma proteome. Strategies to overcome these challenges are discussed.
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