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TRANSLATIONAL PHYSIOLOGY

Challenges in translating plasma proteomics from bench to bedside: update from the NHLBI Clinical Proteomics Programs

¹Cardiology Division and Center for Immunology and Inflammatory Diseases, Massachusetts General Hospital, Boston, Massachusetts; ²Department of Pediatrics, University of Colorado Denver and Health Sciences, Denver, Colorado; ³Division of Allergy, Pulmonary, and Critical Care Medicine, Department of Medicine, Vanderbilt University, Nashville, Tennessee; ⁴Mass Spectrometry Research Center, Vanderbilt University Medical Center, Nashville, Tennessee; ⁵Department of Laboratory Medicine and Pathology, Mayo Clinic and Mayo Foundation, Rochester, Minnesota; ⁶Division of Cardiovascular Diseases, Mayo Clinic, Rochester, Minnesota; ⁷Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, Massachusetts; ⁸Cardiovascular Division, Brigham and Women's Hospital and Department of Medicine, Harvard Medical School, Boston, Massachusetts; and ⁹National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland

Submitted 24 January 2008 ; accepted in final form 27 April 2008

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 (i.e., 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.

protein content; proteome