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CE-MS analysis of the human urinary proteome for biomarker discovery and disease diagnostics

  • Joshua J. Coon
  • , Petra Zürbig
  • , Mohammed Dakna
  • , Anna F. Dominiczak
  • , Stéphane Decramer
  • , Danilo Fliser
  • , Moritz Frommberger
  • , Igor Golovko
  • , David M. Good
  • , Stefan Herget-Rosenthal
  • , Joachim Jankowski
  • , Bruce A. Julian
  • , Markus Kellmann
  • , Walter Kolch
  • , Ziad Massy
  • , Jan Novak
  • , Kasper Rossing
  • , Joost P. Schanstra
  • , Eric Schiffer
  • , Dan Theodorescu
  • Raymond Vanholder, Eva M. Weissinger, Harald Mischak, Philippe Schmitt-Kopplin
  • University of Wisconsin-Madison
  • Mosaiques Diagnostics and Therapeutics AG
  • University of Glasgow
  • INSERM U70
  • Université de Toulouse
  • Children's Hospital
  • Hannover Medical School
  • Helmholtz Zentrum München German Research Center for Environmental Health
  • University Hospital of Essen
  • Charité – Universitätsmedizin Berlin
  • Uni-versity of Alabama at Birmingham
  • Thermo Fisher Scientific, Clinical Diagnostics, BRAHMS GmbH
  • University of Glasgow
  • CHU Sud
  • Steno Diabetes Center Copenhagen
  • University of Virginia School of Medicine
  • Ghent University Hospital

Research output: Contribution to journalReview articlepeer-review

181 Scopus citations

Abstract

Owing to its availability, ease of collection, and correlation with pathophysiology of diseases, urine is an attractive source for clinical proteomics. However, many proteomic studies have had only limited clinical impact, due to factors such as modest numbers of subjects, absence of disease controls, small numbers of defined biomarkers, and diversity of analytical platforms. Therefore, it is difficult to merge biomarkers from different studies into a broadly applicable human urinary proteome database. Ideally, the methodology for defining the biomarkers should combine a reasonable analysis time with high resolution, thereby enabling the profiling of adequate samples and recognition of sufficient features to yield robust diagnostic panels. CE-MS, which was used to analyze urine samples from healthy subjects and patients with various diseases, is a suitable approach for this task. The database of these datasets compiled from the urinary peptides enables the diagnosis, classification, and monitoring of a wide range of diseases. CE-MS exhibits excellent performance for biomarker discovery and allows subsequent biomarker sequencing independent of the separation platform. This approach may elucidate the pathogenesis of many diseases, and better define especially renal and urological disorders at the molecular level.

Original languageEnglish
Pages (from-to)964-973
Number of pages10
JournalProteomics - Clinical Applications
Volume2
Issue number7-8
DOIs
StatePublished - Jul 2008
Externally publishedYes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

  • CE
  • Database
  • MS
  • Urine

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