TY - JOUR
T1 - CE-MS analysis of the human urinary proteome for biomarker discovery and disease diagnostics
AU - Coon, Joshua J.
AU - Zürbig, Petra
AU - Dakna, Mohammed
AU - Dominiczak, Anna F.
AU - Decramer, Stéphane
AU - Fliser, Danilo
AU - Frommberger, Moritz
AU - Golovko, Igor
AU - Good, David M.
AU - Herget-Rosenthal, Stefan
AU - Jankowski, Joachim
AU - Julian, Bruce A.
AU - Kellmann, Markus
AU - Kolch, Walter
AU - Massy, Ziad
AU - Novak, Jan
AU - Rossing, Kasper
AU - Schanstra, Joost P.
AU - Schiffer, Eric
AU - Theodorescu, Dan
AU - Vanholder, Raymond
AU - Weissinger, Eva M.
AU - Mischak, Harald
AU - Schmitt-Kopplin, Philippe
N1 - Funding Information:
This work was carried out for the U.S. Army Advanced Research and Development Command, Dover, New Jersey, Contract DAAK10-78-C-0434.
PY - 2008/7
Y1 - 2008/7
N2 - 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.
AB - 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.
KW - CE
KW - Database
KW - MS
KW - Urine
UR - http://www.scopus.com/inward/record.url?scp=50849092540&partnerID=8YFLogxK
U2 - 10.1002/prca.200800024
DO - 10.1002/prca.200800024
M3 - Review article
AN - SCOPUS:50849092540
SN - 1862-8346
VL - 2
SP - 964
EP - 973
JO - Proteomics - Clinical Applications
JF - Proteomics - Clinical Applications
IS - 7-8
ER -